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Speaker 1: From Marhard where Innovation, money and power Collie in Silicon Valley, NBN.

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Speaker 2: This is Bloomberg Technology with Caroline Hyde and Ed Ludlove.

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Speaker 3: I'm Carolin Hyde and Bloomberg's word lad Quarters in New York,

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Speaker 3: and I'm Ed Lovelow in San Francisco.

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Speaker 4: This is Bloomberg Technology.

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Speaker 3: Ed caution consumes investors as were a torrent of tech earnings.

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Speaker 5: This week, we discuss what to watch.

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Speaker 4: But artificial intelligence momentum. We speak to the CEO of

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Speaker 4: Runway on generative AI powered video and we're joined by

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Speaker 4: the executive leading fidelities charge into AI and machine learning.

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Speaker 3: Meanwhile, legal headaches to blue check chaos. What on earth

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Speaker 3: is going on at Twitter? The latest is all coming up.

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Speaker 4: It is a big week where earnings are in focus.

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Speaker 4: You look at this sort of long list of megacat

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Speaker 4: names that are coming. We start with Microsoft, then Alphabet,

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Speaker 4: the parent company of Google Meta, and then we work

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Speaker 4: through to May fourth in a couple of weeks time

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Speaker 4: or next week. For Apple. Things are getting very serious

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Speaker 4: because across the S and P five hundred information technology subsector,

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Speaker 4: we expect a pretty big drop when it comes to

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Speaker 4: earning's character.

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Speaker 3: Yeah, we're expecting the biggest drop in tech profits since

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Speaker 3: two thousand and nine. Let's dig in to the key

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Speaker 3: company's got to keep an eye on none of them

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Speaker 3: and Mart Mahiney you know him, of course, senior managing

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Speaker 3: director of Internet Research over at Evercore ISI and we

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Speaker 3: wait with bated breath to the likes of Alphabet, some

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Speaker 3: of the big heavyweights that really shine and light on

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Speaker 3: how much companies are willing to spend on advertising and

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Speaker 3: drive these sorts of companies forward.

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Speaker 6: Mark, That's right, Caroline, So yeah, we're going to get

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Speaker 6: a lot of I think there are three things we

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Speaker 6: want to alter that it and all the noise we're

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Speaker 6: going to get the signal we're going to get this week.

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Speaker 6: Let's try to focus on three things. What are these

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Speaker 6: companies telling us about the state of the economy. Is

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Speaker 6: our advertising retail trends, cloud enterprise spend trends. Are they

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Speaker 6: still deteriorating or improving from where we were in the

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Speaker 6: December quarter? Our best guess is that trends are stabilizing

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Speaker 6: to softening, not recovering yet. The second thing is costs.

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Speaker 6: That's the new sheriff in town, if you will. All

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Speaker 6: of these companies across tech have announced some sort of

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Speaker 6: riffs reduction in forces over the last three six nine months.

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Speaker 6: I mean, it's a dramatic change in the mentality of

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Speaker 6: Silicon Valley and of tech broadly. So is there more

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Speaker 6: to come? And what does it actually mean in terms

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Speaker 6: of the bottom line? How successful are these companies defending

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Speaker 6: their free cash flow and earnings? And then the third

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Speaker 6: thing is on the positive side, is AI generative AI.

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Speaker 6: You already teased it out a little bit, and I

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Speaker 6: imagine that every tech company is going to mention AI

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Speaker 6: and somebody should start a counter right now. How many

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Speaker 6: times AI is mentioned in the in the earnings transcripts

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Speaker 6: over the next of course, of the next couple of weeks.

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Speaker 4: But it's going to be high.

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Speaker 6: But for a reason too, we're at a bit of

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Speaker 6: a tipping point because for a lot of reasons, but

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Speaker 6: you know, chat GPT really brought AI home to most

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Speaker 6: people about what the power is of these models even

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Speaker 6: though they've been deployed for a while. But the increase

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Speaker 6: in compute capacity really allows kind of a step level

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Speaker 6: increase in the applications to the deployments of AI. So

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Speaker 6: talk about it. We want these companies to talk about

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Speaker 6: how well positioned they are and what their deployment ideas are.

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Speaker 4: Mark, let's go back to that sheriff in town being

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Speaker 4: cost control. You know, probably the one point of commonality

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Speaker 4: between those names is that they have done layoffs and

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Speaker 4: other cost reduction action and how it shows up. I

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Speaker 4: find Meta to be really interesting in that respect because

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Speaker 4: the top line growth might not be there, but I

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Speaker 4: think we're expecting a sequential improvement in margins, right because

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Speaker 4: of the actions that they've taken.

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Speaker 2: I think that's right.

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Speaker 6: I just think of these companies, probably the one that's

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Speaker 6: been most aggressive in taking out costs so far, maybe

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Speaker 6: surprisingly it is Meta, and I think these other companies,

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Speaker 6: I think the market. I think investors want Google to

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Speaker 6: do more with their cost structure, that's probably, and then

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Speaker 6: I think market once and investors want Amazon to do

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Speaker 6: more too. I don't know about Microsoft. Investors may be

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Speaker 6: comfortable with where they are now, but yeah, I think

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Speaker 6: at the whole, investors kind.

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Speaker 4: Of want a little bit more.

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Speaker 6: Look, we overbuilt, we over hired because we overextrapolated from

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Speaker 6: COVID demand trends, but also because we're going into a

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Speaker 6: softening demand environment. You know, households are going to have

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Speaker 6: to tighten our belts and solo companies. So I think

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Speaker 6: there's gonna be an expectation or at least a hope.

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Speaker 6: This question is whether it's going to be realized that

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Speaker 6: we're going to hear more on the cost front from companies.

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Speaker 6: I think we will from Meta, I'm not sure we

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Speaker 6: will from Google, and I think we will from Amazon,

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Speaker 6: but you know, it's hard to know.

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Speaker 3: The three big beasts that you analyze Mark Amazon also

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Speaker 3: will shine a light, as you said so rightly, on

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Speaker 3: how willing companies are to invest in things like cloud.

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Speaker 3: At the moment, AWS a big profit juggernaut for them,

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Speaker 3: but also for alphabet who are hoping to break even

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Speaker 3: in some way on that. What do you expect for

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Speaker 3: how much companies are showing resilience and needing compute power

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Speaker 3: right now?

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Speaker 6: Well, we're decelerating. I think cloud demand is going down.

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Speaker 6: It shot up during part of the COVID crisis. It's

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Speaker 6: been a super hot, high priority level area for enterprise

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Speaker 6: spend for the better part of five, six, seven years,

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Speaker 6: but it's slowed down and we're through what we're going

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Speaker 6: through what's called an optimization cycle, where companies are going

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Speaker 6: to you know, when the renewals are coming up, they're

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Speaker 6: demanding better, a bigger discounts, or more capacity for the

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Speaker 6: same price. That's clearly happening in the market. That means

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Speaker 6: that these revenue growth rates that Microsoft is going to

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Speaker 6: report with a zer, a, Google Cloud, and AWS are

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Speaker 6: going to report, they're all going to show a slow down.

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Speaker 6: And the question we have is just how low will it go?

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Speaker 6: And I think we hope that the trough is kind

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Speaker 6: of the June quarter, and we think with Amazon with

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Speaker 6: AWS you're going to have like a single digit percent

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Speaker 6: growth and AWS revenue. I mean, that's kind of shocking

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Speaker 6: if you go back two years, nobody would have expected

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Speaker 6: that to happen, But that's probably what's going to happen.

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Speaker 6: And then the question is how quickly does it base

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Speaker 6: from there and start reaccelerating. We think it will base,

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Speaker 6: We think it will reaccelerate. We just don't really know

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Speaker 6: just how quickly it will. So that's that's going to

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Speaker 6: be an overhang on the stock mark.

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Speaker 4: If we do get some upside surprise across this kind

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Speaker 4: of pretty broad range of names, where do you think

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Speaker 4: it will be.

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Speaker 6: I think it's probably going to come on the margin side.

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Speaker 6: Ed I think it's probably going to come on the

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Speaker 6: cost side, because that's what these companies can control. It

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Speaker 6: can't do much about demand trends in a softening macro environment.

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Speaker 6: So I'd be really surprised if we had positive, you know,

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Speaker 6: material upwards revisions on cloud enterprise spend, or on retail

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Speaker 6: spend or on advertising spend. I mean, I hope we

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Speaker 6: get them, but as a bull on these stocks, but

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Speaker 6: I don't.

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Speaker 4: I think that's.

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Speaker 6: Highly highly unlikely and what we should so I think

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Speaker 6: there'll be positive news on the margins. We should all

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Speaker 6: be just watching out for the chance that if we

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Speaker 6: go into a hard landing in the second half of

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Speaker 6: this year. I don't think that's modeled in. It may

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Speaker 6: be priced in, but I don't think it's modeled into

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Speaker 6: these companies. So that's what we're you know, Like I

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Speaker 6: do worry like fund valuation wise, I think there's upside

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Speaker 6: to be stock because they were so de risk last year.

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Speaker 6: But estimates wise, I think the first half the year

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Speaker 6: estimates are fine, a little bit of an upwards bias

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Speaker 6: because of cost cutting, But I just worry about the

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Speaker 6: demand tens if we have a hard landing estimates may

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Speaker 6: need to come down in the back half of the year,

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Speaker 6: So I don't know if that's more cautious than you

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Speaker 6: wanted to hear it. That's kind of how we come out.

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Speaker 3: And what's interesting about timing this year for this quarter.

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Speaker 3: It used to be that Snap was the bell weather

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Speaker 3: for where we would expect Alphabet and Meta to go,

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Speaker 3: but they're actually behind those two ones in terms of

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Speaker 3: timing this year.

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Speaker 4: Yeah, you're right, we showed the calendar earlier. Mark. It's

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Speaker 4: just the cadence of the week gets bigger and bigger

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Speaker 4: and bigger. But we don't have an early sense apart

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Speaker 4: from Tesla that's reported on broadly where we sit in

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Speaker 4: this market.

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Speaker 6: You're right, Carolina, Then you're right, and it's odd the

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Speaker 6: way you set it up. Caroline, you're right that the

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Speaker 6: market took Snap as a bell weather, but come on,

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Speaker 6: it's tiny compared to these other companies, and they haven't

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Speaker 6: executed nearly as well, so they've really been a false

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Speaker 6: false indicator most quarters, I would argue, So I think

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Speaker 6: it's better for the market that they report later because

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Speaker 6: it's just noise that comes out of them. You and

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Speaker 6: I don't mean I don't mean that disrespectfully. I just

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Speaker 6: think that they're less of a read through into the

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Speaker 6: larger and larger players. That's what I really meant to

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Speaker 6: say in a respectful way. Whereas Google is going to

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Speaker 6: give you a really good view on what's happening to

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Speaker 6: overall advertising trends. Google is GDP advertising, so and I

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Speaker 6: think they're going to talk about trends that are softening,

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Speaker 6: not deterior and dramatically, but are modestly deteriorating. And I

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Speaker 6: think we should be prepared for that. I think it's

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Speaker 6: going to be a really reasonable read through, certainly sizable.

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Speaker 6: It's the single largest advertising data point you can get worldwide.

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Speaker 6: Pay attention to Google.

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Speaker 3: Mamahini well said, and pay attention to all of those

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Speaker 3: AI headlines that come out of them as well. Av

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Speaker 3: Aquo II, we thank you as always for joining.

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Speaker 5: Us a ed.

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Speaker 2: Yeah.

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Speaker 4: Look, layoffs were a common theme of that conversation. Let's

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Speaker 4: take a look at Disney, which began its second round

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Speaker 4: of job cuts today, part of its broader push to

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Speaker 4: eliminate about seven thousand jobs this year. The company says

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Speaker 4: that by Thursday, around four four thousand jobs will have

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Speaker 4: been cut. The cut stretch from Disney's headquarters in Burbank,

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Speaker 4: California to the ESPN Sports network over in Connecticut. Now

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Speaker 4: coming up, Bitcoin hit new records after each of its

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Speaker 4: previous harvings. Now cryptoanalysts are projecting for a new high

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Speaker 4: when the process takes place next year. We have more

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Speaker 4: and how this will push the price of the digital

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Speaker 4: token quick check and the shares of First Republic higher

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Speaker 4: seven percent due out of earnings after the bell later today,

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Speaker 4: have really keen eye on deposit flows still going the

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Speaker 4: health of that bank long term, bringing the details.

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Speaker 7: This is bloombow.

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Speaker 3: So crypto taking a bit of a breathe over the

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Speaker 3: last few trading days, but analysts through actually saying Bitcoin

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Speaker 3: in particular, it's rebound, just the start of the rally

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Speaker 3: that will take the coin past fifty thousand dollars next year,

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Speaker 3: all because of what is the process known as halving,

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Speaker 3: that's basically curbing the amount of tokens that miners receive

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Speaker 3: as a reward for their work. Mike mccloin seeing in

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Speaker 3: macro strutches and bringing back intelligence joins us. Now with

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Speaker 3: more and this so called harving, we're anticipating what happens

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Speaker 3: in about April of next year.

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Speaker 8: Well, Hi, Caroline, it makes me put on my trader's

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Speaker 8: hat versus my investor's hat. The having, i think is

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Speaker 8: the key part of bitcoin's definable diminishing supply, which is

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Speaker 8: unique to all common is the theater is kind of

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Speaker 8: like that, just less more difficult to measure. But with

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Speaker 8: increasing the demand and adoption of price must up over

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Speaker 8: time that it's a known known. The thing I'm worried

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Speaker 8: about right now is this bigger unknown of the world

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Speaker 8: tilting towards the recession, risk assets kind of tilting downward,

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Speaker 8: and the FED still tightening. In bitcoin and in cryptos

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Speaker 8: among the riskiest of acts. So I'm worried that we're

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Speaker 8: more likely to have peaked around thirty have some pressure

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Speaker 8: for a while, particularly if we see more in the

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Speaker 8: screen we're seeing today with the stock market going lower

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Speaker 8: and NASDA kind trickling down below that thirteen thousand level.

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Speaker 4: Mike, let's go back to basics a little bit. A

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Speaker 4: harving or harthening as it's otherwise known, reducing the amount

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Speaker 4: of tokens that bitcoin minors get as a reward for

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Speaker 4: their work. What is the link between that activity and

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Speaker 4: the anticipation of the price of bitcoin going higher. What

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Speaker 4: are the mechanics behind that.

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Speaker 1: So right now the most you can we can produce

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Speaker 1: on a daily basis of bitcoin mine is nine hundred

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Speaker 1: coins a day. Before that was eighteen hundred before the

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Speaker 1: last having, So you get the next having that's going

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Speaker 1: to cut it back to four hundred and fifty coins

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Speaker 1: a day. It's by code, and then four years from

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Speaker 1: now it's going to do it again. That's just the

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Speaker 1: beauty of the code. It has to go down the

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Speaker 1: supply decline.

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Speaker 9: So right now the total supply is running around two percent,

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Speaker 9: so right around the historical measure of goal. A year

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Speaker 9: from now it's going to drop below one percent because

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Speaker 9: it's just definable diminishing. And that's the beauty of it.

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Speaker 9: All those miners are fighting for more and more. But

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Speaker 9: it's a unique thing about what bitcoin does. No other

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Speaker 9: commodity does that. You can say prices go up, supply

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Speaker 9: comes on tip. Particularly with gold it does too, but

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Speaker 9: most notably learned that when crude all last year. But

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Speaker 9: what's happening now is people are anticipating now it's going

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Speaker 9: to just do what it did in the past. And

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Speaker 9: I think the thing that's different this time, ed is

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Speaker 9: bitcoin was born out of the last great financial crisis

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Speaker 9: in that recession. I think this one's going to define it,

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Speaker 9: but it's going to be its first recession, and I

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Speaker 9: think it's the first time we might see that the

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Speaker 9: S and B five hundred drop more than twenty percent

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Speaker 9: into a recession, which should pull all risk acids lower.

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Speaker 9: So I think i'll end with this one thing you

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Speaker 9: saw today was a little bit off topic, but similar

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Speaker 9: as record shorts and ten year thirty year in bond futures,

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Speaker 9: might be indicative of gold continued toll performing, Bitcoin being

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Speaker 9: pushed a little to the waistside.

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Speaker 4: All right, Mike mcglom Bloomberg Intelligence on a roll out

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Speaker 4: of Miami. Thank you. Meanwhile, Caroline, don't know if you

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Speaker 4: saw this one. Franklin Templeton says it's money market fund

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Speaker 4: used to record share ownership on a blockchain, is seeing

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Speaker 4: inflows specifically from crypto related firms. The fund's total assets

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Speaker 4: increased to around two hundred and seventy million dollars. This

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Speaker 4: is a fund that invests in US government securities, does

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Speaker 4: not hold crypto assets, but it's who's putting the money in.

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Speaker 3: Yeah, because remember a lot of at the moment, these

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Speaker 3: overall crypto investors or crypto related companies, they've just lost

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Speaker 3: a load of their financial infrastructure with some of the

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Speaker 3: bank's silver gate. We think, of course Signature Bank coming

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Speaker 3: out of the market. They want safer places to put

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Speaker 3: their money. Of course, you kind of have to be

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Speaker 3: banked to get into a money market fund, even if

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Speaker 3: it's Franklin Templeton. But I suppose it is no surprise

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Speaker 3: that if you're going to look for a money market

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Speaker 3: fund to gain you exposure, safe exposure overall, you're likely

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Speaker 3: to go to one that's at least in some way

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Speaker 3: in your line of sight and liking. And of course

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Speaker 3: the Franklin on shae US government money funds they're going

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Speaker 3: to like that. There's sort of play there, isn't there,

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Speaker 3: And at the moment it's off likely to be on

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Speaker 3: chain without Benji coin.

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Speaker 4: We saw the vcs do it, and now we're seeing

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Speaker 4: the industry itself do it with their own money. Let's

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Speaker 4: continue talking with the crypto market. Sunny Singers here with

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Speaker 4: more insights. The CEO of CS Labs twenty one in

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Speaker 4: stealth mode, but also of course a well known person

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Speaker 4: in this industry. Let's go straight actually to the harving

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Speaker 4: or the harvining. What do you make of that?

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Speaker 2: Yeah, it happens everything years.

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Speaker 4: You're a believer in the logic behind it.

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Speaker 10: Yes, it makes sure like central governments can keep producing

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Speaker 10: new currency, which creates inflation. Bitcoin was created the other

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Speaker 10: way to reduce the supply, which makes it more deflation area.

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Speaker 10: So it's a great mechanism they built into the system,

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Speaker 10: and it happens every four years, and it's scheduled to

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Speaker 10: happen next April twenty seventh, round rough with that date,

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Speaker 10: and so right around a year from now, and you're

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Speaker 10: gonna start seeing a lot of media frenzy start happening.

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Speaker 10: Six months to you know, rout and that should start happening,

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Speaker 10: and then you start seeing the price run up a

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Speaker 10: little bit.

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Speaker 3: Look, it's funny to overall think about how much we've

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Speaker 3: seen this torrid time within crypto, whether it's actually the

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Speaker 3: likes of Bitcoin eighth sort of taking up a lot

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Speaker 3: of the oxygen in the room when it comes to

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Speaker 3: allocation at the moment, or at least.

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Speaker 5: Where the rallies have been.

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Speaker 3: Then there's also the question we were just talking about

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Speaker 3: the fact that a lot of crypto players find themselves

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Speaker 3: without the banking infrastructure that were used to the signatures,

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Speaker 3: the silver banks or the overall silver gates as well.

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Speaker 5: How difficult is it in the crypto space for you?

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Speaker 2: It's very bad.

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Speaker 10: So my company we just raised a four million dollar

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Speaker 10: seed around. We did it in forty five days, which

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Speaker 10: is great from an all starting of crypto investors as

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Speaker 10: all autritional investors, But the mood has definitely changed. The

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Speaker 10: banking regulations have become increasingly hard. Getting banks is increasingly hard.

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Speaker 10: Everything that could go wrong has gone wrong for the

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Speaker 10: crypto industry. And yet the price of bitcoin has rallied

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Speaker 10: from sixteen thousand to almost thirty thousand in the last

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Speaker 10: six months, which is pretty remarkable, and I wouldn't be

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Speaker 10: surprised if bitcoin hits forty thousand this year.

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Speaker 4: Can I just jump in on that seed stage? Just

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Speaker 4: broadly speaking, how difficult was that following the collapse of

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Speaker 4: Silicon Valley Bank? For how long you would negotiate around

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Speaker 4: But you're basically taking a crypto proposition to investors who

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Speaker 4: are a bit more cautious than perhaps they previously were

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Speaker 4: in that industry.

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Speaker 10: Yes, it was much harder and we wanted to get

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Speaker 10: a mix of crypto investors as well as silicon value investors.

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Speaker 2: And the silicon value investors.

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Speaker 10: Might have loved the idea, but they're no longer doing

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Speaker 10: crypto investing anymore. A while of teams said they're now

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Speaker 10: doing AI and pivot, so it's much more difficult that way,

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Speaker 10: and they would rarely to wire our money the day

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Speaker 10: before SVB had the issues, and we're going to SVB

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Speaker 10: two as we did away that three weeks too. So again,

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Speaker 10: everything that could go wrong has gone wrong in the

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Speaker 10: crypto space for the last year, I would.

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Speaker 3: Say, and that's not even talking about some of the

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Speaker 3: overall regulatory headwinds that still everyone is very worried about

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Speaker 3: when it comes to how people are going to analyze

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Speaker 3: the SEC is going to look.

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Speaker 5: At some of these crypto assets.

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Speaker 3: All these headlines you see Gemini thinking of a non

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Speaker 3: US based derivative platform. We know that other key players

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Speaker 3: are looking at licenses, crypto licensings in Bermuda. How tempting

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Speaker 3: is it to take whatever you're building out of the

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Speaker 3: United States.

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Speaker 10: Yeah, and again companies that coin based Gemini, my previous company,

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Speaker 10: bit pay. We try to play by the rules. Work

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Speaker 10: with the SEC. We all got in New York bit licenses.

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Speaker 10: We're trying to do it the right way. Fighting the

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Speaker 10: SEC is now really getting a little harder to work with,

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Speaker 10: and they keep changing their mind on things and becoming

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Speaker 10: like coin based of Gema. It's trying to keep growing

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Speaker 10: during Crypto Winner, it's hard for them to get full

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Speaker 10: clarity on what to do.

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Speaker 2: That's why they're looking offshore.

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Speaker 10: And again ftxus FTX, which went out of business, was

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Speaker 10: created because it compan called bit mechs random issues. And

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Speaker 10: now with FTX gone, there's really a void in the

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Speaker 10: international trading markets that's going to buyans instead, and companies

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Speaker 10: like Gemini and coinbas see a big opportunity if they

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Speaker 10: can get this derivers market and things like that that

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Speaker 10: Bias has been focusing on.

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Speaker 4: All right, Thanks to Sonny Saying CEO of cs Labs

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Speaker 4: twenty one, which I hope to hit more about. Twitter's

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Speaker 4: legal battles over its mass layoffs. Last Full continue to grow,

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Speaker 4: with two more formal workers filing class action complaints at

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Speaker 4: about two thousand Index employees pursuing claims in individual arbitration.

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Speaker 4: Let's bring in Bloomberg. Sarah Fryar for more two thousand,

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Speaker 4: making claims. It's quite a big chunk of the workforce

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Speaker 4: that was laid off.

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Speaker 11: And it's all in individual arbitration and mass arbitration, which

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Speaker 11: often when companies put the arbitration clauses into contracts to

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Speaker 11: expect that they'll be able to get things done quietly

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Speaker 11: behind the scenes. In this case, I don't think that's

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Speaker 11: going to work. I think it's going to become a

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Speaker 11: very arduous and expensive process for Twitter.

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Speaker 3: Meanwhile, there's been several people completely well many people completely

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Speaker 3: baffled by what's happening at Twitter in general, particularly around

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Speaker 3: who's got those blue check.

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Speaker 5: Marks who doesn't. It feels as though there's a lot

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Speaker 5: of chaos there at the moment.

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Speaker 3: I just want to listen into what Ron Reynolds had

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Speaker 3: to say last week about the service to take a listen.

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Speaker 12: Sarah, I don't know. I mean, I see they all

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Speaker 12: have sort of different footprints. I mean, Twitter has is

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Speaker 12: now and has always been a piping high dumpster fire

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Speaker 12: of trash, and it is it can be a very

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Speaker 12: difficult place, but it's also a place that there can

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Speaker 12: also be incredible good.

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Speaker 3: When you have words to say like that, are we

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Speaker 3: unsurprised that with twenty one million followers, his Blue Legacy

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Speaker 3: check mark isn't being paid by Ela Musk.

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Speaker 11: I think that we're actually seeing the check mark return

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Speaker 11: to a lot of accounts in particular that have been

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Speaker 11: critical of Musk is almost applying that brand to them

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Speaker 11: as a punishment, which seems like a strange a strange

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Speaker 11: reward slash punishment. If you want people to buy a

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Speaker 11: blue check, using it against your enemies is not the

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Speaker 11: strategy I would pick, but hey, Musk has he works

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Speaker 11: in mysterious ways. I think we're also seeing the check

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Speaker 11: reappear among the accounts of you know, rather popular accounts

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Speaker 11: with more than a million followers, including some accounts of

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Speaker 11: people who don't exist or who have since died from

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Speaker 11: before the checks were rolled out. So it is all

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Speaker 11: getting a whole lot more confusing, And I think that

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Speaker 11: it just means the Twitter Blue experiment nobody knows what

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Speaker 11: it's for. Nobody knows all.

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Speaker 4: Right, Bloomberg Surah for I thank you. Cara would point

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Speaker 4: out in Ryan Reynolds's case, in particular, he is using

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Speaker 4: Twitter right. Think about the reaction video he shared film

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Speaker 4: by Paul Rudd after his football team Rexham got promoted.

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Speaker 3: Millions of people saw that particular clips, several clips.

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Speaker 5: What a story They've got to make another documentary out

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Speaker 5: of it.

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Speaker 13: Ed.

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Speaker 4: Yeah, and by the way, my mum was born in Wrexham,

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Speaker 4: so I was cheering from AFAR.

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Speaker 3: Welcome back to bloombag Technology. I'm Carolin Hide in New York.

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Speaker 4: And Imed Ludlow in San Francisco. Carrige. We get a

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Speaker 4: quick check on the markets and where we're training in

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Speaker 4: the technology sector, and that's that one hundred softer by

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Speaker 4: about seven tens percent. Tesla kind of a big drag

439
00:20:59,359 --> 00:21:02,639
Speaker 4: on that index from a points perspective. Underperformance as well

440
00:21:03,000 --> 00:21:06,359
Speaker 4: in the chip space semiconductors and the socks off by

441
00:21:06,440 --> 00:21:09,159
Speaker 4: nine tens percent short end of the curve. That is interesting.

442
00:21:09,200 --> 00:21:11,520
Speaker 4: You see the two year off by about three basis

443
00:21:11,600 --> 00:21:13,800
Speaker 4: points four point one four percent, and as we said,

444
00:21:14,040 --> 00:21:17,200
Speaker 4: we kind of cooled it on Bitcoin twenty seven three

445
00:21:17,280 --> 00:21:20,960
Speaker 4: hundred dollars or so per token. There's a few specific

446
00:21:21,080 --> 00:21:22,800
Speaker 4: movers that we're looking at in the markets as well

447
00:21:22,840 --> 00:21:25,280
Speaker 4: when it comes to big tech names, actually tech names

448
00:21:25,359 --> 00:21:27,359
Speaker 4: large and small. Mister director, if you change up the

449
00:21:27,400 --> 00:21:30,120
Speaker 4: board Tesla, as I said to the downside, a pretty

450
00:21:30,160 --> 00:21:33,800
Speaker 4: big drag. Microsoft also markedly lower off by two percent

451
00:21:34,240 --> 00:21:37,800
Speaker 4: ahead of earnings coming this week. Amazon also softer C

452
00:21:38,000 --> 00:21:42,359
Speaker 4: three AI, an AI name that has been downgraded, not

453
00:21:42,720 --> 00:21:45,800
Speaker 4: for anything reasons to do with artificial intelligence, Caro, simply

454
00:21:45,880 --> 00:21:48,719
Speaker 4: to do with macro and fundamentals. And you wonder now

455
00:21:48,800 --> 00:21:51,119
Speaker 4: how much this market is getting a bit more realistic,

456
00:21:51,440 --> 00:21:53,960
Speaker 4: some of the momentum on the publicly traded AI names

457
00:21:53,960 --> 00:21:56,720
Speaker 4: at least kind of being taken out. Should we move on.

458
00:21:57,000 --> 00:21:58,720
Speaker 4: Here's what some of our guests on the show have

459
00:21:58,800 --> 00:22:01,639
Speaker 4: had to say about the rise of generative AI in

460
00:22:01,720 --> 00:22:04,360
Speaker 4: that space, both but for the sort of investing standpoint,

461
00:22:04,560 --> 00:22:06,520
Speaker 4: but also let's have a little thing about research too.

462
00:22:06,680 --> 00:22:07,160
Speaker 4: Have a listen.

463
00:22:08,160 --> 00:22:10,920
Speaker 14: Some of the best opportunities are are sitting right in

464
00:22:10,960 --> 00:22:11,439
Speaker 14: front of us.

465
00:22:11,560 --> 00:22:15,360
Speaker 15: AI I believe will be the third largest compute revolution

466
00:22:15,520 --> 00:22:16,920
Speaker 15: that we've seen in our generation.

467
00:22:17,080 --> 00:22:19,920
Speaker 4: So we think the biggest opportunity and the safest place

468
00:22:19,960 --> 00:22:23,040
Speaker 4: to deploy these type of technology is inside the enterprise itself.

469
00:22:23,119 --> 00:22:25,960
Speaker 6: We'll see more of that AI to go into traditional

470
00:22:26,040 --> 00:22:28,280
Speaker 6: industries and helping them move faster and be more efficient.

471
00:22:28,400 --> 00:22:31,880
Speaker 5: AI is global, it's not something local. It's not a fan.

472
00:22:32,320 --> 00:22:35,199
Speaker 5: It's going to be here and it is affecting all

473
00:22:35,240 --> 00:22:35,480
Speaker 5: of us.

474
00:22:35,720 --> 00:22:37,600
Speaker 4: This space is emerging so fast.

475
00:22:37,680 --> 00:22:38,960
Speaker 16: Frankly, it's very hard at track.

476
00:22:39,280 --> 00:22:40,600
Speaker 4: Even from an investment perspective.

477
00:22:40,720 --> 00:22:45,240
Speaker 15: There's never like one very obvious clear answer when you're

478
00:22:45,240 --> 00:22:48,280
Speaker 15: getting into the weeds of how this technology can affect people.

479
00:22:48,440 --> 00:22:51,399
Speaker 15: I'm a huge fan of making sure that the government

480
00:22:51,480 --> 00:22:54,640
Speaker 15: authorities work closely with the industry.

481
00:22:56,119 --> 00:22:59,000
Speaker 3: Well, we've got yet more insight, this time from a

482
00:22:59,280 --> 00:23:03,280
Speaker 3: heavy backe see backed player in the space already making things,

483
00:23:03,359 --> 00:23:04,520
Speaker 3: creating things in the real world.

484
00:23:04,760 --> 00:23:06,359
Speaker 5: Chris valence Uler is with our.

485
00:23:06,280 --> 00:23:09,679
Speaker 3: CEO of the applied AI research company Runway. Basically, you're

486
00:23:09,680 --> 00:23:13,680
Speaker 3: all about human creativity, you're about films. We've already seen

487
00:23:13,800 --> 00:23:17,520
Speaker 3: what your own AI generalor AI being used within everything everywhere,

488
00:23:17,560 --> 00:23:18,080
Speaker 3: all at once.

489
00:23:18,800 --> 00:23:21,920
Speaker 5: How is it being used in a way that that surprises.

490
00:23:21,480 --> 00:23:22,920
Speaker 16: You many ways.

491
00:23:23,359 --> 00:23:26,160
Speaker 14: We started to see things that we're just literally impossible

492
00:23:26,200 --> 00:23:29,280
Speaker 14: to do for now being possible with these technologies. I

493
00:23:29,320 --> 00:23:32,000
Speaker 14: think we're still very early to understand the full creative

494
00:23:32,040 --> 00:23:34,720
Speaker 14: potential of everything that will come. But yeah, you have

495
00:23:35,000 --> 00:23:37,920
Speaker 14: thoughts like everything about all once using Runway to added

496
00:23:37,960 --> 00:23:41,040
Speaker 14: some scenes in there, you have musicians and artists taking

497
00:23:41,200 --> 00:23:43,200
Speaker 14: like really the technology to the next level, which we

498
00:23:43,280 --> 00:23:45,119
Speaker 14: think it's really the right thing to do, which is

499
00:23:45,440 --> 00:23:48,200
Speaker 14: start experimenting and exploring more with these capacities.

500
00:23:48,560 --> 00:23:48,800
Speaker 12: You have.

501
00:23:48,960 --> 00:23:51,920
Speaker 3: Of course all of this excitement euphoria, there's also a

502
00:23:51,960 --> 00:23:54,560
Speaker 3: lot of nerves, particularly around people who worry that their

503
00:23:54,600 --> 00:23:57,800
Speaker 3: own are their own imaging is being used to train

504
00:23:57,880 --> 00:24:01,440
Speaker 3: Aiyes on, how are you squaring that circle?

505
00:24:01,440 --> 00:24:02,879
Speaker 5: Because it's a difficult one to navigate.

506
00:24:03,119 --> 00:24:05,920
Speaker 14: Yeah, I know, and we've always thought about that in

507
00:24:06,000 --> 00:24:08,879
Speaker 14: a very particular, interesting different way. I come from on

508
00:24:09,040 --> 00:24:11,720
Speaker 14: our distant background, and so I have deep empathy for

509
00:24:11,920 --> 00:24:14,800
Speaker 14: those arties and those creatives who are asking themselves what's

510
00:24:14,800 --> 00:24:16,359
Speaker 14: going to happen next with this technology?

511
00:24:16,760 --> 00:24:18,960
Speaker 16: I think, I think to remind ourselves is that we're.

512
00:24:18,800 --> 00:24:20,879
Speaker 14: Still very early, and what we need to do is

513
00:24:21,000 --> 00:24:23,280
Speaker 14: open the conversation to have more people come in and

514
00:24:23,440 --> 00:24:26,119
Speaker 14: understand how you're going to start leveraging this technology in

515
00:24:26,160 --> 00:24:29,080
Speaker 14: your art practice. I think that for me is really important,

516
00:24:29,119 --> 00:24:31,280
Speaker 14: and I'm something we practicing runway kind of like from

517
00:24:31,320 --> 00:24:32,320
Speaker 14: the very beginning.

518
00:24:33,359 --> 00:24:37,560
Speaker 4: Chris, your Gentoo system is still on a witless basis, right,

519
00:24:37,560 --> 00:24:39,119
Speaker 4: I know a lot of folks that I've spoken to

520
00:24:39,119 --> 00:24:41,720
Speaker 4: about this way are keen to get access. But the

521
00:24:41,840 --> 00:24:45,160
Speaker 4: reality is, you know, text to video it generates about

522
00:24:45,200 --> 00:24:48,440
Speaker 4: three seconds, and I wondered how much you're emphasizing the

523
00:24:48,560 --> 00:24:51,240
Speaker 4: kind of early stage that this is In three seconds.

524
00:24:51,320 --> 00:24:53,640
Speaker 4: Not a lot of contents generate using the tool.

525
00:24:53,880 --> 00:24:55,840
Speaker 16: I agree three seconds is not enough.

526
00:24:56,320 --> 00:25:00,160
Speaker 14: We already have fifteen second like long support Engen, you're

527
00:25:00,200 --> 00:25:02,760
Speaker 14: working towards including and adding more to that. I think

528
00:25:02,760 --> 00:25:04,760
Speaker 14: the reason we released with three seconds is we have

529
00:25:04,880 --> 00:25:07,280
Speaker 14: to make sure that more people can get their hands

530
00:25:07,440 --> 00:25:10,920
Speaker 14: on the research, on the product, on this new way

531
00:25:11,000 --> 00:25:11,720
Speaker 14: of creating.

532
00:25:11,440 --> 00:25:14,159
Speaker 16: Content and really have thousands of people using it, and

533
00:25:14,280 --> 00:25:15,439
Speaker 16: so the amount of.

534
00:25:15,440 --> 00:25:17,879
Speaker 14: Feedback we've gotten to improve the quality of the model

535
00:25:17,920 --> 00:25:20,440
Speaker 14: the experience as well. I think text to videos one

536
00:25:20,480 --> 00:25:23,600
Speaker 14: of many modes of using Gen two actually has eight

537
00:25:23,640 --> 00:25:28,080
Speaker 14: different modes. Today release the mobile app for the first

538
00:25:28,080 --> 00:25:31,680
Speaker 14: time ever, you can try Gen one actually on your iPhone,

539
00:25:32,080 --> 00:25:34,280
Speaker 14: and so there's so many things yet to be explored

540
00:25:34,680 --> 00:25:37,160
Speaker 14: and discovered here that for us really just making sure

541
00:25:37,160 --> 00:25:38,560
Speaker 14: that we could put it in the hands of more

542
00:25:38,880 --> 00:25:39,600
Speaker 14: creatives out there.

543
00:25:40,520 --> 00:25:43,240
Speaker 4: I was listening to Noam Shazir, the CEO of character

544
00:25:43,280 --> 00:25:45,879
Speaker 4: Ai on the No Prize podcast the other day, and

545
00:25:46,000 --> 00:25:49,520
Speaker 4: he faces a similar debate, Right, how do you onboard

546
00:25:49,720 --> 00:25:52,440
Speaker 4: users and grow the user base and then in the

547
00:25:52,560 --> 00:25:56,240
Speaker 4: future monetize And I wonder how that equation looks for

548
00:25:56,320 --> 00:25:58,800
Speaker 4: you as well, the focus on kind of making money

549
00:25:59,000 --> 00:26:02,080
Speaker 4: or just grow the number of users and investing in

550
00:26:02,160 --> 00:26:03,760
Speaker 4: the infrastructure needed to support it.

551
00:26:04,119 --> 00:26:07,320
Speaker 14: I think it's important that remember that we're very early

552
00:26:07,440 --> 00:26:10,200
Speaker 14: on the journey of both value capture and one of

553
00:26:10,200 --> 00:26:13,320
Speaker 14: the station industry wide, and so making sure that you

554
00:26:13,440 --> 00:26:15,640
Speaker 14: work with as many people and as many companies as

555
00:26:15,720 --> 00:26:19,040
Speaker 14: you can will help you on tab possible value captures.

556
00:26:19,320 --> 00:26:22,119
Speaker 14: I think overall, where we're starting to digest the initial

557
00:26:22,160 --> 00:26:24,240
Speaker 14: stages of that and for us is really making sure

558
00:26:24,240 --> 00:26:26,000
Speaker 14: that we're going to keep building the research and the

559
00:26:26,080 --> 00:26:29,840
Speaker 14: foundational effort to build better models and more safe models

560
00:26:29,880 --> 00:26:30,240
Speaker 14: every time.

561
00:26:31,280 --> 00:26:33,480
Speaker 4: Hey Caro, I think back to when Sonia Juang of

562
00:26:33,560 --> 00:26:36,920
Speaker 4: Sequoia is on the show a Venture Capitalist explaining why

563
00:26:37,560 --> 00:26:40,800
Speaker 4: she sees potential here, you know, think about one's ability

564
00:26:40,920 --> 00:26:44,000
Speaker 4: to dream up anything and use the generative tool to

565
00:26:44,080 --> 00:26:46,720
Speaker 4: just create your own video. That's where the vcs seemed

566
00:26:46,720 --> 00:26:48,480
Speaker 4: to be wanting to put their money.

567
00:26:48,440 --> 00:26:51,800
Speaker 3: To augment humanity, not to come against it at the moment.

568
00:26:51,920 --> 00:26:54,159
Speaker 3: But Chris, I turned to you and think about how

569
00:26:54,359 --> 00:26:56,840
Speaker 3: enthusiastics certain vcs have been around you. I think a

570
00:26:56,920 --> 00:27:00,560
Speaker 3: CO two Lux Capital for Lie's. You've got a lot

571
00:27:00,600 --> 00:27:03,680
Speaker 3: of money come in your direction. Are you turning it away?

572
00:27:03,840 --> 00:27:06,080
Speaker 3: Are you thinking about raising funds opportunistically?

573
00:27:06,560 --> 00:27:08,840
Speaker 14: So we started Runway like four or five years ago,

574
00:27:08,880 --> 00:27:11,200
Speaker 14: I mean working on jogging models and creative tools for

575
00:27:11,240 --> 00:27:13,680
Speaker 14: the last eight years or so. When we started, it

576
00:27:13,760 --> 00:27:16,359
Speaker 14: was hard. Jenneyvi I wasn't really a thing, and people

577
00:27:16,480 --> 00:27:18,800
Speaker 14: told us we were crazy, Like Jenndyvi, I was never

578
00:27:18,880 --> 00:27:19,440
Speaker 14: going to be a thing.

579
00:27:19,880 --> 00:27:21,680
Speaker 5: Really, I did as little as four years.

580
00:27:21,760 --> 00:27:23,480
Speaker 16: Yeah, four years. I will be broiled at Chris. This

581
00:27:23,600 --> 00:27:24,280
Speaker 16: is not a thing.

582
00:27:24,359 --> 00:27:26,480
Speaker 14: JENNEYV is not a market And so the one thing

583
00:27:26,560 --> 00:27:28,000
Speaker 14: that I think has changed now is I don't have

584
00:27:28,080 --> 00:27:30,480
Speaker 14: to convince more people that this is worth paying attention to.

585
00:27:30,760 --> 00:27:33,680
Speaker 14: That's that's for sure. But we're very fortunate to be

586
00:27:34,080 --> 00:27:36,760
Speaker 14: able to work with investors that believed in that very

587
00:27:36,800 --> 00:27:39,800
Speaker 14: early on, where perhaps they're wandering as much proof points

588
00:27:40,400 --> 00:27:42,240
Speaker 14: and it's a long term bad. But I go back

589
00:27:42,280 --> 00:27:43,680
Speaker 14: to the mentioned before, it's still.

590
00:27:43,600 --> 00:27:45,200
Speaker 4: Very early, and a lot of one need to be

591
00:27:45,280 --> 00:27:46,200
Speaker 4: built around.

592
00:27:45,960 --> 00:27:48,200
Speaker 14: The industry at large is still yet to be built,

593
00:27:48,760 --> 00:27:50,760
Speaker 14: and so there's a lot of opportunities for more investors

594
00:27:50,800 --> 00:27:54,359
Speaker 14: to invest in more kind of likes in you can't

595
00:27:54,359 --> 00:27:57,359
Speaker 14: really speak about like fundraising, but I think the industry

596
00:27:57,400 --> 00:27:59,520
Speaker 14: at large will will continue to grow very very.

597
00:27:59,480 --> 00:28:03,040
Speaker 4: Fast, all right, Chris Balezuela, see a runway. Good to

598
00:28:03,080 --> 00:28:06,360
Speaker 4: see David Cumming on the show. Keep us updated. Now

599
00:28:06,520 --> 00:28:08,600
Speaker 4: when this one physics teacher isn't in the middle of

600
00:28:08,680 --> 00:28:11,359
Speaker 4: a physics lesson, he's in the middle of building one

601
00:28:11,400 --> 00:28:14,800
Speaker 4: of the world's biggest free AI training data sets. His

602
00:28:14,960 --> 00:28:16,240
Speaker 4: Bloomberg Zaggie Cantrell with.

603
00:28:16,280 --> 00:28:21,680
Speaker 17: More Luranza's time DAI one came out and I was

604
00:28:21,800 --> 00:28:26,400
Speaker 17: instantly wow, Wow, this is so extremely powerful, Like now

605
00:28:26,800 --> 00:28:31,400
Speaker 17: we can draw images from text and this will change everything.

606
00:28:31,600 --> 00:28:35,199
Speaker 17: And I instantly understood that if this is like centralized

607
00:28:35,240 --> 00:28:38,360
Speaker 17: to one, two or three companies, this will have really

608
00:28:38,600 --> 00:28:40,400
Speaker 17: bad effects for society.

609
00:28:40,560 --> 00:28:43,040
Speaker 18: We've taught for months about the benefits and pitfalls of

610
00:28:43,200 --> 00:28:46,200
Speaker 18: artificial intelligence, but what we talked a lot less about

611
00:28:46,400 --> 00:28:49,160
Speaker 18: is that these AI products are built on decades of

612
00:28:49,320 --> 00:28:52,840
Speaker 18: data from across the Internet. One data set, which has

613
00:28:52,880 --> 00:28:56,640
Speaker 18: been used to train image generators Stable Diffusion and Google's

614
00:28:56,680 --> 00:28:58,280
Speaker 18: Imagen is called.

615
00:28:58,160 --> 00:28:59,080
Speaker 4: Lion five B.

616
00:29:00,080 --> 00:29:02,480
Speaker 18: Lion five B was created by a team of hobbyists,

617
00:29:02,560 --> 00:29:05,240
Speaker 18: including a high school student in London and a high

618
00:29:05,280 --> 00:29:09,320
Speaker 18: school teacher in Hamburg. Lyon is built on open source data.

619
00:29:09,680 --> 00:29:12,560
Speaker 18: It scrapes public images from across the web and anyone

620
00:29:12,640 --> 00:29:16,280
Speaker 18: can see what's in it. Stability AI, the company behind

621
00:29:16,360 --> 00:29:19,920
Speaker 18: Stable Diffusion, is now seeking a four billion dollar valuation

622
00:29:20,400 --> 00:29:23,040
Speaker 18: thanks in a large part to the free data from Lyon.

623
00:29:23,360 --> 00:29:29,880
Speaker 17: I think it's really important for this technology, the models

624
00:29:29,960 --> 00:29:34,960
Speaker 17: we train, the data sets we produce to stay openly accessible.

625
00:29:35,080 --> 00:29:37,720
Speaker 18: But after discovering that their work was used to train

626
00:29:37,880 --> 00:29:42,240
Speaker 18: Stable Diffusion, artists are now suing Stability. Lyon is cited

627
00:29:42,280 --> 00:29:45,160
Speaker 18: in these suits, but not as a defendant. It's still

628
00:29:45,160 --> 00:29:48,680
Speaker 18: an open question if these artists work, or any images

629
00:29:48,760 --> 00:29:51,560
Speaker 18: that are publicly available online a fair game for these

630
00:29:51,640 --> 00:29:55,000
Speaker 18: data sets. An answer may come soon with the European

631
00:29:55,120 --> 00:29:59,160
Speaker 18: Union's AI Act, a government's first ever serious effort to

632
00:29:59,280 --> 00:30:01,080
Speaker 18: regulate our ti official intelligence.

633
00:30:01,560 --> 00:30:07,320
Speaker 4: If you want society to embrace technology and to trust technology,

634
00:30:07,480 --> 00:30:10,760
Speaker 4: you need to have that social contract with society.

635
00:30:10,920 --> 00:30:15,160
Speaker 18: The generative AI boom has also generated calls to ensure

636
00:30:15,240 --> 00:30:20,880
Speaker 18: that companies disclose what images train their machines. Models like Openaies,

637
00:30:21,000 --> 00:30:24,440
Speaker 18: Daly Too, and chat GBT, for example, have not really

638
00:30:24,520 --> 00:30:29,600
Speaker 18: shared any details about their databases. While such regulation could

639
00:30:29,720 --> 00:30:33,320
Speaker 18: be a win for artists and creators, advocates for open

640
00:30:33,400 --> 00:30:37,360
Speaker 18: source data sets believe over regulation will only benefit big

641
00:30:37,440 --> 00:30:38,320
Speaker 18: tech in the long run.

642
00:30:38,520 --> 00:30:42,360
Speaker 17: If we try to slow things down and over regulate,

643
00:30:42,520 --> 00:30:46,760
Speaker 17: there's a big danger that in the end a few

644
00:30:47,080 --> 00:30:54,440
Speaker 17: big corporate players can afford to fulfill all formal requirements,

645
00:30:54,720 --> 00:30:59,440
Speaker 17: and in the end this technology and all data that

646
00:30:59,640 --> 00:31:04,240
Speaker 17: flows through it, will it be monopolized or at least

647
00:31:04,520 --> 00:31:05,800
Speaker 17: highly centralized.

648
00:31:07,480 --> 00:31:10,400
Speaker 16: And I see this as the real danger.

649
00:31:10,720 --> 00:31:13,320
Speaker 18: As AI data sets and the rules around them are

650
00:31:13,480 --> 00:31:17,600
Speaker 18: being developed in tandem, the question remains how closely should

651
00:31:17,640 --> 00:31:19,400
Speaker 18: we regulate what goes into them?

652
00:31:21,880 --> 00:31:25,360
Speaker 4: That was the latest from Bloomberg Zaggie count out of Germany. Now,

653
00:31:25,440 --> 00:31:29,120
Speaker 4: the CEO of NBC Universal is leaving after admitting to

654
00:31:29,200 --> 00:31:33,440
Speaker 4: an inappropriate relationship with an employee. In a statement after

655
00:31:33,480 --> 00:31:37,440
Speaker 4: an investigation, Jeff Shell said he deeply regrets the incident.

656
00:31:37,520 --> 00:31:40,840
Speaker 4: He served as CEO since January twenty twenty and worked

657
00:31:40,880 --> 00:31:44,440
Speaker 4: at NBC Universal parent Comcast for almost two decades. A

658
00:31:44,520 --> 00:31:47,040
Speaker 4: replacement has not yet been announced.

659
00:31:47,080 --> 00:31:50,160
Speaker 3: Caroline and just sticking with media, just check out the

660
00:31:50,240 --> 00:31:54,120
Speaker 3: market capitalization hit to Fox today. Seven hundred million dollars

661
00:31:54,160 --> 00:31:56,080
Speaker 3: has been lost as it's down four percent.

662
00:31:56,200 --> 00:31:58,800
Speaker 5: Why but as parting ways with Taker, Carlson.

663
00:31:58,480 --> 00:32:00,400
Speaker 3: Seems to be the real catalyst of the news, it's

664
00:32:00,480 --> 00:32:02,920
Speaker 3: most popular primetime host we know, also the source of

665
00:32:03,360 --> 00:32:07,240
Speaker 3: repeated controversy over his statements and everything from election fairness

666
00:32:07,360 --> 00:32:11,400
Speaker 3: to LGBTQ rights. We currently see also plenty more news

667
00:32:11,440 --> 00:32:12,400
Speaker 3: coming from the medial world.

668
00:32:12,400 --> 00:32:14,040
Speaker 5: We're going to dig into that in a little bit.

669
00:32:14,200 --> 00:32:16,040
Speaker 5: But meanwhile, coming up, we're going to dig in more to.

670
00:32:16,160 --> 00:32:19,480
Speaker 3: Artificial intelligence, of course, and its impact on wealth management.

671
00:32:19,680 --> 00:32:21,760
Speaker 3: We're going to be joined by Sarah Hoffman, vice president

672
00:32:21,840 --> 00:32:24,480
Speaker 3: of AI Machine Learning Research at Fidelity Investments.

673
00:32:24,560 --> 00:32:26,160
Speaker 5: That's next, listen Bloomberg.

674
00:32:42,080 --> 00:32:45,080
Speaker 4: Time now for the VC roundup. Aerodyne Group, a Malaysian

675
00:32:45,240 --> 00:32:48,520
Speaker 4: drone services company, has picked City Group for a funding

676
00:32:48,640 --> 00:32:51,600
Speaker 4: round ahead of its planned IPO. According to sources, the

677
00:32:51,680 --> 00:32:54,400
Speaker 4: startup seeking to raise up to two hundred million dollars

678
00:32:54,560 --> 00:32:57,640
Speaker 4: and aims to make its first close in midyear, ahead

679
00:32:57,680 --> 00:33:01,640
Speaker 4: of an IPO in twenty twenty four or five. And

680
00:33:01,760 --> 00:33:05,760
Speaker 4: Schroeder's a shareholder in the UK financial technology company Revolute,

681
00:33:06,080 --> 00:33:09,520
Speaker 4: has cut the value of its stake by forty six percent.

682
00:33:09,640 --> 00:33:12,080
Speaker 4: This is the second right down to hit the company

683
00:33:12,160 --> 00:33:16,600
Speaker 4: after the fintech's firms auditors raised questions on its twenty

684
00:33:16,720 --> 00:33:17,880
Speaker 4: twenty one revenue.

685
00:33:18,120 --> 00:33:19,560
Speaker 3: Caroline and we're going to stick a bit on the

686
00:33:19,600 --> 00:33:21,360
Speaker 3: world of fintech, but we're going to focus in on

687
00:33:21,440 --> 00:33:24,880
Speaker 3: the impact of artificial intelligence on it because we've talked

688
00:33:24,880 --> 00:33:27,680
Speaker 3: about perhaps how AI is affecting yours and my life

689
00:33:27,720 --> 00:33:30,960
Speaker 3: in general generative AI, but what about wealth management in particular.

690
00:33:31,280 --> 00:33:32,640
Speaker 3: We've got a bit of an optimist in the house,

691
00:33:32,640 --> 00:33:35,160
Speaker 3: Sarah Hoffman, and vice president of AI and Machine learning

692
00:33:35,240 --> 00:33:38,640
Speaker 3: research over at Fidelity Investments, And really what takes us

693
00:33:38,720 --> 00:33:41,240
Speaker 3: about some of your thoughts in AI and wealth management.

694
00:33:41,320 --> 00:33:43,560
Speaker 3: There is the way in which you think chat, GBT

695
00:33:43,880 --> 00:33:46,480
Speaker 3: or those sorts of large language models could assist with

696
00:33:46,640 --> 00:33:48,720
Speaker 3: upgrading our approach to financial literacy.

697
00:33:49,120 --> 00:33:50,160
Speaker 5: How do you see that happening?

698
00:33:51,560 --> 00:33:56,200
Speaker 13: Yeah, so obviously the financial world has become so complicated

699
00:33:56,600 --> 00:33:58,960
Speaker 13: and would really want more people to be able to

700
00:33:59,160 --> 00:34:02,640
Speaker 13: access and under stand finances and one thing that I

701
00:34:02,720 --> 00:34:05,160
Speaker 13: could see happening with generative AI. I mean, we've been

702
00:34:05,200 --> 00:34:09,520
Speaker 13: talking about per personalization for so long, but this technology

703
00:34:09,640 --> 00:34:13,359
Speaker 13: really takes personalization to the next level when it comes

704
00:34:13,440 --> 00:34:17,399
Speaker 13: to any type of education, but specifically financial literacy, which

705
00:34:17,480 --> 00:34:20,960
Speaker 13: is so complicated. You can ask this technology, you know,

706
00:34:21,120 --> 00:34:23,920
Speaker 13: explain this to me as though I'm a fourth grader,

707
00:34:24,480 --> 00:34:27,120
Speaker 13: Or if you're somebody who's very into sports, you could say,

708
00:34:27,160 --> 00:34:29,839
Speaker 13: explain this to me with a sports metaphor we can

709
00:34:30,000 --> 00:34:32,759
Speaker 13: all really learn in our own way now, which is

710
00:34:32,880 --> 00:34:33,800
Speaker 13: super powerful.

711
00:34:34,239 --> 00:34:36,120
Speaker 3: Yeah, I've already been doing it to explain it to

712
00:34:36,200 --> 00:34:38,480
Speaker 3: my five year old I'm interested in those serah As

713
00:34:38,520 --> 00:34:42,440
Speaker 3: to how much information you talk about personalization, how much

714
00:34:42,480 --> 00:34:45,640
Speaker 3: are people willing to share their own financial information and

715
00:34:46,480 --> 00:34:48,360
Speaker 3: asking for examples and advice?

716
00:34:48,400 --> 00:34:50,960
Speaker 5: Shall we say it by chat ChiPT? How right is

717
00:34:51,040 --> 00:34:52,359
Speaker 5: that to do it at the moment as well?

718
00:34:54,120 --> 00:34:57,600
Speaker 13: Right now, I would not recommend using it for advice,

719
00:34:57,840 --> 00:35:01,800
Speaker 13: but I do believe this could be great before you

720
00:35:01,960 --> 00:35:06,719
Speaker 13: meet with a financial representative. So if a customer could

721
00:35:07,160 --> 00:35:10,200
Speaker 13: really learn on their own before that meeting, and even

722
00:35:10,239 --> 00:35:13,520
Speaker 13: the financial representative could use these tools before the meeting,

723
00:35:13,880 --> 00:35:16,440
Speaker 13: I think we can have a much better, more informed

724
00:35:16,480 --> 00:35:21,040
Speaker 13: discussion with better questions where even though I think the

725
00:35:21,160 --> 00:35:24,880
Speaker 13: human is still essential, everybody's gaining and coming in a

726
00:35:25,440 --> 00:35:25,919
Speaker 13: better place.

727
00:35:27,280 --> 00:35:30,239
Speaker 4: Sarah, I'm interested in you and what you're up to

728
00:35:30,360 --> 00:35:33,560
Speaker 4: at Fidelity. Give me a typical day in the life

729
00:35:33,640 --> 00:35:36,719
Speaker 4: of Sarah Hoffman working in the field of machine learning

730
00:35:36,760 --> 00:35:37,120
Speaker 4: and AI.

731
00:35:37,239 --> 00:35:43,279
Speaker 13: But at Fidelity, I focus specifically on what's coming with

732
00:35:43,480 --> 00:35:48,040
Speaker 13: technology over the next five years. Fidelity is a company

733
00:35:48,120 --> 00:35:51,160
Speaker 13: that really wants to make sure we are ready for

734
00:35:51,280 --> 00:35:54,920
Speaker 13: what's coming across our business units, and so at Fidelity

735
00:35:55,080 --> 00:35:59,680
Speaker 13: we focus specifically on building the next generation digital platform.

736
00:36:00,080 --> 00:36:04,760
Speaker 13: I'll also focusing on the human element of the industry,

737
00:36:04,800 --> 00:36:07,839
Speaker 13: which we believe is very important, and so my role

738
00:36:07,880 --> 00:36:11,000
Speaker 13: specifically is really making sure we are aware of the

739
00:36:11,080 --> 00:36:12,000
Speaker 13: trends that are coming.

740
00:36:13,200 --> 00:36:15,759
Speaker 4: Would you say that you work closely more internally with

741
00:36:15,880 --> 00:36:18,840
Speaker 4: your technology teams or how close are you also working

742
00:36:19,360 --> 00:36:21,640
Speaker 4: with the investment teams to basically say, look, this is

743
00:36:21,719 --> 00:36:23,880
Speaker 4: a tool that we could use and here's how we

744
00:36:24,000 --> 00:36:25,080
Speaker 4: might potentially use it.

745
00:36:26,400 --> 00:36:26,560
Speaker 2: Yeah.

746
00:36:26,600 --> 00:36:30,680
Speaker 13: So I write research papers and give presentations and talks

747
00:36:30,719 --> 00:36:34,719
Speaker 13: and have dialogue across the company across roles and for

748
00:36:35,840 --> 00:36:37,440
Speaker 13: both of these types of roles.

749
00:36:37,200 --> 00:36:40,600
Speaker 3: And how much so is everybody chat GIPT to help

750
00:36:40,640 --> 00:36:41,920
Speaker 3: write those research papers now.

751
00:36:41,920 --> 00:36:45,680
Speaker 13: Sarah, I've tried, I would right now. One of the

752
00:36:46,800 --> 00:36:50,080
Speaker 13: main issues with tools like chat GPT is that they

753
00:36:50,239 --> 00:36:54,520
Speaker 13: really don't have current information, so you can't really rely

754
00:36:54,719 --> 00:36:57,200
Speaker 13: on it for if you're looking for trends or anything

755
00:36:57,719 --> 00:37:01,080
Speaker 13: that's it doesn't understand or know about what's happening right now.

756
00:37:01,480 --> 00:37:05,400
Speaker 13: But it's still useful for if you're trying to figure

757
00:37:05,440 --> 00:37:09,359
Speaker 13: out a nice structure or even brainstorming. You know, how

758
00:37:09,480 --> 00:37:12,880
Speaker 13: can generative AI be useful in wealth management? It's a

759
00:37:13,000 --> 00:37:17,400
Speaker 13: useful brainstorming tool, but because it doesn't know today, I

760
00:37:17,440 --> 00:37:21,080
Speaker 13: wouldn't recommend it to trust it blindly. One nice thing

761
00:37:21,120 --> 00:37:23,520
Speaker 13: I would add about using it for brainstorming is that

762
00:37:24,280 --> 00:37:28,000
Speaker 13: today it could produce things that you cannot trust. And

763
00:37:28,200 --> 00:37:30,759
Speaker 13: when it comes to brainstorming and trying to find a

764
00:37:30,880 --> 00:37:36,120
Speaker 13: novel insight, it doesn't actually matter if you can't verify

765
00:37:36,200 --> 00:37:38,560
Speaker 13: the results, as long as it helps you think more

766
00:37:38,640 --> 00:37:42,080
Speaker 13: creatively and think differently. So to me, brainstorming is actually

767
00:37:42,160 --> 00:37:43,880
Speaker 13: a perfect use case for these tools.

768
00:37:44,120 --> 00:37:44,400
Speaker 4: Sarah.

769
00:37:44,440 --> 00:37:47,439
Speaker 3: It was interesting we just had Chris Valenzuela on who's

770
00:37:47,480 --> 00:37:50,800
Speaker 3: the CEO of Runway, thinking about generative AI and the

771
00:37:50,880 --> 00:37:54,479
Speaker 3: world of creation and particularly in images. But he said

772
00:37:54,520 --> 00:37:56,360
Speaker 3: when four or five years ago he was speaking to

773
00:37:56,480 --> 00:37:57,400
Speaker 3: vcs and they were like.

774
00:37:57,840 --> 00:37:59,800
Speaker 5: Generative AI is not a thing. It's not going to

775
00:37:59,840 --> 00:38:00,359
Speaker 5: be thing.

776
00:38:01,000 --> 00:38:02,960
Speaker 3: Did you, as someone who has to look at what's

777
00:38:03,040 --> 00:38:05,640
Speaker 3: the trend in four or five years, see this coming?

778
00:38:07,600 --> 00:38:08,920
Speaker 5: So I saw this coming.

779
00:38:09,000 --> 00:38:12,200
Speaker 13: I would save me personally with GPT three, the previous

780
00:38:12,360 --> 00:38:15,800
Speaker 13: version of chat GPT which was released in twenty twenty,

781
00:38:17,160 --> 00:38:19,919
Speaker 13: it was so much better than and your generative AI

782
00:38:20,040 --> 00:38:23,120
Speaker 13: existed before, but this was so much better than anything

783
00:38:23,200 --> 00:38:27,320
Speaker 13: we had seen before that you know that I and

784
00:38:27,440 --> 00:38:30,319
Speaker 13: many others were, you know, looking at you know, how can,

785
00:38:30,560 --> 00:38:30,879
Speaker 13: what can?

786
00:38:31,040 --> 00:38:31,759
Speaker 5: What does this mean?

787
00:38:32,040 --> 00:38:35,439
Speaker 13: Basically, the conversations that started in November with everybody else

788
00:38:35,800 --> 00:38:38,800
Speaker 13: really started back in twenty twenty, twenty twenty one with

789
00:38:38,920 --> 00:38:42,279
Speaker 13: GPT three. For those of us in the AI world.

790
00:38:42,320 --> 00:38:45,440
Speaker 3: Sarah Hoffmann me thank you for audio advice and was

791
00:38:45,480 --> 00:38:47,600
Speaker 3: back to them. Vice president of AI and Machine Learning

792
00:38:47,640 --> 00:38:59,040
Speaker 3: Research every Fidelity Investments, there is quite the whirlwind. Immediate

793
00:38:59,080 --> 00:39:02,920
Speaker 3: departures today from Tucker Carlson departing Fox News immediately sending

794
00:39:02,960 --> 00:39:05,560
Speaker 3: Fox shares plunging, as we've already seen in the showdown

795
00:39:05,560 --> 00:39:07,560
Speaker 3: six hundred million dollars in terms of market cap.

796
00:39:07,760 --> 00:39:10,440
Speaker 5: We've also seen CNN star anchor Don Lemon.

797
00:39:10,320 --> 00:39:13,640
Speaker 3: Leaving after being under intense scrutiny following remarks all went

798
00:39:13,680 --> 00:39:17,239
Speaker 3: back in February about women and aging. Let's break all

799
00:39:17,280 --> 00:39:21,040
Speaker 3: of this down. I Meg's Felix Chillette extraordinary. Let's start

800
00:39:21,160 --> 00:39:24,799
Speaker 3: with Tucker Carlson. How much is this linked to settlements

801
00:39:24,880 --> 00:39:26,200
Speaker 3: with voting systems in the life.

802
00:39:26,480 --> 00:39:31,160
Speaker 19: I think it's definitely fallout from the dominion settlement. Rupert

803
00:39:31,239 --> 00:39:34,080
Speaker 19: Murdoch in the past has shown that during times of

804
00:39:34,160 --> 00:39:38,880
Speaker 19: controversy he's totally willing to get rid of talent. And

805
00:39:39,160 --> 00:39:42,040
Speaker 19: if you think back to the phone hacking scandal, closing

806
00:39:42,120 --> 00:39:44,799
Speaker 19: News of the World, getting rid of writers reporters left

807
00:39:44,840 --> 00:39:48,080
Speaker 19: and right, I think you know Murdoch has always felt

808
00:39:48,200 --> 00:39:50,759
Speaker 19: confident that he can replace the talent, even if it's

809
00:39:50,800 --> 00:39:54,160
Speaker 19: someone like Tucker Carlson, who's currently the top rated primetime

810
00:39:54,239 --> 00:39:56,919
Speaker 19: host for Fox News. You think back of when Fox

811
00:39:57,040 --> 00:40:01,440
Speaker 19: News got rid of Glenn Beck reread events, Meg and Kelly,

812
00:40:01,520 --> 00:40:03,680
Speaker 19: they're always this question, Oh, how are they going to recover?

813
00:40:04,200 --> 00:40:07,040
Speaker 19: And yet whoever they plug into those time slots always

814
00:40:07,080 --> 00:40:08,439
Speaker 19: seem to do quite well.

815
00:40:09,400 --> 00:40:12,120
Speaker 4: Felix, our colleagues at Bloomberg Intelligence have a react out

816
00:40:12,160 --> 00:40:14,760
Speaker 4: at pointing out Fox News two point two billion dollars

817
00:40:14,800 --> 00:40:17,400
Speaker 4: of EBIT DARR or seventy percent of profits. You can

818
00:40:17,440 --> 00:40:20,440
Speaker 4: see why there's that market reaction. CNN Don Lemon, what

819
00:40:20,520 --> 00:40:20,800
Speaker 4: do we know?

820
00:40:21,800 --> 00:40:25,080
Speaker 19: Don Lemon has been, you know, going from one controversy

821
00:40:25,120 --> 00:40:26,799
Speaker 19: to the next for the last couple of months. They

822
00:40:26,880 --> 00:40:29,520
Speaker 19: brought him back after he made these comments about Nicki

823
00:40:29,560 --> 00:40:33,120
Speaker 19: Haley that upset people. You know, the problem with Don

824
00:40:33,239 --> 00:40:36,400
Speaker 19: Lemon is not only is the generating negative headlines for CNN,

825
00:40:36,560 --> 00:40:40,239
Speaker 19: but also the ratings have been lousy. So combine those

826
00:40:40,280 --> 00:40:43,640
Speaker 19: two things. Chris Lick, who's overseeing CNN now for Warner

827
00:40:43,680 --> 00:40:47,080
Speaker 19: Brothers Discovery, has been under a lot of pressure. You know,

828
00:40:47,400 --> 00:40:51,000
Speaker 19: ratings have been way down ever since Donald Trump left

829
00:40:51,080 --> 00:40:54,520
Speaker 19: office for CNN, and so the fact that they would

830
00:40:54,560 --> 00:40:56,920
Speaker 19: make this switch, it almost seems surprising to me that

831
00:40:57,000 --> 00:40:58,440
Speaker 19: they haven't done this previously.

832
00:40:59,280 --> 00:41:01,960
Speaker 3: Felix Jalet with the inside track and what is a

833
00:41:02,000 --> 00:41:04,560
Speaker 3: whirlwinder headlines we'll let him get back to his analysis

834
00:41:04,640 --> 00:41:05,240
Speaker 3: of reporting.

835
00:41:05,760 --> 00:41:06,960
Speaker 5: We thank you so much.

836
00:41:07,280 --> 00:41:11,040
Speaker 3: Meanwhile, Oh, quite an extraordinary start to a Monday, and Ed,

837
00:41:11,160 --> 00:41:13,280
Speaker 3: I thank you for rolling with my rather horse voice.

838
00:41:13,360 --> 00:41:15,279
Speaker 3: I wasn't at partying, I'm afraid to say.

839
00:41:15,640 --> 00:41:18,240
Speaker 5: But that does it for this edition of Bloomberg Technology.

840
00:41:18,280 --> 00:41:21,200
Speaker 3: Tune in tomorrow for our gear up the coverage, your

841
00:41:21,239 --> 00:41:24,400
Speaker 3: coverage of RSA cybersecurity conference over in San Francisco.

842
00:41:24,440 --> 00:41:27,160
Speaker 5: We got the eighteen t COO among many more.

843
00:41:28,280 --> 00:41:32,160
Speaker 4: Yeah. Look, huge weeks of earnings, just megacap names coming left,

844
00:41:32,239 --> 00:41:33,959
Speaker 4: right and center. If you want to recap, don't forget.

845
00:41:34,040 --> 00:41:37,319
Speaker 4: We have the podcast on Bloomberg or other platforms where

846
00:41:37,320 --> 00:41:39,920
Speaker 4: you get your podcasts. So much to think on. This

847
00:41:40,080 --> 00:41:40,680
Speaker 4: is Bloomberg