WEBVTT - SpaceX IPO Multiple Times Oversubscribed

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<v Speaker 1>Bloomberg Audio Studios, podcasts, radio news. Bloomberg Tech is alive

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<v Speaker 1>from coast to coast with Caroline Hide in New York

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<v Speaker 1>and ever though in sentences.

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<v Speaker 2>Go this is Bloomberg Tech coming up. Wall Street can't

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<v Speaker 2>get enough of SpaceX. With demand from big institutional investors

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<v Speaker 2>and the biggest IPO in history, way over subscribed, class.

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<v Speaker 3>Google backstops and thropping data centers, a Silicon valley raises

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<v Speaker 3>to build AI infrastructure with ever more intertwined deals.

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<v Speaker 2>An Oracle reports after the closing bell, it's a race

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<v Speaker 2>between building data centers and booking AI cloud.

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<v Speaker 4>Revenues, AI AI AI and space SpaceX's over subscribed IPO

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<v Speaker 4>is where we have to start ed because the geographical

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<v Speaker 4>reach of the level of demand.

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<v Speaker 5>We've been mesmerized by this record breaking.

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<v Speaker 2>Yeah, it's out of this world. I don't apologize for

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<v Speaker 2>that for one bit. The state of players this right.

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<v Speaker 2>The order book for institution investors closes four pm Eastern today,

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<v Speaker 2>and as we've reported, there are several long only asset

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<v Speaker 2>managers basically that want ten billion dollars worth of shares.

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<v Speaker 5>It's seventy five billion dollars worth.

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<v Speaker 2>So somebody is going to miss out. Now the retail

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<v Speaker 2>investor can still place orders I think through Thursday on

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<v Speaker 2>whatever platforms are available, but they're not guaranteed to get

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<v Speaker 2>hold of those shares either. So that's the state of play.

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<v Speaker 2>And believe it or not, there is a roadshow happening

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<v Speaker 2>in the background, and.

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<v Speaker 5>We're learning ever more on that roadshow. That's the entire point.

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<v Speaker 3>We're understanding the transparency, the business model. We're learning much

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<v Speaker 3>about those orbital data centers.

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<v Speaker 2>Yeah, I think the focus in the pitch has still

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<v Speaker 2>been let us explain orbital data centers. That brings us

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<v Speaker 2>to today's big number, two hundred and fifty billion. That's

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<v Speaker 2>a total amount of SpaceX IPO orders we've reported this morning,

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<v Speaker 2>one to five billion of which is coming from Saudi Qate,

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<v Speaker 2>other Middle East funds, sovereign funds. That's according to sources.

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<v Speaker 2>That's the absolute latest. Joining us now to talk all

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<v Speaker 2>things SpaceX. It's IPO. Also the general landscape Peter Singlehurst,

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<v Speaker 2>head of private Companies are Bailey giff and we just know,

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<v Speaker 2>you know SpaceX is a really important investment for you

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<v Speaker 2>guys prior to the offering. Let's start there. You know,

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<v Speaker 2>what does this the biggest IPO in history represent to

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<v Speaker 2>you and to the firm and to I guess support

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<v Speaker 2>the thesis when you first made the investment way back when.

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<v Speaker 6>I think that the SpaceX IPO needs to be seen

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<v Speaker 6>as the culmination of a trend which has been playing

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<v Speaker 6>out now for fifteen years or longer, of companies staying

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<v Speaker 6>private for longer. And this is something that we started

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<v Speaker 6>to see in twenty twelve when we first started investing

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<v Speaker 6>in private companies. Now, we didn't think the companies would

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<v Speaker 6>get this big and stay private this long, But here

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<v Speaker 6>we are with, you know, SpaceX going public at something

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<v Speaker 6>like a one point eight trillion dollar valuation. That's nine

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<v Speaker 6>hundred times larger and more valuable than Tesla was when

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<v Speaker 6>it went public in twenty twelve. So, on the one hand,

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<v Speaker 6>this is a story of a truly exceptional company which

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<v Speaker 6>has compounded its growth at a very high rate. On

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<v Speaker 6>the other hand, it's a story of a bigger structural

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<v Speaker 6>trend of companies staying private longer and more and more

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<v Speaker 6>return to accruing within the high growth private.

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<v Speaker 5>Market and peter to that end.

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<v Speaker 3>When you think about Tesla after it's gone public, it

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<v Speaker 3>was a volatile ride, but it's twenty five thousand percent

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<v Speaker 3>higher than when it listed, And so will we see

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<v Speaker 3>a level of returns do you think in the public

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<v Speaker 3>market or does that have to be in some ways

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<v Speaker 3>pushed against the meat and bones of returns going to

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<v Speaker 3>have happened to private investors.

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<v Speaker 6>I think it's mathematically it's very hard to see how

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<v Speaker 6>you could see SpaceX delivering the same kind of returns

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<v Speaker 6>as a public company as Tesla did. But I think

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<v Speaker 6>what this speaks to is a requirement for investors to

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<v Speaker 6>have exposure to growth in both the private and the

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<v Speaker 6>public markets. Has been set up to almost divide these

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<v Speaker 6>things and say there's kind of private growth and there's

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<v Speaker 6>public growth, and these things are different, and we've taken

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<v Speaker 6>a different approach. We've sort of taken the view that actually,

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<v Speaker 6>if you want to do growth equity investing, you want

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<v Speaker 6>to do it properly, you have to do it in

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<v Speaker 6>the private markets, and you have to do it in

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<v Speaker 6>the public markets. And what our clients are and beneficiaries

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<v Speaker 6>who are predominantly pension funds, what they need and what

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<v Speaker 6>they ask from us is that we give them exposures

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<v Speaker 6>to the world's best growth stage companies starting in the

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<v Speaker 6>private markets. Earning the returns that we can generate there,

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<v Speaker 6>and then also only those in the public markets from

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<v Speaker 6>within our public funds to make sure that they're still

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<v Speaker 6>capturing that growth even once companies transition into the public markets.

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<v Speaker 2>Peter, I think it's important to pose the question why

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<v Speaker 2>is SpaceX going public? And when Elon Musk spoke to

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<v Speaker 2>Jamie Diamond, he eventually got to the answer, which is

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<v Speaker 2>they need capital for this growth phase. But what we

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<v Speaker 2>are seeing outside of just this fixation on IPOs is

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<v Speaker 2>a race for capital through equity. How comfortable do you

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<v Speaker 2>feel as a firm at Bailey gifed, whatever mechanism it

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<v Speaker 2>is raising money at that volume, but it basically then

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<v Speaker 2>goes directly into capital expenditure. That's what's happening here.

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<v Speaker 6>You want to invest in companies that are able to

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<v Speaker 6>deploy capital or high rates of return. So I don't

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<v Speaker 6>think there's anything wrong, per se in investing in capital

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<v Speaker 6>intensive businesses. In fact, what you want as a company

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<v Speaker 6>that can deploy large amounts of capital, but where you

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<v Speaker 6>can earn high returns on that capital. And ultimately that's

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<v Speaker 6>what separates a good business from a bad business. It's

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<v Speaker 6>return on equity. And so when we're looking at a company,

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<v Speaker 6>whether it's SpaceX or Anthropic, what any other company that

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<v Speaker 6>we invest in privately or publicly. Ultimately, what we're asking

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<v Speaker 6>is how do you get to high returns on equity?

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<v Speaker 6>And it's building those thesis that then leads us to

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<v Speaker 6>invest in companies. And in the case of SpaceX, increasingly

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<v Speaker 6>that thesis is going to have to rely on AI.

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<v Speaker 6>They've shown that they can invest capital or high rates

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<v Speaker 6>of return in rockets in starlink, and of course the

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<v Speaker 6>next leg of that is going to be in AI

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<v Speaker 6>data center build out, quite possibly in space but that's.

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<v Speaker 3>Where it becomes so fascinating, particularly Peter for Bailey Gifford,

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<v Speaker 3>which in the private markets backspace X on a thesis

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<v Speaker 3>of SPACE, Backtindthropic on a thesis of AI, and now

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<v Speaker 3>they're all overlapping in terms of business models. What is

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<v Speaker 3>your perspective of commoditization or a winner takes all or

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<v Speaker 3>is there room for all.

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<v Speaker 5>Of these giant AI players? Do we winning in.

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<v Speaker 3>The technology as well as perhaps in the public markets.

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<v Speaker 6>I think what your question gets to is this very

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<v Speaker 6>important question of where does value accrue in the AI stack? Now,

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<v Speaker 6>hopefully lots of value is going to accrue to the

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<v Speaker 6>end customers. That has to happen. Historically, we've seen value

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<v Speaker 6>accruing to the chip manufacturers, initially with Nvidio, but now

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<v Speaker 6>increasing league so memory manufacturers. But I think what we're

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<v Speaker 6>also starting to see is value a crew at the

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<v Speaker 6>foundational model level. And I suppose with the Grock acquisition,

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<v Speaker 6>SpaceX is making a bet not just on the foundational

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<v Speaker 6>model level, but also on the infrastructure level. And I

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<v Speaker 6>think what we're seeing with the deal that they did

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<v Speaker 6>recently with Anthropic is that they have options in terms

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<v Speaker 6>of how they can monetize in the AI transition, both

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<v Speaker 6>through their own models, but also importantly through the infrastructure itself.

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<v Speaker 2>I have so many questions about this. You know, let's

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<v Speaker 2>be honest. The hedge that SpaceX has put in place

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<v Speaker 2>in the interim is to become a hyperscaler and sell

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<v Speaker 2>compute played a blinder with that. We got the design,

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<v Speaker 2>or at least the renderings of Orbital Data Center. I

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<v Speaker 2>think the team are going to put them up on

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<v Speaker 2>the screen now in that presentation that Elon must make,

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<v Speaker 2>like there's the body, there's the solar arrays, there's the radiator,

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<v Speaker 2>which part of the thesis, Peter is most important to you, right,

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<v Speaker 2>It is a long way from the tam of twenty

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<v Speaker 2>six point five trillion that they're basically packaging it as

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<v Speaker 2>enterprise AI and in the interim, this plan for orbital

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<v Speaker 2>data center like it needs to work. That's what they're

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<v Speaker 2>telling people on the road show.

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<v Speaker 6>So there's absolutely no question that the orbital Data center

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<v Speaker 6>strategic that they're making widens the range of outcomes for SpaceX.

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<v Speaker 6>On the one hand, if it works, it increases the

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<v Speaker 6>potential upsides for the business. On the other hand, if

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<v Speaker 6>this doesn't work, it's going to increase the downside for

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<v Speaker 6>this company. And investing ultimately is about ranges of outcomes.

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<v Speaker 6>It's about probabilities, and it's about payoffs in those range

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<v Speaker 6>of outcomes. What we've seen with SpaceX over the years

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<v Speaker 6>is that they have continuously tested and validated a series

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<v Speaker 6>of outlandish hypotheses. The very notion of the business starting

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<v Speaker 6>off as a private rocket company was a self an

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<v Speaker 6>outlandish hypothesis that they've validated. Then the idea that you

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<v Speaker 6>could have reusable rockets was also an outlanded hypothesis, and

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<v Speaker 6>they validated it. They did the same with starlink with

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<v Speaker 6>satellite based broadband. They've done the same with rockets on

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<v Speaker 6>the scale of Starship and the orbital data centillate is

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<v Speaker 6>that is the next hypothesis that they are seeking to validate.

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<v Speaker 6>But everybody should be totally aware of the risks that

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<v Speaker 6>are involved in this. It is unproven. In the event

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<v Speaker 6>that they prove it, the payoffs will be large. But

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<v Speaker 6>as we've already touched on the amount of cattle that

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<v Speaker 6>is going into this means that the event that they

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<v Speaker 6>don't validate it, it's going to increase the scope of

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<v Speaker 6>downside in the investments as well, and investors just need

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<v Speaker 6>to understand the range of outcomes and the payoffs that

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<v Speaker 6>go with that.

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<v Speaker 3>Can I ask about payoffs, Peter, because I don't want

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<v Speaker 3>to go into the granularity of how much SpaceX E

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<v Speaker 3>suppose you have, et cetera, but how long do you

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<v Speaker 3>think you'll hold it and how much do you think

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<v Speaker 3>it's a warrior or an anxiety that all these other

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<v Speaker 3>big public companies are selling equity into this market at

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<v Speaker 3>the same time Alphabet trying to fund its own capex

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<v Speaker 3>in the equity market, Meta might be doing as well,

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<v Speaker 3>And does that take oxygen out the room.

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<v Speaker 6>I think that's probably part of the thinking that's going

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<v Speaker 6>on for these different companies trying to raise these large

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<v Speaker 6>amounts of cattle. They're sort of trying to soak up

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<v Speaker 6>what available castle there is. But to your first question,

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<v Speaker 6>different funds within Bailey Gifford are going to be in

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<v Speaker 6>very different positions. For those funds that have owned SpaceX

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<v Speaker 6>since twenty eighteen, since it was a thirty billion dollar company,

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<v Speaker 6>those funds have very very large exposure, large positions in SpaceX.

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<v Speaker 6>Now it might make sense post lock up for those

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<v Speaker 6>funds to start selling down, even if they want to

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<v Speaker 6>maintain a meaningful exposure, because ultimately, we are beholding to

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<v Speaker 6>our clients and we have to provide them with a

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<v Speaker 6>level of diversification within their funds. And then, of course

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<v Speaker 6>funds that don't own it, funds that are solely public funds,

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<v Speaker 6>they then faced with a question of whether to buy

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<v Speaker 6>it for their funds. So it might well be that

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<v Speaker 6>you see different funds within Bailey Gifford doing different things

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<v Speaker 6>over the coming months, and that will be a function

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<v Speaker 6>of the history and the portfolio context. I think there's

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<v Speaker 6>universe agreement that SpaceX has been an exceptional company. The

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<v Speaker 6>real question from here is what is the right price

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<v Speaker 6>and what is the right position size in SpaceX.

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<v Speaker 3>From thirty billion to potentially one point eight trillion this

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<v Speaker 3>week Peter Senglehurst A Bailey Gifford A joy to have

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<v Speaker 3>you on about all things SpaceX and the IPO landscape.

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<v Speaker 3>More broadly, let's get though, also to political tensions. They

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<v Speaker 3>are continuing to whipsaw markets. We are down a percentage

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<v Speaker 3>point again on the NASA one hundred s and p

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<v Speaker 3>is under pressure. You're seeing a really hardware of by

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<v Speaker 3>two percent if you're looking at the semiconductor index. President

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<v Speaker 3>Trump is saying that Iran would pay the price.

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<v Speaker 5>For delaying peace negotiations.

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<v Speaker 3>Let's get you up to speakably the most Tyler Kendall,

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<v Speaker 3>the latest sim of the White House.

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<v Speaker 7>What do we need to know, hey, Caroline, Well, at

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<v Speaker 7>this point, President Trump is renewing his threat, really just

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<v Speaker 7>underscoring that this White House has mounting frustration with the

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<v Speaker 7>ongoing negotiations, as the US has repeatedly maintained that they

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<v Speaker 7>were trying to prioritize a diplomatic solution to end the conflict. Now,

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<v Speaker 7>President Trump's remarks aren't totally clear if this means that

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<v Speaker 7>we're going to see an end to the ceasefire agreement

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<v Speaker 7>after we saw the worst flare up in fighting between

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<v Speaker 7>the sides just overnight with both the US and Iran

0:11:51.280 --> 0:11:56.320
<v Speaker 7>exchanging strikes after an American military helicopter was shot down.

0:11:56.600 --> 0:12:00.920
<v Speaker 7>After the strike from the US then on Iranian military assets,

0:12:01.000 --> 0:12:04.200
<v Speaker 7>we saw Iran put forward some strikes and attempts to

0:12:04.280 --> 0:12:07.720
<v Speaker 7>hit American military assets. It's really been escalating from here.

0:12:07.760 --> 0:12:11.000
<v Speaker 7>But our own analysts at Bloomberg Economics, perhaps this is

0:12:11.040 --> 0:12:14.079
<v Speaker 7>a bid to escalate in a bid to de escalate.

0:12:14.360 --> 0:12:17.480
<v Speaker 7>In one positive sign for the negotiation front, Irani and

0:12:17.520 --> 0:12:20.400
<v Speaker 7>state media reported within the last hour that a Katari

0:12:20.559 --> 0:12:23.520
<v Speaker 7>delegation has landed in Tehran in a bid to keep

0:12:23.559 --> 0:12:26.240
<v Speaker 7>diplomacy on track. But Ed and Caroline, I want to

0:12:26.360 --> 0:12:29.440
<v Speaker 7>highlight this renewed risk that we are seeing moments ago

0:12:29.760 --> 0:12:33.040
<v Speaker 7>flashing across the Bloomberg terminal. India is now condemning an

0:12:33.040 --> 0:12:37.320
<v Speaker 7>apparent attack on a commercial vessel near the Strait of

0:12:37.320 --> 0:12:40.600
<v Speaker 7>her Moves off the coast of Oman. So definitely still

0:12:40.760 --> 0:12:43.760
<v Speaker 7>very high intentions contributing to the situation that we're seeing

0:12:43.840 --> 0:12:44.400
<v Speaker 7>on the ground.

0:12:45.559 --> 0:12:48.040
<v Speaker 2>Goodvoks Tyler Kendall, Thank you very much. So coming up,

0:12:48.080 --> 0:12:51.400
<v Speaker 2>Google steps up to backstop a massive thirty five billion

0:12:51.440 --> 0:12:54.360
<v Speaker 2>dollar financing deal from Propic. We had the details. Next,

0:12:54.480 --> 0:13:03.240
<v Speaker 2>this is Bloomberg Tech. Super Micro is targeting public markets

0:13:03.240 --> 0:13:05.920
<v Speaker 2>for a massive seven billion dollar equity raise. Sales to

0:13:06.000 --> 0:13:09.800
<v Speaker 2>super Micro servers fitted with video chips have surged for

0:13:09.960 --> 0:13:13.160
<v Speaker 2>AI workloads. The server manufacturer is moving quickly to fund

0:13:13.200 --> 0:13:16.560
<v Speaker 2>a staggering thirty nine billion dollars in orders, using the

0:13:16.600 --> 0:13:19.800
<v Speaker 2>fresh cash injection to pay for the equipment needed to

0:13:19.840 --> 0:13:23.760
<v Speaker 2>make the servers. That's greasing the wheels. Google is backstopping

0:13:23.800 --> 0:13:27.319
<v Speaker 2>a massive thirty five billion dollar financing deal from Fropic,

0:13:27.600 --> 0:13:30.480
<v Speaker 2>The creator of Claud, is leasing AI chips across five

0:13:30.760 --> 0:13:33.760
<v Speaker 2>different data centers with help from the long established tech

0:13:33.800 --> 0:13:37.800
<v Speaker 2>giant Doomscott Carpenter joins us now to break down the mechanics, like,

0:13:38.280 --> 0:13:41.839
<v Speaker 2>you know, to our audience, what does backstopping mean? I

0:13:41.880 --> 0:13:44.559
<v Speaker 2>think what we're saying is guaranteeing the funds for it

0:13:44.920 --> 0:13:46.360
<v Speaker 2>and the event that something goes wrong.

0:13:46.440 --> 0:13:50.360
<v Speaker 8>But but just go with the basics, right, So, first

0:13:50.400 --> 0:13:55.240
<v Speaker 8>of all, Broadcom is providing a huge guarantee on the

0:13:55.440 --> 0:14:00.439
<v Speaker 8>chips themselves, as the biggest part of the thirty five years. Yes,

0:14:00.480 --> 0:14:03.120
<v Speaker 8>these are Google's TPUs that are going to be involved,

0:14:03.600 --> 0:14:06.319
<v Speaker 8>So Broadcom is backstopping the debt itself.

0:14:07.080 --> 0:14:09.000
<v Speaker 2>Now, the chips, when.

0:14:08.880 --> 0:14:12.440
<v Speaker 8>They are delivered and they have to be manufactured, are

0:14:12.520 --> 0:14:15.400
<v Speaker 8>going to be used in these five data centers that

0:14:15.600 --> 0:14:20.080
<v Speaker 8>we identify in the story. The leases on those five

0:14:20.200 --> 0:14:24.200
<v Speaker 8>data centers are backstopped by Google. So you could think

0:14:24.200 --> 0:14:28.560
<v Speaker 8>of it as two different forms of guarantees being involved

0:14:29.040 --> 0:14:32.240
<v Speaker 8>in this to put together this deal. There's the Broadcom

0:14:32.280 --> 0:14:34.080
<v Speaker 8>one and there's the Google ones underneath.

0:14:34.280 --> 0:14:37.880
<v Speaker 3>So in a way, Broadcom's saying almost Alphabet's going to

0:14:37.880 --> 0:14:40.560
<v Speaker 3>get its money from Anthropic for buying the chips.

0:14:40.800 --> 0:14:41.280
<v Speaker 5>Is that right?

0:14:41.400 --> 0:14:44.080
<v Speaker 3>Meanwhile, who's getting the money for the leases and who

0:14:44.160 --> 0:14:46.480
<v Speaker 3>therefore is Alphabet saying like, you're good for the money,

0:14:46.480 --> 0:14:48.920
<v Speaker 3>don't worry. Is that the people actually constructing the data

0:14:48.920 --> 0:14:50.160
<v Speaker 3>centers owning the land.

0:14:50.600 --> 0:14:54.880
<v Speaker 8>It's the leases are to Fluid Stack, which is a

0:14:54.920 --> 0:14:58.680
<v Speaker 8>company that Anthropic has said it's been working with to

0:14:58.800 --> 0:15:03.080
<v Speaker 8>develop these data seen. So you see how it's it's complicated, right,

0:15:03.480 --> 0:15:04.400
<v Speaker 8>there's many.

0:15:04.400 --> 0:15:05.560
<v Speaker 2>See how it's complicated.

0:15:05.640 --> 0:15:08.480
<v Speaker 8>Yeah, yeah, I mean to pull off a deal of

0:15:08.520 --> 0:15:12.040
<v Speaker 8>this magnitude, which I think is the largest private credit

0:15:12.040 --> 0:15:16.160
<v Speaker 8>deal in history, definitely the largest chip deal. There's a

0:15:16.160 --> 0:15:18.560
<v Speaker 8>lot of moving pieces. One of the key things is

0:15:18.560 --> 0:15:20.840
<v Speaker 8>that these chips are not I mean, they need to

0:15:20.920 --> 0:15:21.720
<v Speaker 8>be created.

0:15:21.760 --> 0:15:27.200
<v Speaker 2>They don't exist right now. But yeah, there's a lot

0:15:27.280 --> 0:15:27.960
<v Speaker 2>that goes into this.

0:15:28.600 --> 0:15:33.880
<v Speaker 3>Who's the manufacturer of questions around Intel? VISs TSMC absolutely fascinating.

0:15:33.920 --> 0:15:36.440
<v Speaker 3>Scott Carpenter. He broke it down so clearly, we so

0:15:36.480 --> 0:15:39.400
<v Speaker 3>appreciate it. Meanwhile, let's turn our attention to soft Bank.

0:15:39.520 --> 0:15:42.080
<v Speaker 3>It's attempt to leverage its massive AI bets. It's hitting

0:15:42.200 --> 0:15:44.680
<v Speaker 3>a bit of a wall. Sources told Bloomberg that talks

0:15:44.680 --> 0:15:46.960
<v Speaker 3>are stalled with potential creditors to raise at least six

0:15:46.960 --> 0:15:50.200
<v Speaker 3>billion dollars from a margin loan backed by its opening

0:15:50.240 --> 0:15:53.360
<v Speaker 3>eye steak. So it's unclear why the pores comes just

0:15:53.400 --> 0:15:56.200
<v Speaker 3>weeks after soft Bank slashed it's fundraising target from ten

0:15:56.240 --> 0:15:59.080
<v Speaker 3>billion dollars, and soft Bank shares have tumbled nearly ten percent.

0:15:59.120 --> 0:16:01.960
<v Speaker 3>On the news today, A says the firm ways alternative

0:16:02.240 --> 0:16:04.400
<v Speaker 3>funding options coming up.

0:16:04.840 --> 0:16:05.760
<v Speaker 5>We are going to be speaking a.

0:16:05.840 --> 0:16:10.240
<v Speaker 3>Saphia Noble, Professor, director of the Center of Resilience and

0:16:10.280 --> 0:16:13.800
<v Speaker 3>Digital Justice, at the UCLA to discuss bias discrimination within

0:16:13.840 --> 0:16:14.520
<v Speaker 3>this world of AI.

0:16:14.560 --> 0:16:16.720
<v Speaker 5>We keep talking more on that next as a Blueberg tech.

0:16:24.040 --> 0:16:26.680
<v Speaker 3>This week, as Open AI filed its S one confidentially,

0:16:26.760 --> 0:16:29.600
<v Speaker 3>CEO Samaltman was also out with a sweeping long term

0:16:29.640 --> 0:16:33.160
<v Speaker 3>vision for generative AIS alignment with humanity and warned that

0:16:33.440 --> 0:16:37.600
<v Speaker 3>transformative technologies such as AI can concentrate power, stating Open

0:16:37.640 --> 0:16:40.400
<v Speaker 3>AIS quote clear ride about the risks as it aims

0:16:40.440 --> 0:16:43.680
<v Speaker 3>to build powerful systems that remain safe subject to human control.

0:16:43.720 --> 0:16:47.600
<v Speaker 3>But critics have long voiced concerns about AI risks such

0:16:47.600 --> 0:16:51.280
<v Speaker 3>as algorithmic bias in equality joining us now, Sofia Noble,

0:16:51.440 --> 0:16:55.800
<v Speaker 3>UCLA professor author of the acclaimed book Algorithms of Oppression

0:16:56.800 --> 0:16:59.800
<v Speaker 3>clear Ride, is that enough. Are we seeing some of

0:16:59.840 --> 0:17:04.200
<v Speaker 3>the risks being digested and answered for within these models?

0:17:04.640 --> 0:17:06.960
<v Speaker 9>I don't think so. I don't think we're anywhere near

0:17:08.160 --> 0:17:12.400
<v Speaker 9>a call for or an ability to realize safe AI.

0:17:13.359 --> 0:17:17.160
<v Speaker 9>What we see, in fact, are chapbot technologies and large

0:17:17.200 --> 0:17:21.000
<v Speaker 9>language models that for the most part don't have markets.

0:17:21.359 --> 0:17:24.560
<v Speaker 9>I mean, they were built for Corporate America to reduce

0:17:24.640 --> 0:17:27.760
<v Speaker 9>labor costs, but corporate America is moving away from them

0:17:27.800 --> 0:17:31.320
<v Speaker 9>because they're very expensive, they're not really reliable, they are

0:17:31.560 --> 0:17:35.160
<v Speaker 9>incredibly negatively impactful on the environment.

0:17:35.400 --> 0:17:37.360
<v Speaker 3>Moving away from them, you think corporate America is moving

0:17:37.359 --> 0:17:38.439
<v Speaker 3>away from lagenguge porp.

0:17:38.520 --> 0:17:38.760
<v Speaker 2>I do.

0:17:38.840 --> 0:17:42.200
<v Speaker 9>We've been seeing studies where companies are saying that it's

0:17:42.280 --> 0:17:46.639
<v Speaker 9>more expensive for them to use these chatbots because human

0:17:46.680 --> 0:17:50.200
<v Speaker 9>beings have to check the efficacy and the reliability. There

0:17:50.200 --> 0:17:54.520
<v Speaker 9>are so many errors, factual errors that are proliferating through

0:17:54.520 --> 0:17:58.600
<v Speaker 9>these technologies. So if the technology itself is that flawed

0:17:59.320 --> 0:18:02.960
<v Speaker 9>and it's being now on the public as some type

0:18:03.000 --> 0:18:06.119
<v Speaker 9>of solution, I think we're in trouble. And of course

0:18:06.240 --> 0:18:10.760
<v Speaker 9>we know that we have the racial bias, the gender bias,

0:18:11.040 --> 0:18:14.960
<v Speaker 9>the kind of geographic and political concerns about what comes

0:18:14.960 --> 0:18:18.840
<v Speaker 9>out of these technologies. I think that we are moving

0:18:18.880 --> 0:18:24.120
<v Speaker 9>into very dangerous territory trying to bolster our society on

0:18:24.359 --> 0:18:26.280
<v Speaker 9>large language models fascinatable.

0:18:26.560 --> 0:18:29.680
<v Speaker 2>The large body of your work looked at the data

0:18:29.720 --> 0:18:33.560
<v Speaker 2>issues for commercial search engines. Basically the net result is

0:18:34.119 --> 0:18:38.800
<v Speaker 2>that the search engines, as per your books title, reinforce racism. Yes,

0:18:39.520 --> 0:18:43.840
<v Speaker 2>what was the underlying issue in the search engine case study?

0:18:44.119 --> 0:18:47.480
<v Speaker 2>And what is different or the same about the large

0:18:47.520 --> 0:18:50.560
<v Speaker 2>language models that I think you're saying yield a similar

0:18:50.600 --> 0:18:51.560
<v Speaker 2>result they do.

0:18:52.280 --> 0:18:56.480
<v Speaker 9>So what we've seen over the last fifteen twenty years

0:18:56.640 --> 0:19:00.000
<v Speaker 9>is that all of the discrimination that's in our society,

0:19:00.400 --> 0:19:04.520
<v Speaker 9>all of the kind of stereotyping, all of the inequality,

0:19:05.160 --> 0:19:08.920
<v Speaker 9>just gets packaged up and then used to train models.

0:19:09.240 --> 0:19:11.000
<v Speaker 2>So it's within the data.

0:19:10.640 --> 0:19:13.480
<v Speaker 9>Within the data, but it's also the people who are

0:19:13.480 --> 0:19:16.960
<v Speaker 9>designing the models are really not aware. They don't understand

0:19:17.040 --> 0:19:21.320
<v Speaker 9>the kind of social, historical, economic processes. These are software

0:19:21.320 --> 0:19:23.640
<v Speaker 9>engineers who don't even think about they don't even ask

0:19:23.680 --> 0:19:26.480
<v Speaker 9>the kinds of questions that let's say, a sociologist like

0:19:26.520 --> 0:19:30.240
<v Speaker 9>I would ask. And so we have discriminatory data that

0:19:30.359 --> 0:19:34.680
<v Speaker 9>is training models. But what's different now is that these

0:19:34.800 --> 0:19:40.880
<v Speaker 9>models obfuscate the inequality. They appear to be factual and reliable.

0:19:41.160 --> 0:19:43.920
<v Speaker 9>And if you don't know, if you don't have deep expertise,

0:19:44.200 --> 0:19:45.880
<v Speaker 9>you're not going to know that the kinds of things

0:19:45.920 --> 0:19:49.640
<v Speaker 9>that are being served up in these products are actually faulty.

0:19:49.840 --> 0:19:55.040
<v Speaker 9>And imagine putting your whole business enterprise, your public institutions,

0:19:55.080 --> 0:19:59.240
<v Speaker 9>your schools, your libraries, making that the backbone. I mean

0:19:59.320 --> 0:20:02.160
<v Speaker 9>that is to me quite dangerous Sophia.

0:20:03.040 --> 0:20:05.800
<v Speaker 3>We can talk at length about the risks and the problems.

0:20:06.200 --> 0:20:08.479
<v Speaker 3>What about the solutions here because we saw but at

0:20:08.560 --> 0:20:10.400
<v Speaker 3>least two years ago, I think it was when alphabet

0:20:10.480 --> 0:20:13.520
<v Speaker 3>was struggling to ensure that some of the images AI

0:20:13.640 --> 0:20:18.080
<v Speaker 3>generated images didn't overcompensate for some of the worries about

0:20:18.480 --> 0:20:20.480
<v Speaker 3>racism and bias within the algorithm.

0:20:20.640 --> 0:20:23.879
<v Speaker 5>So what have you been done that works. Let's not

0:20:23.920 --> 0:20:26.840
<v Speaker 5>just beautify the problem, let's give us the solution.

0:20:27.560 --> 0:20:30.000
<v Speaker 9>Well, I think that we don't want to give up

0:20:30.240 --> 0:20:37.040
<v Speaker 9>what it means to have human expertise, human journalists, fact checkers, teachers, thinkers.

0:20:37.440 --> 0:20:39.560
<v Speaker 10>This is our most powerful asset.

0:20:39.680 --> 0:20:43.280
<v Speaker 9>These human beings are people, and we can't replace people

0:20:43.320 --> 0:20:46.320
<v Speaker 9>with these kinds of machines. So to me, you know,

0:20:46.880 --> 0:20:50.240
<v Speaker 9>having deep knowledge in the humanities and social sciences, these

0:20:50.240 --> 0:20:52.719
<v Speaker 9>are the things that are going to really be important

0:20:52.760 --> 0:20:56.840
<v Speaker 9>as we go forward in society. And we're over investing,

0:20:56.960 --> 0:20:59.199
<v Speaker 9>I think, in the wrong things. We need to be

0:20:59.280 --> 0:21:04.960
<v Speaker 9>investing in putting resources into pro social, pro rights respecting technology.

0:21:05.280 --> 0:21:09.320
<v Speaker 9>There's a whole world of small language models and different

0:21:09.400 --> 0:21:13.680
<v Speaker 9>kinds of very interesting kinds of technologies that women are

0:21:13.720 --> 0:21:17.040
<v Speaker 9>thinking about that people of color are working on and

0:21:17.080 --> 0:21:19.800
<v Speaker 9>these are the least invested in, but they are I

0:21:19.840 --> 0:21:23.800
<v Speaker 9>think the kinds of technologies that are going to help

0:21:23.920 --> 0:21:25.160
<v Speaker 9>us find a way forward.

0:21:25.240 --> 0:21:29.359
<v Speaker 2>Sophea, How conscious of and open about are the companies

0:21:29.760 --> 0:21:32.879
<v Speaker 2>on the issue, And you know, in research and writing

0:21:32.920 --> 0:21:34.720
<v Speaker 2>your book, but your ongoing work, how much do they

0:21:34.720 --> 0:21:35.760
<v Speaker 2>engage with you on it?

0:21:36.440 --> 0:21:40.320
<v Speaker 9>The companies for the most part want to deny, deny

0:21:40.920 --> 0:21:44.560
<v Speaker 9>the most dangerous dimensions of their products, and of course

0:21:44.640 --> 0:21:48.840
<v Speaker 9>they are only interested in regulation that they're writing. We've

0:21:48.920 --> 0:21:54.480
<v Speaker 9>just saw the landmark ruling against Meta, where they knew

0:21:54.760 --> 0:21:58.879
<v Speaker 9>that their products were harmful, especially to girls and to women.

0:21:59.160 --> 0:22:01.199
<v Speaker 9>And of course this includes all of the kind of

0:22:01.240 --> 0:22:06.600
<v Speaker 9>deep fake technologies that these companies are invested toute.

0:22:06.119 --> 0:22:07.960
<v Speaker 2>That right, and we covered that case in detail on

0:22:08.000 --> 0:22:08.520
<v Speaker 2>the program.

0:22:08.600 --> 0:22:11.720
<v Speaker 9>But yeah, well, I think you know what we have

0:22:12.080 --> 0:22:15.560
<v Speaker 9>is more and more litigation against these companies because there's

0:22:15.720 --> 0:22:16.920
<v Speaker 9>evidence of harm.

0:22:17.440 --> 0:22:20.800
<v Speaker 2>Sophear Noble, Professor and director of the Center and Resilience

0:22:20.800 --> 0:22:23.080
<v Speaker 2>and Digital Justice. You see, La, thank you very much

0:22:23.080 --> 0:22:25.800
<v Speaker 2>for joining us coming up on the show. The excitement

0:22:25.840 --> 0:22:30.560
<v Speaker 2>around SpaceX's IPO is putting pressure on market operators to

0:22:30.600 --> 0:22:34.360
<v Speaker 2>make sure this goes smoothly, we get really in the weeds,

0:22:34.520 --> 0:22:37.199
<v Speaker 2>very technical about what pulling off the biggest IPO and

0:22:37.280 --> 0:22:40.760
<v Speaker 2>history means for the market. That's next, That's what markets

0:22:40.760 --> 0:22:43.320
<v Speaker 2>look like. Stay with us. It's half time and this

0:22:43.359 --> 0:22:44.200
<v Speaker 2>is Bloomberg Tech.

0:22:57.440 --> 0:22:59.120
<v Speaker 5>Welcome back to Bloomberg Tech.

0:22:59.160 --> 0:23:01.080
<v Speaker 3>We check in on these market which are under pressure

0:23:01.119 --> 0:23:04.720
<v Speaker 3>as we await the biggest IPO in history. Then as

0:23:04.720 --> 0:23:07.120
<v Speaker 3>that one hundred is off five a percentage point, there's

0:23:07.160 --> 0:23:11.000
<v Speaker 3>geopolitical tensions, risks and terms about yet further conflict in

0:23:11.040 --> 0:23:12.840
<v Speaker 3>the Middle East between Iran the United States.

0:23:12.880 --> 0:23:15.119
<v Speaker 5>We see the semic conduct To Index hardware.

0:23:14.720 --> 0:23:17.359
<v Speaker 3>Once again, having risen so much, gets pulled back somewhat

0:23:17.400 --> 0:23:18.119
<v Speaker 3>of by two percent.

0:23:18.160 --> 0:23:21.840
<v Speaker 5>Magnificent seven also down, but some aren't. For men. ASML

0:23:22.160 --> 0:23:23.560
<v Speaker 5>just finishing trading.

0:23:23.200 --> 0:23:27.400
<v Speaker 3>In Europe record high I since nineteen ninety five. We're

0:23:27.480 --> 0:23:30.840
<v Speaker 3>up another percentage point on ASML and its European trading

0:23:30.880 --> 0:23:31.240
<v Speaker 3>on the day.

0:23:31.280 --> 0:23:33.000
<v Speaker 5>But we really do shine light and what's.

0:23:32.840 --> 0:23:35.320
<v Speaker 3>Been happening more broadly in the American indices and there is.

0:23:35.280 --> 0:23:35.840
<v Speaker 5>Some concern there.

0:23:35.920 --> 0:23:39.120
<v Speaker 2>Yeah, tech trians are driving us lower, but SpaceX there

0:23:39.160 --> 0:23:42.120
<v Speaker 2>is going to be an element of volatility whatever happens

0:23:42.119 --> 0:23:46.000
<v Speaker 2>and outside demand for SpaceX shares has market operators stress

0:23:46.080 --> 0:23:49.679
<v Speaker 2>testing their systems to ensure smooth trading for the largest

0:23:49.720 --> 0:23:52.560
<v Speaker 2>IPO in history. Bloomberg yzabel Lee has been speaking with

0:23:52.600 --> 0:23:54.760
<v Speaker 2>some of those firms. And the way that you put

0:23:54.760 --> 0:23:58.840
<v Speaker 2>it third paragraph of a critically important story is when

0:23:58.880 --> 0:24:02.040
<v Speaker 2>this IPO hits, you're talking millions and millions of orders,

0:24:02.359 --> 0:24:05.240
<v Speaker 2>and with those orders comes millions and millions of messages

0:24:05.560 --> 0:24:10.720
<v Speaker 2>and transactions. This becomes a technology story. How does that work?

0:24:10.960 --> 0:24:13.280
<v Speaker 2>What is it that they're stress testing right now?

0:24:13.920 --> 0:24:15.840
<v Speaker 11>Thank you for reading, and that's proof that you read

0:24:15.840 --> 0:24:17.920
<v Speaker 11>this story. But indeed, I think much has been said

0:24:17.920 --> 0:24:21.160
<v Speaker 11>about the excitement surrounding SpaceX IPO, but what is often

0:24:21.240 --> 0:24:24.520
<v Speaker 11>left un said is the plumbing that powers this IPO,

0:24:24.560 --> 0:24:26.840
<v Speaker 11>because for the IPO to be successful, the plumbing has

0:24:26.880 --> 0:24:27.560
<v Speaker 11>to work smoothly.

0:24:27.600 --> 0:24:29.399
<v Speaker 10>And we talked to a couple of those players.

0:24:29.520 --> 0:24:33.160
<v Speaker 11>The TCC, for one, they're the Depository Trust and Clearing Corporation.

0:24:33.480 --> 0:24:35.439
<v Speaker 11>They like to say they're the most important company that

0:24:35.480 --> 0:24:36.080
<v Speaker 11>no one has.

0:24:35.920 --> 0:24:36.639
<v Speaker 10>Ever heard of.

0:24:36.920 --> 0:24:39.440
<v Speaker 11>Think of them as the central plumbing that is basically

0:24:39.440 --> 0:24:42.919
<v Speaker 11>in charge of virtually all transactions that processes, clears and

0:24:42.960 --> 0:24:46.400
<v Speaker 11>settles us financial assets in the US. We also talk

0:24:46.480 --> 0:24:48.960
<v Speaker 11>to the S andp's equity book builder. They think of

0:24:49.000 --> 0:24:52.440
<v Speaker 11>them as like the financial technology used by global investment

0:24:52.480 --> 0:24:55.560
<v Speaker 11>banks to really power a lot of these underwritings like IPO.

0:24:55.760 --> 0:24:59.119
<v Speaker 11>So they have been preparing for weeks for this SpaceX

0:24:59.119 --> 0:25:00.800
<v Speaker 11>a IPO for DTCC.

0:25:00.880 --> 0:25:03.000
<v Speaker 10>They're going to have a watch party over the weekend.

0:25:03.240 --> 0:25:05.320
<v Speaker 11>For the SMP, they're using AI to make sure that

0:25:05.359 --> 0:25:06.479
<v Speaker 11>all systems are smooth.

0:25:07.320 --> 0:25:11.119
<v Speaker 3>One are the biggest fas is it just slowness or

0:25:11.160 --> 0:25:14.080
<v Speaker 3>is it anything that could go more deeply awry here

0:25:14.119 --> 0:25:14.480
<v Speaker 3>as well?

0:25:15.160 --> 0:25:17.679
<v Speaker 11>For DTCC, they said, it's not one big risk, but

0:25:17.760 --> 0:25:20.119
<v Speaker 11>it's just how interconnected everything is, so it could be

0:25:20.160 --> 0:25:23.280
<v Speaker 11>one small broker dealer or maybe one small market maker

0:25:23.280 --> 0:25:25.040
<v Speaker 11>that may not be as prepared and it will be

0:25:25.119 --> 0:25:28.160
<v Speaker 11>just a huge domino effect that will really affect everything.

0:25:28.200 --> 0:25:31.800
<v Speaker 11>Because they really are kind of worried about what happened

0:25:31.840 --> 0:25:33.840
<v Speaker 11>in Facebook in twenty twelve, and a lot of retail

0:25:33.840 --> 0:25:36.160
<v Speaker 11>investors were left in the dark as well as investment

0:25:36.200 --> 0:25:38.679
<v Speaker 11>bankers because it was marred by a lot of technical

0:25:38.720 --> 0:25:42.280
<v Speaker 11>failures that left some traders really uncertain. So I think

0:25:42.320 --> 0:25:44.080
<v Speaker 11>they've learned from that. It's been more than a decade

0:25:44.080 --> 0:25:46.800
<v Speaker 11>since technology has grown leaps and bounds, or they're really

0:25:47.000 --> 0:25:49.520
<v Speaker 11>preparing for it. At SMP, they have what they call

0:25:49.600 --> 0:25:52.040
<v Speaker 11>a pre mortem, which is the opposite of post mortem,

0:25:52.280 --> 0:25:54.720
<v Speaker 11>so they're really ensuring that everything is really going to

0:25:54.720 --> 0:25:57.119
<v Speaker 11>go smoothly. For example, they made sure that the tripling

0:25:57.160 --> 0:25:59.920
<v Speaker 11>of order handling capacity is going to be possible and

0:26:00.160 --> 0:26:04.960
<v Speaker 11>fourfold improvement in response time, so it really allows to digest.

0:26:05.040 --> 0:26:06.800
<v Speaker 11>But I want to join our watch party over the

0:26:06.800 --> 0:26:08.640
<v Speaker 11>weekend they're going to be online twenty four to seven.

0:26:08.680 --> 0:26:09.760
<v Speaker 10>They said, we'll.

0:26:09.600 --> 0:26:12.280
<v Speaker 3>Start our own as VALI, who has just been so

0:26:12.400 --> 0:26:16.320
<v Speaker 3>watchful on this IPO for us, thank you very much. Indeed, Meanwhile,

0:26:16.480 --> 0:26:19.520
<v Speaker 3>the AI economy, well it's moving fast and now we

0:26:19.600 --> 0:26:22.119
<v Speaker 3>may have a clearer way to track how it's changing

0:26:22.160 --> 0:26:25.080
<v Speaker 3>the way people work. ADP has partnered with the Stanford

0:26:25.119 --> 0:26:28.399
<v Speaker 3>Digital Economy Lab to launch the Canaries Dashboard.

0:26:28.600 --> 0:26:30.280
<v Speaker 5>It's a real time indicator.

0:26:29.840 --> 0:26:33.840
<v Speaker 3>Designed to show how AI is reshaping different occupations based

0:26:33.880 --> 0:26:37.560
<v Speaker 3>on actual labor market data. Joining us some More is

0:26:37.600 --> 0:26:40.200
<v Speaker 3>one of the researchers involved in this project. Nila Richardson,

0:26:40.280 --> 0:26:43.560
<v Speaker 3>chief economist at ADP, former senior economist actually at Bloomberg

0:26:43.600 --> 0:26:48.199
<v Speaker 3>and NILA present. You are monitoring the present, not the past.

0:26:48.680 --> 0:26:50.080
<v Speaker 3>But what data do you take in?

0:26:50.240 --> 0:26:51.240
<v Speaker 5>How are you monitoring this?

0:26:51.600 --> 0:26:51.879
<v Speaker 10>Well?

0:26:51.960 --> 0:26:55.400
<v Speaker 12>First of all, we are thrilled because AI is said

0:26:55.440 --> 0:26:58.840
<v Speaker 12>to be one of the most consequential technologies the world

0:26:58.920 --> 0:27:02.760
<v Speaker 12>has ever seen, and yet we have very limited ways

0:27:02.800 --> 0:27:05.679
<v Speaker 12>of measuring impact. And so this is why I'm so

0:27:05.760 --> 0:27:10.040
<v Speaker 12>excited to partner with Stanford Digital CONNOMU Lab led by

0:27:10.160 --> 0:27:13.399
<v Speaker 12>Eric Burne Jolson on these AI indicators and namely the

0:27:13.440 --> 0:27:18.600
<v Speaker 12>Canaries Dashboard, which tracks the impact of AI on occupations

0:27:18.640 --> 0:27:22.760
<v Speaker 12>in almost real time. This is about moving the conversation

0:27:22.880 --> 0:27:26.440
<v Speaker 12>about AI's impact from what we think to what we

0:27:26.600 --> 0:27:30.200
<v Speaker 12>know and what we can measure with the data Nila.

0:27:30.000 --> 0:27:33.119
<v Speaker 2>There was a section of the labor market that really

0:27:33.200 --> 0:27:34.919
<v Speaker 2>just jumps off the screen at me, and that is

0:27:35.000 --> 0:27:38.239
<v Speaker 2>the early career workers. These are people aged twenty two

0:27:38.320 --> 0:27:40.920
<v Speaker 2>to twenty five. And there is a I don't know

0:27:40.920 --> 0:27:43.960
<v Speaker 2>how you would put it, a bifurcation, right, industries that

0:27:44.000 --> 0:27:46.920
<v Speaker 2>have exposure to AI, industries that have nothing to do

0:27:47.040 --> 0:27:49.400
<v Speaker 2>with it whatsoever. What is the data telling us there?

0:27:49.520 --> 0:27:52.560
<v Speaker 2>And if you can the why the why?

0:27:52.880 --> 0:27:55.679
<v Speaker 12>Well, the important part of this data series is that

0:27:55.760 --> 0:28:00.159
<v Speaker 12>it is able to categorize over seven hundred occupations by

0:28:00.200 --> 0:28:02.960
<v Speaker 12>AI exposure in a very granular way.

0:28:03.240 --> 0:28:04.399
<v Speaker 10>But it's more than.

0:28:04.280 --> 0:28:09.000
<v Speaker 12>That because it's not just how the AI is affecting occupations,

0:28:09.240 --> 0:28:14.040
<v Speaker 12>but it's about how AIS is affecting workers people young career.

0:28:14.119 --> 0:28:16.679
<v Speaker 12>So we're able to use the demographics and the ADP

0:28:16.800 --> 0:28:19.800
<v Speaker 12>payroll data and segment it by early career twenty two

0:28:19.880 --> 0:28:23.160
<v Speaker 12>to twenty six, and look, AI, you know this as

0:28:23.200 --> 0:28:26.879
<v Speaker 12>well as anyone has two different roles in a business context,

0:28:27.160 --> 0:28:30.359
<v Speaker 12>it can augment. That's the old school story. That's the

0:28:30.440 --> 0:28:35.320
<v Speaker 12>dinosaur story of technology. How do you sorry automate work?

0:28:35.640 --> 0:28:38.160
<v Speaker 12>That's the old school story. The new school story. The

0:28:38.200 --> 0:28:41.320
<v Speaker 12>frontier is how to augment. And so what AI does

0:28:41.400 --> 0:28:44.280
<v Speaker 12>for early career in AI exposed fields, it looks like

0:28:44.320 --> 0:28:46.160
<v Speaker 12>it's automating certain tasks.

0:28:46.520 --> 0:28:48.560
<v Speaker 10>So the trick is how do we move.

0:28:48.440 --> 0:28:54.280
<v Speaker 12>From automation to augmentation and look for those higher value

0:28:54.440 --> 0:28:56.360
<v Speaker 12>tasks higher value work?

0:28:56.520 --> 0:28:59.760
<v Speaker 2>And the result is that in that space employment is contracting.

0:29:00.200 --> 0:29:03.760
<v Speaker 12>Right, So for AI exposed careers, there is a contraction

0:29:03.880 --> 0:29:07.840
<v Speaker 12>for early career in like software developers. So let's take

0:29:07.880 --> 0:29:11.440
<v Speaker 12>software developers. Since the rollout of chat GPT. In November

0:29:11.440 --> 0:29:14.800
<v Speaker 12>twenty twenty two, the dashboard shows there's been a twenty

0:29:14.920 --> 0:29:20.040
<v Speaker 12>percent decline in early career software developers, but when you

0:29:20.080 --> 0:29:23.480
<v Speaker 12>look at older workers, no decline at all. In fact,

0:29:23.520 --> 0:29:26.120
<v Speaker 12>you're seeing growth. That shows you that there is a

0:29:26.320 --> 0:29:30.520
<v Speaker 12>disparate impact here. For skills and tasks that are easily automated,

0:29:30.560 --> 0:29:33.360
<v Speaker 12>you're seeing in an effect that's the early career, But

0:29:33.400 --> 0:29:36.720
<v Speaker 12>for work where it's more complex, AI becomes a helpmate,

0:29:36.880 --> 0:29:41.880
<v Speaker 12>a coworker, an augmentation tool as opposed to an automation tool.

0:29:42.200 --> 0:29:44.840
<v Speaker 3>This is going to be released every Wednesday after Jobs Week,

0:29:44.880 --> 0:29:47.920
<v Speaker 3>so it's monthly data. How are you thinking about when

0:29:47.960 --> 0:29:51.240
<v Speaker 3>you realize an industry is becoming AI exposed At the moment,

0:29:51.280 --> 0:29:53.920
<v Speaker 3>you've been so fascinating with the fact that we've got developers,

0:29:54.000 --> 0:29:58.000
<v Speaker 3>customer services, but which one to understand luhees the next

0:29:58.280 --> 0:30:01.480
<v Speaker 3>How are you seeing low AIX posed operations starting to

0:30:01.600 --> 0:30:03.480
<v Speaker 3>change will become AI exposed.

0:30:03.600 --> 0:30:06.160
<v Speaker 12>That's a great question, and that's really the purpose and

0:30:06.240 --> 0:30:09.320
<v Speaker 12>mission of this work. It is to track value creation

0:30:09.560 --> 0:30:11.760
<v Speaker 12>in real time. And the thing about it is you

0:30:11.800 --> 0:30:14.320
<v Speaker 12>can't track it in a macro way. You can't track

0:30:14.360 --> 0:30:18.880
<v Speaker 12>it in the markets those yeah, market IPO creation. Value

0:30:18.880 --> 0:30:21.320
<v Speaker 12>creation is very different to how it affects the real

0:30:21.400 --> 0:30:24.959
<v Speaker 12>economy and what people are really experiencing at work. And

0:30:25.040 --> 0:30:28.080
<v Speaker 12>so at the task level is where you see value creation,

0:30:28.520 --> 0:30:32.680
<v Speaker 12>and you see that in certain complex jobs. So let's

0:30:32.720 --> 0:30:37.320
<v Speaker 12>move on from software developers. Maybe look at radiologists where

0:30:37.720 --> 0:30:41.680
<v Speaker 12>AI becomes a really important diagnostic tool and you can

0:30:41.760 --> 0:30:45.800
<v Speaker 12>see that value creation and delivery helping them concentrate on

0:30:45.840 --> 0:30:48.920
<v Speaker 12>the work that is necessary for human to human interaction

0:30:49.280 --> 0:30:53.680
<v Speaker 12>as opposed to simply diagnosing different patterns, which AI is

0:30:53.720 --> 0:30:56.720
<v Speaker 12>good at. So the key for employers is how to

0:30:57.000 --> 0:31:01.280
<v Speaker 12>extend human capability, not limit it, not replace it, but

0:31:01.400 --> 0:31:05.000
<v Speaker 12>extend that capability to new task and new value creation.

0:31:05.560 --> 0:31:08.200
<v Speaker 2>We have a good case study for that. Bloomboats remain Bostic.

0:31:08.360 --> 0:31:11.280
<v Speaker 2>Just spoke to the IBM CEO about this exact point.

0:31:12.080 --> 0:31:13.440
<v Speaker 2>Let's listen to what he had to say, and then

0:31:13.480 --> 0:31:15.120
<v Speaker 2>you can say if it shows up in the data.

0:31:15.240 --> 0:31:19.120
<v Speaker 13>Okay, using AI tools, now we can probably add ten

0:31:19.200 --> 0:31:23.440
<v Speaker 13>points of profit energy on day one because the amount

0:31:23.440 --> 0:31:25.560
<v Speaker 13>of time it used to take to move contracts over,

0:31:25.760 --> 0:31:28.400
<v Speaker 13>to do all of the sales automation, to do all

0:31:28.440 --> 0:31:32.920
<v Speaker 13>of the revenue forecasting. All of that now using AI

0:31:33.160 --> 0:31:35.840
<v Speaker 13>can be shrunk down literally a few weeks.

0:31:36.680 --> 0:31:39.680
<v Speaker 2>When I listen to that, I just can't draw a conclusion.

0:31:39.680 --> 0:31:43.440
<v Speaker 2>Are we talking about role elimination? Are we talking about

0:31:43.840 --> 0:31:46.120
<v Speaker 2>a boost of productivity? Which Caroline and I were told

0:31:46.120 --> 0:31:49.640
<v Speaker 2>by SFF president Mary Daily last week, isn't showing up

0:31:49.640 --> 0:31:52.120
<v Speaker 2>in the data yet, Like, how do you read the

0:31:52.200 --> 0:31:54.720
<v Speaker 2>corporate speak in this job market?

0:31:55.800 --> 0:31:58.800
<v Speaker 12>The way I read it is this AI has the

0:31:58.880 --> 0:32:03.040
<v Speaker 12>ability to reach shape work, and yet it's still a

0:32:03.080 --> 0:32:08.040
<v Speaker 12>tool that decision lies with the employer, and so this

0:32:08.320 --> 0:32:12.040
<v Speaker 12>data is about empowering employers to make that decision. Is

0:32:12.080 --> 0:32:16.080
<v Speaker 12>AI going to be your efficiency tool? There's a clear

0:32:16.160 --> 0:32:19.080
<v Speaker 12>case for that. There's a clear use case that AI

0:32:19.200 --> 0:32:22.240
<v Speaker 12>is making certain work more efficient. You can do more

0:32:22.320 --> 0:32:25.360
<v Speaker 12>with less. But I think AI has the potential for

0:32:25.480 --> 0:32:28.400
<v Speaker 12>more than that. It is a productivity tool in the

0:32:28.440 --> 0:32:32.160
<v Speaker 12>sense that it enhances work, it makes work better, it

0:32:32.200 --> 0:32:36.040
<v Speaker 12>makes problems easier to solve, and therefore you can tackle

0:32:36.080 --> 0:32:40.840
<v Speaker 12>more problems. If it's an augmentation tool, that is something

0:32:40.920 --> 0:32:45.080
<v Speaker 12>completely different, and hopefully data will help employers find that

0:32:45.200 --> 0:32:48.479
<v Speaker 12>value creation within their own businesses. So right now, I

0:32:48.480 --> 0:32:52.480
<v Speaker 12>think the narrative is really really wide. The data can

0:32:52.560 --> 0:32:54.960
<v Speaker 12>help anchor it on the truth of the moment.

0:32:54.840 --> 0:32:57.960
<v Speaker 3>And Avin would actually say they are hiring more graduates

0:32:58.240 --> 0:33:00.360
<v Speaker 3>than they happened in the past, more than ever the moment.

0:33:00.400 --> 0:33:03.000
<v Speaker 5>So it is interesting where the new grudge is coming

0:33:03.040 --> 0:33:03.840
<v Speaker 5>out and whether or not.

0:33:04.000 --> 0:33:06.320
<v Speaker 2>Now we have real time data to keep the monest

0:33:06.360 --> 0:33:08.880
<v Speaker 2>on it. Anita Richardson chief promise that ADP is great

0:33:08.880 --> 0:33:10.720
<v Speaker 2>to have you back on the show. Thank you very much.

0:33:10.760 --> 0:33:13.880
<v Speaker 2>Now coming up, Anthropic CEO Dario M. O Day says

0:33:13.920 --> 0:33:17.360
<v Speaker 2>his company's AI doesn't spell the death of software, that

0:33:17.760 --> 0:33:21.040
<v Speaker 2>some firms will be losers at that part of the conversation. Next,

0:33:21.080 --> 0:33:22.160
<v Speaker 2>this is an a big tech.

0:33:29.240 --> 0:33:32.800
<v Speaker 14>Now early on others focused on fund splashy consumer apps.

0:33:33.160 --> 0:33:36.640
<v Speaker 14>You made a bet on coding and enterprise. Why did

0:33:36.640 --> 0:33:40.400
<v Speaker 14>you make that bet? Was it a values decision or

0:33:40.440 --> 0:33:41.360
<v Speaker 14>a business decision?

0:33:41.800 --> 0:33:44.800
<v Speaker 15>Look, if you pick a business model that fundamentally conflicts

0:33:44.840 --> 0:33:47.840
<v Speaker 15>with your values, you're going to have a hard time, right,

0:33:48.080 --> 0:33:51.880
<v Speaker 15>either you betray your own values or you become irrelevant.

0:33:52.120 --> 0:33:54.080
<v Speaker 15>And so when we thought about it, we said, look,

0:33:54.320 --> 0:33:56.720
<v Speaker 15>you know, we've seen the world of social media, the

0:33:56.840 --> 0:34:01.360
<v Speaker 15>consumer world. It really seems to you know, encourage engagement

0:34:01.840 --> 0:34:05.240
<v Speaker 15>even addiction. You know, the slop we've seen with AI

0:34:05.320 --> 0:34:07.479
<v Speaker 15>video models, It's like, what's going on? Is it want

0:34:07.520 --> 0:34:10.120
<v Speaker 15>to maximize the number of minutes that you're you're paying

0:34:10.160 --> 0:34:14.200
<v Speaker 15>attention to because that's the advertising revenue driven incentive. Whereas

0:34:14.200 --> 0:34:17.279
<v Speaker 15>if we look at enterprise, look, I mean, you know,

0:34:17.640 --> 0:34:20.319
<v Speaker 15>we want to make these models useful to people. We

0:34:20.400 --> 0:34:24.480
<v Speaker 15>want to use AI to you know, cure diseases that

0:34:24.480 --> 0:34:27.040
<v Speaker 15>we couldn't cure before. Right, Well, that's working with biotech,

0:34:27.080 --> 0:34:30.640
<v Speaker 15>it's working with pharma, it's working with academic research groups.

0:34:30.680 --> 0:34:32.200
<v Speaker 15>All of those are enterprises.

0:34:32.320 --> 0:34:32.480
<v Speaker 6>Right.

0:34:32.760 --> 0:34:35.279
<v Speaker 15>We want to use AI to like, you know, to

0:34:35.400 --> 0:34:37.280
<v Speaker 15>make energy cheaper and more efficient.

0:34:37.440 --> 0:34:38.760
<v Speaker 2>That's that's all enterprise.

0:34:39.080 --> 0:34:40.880
<v Speaker 15>And so I think it served us well to have

0:34:41.000 --> 0:34:44.280
<v Speaker 15>this business model that largely aligns with our values.

0:34:44.719 --> 0:34:47.560
<v Speaker 14>Soon after Claude Cowork was released, two hundred and eighty

0:34:47.560 --> 0:34:51.680
<v Speaker 14>five billion dollars in market value vanished overnight. Traders called

0:34:51.719 --> 0:34:52.839
<v Speaker 14>it the SaaS apocalypse.

0:34:53.040 --> 0:34:55.760
<v Speaker 15>This kind of white collar wipeout story in the software

0:34:55.800 --> 0:34:57.360
<v Speaker 15>set to terrifying.

0:34:57.480 --> 0:34:59.359
<v Speaker 3>Some of those are down for nine days in a row,

0:34:59.400 --> 0:35:01.600
<v Speaker 3>So clearly the is building if.

0:35:01.480 --> 0:35:05.360
<v Speaker 14>AI continues improving at this pace, how much of traditional

0:35:05.400 --> 0:35:09.359
<v Speaker 14>software gets replaced and how fast I think.

0:35:09.480 --> 0:35:12.400
<v Speaker 15>With AI, like the pie is getting bigger. Right, so

0:35:12.920 --> 0:35:16.839
<v Speaker 15>the existing incumbents may be smaller and relative terms, some

0:35:16.920 --> 0:35:19.040
<v Speaker 15>of them may may go down in value. Some of

0:35:19.040 --> 0:35:21.080
<v Speaker 15>them may even may even go out of business if

0:35:21.120 --> 0:35:23.239
<v Speaker 15>they don't, if they don't adapt in the right way.

0:35:23.400 --> 0:35:26.640
<v Speaker 15>But like I would guess that the software industry gets larger,

0:35:26.760 --> 0:35:29.600
<v Speaker 15>not smaller, although there will be some big losers, those

0:35:29.640 --> 0:35:32.360
<v Speaker 15>who don't kind of see what's coming, who don't identify

0:35:32.400 --> 0:35:33.719
<v Speaker 15>the motes they have, they're going to have a really

0:35:33.760 --> 0:35:34.240
<v Speaker 15>hard time.

0:35:35.600 --> 0:35:39.320
<v Speaker 3>That was Bloombg Examine Chang speaking with Anthropic CEO Daria Amiday,

0:35:39.400 --> 0:35:41.440
<v Speaker 3>and you can catch part one of this two but

0:35:41.680 --> 0:35:44.360
<v Speaker 3>episode of the circuit it comes out later today. It

0:35:44.400 --> 0:35:48.280
<v Speaker 3>airs on Bloombg TV at six pm Eastern and sticking

0:35:48.280 --> 0:35:50.360
<v Speaker 3>with Anthropic, but the company has released a new model

0:35:50.400 --> 0:35:53.600
<v Speaker 3>called Claude Fable five for the capabilities of it SMITH

0:35:53.680 --> 0:35:57.320
<v Speaker 3>or SAI, but includes godrails so prevent it from responding

0:35:57.320 --> 0:36:00.359
<v Speaker 3>to queries on topics including cybersecurity and biology.

0:36:00.400 --> 0:36:01.960
<v Speaker 5>Now you'll remember Anthropic.

0:36:01.600 --> 0:36:05.480
<v Speaker 3>Initially released with us only to select organizations after warning

0:36:05.520 --> 0:36:07.920
<v Speaker 3>that it could exploit cyber vulnerabilities.

0:36:08.680 --> 0:36:12.760
<v Speaker 2>AI startup Poetic has emerged from sealth with fifty million

0:36:12.800 --> 0:36:16.160
<v Speaker 2>dollars in funding and a half a billion dollar valuation.

0:36:16.400 --> 0:36:20.480
<v Speaker 2>Right out of the gate. Poetic system helps businesses streamline complex,

0:36:20.800 --> 0:36:24.400
<v Speaker 2>long running tasks. Its founder and CEO, Markey Wagner, previously

0:36:24.440 --> 0:36:27.880
<v Speaker 2>launched AI consultancy Delphi Labs and worked on machine learning

0:36:28.080 --> 0:36:32.600
<v Speaker 2>at Google and Waimo and Markey joins us. Now this

0:36:32.880 --> 0:36:35.480
<v Speaker 2>was one of the I guess we call it a

0:36:35.520 --> 0:36:38.799
<v Speaker 2>coconut round or a mango seed round. But right out

0:36:38.840 --> 0:36:42.560
<v Speaker 2>the gate open AI Kleine Perkins founder's fund are backing you.

0:36:43.760 --> 0:36:46.240
<v Speaker 2>What is it they know that we don't yet about Poetic?

0:36:46.280 --> 0:36:47.279
<v Speaker 2>What is Poetic up to?

0:36:48.920 --> 0:36:51.040
<v Speaker 16>Yeah, so a bit about Poetic.

0:36:51.239 --> 0:36:54.680
<v Speaker 17>So Poetic is in an AI system that can learn

0:36:54.719 --> 0:36:58.920
<v Speaker 17>and execute extremely complex, multi hour processes at some of

0:36:58.960 --> 0:37:01.520
<v Speaker 17>the biggest companies in the place with over ninety nine

0:37:01.560 --> 0:37:04.400
<v Speaker 17>percent accuracy and ten times less tokens. And so what

0:37:04.440 --> 0:37:06.480
<v Speaker 17>they've seen is they know our customers and they've seen

0:37:06.520 --> 0:37:09.920
<v Speaker 17>the results, and they've seen a lot of AI pilots

0:37:09.920 --> 0:37:12.400
<v Speaker 17>that have gone well and poorly. And you know, we've

0:37:12.800 --> 0:37:15.520
<v Speaker 17>scaled up every single customer you've had into production in

0:37:15.600 --> 0:37:16.560
<v Speaker 17>a time when a.

0:37:16.480 --> 0:37:19.000
<v Speaker 16>Lot of these things are getting stuck in demo land.

0:37:19.120 --> 0:37:20.319
<v Speaker 16>And so that's what they've seen.

0:37:20.640 --> 0:37:23.960
<v Speaker 3>I mean, marketing people describe you to me as the

0:37:24.000 --> 0:37:27.799
<v Speaker 3>AI whisperer to some of the most important companies out there.

0:37:28.040 --> 0:37:30.399
<v Speaker 3>And Clin Perkins has put out a blog about them

0:37:30.440 --> 0:37:32.520
<v Speaker 3>backing you, and they reference Anthony Noto.

0:37:32.600 --> 0:37:33.120
<v Speaker 5>It's so far.

0:37:33.200 --> 0:37:35.840
<v Speaker 3>The CEO just saying how in weeks your company is

0:37:35.840 --> 0:37:39.400
<v Speaker 3>sort of turned around food processes and to end, how

0:37:39.440 --> 0:37:42.239
<v Speaker 3>and how are you doing it with not really that

0:37:42.320 --> 0:37:45.760
<v Speaker 3>much compute, that much token being used at that time.

0:37:46.800 --> 0:37:48.840
<v Speaker 17>Yeah, so we have a bit of a different approach

0:37:48.920 --> 0:37:51.839
<v Speaker 17>than what most folks are doing right now. And so

0:37:52.400 --> 0:37:54.400
<v Speaker 17>we have this system that is kind of the synthesis

0:37:54.480 --> 0:37:57.960
<v Speaker 17>of both AI and code. So, you know code today

0:37:58.000 --> 0:38:00.400
<v Speaker 17>is you know, it's very static, and so the innovations

0:38:00.400 --> 0:38:03.320
<v Speaker 17>of the past written in code. If something small changes

0:38:03.360 --> 0:38:05.600
<v Speaker 17>like a column name, it would break an AI. On

0:38:05.640 --> 0:38:09.000
<v Speaker 17>the other hand, agents are incredible, but they figure out

0:38:09.000 --> 0:38:10.640
<v Speaker 17>what to do step by step and they can easily

0:38:10.680 --> 0:38:11.440
<v Speaker 17>go off the rails.

0:38:11.840 --> 0:38:12.319
<v Speaker 2>We have this.

0:38:12.320 --> 0:38:15.080
<v Speaker 16>System that takes the best of both.

0:38:15.520 --> 0:38:18.080
<v Speaker 17>So a task is written in English similarly to an

0:38:18.120 --> 0:38:19.839
<v Speaker 17>operating procedure right, and our.

0:38:19.760 --> 0:38:21.399
<v Speaker 16>System will turn that into code under the hood.

0:38:22.120 --> 0:38:25.000
<v Speaker 3>I'm terribly sorry, Maki Wagne, but this President of the

0:38:25.080 --> 0:38:25.919
<v Speaker 3>United States is talking.

0:38:25.960 --> 0:38:26.880
<v Speaker 5>At this moment. We must go on.

0:38:26.920 --> 0:38:29.640
<v Speaker 3>Iran CEO founder a poetic We tend our attention to

0:38:29.680 --> 0:38:30.360
<v Speaker 3>President Trump

0:38:31.320 --> 0:38:32.799
<v Speaker 16>At speeds that you wouldn't want to go.