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Speaker 1: From hard where Innovation, money and power Collie in Silicon Valley, NBR.

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Speaker 1: This is Bloomberg Technology with Caroline Hide and Ed Ludlove.

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Speaker 1: Ed Love o here in San Francisco. Caroline Hide is

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Speaker 1: off this week. This is Bloomberg Technology coming up Open

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Speaker 1: AI targeted in a complaint to the FTC demanding a

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Speaker 1: halt to further deployment of generative AI technology. Will Speaks,

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Speaker 1: one of the signatories who called for the original pause

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Speaker 1: of further jack GPT rollouts. In just a few moments

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Speaker 1: time and the bulls are back full market coverage ahead.

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Speaker 1: Is the tech sector leads the way? Then? Has that

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Speaker 1: one hundred heading for its second best quarter of the decade.

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Speaker 1: How does that translate to private markets? Plus conversations spanning cybersecurity, venture, cabital,

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Speaker 1: the metaverse and beyond. We'll bring you the inside from

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Speaker 1: the c suite for all the latest and all corners

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Speaker 1: for the technology sector. Let's get your markets. We're moving

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Speaker 1: away from jitters around the banking sector, the FED firmly

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Speaker 1: and focus. There's this kind of growing feeling that the

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Speaker 1: rate heights cycle is coming to an end. You look

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Speaker 1: at them as that one hundred up eight tenths of

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Speaker 1: one percent, but outperforming the broader market. Continued out performance

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Speaker 1: as semiconductors, but also the US listed chairs of Chinese technology.

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Speaker 1: Little action in the bond market, kind of muted trading,

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Speaker 1: but you look at the short end of the curve,

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Speaker 1: the US two year at four point one percent. We're

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Speaker 1: talking a lot about yield curve inversion of what that

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Speaker 1: signals for a potential recession, but that is where the

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Speaker 1: muted actions at at that short end of the curve.

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Speaker 1: This is where we are the last few days of March,

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Speaker 1: the last few days of the quarter, and then as

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Speaker 1: that one hundred is kind of heading for its best quarter,

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Speaker 1: second best quarter in the last decade, we're looking for

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Speaker 1: signals in the trajectory of rates still, but also thinking

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Speaker 1: about the Fed's balance sheet and what they're doing in

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Speaker 1: terms of easing and how it's impacted the technology sector evaluations.

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Speaker 1: That's the Matt grow picture. Hey with the micro out

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Speaker 1: in New York Bloombos. Katy Gryfeld, Hey, Katie, Hey, ed Well,

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Speaker 1: I want to start with bitcoin just for fun, because

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Speaker 1: that's been one of the big stories this year, is

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Speaker 1: this big bounce back that we've seen in the crypto

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Speaker 1: space as a whole, led by Bitcoin, as you can

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Speaker 1: see up year to date by more than seventy percent,

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Speaker 1: and it's a real choose your own narrative. The true

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Speaker 1: believers would tell you what we're seeing in the banking

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Speaker 1: sector that just proves the promise of crypto and why

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Speaker 1: you want to be in it. Maybe a more salable

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Speaker 1: narrative would be what you're just talking about, what we're

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Speaker 1: seeing with tech shares and a potential pivot coming from

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Speaker 1: the FED. But in any case, Bitcoin up by seventy

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Speaker 1: one percent year to day. Let's go back to reality, though,

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Speaker 1: and talk about some of those single stock names, bed

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Speaker 1: Bath and beyond. One of the stories today, the company

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Speaker 1: filed to sell as much as three hundred million dollars

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Speaker 1: worth of shares, has also came out with some preliminary

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Speaker 1: sales figures that disappointed in a big way. You can

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Speaker 1: see shares off by more than nineteen person. Moving on

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Speaker 1: Ali Bob, of course, the news from earlier this week

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Speaker 1: is that it's going to split the Empire into six units.

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Speaker 1: The news today, according to people familiar with the matter,

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Speaker 1: is that its logistics arm is exploring a Hong Kong

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Speaker 1: ipo that could come as soon as the end of

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Speaker 1: this year, and JD dot Com maybe following suit. Two

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Speaker 1: of its subsidiaries also potentially exploring a Hong Kong listed ipis.

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Speaker 1: You can see JD dot Com up about nine and

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Speaker 1: a half percent or so and C three AI at

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Speaker 1: This is interesting. Even with all the headlines around AI

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Speaker 1: that I know you've been following very closely, AI stocks

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Speaker 1: not really feeling the heat yet. This stock in particular

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Speaker 1: up about three points seven percent. Yeah, I think you know.

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Speaker 1: The availability of capital, the energy from investors in this

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Speaker 1: space is ongoing despite what's happening behind the scenes. Speaking

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Speaker 1: of a prominent tech ethics group filed a complaint on

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Speaker 1: Thursday with the US Federal Trade Commission urging the regulator

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Speaker 1: to halt further commercial deployments of the next generations of

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Speaker 1: AI technology that power popular tools like chat GPT. The

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Speaker 1: Center for Artificial Intelligence and Digital Policy ask the FTTC

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Speaker 1: to open an investigation into open AI to determine if

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Speaker 1: the commercial release of the fourth generation of the tool

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Speaker 1: violates US or global regulations. For more, let's bring a

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Speaker 1: Nindo Managing editor covering tech and cybersecurity, Adam new York Lin.

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Speaker 1: What do we know, Hied. It's nice to be here,

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Speaker 1: thanks for having me. So, as you said, this very

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Speaker 1: prominent tech ethics group has filed this complaint with the FTC,

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Speaker 1: and very specifically, it's asking the US government to make

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Speaker 1: a very rare intervention here and hit the puzz button

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Speaker 1: on this huge boom and rush into building smarter and

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Speaker 1: smarter generations of AI. Like you said, the complaint right

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Speaker 1: now is focused on this GPT four language model, the

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Speaker 1: latest one that is behind chat GPT, and as we

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Speaker 1: all know by now, it's a very convincing simulation of

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Speaker 1: human conversation, and that is the fear here. The group

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Speaker 1: is specifically, as you said, asking for this investigation into

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Speaker 1: open AI to see if GPT four's release actually violates

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Speaker 1: existing regulations not just in the US but worldwide. And

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Speaker 1: of course this comes just a day after more than

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Speaker 1: a thousand people, some of them very prominent people in

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Speaker 1: the AI and tech space, signed a letter urging the

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Speaker 1: same Lynn, I think there's an important point of clarification

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Speaker 1: that we've got to mate to our audience, which is

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Speaker 1: this is not a complaint from the FTC. It's a

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Speaker 1: complaint to the FTC by basically an interested party. Right,

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Speaker 1: That is right. The complaint is being led by this

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Speaker 1: center that is a tech ethics group that is very

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Speaker 1: well known. Behind it is this long time privacy advocate

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Speaker 1: Mark Rottenberg, who is trying to press the FTC to

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Speaker 1: take action here. All right, Lyndon, managing editor covering tech

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Speaker 1: cybersecurity out of New York, Thank you so much. Let's

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Speaker 1: stick with this conversation. Bring in Stuart Russell Berkeley, director

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Speaker 1: of the Center for Intelligent Systems for More's the second

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Speaker 1: name on the list and that petition calling for a

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Speaker 1: halt to AI development. He's also, of course the co

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Speaker 1: author of the standard textbook Artificial Intelligence A Modern Approach.

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Speaker 1: Let's start the basics, professor, why did you sign that petition?

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Speaker 1: So the petition expresses the concern that these kinds of

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Speaker 1: systems are extremely unpredictable. In essence, we have really no

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Speaker 1: idea how they work inside, and so they simply don't

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Speaker 1: comply with principles that have already been established. For example,

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Speaker 1: the OECD has a set of principles that have been

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Speaker 1: signed up to by the United States and all the

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Speaker 1: other advanced economies saying that AI systems need to be

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Speaker 1: shown that they will not present undue risk two users.

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Speaker 1: And at the moment, there's no way that we can

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Speaker 1: show that these large language models don't present undue risks

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Speaker 1: because we don't know how they work. And there are

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Speaker 1: already many many stories of apparently psychotic conversations the one

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Speaker 1: reported in the New York Times, for example, a one

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Speaker 1: reported in the Belgian press just the other day of

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Speaker 1: chat GPT working with someone as they prepared to commit

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Speaker 1: suicide and in some ways encouraging them to continue with

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Speaker 1: that student. Those are the broad risks that the petition identifies.

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Speaker 1: There are lots of questions about the petition itself. For example,

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Speaker 1: when it was first published online, Sam Autman, the CEO

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Speaker 1: of open AI's name was listed open Ai, told us

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Speaker 1: at Bloomberg that he never signed it. Have any of

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Speaker 1: the organizers or you as signatories among yourself of discussed

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Speaker 1: that that's some of the names on that petition may

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Speaker 1: not actually never have signed it. So I think what

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Speaker 1: happens when you open one of these petitions for people

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Speaker 1: to sign up online and add their names. A lot

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Speaker 1: of jokers come along and put somebody else's name for fun,

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Speaker 1: and probably someone thought it would be cool to pretend

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Speaker 1: to be sample. When I think Shijinping, that name also appeared,

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Speaker 1: and so very quickly the organizers realized that they were

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Speaker 1: being subjected to a campaign of disinformation, so to speak,

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Speaker 1: and so they put in steps to slow that down.

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Speaker 1: Elon Musk is a signatory to this petition. I've written

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Speaker 1: to Elon Musk multiple times in the last four d

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Speaker 1: eight hours to try and confirm that he actually signed it.

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Speaker 1: Are you confident that it was actually him that signed it?

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Speaker 1: So I have to say I had no role in

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Speaker 1: the preparation of the petition or in the vetting of

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Speaker 1: the signatures. But Max Tegmark, who is the president of

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Speaker 1: the Future of Life Institute, which organized the petition, is

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Speaker 1: in direct contact with Elon Musk, and so I'm pretty

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Speaker 1: confident that that's a real signature. What you're asking for

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Speaker 1: is a halt of six months, a specific period where

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Speaker 1: next generations of large language models or the underlying technology

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Speaker 1: that powers the generative AI tools we're talking about, are

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Speaker 1: not released. How realistic do you think that is? To

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Speaker 1: coordinate that everyone agrees to just stop. I think there

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Speaker 1: are different ways of viewing what we're asking for. I

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Speaker 1: think the idea that you know the CEOs of Microsoft

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Speaker 1: and Google and deep Mind are going to read this

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Speaker 1: and say, oh, sorry, yeah, you're right, we completely messed up,

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Speaker 1: and we'll definitely stop right now. I don't think that's

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Speaker 1: going to happen, but I hope that it at least

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Speaker 1: begins a serious conversation about what would be necessary to

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Speaker 1: develop systems that we can have confidence in. And there's

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Speaker 1: two parts to that, right, Could we develop ways of

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Speaker 1: testing the systems that we already have so that we

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Speaker 1: can show that they do not present risks that they

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Speaker 1: will not help people to commit suicide and go on,

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Speaker 1: or we change the way we design the system so

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Speaker 1: that we can do that, And I actually think the

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Speaker 1: latter is probably more likely. I would also say that

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Speaker 1: legislators the European Union, for example, are close to passing

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Speaker 1: the AI Act, which would require these kinds of steps

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Speaker 1: as a matter of law, so that you could not

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Speaker 1: put systems on the market unless you could satisfy the

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Speaker 1: regulators that they were safe. Professor twenty four hours go,

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Speaker 1: we had Sarah Gao, who's a founder of a VC

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Speaker 1: firm called Conviction on the show. She's been investing in

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Speaker 1: AI for a long time. She was not a signatory

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Speaker 1: to the petition have listened to what she had to

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Speaker 1: say about this. I really care about access and also

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Speaker 1: a reinforcement of bias. But the thing to do is

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Speaker 1: to address these concerns in like a open and transparent way,

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Speaker 1: not to call for a halt to development. In the

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Speaker 1: context of a halt or a six month period of

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Speaker 1: non new releases, Sarah's point is that she thinks the

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Speaker 1: technology should be made increasingly available. More broadly, what is

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Speaker 1: the risk with that? Well, as she pointed out, the

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Speaker 1: systems already exhibit bias and various other kinds of problems.

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Speaker 1: So by making further generations of this technology available, which

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Speaker 1: might be much more capable of causing serious disruption in

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Speaker 1: our society, that would only make things worse, not or

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Speaker 1: I think what we're asking for is that the developers

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Speaker 1: of the technologies take their responsibilities seriously. A professor, We're

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Speaker 1: grateful for your time. You're staying up late for us

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Speaker 1: out of Singapore. You come at this from the academic perspective,

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Speaker 1: the research and policy perspective. But when I went to

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Speaker 1: our audience and asked about questions for you, they basically ask,

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Speaker 1: is this just sour grapes? Is this those in the

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Speaker 1: field that look at open AI and say you're the

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Speaker 1: leader here, we are trying to catch up, and that's

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Speaker 1: why we want to halt. No, not at all. I mean,

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Speaker 1: we're not in the business of competing in the commercial market.

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Speaker 1: Some of the underlying technologies of large language models emerged

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Speaker 1: from academia, and pretty much all of the deep blening

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Speaker 1: technologies emerge from academic and basic research lamps. So this

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Speaker 1: is really a question of asking that the systems that

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Speaker 1: are deployed by that affect the lives of billions of

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Speaker 1: people are actually systems that we understand and that we

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Speaker 1: can show our safe whose capabilities we get predict I

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Speaker 1: don't think that's too much to ask. Stuart Russell Berkeley,

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Speaker 1: Director of the Center for Intelligent Systems, Thank you again

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Speaker 1: for your time and staying up late for us out

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Speaker 1: of Singapore time. Now for talking tech today. Honey in

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Speaker 1: On China, a new Hong Kong based fund, pans to

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Speaker 1: raise one hundred million dollars this year to invest in

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Speaker 1: digital assets startups. Prodigital Future has raised thirty million dollars

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Speaker 1: so far, and we'll turge it early stage and developing ventures,

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Speaker 1: particularly companies with ties to Web three. The fundraising comes

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Speaker 1: as Hong Kong aggressively caughts crypto companies and talent to

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Speaker 1: revive the financial center after the slowdown spurred by COVID lockdowns.

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Speaker 1: The city sees digital assets is key to financial revival.

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Speaker 1: Chinese regulators held a meeting with banks to gauge interest

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Speaker 1: in taking over Silicon Valley banks stake in a local

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Speaker 1: joint venture in a bid to safeguard the banking system

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Speaker 1: from the lenders collapse here in the United States. The

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Speaker 1: China Banking and Insurance Regulatory Commission convener meeting this week

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Speaker 1: to discuss the disposal of svob's fifty percent holding in

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Speaker 1: SPD Silicon Valley Banks. Some Chinese banks have already indicated interest,

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Speaker 1: although discussions that are a very early stage. It's also

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Speaker 1: unclear if regulators prefer a foreign buyer. An Ali Barber

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Speaker 1: will consider gradually giving up controls some of its main

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Speaker 1: businesses over time, after completing a major overhaul to create

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Speaker 1: six new companies that may debut on public markets. The

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Speaker 1: company's gain more than thirty billion US dollars of market

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Speaker 1: value since Tuesday's announcement, which also fired up a rally

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Speaker 1: in other Chinese technology shares. The new divisions will begin

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Speaker 1: separate strategy planning. And now, according to Bloomberg's reporting, let's

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Speaker 1: get to that reporting for more on Ali Baba. Bring

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Speaker 1: in Bloomberg, says Bill Lee out in New York. Where

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Speaker 1: are we right now with Ali Baba? Hi, Yeah, that's

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Speaker 1: definitely the big news of today. Ali Baba said it

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Speaker 1: will see control over its unit two of the six units,

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Speaker 1: although when and where and by how much we still

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Speaker 1: have to find out. Yesterday overnight there was a press

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Speaker 1: conference or a press briefing blue the CEO, Daniel Zang,

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Speaker 1: and there were three takeaways. So first is that it

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Speaker 1: will be a case to case basis, as you've mentioned.

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Speaker 1: The second is that the restructuring is already underway. We

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Speaker 1: just don't know when or when it will be finalized,

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Speaker 1: but it is underway. We've talked about this extensively this week.

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Speaker 1: And the third is the CEO admitted that this is

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Speaker 1: so that they will become more nimble and they will

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Speaker 1: respond to the markets, market regulatory environment more positively. They

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Speaker 1: said that they want to be more of an asset

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Speaker 1: and capital operator rather than a business operator. But the

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Speaker 1: CEO did stress that he wants to have more synergy

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Speaker 1: still and it's also interesting because Ali Baba Holding that's

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Speaker 1: its company names that will literally be the whole day company.

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Speaker 1: And another news that also came across the bloombergwire today

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Speaker 1: is the logistics arm of Ali Baba, that's Tinieo. It's

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Speaker 1: already preparing for its first share sales, so it's targeting

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Speaker 1: a listing as soon as the end of this year.

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Speaker 1: And we've talked about this also that the two things

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Speaker 1: that will come out of this is that it will

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Speaker 1: revive a lackluster IPO market, most of which are I

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Speaker 1: am Hong Kong. And the second is that it will

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Speaker 1: just be more nimble in a piece of regulators after

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Speaker 1: a crackdown that we've seen when Ali Baba tried to

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Speaker 1: list its financial group for an IPO in twenty twenty.

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Speaker 1: Right bloombos Isabella, we're sharing that chart of how long

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Speaker 1: or how far the shares have to catch up before

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Speaker 1: they hit the average twelve month price target. All right,

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Speaker 1: Coming up, we're going to discuss cybersecurity risks and the

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Speaker 1: threat posed by North Korean hackers. That's with MANNYANS Director

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Speaker 1: of Intelligence John holt Quist also taking a look at

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Speaker 1: crips a bit quite interesting. We've seen some undulations i'd

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Speaker 1: put it in the last twenty four hours or so.

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Speaker 1: We're at a twenty eight thousand level right now in

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Speaker 1: terms of dollars per token, but there's a lot of

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Speaker 1: momentum right now in discussion about bitcoin pushing higher to

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Speaker 1: thirty thousand US dollars per token. In this session, we're

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Speaker 1: kind of moving to the downside softer by a round

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Speaker 1: a percentage point. We'll keep our sites set on bitcoin.

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Speaker 1: This is Bloomberg North Korean hackers from a group known

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Speaker 1: as APT forty three opposing as journalists, trying to gather

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Speaker 1: intelligence about international officials approach to nuclear security policy and

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Speaker 1: Kim Johnson's government. That's according to new research. For more

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Speaker 1: on the nation's espionage capabilities, let's bringing John Holtquist, head

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Speaker 1: of Mandian Threat Intelligence and Google Cloud. John, these are

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Speaker 1: really interesting findings in the research that you've been driving.

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Speaker 1: What are they trying to find out at specifically posing

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Speaker 1: as journalists? Why that tactic? Well, people answer questions from journalists,

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Speaker 1: So they're reaching out to people and think tanks and

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Speaker 1: the defense industry, and you know, ultimately they want to

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Speaker 1: compromise their systems. But what's so fascinating about this is

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Speaker 1: they could just ask questions to these people and get responses.

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Speaker 1: And really what North Korea is trying to do is

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Speaker 1: gain decision advantage. They want to know if they'd launch

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Speaker 1: a missile, how people are going to react, and these

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Speaker 1: are the experts on those questions. So what do your

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Speaker 1: findings tell us about their capabilities? How serious is what

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Speaker 1: they're able to achieve. Well, you know what's so interesting

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Speaker 1: about North Korean actors is a few years ago they

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Speaker 1: started doing this espionage activity and it's clearly supporting their

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Speaker 1: nuclear ambitions. But the other half of this is all

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Speaker 1: the crypto to activity that they're involved in. They started

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Speaker 1: using their spies to steal a money globally a few

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Speaker 1: years ago, and even this team that has this espionage

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Speaker 1: operation is stealing crypto on the side that there's a

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Speaker 1: digital cyber component to this. But as part of your research,

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Speaker 1: you point out in person interviews between those posing as

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Speaker 1: journalists and experts are also taking place. So what is

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Speaker 1: the Mandian strategy? How do you protect against both of

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Speaker 1: those issues? Well, you know, we see them coming and

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Speaker 1: pretending to be these people, and what we're lacking or

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Speaker 1: the people who are getting hit are lacking is really

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Speaker 1: a strategy of identifying who they're talking to, right I

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Speaker 1: don't think, you know, if you are in a position

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Speaker 1: where you're likely to be targeted by spies, you really

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Speaker 1: need to up your game as far as who you're

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Speaker 1: responding to, how you authenticate them, and what you know

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Speaker 1: countermess you're taking. Is this unique to North career or

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Speaker 1: did you see evidence that it's a tactic used elsewhere?

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Speaker 1: Not at all. You know, we see Iranian actor carry

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Speaker 1: out targeting pretinning to be journalists. The Russians are doing

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Speaker 1: the exact same thing. They've got obviously a huge need

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Speaker 1: for decision advantage right now, given that the war that

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Speaker 1: they're in. If you are in a business that is

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Speaker 1: likely to be targeted by spies, you really got to

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Speaker 1: take care of who you're responding to. Will use the

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Speaker 1: word spies, you know, we identify that according to the research,

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Speaker 1: North Korean intelligence services are the ones directing this activity.

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Speaker 1: Why is it that significant that the intelligence services of

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Speaker 1: that country are kind of putting the strings. Well, you know,

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Speaker 1: a long time ago, it took so much money and

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Speaker 1: time to do human operations, and those still happen. People

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Speaker 1: get We catch this human spies all the time, but

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Speaker 1: it's so much cheaper and more efficient to use hackers

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Speaker 1: or so many of these problems. And these guys can

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Speaker 1: get answers in hours. They can literally go and ask

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Speaker 1: somebody for a question and get the kind of answers

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Speaker 1: intelligence services used to spend, you know, years trying to answer.

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Speaker 1: What do you think happens next? I understand you don't

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Speaker 1: have a crystal ball, but what are you bracing for? Well,

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Speaker 1: you know what I'm really bracing for is them to

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Speaker 1: up their game on the crypto side. We're right now

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Speaker 1: in the midst of a major incident that is involving

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Speaker 1: potentially thousands of organizations, and we think that, you know,

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Speaker 1: one of the reasons that they're doing, that they're behind

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Speaker 1: this incident is to gain access to crypto users. So

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Speaker 1: that's going to affect organizations involved in crypto, that's going

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Speaker 1: to affect everyday users. And that's one of the most

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Speaker 1: interesting parts of this. You you, an average crypto investor

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Speaker 1: could affect it all, right, John hold Quist and Mandy

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Speaker 1: and bringing us the data. Thank you. Welcome back to

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Speaker 1: bloom Big Technology. Ed Ludlow here in San Francisco now

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Speaker 1: Twili out with its fourth annual State of Customer Engagement

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Speaker 1: Report this year. The report states that data driven customer

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Speaker 1: engagement does drive revenue growth and resilience for the brands

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Speaker 1: they lean into. Its relio CEO Jeff Lawson back with

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Speaker 1: us on Bloomberg Technology. Jeff, it's good to see you

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Speaker 1: break that down into Layman's of course, break that down

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Speaker 1: for me into Layman's terms. What are we talking about there?

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Speaker 1: What does any of that actually mean? Companies that bother

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Speaker 1: to pay attention to their customers and to build a

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Speaker 1: relevant relationship and maintain relevance with that customer make more money.

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Speaker 1: I mean, that's the basic Layman's term for it. And

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Speaker 1: if you think about it, it makes sense. Right in

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Speaker 1: a period of time where people had a lot of

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Speaker 1: money burning a hole in their pocket, right, you didn't

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Speaker 1: have to be very good to go build customer basis

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Speaker 1: audiences get them to spend that money. But in our

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Speaker 1: current environment, when there's more of a fixed wallet world

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Speaker 1: that we're in, it's the companies that actually pay more

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Speaker 1: attention to their customers and create a relationship with those customers, right,

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Speaker 1: they're tending to win the hearts, minds, and all of

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Speaker 1: those customers. And we had a you know, in the report,

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Speaker 1: there was a survey of many, many, many companies, and

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Speaker 1: what we found is that the companies that invested most

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Speaker 1: in building those digital relationships engaging with those customers in

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Speaker 1: targeted and relevant ways. So a ninety percent increase in revenue. Now,

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Speaker 1: think about that. In an environment like this, it makes

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Speaker 1: sense that the companies that do a really good job

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Speaker 1: of using all this very efficient digital technology will understand

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Speaker 1: their customers and build a relationship would actually be doing

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Speaker 1: better than those that don't. So, Jeff, I'm assuming there

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Speaker 1: as a reason that Tuilio carries out this this research, right,

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Speaker 1: how do you put into practice your findings, put into

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Speaker 1: practice what you preach at Well, what it does is, yeah,

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Speaker 1: what it does is it helps companies to understand the

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Speaker 1: nature of the ROI of these investments. So I love

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Speaker 1: the story of Domino's Pizza. Actually, they have many touches

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Speaker 1: with their customers, online, offline, et cetera. As you can imagine,

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Speaker 1: they put in a Tuilio segment, the customer data platform,

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Speaker 1: the leading customer data platform the market to go understand

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Speaker 1: who are these customers? And because of that, they were

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Speaker 1: able to better target their messages and their advertisements to

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Speaker 1: get more customers, and they saw a seven hundred percent

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Speaker 1: increase in the return on ad spend seven right, And

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Speaker 1: so it's those types of things that in this market environment,

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Speaker 1: free company is looking for waves to get more while

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Speaker 1: spending less, and those are the kind of results that

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Speaker 1: we can return. Jeff, you've just stow the debate that

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Speaker 1: Jackie Lopez, our senior producer on the show, has been

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Speaker 1: having for some time that Domino's is a technology company.

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Speaker 1: I think it's a company. Let's go to AI. Let's

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Speaker 1: go to AI, because right now, how companies are doing that.

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Speaker 1: Think about chatbots, how they engage and I'm talking about

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Speaker 1: enterprise companies of all shapes and sizes. They are launching

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Speaker 1: GPT or other AI related services. What's Twilio doing in

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Speaker 1: that space, Well, you know, at the leading customer data platform,

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Speaker 1: we have the free party customer data for so many

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Speaker 1: tens of thousands of our customers and that allows us

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Speaker 1: to help them build relevance and personalization into these generative

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Speaker 1: large language model tools that everyone is building. And so

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Speaker 1: if you think about it, like imagine a website, if

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Speaker 1: the website doesn't know anything about you. Went to Amazon

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Speaker 1: and it didn't know anything about who you are, being

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Speaker 1: a not a very good website. But the fact that

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Speaker 1: Amazon or Google know who you are, impersonalized to who

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Speaker 1: you are makes them great products. Well, I think of

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Speaker 1: you know, you've got the web on one side, you've

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Speaker 1: got mobile over here, and the new world is going

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Speaker 1: to be the results of what these large language models

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Speaker 1: are able to do. It's a new interface into companies,

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Speaker 1: and those interfaces need to understand who they're talking to customer,

431
00:25:42,280 --> 00:25:45,080
Speaker 1: and that's what Tulio can provide. Hey, Jeff, let's talk

432
00:25:45,080 --> 00:25:47,320
Speaker 1: a little bit about the company and you. I think

433
00:25:47,320 --> 00:25:49,760
Speaker 1: I'm right in saying that you're a founda CEO, right

434
00:25:49,840 --> 00:25:53,480
Speaker 1: co found a CEO? Your founder shares convert to common

435
00:25:53,560 --> 00:25:56,000
Speaker 1: stock later in the year, I think in the summer.

436
00:25:56,720 --> 00:26:00,639
Speaker 1: Mark Benioff's spin on this program Active Vision is a

437
00:26:00,680 --> 00:26:03,600
Speaker 1: big topic right now. Talk to me about how you

438
00:26:03,840 --> 00:26:06,919
Speaker 1: where your head's at with that. Well, look, you know,

439
00:26:06,960 --> 00:26:10,240
Speaker 1: the conversion of our shares later this year, it doesn't

440
00:26:10,240 --> 00:26:12,720
Speaker 1: really change anything. I mean, since we went public, we

441
00:26:12,760 --> 00:26:16,159
Speaker 1: have been very responsive in listening to our investors, understanding,

442
00:26:16,440 --> 00:26:18,960
Speaker 1: you know, their needs, their motivations, their ideas for the

443
00:26:19,000 --> 00:26:22,240
Speaker 1: company and having those be reflected in our actions as

444
00:26:22,240 --> 00:26:24,080
Speaker 1: a management team. And I think you see that in

445
00:26:24,560 --> 00:26:27,560
Speaker 1: just last month, we took a number of substantive actions

446
00:26:28,200 --> 00:26:31,400
Speaker 1: to get the company fit for the current market environment

447
00:26:31,440 --> 00:26:36,040
Speaker 1: that we're in, to focus on profitability, including gap profitability,

448
00:26:36,920 --> 00:26:38,960
Speaker 1: and that is all based on feedback that we get

449
00:26:38,960 --> 00:26:41,400
Speaker 1: from our investors about what kind of company they want

450
00:26:41,440 --> 00:26:44,720
Speaker 1: to be invested in. And so this doesn't change are

451
00:26:44,920 --> 00:26:46,760
Speaker 1: the fact that we listen to our investors and they

452
00:26:46,760 --> 00:26:49,440
Speaker 1: are an important stakeholder, and how we are running the company.

453
00:26:50,840 --> 00:26:55,320
Speaker 1: There's also discussions we had around the technology sector right now,

454
00:26:55,359 --> 00:26:58,320
Speaker 1: the health of it, and San Francisco. I don't want

455
00:26:58,359 --> 00:27:00,760
Speaker 1: you to roll your eyes at May you've gone there

456
00:27:00,880 --> 00:27:04,480
Speaker 1: with the debate on remote working. Coming back to the office.

457
00:27:05,080 --> 00:27:07,879
Speaker 1: Just taught me through the logic behind the system that

458
00:27:07,960 --> 00:27:12,760
Speaker 1: Twilio has. Why you think that makes for a better company. Well,

459
00:27:12,800 --> 00:27:14,080
Speaker 1: first of all, let me start up saying I'm a

460
00:27:14,119 --> 00:27:16,520
Speaker 1: big believer in San Francisco. I'm in San Francisco right now.

461
00:27:16,560 --> 00:27:18,840
Speaker 1: This is where I live, this is where our headquarters is,

462
00:27:18,880 --> 00:27:21,400
Speaker 1: and so I think San Francisco is the greatest town

463
00:27:21,440 --> 00:27:24,760
Speaker 1: on earth. As far as Twilio goes, we have moved

464
00:27:24,760 --> 00:27:27,040
Speaker 1: to a distributed work mode and that is both the

465
00:27:27,080 --> 00:27:30,000
Speaker 1: reality of our works right. We are hiring great people

466
00:27:30,040 --> 00:27:33,040
Speaker 1: wherever they are in the world. Before the pandemic, about

467
00:27:33,160 --> 00:27:35,679
Speaker 1: ten to fifteen percent of our employees lived in a

468
00:27:35,680 --> 00:27:38,480
Speaker 1: place that was not attached to a physical office of Twilio's.

469
00:27:38,480 --> 00:27:41,080
Speaker 1: Now that number is it's getting close to sixty percent

470
00:27:41,119 --> 00:27:43,919
Speaker 1: of our population. So what that means is we are

471
00:27:43,960 --> 00:27:46,920
Speaker 1: able to tap into the global talent, which a lot

472
00:27:46,920 --> 00:27:49,760
Speaker 1: of companies did during the pandemic. But now when you say, oh,

473
00:27:49,760 --> 00:27:51,200
Speaker 1: but you got to come back to the office, well,

474
00:27:51,520 --> 00:27:53,000
Speaker 1: there's a lot of people who won't be able to

475
00:27:53,000 --> 00:27:55,160
Speaker 1: go back to the office, and you'll get a first class,

476
00:27:55,200 --> 00:27:57,119
Speaker 1: second class world that are people in offices and the

477
00:27:57,119 --> 00:27:59,640
Speaker 1: people who are remote. Well, Twilio we've said, look, we're

478
00:27:59,640 --> 00:28:03,040
Speaker 1: gonna just continue leaning into this world of hiring great

479
00:28:03,080 --> 00:28:07,439
Speaker 1: talent where it is and make them super productive and

480
00:28:07,600 --> 00:28:11,240
Speaker 1: successful in this distributed world. And you know, someone said

481
00:28:11,280 --> 00:28:13,360
Speaker 1: something smart to me once that I've always thought about,

482
00:28:13,400 --> 00:28:16,719
Speaker 1: which is hybrid is the worst of both worlds. You know,

483
00:28:16,920 --> 00:28:21,080
Speaker 1: hybrid cars, hybrid work, hybrid whatever hybrid's applied to and

484
00:28:21,119 --> 00:28:23,280
Speaker 1: I think there's actually some truth to that. Yes, I'd

485
00:28:23,359 --> 00:28:25,600
Speaker 1: rather have a full gas car or an electric car,

486
00:28:26,640 --> 00:28:28,640
Speaker 1: but hybrid work, I think it is a similar thing.

487
00:28:28,720 --> 00:28:30,399
Speaker 1: So we are leaning into this idea that we are

488
00:28:30,400 --> 00:28:33,119
Speaker 1: a fully distributed company and that our job is to

489
00:28:33,160 --> 00:28:36,440
Speaker 1: get teams together periodically to build that yes, free decor

490
00:28:36,720 --> 00:28:39,240
Speaker 1: just build those relationships to get a lot done. But

491
00:28:39,280 --> 00:28:42,360
Speaker 1: then ultimately where people live is not the most important

492
00:28:42,360 --> 00:28:45,320
Speaker 1: factor in how we build a successful company. Wherever the

493
00:28:45,360 --> 00:28:48,600
Speaker 1: work is happening, you're trying to make your product competitive.

494
00:28:48,760 --> 00:28:50,280
Speaker 1: You know, there are people I speak to out in

495
00:28:50,320 --> 00:28:52,960
Speaker 1: the market that look at what some of the telcos

496
00:28:53,000 --> 00:28:57,600
Speaker 1: are doing around API based messaging platforms, in other words,

497
00:28:57,600 --> 00:29:02,120
Speaker 1: competitions coming, and I wonder how you respond onto that. Well, look,

498
00:29:02,120 --> 00:29:04,600
Speaker 1: we're just for injured a competition. There's been APIs for

499
00:29:05,000 --> 00:29:07,880
Speaker 1: things that we do communications. We didn't invent phone calls

500
00:29:07,960 --> 00:29:10,400
Speaker 1: or text messages. We've just had our finger on the

501
00:29:10,400 --> 00:29:12,480
Speaker 1: pulse of customers better than anyone else. And that's why

502
00:29:12,480 --> 00:29:15,760
Speaker 1: we're a leader and a creator of this communications platform

503
00:29:15,760 --> 00:29:19,400
Speaker 1: as a service market. And there have been many other

504
00:29:19,600 --> 00:29:22,760
Speaker 1: offerings in the market throughout the years, and I think

505
00:29:22,800 --> 00:29:24,800
Speaker 1: We've always had a really good sense of what customers want,

506
00:29:24,840 --> 00:29:27,400
Speaker 1: and that's why we've been the winner. All right, Tuilio,

507
00:29:27,480 --> 00:29:30,160
Speaker 1: CEO Jeff Lawson. Good to catch up, goods talk San

508
00:29:30,200 --> 00:29:34,000
Speaker 1: Francisco and tech. Turning now to a note out from

509
00:29:34,040 --> 00:29:38,440
Speaker 1: Goldman Sack Strategists saying investors should buy US growth stocks

510
00:29:38,480 --> 00:29:42,160
Speaker 1: with high margins while avoiding low margin growth stocks, even

511
00:29:42,160 --> 00:29:44,640
Speaker 1: as equity and rates markets are at odds over the

512
00:29:44,640 --> 00:29:47,520
Speaker 1: likelihood of a recession. If the economy enters a recession,

513
00:29:47,960 --> 00:29:52,320
Speaker 1: equity market expectations for growth will likely deteriorate. In history

514
00:29:52,360 --> 00:29:57,280
Speaker 1: suggests investors will be rewards so called quality attributes, including

515
00:29:57,800 --> 00:30:01,800
Speaker 1: high margin growth stocks. Now coming up, how spob's collapse

516
00:30:01,960 --> 00:30:05,200
Speaker 1: might be a symptom of a broader breakdown in the

517
00:30:05,280 --> 00:30:09,000
Speaker 1: financial machine powering the startup industry. We'll discuss how that's

518
00:30:09,000 --> 00:30:14,640
Speaker 1: also impacting the European ecosystem and the opportunities in that market. Next,

519
00:30:14,760 --> 00:30:17,280
Speaker 1: checking back in on those markets. And as that one

520
00:30:17,320 --> 00:30:19,920
Speaker 1: hundred we talked about it so much, heading for its

521
00:30:19,920 --> 00:30:23,280
Speaker 1: second best quarter of the decade, A lot of the

522
00:30:24,200 --> 00:30:42,560
Speaker 1: narrative around the federal was this is Bloomberg. There's still

523
00:30:42,600 --> 00:30:45,880
Speaker 1: plenty of opportunity out there. There's growing biotech sector in

524
00:30:45,920 --> 00:30:49,960
Speaker 1: the United States and Boston, San Francisco, there's still technology

525
00:30:49,960 --> 00:30:52,520
Speaker 1: companies that are doing well. Many of these tech cuts

526
00:30:52,560 --> 00:30:56,080
Speaker 1: are because the tech companies overspend or with cheap money,

527
00:30:56,280 --> 00:30:58,280
Speaker 1: just went into areas they should have never gone into.

528
00:30:58,680 --> 00:31:01,240
Speaker 1: So I don't believe those are fun the middle cutting

529
00:31:01,240 --> 00:31:02,720
Speaker 1: to the core of the two companies. That's just a

530
00:31:02,800 --> 00:31:05,400
Speaker 1: restructuring to get back to the basics and get back

531
00:31:05,440 --> 00:31:07,680
Speaker 1: to where they're really ending value and making money rather

532
00:31:07,720 --> 00:31:13,000
Speaker 1: than trying to send a man. Look. That was Boston

533
00:31:13,080 --> 00:31:16,040
Speaker 1: Celtics co owner and Bank Capital Senior revised as Steve

534
00:31:16,080 --> 00:31:19,000
Speaker 1: Poweruka there saying he still sees a lot of opportunity

535
00:31:19,280 --> 00:31:21,680
Speaker 1: in tech and that's the conversation that I want to

536
00:31:21,720 --> 00:31:25,280
Speaker 1: continue with Beyonna Lee, managing partner and chief investment officer

537
00:31:25,360 --> 00:31:28,800
Speaker 1: over a Verdane, a specialist growth investment firm that partners

538
00:31:29,000 --> 00:31:33,440
Speaker 1: with tech enabled but sustainable European business four billion dollars

539
00:31:34,040 --> 00:31:37,320
Speaker 1: in committed capital. Beyonda welcome to the program, you kind

540
00:31:37,320 --> 00:31:40,800
Speaker 1: of heard what Steve had to say. They're the opportunities

541
00:31:40,840 --> 00:31:43,760
Speaker 1: he sees, but those were in US cities. You sit

542
00:31:43,840 --> 00:31:48,880
Speaker 1: in Oslo looking closely at European tech given macro conditions

543
00:31:48,960 --> 00:31:51,760
Speaker 1: right now, how healthy is the technology sector from a

544
00:31:51,800 --> 00:31:56,640
Speaker 1: private markets perspective, Well, it depends on what perspective you're

545
00:31:56,640 --> 00:31:58,760
Speaker 1: looking at from, of course. I mean we are looking

546
00:31:58,800 --> 00:32:02,120
Speaker 1: at from sort of fundamental, fundamental perspective, which is, you know,

547
00:32:03,160 --> 00:32:06,960
Speaker 1: the digitalization and decobanization is eating up an ever larger

548
00:32:07,040 --> 00:32:09,280
Speaker 1: share of GMP. And if you want to invest in

549
00:32:09,280 --> 00:32:11,320
Speaker 1: growth as where you want to be, and for us,

550
00:32:11,320 --> 00:32:13,880
Speaker 1: they've been doing this for twenty years. Having some of

551
00:32:13,880 --> 00:32:17,080
Speaker 1: these sort of what we call digital tourists or private

552
00:32:17,120 --> 00:32:20,720
Speaker 1: market tourists to retract from the market I think creates

553
00:32:20,720 --> 00:32:23,560
Speaker 1: a healthier and more exciting balance as an investor. So

554
00:32:24,240 --> 00:32:27,959
Speaker 1: we see fantastic opportunity sets around us, our average, our

555
00:32:28,000 --> 00:32:29,920
Speaker 1: PORTFOLI we're in the growth segment, right We're not on

556
00:32:29,920 --> 00:32:32,880
Speaker 1: a sort of VC ecosystem. We're not reading the leveraged

557
00:32:32,880 --> 00:32:35,360
Speaker 1: buyout world. We're in the growth ecosystem. And our companies

558
00:32:35,400 --> 00:32:38,440
Speaker 1: grew have on average grown about twenty percent per year

559
00:32:38,480 --> 00:32:41,640
Speaker 1: for the last twenty years, returned three point eight times

560
00:32:41,640 --> 00:32:44,680
Speaker 1: and sixty one percent are and last year it actually

561
00:32:44,720 --> 00:32:48,240
Speaker 1: grew twenty six percent, So it's healthy underlying growth, structural

562
00:32:48,320 --> 00:32:52,040
Speaker 1: thematics keep going, and a more sort of healthy, unstable

563
00:32:52,120 --> 00:32:56,840
Speaker 1: valuation environment. I think here in the United States we're

564
00:32:56,880 --> 00:32:59,320
Speaker 1: talking a lot about how than as that one hundred

565
00:33:00,120 --> 00:33:04,080
Speaker 1: heavy index lots of higher multiple software names is heading

566
00:33:04,120 --> 00:33:06,800
Speaker 1: for its second best quarter of the decade. There's a

567
00:33:06,840 --> 00:33:09,520
Speaker 1: lot of discussion in the public markets about tech shares

568
00:33:09,640 --> 00:33:14,920
Speaker 1: performance in Europe. How are you benchmarking portfolio companies in Europe,

569
00:33:15,040 --> 00:33:19,240
Speaker 1: European startups against that public sector performance we've seen recently.

570
00:33:20,800 --> 00:33:22,640
Speaker 1: We've always tried to be quite cautious in our in

571
00:33:23,080 --> 00:33:26,800
Speaker 1: our valuations. We've had statistically about eighty percent uplifts from

572
00:33:26,800 --> 00:33:30,240
Speaker 1: three quarters before exit at Rodin, we've had the same statistics.

573
00:33:31,120 --> 00:33:33,320
Speaker 1: We actually are old enough that we've sort of seen

574
00:33:33,400 --> 00:33:35,080
Speaker 1: this record play a couple of times before, you know,

575
00:33:35,080 --> 00:33:37,280
Speaker 1: we sort of conquest of the financrist in this sort

576
00:33:37,280 --> 00:33:40,520
Speaker 1: of period where our benchmarks actually where our valuations actually

577
00:33:41,000 --> 00:33:44,560
Speaker 1: shaken out, and and it's really that feel that that

578
00:33:44,560 --> 00:33:49,240
Speaker 1: that sort of very schizophrenic or inconsistent behaviors where you're

579
00:33:49,240 --> 00:33:52,000
Speaker 1: seeing both I think in European tech stock world and

580
00:33:52,280 --> 00:33:54,600
Speaker 1: in the private markets. That is still this period of

581
00:33:54,960 --> 00:33:56,800
Speaker 1: trying to figure out where the world is really headed.

582
00:33:57,080 --> 00:34:01,080
Speaker 1: We're not that affected shortcurring in our valuation parameters because

583
00:34:01,120 --> 00:34:04,080
Speaker 1: we have such such sort of deep discounts to public

584
00:34:04,160 --> 00:34:07,120
Speaker 1: markets in our sort of holding values. But but clearly

585
00:34:07,120 --> 00:34:09,239
Speaker 1: when you're discussing with the founders or they're kind of

586
00:34:09,239 --> 00:34:12,680
Speaker 1: bootstrapped companies we typically talk to, of course they're looking

587
00:34:12,719 --> 00:34:16,080
Speaker 1: at those kind of indexes, right, And clearly it was

588
00:34:16,400 --> 00:34:20,000
Speaker 1: a lot more tricky conversations to do proprietary investments when

589
00:34:20,120 --> 00:34:22,880
Speaker 1: when the BESI mere intercoed index or the growth was

590
00:34:22,920 --> 00:34:26,359
Speaker 1: at sixteen times and is now. But I still think

591
00:34:26,360 --> 00:34:28,640
Speaker 1: those sort of massive durations are still just adding to

592
00:34:28,680 --> 00:34:30,239
Speaker 1: the confusion. I think we're going to see another six

593
00:34:30,360 --> 00:34:33,919
Speaker 1: or twelve months before things things sort of flesh flesh out.

594
00:34:34,320 --> 00:34:36,160
Speaker 1: But mind you, you know, we have more developers in

595
00:34:36,200 --> 00:34:40,040
Speaker 1: Europe than the US where we play sort of northwestern

596
00:34:40,080 --> 00:34:42,719
Speaker 1: Europe is much more digitized economies than the US one

597
00:34:43,400 --> 00:34:46,879
Speaker 1: and so and much less capital available, so we don't

598
00:34:46,880 --> 00:34:49,280
Speaker 1: we haven't had the same massive run up in valuations

599
00:34:49,320 --> 00:34:51,480
Speaker 1: and therefore there's not quite the same pressure downwards. I

600
00:34:51,480 --> 00:34:54,239
Speaker 1: would say, well, you know, you think you reflect on

601
00:34:54,280 --> 00:34:59,080
Speaker 1: how many Northern European and even Eastern European tech talents

602
00:34:59,160 --> 00:35:02,040
Speaker 1: make their way over San Francisco, where I am. You know,

603
00:35:02,080 --> 00:35:05,920
Speaker 1: the debate across the Atlantic has been about the impact

604
00:35:06,000 --> 00:35:09,359
Speaker 1: of the SVB collapse in a tightening of financial conditions.

605
00:35:09,719 --> 00:35:12,200
Speaker 1: How did that play out for you in Europe? Was

606
00:35:12,239 --> 00:35:16,640
Speaker 1: there a material impact to operating cash flow for your

607
00:35:16,680 --> 00:35:20,879
Speaker 1: portfolio companies and and how has that impacted their kind

608
00:35:20,880 --> 00:35:23,239
Speaker 1: of outlook for the rest of the year. It was

609
00:35:23,280 --> 00:35:25,440
Speaker 1: really quite interesting because in in you know, in the

610
00:35:25,520 --> 00:35:27,480
Speaker 1: US as we be, and being on the West coast,

611
00:35:27,520 --> 00:35:30,759
Speaker 1: it's such a sort of pillar of the community. We

612
00:35:30,840 --> 00:35:36,640
Speaker 1: have seven officers across northwestern Europe's UK, and it's really

613
00:35:36,640 --> 00:35:39,000
Speaker 1: only the UK that was effected in the meaningful way

614
00:35:39,440 --> 00:35:41,600
Speaker 1: when we ran a sort of over eighty portfolio companies.

615
00:35:41,600 --> 00:35:44,000
Speaker 1: Are really the three of them that banked the with

616
00:35:44,120 --> 00:35:47,239
Speaker 1: this bib SO and none of the none of the

617
00:35:47,280 --> 00:35:50,080
Speaker 1: regional firms here did So it's in the Europe. It's

618
00:35:50,160 --> 00:35:53,560
Speaker 1: largely sort of isolated in the UK, where it was

619
00:35:53,960 --> 00:35:57,680
Speaker 1: it was important. Um, but I think it's interesting that

620
00:35:57,680 --> 00:36:00,759
Speaker 1: there's been an explosion adventure that part clear last year

621
00:36:01,120 --> 00:36:03,120
Speaker 1: more less double look only some sort of into about

622
00:36:03,120 --> 00:36:05,439
Speaker 1: thirty billion and about a third of all venture money

623
00:36:05,480 --> 00:36:07,399
Speaker 1: race last year we ventured it. I think that story

624
00:36:07,440 --> 00:36:10,320
Speaker 1: is going to draw up for many reasons, not the

625
00:36:10,440 --> 00:36:12,839
Speaker 1: sort of beer learnings, but also the general interest rates

626
00:36:12,840 --> 00:36:14,879
Speaker 1: and everything else going on. So I think that sort

627
00:36:14,880 --> 00:36:17,360
Speaker 1: of reckoning that libits that are reckoning the new reality

628
00:36:17,360 --> 00:36:20,600
Speaker 1: of new valuations in the private markets has been delayed

629
00:36:20,640 --> 00:36:22,600
Speaker 1: by the availables of ectual death and that will go

630
00:36:22,680 --> 00:36:25,520
Speaker 1: down now. Well, I kind of crystallized that for me.

631
00:36:25,600 --> 00:36:30,520
Speaker 1: You have offices across basically euro denominated markets Mainland Europe,

632
00:36:30,560 --> 00:36:36,760
Speaker 1: Sterling denominated in the UK, Corona denominated. Where are the opportunities?

633
00:36:36,760 --> 00:36:38,640
Speaker 1: How do you play that environment when it comes to

634
00:36:38,680 --> 00:36:43,439
Speaker 1: investing in private technology companies now? So we basically tried

635
00:36:43,480 --> 00:36:46,160
Speaker 1: to identify Nichous or Subnichus, you know it could be,

636
00:36:47,040 --> 00:36:50,040
Speaker 1: but typically areas whether the Nortyes and northwest beer drinking

637
00:36:50,080 --> 00:36:53,120
Speaker 1: Europe is what we call our results. And basically it's

638
00:36:53,200 --> 00:36:55,239
Speaker 1: really interesting that, you know, when I remember I moved

639
00:36:55,280 --> 00:36:57,360
Speaker 1: to the US in ninety seven to do my MBA

640
00:36:57,440 --> 00:36:59,520
Speaker 1: and I was shocked that it was still cash in news,

641
00:36:59,560 --> 00:37:02,040
Speaker 1: so check books and news. Sorry, move back from my

642
00:37:02,080 --> 00:37:03,839
Speaker 1: reunion in ten years lad news to these in cash.

643
00:37:04,120 --> 00:37:07,080
Speaker 1: These these very sort of digital environments, which is really interesting.

644
00:37:07,160 --> 00:37:09,240
Speaker 1: We can use that as a laboratory to build global

645
00:37:09,280 --> 00:37:12,440
Speaker 1: winners from. And interestingly, you know when you have something

646
00:37:13,239 --> 00:37:16,880
Speaker 1: a flatter salary structure. Our big companies, for example, easy

647
00:37:16,960 --> 00:37:19,440
Speaker 1: Park has one and a half million parking transactions to

648
00:37:19,480 --> 00:37:21,160
Speaker 1: day through the system. We have ten million customers in

649
00:37:21,160 --> 00:37:23,040
Speaker 1: the US now we know them on the name park.

650
00:37:23,120 --> 00:37:26,440
Speaker 1: Now when we want to hire a developer, that actually

651
00:37:26,440 --> 00:37:29,120
Speaker 1: costomers double in Atlanta compared to Stockholm. So you have

652
00:37:29,200 --> 00:37:31,200
Speaker 1: this perception that these are high cost economies, but it's

653
00:37:31,200 --> 00:37:34,600
Speaker 1: actually quite cheap to build technology. But because blue color

654
00:37:34,680 --> 00:37:38,040
Speaker 1: is quite expensive, the return on deploying technologies very high.

655
00:37:38,040 --> 00:37:40,600
Speaker 1: So that's often what we see be to being driven

656
00:37:40,760 --> 00:37:46,200
Speaker 1: sas lative Yes, healthcare, those kind of bond al managing partner,

657
00:37:46,280 --> 00:37:49,000
Speaker 1: chief investment officer of good catch up, good to see

658
00:37:49,360 --> 00:38:02,120
Speaker 1: from US life. Netflix to expand its budding video game

659
00:38:02,239 --> 00:38:06,600
Speaker 1: service beyond smartphones and tablets and bring it to your TV.

660
00:38:06,760 --> 00:38:09,600
Speaker 1: App developer Steve Mosa is the one who discovered it,

661
00:38:09,640 --> 00:38:14,160
Speaker 1: finding some code hidden within Netflix's app that includes references

662
00:38:14,200 --> 00:38:18,200
Speaker 1: to games played on TV. Netflix launched its gaming effort

663
00:38:18,200 --> 00:38:21,440
Speaker 1: on iPhones iPads and Android devices back in twenty twenty one,

664
00:38:21,719 --> 00:38:25,120
Speaker 1: releasing titles like Into the Dead two, Unleashed or Stranger

665
00:38:25,200 --> 00:38:28,440
Speaker 1: Things three, The Game, and Card Blast. The idea here

666
00:38:28,520 --> 00:38:32,200
Speaker 1: is to expand the experience to TV, supplement Netflix's shows

667
00:38:32,200 --> 00:38:35,280
Speaker 1: and movies, and help retain customers as the company raises

668
00:38:35,320 --> 00:38:39,160
Speaker 1: subscription prices. Now, the findings in Netflix's app code don't

669
00:38:39,520 --> 00:38:42,480
Speaker 1: guarantee that the company will follow through with the TV idea,

670
00:38:42,880 --> 00:38:45,759
Speaker 1: but it does indicate that's what the company has been

671
00:38:45,800 --> 00:38:50,360
Speaker 1: turning testing internally. All right, let's go from gaming to

672
00:38:50,440 --> 00:38:53,759
Speaker 1: the metaverse, because the metaverse dream is not dead, at

673
00:38:53,800 --> 00:38:56,880
Speaker 1: least that's according to Meta's Nick Clegg, the social media's

674
00:38:56,880 --> 00:38:59,799
Speaker 1: head of global affairs, took to the metaverse yesterday to

675
00:39:00,120 --> 00:39:03,319
Speaker 1: cyst that the future of computing will take place on

676
00:39:03,400 --> 00:39:07,600
Speaker 1: that still not quite yet defined virtual world. That's sorts

677
00:39:07,600 --> 00:39:11,920
Speaker 1: Bloomberg's Match Taffkin BusinessWeek columnists are they are? They know?

678
00:39:12,160 --> 00:39:14,439
Speaker 1: I've done a lot of reporting about and cutting back.

679
00:39:14,600 --> 00:39:16,640
Speaker 1: Nick Clegg puts a headset on and comes out what

680
00:39:16,760 --> 00:39:20,279
Speaker 1: they are? What's the latest? Well, I mean it sounds like,

681
00:39:20,480 --> 00:39:23,360
Speaker 1: first of all, Meta Facebook has put so much money

682
00:39:23,360 --> 00:39:26,240
Speaker 1: into this thing and it's and as we know, it's

683
00:39:26,280 --> 00:39:29,239
Speaker 1: it's very important to Mark Zuckerberg. And for all the

684
00:39:29,280 --> 00:39:32,560
Speaker 1: talk of you know, the Year of Efficiency and all that,

685
00:39:33,000 --> 00:39:35,719
Speaker 1: this is still Mark Zuckerberg's company. He thinks it's the future,

686
00:39:35,840 --> 00:39:38,120
Speaker 1: and he thinks it's a way for Facebook meta to

687
00:39:38,360 --> 00:39:41,319
Speaker 1: own the next big platform. Um, I think, you know,

688
00:39:41,400 --> 00:39:43,560
Speaker 1: the jury really very much is still out here. We're

689
00:39:43,560 --> 00:39:46,839
Speaker 1: seeing all these other big companies pull back on their

690
00:39:46,880 --> 00:39:51,280
Speaker 1: metaverse ambitions. And when you look at the investment that

691
00:39:51,480 --> 00:39:54,239
Speaker 1: this company has put in to this new technology in

692
00:39:54,360 --> 00:39:57,400
Speaker 1: terms of advertising, in terms of research, they are not

693
00:39:57,520 --> 00:39:59,840
Speaker 1: seeing much of a return. Here. We're talking about, you know,

694
00:40:00,040 --> 00:40:03,560
Speaker 1: tens of billions of dollars Super Bowl ads and relatively

695
00:40:03,600 --> 00:40:06,480
Speaker 1: few users at least when we're talking about the sort

696
00:40:06,480 --> 00:40:09,200
Speaker 1: of horizon product, which is the product that you know,

697
00:40:09,280 --> 00:40:12,839
Speaker 1: Zuckerberg thinks is the future. Yeah, all right, Bloomberg's match

698
00:40:12,880 --> 00:40:14,799
Speaker 1: Chaffkin keeping us up to date with the metaverse, Well,

699
00:40:14,840 --> 00:40:17,680
Speaker 1: little won't it. We're not going to decide today now.

700
00:40:17,960 --> 00:40:22,040
Speaker 1: The weeks after Elon Musk acquired Twitter, hundreds of advertisers

701
00:40:22,080 --> 00:40:25,600
Speaker 1: pause spending on the platform, where the changes the billionaire

702
00:40:25,719 --> 00:40:30,400
Speaker 1: might bring. Months later, many of those advertisers still haven't returned,

703
00:40:30,400 --> 00:40:33,120
Speaker 1: despite efforts by Twitter sales team to woo them back

704
00:40:33,160 --> 00:40:36,719
Speaker 1: with steep discounts and new safety tools. With the reporting

705
00:40:37,000 --> 00:40:41,839
Speaker 1: Bloomberg's Iish accounts interesting story. You know, Elon's been kind

706
00:40:41,840 --> 00:40:44,360
Speaker 1: of public about some of the changes to the platform.

707
00:40:44,560 --> 00:40:46,759
Speaker 1: What's less public is how it's been received. What have

708
00:40:46,800 --> 00:40:49,160
Speaker 1: you learned in your reporting? Yeah, so what we heard.

709
00:40:49,200 --> 00:40:52,239
Speaker 1: We talked to some of the major advertising agencies, and

710
00:40:52,320 --> 00:40:54,839
Speaker 1: really the discounts and all that are great, but that's

711
00:40:54,920 --> 00:40:57,840
Speaker 1: not moving they needed for them. It really is Musk.

712
00:40:58,160 --> 00:41:01,520
Speaker 1: It's some of his erratic decison making, right. It's the

713
00:41:01,600 --> 00:41:04,279
Speaker 1: lack of content moderation on the platform. Right, there's been

714
00:41:04,280 --> 00:41:08,040
Speaker 1: a resurgence of hate speech, and so it's really those things, Right,

715
00:41:08,080 --> 00:41:10,480
Speaker 1: what kind of content is their ad going to be

716
00:41:10,520 --> 00:41:14,600
Speaker 1: next to? That's keeping them off of the platform Twitter structurally,

717
00:41:14,680 --> 00:41:17,319
Speaker 1: his Mason changes, the platform looks different basically to how

718
00:41:17,320 --> 00:41:19,880
Speaker 1: it did a year ago. Which parts of that are

719
00:41:19,880 --> 00:41:21,759
Speaker 1: advertising is worried about. I mean, did they just not

720
00:41:21,920 --> 00:41:25,680
Speaker 1: see the value of advertising on it? Yeah, it's part

721
00:41:25,719 --> 00:41:29,240
Speaker 1: of that, right, Like in although Twitter has had actually

722
00:41:29,320 --> 00:41:32,440
Speaker 1: more daily active users right from us, right, it's but

723
00:41:32,600 --> 00:41:35,759
Speaker 1: even still like there's been so much internal change in

724
00:41:35,880 --> 00:41:38,799
Speaker 1: chaos that it's made it really difficult. So the head

725
00:41:38,840 --> 00:41:41,720
Speaker 1: of sales they thought was gone, but then he actually

726
00:41:41,760 --> 00:41:44,560
Speaker 1: wasn't gone. And there's been so many internal changes within

727
00:41:44,600 --> 00:41:48,160
Speaker 1: the sales organization that advertisers are losing their points of contact.

728
00:41:48,239 --> 00:41:50,719
Speaker 1: They don't even know who to contact or talk to

729
00:41:50,760 --> 00:41:53,760
Speaker 1: anymore about their ads, and so that makes it difficult.

730
00:41:53,760 --> 00:41:55,960
Speaker 1: So it's really a lot of the internal chaos and

731
00:41:56,080 --> 00:41:58,760
Speaker 1: organizational changes that make it confusing for them to even

732
00:41:58,920 --> 00:42:01,399
Speaker 1: know who to talk to. All right, Bloomberg's actually counts

733
00:42:01,480 --> 00:42:04,600
Speaker 1: terrific reporting. Thank you. That does it for this edition

734
00:42:04,600 --> 00:42:08,120
Speaker 1: of Bloomberg Technology, don't forget so much to recap across AI,

735
00:42:08,239 --> 00:42:11,200
Speaker 1: across social. You can check out the podcast wherever you

736
00:42:11,239 --> 00:42:16,360
Speaker 1: find your podcast Apple, Spotify, iHeart, or on our Bloomberg channels.

737
00:42:16,960 --> 00:42:18,719
Speaker 1: One more day to go in this week. The tech

738
00:42:18,800 --> 00:42:21,480
Speaker 1: sector ending the first quarter on a high and has

739
00:42:21,520 --> 00:42:24,680
Speaker 1: that one hundred second best quarter of the decade. More

740
00:42:24,719 --> 00:42:25,920
Speaker 1: to come. This is Bloomberg