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Speaker 1: From Marhart where Innovation, Money and power Collie in Silicon

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Speaker 1: Vallet NBN.

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Speaker 2: This is Bloomberg Technology with Caroline Hide and Ed Lodlove.

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Speaker 3: I'm Caroline Heyde of Bloomberg's World headquarters in New York,

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Speaker 3: and I'm Ed Ludlow in San Francisco.

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Speaker 4: This is Bloomberg Technology.

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Speaker 3: Coming up. Microsoft and labor Unions. They form a historic

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Speaker 3: alliance on artificial intelligence. While the software giant's president says

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Speaker 3: there's no guarantee that AI won't displace jobs, will break

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Speaker 3: down the announcement, and.

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Speaker 2: As global regulators examine Microsoft's thirteen billion dollar investment in

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Speaker 2: Open AI, the company has a simple response, it doesn't

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Speaker 2: own a stake. Will discuss the antitrust concern SLUS.

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Speaker 3: The EU reaches a preliminary deal in what's seen as

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Speaker 3: a key part of the word well's first comprehensive I

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Speaker 3: guessed it artificial intelligence regulation. We'll discuss that and so

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Speaker 3: much more throughout the hour.

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Speaker 2: The big piece of news is Microsoft. Actually there were

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Speaker 2: two pieces of news. Is news news On the one hand,

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Speaker 2: this kind of relationship with afl CIO, a broad agreement

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Speaker 2: with unions to cooperate on artificial intelligence. The other more

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Speaker 2: specific piece of news is a case study involving a

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Speaker 2: few hundred staff and a very specific video games unit

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Speaker 2: where AI has been brought into the collective bargaining agreement.

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Speaker 2: I want to bring in Bloomberg's Jackie Davlos. She is

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Speaker 2: the host of AIRL on Bloomberg. Let's start with the

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Speaker 2: video game unit. Tell us the details of what happened overnight.

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Speaker 5: Well, the Communication Workers of America basically agreed with Microsoft

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Speaker 5: that they will be allowed to incorporate some AI language

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Speaker 5: into their collective bargaining agreement.

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Speaker 3: And that move was really one.

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Speaker 5: Of the first where you saw Microsoft agreeing to have

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Speaker 5: some language in those contracts. And that goes hand in

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Speaker 5: hand with that broader announcement that we saw today in

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Speaker 5: collaboration with afl CIO. Now, many people may not realize

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Speaker 5: that this labor organization encompasses over twelve point five million workers.

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Speaker 5: That includes other affiliated unions like SAGAFTERRA, like the Writers Guilled,

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Speaker 5: the Teachers' Union. So a real broad based organization coming

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Speaker 5: together with Microsoft in one of the first partnerships of

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Speaker 5: its kind that is tackling how to handle artificial intelligence

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Speaker 5: impact on workers, what they can do to both come

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Speaker 5: to the table and say where are workers being impact

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Speaker 5: and how can we best prepare them?

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Speaker 3: And to that end, we had, of course, President of Microsoft,

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Speaker 3: Brad Smith saying, like, look, honestly, I cannot say that

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Speaker 3: Joels won't be impacted and indeed become obsolete due to AI.

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Speaker 3: How straight toolking. We'll see at the announcement Jackie.

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Speaker 5: He was very candid because you had a room full

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Speaker 5: of people who have serious questions. You had teachers who

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Speaker 5: were wondering, how is this going to impact my students?

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Speaker 5: Randy Weingarten, the president of the American Federation of Teachers, sorry,

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Speaker 5: the Teachers' Union basically also they're in collaboration with Microsoft,

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Speaker 5: saying we want to understand how the company is going

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Speaker 5: to help us understand what artificial intelligence is doing to

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Speaker 5: our teachers, to our students, what can we do to

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Speaker 5: prepare them? And two things came out of this partnership.

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Speaker 5: The first being you have Microsoft agreeing to basically host

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Speaker 5: some training sessions for workers across the board. How is

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Speaker 5: artificial intelligence being developed?

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Speaker 3: How can it affect you?

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Speaker 5: Basically kind of giving them the AI one oh one

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Speaker 5: on what the technology is. The next being how can

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Speaker 5: we incorporate your feedback into the room where developers are

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Speaker 5: creating this technology. And the third is where can we

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Speaker 5: team up on policy proposals. They said they want to

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Speaker 5: quote join forces on putting forth legislation. When you have

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Speaker 5: members of the Senate in Congress basically coming together and saying, well,

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Speaker 5: we're accepting suggestions and the two want to create an

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Speaker 5: alliance of sorts to put together some proposals that are

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Speaker 5: the best interest of workers. Now, the other thing is

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Speaker 5: that came out of this is basically saying, look, we

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Speaker 5: Microsoft understand that the collective bargaining process is important and

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Speaker 5: we don't want to stand in the way of that.

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Speaker 5: And so the agreement also includes kind of this neutrality template,

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Speaker 5: basically terms that can say we're not going to stand

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Speaker 5: in the way of or people organizing. And that's a

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Speaker 5: big step coming from a technology company. It really puts

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Speaker 5: the spotlight on other companies like Amazon, which have not

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Speaker 5: taken such a friendly approach when it comes to their

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Speaker 5: workers organizing.

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Speaker 3: Great context, Jackie Davilos, thank you so much on the

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Speaker 3: world when it comes to Microsoft and the Laby unions.

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Speaker 3: But meanwhile, Smith and Microsoft in general, they've been busy

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Speaker 3: because of course having to defend that relationship with open

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Speaker 3: Ai as well. It's been drawing a lot of scrutiny

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Speaker 3: from global regulators, and the software giant has a simple

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Speaker 3: argument when it comes to its investment in open Ai,

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Speaker 3: and it hopes it will resonate with anti trust officials

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Speaker 3: the messages it doesn't own a traditional stake in the

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Speaker 3: startup with US. Now to discuss is Rebecca Allensworth, professor

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Speaker 3: at Vanderbilt University Law School, And all of this comes

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Speaker 3: about from Friday. We understand first the UK the CMA,

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Speaker 3: they're wanting to start sort of requesting people's input as

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Speaker 3: to whether or not de facto Microsoft controls open Ai

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Speaker 3: more than would be well seen on the surface of things.

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Speaker 3: Then we have the UK, as the US as well

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Speaker 3: regulators here looking into the relationship more broadly. From your perspective,

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Speaker 3: from a legal perspective, how strong is the argument that

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Speaker 3: Microsoft is some way acquired open Ai.

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Speaker 6: I think we don't know the answer to that question

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Speaker 6: because we don't know the terms of that deal. I mean,

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Speaker 6: you'll notice that he said traditional steak. So what I

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Speaker 6: want to know and I think what the FTC would

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Speaker 6: want to know, and the CMA is well in what

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Speaker 6: sense do they have a non traditional steak. There's some

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Speaker 6: reporting that they have a non voting seat at the table,

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Speaker 6: a position on the board. Is that going to be

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Speaker 6: a situation where that member says, hey, I'm not voting

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Speaker 6: on this, but if you vote for this, then I

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Speaker 6: think Microsoft will pull out. That could be seen as

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Speaker 6: de facto having some kind of control, and that could

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Speaker 6: potentially raise antitrust problems.

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Speaker 4: So here's what we know about the structure.

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Speaker 2: Based on Boobog's reporting, the thirteen billion dollars to date

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Speaker 2: did not equate to taking an equity investment. According to

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Speaker 2: Boonbo's reporting, Rebecca, it was that Microsoft would derive half

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Speaker 2: of the profits open Ai generates up into a capped

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Speaker 2: limit due to its closed profit model. And that is

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Speaker 2: the argument that Microsoft's saying it is not a state

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Speaker 2: because it was not an investment in return for equity.

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Speaker 2: The question is do regulators or will regulators by that.

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Speaker 6: I think it's an open question that we don't really

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Speaker 6: know the answer to. So anti trust law is not

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Speaker 6: well positioned to challenge investments. It actually is specifically said

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Speaker 6: in the Clayton Act that acquisitions that are merely an

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Speaker 6: investment essentially that don't involve any decision making authority are

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Speaker 6: not covered by that statute. And likewise, if we're going

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Speaker 6: to talk about Section one of the Sherman Act or

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Speaker 6: Section two of the Sherman Acts, other ways of challenging

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Speaker 6: it under anti trust laws, I think the regulators would

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Speaker 6: have to see this as some sort of merging of

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Speaker 6: decision making authority, and I just don't think that we know.

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Speaker 6: At the same time, the antrust laws are pretty flexible

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Speaker 6: about determining whether or not there is some de facto

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Speaker 6: decision making authority, so this won't be decided merely by

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Speaker 6: the corporate form. This will be a fact intensive inquiry

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Speaker 6: and the facts I think we don't have yet.

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Speaker 3: When we have to. Therefore, try not to speculate on facts,

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Speaker 3: but instead look at really what legal groundings. The FTC

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Speaker 3: has pursued a number of well cases of late where

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Speaker 3: ultimately they haven't won out, but they've been trying to

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Speaker 3: sort of swing the perception of where regulators should start

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Speaker 3: to get involved, what really consumer protection looks like. Over

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Speaker 3: in the UK, we know the CMA sort of backed

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Speaker 3: off from its original view of Microsoft and activision in

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Speaker 3: but did force change on that particular deal. What do

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Speaker 3: you think ultimately is trying to be got across here?

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Speaker 3: Are they worrying about some sort of ultimate monopolization that

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Speaker 3: could go into the world of artificial intelligence, and how

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Speaker 3: do they get ahead of that curve?

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Speaker 6: I think that's right, and I think the concern here is,

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Speaker 6: like so many of the FTC's actions happening right now,

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Speaker 6: which is a major deep pockets competitor, one might say

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Speaker 6: a monopolist, although I think in this case that's a

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Speaker 6: little bit of problematic. Holding an input that everybody needs

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Speaker 6: to effectively compete, and that input here is the GPT model,

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Speaker 6: and the idea would be this is going to become

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Speaker 6: essential to compete in so many markets actually, and if

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Speaker 6: it's controlled by one single entity that has the power

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Speaker 6: to bring it to market and can exclude other people

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Speaker 6: who might compete, that could be really bad for competition.

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Speaker 6: As you point out, though, that theory of competition, which

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Speaker 6: is not about head to head, it's not about Microsoft

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Speaker 6: competes with the chat GPT.

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Speaker 4: Product right now head to head.

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Speaker 6: That makes it a little bit of a different type

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Speaker 6: of challenge than the antitrust laws have been used to

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Speaker 6: over the last forty years. It's not precluded by the statute,

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Speaker 6: but it is a little bit unorthodox.

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Speaker 2: How much weight do you think a regulator would give

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Speaker 2: to the idea that there are now many companies offering

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Speaker 2: similar foundation or large language models. They're not the same

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Speaker 2: as GPT, but there are other models out there.

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Speaker 7: So this is a great.

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Speaker 6: Question because this has always been a problem in anti

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Speaker 6: trust and what you're talking aout about it's basically market definition.

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Speaker 6: Is the market the chat GPT model because it is

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Speaker 6: so different, it is so important, it does not have

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Speaker 6: a substitute that we're prepared to call that a market,

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Speaker 6: or is the market as I'm sure Microsoft and open

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Speaker 6: AI will be arguing artificial intelligence, which of course is

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Speaker 6: broad and people have been using artificial intelligence, you know,

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Speaker 6: for many, many years, and there's a lot of competitors

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Speaker 6: within it. The question should be about substitution. Is there

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Speaker 6: a substitutable product for open AIS technology? And I think

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Speaker 6: that is a pretty good argument that the answer to that.

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Speaker 8: Question is no.

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Speaker 2: Oh thanks to Rebecca Allen's work, professor at Vanderbilt Law

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Speaker 2: School there on potential interrust action against open Microsoft.

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Speaker 4: Right coming up on the show, European.

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Speaker 2: Regulators striking a landmark deal to regulate artificial intelligence. We'll

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Speaker 2: discuss with Ashley Casavan, Managing director at the iapp's AI

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Speaker 2: Governance Center. That's our conversation to next. This is Bloomberg technology.

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Speaker 9: That we lose control of the machines I think to

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Speaker 9: some extent, and the howser still very much being debated obviously,

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Speaker 9: of having the fail safe mechanisms in place that humans

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Speaker 9: can override the systems. That's probably the single largest thing

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Speaker 9: I think and worry about, is that if the machines

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Speaker 9: can be or the algorithms or there was being generated,

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Speaker 9: can be developed in such a way that there is

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Speaker 9: no fail safe mechanism that it can be overridden by

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Speaker 9: a human.

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Speaker 8: That's what worries me.

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Speaker 2: Those Armed CEO Renee has speaking exclusively to Bloomberg back

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Speaker 2: at the end of November about his concerns regarding generative AI.

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Speaker 2: He shares concerns with European regulators, who over the weekend

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Speaker 2: reached a deal to formally regulate the technology. Joining us

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Speaker 2: now with more is as Casavan, Managing director of the

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Speaker 2: International Association of Privacy Professionals AI Governance Center, one of

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Speaker 2: the largest and most comprehensive resources for global privacy and information.

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Speaker 2: There is a lot in this EUAI Act. The top

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Speaker 2: lines as I see it, is the acceptable use policy,

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Speaker 2: some disclosures about the data.

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Speaker 4: Used to train models.

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Speaker 2: What is your kind of takeaway on the depth of

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Speaker 2: how this has been regulated in Europe?

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Speaker 8: It's quite significant.

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Speaker 10: This is definitely a landmark deal that has broad implications,

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Speaker 10: not just for European companies, but companies all over the world.

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Speaker 10: I think that the fact that they're really looking to

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Speaker 10: align with international definitions or following and changing through these

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Speaker 10: discussions to OECD definitions, indicates just how big of an

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Speaker 10: impact this will have globally.

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Speaker 3: The whole difficulty was on one side fostering innovation, on

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Speaker 3: the other side, well, protecting the rights of people the user,

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Speaker 3: and of course this is why what it was a

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Speaker 3: thirty seven hours of negotiations that took place. Actually, from

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Speaker 3: your perspective, does this in any way protect and ultimately

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Speaker 3: innovation in Europe because many will worried about some of

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Speaker 3: the startup's mistyle for example in France.

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Speaker 10: Sure, I think that it's really to be determined how

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Speaker 10: this is going to be enforced and the implications. But

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Speaker 10: I do think that what's been drafted is really what

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Speaker 10: we've seen from the dialogues, because we haven't actually seen

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Speaker 10: the text of the final Act will be left to

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Speaker 10: how it's enforced. That said, I do think that really

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Speaker 10: relying on product safety assurance mechanisms like third party audience

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Speaker 10: will hopefully provide that balance between innovation for companies and

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Speaker 10: then protection of the public.

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Speaker 3: What's notable, of course, is yes, we've had an EO

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Speaker 3: here in the US. Yes we've had much talk of regulation,

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Speaker 3: but ultimately this is the first time you get real

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Speaker 3: fines being outlined. I mean, look, they only add out

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Speaker 3: to about thirty five million euros, but that's a lot

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Speaker 3: if you're a small company, and indeed it could be

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Speaker 3: seven percent of global turnover for big companies. Jerry Breton,

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Speaker 3: of course, key negotiator in all of this, trying to

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Speaker 3: drive it across the line when it comes to the EU.

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Speaker 3: He was talking about basically how much the EU is

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Speaker 3: leading here, is it? And how much do you think

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Speaker 3: this is going to set the scene for the global

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Speaker 3: AI players here, because ultimately it's only open AI that

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Speaker 3: seems to be affected thus far.

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Speaker 8: It's a great question.

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Speaker 10: It's funny that you're sharing that that post. I guess

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Speaker 10: is what we're calling tweets now, and the reason why

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Speaker 10: is because there's been a lot of conversations in nations

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Speaker 10: all over the world.

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Speaker 8: You mentioned the US's.

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Speaker 10: Recent AI Executive Order that's really looking to understand the

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Speaker 10: implications of these systems and drive some good guardrails around

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Speaker 10: how to again kind of balance innovation, protect the public,

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Speaker 10: but even really think through what different implications of these

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Speaker 10: systems are, given that AI is not one specific thing,

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Speaker 10: and I think that getting there was a lot of

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Speaker 10: countries that wanted to get to the gate first did

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Speaker 10: get some regulation out there, and so it's great that

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Speaker 10: Europe did that. But I do think that there's actually

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Speaker 10: a converging of a lot of these different guard rails

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Speaker 10: in different formats all over the world.

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Speaker 2: Actually, whether it's at the parliament or commission level in

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Speaker 2: Europe or Congress here in America, do the people writing

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Speaker 2: the rules and the regulation have a deep enough understanding

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Speaker 2: of what it is that they're regulating.

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Speaker 10: It's definitely going to be a resource implication in any

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Speaker 10: country that's looking to over see some of these rules.

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Speaker 10: That said, I don't think that they're doing it alone.

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Speaker 10: We've seen how there's been a large amount of public

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Speaker 10: participation in these processes, civil society organizations providing feedback on

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Speaker 10: an ongoing basis, companies that are brought to the table

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Speaker 10: through not just some of this drafting dialogue, but as

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Speaker 10: we've seen with some of the voluntary codes that have

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Speaker 10: come out and that we're even referenced in this through

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Speaker 10: in the work from the Commission, the G seven Hiroshima process,

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Speaker 10: that those companies are at the table providing inputs, and

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Speaker 10: I think we'll start to see that through enforcements. And

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Speaker 10: again there's a reliance on standards which are typically developed

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Speaker 10: by industry.

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Speaker 8: So I think it's a bit of a misnomer.

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Speaker 10: To think that it's just going to be relying on

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Speaker 10: resources provided by the government.

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Speaker 3: Public and private relationship one that you know well actually

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Speaker 3: of course previously data and digital for the Government of Canada.

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Speaker 3: So having to look around the AI and responsible AI

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Speaker 3: a long time before all of this. Actually, Kasavan, sorry,

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Speaker 3: we thank you so much, Managing director of the iapp's

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Speaker 3: AI Governance Center.

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Speaker 2: Time for talking tech and first stuff in the news.

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Speaker 2: Apple said over the weekend that it shut down third

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Speaker 2: party applications enabling Android devices to use I Message to

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Speaker 2: communicate with iPhone users, citing significant risks to user security

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Speaker 2: and privacy, and shares with the South Korean firm wider Planet,

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Speaker 2: which uses AI to produce advertising, jumped sixty nine percent

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Speaker 2: in two sessions after it said that Squid Games lead

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Speaker 2: Star would become its biggest shareholder with three million shares plus.

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Speaker 2: TikTok agreed to invest one point five billion dollars to

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Speaker 2: combine its shopping businesses with Indonesia's go to group. TikTok

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Speaker 2: gets a seventy five percent stay in that combination, which

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Speaker 2: will run it shopping features in Indonesia, the company's biggest

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Speaker 2: online retail market, Carrot.

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Speaker 3: And let's talk about e commerce more broadly in the

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Speaker 3: globalization of it because a little known PDD or pinto

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Speaker 3: duo has been surging in China. Now it's Timu discounts app.

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Speaker 3: It's rivaling Amazon and Walmart here in the United States.

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Speaker 3: On Bloomberg's latest big take, we take a look at

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Speaker 3: how the company's behind the addictive app is actually outpass

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Speaker 3: outpacing Jack mars Ali Baba also in terms of market capitalization.

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Speaker 3: Now it's even earning the celebrity CEO's praise. Bloomberg expensive

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Speaker 3: Sofa joins us now for what has become a bit

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Speaker 3: of an American addiction too. They might not know that

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Speaker 3: PDD is a company behind it, but it feels like

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Speaker 3: everyone's using Temu.

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Speaker 11: Yeah, it's come on into the US by storm in

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Speaker 11: a little over a year. Is really starting to gobble

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Speaker 11: up spending in market share. It had its big Super

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Speaker 11: Bowl advertising blitz back in February, you know, saying shop

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Speaker 11: like a billionaire. You can can splurge as if you

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Speaker 11: have a ton of money, even if you don't.

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Speaker 4: It's really like.

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Speaker 11: An online dollar General in your phone, you know, just

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Speaker 11: a broad assortment of stuff, very very low prices, and

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Speaker 11: then the sacrifice US shoppers have to make is waiting

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Speaker 11: for delivery time.

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Speaker 4: So that would be the downside.

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Speaker 11: You're gonna you're gonna get prices you can't beat anywhere else,

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Speaker 11: but you're gonna have to you have to wait for

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Speaker 11: the stuff to come to your doorstep.

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Speaker 2: Hey Spencer, what's the threat to your main beat company,

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Speaker 2: Amazon dot Com?

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Speaker 11: Well, I think right now that the threat is exactly

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Speaker 11: that price sensitivity. Is it going to win some of

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Speaker 11: the market share from Amazon, especially maybe like a stocking

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Speaker 11: stuff from market share Amazon CEO A. D. Jasse has

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Speaker 11: given interviews where he says, you know, their customers are

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Speaker 11: still being pretty cautious. They're not buying big ticket items.

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Speaker 11: They're buying the low cost things. They're buying the consumables.

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Speaker 11: That's right where Timu is. You know, most of the

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Speaker 11: products are are low cost ten twenty bucks. And then

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Speaker 11: they also seem to kind to grab you a little

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Speaker 11: more with a social element. They have a lot of

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Speaker 11: games in the app. If you open it, it could almost

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Speaker 11: be like overwhelming and jarring. It's like a like a

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Speaker 11: casino in your phone with lots of spinning wheels and

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Speaker 11: games about raising fish and farming. So they try to

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Speaker 11: kind of suck you in and grab your attention and

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Speaker 11: not just your money.

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Speaker 2: Bloomberg expense is so for a part of the team

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Speaker 2: with the big take and all the things findor Duo

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Speaker 2: and Timu, thank you very much.

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Speaker 3: Welcome back to Bluemore Technology. I'm Karen Hide in New

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Speaker 3: York and.

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Speaker 4: I met Love Low in San Francisco.

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Speaker 2: Quick checking in the markets and two stories that have

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Speaker 2: been driving moves to start in Europe with Worldline. This

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Speaker 2: is a fintech company that closed up one point five

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Speaker 2: percent after Bluebelt, which is basically an activist investor, said

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Speaker 2: get rid of the chair and change the board. You'll

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Speaker 2: remember Worldline, it was a stock that at the end

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Speaker 2: of October fell sixty percent in a single day after

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Speaker 2: it basically dramatically revised its growth for car and everyone said,

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Speaker 2: what on earth is going on? Blue Bell saying let's

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Speaker 2: bring some confidence and trust back to that name in

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Speaker 2: the fintech space and calling for those changes, which investors

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Speaker 2: responded to positively. The other one is an earning story.

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Speaker 2: Oracle is put reporting earnings after the Bell, just a

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Speaker 2: big focus on their data center business. It's about a

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Speaker 2: third of revenue. But the story is can they get

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Speaker 2: access to the high performance GPUs that they need to

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Speaker 2: build out data center infrastructure relevant to the AI story

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Speaker 2: both on the training and inference side. We have so

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Speaker 2: many people on this show, Carr that come on and say,

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Speaker 2: why are we not talking more about Oracle in the

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Speaker 2: same context as the other hyperscalers.

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Speaker 4: They can offer the same thing.

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Speaker 2: There's a big addressable market out there for people who

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Speaker 2: want to train foundation or large language models. Oracle just

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Speaker 2: needs to build out its infrastructure.

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Speaker 4: To support that.

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Speaker 3: And boy hasn't just AI sucked all the oxygen out

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Speaker 3: of this show and likely the entirety of twenty twenty

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Speaker 3: three when it comes to investment, and let's just go

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Speaker 3: from those public market types of investments to the private

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Speaker 3: the bench Capital side Green is on today's at VC Spotlight.

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Speaker 3: He is a founding managing partner of lead Edge Capital

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Speaker 3: growth equity firm five billion dollars in assets under management,

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Speaker 3: investing across public private tech companies and also looking for

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Speaker 3: areas of liquidity when it comes to the secondary market. Mitchell,

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Speaker 3: I'm interested as to how much you think AI is

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Speaker 3: just going to be the play for twenty twenty four,

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Speaker 3: whether it's buying on the secondary market or indeed investing

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Speaker 3: in early rounds.

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Speaker 8: Thanks so much for having me. And I think that

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Speaker 8: there's gonna be a lot of things going on. It's

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Speaker 8: not just AI.

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Speaker 12: You know, there's a lot of interesting software companies being

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Speaker 12: built right now, and I think it's a function of

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Speaker 12: lots of different industries are going to be you know,

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Speaker 12: the top of the town is just AI in Silicon Valley,

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Speaker 12: but there's a lot of people build an interesting software

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Speaker 12: companies outside of AI.

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Speaker 4: Mitchell.

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Speaker 2: The Friday lunch time that Sam Outman was fired by

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Speaker 2: the then board of open AI. I don't think many

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Speaker 2: of us will forget, but the story I was looking

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Speaker 2: into that week was the shocking liquidity on the secondaries

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Speaker 2: market for open ai shares, largely through SPV transactions, some

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Speaker 2: of which blocks of shares or units of SPVs. We're

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Speaker 2: valuing open AI at one hundred billion dollars. I don't

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Speaker 2: think our audience knows just how liquid markets for shares

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Speaker 2: of open AIS SpaceX are. Which names do you expect

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Speaker 2: to be in this big twenty twenty four market that

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Speaker 2: you've outlined.

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Speaker 12: I think you're going to see in an area in

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Speaker 12: a world where investors, so people who invest in funds

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Speaker 12: are limited partners, they are you know, demanding investors private

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Speaker 12: equity funds give capital back to their investors, in which

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Speaker 12: case people are going to look to secondary markets to sell.

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Speaker 12: I think you're going to see, you know, continue to

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Speaker 12: see increase in companies, you know, turning into selling stuff

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Speaker 12: through SPVs or secondary markets. You're going to see a

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Speaker 12: lot of continuation funds. Anything that drives DPI, which is

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Speaker 12: money back to LPs on that basis, is going to

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Speaker 12: be a focus.

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Speaker 8: It because it's it's funds trying to.

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Speaker 12: Return money to their LPs, and you know, with a

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Speaker 12: slow IPO and M and a market that's just going

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Speaker 12: to be exaggerated.

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Speaker 4: Well that's my question.

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Speaker 2: Is all this activity in the secondary's market a precursor

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Speaker 2: or leading indicator that we will start to see more

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Speaker 2: primary rounds and more listings or exits in twenty twenty four.

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Speaker 12: I don't think it's a h think. I don't think

443
00:24:32,200 --> 00:24:35,080
Speaker 12: it's a precursor to it. I think it is a

444
00:24:35,160 --> 00:24:38,280
Speaker 12: it's a result of not having an IPO market right now.

445
00:24:38,359 --> 00:24:39,120
Speaker 8: Why I've seen it.

446
00:24:39,280 --> 00:24:41,640
Speaker 12: That being said, they'll go going all the way back

447
00:24:41,680 --> 00:24:45,720
Speaker 12: to you know, Facebook and Ali Baba and Twitter and Uber.

448
00:24:45,960 --> 00:24:49,000
Speaker 12: There's been secondary markets for a lot of these big companies,

449
00:24:49,080 --> 00:24:50,840
Speaker 12: even in robust IPO markets.

450
00:24:51,920 --> 00:24:52,800
Speaker 8: I think you need to FED.

451
00:24:52,880 --> 00:24:55,880
Speaker 12: I think investors need to get confident that the FED

452
00:24:55,960 --> 00:24:58,240
Speaker 12: is done raising rates. I don't think they need to

453
00:24:58,320 --> 00:25:00,639
Speaker 12: lower them a bunch for the IPO market to happen,

454
00:25:00,720 --> 00:25:03,080
Speaker 12: but I think are to pick up.

455
00:25:03,359 --> 00:25:04,920
Speaker 8: But I do think they need to get a sense

456
00:25:04,960 --> 00:25:08,240
Speaker 8: that rate rising is done, which you know, who knows.

457
00:25:09,080 --> 00:25:11,480
Speaker 12: I expect we'll see more tech IPOs in twenty twenty

458
00:25:11,480 --> 00:25:12,920
Speaker 12: four than we saw on twenty twenty three.

459
00:25:13,040 --> 00:25:16,000
Speaker 8: But that's not very hard to do. You don't need many.

460
00:25:16,680 --> 00:25:19,720
Speaker 3: Well said Mitchell. I'm interested because you, of course that

461
00:25:19,920 --> 00:25:23,840
Speaker 3: lead edged capital do take part in the liquidity movement

462
00:25:23,960 --> 00:25:26,280
Speaker 3: and buying up on the secondary market. And one of

463
00:25:26,280 --> 00:25:28,360
Speaker 3: the valuations that we're looking at, I mean, we were

464
00:25:28,359 --> 00:25:30,600
Speaker 3: hearing of the top evaluations. It's still getting for an

465
00:25:30,640 --> 00:25:33,919
Speaker 3: open AI. But while a lot of these gps are

466
00:25:33,920 --> 00:25:36,120
Speaker 3: going to be under stress from their LPs to be

467
00:25:36,560 --> 00:25:39,440
Speaker 3: well selling out, perhaps at evaluation that isn't as high

468
00:25:39,440 --> 00:25:40,320
Speaker 3: as it was previously.

469
00:25:41,520 --> 00:25:45,920
Speaker 8: Yeah, so I think twenty twenty four will be.

470
00:25:46,080 --> 00:25:48,480
Speaker 12: Or maybe not twenty twenty four, twenty twenty four, twenty five,

471
00:25:48,560 --> 00:25:50,520
Speaker 12: twenty six will be. You're going to see a lot

472
00:25:50,560 --> 00:25:53,520
Speaker 12: of down round IPOs and that doesn't mean anything. That

473
00:25:53,640 --> 00:25:56,399
Speaker 12: just means a handful of fools who paid a higher

474
00:25:56,440 --> 00:25:58,560
Speaker 12: price in the last round are now paying you know,

475
00:25:58,600 --> 00:25:59,840
Speaker 12: are now having a down round.

476
00:26:00,119 --> 00:26:02,600
Speaker 8: Doesn't really affect the company they raised. The company is

477
00:26:02,600 --> 00:26:03,399
Speaker 8: actually really smart.

478
00:26:03,440 --> 00:26:05,760
Speaker 12: They raised money at X and when they decided to

479
00:26:05,760 --> 00:26:08,720
Speaker 12: list the shares they became lower than XT.

480
00:26:08,840 --> 00:26:10,280
Speaker 8: That's fine, there's nothing wrong with that.

481
00:26:12,000 --> 00:26:14,800
Speaker 12: But you know, and very few of these investors have

482
00:26:14,880 --> 00:26:17,480
Speaker 12: blocks on these IPOs. So I actually think you're going

483
00:26:17,560 --> 00:26:20,040
Speaker 12: to see a lot of down round IPOs start to happen.

484
00:26:20,080 --> 00:26:21,879
Speaker 8: It's going to take time. Now again we've seen them before.

485
00:26:21,960 --> 00:26:24,359
Speaker 12: Stripe was like a down round IPO, and by the way,

486
00:26:24,400 --> 00:26:25,280
Speaker 12: down ound ipo.

487
00:26:25,080 --> 00:26:25,960
Speaker 8: Doesn't mean anything.

488
00:26:26,640 --> 00:26:28,560
Speaker 12: It can still be a great opportunity to buy the

489
00:26:28,560 --> 00:26:31,000
Speaker 12: stock over the long term. That just means at that

490
00:26:31,040 --> 00:26:33,280
Speaker 12: point in time, somebody at one point was going to

491
00:26:33,280 --> 00:26:36,440
Speaker 12: by a higher price. I think you're going to continue

492
00:26:36,480 --> 00:26:39,320
Speaker 12: now in the secondary markets, it just depends. I think

493
00:26:39,320 --> 00:26:42,560
Speaker 12: the price of open Ai probably is a bit nutty,

494
00:26:42,600 --> 00:26:48,000
Speaker 12: but what do I know. Look, there are interesting opportunities,

495
00:26:48,000 --> 00:26:51,760
Speaker 12: and are in a world where gps need to you know,

496
00:26:51,800 --> 00:26:55,399
Speaker 12: people that run funds need to get liquidity.

497
00:26:55,600 --> 00:26:57,520
Speaker 8: There's probably interesting.

498
00:26:57,320 --> 00:26:59,600
Speaker 12: And I've been sitting on positions for a long time,

499
00:27:00,000 --> 00:27:03,560
Speaker 12: probably you know, interesting opportunities out there. We just bought

500
00:27:03,600 --> 00:27:07,159
Speaker 12: something at less than fifteen times EBITDA and it's a

501
00:27:07,240 --> 00:27:09,399
Speaker 12: rule of sixty business, which means it grows you know,

502
00:27:09,480 --> 00:27:11,919
Speaker 12: twenty five and had thirty five percent ebitdet margians. That

503
00:27:11,920 --> 00:27:13,360
Speaker 12: doesn't seem crazy to us at all.

504
00:27:13,680 --> 00:27:16,119
Speaker 3: So maybe if people are looking to be getting into

505
00:27:16,359 --> 00:27:20,200
Speaker 3: stripe at evaluation that they find slightly more digestible. They're

506
00:27:20,200 --> 00:27:21,360
Speaker 3: going to be able to do that in the secondary

507
00:27:21,359 --> 00:27:23,680
Speaker 3: market right now or indeed better in twenty twenty four.

508
00:27:24,000 --> 00:27:27,160
Speaker 3: Who are those buyers at the moment, and indeed, who

509
00:27:27,200 --> 00:27:29,680
Speaker 3: do you tend to be the sellers in this particular

510
00:27:29,720 --> 00:27:30,440
Speaker 3: type of market.

511
00:27:31,240 --> 00:27:32,240
Speaker 8: Yeah, that's a great question.

512
00:27:32,320 --> 00:27:35,359
Speaker 12: So on the on the cell side, it typically could

513
00:27:35,400 --> 00:27:38,200
Speaker 12: be an early a fund that was an early investor

514
00:27:38,280 --> 00:27:40,800
Speaker 12: in it. It could be a late investor that just

515
00:27:40,880 --> 00:27:44,800
Speaker 12: needs to get liquidity to their LPs, your crossover hedge

516
00:27:44,800 --> 00:27:47,919
Speaker 12: fund or something. It could be an early employee or

517
00:27:47,960 --> 00:27:52,480
Speaker 12: a former employee, an early angel investor, and it just

518
00:27:52,480 --> 00:27:56,600
Speaker 12: think abouting that own stock at my own liquidity. On

519
00:27:56,680 --> 00:28:00,320
Speaker 12: the buy side, you know, depending on the type of company,

520
00:28:00,800 --> 00:28:03,199
Speaker 12: there are a lot of secondary funds set up to

521
00:28:03,240 --> 00:28:08,000
Speaker 12: do this, whether it's Globe and Sachs's secondary fund, you know,

522
00:28:08,400 --> 00:28:13,000
Speaker 12: Blackstone secondary fund, Lexington Collie, or there's a bunch of them. Also,

523
00:28:13,000 --> 00:28:18,280
Speaker 12: people like Industry A Ventures, We obviously participated in secondaries. Additionally,

524
00:28:19,000 --> 00:28:22,000
Speaker 12: existing investors in the company often have you know, writer

525
00:28:22,160 --> 00:28:25,320
Speaker 12: first refusals to buy stock in these businesses, and if

526
00:28:25,359 --> 00:28:29,000
Speaker 12: the prices are attractive. They often do you know, and

527
00:28:29,040 --> 00:28:32,440
Speaker 12: then other other venture firms and if they want exposure

528
00:28:32,480 --> 00:28:34,119
Speaker 12: to a company, that's another you know.

529
00:28:34,160 --> 00:28:35,680
Speaker 8: We we've become big.

530
00:28:35,760 --> 00:28:39,000
Speaker 12: Through a business called transfer Wise, and it was primarily

531
00:28:39,040 --> 00:28:42,800
Speaker 12: through company facilitated secondary because there are two types of

532
00:28:42,800 --> 00:28:43,800
Speaker 12: secondary transactions.

533
00:28:43,800 --> 00:28:46,080
Speaker 8: I mean there's multiple year involved, so it.

534
00:28:46,080 --> 00:28:49,120
Speaker 12: Could be company facilitated or just like you know, one

535
00:28:49,160 --> 00:28:51,400
Speaker 12: off rogue and rogue isn't bad.

536
00:28:51,240 --> 00:28:52,160
Speaker 8: It's just different ways.

537
00:28:52,280 --> 00:28:55,080
Speaker 12: This depends on sometimes their company organized secondaries.

538
00:28:55,840 --> 00:28:58,720
Speaker 2: Mitchell, quickly, what was it the name that you bought

539
00:28:58,760 --> 00:28:59,640
Speaker 2: at fifteen times E?

540
00:28:59,840 --> 00:29:01,160
Speaker 4: Does I make?

541
00:29:01,520 --> 00:29:03,600
Speaker 8: I can't think that it's just a software company.

542
00:29:03,840 --> 00:29:06,760
Speaker 12: I would assure you that not one of your viewers

543
00:29:06,800 --> 00:29:08,600
Speaker 12: has ever heard of the company.

544
00:29:08,640 --> 00:29:11,080
Speaker 8: It makes time tracking software.

545
00:29:12,520 --> 00:29:13,680
Speaker 4: Interesting. We'll dig into it.

546
00:29:13,720 --> 00:29:16,600
Speaker 2: Michaell Green founding, a managing partner of lead Edge Capital.

547
00:29:16,880 --> 00:29:19,120
Speaker 2: Deep dive on the secondaries market. We're doing that more

548
00:29:19,120 --> 00:29:21,200
Speaker 2: and more on the show. Thank you very much. Now,

549
00:29:21,200 --> 00:29:25,440
Speaker 2: coming up, Elon Musk reinstates Alex Jones's account on X

550
00:29:25,680 --> 00:29:29,320
Speaker 2: after a five year fan We have the details. Next,

551
00:29:29,520 --> 00:29:45,520
Speaker 2: this is Bloomberg Technology appsent websites that use AI to

552
00:29:45,680 --> 00:29:47,280
Speaker 2: undress women in photos.

553
00:29:47,360 --> 00:29:49,240
Speaker 4: Are soaring in popularity.

554
00:29:49,280 --> 00:29:53,720
Speaker 2: In September alone, twenty four million people visited undressing websites.

555
00:29:53,760 --> 00:29:57,280
Speaker 2: That's according to the social network analysis company Graphica. It's

556
00:29:57,320 --> 00:30:01,240
Speaker 2: all part of a worrying trend known as deep fake pornography.

557
00:30:01,360 --> 00:30:04,360
Speaker 2: Bloomberg's Margie Murphy joins me on set with her reporting.

558
00:30:04,400 --> 00:30:07,760
Speaker 2: At the center of that reporting is also data around

559
00:30:08,640 --> 00:30:12,600
Speaker 2: the volume of advertising behind those apps and sites and

560
00:30:12,640 --> 00:30:14,000
Speaker 2: where they're being advertised.

561
00:30:14,400 --> 00:30:17,120
Speaker 13: Right, So one of the big problems here is that

562
00:30:17,200 --> 00:30:19,920
Speaker 13: these app developers are getting a load of free marketing

563
00:30:20,280 --> 00:30:24,200
Speaker 13: using social media platforms. So we saw a two four

564
00:30:24,280 --> 00:30:27,760
Speaker 13: hundred percent increase from last year according to Graphica of

565
00:30:28,120 --> 00:30:31,640
Speaker 13: referral links on x and Reddit, and you can see

566
00:30:31,640 --> 00:30:34,479
Speaker 13: if you just search keywords that are associated with these

567
00:30:34,560 --> 00:30:38,320
Speaker 13: kind of apps that the adverts pop up their accounts.

568
00:30:38,440 --> 00:30:41,760
Speaker 13: Underneath them there are pictures of women all kind of

569
00:30:41,840 --> 00:30:44,959
Speaker 13: teasing and winking at you, saying come come.

570
00:30:45,120 --> 00:30:47,640
Speaker 3: If you follow this link, you'll get to this app.

571
00:30:48,360 --> 00:30:51,520
Speaker 13: And it's just a free way for them to provide

572
00:30:51,560 --> 00:30:55,520
Speaker 13: their services. But the social networks since her reporting came out,

573
00:30:55,600 --> 00:30:57,680
Speaker 13: have said that they're cracking down on it, but it's

574
00:30:58,200 --> 00:31:01,200
Speaker 13: they've kind of had free marketing for a good year now.

575
00:31:01,920 --> 00:31:04,120
Speaker 3: I mean some of them have also paid for sponsored

576
00:31:04,200 --> 00:31:07,480
Speaker 3: content on Google's YouTube, for example, and a Google spokesperson

577
00:31:07,520 --> 00:31:11,640
Speaker 3: so the company doesn't allow ads that contain sexually explicit content.

578
00:31:11,760 --> 00:31:14,360
Speaker 3: But Margie, the also really worrying thing is a lot

579
00:31:14,360 --> 00:31:18,080
Speaker 3: of the people who are being sort of undressed don't

580
00:31:18,120 --> 00:31:21,920
Speaker 3: realize ultimately. And also, I mean, of course it's a

581
00:31:21,960 --> 00:31:23,920
Speaker 3: deep fake, so it's not actually real, but there's no

582
00:31:23,960 --> 00:31:26,320
Speaker 3: sort of legal recourse to this from a federal level

583
00:31:26,360 --> 00:31:26,880
Speaker 3: at the moment.

584
00:31:27,920 --> 00:31:31,080
Speaker 13: Absolutely, there's no federal law at the moment that prohibits

585
00:31:31,640 --> 00:31:36,080
Speaker 13: non consensual deep fake pornography. And it's something that people

586
00:31:36,080 --> 00:31:40,239
Speaker 13: I've interviewed about this research is experts in AI are

587
00:31:40,280 --> 00:31:43,800
Speaker 13: really concerned about because we've heard for years about celebrity

588
00:31:43,840 --> 00:31:47,720
Speaker 13: deep fake pornography, which is awful, but now we're really

589
00:31:47,720 --> 00:31:52,040
Speaker 13: seeing normal people becoming part of this story. BusinessWeek we

590
00:31:52,080 --> 00:31:56,600
Speaker 13: had a cover story recently about these awful story about

591
00:31:57,240 --> 00:32:01,760
Speaker 13: some high schoolers in Levittown and they were deep faked.

592
00:32:01,840 --> 00:32:04,280
Speaker 13: They actually found the person who deep faked them. But

593
00:32:04,760 --> 00:32:07,760
Speaker 13: it's just one story that I think will just keep happening,

594
00:32:07,800 --> 00:32:09,400
Speaker 13: and we're just going to see more of it because

595
00:32:09,400 --> 00:32:14,440
Speaker 13: there's no legal recourse for victims and the technology is

596
00:32:14,520 --> 00:32:16,080
Speaker 13: just kind of spiraling out of control.

597
00:32:16,120 --> 00:32:19,400
Speaker 3: Really extraordinary reporting that both you and Olivia and the

598
00:32:19,480 --> 00:32:21,640
Speaker 3: team of Bloomberg continue to do on it, Margaret Murphy,

599
00:32:21,680 --> 00:32:24,560
Speaker 3: and we thank you for breaking that particular really unnerving

600
00:32:24,640 --> 00:32:27,200
Speaker 3: story down for us. Meanwhile, sticking with the world of

601
00:32:27,240 --> 00:32:30,560
Speaker 3: social media, Elon Musk has restored the account of right

602
00:32:30,600 --> 00:32:35,040
Speaker 3: wing conspiracy theorist Alex Jones on X after users themselves

603
00:32:35,120 --> 00:32:38,160
Speaker 3: voted for his reinstatement, and of course it was what

604
00:32:38,360 --> 00:32:41,720
Speaker 3: some five years after his initial ban, Luma's Kurt Wagner

605
00:32:41,880 --> 00:32:44,680
Speaker 3: joins us for more and not only was he reinstated,

606
00:32:44,920 --> 00:32:48,280
Speaker 3: he then was well put onto the X platform. In

607
00:32:48,320 --> 00:32:52,000
Speaker 3: an audio recording with Elon Musk plus Alex Jones plus

608
00:32:52,200 --> 00:32:57,320
Speaker 3: others some lawmakers to well discuss his return. What did

609
00:32:57,320 --> 00:32:58,000
Speaker 3: you make of it all.

610
00:32:57,960 --> 00:33:01,840
Speaker 14: Cut, Yeah, this is a kind of what Elon has

611
00:33:01,880 --> 00:33:04,000
Speaker 14: been doing since he took over the company, right, is

612
00:33:04,040 --> 00:33:07,120
Speaker 14: that he's been kind of rescinding a lot of these

613
00:33:07,200 --> 00:33:09,880
Speaker 14: rules and punishments that Twitter one point zero had put out,

614
00:33:10,080 --> 00:33:12,600
Speaker 14: and not only that, but welcoming these people back with

615
00:33:12,640 --> 00:33:15,760
Speaker 14: open arms, giving them a platform. You know, by Elon

616
00:33:15,880 --> 00:33:18,680
Speaker 14: showing up on that spaces chat with Alex Jones really

617
00:33:18,960 --> 00:33:23,920
Speaker 14: you know, kind of bringing his audience along to Jones's

618
00:33:24,000 --> 00:33:27,040
Speaker 14: you know, rhetoric and message, right. And so I think

619
00:33:27,040 --> 00:33:29,520
Speaker 14: this is all part of Elon's plan to sort of

620
00:33:29,880 --> 00:33:34,600
Speaker 14: drastically change what he views as X versus Twitter and

621
00:33:34,640 --> 00:33:36,800
Speaker 14: to you know, reimagine what this company is.

622
00:33:36,760 --> 00:33:37,520
Speaker 4: Supposed to look like.

623
00:33:38,680 --> 00:33:42,960
Speaker 2: As Caro outlined, Musk did a poll, right, asked the

624
00:33:43,120 --> 00:33:46,520
Speaker 2: user base vote on this. I think it's worth reminding

625
00:33:46,520 --> 00:33:49,960
Speaker 2: our audience the origin story why Alex Jones was removed

626
00:33:49,960 --> 00:33:50,800
Speaker 2: in the first place.

627
00:33:51,960 --> 00:33:54,560
Speaker 4: Yeah, so he was banned back in twenty eighteen.

628
00:33:54,680 --> 00:33:59,520
Speaker 14: He had been a repeat rules violator under Twitter's prior management.

629
00:34:00,200 --> 00:34:02,200
Speaker 14: You know, I believe the last straw is ultimately that

630
00:34:02,240 --> 00:34:04,840
Speaker 14: he had come out and sort of attacked or been

631
00:34:04,920 --> 00:34:07,400
Speaker 14: attacking members of the media. If I recall, I think

632
00:34:07,400 --> 00:34:09,880
Speaker 14: he even said something in a video of his around

633
00:34:09,920 --> 00:34:12,600
Speaker 14: you know, get your battle rifles ready for the media, right.

634
00:34:12,640 --> 00:34:15,560
Speaker 14: And so he was banned ultimately for violating the rules

635
00:34:15,560 --> 00:34:20,560
Speaker 14: around you know, harassment and glorifying violence, and so, you know,

636
00:34:20,840 --> 00:34:23,480
Speaker 14: those are the types of things of course that Musk

637
00:34:23,600 --> 00:34:26,040
Speaker 14: has sort of said he doesn't care much about right.

638
00:34:26,040 --> 00:34:28,880
Speaker 14: As long as something is not illegal, he thinks it

639
00:34:28,880 --> 00:34:31,000
Speaker 14: should be fine. And so that's why we're seeing a

640
00:34:31,000 --> 00:34:32,879
Speaker 14: lot of these people that Twitter one point zero had

641
00:34:32,880 --> 00:34:35,200
Speaker 14: banned or punished start to return to X.

642
00:34:35,320 --> 00:34:38,240
Speaker 3: I mean, of course Elon did say I vemently disagree

643
00:34:38,280 --> 00:34:41,040
Speaker 3: with what he said about Sanny Hook, but as a

644
00:34:41,080 --> 00:34:43,279
Speaker 3: platform that believes in freedom of speech, or are we not?

645
00:34:43,320 --> 00:34:45,880
Speaker 3: But what's notable is November twenty twenty two he posted

646
00:34:45,920 --> 00:34:48,640
Speaker 3: saying and this of course is an emotive subject. For

647
00:34:49,000 --> 00:34:52,520
Speaker 3: crucially elonam anymore, his firstborn child died in his arms.

648
00:34:52,960 --> 00:34:55,520
Speaker 3: I felt this last heartbeat. I'm no mercy for anyone

649
00:34:55,560 --> 00:34:58,399
Speaker 3: who would use deaths of children for gain politics of fame.

650
00:34:58,480 --> 00:35:00,799
Speaker 3: So we sort of did in about turn. Now what's

651
00:35:00,800 --> 00:35:02,879
Speaker 3: interesting in all of this, and we make a sort

652
00:35:02,880 --> 00:35:05,920
Speaker 3: of a movement here to a different story that's occurring

653
00:35:05,920 --> 00:35:09,960
Speaker 3: on X, is that another voice has taken actually sort

654
00:35:10,000 --> 00:35:13,440
Speaker 3: of off the platform to start a different version of

655
00:35:13,719 --> 00:35:16,239
Speaker 3: subscriber growth for himself. And I just want to ask

656
00:35:16,280 --> 00:35:18,719
Speaker 3: you about Tucker Carlson. Of course, we understand he's not

657
00:35:19,520 --> 00:35:21,959
Speaker 3: launching a new news service on X, when in fact

658
00:35:21,960 --> 00:35:24,439
Speaker 3: he's doing a streaming service of his own, called Tucker

659
00:35:24,480 --> 00:35:27,200
Speaker 3: Carlson Network, and it seems as though it's he explored

660
00:35:27,280 --> 00:35:28,880
Speaker 3: launching on X but it didn't work outcut.

661
00:35:29,960 --> 00:35:32,400
Speaker 14: Yeah, well, I mean, the thing is that there's a

662
00:35:32,440 --> 00:35:36,000
Speaker 14: reason that X is not TV, right, And we saw

663
00:35:36,040 --> 00:35:38,359
Speaker 14: them try to do this. You may recall many many

664
00:35:38,440 --> 00:35:41,080
Speaker 14: years ago in twenty sixteen, they kind of tried to

665
00:35:41,160 --> 00:35:44,760
Speaker 14: make Twitter a streaming service, right, They got the NFL

666
00:35:44,800 --> 00:35:47,480
Speaker 14: on there, they got a bunch of deals with other

667
00:35:47,560 --> 00:35:50,440
Speaker 14: media publishers, and it just didn't work. And so I

668
00:35:50,480 --> 00:35:53,560
Speaker 14: think that Twitter or now X obviously usually serves best

669
00:35:53,560 --> 00:35:55,560
Speaker 14: as a compliment to TV. And my guess is that

670
00:35:55,640 --> 00:35:58,920
Speaker 14: Tucker Carlson probably figured that out himself as he was

671
00:35:59,360 --> 00:36:02,160
Speaker 14: trying to build up this new platform. And so I

672
00:36:02,320 --> 00:36:04,920
Speaker 14: have no doubt, given his relationship with Elon and the

673
00:36:04,960 --> 00:36:06,600
Speaker 14: fact that they seem to be buddy buddy, I have

674
00:36:06,680 --> 00:36:08,560
Speaker 14: no doubt that X will continue to be sort of

675
00:36:08,600 --> 00:36:12,839
Speaker 14: an important distribution channel for him. But you know, x's

676
00:36:12,920 --> 00:36:17,480
Speaker 14: nonve video or TV service, and despite Elon's ambitions, you know,

677
00:36:17,520 --> 00:36:19,279
Speaker 14: they haven't been able to turn it into one yet.

678
00:36:20,320 --> 00:36:22,799
Speaker 2: The mess Kurtwagner, good to have you back after a

679
00:36:22,840 --> 00:36:25,920
Speaker 2: few weeks and months away from the show, Kurt Wagner,

680
00:36:25,960 --> 00:36:37,040
Speaker 2: they're out in Denver, Okay. So today's going viral. We're

681
00:36:37,080 --> 00:36:40,920
Speaker 2: looking at Apple, the iPhone maker, offering incentives to artists

682
00:36:40,920 --> 00:36:44,960
Speaker 2: and record labels to produce music using a spatial audio

683
00:36:45,000 --> 00:36:49,000
Speaker 2: technology that surrounds listeners in sound. Starting next year, the

684
00:36:49,040 --> 00:36:52,000
Speaker 2: company plans to give added waiting to streams of songs

685
00:36:52,200 --> 00:36:56,760
Speaker 2: that are mixed in Dolby Atmos technology. According to Bloomberg sources,

686
00:36:56,800 --> 00:36:59,840
Speaker 2: that could mean higher royalty payments for artists who are

687
00:36:59,880 --> 00:37:04,120
Speaker 2: the first to embrace the technology made by Dolby Laboratories.

688
00:37:04,400 --> 00:37:08,400
Speaker 3: Caroline, let's stick with Apple and well turn our attention

689
00:37:08,480 --> 00:37:12,080
Speaker 3: to health to wellness. In twenty twenty four, Bloomberg reportedly

690
00:37:12,120 --> 00:37:15,200
Speaker 3: showed that Apple is planning on an updated watch that

691
00:37:15,200 --> 00:37:18,480
Speaker 3: will tech to tect potentially blood pressure, sleep, ATNA, and

692
00:37:18,560 --> 00:37:20,880
Speaker 3: much more. For us to just discuss that where we

693
00:37:20,920 --> 00:37:23,080
Speaker 3: are with wearables, where we are with digital fitness, how

694
00:37:23,120 --> 00:37:25,080
Speaker 3: we're consuming it. It's a CEO of future. It's a

695
00:37:25,120 --> 00:37:28,160
Speaker 3: company that pairs users with coaches and is backed by

696
00:37:28,200 --> 00:37:30,920
Speaker 3: investors such as Clena Perkins, Coase ad Ventures, found Us, Fun,

697
00:37:31,000 --> 00:37:34,200
Speaker 3: and many more. But please to welcome, Rishie Mandel. Great

698
00:37:34,239 --> 00:37:37,919
Speaker 3: to have you here. And ultimately we are seeing this

699
00:37:38,120 --> 00:37:40,640
Speaker 3: new type of relationship with us and our fitness and

700
00:37:40,680 --> 00:37:43,839
Speaker 3: actually knowledge of our own wellness at this moment. How

701
00:37:43,880 --> 00:37:46,279
Speaker 3: much that being driven by wearables, VR and the like.

702
00:37:46,560 --> 00:37:49,359
Speaker 1: Yeah, there's one hundred million plus wearables out there now

703
00:37:49,440 --> 00:37:51,760
Speaker 1: and better than ever.

704
00:37:51,719 --> 00:37:53,160
Speaker 7: We can understand each individual.

705
00:37:53,560 --> 00:37:57,040
Speaker 1: What's interesting is the history of health understanding in the

706
00:37:57,040 --> 00:38:01,840
Speaker 1: Western world is actually based on men recruited study small populations,

707
00:38:01,920 --> 00:38:04,840
Speaker 1: and so imagine a world where now we're getting inputs

708
00:38:04,880 --> 00:38:08,480
Speaker 1: about every individual, how women's health might change and evolve.

709
00:38:08,520 --> 00:38:11,240
Speaker 1: That's really exciting to see. As you said, the consumers

710
00:38:11,239 --> 00:38:14,200
Speaker 1: thinking a lot about health and fitness, and in an

711
00:38:14,200 --> 00:38:17,560
Speaker 1: AI world, oftentimes the biggest winners are those who have

712
00:38:17,640 --> 00:38:20,080
Speaker 1: the largest proprietary data sets. And so you think about

713
00:38:20,080 --> 00:38:23,920
Speaker 1: a company like Apple, now not just has how you're

714
00:38:23,960 --> 00:38:26,520
Speaker 1: moving through the world, and maybe blood glucose and certain

715
00:38:26,560 --> 00:38:30,359
Speaker 1: types of markers bridging across all that information can help

716
00:38:30,400 --> 00:38:34,120
Speaker 1: tailor to an individual better what they should be doing, when, why,

717
00:38:34,880 --> 00:38:37,920
Speaker 1: and then you can build really innovative delivery mechanisms on

718
00:38:37,960 --> 00:38:38,440
Speaker 1: top of that.

719
00:38:39,040 --> 00:38:41,839
Speaker 3: Yeah, I mean, I've been sort of a guinea pig

720
00:38:41,880 --> 00:38:44,840
Speaker 3: to certain extent got a wearable ring because it was

721
00:38:44,880 --> 00:38:47,239
Speaker 3: meant to be a better for women tracking cycles and

722
00:38:47,280 --> 00:38:49,440
Speaker 3: the like, But ultimately I haven't found it that good

723
00:38:49,440 --> 00:38:52,000
Speaker 3: at it. It didn't realize I had COVID. I have no

724
00:38:52,080 --> 00:38:54,120
Speaker 3: idea if the calorie content is actually true from what

725
00:38:54,200 --> 00:38:57,320
Speaker 3: I'm currently monitoring point, but I am interested in your perspective.

726
00:38:57,360 --> 00:39:00,719
Speaker 3: How many therefore are using you from women versus men?

727
00:39:00,760 --> 00:39:03,560
Speaker 3: What are the demographics who are coming to your platform

728
00:39:03,640 --> 00:39:06,680
Speaker 3: when wanting this one on one sort of service at

729
00:39:06,719 --> 00:39:07,360
Speaker 3: the moment.

730
00:39:07,200 --> 00:39:09,400
Speaker 1: Yeah, no, And I love what you're talking about about

731
00:39:09,440 --> 00:39:12,279
Speaker 1: the lack of accuracy or frankly, a lot of that

732
00:39:12,360 --> 00:39:14,320
Speaker 1: data is just hard to parse through on your own.

733
00:39:14,400 --> 00:39:18,240
Speaker 1: So with future, we match every single person with a coach.

734
00:39:18,760 --> 00:39:21,919
Speaker 1: Now that coach is AI assisted able to sift through

735
00:39:21,920 --> 00:39:24,560
Speaker 1: so much data, not because they're manually doing it, but

736
00:39:24,680 --> 00:39:28,280
Speaker 1: rather we've built some technology to allow them to spot trends,

737
00:39:28,680 --> 00:39:33,120
Speaker 1: interpret different markers about you. And so right now what

738
00:39:33,160 --> 00:39:36,480
Speaker 1: we see is a lot of AI is rudimentary in health,

739
00:39:36,920 --> 00:39:38,600
Speaker 1: and what we're going to see over the next five

740
00:39:38,680 --> 00:39:42,000
Speaker 1: years I think is an explosion of this idea of

741
00:39:42,040 --> 00:39:45,840
Speaker 1: augmented intelligence taking your physician and making sure they're backed

742
00:39:45,880 --> 00:39:49,120
Speaker 1: by the latest and greatest armed with that information, taking

743
00:39:49,120 --> 00:39:52,000
Speaker 1: a radiologist, maybe double checking a scan to make sure

744
00:39:52,000 --> 00:39:52,880
Speaker 1: they don't miss something.

745
00:39:53,120 --> 00:39:54,399
Speaker 7: But there's still that human there.

746
00:39:54,760 --> 00:39:57,440
Speaker 1: And with Future, that's what we see is with fitness,

747
00:39:57,440 --> 00:39:58,280
Speaker 1: we give you a coach.

748
00:39:58,680 --> 00:40:00,400
Speaker 7: That coach is highly assisted.

749
00:40:00,040 --> 00:40:03,680
Speaker 1: And building your program, your training program, whether you're running outside,

750
00:40:03,760 --> 00:40:05,919
Speaker 1: working out in a gym, at home, or all three,

751
00:40:06,000 --> 00:40:08,600
Speaker 1: which is very common. And then we actually use a

752
00:40:08,640 --> 00:40:10,759
Speaker 1: lot of AI to help augment for that coach. What's

753
00:40:10,760 --> 00:40:13,520
Speaker 1: hav a day to reach out to Caroline and what's

754
00:40:13,520 --> 00:40:15,120
Speaker 1: the right thing to say and what are the trends

755
00:40:15,160 --> 00:40:17,720
Speaker 1: that we're observing. That augmentation I think is really powerful.

756
00:40:17,960 --> 00:40:19,880
Speaker 1: And to answer your question, it's about fifty to fifty

757
00:40:19,880 --> 00:40:21,960
Speaker 1: men and women who reach out to get a coach

758
00:40:22,000 --> 00:40:22,480
Speaker 1: on future.

759
00:40:23,200 --> 00:40:24,360
Speaker 4: Hey, Ritchie, real quick.

760
00:40:25,200 --> 00:40:27,279
Speaker 2: Is it fitness that's going to be the driver of

761
00:40:27,280 --> 00:40:30,960
Speaker 2: wearables adoption or is it health data that's going to

762
00:40:30,960 --> 00:40:31,880
Speaker 2: be the principal driver?

763
00:40:32,160 --> 00:40:35,520
Speaker 1: You know, the reason we started with fitness is because

764
00:40:35,719 --> 00:40:42,640
Speaker 1: there is a daily and very common interaction with fitness.

765
00:40:42,640 --> 00:40:45,400
Speaker 1: People typically who are engaging with it are doing it

766
00:40:45,560 --> 00:40:48,920
Speaker 1: daily weekly. That kind of cadence. And what I was

767
00:40:48,960 --> 00:40:51,399
Speaker 1: saying earlier is the biggest winners in an AI world

768
00:40:51,400 --> 00:40:54,400
Speaker 1: are those We have the largest proprietary data sets, and

769
00:40:54,480 --> 00:40:57,040
Speaker 1: so when we interact with a member every single day,

770
00:40:57,320 --> 00:40:59,879
Speaker 1: we can come to understand their life in a fulsome way.

771
00:41:00,280 --> 00:41:03,279
Speaker 1: Our average member will trade three text messages every single

772
00:41:03,320 --> 00:41:05,799
Speaker 1: day with their coach one thousand a year. Lay that

773
00:41:05,920 --> 00:41:08,440
Speaker 1: on top of the biometrics we get from wearables. Lay

774
00:41:08,440 --> 00:41:10,719
Speaker 1: that on top of the understanding of your behaviors, and

775
00:41:10,800 --> 00:41:12,440
Speaker 1: now you have a really big picture.

776
00:41:12,800 --> 00:41:15,480
Speaker 3: Rishi Mandaw wish we had longer future CEO there. That

777
00:41:15,760 --> 00:41:20,319
Speaker 3: is it from this edition of Bloomberg Technology