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Speaker 1: From the hard of We're Innovation, Money and Power Collie

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Speaker 1: in Silicon Vallet, NBM. This is Bloomberg Technology with Caroline

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Speaker 1: Hide and Ted loved Love. I'm Caroline Hide at work

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Speaker 1: Bloomberg's World headquarters in New York and I made Love

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Speaker 1: law out here in San Francisco. This is Bloomberg Technology

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Speaker 1: coming up. Amazon the latest to show love for AI.

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Speaker 1: In Andy Jesse's annual shareholder letter, the CEO pledges to

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Speaker 1: keep investing in such growth areas despite cost cutting initiatives.

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Speaker 1: We're going to dive deep into the world of crypto

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Speaker 1: as Ether tops two thousand U S dollars after its

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Speaker 1: software update went off without a hitch. John wuerav Labs

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Speaker 1: joints to discuss plus, how does a four day work

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Speaker 1: week sound. We'll speak with Nobel Prize winning economist Christopher Sarides,

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Speaker 1: who says AI it may increase productivities so much will

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Speaker 1: shave a day off your weekly work calendar. At first,

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Speaker 1: a big name and a big outperformer in the market

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Speaker 1: is Amazon as well. You look at the gains the

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Speaker 1: stock has made. It took it a while to get

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Speaker 1: going after they introduced two new large language models. We're

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Speaker 1: going to talk a lot about this throughout the day

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Speaker 1: on Bloomberg Television, but it's focused pretty firmly on cloud customers,

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Speaker 1: a tool for generating text, but also a sort of

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Speaker 1: platform to foster an ecosystem of AI. That stock up

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Speaker 1: eight tens of percent on a two day basis, but

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Speaker 1: really jumping higher caroline in this early trading of Thursday's session. Yeah,

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Speaker 1: and the Titan announcement all came within a focus from

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Speaker 1: Amazon CEO Andy Jase on or where they're going to

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Speaker 1: be investing. It's his second annual letter to shareholders, and

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Speaker 1: he read a pledge to keep putting money into big,

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Speaker 1: long term mets despite those cost cuts and an uncertain economy.

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Speaker 1: He's moving ahead with a global expansion, looking at efforts

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Speaker 1: to become a bigger player on groceries and healthcare and

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Speaker 1: of course as a generative AI part. Let's talk all

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Speaker 1: of that with Bloomberg's Spencer Soap, and let's go to

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Speaker 1: the old school Amazon and they might say that AI

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Speaker 1: is an old school for them too, but let's talk

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Speaker 1: about groceries. Let's talk about e commerce. I mean, where

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Speaker 1: are they looking to expand there on groceries in e commerce?

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Speaker 1: Just because there's such huge consumer spending categories, and it's

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Speaker 1: not really new. You know, Amazon has been trying that

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Speaker 1: for a while now, you know, groceries more than a decade,

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Speaker 1: and then they had that the big Whole Foods acquisition

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Speaker 1: several years ago, which has kind of fallen flat. They've

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Speaker 1: got this new iteration of a supermarket. They've tried, they

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Speaker 1: tried Amazon Go. They've had a lot of false starts

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Speaker 1: on it, but it's still just such a huge spending category.

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Speaker 1: And then it doves tails nicely with healthcare, particularly pharmacy,

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Speaker 1: and Amazon is trying a lot of the same old

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Speaker 1: tricks that other big retailers try, like you know, cheap,

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Speaker 1: cheap prescription drugs. You know, everybody and his brother does that,

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Speaker 1: so it's it's not really differentiating, but they remain they

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Speaker 1: remain important targets for Amazon just as a way to

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Speaker 1: grow revenue because there's so much money spent in those

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Speaker 1: in those areas spend. So the commentary around AWS is

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Speaker 1: really interesting. You know, this has been the cash cow

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Speaker 1: for Amazon, and let yeah, Jasse points out the short

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Speaker 1: term headwinds the challenges facing AWS, and then gave us

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Speaker 1: this kind of long time horizon of how well it

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Speaker 1: can do even the next decade. Yeah, and he was

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Speaker 1: he was definitely trying to you know, fleck back to

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Speaker 1: history where Amazon faced difficult economic conditions and prevailed, you know,

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Speaker 1: did made the hard decisions, made tough choices, but still

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Speaker 1: came out at the end of the at the end

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Speaker 1: of the tunnel. Anythink, trying to emphasize that Amazon is

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Speaker 1: still a strong company, that that can emerge through this uh,

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Speaker 1: through it through a downturn, and come out stronger on

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Speaker 1: the other end. And it's really the one area where

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Speaker 1: he can kind of gin up some some excitement and enthusiasm.

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Speaker 1: You know, that's really what he had to do with

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Speaker 1: this letter. The stocks down you know, about half nearly

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Speaker 1: nearly half from its pandemic highs. You know that that

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Speaker 1: rips you know, not just investors, but also employees who

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Speaker 1: get a lot of their compensation by the by the

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Speaker 1: stock value. Then also all of the cuts of deteriorated morale.

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Speaker 1: So he's got to you know, it's a heavy lift

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Speaker 1: for him, but he's got to do something to try

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Speaker 1: to try to energize this company right now. And at

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Speaker 1: the end of the letter he tries to do just that.

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Speaker 1: With the focus on artificial intelligence, trying to remind us

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Speaker 1: that for decades they've been looking at machine learning, they're

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Speaker 1: looking at chip investment there, but just talk to us

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Speaker 1: about the large language models that are being offered and

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Speaker 1: what makes it different because it's not a chatbot. Yeah,

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Speaker 1: and I think what Amazon is really trying to say

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Speaker 1: is listen. You know, if you look at the history

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Speaker 1: in the marketplace model, they just kind of want to

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Speaker 1: be in the middle of it. Amazon wants to position

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Speaker 1: itself as a as a as a marketplace where you

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Speaker 1: go to access these tools, and as long as it's

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Speaker 1: got people coming for those tools, it's going to get

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Speaker 1: the best tools that people want exposure to. So I

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Speaker 1: think it's really looking looking to make itself, uh, you know,

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Speaker 1: a marketplace for ideas and a can do it for

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Speaker 1: these ideas to be utilized. Right Bloomberg Spencer, Soapa or

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Speaker 1: Addie Seattle, thank you and stay with us on Bloomberg Television.

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Speaker 1: We have a conversation later today with AWS CEO Adams

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Speaker 1: Alpski that you don't want to miss at two pm

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Speaker 1: East and eleven am Pacific. I've been speaking carow to

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Speaker 1: some AWS custom this morning. We certainly have some interesting

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Speaker 1: AI related questions to put to him. And another key

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Speaker 1: story that we're following SoftBank moving to sell more of

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Speaker 1: its stake in Chinese Internet. John Ali Baba, I'm winding

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Speaker 1: with that initial bed that spurred Massochi's son to really

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Speaker 1: take ambitions to invest billions of dollars into other startups,

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Speaker 1: trying out some pieces safe more is isabel Lee and

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Speaker 1: why sell out and in what way are they setting out?

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Speaker 1: So this is really interesting. So SoftBank is looking to

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Speaker 1: sell a majority of its steake to China to really

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Speaker 1: trim to Availibaba, to trim it stake in China and

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Speaker 1: to raise fund. So this is quite a move because

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Speaker 1: from holding a majority stake at one point around thirty

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Speaker 1: four percent, they're whittling it down to four percent three

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Speaker 1: point ninety eight to be exact. So this year they're

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Speaker 1: looking to sell seven billion worth of shares. That's following

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Speaker 1: last year where they sold around twenty nine billion. And

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Speaker 1: the important thing to note here is that these are

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Speaker 1: prepaid forward contracts, which means that they could still buy

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Speaker 1: back the shares, but from the previous deals they just

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Speaker 1: didn't hold on to those and just sold them. And

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Speaker 1: this all comes at a time that the Vision Fund

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Speaker 1: bet the bets are startups to SoftBank, I just haven't

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Speaker 1: fed that overall business. Well, it's why the market's interpretation

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Speaker 1: of this is so interesting. You know, it says something

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Speaker 1: about China and investors exposure to China, but it also

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Speaker 1: says quite a lot about soft Bank, right about what

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Speaker 1: do we take away from this, about how soft Banks

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Speaker 1: having to maneuver given the losses that it suffered from

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Speaker 1: its startup bets. So this is really quite a pivotal

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Speaker 1: moment because you're right, there's so many things at play here. First,

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Speaker 1: for SoftBank, they lost a lot of money when they

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Speaker 1: were investing a lot of money in Vision Fund, and

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Speaker 1: as we know, a lot of those startups are now

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Speaker 1: fledgend companies. And SoftBank said that they want to exercise

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Speaker 1: regular they want excess financial prudence, they want to just

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Speaker 1: cut back on risks, and they also want to raise

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Speaker 1: money for the chip making design firm they're looking to

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Speaker 1: take a public called arm But at the same time,

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Speaker 1: this comes at a time when Ali Baba two hundred

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Speaker 1: billion giant has broken down into six units, six baby

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Speaker 1: babas as some people have called it. But we don't

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Speaker 1: know how that's going to play out, because clearly there

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Speaker 1: will be favorites. For instance, how bout that's a pow

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Speaker 1: joel of Ali Baba, Likely that will do well, but

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Speaker 1: what about the others. So there's so many factors that

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Speaker 1: play here. China is now looking to really be more

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Speaker 1: friendly to entrepreneurs, but then who still knows. I mean,

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Speaker 1: this comes after several years of intense crackdown on Chinese

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Speaker 1: tech giants. Ali Baba included all Right bloombergs Isabel Lee

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Speaker 1: on the China Tech Beat. Thank you. Let's talk crypto.

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Speaker 1: Let's talk ef right now at top two thousand dollars

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Speaker 1: for the first time since August, after a widely anticipated

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Speaker 1: software upgrade to what is the most commercially important blockchain

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Speaker 1: went pretty according to plan and worries those initial rapid outflows,

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Speaker 1: it seemed to be proved unfounded to see yup more

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Speaker 1: than five percent as we speak. John Woo, president of

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Speaker 1: Ava Labs building smart contracts platform Avalanche, is with us.

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Speaker 1: Always great to have you in the house. It's nice

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Speaker 1: to see you, Carol. Let's talk about what this upgrade means.

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Speaker 1: I know that there's interoperability with Ethan, your platform that

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Speaker 1: you're building. Is this going to help yet further just

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Speaker 1: build back a little bit of confidence in the space

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Speaker 1: a little yes. I mean it's a fantastic thing in

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Speaker 1: the sense that going from proof for work to proof

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Speaker 1: for stake basically decreases the energy requirement to run ethereum.

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Speaker 1: The short term relief is basically it went without a

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Speaker 1: technical glitch and there was not as much selling of

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Speaker 1: eth out of this. So that's why ethereum is breaking

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Speaker 1: through two thousand and the whole space is actually frankly

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Speaker 1: doing decent, but it's important to understand why it's sing

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Speaker 1: so well and who's buying it and that kind of thing. Yeah,

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Speaker 1: because there is This is where we get the distinction

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Speaker 1: between Bitcoin, which generally is just an asset and meant

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Speaker 1: to be a store of value, then got e which

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Speaker 1: the hope is around smart contracts, That hope is around adoption.

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Speaker 1: But is that going to become an obvious use case

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Speaker 1: right here, right now because we don't wait for the

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Speaker 1: killer DAP And it does, and that is a use

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Speaker 1: case for people in the crypto ecosystem in order to

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Speaker 1: get yield. Now they can stake their ethereum, So that's

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Speaker 1: very positive for in etherium, in crypto asset people. In fact,

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Speaker 1: a lot of this rally we've seen in the last

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Speaker 1: year to data to speak, it's been a better asset

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Speaker 1: class than any other asset class, even tech stocks. Is

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Speaker 1: because it's been driven by on chain people, crypto natives,

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Speaker 1: and also international people. It's not so much that new

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Speaker 1: money or the institutional adoption interesting and naturally it's all

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Speaker 1: about well teasing out potentially future international and institutional adoption

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Speaker 1: at the moment end where well, there's an NFT week

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Speaker 1: going on here in New York and of a plenty

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Speaker 1: of people focusing on the upgrade. But were you doing

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Speaker 1: some sort of watch party out there at SF No,

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Speaker 1: no watch party for me. For context, John twenty four

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Speaker 1: hours ago, Kate Lawrence of bloc Celerat was on, We're

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Speaker 1: talking about how for the industry, right amid all of

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Speaker 1: the volatility in the pricing of certain tokens, people still

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Speaker 1: watch the technological for the technology grow updates. My question

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Speaker 1: is why why is it such a key moment for

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Speaker 1: an industry to track the underlying technology that powers those platforms.

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Speaker 1: So earlier I just said that the rally has been

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Speaker 1: drawn by native crypto people and in my opinion for

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Speaker 1: this asset class that continue to grow in price, at least,

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Speaker 1: you really really need institutional buyers to come into the space.

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Speaker 1: And for real institutional buyers to come into the space,

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Speaker 1: you need real utility, real world use cases, and you're

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Speaker 1: seeing that. You know, one of the big things for

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Speaker 1: NFT NYC this time around versus last year is that

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Speaker 1: although there are fewer people here, there's quality brands. A

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Speaker 1: lot of brands are coming to see how the NFT

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Speaker 1: construct can help them with their loyalty plans and loyalty

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Speaker 1: programs and also increase engagement. So you're seeing some real

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Speaker 1: use cases and discussion of building real things for big brands.

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Speaker 1: You know, John, when last you were on with us

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Speaker 1: is the afternoon the SVB collapsed, and you were going

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Speaker 1: to come on and talk about what we're talking about

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Speaker 1: right now, and we got sidetracked. In of all the

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Speaker 1: volatility in recent weeks in markets, the industry's access to funding,

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Speaker 1: what are you seeing in New York City among that

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Speaker 1: NFT conversation is that a marketplace that's back on track

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Speaker 1: and that people are kind of optimistic about investing in.

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Speaker 1: So you're not seeing the OTC markets or the on

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Speaker 1: ramps like FIAT like through coin Base into the crypto

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Speaker 1: ecosystem really picking up. Yet they are still hesitant, but

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Speaker 1: the excitement inside the ecosystem of crypto has been reinvigorated.

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Speaker 1: Most of this price rally, people who don't realize happened

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Speaker 1: right after SVB. It was a reminder to the crypto

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Speaker 1: native people as to why they got into this space originally,

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Speaker 1: the mistrust they have for large institutions. You know, that's

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Speaker 1: why bigcoin became very popular after two thousand and eight.

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Speaker 1: And because of that reminder, I think it reinvigorated the

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Speaker 1: crypto native community. We saw a lot of uptick in

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Speaker 1: Bitcoin and if maybe not so much the old coins job,

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Speaker 1: but talk to us about your own initiatives right now,

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Speaker 1: because you say there's utility, there's purpose. Big brands, well,

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Speaker 1: you've got some key financial players coming on to try

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Speaker 1: and understand how they can use blockchain technology, right name

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Speaker 1: some of the people and what you're really building. So

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Speaker 1: from what I'm seeing, especially in the US asset managers,

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Speaker 1: they want to be involved in the space, but they're hesitant.

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Speaker 1: They're not sure of the regulatory framework, and they need

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Speaker 1: to learn the technology better. So this is why we

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Speaker 1: announced yesterday a project called Spruce, which is a public

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Speaker 1: blockchain permission only by the ky c k y b

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Speaker 1: AML functionality. We're doing this in part with Wellington, trow

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Speaker 1: Price and whis Entry. What they're going to experience is

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Speaker 1: what the ability to see the power of execution and

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Speaker 1: settlement in real time, the cost savings of putting this

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Speaker 1: in this system together, and it's a safe place because

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Speaker 1: it's a token valueless transfer. So it's a test net,

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Speaker 1: but they will be able to experience an institutional defy component.

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Speaker 1: No one's been able to do this so far, and

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Speaker 1: this is gonna be the first time they can really

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Speaker 1: explore defy. John, I get what the Spruce test net

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Speaker 1: offers to both the buy and sell side in terms

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Speaker 1: of access. You're a businessman, I'm actually quite curious about

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Speaker 1: how you make money off this model. What is the

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Speaker 1: business that you've created here? Well, first we have to

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Speaker 1: create adoption, and this is the first step towards adopting this.

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Speaker 1: So All the Labs is a software technology company. At

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Speaker 1: some point we will provide services no different from AWS

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Speaker 1: for things in the cloud. Instead of storage and compute power.

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Speaker 1: Services will be providing different features and tools for developers

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Speaker 1: and for places like Wellington or Tiro or Wisdom Tree

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Speaker 1: to create their own applications, for them to create their

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Speaker 1: own them or their marketplace, for them to create their

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Speaker 1: own lending barring DAP. And right now we've gotten partners

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Speaker 1: in the applications and we've gotten the traditional institution to

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Speaker 1: try things out and see what DeFi is all about. John,

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Speaker 1: we have a lab's good to catch up. Thank you

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Speaker 1: for coming back on the show. Now coming up. In

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Speaker 1: an effort to reduce its reliance on China, Apple is

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Speaker 1: putting India in the spotlight and tripling production in what's

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Speaker 1: now considered the world's fastest growing smartphone market. Caroline sick

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Speaker 1: with India because we're also watching shows of emphasis. Sales

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Speaker 1: outlook look pretty disappointing. End that really under schools just

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Speaker 1: how clients are tightening it budgets. They're trying to weather

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Speaker 1: this economic slowdown. Is a sea off by ten percent

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Speaker 1: in the last couple of days. From New York and

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Speaker 1: from San Francisco, this is ret It's time now for

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Speaker 1: talking tech trucking startup Trailler. Well, it's winding down its

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Speaker 1: business in Pakistan and stopped taking new orders last month.

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Speaker 1: It's according to sources, now says the Egypt based platform

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Speaker 1: that connects hippers to carriers. I was looking to exit

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Speaker 1: an economy that's going through one of its biggest crises. Meanwhile,

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Speaker 1: US purchases from machine of machines from Taiwan to make

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Speaker 1: computer chips they rose to a record hive seventy one

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Speaker 1: million in March. That says the Biden ministration here is

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Speaker 1: working to reinvigorate the domestic chip industry. Taiwan saw its

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Speaker 1: USX sports wherese one the forty two percent in the

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Speaker 1: month in March from a year earlier. It's all according

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Speaker 1: to data from the Ministry of Finance. An Apple assembled

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Speaker 1: more than seven billion dollars worth of iPhones in India

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Speaker 1: last fischool year, tripling the production there after accelerating a

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Speaker 1: move beyond China. Now the iphonemaker now makes almost seven

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Speaker 1: percent of its phones in India, according to sources and

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Speaker 1: ed we're going to stick on that theme. Yeah, it's

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Speaker 1: a really key piece of reporting in an ongoing situation

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Speaker 1: for Apple. That's bringing Anna Agran, a senior tech analyst

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Speaker 1: at Bloomberg Intelligence, so in shore. Apple is building more

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Speaker 1: iPhones in India and exporting more iPhones in India. In

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Speaker 1: your fundamental analysis, what's the net result for Apple financially. Yeah, so,

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Speaker 1: you know, when you look at it, I think it's

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Speaker 1: just more of a supply chain issue than financial because,

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Speaker 1: frankly speaking, the best supply chain is in China. It's

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Speaker 1: taken about twenty years to build that. So they build

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Speaker 1: it anywhere outside it's going to be slightly more expensive

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Speaker 1: in the first few years. But frankly speaking, when we

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Speaker 1: saw back in November when because of COVID the iPhone

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Speaker 1: pro factory shut down, it did have an impact on

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Speaker 1: them financially. That too, plus the ongoing geopolitical tensions between

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Speaker 1: the US and China. You know, it is in the

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Speaker 1: best interest of Apple and Apple shareholders that they diversify

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Speaker 1: the supply chain outside of China to other regions, whether

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Speaker 1: it's Vietnam, India, Mexico, or almost everywhere. I would say,

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Speaker 1: and what about the end user in India as well.

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Speaker 1: It's not only about supply from there, it's the demand

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Speaker 1: there that they want to satiate. You Yeah, I think

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Speaker 1: this is one of one of the most fun things.

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Speaker 1: You look at it. You know, if Apple's installed base

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Speaker 1: in India is so small compared to the population, which

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Speaker 1: means it is going to be a very big growth

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Speaker 1: market for them over the next decade. They're opening a

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Speaker 1: store next you know, next week in India, and you know,

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Speaker 1: we have looked at some numbers. So if they are

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Speaker 1: about let's say, six hundred million smartphones in India, you know,

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Speaker 1: Apple only plays in that ten percent of the market.

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Speaker 1: I mean, they have a very small market share, not

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Speaker 1: even ten percent there. The market share is miniscal. Less

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Speaker 1: than five percent of Apple's iPhone revenue comes from India

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Speaker 1: based on artwork, and I think this is this is

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Speaker 1: a big growth opportunity for them in the coming years,

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Speaker 1: and largely because as the middle class in India you know,

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Speaker 1: becomes richer and and and it's more affluent, they're able

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Speaker 1: to afford a luxury product like Apple. You know, currently

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Speaker 1: that market is dominated by Android. We also reported that

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Speaker 1: like local regulations require you know, components sourced and supply

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Speaker 1: chain source within that nation. Apple seems to be kind

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Speaker 1: of rethinking it's global strategy right now when it comes

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Speaker 1: to India. How quick does that ramp in market opportunity come.

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Speaker 1: It seems like this is a long term project, right Yeah, yeah, Yeah,

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Speaker 1: it is going to be a long term project because

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Speaker 1: you know, but I mean, as you can think about it.

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Speaker 1: If ninety percent of the iPhones are under let's say,

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Speaker 1: you know, three hundred four hundred dollars, I shouldn't say iPhone,

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Speaker 1: but of the smartphones are under three hundred dollars, Apple

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Speaker 1: doesn't play in that market, so it's can't even tap

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Speaker 1: into it. But it's as that middle class becomes more

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Speaker 1: raflu when they are richer, the per capita income goes up,

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Speaker 1: and I think that's more of a five to ten

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Speaker 1: year trend rather than something that will happen over the

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Speaker 1: next couple of years and giving us the global perspective.

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Speaker 1: We love it. Granna, thank you of Blomberg Intelligence. Let's

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Speaker 1: talk about now we Work a bench just started by

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Speaker 1: we Work and a private equity firm, Roan Group. Oh, accordingly,

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Speaker 1: it's defaulted on a loan for a San Francisco office tower. Now,

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Speaker 1: the two hundred and forty million dollars loan was for

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Speaker 1: a building in San Francisco's Financial District, which is owned

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Speaker 1: by funds managed by the venture formed by we Work

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Speaker 1: and Roan back in twenty nineteen. That was to buy

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Speaker 1: an oversea with estate led. Welcome back to Bloomberg Technology.

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Speaker 1: I'm Caroline Hide in New York and Imed Ludlow here

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Speaker 1: in San Francisco. It's going to check on those markets. Caroline.

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Speaker 1: This is what your technology sector looks like in the

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Speaker 1: equity space, and that's that one hundred continuing to push higher,

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Speaker 1: up one point six percent. You've got the kind of

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Speaker 1: softening of data both on the jobs and inflation front,

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Speaker 1: which is kind of fueling the idea that actually, when

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Speaker 1: you come to the FED in this rate hiking cycle,

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Speaker 1: we might be nearing the end. You can see out

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Speaker 1: performance as well in the US listed shares of Chinese

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Speaker 1: technology companies and bitcoin off session high as we were

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Speaker 1: at a fifty week high on that bitcoin, but kind

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Speaker 1: of pulled back a little bit near thirty thirty thousand,

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Speaker 1: five hundred dollars per token. A lot of movement in

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Speaker 1: the single names in the stock market around AI, Amazon

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Speaker 1: pushing higher, both based on the annual shareholder letter from

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Speaker 1: CEO Andy Jats up three point five percent, but also

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Speaker 1: the news around their large language models, which I know

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Speaker 1: you're going to get into. Other names gaining C three

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Speaker 1: AI and some giving games back big Bear down seven percent,

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Speaker 1: all of them kind of looking at how to use

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Speaker 1: generative AI tools, but in their enterprise or cloudspace, which

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Speaker 1: is where Amazon is really focused. Carrect Yeah, Big Bear

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Speaker 1: looking to potentially sell more stock in the future, supply

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Speaker 1: wearing a lower But let's go back to where you're

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Speaker 1: just hinting out the Amazon News because it's joining Microsoft,

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Speaker 1: it's joining Google, it's joining everyone basically in the generative

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Speaker 1: AI race. End announcing technology aimed at its cloud customers

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Speaker 1: in particular, as well as marketplace for AI tools from

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Speaker 1: other companies. Now, the Amazon Web Services unit announced not one,

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Speaker 1: but two of its own large language models, one designed

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Speaker 1: to generate text and another that could help power web

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Speaker 1: search personalization. But and it's big, but plans to release

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Speaker 1: a chatbot as yet. But let's dig into this whole

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Speaker 1: world of generative AI and in particular chat GPT. The

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Speaker 1: revolution that basically seems to have opened, could it actually

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Speaker 1: open for productivity to mean that you and I have

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Speaker 1: just a four day workweek. Let's ask Nobel Prize winning

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Speaker 1: labor economists. Sir Christopher Pisorealis is with US recently said

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Speaker 1: that the labor market could adapt quickly enough your work

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Speaker 1: has been so thoughtful about automation, the impact of technology

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Speaker 1: on the way in which we can be productive. Do

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Speaker 1: you think we'd really get to a four day work week?

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Speaker 1: I think so, yeah, I think it's feasible. I mean,

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Speaker 1: let me put it that way. If you see the

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Speaker 1: skills or the componenses that companies are demanding now they're

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Speaker 1: mostly demanding, it's basically down to data processing. Data is

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Speaker 1: the big resource that companies have now, it's data processing.

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Speaker 1: It's communication of that process. Seem to both subordinates and

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Speaker 1: light managers. Now when you get it technology that chatter

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Speaker 1: GPT or other language, well, you basically have the analysis

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Speaker 1: of that day right in front of you ready, much

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Speaker 1: faster than you must can do it. Well, that's what

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Speaker 1: we mean by higher productivity, and Professor, get the machinery

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Speaker 1: to do it. Then obviously then we have spare time

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Speaker 1: ourselves and we have your money because we are more productive.

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Speaker 1: At the same time, Professor Pusaridis, we asked our audience

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Speaker 1: if they share your optimism on the impact of generative

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Speaker 1: AI and a four day work week. This is what

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Speaker 1: they had to say. The results fifty two percent of

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Speaker 1: respondents fifty three percent saying that a four day work

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Speaker 1: week is coming soon because of generative AI. Twenty nine

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Speaker 1: percent of them said, not in our lifetime. What is

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Speaker 1: the timeline that you see. Is this a decade out

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Speaker 1: from being true? I would think so, I would place

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Speaker 1: it at a decade. Yeah, I was thinking twenty thirty maybe,

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Speaker 1: But these things are usually slower, And actually, if I

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Speaker 1: were to recommend it, to advise someone about it, I

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Speaker 1: would advise to be slower because it is going to

429
00:23:14,840 --> 00:23:18,400
Speaker 1: require some readjustments of the things we're doing. I mean,

430
00:23:18,440 --> 00:23:22,560
Speaker 1: not least about families, schooling, education, you know, I mean,

431
00:23:22,760 --> 00:23:25,600
Speaker 1: we might have full day week in the labor market,

432
00:23:25,680 --> 00:23:28,080
Speaker 1: but you do have to take into account and what

433
00:23:28,200 --> 00:23:30,320
Speaker 1: happens with schools, for example, do they also have a

434
00:23:30,359 --> 00:23:35,240
Speaker 1: full day week? What happens with public services, what happens

435
00:23:35,240 --> 00:23:37,240
Speaker 1: with the people who will work there? You know, it's

436
00:23:36,840 --> 00:23:42,480
Speaker 1: it's it's it's a complicated union. It's feasible and if

437
00:23:42,520 --> 00:23:44,800
Speaker 1: there is will you're going to find a way. Well,

438
00:23:44,800 --> 00:23:46,959
Speaker 1: you want your Nobel Prize from work on the impact

439
00:23:46,960 --> 00:23:50,320
Speaker 1: of regulation on policy on the labor market. So is

440
00:23:50,320 --> 00:23:52,359
Speaker 1: it regulation and policy that needs to sort of be

441
00:23:52,920 --> 00:23:57,000
Speaker 1: the car that leads a horse there? Yes, they keep

442
00:23:57,040 --> 00:24:01,359
Speaker 1: problem with the combination of good regulation so it doesn't

443
00:24:01,359 --> 00:24:04,119
Speaker 1: stifle the labor market, which is basically what I studied

444
00:24:04,200 --> 00:24:07,639
Speaker 1: from my Noble winning work work which will call frictions

445
00:24:07,720 --> 00:24:11,639
Speaker 1: and at the same time making sure that it allows

446
00:24:11,760 --> 00:24:17,240
Speaker 1: enough flexibility to the people to show their individual individual

447
00:24:17,359 --> 00:24:19,840
Speaker 1: talents in the labor market, you know, to sort of

448
00:24:19,880 --> 00:24:21,880
Speaker 1: unleash that talent if you like, and do the things

449
00:24:21,920 --> 00:24:25,639
Speaker 1: they are doing best, which my work I call matching

450
00:24:25,720 --> 00:24:30,600
Speaker 1: and the equality of matching. So essentially the way that

451
00:24:30,680 --> 00:24:33,840
Speaker 1: the labor market will work in the best way is

452
00:24:33,880 --> 00:24:38,719
Speaker 1: to improve the match between the worker and the capital

453
00:24:38,880 --> 00:24:43,240
Speaker 1: equipment that that worker has, and I would include computers

454
00:24:43,240 --> 00:24:46,040
Speaker 1: and software as the capital equipment that the worker has,

455
00:24:46,640 --> 00:24:49,880
Speaker 1: and at the same time make sure that sufficiently well

456
00:24:50,000 --> 00:24:56,240
Speaker 1: regulated that it doesn't lead to fraud to abuse of

457
00:24:56,320 --> 00:24:59,679
Speaker 1: the technology. As I mentioned before, you know, these technologies

458
00:24:59,680 --> 00:25:03,480
Speaker 1: are very they're very difficult to monitor. Yeah, and they

459
00:25:03,480 --> 00:25:05,800
Speaker 1: are open to abuse, and we need to work very

460
00:25:05,840 --> 00:25:09,399
Speaker 1: hard to think how to avoid that. Circristopher to that point,

461
00:25:09,640 --> 00:25:11,680
Speaker 1: I mean, you let enough field funded review of the

462
00:25:11,720 --> 00:25:15,119
Speaker 1: effects of automation on jobs, and a lot of the

463
00:25:15,160 --> 00:25:19,160
Speaker 1: time there is a pushback from individuals from unions, from

464
00:25:19,200 --> 00:25:21,800
Speaker 1: a nervousness of what it means for all of us.

465
00:25:21,840 --> 00:25:23,560
Speaker 1: We see it in school children at the moment. We

466
00:25:23,600 --> 00:25:26,320
Speaker 1: see it in people coming through university, people terrified of

467
00:25:26,400 --> 00:25:30,440
Speaker 1: what generative AI really means for their skill set. How

468
00:25:30,480 --> 00:25:32,639
Speaker 1: important is it to win hearts and minds and make

469
00:25:32,680 --> 00:25:35,880
Speaker 1: sure that everyone feels that this actually builds on their skills,

470
00:25:35,960 --> 00:25:39,680
Speaker 1: not detracts on them or put them out of work. Exactly,

471
00:25:39,680 --> 00:25:42,280
Speaker 1: we need to train people, We need to give better

472
00:25:42,320 --> 00:25:47,000
Speaker 1: information and show that it doesn't require that much upskilling

473
00:25:47,480 --> 00:25:51,720
Speaker 1: to ensure that you can have a good job. Unfortunately,

474
00:25:51,760 --> 00:25:55,639
Speaker 1: that comes from two different directions the way I see it.

475
00:25:56,080 --> 00:25:58,600
Speaker 1: One of them is that is that people have naturally

476
00:25:59,040 --> 00:26:01,720
Speaker 1: conservative in if we had changed, you know, if they're

477
00:26:01,760 --> 00:26:06,280
Speaker 1: setting their ways, we're okay, you know what's all these

478
00:26:06,359 --> 00:26:08,520
Speaker 1: new technologies they are going to disrupt us. They're going

479
00:26:08,520 --> 00:26:12,600
Speaker 1: to make us change. That's the way that unions, for example,

480
00:26:12,720 --> 00:26:14,919
Speaker 1: especially in Europe, actually that's the way they look at it.

481
00:26:15,400 --> 00:26:19,800
Speaker 1: They try. Now now we have to we have to

482
00:26:19,920 --> 00:26:22,520
Speaker 1: educate them somehows when we say we have it is

483
00:26:22,520 --> 00:26:25,840
Speaker 1: not really so much to get academics like myself, but

484
00:26:25,920 --> 00:26:33,080
Speaker 1: also the relevant government departments, employers, associations. But throughout modern history,

485
00:26:33,760 --> 00:26:37,040
Speaker 1: technologies has changed our lives and has changed for the better.

486
00:26:37,440 --> 00:26:40,040
Speaker 1: I mean, just think if we resisted electricity, for example,

487
00:26:40,160 --> 00:26:43,640
Speaker 1: now where we will be. You know, it's we had

488
00:26:43,680 --> 00:26:46,359
Speaker 1: so many you know, if we if we resisted, we

489
00:26:46,400 --> 00:26:51,040
Speaker 1: will have all those appliances in our homes to that

490
00:26:51,640 --> 00:26:54,920
Speaker 1: have made life so much easier. So that's one. That's

491
00:26:55,000 --> 00:26:59,480
Speaker 1: one direction that is coming. The other one is that

492
00:27:00,720 --> 00:27:03,919
Speaker 1: a lot of work you read now and what you

493
00:27:04,040 --> 00:27:06,840
Speaker 1: hear about new technologies, it's about how many jobs would

494
00:27:06,840 --> 00:27:11,720
Speaker 1: be lost, exactly three hundred medium jobs. But they never

495
00:27:11,720 --> 00:27:13,640
Speaker 1: talk about how many jobs are going to be created.

496
00:27:14,119 --> 00:27:18,320
Speaker 1: Because Professor pissardes that this is the human tension in

497
00:27:18,359 --> 00:27:23,480
Speaker 1: the story, Right, will my job be eliminated by artificial

498
00:27:23,480 --> 00:27:28,480
Speaker 1: intelligence tools? Are there specific sectors, specific areas of the

499
00:27:28,480 --> 00:27:31,960
Speaker 1: technology industry where you think, yeah, generative AI is just

500
00:27:32,560 --> 00:27:36,679
Speaker 1: it's just going to eliminate the need for a certain role. Oh,

501
00:27:36,760 --> 00:27:39,040
Speaker 1: you can't think of plenty of those, you know. I mean,

502
00:27:39,119 --> 00:27:43,240
Speaker 1: take legal assistance. What is a legal assistant? The job

503
00:27:43,280 --> 00:27:44,920
Speaker 1: may have gone already. If it hasn't gone, an will

504
00:27:44,960 --> 00:27:47,919
Speaker 1: go already. You basically have a lawyer who needs an

505
00:27:47,960 --> 00:27:52,200
Speaker 1: assistant to go through all previous court cases, through all

506
00:27:52,280 --> 00:27:55,359
Speaker 1: those legal books, through all those arpheads to find all

507
00:27:55,400 --> 00:27:59,680
Speaker 1: the information is relevant to your case. Now, just as

508
00:28:00,080 --> 00:28:04,840
Speaker 1: up chatter GPT tell me about sagic case, what do

509
00:28:04,880 --> 00:28:07,639
Speaker 1: we know? And you get your four pages with all

510
00:28:07,680 --> 00:28:10,359
Speaker 1: the information that you're a legal assistant will have got

511
00:28:10,359 --> 00:28:17,240
Speaker 1: working seven for several days. You know, there are jobs

512
00:28:17,240 --> 00:28:21,680
Speaker 1: that we've disappeared. But those those people, they are educated,

513
00:28:21,720 --> 00:28:24,800
Speaker 1: they are highly people that can be more creative than

514
00:28:26,160 --> 00:28:28,800
Speaker 1: our coad rooms going through pages and pages of script.

515
00:28:30,600 --> 00:28:33,520
Speaker 1: So Christopher, Caroline and I have played with many of

516
00:28:33,520 --> 00:28:38,280
Speaker 1: these generative AI tools. We've experimented using them for scripts

517
00:28:38,320 --> 00:28:40,840
Speaker 1: in television news. I think both of us are relatively

518
00:28:40,840 --> 00:28:43,200
Speaker 1: confident we won't be out of the job, at least

519
00:28:43,200 --> 00:28:46,640
Speaker 1: in twenty twenty three. What other areas of the economy

520
00:28:46,760 --> 00:28:51,360
Speaker 1: is productivity unlocked? Forget about replacing jobs. How does this

521
00:28:51,440 --> 00:28:56,680
Speaker 1: make money for global economy? Is really quick? Okay? Number

522
00:28:56,760 --> 00:29:01,400
Speaker 1: one data processing. If we use these technologies to generate

523
00:29:01,480 --> 00:29:05,360
Speaker 1: more data, then they're all businesses with benefit from knowing

524
00:29:05,440 --> 00:29:08,840
Speaker 1: more about their customer. You know the famous KYC No,

525
00:29:08,960 --> 00:29:13,040
Speaker 1: you're a customer, We can get better information, and if

526
00:29:13,080 --> 00:29:17,160
Speaker 1: we have better information, we can target our market in

527
00:29:17,200 --> 00:29:21,800
Speaker 1: a much better way, less time, more productive, more money.

528
00:29:22,320 --> 00:29:24,840
Speaker 1: And that's why I'm saying that we're going to have

529
00:29:24,960 --> 00:29:27,520
Speaker 1: enough money that we can go on their fourth day

530
00:29:27,560 --> 00:29:31,000
Speaker 1: week and have an extract on the weekend, which is

531
00:29:31,080 --> 00:29:33,520
Speaker 1: which is wonderful at least what I'm saying. Yeah, it

532
00:29:33,520 --> 00:29:37,479
Speaker 1: will be wonderful to have one next day. Wow, I

533
00:29:37,520 --> 00:29:39,920
Speaker 1: think many people think it would be wonderful. London School

534
00:29:39,920 --> 00:29:43,840
Speaker 1: of Economics Professor Nobel Prize winning economists, Sir Christopher Pissaridis,

535
00:29:43,880 --> 00:29:46,200
Speaker 1: thank you for your time. Now coming up, we're going

536
00:29:46,240 --> 00:29:48,440
Speaker 1: to get the pulse on the VC ecosystem in the

537
00:29:48,480 --> 00:29:51,600
Speaker 1: first quarter of this year with pitchbook lead VC analyst

538
00:29:51,840 --> 00:29:54,520
Speaker 1: Carl Stanford, who's going to break down the firm's latest

539
00:29:54,560 --> 00:30:23,880
Speaker 1: reports and data. That's next. This is blamed Berg time

540
00:30:23,920 --> 00:30:26,240
Speaker 1: for the VC round up. Lot of Mattica is looking

541
00:30:26,280 --> 00:30:28,800
Speaker 1: to raise four hundred and twenty five million euros that's

542
00:30:28,840 --> 00:30:31,680
Speaker 1: four hundred and sixty seven million dollars for a milan

543
00:30:31,840 --> 00:30:35,400
Speaker 1: IPO this year and it's seeking evaluation of around five

544
00:30:35,480 --> 00:30:39,240
Speaker 1: billion dollars including debt. The Italian gambling company backed by

545
00:30:39,240 --> 00:30:43,880
Speaker 1: Apollo operates its Italy's regulated gaming market, is also active

546
00:30:43,880 --> 00:30:48,479
Speaker 1: in online sports, bedding, and slot machine segments. Relativity Space

547
00:30:48,600 --> 00:30:51,880
Speaker 1: plans to abandon future flights of its Terran one rocket,

548
00:30:52,360 --> 00:30:55,040
Speaker 1: less than a month after it first tested the three

549
00:30:55,160 --> 00:30:58,880
Speaker 1: D printed vehicle. The company will instead shift operations towards

550
00:30:58,920 --> 00:31:02,000
Speaker 1: a previously planned larger rocket in hopes of filling a

551
00:31:02,080 --> 00:31:06,840
Speaker 1: growing market need and better competing with industry leader leader

552
00:31:07,000 --> 00:31:10,840
Speaker 1: space X and Bicosal. Venture firm Lux Capital has raised

553
00:31:10,920 --> 00:31:13,400
Speaker 1: one point one five billion dollars in new funds that

554
00:31:13,480 --> 00:31:16,719
Speaker 1: it plans to invest in startups focused on science and

555
00:31:16,840 --> 00:31:22,000
Speaker 1: deep technologies, including biotech and artificial intelligence. It's the largest

556
00:31:22,000 --> 00:31:24,960
Speaker 1: fund to date, and we'll be called Lux Ventures eight,

557
00:31:25,160 --> 00:31:30,000
Speaker 1: taking the firm's total assets under management beyond five billion dollars. Caroline,

558
00:31:30,600 --> 00:31:32,600
Speaker 1: I've got a nice close up of you there, a

559
00:31:32,640 --> 00:31:36,000
Speaker 1: little bit of a technically which of ourselves a little lovely. Meanwhile,

560
00:31:36,040 --> 00:31:37,680
Speaker 1: I mean it's interesting that you talk about a new

561
00:31:37,680 --> 00:31:40,000
Speaker 1: fund coming from Lux Capital, because we're going to talk

562
00:31:40,000 --> 00:31:42,280
Speaker 1: to us now about VC and perhaps how it's been

563
00:31:42,560 --> 00:31:46,240
Speaker 1: stifled of late. Kyle Stamford, Today's VC spotlighters. Here here's

564
00:31:46,240 --> 00:31:48,320
Speaker 1: the lead VC analyst O rout pitch Book, which just

565
00:31:48,520 --> 00:31:51,200
Speaker 1: released its data about the health of the VC ecosystem

566
00:31:51,240 --> 00:31:53,600
Speaker 1: in the first care of twenty twenty three, and health

567
00:31:53,640 --> 00:31:56,480
Speaker 1: being the operative word here, right, How healthy or not

568
00:31:56,600 --> 00:31:59,800
Speaker 1: healthy is it depends which you you are a comparing team.

569
00:32:00,040 --> 00:32:02,239
Speaker 1: Comparing to twenty twenty one, it is not healthy at all.

570
00:32:02,240 --> 00:32:05,040
Speaker 1: Comparing it to any year before that, it's holding up

571
00:32:05,080 --> 00:32:07,680
Speaker 1: a little bit better than what the data might look like. Right.

572
00:32:08,320 --> 00:32:10,520
Speaker 1: Exits are way down five point eight billion dollars in

573
00:32:10,560 --> 00:32:12,959
Speaker 1: exit val you generated, extremely low. That will have impacts

574
00:32:12,960 --> 00:32:15,600
Speaker 1: down the road. Fundraising was also low at eleven point

575
00:32:15,640 --> 00:32:18,280
Speaker 1: seven billion close in Q one. That will have knock

576
00:32:18,280 --> 00:32:20,840
Speaker 1: on effets down the road. But deal count is surprisingly high.

577
00:32:20,840 --> 00:32:23,400
Speaker 1: We're asking me about thirty thirty nine hundred deals closed

578
00:32:23,480 --> 00:32:26,080
Speaker 1: in Q one, which is higher than any quarter before

579
00:32:26,240 --> 00:32:29,520
Speaker 1: twenty twenty one. So okay, and so that deal count,

580
00:32:29,960 --> 00:32:31,920
Speaker 1: where are those checks being written at the moment? I'm

581
00:32:31,960 --> 00:32:34,600
Speaker 1: not talking sect from talking like geography. Is it with

582
00:32:34,720 --> 00:32:36,360
Speaker 1: that home in New York but you'll usually on the

583
00:32:36,360 --> 00:32:38,440
Speaker 1: West coast? How does it break down from an America

584
00:32:38,520 --> 00:32:41,040
Speaker 1: and global perspective? Sure, we're still seeing about you know,

585
00:32:41,080 --> 00:32:42,880
Speaker 1: fifty percent of the deal count in the US is

586
00:32:42,880 --> 00:32:45,600
Speaker 1: happening in San Francisco, New York, Boston, or LA. Those

587
00:32:45,600 --> 00:32:48,080
Speaker 1: are by far the four largest markets, and San Francisco

588
00:32:48,120 --> 00:32:49,760
Speaker 1: has dropped a little bit. Only about eighteen and a

589
00:32:49,760 --> 00:32:52,280
Speaker 1: half percent of the deals were completed in San Francisco,

590
00:32:52,760 --> 00:32:55,280
Speaker 1: which had been above twenty percent every year until now.

591
00:32:55,280 --> 00:32:56,760
Speaker 1: So I think it's three quarters in the road there.

592
00:32:57,200 --> 00:33:00,480
Speaker 1: But venture is getting much more diversified in the US.

593
00:33:00,600 --> 00:33:02,960
Speaker 1: Right We've had four thousand funds closed in the past

594
00:33:03,040 --> 00:33:05,840
Speaker 1: four years. Those are in the Midwest, They're in Miami,

595
00:33:05,840 --> 00:33:07,960
Speaker 1: they're in Awesome, They're kind of everywhere rather than just

596
00:33:08,040 --> 00:33:11,560
Speaker 1: being siloed into San Francisco and New York. Kyle, there

597
00:33:11,560 --> 00:33:15,680
Speaker 1: are so many dichotomies and contradictions right now. On a

598
00:33:15,760 --> 00:33:18,720
Speaker 1: day where Lots Capital comes out and says one point

599
00:33:18,800 --> 00:33:21,760
Speaker 1: one five billion in new funds, largest ever single funds,

600
00:33:22,040 --> 00:33:24,360
Speaker 1: and yet the data shows it's not being deployed. I

601
00:33:24,440 --> 00:33:28,560
Speaker 1: was at startup Grind last night. Everyone is so optimistic

602
00:33:28,600 --> 00:33:32,640
Speaker 1: about early stage precede getting checks written. Why is there

603
00:33:32,640 --> 00:33:36,280
Speaker 1: this contradiction between the backward looking data and the vibe

604
00:33:36,520 --> 00:33:39,240
Speaker 1: right now in this community? Right Well, I think we

605
00:33:39,240 --> 00:33:40,800
Speaker 1: have to go back to those four thousand funds they

606
00:33:40,800 --> 00:33:42,040
Speaker 1: were closed. A lot of those are going to be

607
00:33:42,080 --> 00:33:44,600
Speaker 1: small funds one hundred fifty million under two hundred fifty million,

608
00:33:44,600 --> 00:33:47,440
Speaker 1: which you're going to focus on the early stage. That area.

609
00:33:47,400 --> 00:33:49,760
Speaker 1: The market is also much further away from you know,

610
00:33:49,840 --> 00:33:51,720
Speaker 1: the public volatility that we've seen are the you know,

611
00:33:51,840 --> 00:33:54,040
Speaker 1: the interest rate hikes that are really impacting the late

612
00:33:54,080 --> 00:33:57,480
Speaker 1: stage and venture growth stage. So optimism is coming back

613
00:33:57,480 --> 00:34:00,280
Speaker 1: to seed in early stage. I think this quarter one

614
00:34:00,400 --> 00:34:02,880
Speaker 1: was the highest medium valuation at seed that we've ever

615
00:34:02,920 --> 00:34:05,480
Speaker 1: seen our data at thirteen million dollars, which is, you know,

616
00:34:05,520 --> 00:34:08,080
Speaker 1: extremely high and you know, double what we're seeing even

617
00:34:08,120 --> 00:34:10,680
Speaker 1: three years ago. So there is optimistic at the early stage,

618
00:34:10,760 --> 00:34:13,360
Speaker 1: especially because it's so far away from kind of the

619
00:34:13,440 --> 00:34:15,480
Speaker 1: public market in the problems that we're seeing at the

620
00:34:15,520 --> 00:34:20,040
Speaker 1: late stage. Kyle, you seem like a glass half full

621
00:34:20,160 --> 00:34:23,160
Speaker 1: kind of guy. Let's go to the world of crypto

622
00:34:23,280 --> 00:34:27,040
Speaker 1: and talk about your report. In the plunge in funding

623
00:34:27,160 --> 00:34:30,360
Speaker 1: back to sort of twenty twenty or pre twenty twenty levels,

624
00:34:30,360 --> 00:34:33,320
Speaker 1: what's happening in that sector. Well, again, I think, you know,

625
00:34:33,360 --> 00:34:37,120
Speaker 1: crypto was the you know industry of this of the

626
00:34:37,160 --> 00:34:40,080
Speaker 1: market two years ago, right, everything was happening through crypto.

627
00:34:40,120 --> 00:34:42,040
Speaker 1: It was Web three, it was blockchain, it was everything

628
00:34:42,400 --> 00:34:44,680
Speaker 1: that has now switched shifted over to AI. If you're

629
00:34:44,680 --> 00:34:47,400
Speaker 1: an AI startup and you have you know, a novel

630
00:34:47,920 --> 00:34:50,480
Speaker 1: use of the technology for general AI or some other

631
00:34:50,800 --> 00:34:53,440
Speaker 1: like I AI technology that can increase the efficiency of

632
00:34:53,440 --> 00:34:57,120
Speaker 1: these companies, that's where investors have shifted their focus now

633
00:34:57,280 --> 00:34:59,960
Speaker 1: right again, looking for the you know, the emerging technology

634
00:35:00,120 --> 00:35:02,759
Speaker 1: that they can really ride the tail winds on for

635
00:35:03,040 --> 00:35:06,880
Speaker 1: the next few years. Told the Mark Grace, I guess, Caroline,

636
00:35:06,920 --> 00:35:09,440
Speaker 1: my point is that on the ground, there's energy and

637
00:35:09,520 --> 00:35:12,600
Speaker 1: optimism across all these spaces. John Wu earlier was in

638
00:35:12,640 --> 00:35:15,840
Speaker 1: New York for a reason. Right he's having meetings talking

639
00:35:15,880 --> 00:35:19,879
Speaker 1: about a very specific section of the crypto market. I mean,

640
00:35:19,880 --> 00:35:22,000
Speaker 1: what are you hearing out there on the East Coast, Well,

641
00:35:22,320 --> 00:35:24,080
Speaker 1: I'm hearing a bat a load of invitations that are

642
00:35:24,080 --> 00:35:26,120
Speaker 1: going around the block at the moment, because it's an

643
00:35:26,200 --> 00:35:28,880
Speaker 1: FT NYC and everyone's got a various event to go,

644
00:35:29,000 --> 00:35:32,080
Speaker 1: and we've still got big VCS companies willing to put

645
00:35:32,160 --> 00:35:35,040
Speaker 1: money on and to work to entertain, to bring people

646
00:35:35,080 --> 00:35:38,000
Speaker 1: together to talk about growth. Cayle what's interesting for me

647
00:35:38,120 --> 00:35:40,239
Speaker 1: and I love your takers, whether you can tell us

648
00:35:40,239 --> 00:35:43,600
Speaker 1: about the pool of capital going towards diverse founders or not.

649
00:35:43,800 --> 00:35:47,960
Speaker 1: Because we know that the NFT space is largely built

650
00:35:47,960 --> 00:35:51,080
Speaker 1: by guys. Are we seeing women people of color managing

651
00:35:51,080 --> 00:35:54,120
Speaker 1: to get checks in this environment? We're still seeing female founders,

652
00:35:54,160 --> 00:35:56,279
Speaker 1: you know, not raised nearly the amount of capital that

653
00:35:56,320 --> 00:35:57,919
Speaker 1: we would like to see, or we'd hope to see

654
00:35:57,960 --> 00:36:00,600
Speaker 1: with all the talk in narrative around you know, getting

655
00:36:00,640 --> 00:36:02,920
Speaker 1: more checks to that, you know, those founders over the

656
00:36:02,960 --> 00:36:05,640
Speaker 1: past few years, but when you kind of zoom out further,

657
00:36:06,000 --> 00:36:08,319
Speaker 1: there is more a lot more money going to those

658
00:36:08,360 --> 00:36:10,279
Speaker 1: founders now than we have seen, you know, ten years ago,

659
00:36:10,400 --> 00:36:12,600
Speaker 1: even five years ago. The past few years, there have

660
00:36:12,680 --> 00:36:14,800
Speaker 1: been you know, quite a few women who have started

661
00:36:14,840 --> 00:36:18,120
Speaker 1: their own funds who are focusing on women founders and

662
00:36:18,160 --> 00:36:20,880
Speaker 1: helping get money to that aa in the market, but

663
00:36:21,040 --> 00:36:23,600
Speaker 1: it just hasn't happened as fast as you know, we

664
00:36:23,640 --> 00:36:29,640
Speaker 1: would hope. I guess what is the prediction, the outlook,

665
00:36:29,760 --> 00:36:32,920
Speaker 1: the forward looking nature of all of this, Kyle, You know,

666
00:36:32,960 --> 00:36:35,560
Speaker 1: there's discussion around a mild recession in the back half

667
00:36:35,560 --> 00:36:38,680
Speaker 1: of this year. How does that manifest itself in private

668
00:36:38,719 --> 00:36:41,319
Speaker 1: markets and for the world of startops. Sure. I mean

669
00:36:41,360 --> 00:36:43,480
Speaker 1: what we're looking at really from that perspective is that

670
00:36:43,560 --> 00:36:46,799
Speaker 1: late stage and venture growth stage of the market. There's

671
00:36:46,800 --> 00:36:48,800
Speaker 1: a huge number of companies there. I think we pulled

672
00:36:49,840 --> 00:36:52,040
Speaker 1: a stat that showed that the number of companies that

673
00:36:52,080 --> 00:36:54,319
Speaker 1: are privately backed at the late stage has grown over

674
00:36:54,480 --> 00:36:56,640
Speaker 1: to has more than doubled in the past five years.

675
00:36:56,920 --> 00:36:58,879
Speaker 1: That's a huge number of companies that need a lot

676
00:36:58,920 --> 00:37:01,000
Speaker 1: of capital, and a lot of that capital that is

677
00:37:01,040 --> 00:37:03,879
Speaker 1: coming from hedge funds, mutual funds, private EU funds. Really

678
00:37:03,920 --> 00:37:05,800
Speaker 1: built up the deal value of the past few years.

679
00:37:05,880 --> 00:37:08,480
Speaker 1: They've gone away, so that area the market without the

680
00:37:08,520 --> 00:37:11,920
Speaker 1: exit opportunities that they really need to release the pressure

681
00:37:11,920 --> 00:37:16,120
Speaker 1: the venture market, they're in for a very struggle struggle

682
00:37:16,239 --> 00:37:18,960
Speaker 1: year or you know, long struggles until that money is

683
00:37:18,960 --> 00:37:20,880
Speaker 1: able to come back and get the money they need

684
00:37:20,920 --> 00:37:24,360
Speaker 1: to grow. Kyle, great to have you in the house

685
00:37:24,440 --> 00:37:26,640
Speaker 1: here in New York. We thank you for it. Carl Stamford,

686
00:37:26,719 --> 00:37:38,400
Speaker 1: lead VC analyst Over at Pitchbook, an Air National guardsman

687
00:37:38,520 --> 00:37:41,399
Speaker 1: is being sought by US agents on the intelligence league

688
00:37:41,440 --> 00:37:43,319
Speaker 1: that according to a person familiar with the matter, who

689
00:37:43,320 --> 00:37:46,440
Speaker 1: says Jack takesi Era is the focus of the Documents League.

690
00:37:46,440 --> 00:37:49,960
Speaker 1: Pro Bloomberg's Amory Horndon joins us now with the details, Amh,

691
00:37:50,040 --> 00:37:51,440
Speaker 1: what do we know, Well, there's just a lot of

692
00:37:51,440 --> 00:37:54,360
Speaker 1: reports right now about this is the individual, as you mentioned,

693
00:37:54,360 --> 00:37:57,640
Speaker 1: twenty one year old National guardsmen. The reports are that

694
00:37:57,719 --> 00:38:00,880
Speaker 1: when these leaks happened, he was stationed at Fort Bragg.

695
00:38:01,320 --> 00:38:04,040
Speaker 1: And there's still a ton of a ton of questions

696
00:38:04,040 --> 00:38:06,640
Speaker 1: though that are out there. One is how and why

697
00:38:06,680 --> 00:38:10,440
Speaker 1: this individual had access to these documents, and the documents

698
00:38:10,440 --> 00:38:13,239
Speaker 1: that were posted on this discord chat at months ago,

699
00:38:13,320 --> 00:38:17,280
Speaker 1: actually they have marks in them, clearly they were folded.

700
00:38:17,400 --> 00:38:20,680
Speaker 1: So how did this individual get access and post these

701
00:38:20,920 --> 00:38:22,800
Speaker 1: documents online is going to be one of the biggest

702
00:38:22,840 --> 00:38:27,480
Speaker 1: questions obviously facing this investigation. And also, of course are

703
00:38:27,520 --> 00:38:30,239
Speaker 1: there any more leaks actually out there, because remember this

704
00:38:30,320 --> 00:38:33,279
Speaker 1: happened months ago and we're only finding out some of

705
00:38:33,320 --> 00:38:38,200
Speaker 1: these stories. And for intelligence officials it's incredibly concerning because

706
00:38:38,400 --> 00:38:41,280
Speaker 1: they're worried that potentially some of these documents have insight

707
00:38:41,360 --> 00:38:45,520
Speaker 1: into how the US gathers intel. Interesting though, earlier this morning,

708
00:38:45,520 --> 00:38:48,200
Speaker 1: as Biden is in Ireland on an international trip, He

709
00:38:48,280 --> 00:38:51,280
Speaker 1: said that they were quote getting close to this individual

710
00:38:51,320 --> 00:38:53,440
Speaker 1: and we could end up with an arrest today, A Marie,

711
00:38:53,520 --> 00:38:54,799
Speaker 1: we need to bring it to us, and of course

712
00:38:54,840 --> 00:38:56,920
Speaker 1: there is a deep technology element to all of this

713
00:38:57,200 --> 00:38:59,480
Speaker 1: and the way it was distributed across Discord. We want

714
00:38:59,480 --> 00:39:03,320
Speaker 1: to thank you for bringing us the latest Bloomberg's Amory Horden. Meanwhile,

715
00:39:03,480 --> 00:39:07,040
Speaker 1: that does it for this edition of Blog Technology. Yeah,

716
00:39:07,239 --> 00:39:09,000
Speaker 1: we have a really key conversation coming up with the

717
00:39:09,040 --> 00:39:11,960
Speaker 1: AWS CEO. I want to get into the detail around

718
00:39:12,000 --> 00:39:14,600
Speaker 1: what generative AI is going to do for Amazon. So

719
00:39:14,719 --> 00:39:18,400
Speaker 1: much to recap four days in almost the weekend, busy

720
00:39:18,640 --> 00:39:21,200
Speaker 1: day markets. Don't forget the podcast. You can get the

721
00:39:21,200 --> 00:39:25,239
Speaker 1: podcast for the show on the terminal, Apple, Spotify, iHeart

722
00:39:25,280 --> 00:39:30,439
Speaker 1: wherever Caroline you get your podcasts. Yeah, big show, big show,

723
00:39:30,440 --> 00:39:32,440
Speaker 1: and more to come with your interview that's coming up

724
00:39:32,600 --> 00:39:35,400
Speaker 1: two pm in New York time. But of course the

725
00:39:35,440 --> 00:39:37,840
Speaker 1: CEO of Amazon Web Services, we can't wait for it.

726
00:39:37,840 --> 00:39:40,640
Speaker 1: We're going to talk much more about generative AI across

727
00:39:40,719 --> 00:39:42,960
Speaker 1: the shows today from New York. This is Bloomberg