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Speaker 1: Bloomberg Audio Studios, podcasts, radio news.

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Speaker 2: From the heart of.

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Speaker 3: Where innovation, money and power collide in Silicon Valley and beyond.

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Speaker 4: This is Bloomberg Technology with Caroline Hyde.

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Speaker 5: And Ed Ludlow.

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Speaker 3: Welcome to a special edition of BlueBag Technology. I'm Caroline Hyde.

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Speaker 3: Right here in Las Vegas. We are live from the

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Speaker 3: human x AI conference and we're going to be talking

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Speaker 3: to some of the biggest industry leaders and decision makers

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Speaker 3: in the AI space throughout this next hour. As you

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Speaker 3: can see, a key lineup of expertise. But let's move

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Speaker 3: on to also the discussion of how the public markets

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Speaker 3: feed into the private markets. How does one think about

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Speaker 3: the startup space as we contend with this growth anxiety,

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Speaker 3: as we contend with tariff anxiety. Let's bring in Alfred

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Speaker 3: Lynn Squire Capital Partners, someone who thinks about seed about

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Speaker 3: early stage investment, has been at the forefront of artificial

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Speaker 3: intelligence investment before many others. And Alfred, look, how does

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Speaker 3: the public market feed into the private market from your

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Speaker 3: perspective right now?

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Speaker 5: Well, Caroline, thank you for having me on the show.

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Speaker 6: This is a great honor to be here, you know,

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Speaker 6: to answer your question directly, We just think about building

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Speaker 6: great companies and partnering with great founders and helping them

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Speaker 6: from idea to IPO and beyond. And so in terms

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Speaker 6: of thinking about the public markets, we just think about

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Speaker 6: much much longer term. We work with founders at the

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Speaker 6: idea stage in a y in a decade, that company

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Speaker 6: can then be worth one to ten million dollars in

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Speaker 6: two decades. It could be worth ten to one hundred

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Speaker 6: billion dollars in three decades, like in for invidious case,

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Speaker 6: in Google's case, in Apple's case that we backed very

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Speaker 6: early on, they become potentially a trilling dollar company. And so, yes,

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Speaker 6: markets can go up and down, there might be you know,

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Speaker 6: sort of bumps in the road, but I would just

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Speaker 6: encourage your viewers to think about the long long term

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Speaker 6: and when you're behind a megatrends such as AI, it

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Speaker 6: can last for a long, long period of time.

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Speaker 3: That megatren needs infrastructure and it needs electricity and power.

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Speaker 3: That's one of the key anxieties of Andy Jesse, for example,

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Speaker 3: at Amazon saying his constraint on AWS is power. Is

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Speaker 3: it something that your ceo is are contending with how

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Speaker 3: to ultimately sustain their growth paths?

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Speaker 6: I think it's something that they think about, and it's

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Speaker 6: something that we need to work on, and there are

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Speaker 6: a lot of great minds working on it. It's not

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Speaker 6: something that I'm an expert in, but you're right. Power

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Speaker 6: is something that we need in data centers, and the

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Speaker 6: bigger the data centers we've built, the more powerful the

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Speaker 6: models we've been able to build, the more powerful the applications,

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Speaker 6: and so I'm sure we'll get solved.

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Speaker 3: Also interesting is, of course you mentioned how Sequoia was

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Speaker 3: early into Jensen Huang's vision for AI. You're now thinking

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Speaker 3: about not just sort of moving away from lllms, we're

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Speaker 3: going into application layers, and we're also going into physics.

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Speaker 3: I know that that's really important to you at the moment,

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Speaker 3: the application of robotics. Where is the next growth story

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Speaker 3: in this long term mega trend?

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Speaker 6: Well, I think they're uh, let's break this up. I

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Speaker 6: think s there's still lots to do in the foundation

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Speaker 6: layer model.

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Speaker 5: They're not done. These companies like open.

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Speaker 6: Ai that we backed UH early on, they're still continuing

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Speaker 6: to build a foundation model layer that allows the application

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Speaker 6: founders to build applications that we've not seen before. And

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Speaker 6: we will continue to see UH innovation there will you

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Speaker 6: continue to.

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Speaker 2: See private rounds for those sorts of companies.

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Speaker 3: Is there ever a point in which they've just raised

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Speaker 3: too much money in the private markets?

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Speaker 5: It c It goes up and down.

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Speaker 6: But the p the point is that over time these

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Speaker 6: companies are always surprising us in the long run on

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Speaker 6: what they're capable of doing. Sure, in the short term

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Speaker 6: they may uh surprise us in the I and technology

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Speaker 6: tends to surprise us in the opposite direction, but in

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Speaker 6: the long run it always surprises us in the positive direction.

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Speaker 6: And in in terms of many of the companies who

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Speaker 6: work with, they're just providing automation to.

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Speaker 5: A lot of work flows today. That's what we see today.

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Speaker 6: So companies that we work with, such as Clay, they're

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Speaker 6: trying to help salespeople with their leads and autoutomatically enriching

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Speaker 6: their leads so that the salespeople can focus on actually

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Speaker 6: growing their business and contacting the right leads. And with

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Speaker 6: Khmure or their ambient scribe is helping doctors focus on

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Speaker 6: care not the administrative work. They listen in the background,

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Speaker 6: take notes, and yes they take the notes. It seems

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Speaker 6: like something small, but then once you take the notes,

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Speaker 6: they can code the medical codes properly for insurance claims,

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Speaker 6: and that just allows the doctors to focus on care.

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Speaker 6: And we're going to see more and more innovation because

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Speaker 6: we are left to do some of the more interesting

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Speaker 6: things that humans do better than machines.

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Speaker 3: So you're looking at still LMS still then looking at

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Speaker 3: the application there. What's been interesting is we have had

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Speaker 3: this fomo feel in still in the latest alumni from

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Speaker 3: open ai going up and starting their own company at Ilia,

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Speaker 3: for example, potentially raise get thirty billion dollars without even

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Speaker 3: really a product out there or any revenue stream.

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Speaker 2: Would you still back him at that sort of valuation?

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Speaker 2: Are you doing following an investment?

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Speaker 6: I think the interesting thing is if you think that

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Speaker 6: something could be worth a trillion dollars, whether it's what

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Speaker 6: you the entry price today is important, but not as

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Speaker 6: relevant as what you think the order of magnitude is

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Speaker 6: in the future. And I think the order of magnitude

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Speaker 6: that we think about today will surprise us in the

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Speaker 6: long run.

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Speaker 5: Don't forget a decade ago.

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Speaker 6: Two decades ago, we didn't think a trillion dollar company

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Speaker 6: as possible. If you compound what trillion dollars twenty percent

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Speaker 6: a year of a year for the next decade, we

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Speaker 6: will have ten twenty trillion dollar companies. So you know,

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Speaker 6: you just have to think about the order of magnitude

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Speaker 6: that is possible and we need to change our mindspace

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Speaker 6: around that.

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Speaker 3: Help change our mind space because you have been seeing

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Speaker 3: around these corners longer than nearly anyone. How can you

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Speaker 3: see that earlier is someone to back? How can you

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Speaker 3: see that a founder is in an indel you want

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Speaker 3: to see the vision of and think.

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Speaker 2: That they get to build a trillion on a company.

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Speaker 7: Yeah.

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Speaker 6: I think that it comes down to the founders. And

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Speaker 6: we've been very good at picking founders that are known

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Speaker 6: by their first names. Jensen at Nvidio, that Steve ad Apple,

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Speaker 6: Larry at Google, Sam.

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Speaker 5: And Sam and open Ai.

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Speaker 6: And today's founders are Ilia and Brian at Airbnb and

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Speaker 6: Tony at geor Dash and the list goes on and on.

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Speaker 6: So these founders are special because they have a vision

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Speaker 6: of the world that the world has viewed, the world

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Speaker 6: has sorry, the world has solved a problem incorrectly, and

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Speaker 6: they want to go change it. Yeah, and we get

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Speaker 6: the front row seat to see how they want to

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Speaker 6: go change the future.

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Speaker 2: Is Mara going to be a name that we know

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Speaker 2: and one that you're gonna.

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Speaker 5: Allow you already know.

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Speaker 2: Are you backing her?

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Speaker 3: We're talking to her, You're talking to Merrett, You're talking

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Speaker 3: to Ilia on his latest funding round.

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Speaker 5: Where are investor is in LA's company?

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Speaker 2: I know do follow on because he's building something a

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Speaker 2: big vision.

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Speaker 5: We'll consider that too.

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Speaker 3: Tell us about the thesis when it comes to llms

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Speaker 3: and then application layers and then the robotics and physical AI.

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Speaker 3: What for you is going to be pushing us forward

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Speaker 3: in terms of the next situation of value coming from

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Speaker 3: these companies going to Are we getting to the stage

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Speaker 3: that they will see one trillion across all of those spaces?

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Speaker 5: Do you think I believe that's to be true.

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Speaker 6: And if you sort of go back in time, when

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Speaker 6: I started my career, we thought that all the value

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Speaker 6: will accrue to the software layer. That's why we invest

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Speaker 6: in software companies. Yeah, but over today the Magnificent seven,

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Speaker 6: all of them are both hardware and software companies, and

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Speaker 6: some of them in the physical world. I think the

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Speaker 6: physical world is going to be the next boundary that

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Speaker 6: we're going to break, which is why I'm very excited

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Speaker 6: to be here with Brad Porter from Collaborative robotics.

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Speaker 3: And it's interesting, of course you backed Xai when it

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Speaker 3: comes to AI. Elon himself busy looking at robotics from

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Speaker 3: his Tesla perspective as well. How are you thinking about

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Speaker 3: who wins in the robotics space? They're gonna be countless

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Speaker 3: winners and applications.

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Speaker 6: As you know that there are many many winners, Uh

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Speaker 6: when there when there's a megatron, and so I believe

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Speaker 6: they're gonna be many winners and robotics. And yes, Tesla's

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Speaker 6: building robots. They were building cars before. They also build software.

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Speaker 6: And Elon Uh is someone that we back across a

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Speaker 6: variety of companies such as SpaceX and Boring Company, and

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Speaker 6: the physical world is something that we will continue to

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Speaker 6: attack and make progress over the coming years.

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Speaker 3: How frustrating or how illuminating is it when your founders

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Speaker 3: are having arguments in public like Sam versus Elon?

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Speaker 2: Does that bother you?

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Speaker 6: That's you can ask them that those questions. But no

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Speaker 6: founders have Uh a particular view of the world and

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Speaker 6: they like to be they like to express those views.

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Speaker 5: So it's good.

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Speaker 6: That people sort of talk about what they believe and

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Speaker 6: how they're going to change the world through that and

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Speaker 6: I believe the discussion is always the positive thing.

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Speaker 2: Who and where are they going to be from?

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Speaker 3: Is changing the world at this moment when you think

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Speaker 3: about China versus.

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Speaker 2: The US deep Seek.

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Speaker 3: You wrote a very thoughtful blog about the impact of

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Speaker 3: deep seek and the opportunities for learning from these powerful

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Speaker 3: generative AI models that come and the innovation and the

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Speaker 3: data and the efficiencies we're going to see. Are we

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Speaker 3: going to see a so called winners on either side

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Speaker 3: and around the world.

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Speaker 6: Well, I believe in humanity instead of one country over

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Speaker 6: the other. I am an immigrant to this country, and

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Speaker 6: I believe that the way we operate and build businesses

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Speaker 6: here is much more innovative. And so I'm rooting for

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Speaker 6: the United States, but more but even more than I'm

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Speaker 6: rooting for humanity to continue to progress. And the more

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Speaker 6: progress we make here, the more progress the people in

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Speaker 6: China want to make, And the more progress they make there,

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Speaker 6: the more we want to continue to make progress. And

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Speaker 6: so that just is the thing that I'm I'm most

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Speaker 6: focused on, which is we're going to just make each

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Speaker 6: other better at Competition.

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Speaker 5: Has always made us better.

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Speaker 6: And I believe in the United States and its ability

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Speaker 6: and it's free society to do things in much more

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Speaker 6: innovative ways than almost any other country in the world.

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Speaker 6: It's our innovation, it's our free spirit, and it's also

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Speaker 6: our capital markets. You think about our place in the world.

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Speaker 6: We have what approximately four percent of the world's population,

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Speaker 6: but we have a disproportionate number of the amazing companies,

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Speaker 6: the most valuable companies in the world. We have a

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Speaker 6: disproportionate amount of capital that we have and control.

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Speaker 5: And are able to invest.

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Speaker 6: This is why your viewers listen to you and this show,

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Speaker 6: because Bloomberg is the place where a lot of information

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Speaker 6: about capital is talked about.

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Speaker 3: Alphad thanks that they're listening to you, and they're listening

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Speaker 3: to thinking about how the money comes back ultimately to

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Speaker 3: your LPs, and it's going to be about exits. How

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Speaker 3: are you thinking about the IPA market? How you think

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Speaker 3: about M and A. Boy, we just had service now

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Speaker 3: move works. M and A is still getting done.

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Speaker 6: Yeah, So I think that that's a great question shown

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Speaker 6: lots of people ask that, but I just want to

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Speaker 6: emphasize that at SAKO, over the last five years, we've

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Speaker 6: distributed over forty three billion dollars back to our LPs,

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Speaker 6: and so this is a business that, if done right,

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Speaker 6: can generate a lot of returns for elimited partners. On

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Speaker 6: top of that, I just kind of tell our founders

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Speaker 6: it doesn't matter what's going on in the public markets

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Speaker 6: or in the M and A markets, just focus on

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Speaker 6: building a long term great business. If you do that,

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Speaker 6: as did Tony at Door to Ash and Brian at

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Speaker 6: Airbnb and many others are doing today, you.

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Speaker 5: Will always have options.

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Speaker 6: And that option may come in at M and A,

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Speaker 6: that option may come in IPO.

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Speaker 5: I would contend that the IPO.

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Speaker 6: Market is always open for great companies.

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Speaker 3: What's interesting is we first saw AI come into existence

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Speaker 3: almost in a not for profit mentality, and now we

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Speaker 3: think open AI thinking about trying to restructure and become

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Speaker 3: a for profit entity more clearly.

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Speaker 2: Is that something that you back, that.

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Speaker 3: You support, that you want to see clearer division of

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Speaker 3: for profit and not.

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Speaker 4: I think that.

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Speaker 6: I think if you go back in history, AI has

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Speaker 6: existed for way longer than what you're talking about, which

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Speaker 6: is open AI. I mean, the j Jensen was building

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Speaker 6: GPUs way longer than what we're talking about today. And

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Speaker 6: the first uh, the first set of applications that we're

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Speaker 6: that were AI applications were something like AdWords that Google built,

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Speaker 6: or trading platforms that many of your viewers and that

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Speaker 6: work at large institutions have been using. Those who were

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Speaker 6: called machine learning. We just changed that term for profit nonprofit.

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Speaker 6: Those are just structures for accomplishing what the mission of

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Speaker 6: the company wants to do.

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Speaker 5: And I'm supportive of whatever.

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Speaker 6: The right model is to do that. But as an investor,

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Speaker 6: I want to make sure that the equity that we

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Speaker 6: invest in has appreciation and that's that's that's separate from

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Speaker 6: the mission.

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Speaker 3: Well, you have an extraordinary realm of portfolio companies. You

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Speaker 3: sit on some amazing board Citadel Securities among them, as

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Speaker 3: well as ABNB as you say, and you had a

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Speaker 3: long history of being a founder and a builder yourself.

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Speaker 3: Alfred Lynn, we thank you so much from Sequoia Capital

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Speaker 3: once we return. Now they're the private markets and people

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Speaker 3: building within that space. Mayhobib is a key founder, CEO

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Speaker 3: and indeed the co founder of Writer Generative AI platform

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Speaker 3: for the enterprise.

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Speaker 2: And I've got to.

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Speaker 3: Ask at this time, May, when you're really seeing the

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Speaker 3: growth accelerate for your business, when you're seeing more and

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Speaker 3: more fortune five hundred businesses want to work with you.

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Speaker 3: What are these CEOs telling you about the landscape, about

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Speaker 3: anxiety about putting money into generative AI when they don't

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Speaker 3: know when the next tariff is hitting.

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Speaker 8: Yeah, there's no question that selling generative I in the

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Speaker 8: enterprise right now has to be an ROI first story.

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Speaker 8: Because these are the same CEOs who are telling the

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Speaker 8: street that they're cutting op X and you know they're

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Speaker 8: trimming down their teams. So it really puts a big

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Speaker 8: focus on the kind of return. And the reality is

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Speaker 8: actually said it a couple weekends ago. Right there has

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Speaker 8: never been such a big gulf between what technology can

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Speaker 8: do and then what's actually happening inside of the enterprise.

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Speaker 8: We've got a survey coming out for Fortune five hundred execs.

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Speaker 8: Seventy one percent are saying that their generative AI efforts have.

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Speaker 2: Been very disappointing, very disappointing.

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Speaker 8: And so you know, it absolutely is a big thing.

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Speaker 2: Why is it disappointing?

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Speaker 3: Why is it so hard to bring this once in

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Speaker 3: a lifetime AI exuberance.

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Speaker 2: To bear on your productivity?

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Speaker 8: I think we are bringing knives to knives to a

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Speaker 8: gunfight when it comes to actually building with generative AI.

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Speaker 8: People are taking these really long kind of software development cycles,

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Speaker 8: and it is taking these projects to the business, and

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Speaker 8: business is saying this is not really good enough to

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Speaker 8: actually write benefit my work. So we take a very

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Speaker 8: different approach. Writer is purpose built for that kind of

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Speaker 8: collaboration you need between the business and it to actually

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Speaker 8: deliver generative AI projects and applications and agents that work

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Speaker 8: for the business.

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Speaker 2: So it's sort of legacy software is the issue here.

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Speaker 8: So it's it's the legacy tooling of building right these

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Speaker 8: these products, and so the lms are very very powerful

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Speaker 8: that you need to wrap it with data, with workflow,

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Speaker 8: with no how and that's just not happening right now

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Speaker 8: and most IT departments in the enterprise.

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Speaker 2: So help measure your success.

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Speaker 3: How you articulating to those you're going out in pitch

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Speaker 3: and saying you will get ROI with us.

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Speaker 8: It's a very solution oriented approach. So you know, we're

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Speaker 8: not talking about, hey, buy a full stack platform by

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Speaker 8: our amazing lllms, look at us, how great we are.

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Speaker 8: We're talking about actual end user productivity and end user

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Speaker 8: use cases. So it's very solution oriented.

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Speaker 3: What's also interesting is you've set yourself apart from a

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Speaker 3: pretty crowded field. I'm going to say, like AGENTICKI and

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Speaker 3: generative AI and the workplace and enterprise seems to be

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Speaker 3: everyone's Lexican right now. But you're coming at it from look,

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Speaker 3: we do something very different with the type of data

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Speaker 3: we're using and the type of model building that we're

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Speaker 3: going through. You're almost saying like, actually, you don't look

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Speaker 3: at a reasoning model, look at our type of model.

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Speaker 2: What is your type of model?

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Speaker 8: Yeah, I mean, look at this exhibition floor. It's literally

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Speaker 8: hundreds and hundreds of startups and it's such an exciting opportunity.

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Speaker 8: But when it comes to the enterprise, they are looking

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Speaker 8: at the vendors that they trust, the ones.

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Speaker 2: That already have their data.

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Speaker 8: But yesterday I was with a CIO of a fortune

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Speaker 8: five hundred company where to hire an employee, they need

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Speaker 8: to touch one hundred systems, only ten fifteen of them

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Speaker 8: might be a service now application. Right, So we kind

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Speaker 8: of see ourselves as that Switzerland that ties together a

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Speaker 8: lot of disparate systems. They when it comes to building

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Speaker 8: these agentic applications, and it really is that bringing together

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Speaker 8: structured and unstructured data together to build the toolings.

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Speaker 3: But when we had Jensen Wang come out with his

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Speaker 3: latest earnings. He tried to paint the vision of a

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Speaker 3: new scaling law that's here because of reasoning models. But

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Speaker 3: you're actually taking issue with reasoning models in the general

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Speaker 3: of AI application right now, you think that self evolving

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Speaker 3: models is.

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Speaker 2: Where we should go. Okny, articulate what that is?

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Speaker 8: Yeah, I mean I love Jensen and he just wants

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Speaker 8: more tokens, right, And he's absolutely right right in the

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Speaker 8: spirit of what he's saying, which is the revolution is

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Speaker 8: just getting started.

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Speaker 2: Right, The vast.

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Speaker 8: Majority of enterprises are still not benefiting from AI, So

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Speaker 8: imagine what's going to happen once they do. Right when

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Speaker 8: it comes to the actual LMS, Right, what we're saying

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Speaker 8: is the path to superintelligence is through self evolving models.

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Speaker 8: Models that can update their training data in real time

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Speaker 8: in response to making a mistake and then getting nudged.

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Speaker 2: By a user. You didn't get our process right, right.

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Speaker 8: If we need to go out and retrain or fine

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Speaker 8: tune or you know, rebuild a RAG pipeline every time

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Speaker 8: a model is wrong, we are never going to get

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Speaker 8: to the kind of superintelligence that's possible.

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Speaker 3: What about the talent that's needed at the moment, We've

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Speaker 3: got a new administration that seems to be cutting funding

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Speaker 3: for science in the United States, for universities and next

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Speaker 3: level education.

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Speaker 2: Is that something that worries you? Or is talent still rich?

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Speaker 8: I mean, talent is a struggle no matter you know

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Speaker 8: what the political environment. We just opened up our London

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Speaker 8: and Singapore offices for exactly this reason, to be able

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Speaker 8: to hire outside of the United States. Most of our

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Speaker 8: four hundred employees are here, but as we look to

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Speaker 8: double over the next year, we.

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Speaker 3: Are definitely going to be growing internationally. What about inorganic

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Speaker 3: versus organic? Every time you come on, I say, whoever

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Speaker 3: startups you're looking at, what other aqua hyers can you do?

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Speaker 2: Is it still rich pickings for you to buy other businesses?

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Speaker 8: There are literally hundreds of startups that are doing really,

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Speaker 8: really exciting things. They are finding it difficult to sell

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Speaker 8: into the enterprise. We have kind of risen above the noise,

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Speaker 8: and so there have been, you know, some really interesting

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Speaker 8: things that we're looking at as bolt on acquisitions to

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Speaker 8: the Writer platform.

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Speaker 3: Come on when you can announce some of them, maybe,

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Speaker 3: but it's always great to catch up with her, the

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Speaker 3: CEO of Writer. Now let's return to where we're going next.

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Speaker 3: Much more to expect from Human x conference, but in

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Speaker 3: the interim it looks for happened to Oracle shares after

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Speaker 3: they had their numbers after the bell yesterday, there was

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Speaker 3: disappointment that eight percent revenue growth that was hopeful was

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Speaker 3: not quite matched.

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Speaker 2: We're off by five percent.

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Speaker 3: They did say, look next fiscal year, the year after that,

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Speaker 3: we can have seen fifteen percent twenty percent revenue growth.

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Speaker 3: But in the here and now the disappoint in terms

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Speaker 3: of cloud infrastructure demand and sales return. From Las Vegas,

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Speaker 3: this is Bloom their technology. Welcome back to a special

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Speaker 3: edition of Blue meg Technology.

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Speaker 2: I'm Karen Hyde in Las Vegas.

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Speaker 3: We're live at the human x AI conference, and we

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Speaker 3: are keeping arest what's happening in public markets because after

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Speaker 3: the biggest sell off for the Nazak one hundred yesterday

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Speaker 3: in it since twenty twenty two, we want to see

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Speaker 3: what the Magnificent seven is up to. We're currently bouncing

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Speaker 3: around up now four tens percent, but this is not

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Speaker 3: the sort of bounce back that we anticipated after the

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Speaker 3: hard sell off of yesterday. Their anxiety is still there

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Speaker 3: when it comes to US growth, tariff pressure and the

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Speaker 3: latest that are being imposed, of course on Canadian goods.

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Speaker 3: Let's talk about what it means for the private sector.

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Speaker 3: Vibes and Rachel Metz is here in Las Vegas, lock.

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Speaker 3: This is one of the biggest artificial intelligence gatherings.

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Speaker 2: Are they feeling exuberant?

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Speaker 3: Are they worried about what's happening in the public markets

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Speaker 3: and the sell off that we've seen in the likes

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Speaker 3: of Nvidia.

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Speaker 2: Down a trillion dollars from its highs and market cap.

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Speaker 9: I think it's a little bit of both things, right.

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Speaker 9: I think people are a little concerned about what's going

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Speaker 9: on in the stock market. But frankly, during the time

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Speaker 9: that I've been here, I see a lot of people

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Speaker 9: excited about AI. I mean, this is a conference has

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Speaker 9: not happened before, so people are excited to be here

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Speaker 9: to see what's going to happen here. And a lot

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Speaker 9: of people probably haven't haven't been together, you know, in

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Speaker 9: this space, and it's there are so many people.

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Speaker 2: The vibes are i'd say, prely good.

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Speaker 3: At this moment because the moon music had been still

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Speaker 3: one of Fomo, which is talking with Alfred Lynn about

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Speaker 3: how he's stills been with Elia, who's potentially raising at

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Speaker 3: thirty billion dollars, the ex open AI alumni who are

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Speaker 3: coming out memorialities raising money. We're expecting just these bigger

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Speaker 3: and big e valuations with few and fewer products actually

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Speaker 3: being offered. People are getting in at such an early stage.

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Speaker 3: Is that going to stay with these public market gyrations?

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Speaker 2: That is a really good question.

445
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Speaker 9: I think it's a little bit hard to say, but

446
00:20:52,560 --> 00:20:55,720
Speaker 9: it certainly is still the case that you only have

447
00:20:55,880 --> 00:20:58,520
Speaker 9: I think a handful of people that are able to

448
00:20:58,600 --> 00:21:01,919
Speaker 9: command these kinds of value cuations, these amounts of money

449
00:21:02,560 --> 00:21:06,080
Speaker 9: without even saying, look, here's my minimum viable product.

450
00:21:06,119 --> 00:21:07,960
Speaker 2: You know, like Ilia's company in.

451
00:21:07,880 --> 00:21:11,560
Speaker 9: Particular, they have said specifically like we're not gonna show anything,

452
00:21:11,600 --> 00:21:13,479
Speaker 9: We're I can roll anything out until we get to

453
00:21:13,520 --> 00:21:15,840
Speaker 9: this extremely high level of a product.

454
00:21:15,920 --> 00:21:17,919
Speaker 2: So I think you're not gonna have a ton of

455
00:21:17,920 --> 00:21:18,520
Speaker 2: people like that.

456
00:21:18,600 --> 00:21:21,080
Speaker 9: But you're right that people are asking questions about just

457
00:21:21,119 --> 00:21:22,840
Speaker 9: in general, like how much money you should be put

458
00:21:22,880 --> 00:21:25,720
Speaker 9: toward these things, especially if it's not something we've even

459
00:21:26,000 --> 00:21:27,880
Speaker 9: gotten a hint of what is going to be yet.

460
00:21:28,400 --> 00:21:32,040
Speaker 3: You're speaking with international CEOs as well and leaders of

461
00:21:32,080 --> 00:21:34,560
Speaker 3: AI businesses. How are they viewing the US as a

462
00:21:34,640 --> 00:21:38,160
Speaker 3: place to be building right now versus France and Mistral

463
00:21:38,320 --> 00:21:40,280
Speaker 3: or what's happening in the Middle East or in Asia.

464
00:21:40,640 --> 00:21:46,440
Speaker 9: Yeah, I mean, I think people are feeling like it's

465
00:21:47,080 --> 00:21:49,399
Speaker 9: it's not totally clear yet how things are going to

466
00:21:49,440 --> 00:21:52,600
Speaker 9: shake out. I think there's probably a little bit of hesitation.

467
00:21:52,720 --> 00:21:56,200
Speaker 9: But also I'm not sure that anybody is really saying like, oh,

468
00:21:56,240 --> 00:21:57,680
Speaker 9: I'm not going you know, I'm not going to do.

469
00:21:57,600 --> 00:21:59,959
Speaker 2: That right now, and we're not worried about scaling.

470
00:22:01,320 --> 00:22:03,840
Speaker 9: I mean, I think people should certainly be thinking about it,

471
00:22:04,520 --> 00:22:07,679
Speaker 9: and for a variety of reasons. It's getting more and

472
00:22:07,680 --> 00:22:11,320
Speaker 9: more and more expensive to build larger AI models that

473
00:22:11,440 --> 00:22:12,280
Speaker 9: also uses.

474
00:22:12,040 --> 00:22:13,119
Speaker 2: More and more resources.

475
00:22:13,200 --> 00:22:15,520
Speaker 9: So I think you see people thinking about, well, how

476
00:22:15,560 --> 00:22:17,719
Speaker 9: else might we be able to build these things and

477
00:22:18,040 --> 00:22:21,920
Speaker 9: make them both powerful and not en cost effective and

478
00:22:22,040 --> 00:22:23,280
Speaker 9: also decempro the environment.

479
00:22:23,800 --> 00:22:25,600
Speaker 3: Rachel Metz is going on stage very soon.

480
00:22:25,640 --> 00:22:33,840
Speaker 2: We thank you for joining us.

481
00:22:37,119 --> 00:22:40,000
Speaker 3: Welcome back to the special edition of Blomberg Technology. We

482
00:22:40,080 --> 00:22:43,200
Speaker 3: are live from the Human x AI conference in Las Vegas.

483
00:22:43,480 --> 00:22:46,120
Speaker 3: Apple down two point eight percent even as they unveil

484
00:22:46,400 --> 00:22:48,399
Speaker 3: well as Mark German unveils that they're going to be

485
00:22:48,440 --> 00:22:52,600
Speaker 3: bringing us a whole updated operating system across max iPads

486
00:22:52,640 --> 00:22:56,480
Speaker 3: and indeed iPhones come WWDC in June. Not enough to

487
00:22:56,640 --> 00:22:59,320
Speaker 3: settle some of those worries about the Siri implementation with

488
00:22:59,359 --> 00:23:02,320
Speaker 3: AI and indeed what only what's happening with the Google

489
00:23:02,359 --> 00:23:05,399
Speaker 3: DOJ investigation as well? Yesterday it seems as though the

490
00:23:05,520 --> 00:23:08,639
Speaker 3: current DOJ in this current administration still looking at.

491
00:23:08,480 --> 00:23:12,280
Speaker 2: Stopping any flows of money going from Apple, Google to Apple.

492
00:23:12,359 --> 00:23:15,520
Speaker 3: But let's dig into just where the mindset is right now,

493
00:23:15,560 --> 00:23:18,160
Speaker 3: how a CEO is feeling, how HR department's feeling, how's

494
00:23:18,200 --> 00:23:18,920
Speaker 3: the labor force.

495
00:23:19,200 --> 00:23:21,400
Speaker 2: Sarah Franklin is a person to talk about it.

496
00:23:21,560 --> 00:23:24,960
Speaker 3: That's a CEO where it's bringing artificial intelligence within the

497
00:23:25,240 --> 00:23:30,119
Speaker 3: HR spectrum and ultimately helping companies navigate progress within their

498
00:23:30,119 --> 00:23:32,679
Speaker 3: business culture, within their business. But right now I have

499
00:23:32,720 --> 00:23:36,000
Speaker 3: a feeling maybe the AI output is have fewer people.

500
00:23:37,040 --> 00:23:40,080
Speaker 4: I mean, Caroline, we are in a new economy. It

501
00:23:40,280 --> 00:23:44,679
Speaker 4: has transformed from a data economy to an AI economy overnight,

502
00:23:45,119 --> 00:23:47,199
Speaker 4: and you're seeing the questions of like, what does this

503
00:23:47,280 --> 00:23:48,960
Speaker 4: mean for my jobs? What does this mean for my people?

504
00:23:48,960 --> 00:23:51,000
Speaker 4: What does mean for my business? And everything you were

505
00:23:51,040 --> 00:23:55,200
Speaker 4: just talking about as well, How it's in facting public policy, tariffs,

506
00:23:55,240 --> 00:23:59,240
Speaker 4: everything with electricity to fuel the economy. The AI economy

507
00:23:59,320 --> 00:24:03,200
Speaker 4: is real and it's very much about jobs. And at lattice,

508
00:24:03,520 --> 00:24:05,280
Speaker 4: we want to put people at the center and the

509
00:24:05,280 --> 00:24:07,520
Speaker 4: success of people at the center, so that AI can

510
00:24:07,560 --> 00:24:09,320
Speaker 4: be helpful for people powered future.

511
00:24:09,600 --> 00:24:12,520
Speaker 3: And what are you saying that you all one of

512
00:24:12,560 --> 00:24:15,880
Speaker 3: those people of voices who against the grain came out

513
00:24:15,920 --> 00:24:18,919
Speaker 3: and said we are going to have new coworkers and

514
00:24:18,960 --> 00:24:21,800
Speaker 3: they're going to be artificial intelligence. What does that mean

515
00:24:21,800 --> 00:24:23,600
Speaker 3: for disruption of the labor force? What does it mean

516
00:24:23,600 --> 00:24:26,640
Speaker 3: for the ultimate labor full state we get we can

517
00:24:26,680 --> 00:24:29,000
Speaker 3: wake out, which currently is starting to see a little.

518
00:24:28,760 --> 00:24:31,360
Speaker 2: Bit of a bump up in unemployment. Yeah, I mean Caroline.

519
00:24:31,560 --> 00:24:34,040
Speaker 2: In July, we had this vision.

520
00:24:34,200 --> 00:24:37,280
Speaker 4: We saw what was coming with AI and we wanted

521
00:24:37,280 --> 00:24:39,280
Speaker 4: to get ahead of it because we believe that we

522
00:24:39,320 --> 00:24:41,600
Speaker 4: need to put the success of people as a primary.

523
00:24:41,640 --> 00:24:44,040
Speaker 4: How do we help people understand how to work together

524
00:24:44,320 --> 00:24:47,679
Speaker 4: with AI where they are your coworker, and how do

525
00:24:47,760 --> 00:24:51,040
Speaker 4: you manage AI when it's not just about automation, it's

526
00:24:51,080 --> 00:24:55,040
Speaker 4: about autonomy. People need to understand that AI will be

527
00:24:55,280 --> 00:24:57,959
Speaker 4: acting on your behalf, on your brand's behalf on your

528
00:24:58,000 --> 00:25:00,919
Speaker 4: business behalf And so we are head of this and

529
00:25:00,960 --> 00:25:04,359
Speaker 4: we're helping HR leaders be the people that are helping

530
00:25:04,400 --> 00:25:08,160
Speaker 4: their companies bring AI in responsibly so that we can

531
00:25:08,280 --> 00:25:12,080
Speaker 4: make sure that every career, every job that's changing, we

532
00:25:12,119 --> 00:25:14,600
Speaker 4: can put AI into it in a way that helps

533
00:25:14,600 --> 00:25:16,280
Speaker 4: people be successful in the future.

534
00:25:16,440 --> 00:25:18,360
Speaker 3: May have been the writer was just on saying they've

535
00:25:18,359 --> 00:25:20,520
Speaker 3: got a survey coming out showing that at the moment.

536
00:25:20,359 --> 00:25:22,080
Speaker 2: CEOs are really disappointed with.

537
00:25:22,119 --> 00:25:25,080
Speaker 3: Generative AI, with the productivity, lack of gains.

538
00:25:25,359 --> 00:25:26,680
Speaker 2: How are you measuring success?

539
00:25:26,720 --> 00:25:29,040
Speaker 3: How are you starting to show up that it's helping

540
00:25:29,040 --> 00:25:29,800
Speaker 3: in the world of HI.

541
00:25:30,119 --> 00:25:32,000
Speaker 2: Yeah, so this is a thing. This is new.

542
00:25:32,480 --> 00:25:35,000
Speaker 4: This is an unwritten book for us, and people don't

543
00:25:35,000 --> 00:25:35,439
Speaker 4: know what to do.

544
00:25:35,440 --> 00:25:37,040
Speaker 2: People are saying, okay, let me just bring in.

545
00:25:37,080 --> 00:25:39,840
Speaker 4: AI, but then you have blank slate problems or you

546
00:25:39,880 --> 00:25:41,760
Speaker 4: don't really know how to use it. So we need

547
00:25:41,800 --> 00:25:44,399
Speaker 4: to invest in the education, We need to invest in

548
00:25:44,400 --> 00:25:47,240
Speaker 4: the career pathways, we need to invest.

549
00:25:46,880 --> 00:25:48,520
Speaker 2: In how people can be successful.

550
00:25:48,600 --> 00:25:51,200
Speaker 3: CEOs willing to do that in this anxiety ridden market

551
00:25:51,240 --> 00:25:52,760
Speaker 3: where they're just trying to think about the bottom line.

552
00:25:52,800 --> 00:25:55,919
Speaker 1: They're willing to invest in their people CEOs are and

553
00:25:55,960 --> 00:25:58,439
Speaker 1: this is where HR leaders have an opportunity to be

554
00:25:59,160 --> 00:26:02,320
Speaker 1: that rock for them to lean on and for them

555
00:26:02,359 --> 00:26:05,359
Speaker 1: to really go forward and know what to do to

556
00:26:05,359 --> 00:26:07,760
Speaker 1: invest in their people and their performance, because the ultimate

557
00:26:07,840 --> 00:26:11,280
Speaker 1: judge is how performance is your company, and performance companies

558
00:26:11,680 --> 00:26:14,360
Speaker 1: are that way when their people are performant, they're engaged,

559
00:26:14,400 --> 00:26:16,400
Speaker 1: they're passionate, and they know what to do and they.

560
00:26:16,400 --> 00:26:18,000
Speaker 4: Know how to use the tools. You can't just throw

561
00:26:18,040 --> 00:26:20,320
Speaker 4: AI at them. You have to say, this is how

562
00:26:20,359 --> 00:26:22,240
Speaker 4: you're going to use it, and we want to do

563
00:26:22,280 --> 00:26:24,400
Speaker 4: this in a way that puts the people success as

564
00:26:24,440 --> 00:26:24,960
Speaker 4: the primary.

565
00:26:25,560 --> 00:26:25,760
Speaker 2: Look.

566
00:26:26,119 --> 00:26:28,520
Speaker 3: I'm going to ask a kind of uncomfortable question with

567
00:26:28,560 --> 00:26:30,840
Speaker 3: two women sitting here at an AI conference and thinking

568
00:26:30,840 --> 00:26:34,360
Speaker 3: about who generally is in HL departments, But how will

569
00:26:34,359 --> 00:26:37,880
Speaker 3: women adopting it in particular generative AI versus men? How

570
00:26:37,960 --> 00:26:39,879
Speaker 3: is it disrupting women more in the workface so the

571
00:26:40,080 --> 00:26:43,560
Speaker 3: men or is that just a too basic, simplistic idea.

572
00:26:43,960 --> 00:26:46,080
Speaker 4: The truth is we're too early to tell like this

573
00:26:46,119 --> 00:26:48,440
Speaker 4: is all new. What I do love is that it

574
00:26:48,480 --> 00:26:53,719
Speaker 4: is two incredible women here. A big man is that

575
00:26:53,920 --> 00:26:56,560
Speaker 4: women have that curiosity, they have the resilience, they have

576
00:26:56,640 --> 00:26:58,879
Speaker 4: the ability to adapt to change and they say, Okay,

577
00:26:59,440 --> 00:27:02,239
Speaker 4: this is new, let's bring this in and figure out

578
00:27:02,280 --> 00:27:03,960
Speaker 4: how it can help me, how it can help me

579
00:27:04,040 --> 00:27:06,679
Speaker 4: and my team. And having that mindset of how we

580
00:27:06,720 --> 00:27:10,080
Speaker 4: can use this technology to be helpful is the mindset.

581
00:27:10,119 --> 00:27:12,440
Speaker 4: We don't want to just automate people out of work.

582
00:27:12,880 --> 00:27:15,439
Speaker 4: We want to bring people into the future that we're creating.

583
00:27:15,480 --> 00:27:18,399
Speaker 4: And this is a massive responsibility that we all share

584
00:27:18,480 --> 00:27:21,640
Speaker 4: because we can't just create technology for technology's sake.

585
00:27:21,880 --> 00:27:23,080
Speaker 2: We have to create it for the.

586
00:27:23,000 --> 00:27:25,240
Speaker 4: Better of the world and for the better of business

587
00:27:25,280 --> 00:27:26,560
Speaker 4: and for the better of people.

588
00:27:26,680 --> 00:27:29,359
Speaker 2: That will people be ultimated out of work? Jobs are

589
00:27:29,359 --> 00:27:30,080
Speaker 2: going to change.

590
00:27:30,240 --> 00:27:33,399
Speaker 4: It is no question there is a massive reshaping that

591
00:27:33,560 --> 00:27:36,040
Speaker 4: every CEO is on their mind is how do I

592
00:27:36,119 --> 00:27:40,040
Speaker 4: reshape my business? How do I reshape these career pathways?

593
00:27:40,200 --> 00:27:41,320
Speaker 2: How do I put this in there?

594
00:27:41,320 --> 00:27:43,600
Speaker 4: And we don't There's so many questions we don't have

595
00:27:43,680 --> 00:27:46,159
Speaker 4: the answers to and why we need courage in the

596
00:27:46,240 --> 00:27:50,159
Speaker 4: leadership and we need to be responsible, accountable and transparent.

597
00:27:49,640 --> 00:27:50,800
Speaker 2: In the decisions that we're making.

598
00:27:51,000 --> 00:27:53,800
Speaker 4: And that's what Lattice helps all HR leaders do as

599
00:27:53,840 --> 00:27:55,240
Speaker 4: they're bringing AI into the workforce.

600
00:27:55,280 --> 00:27:57,280
Speaker 3: What about your own talent and bringing people into your

601
00:27:57,320 --> 00:27:59,280
Speaker 3: own business at the moment, is it rich pickings? Is

602
00:27:59,320 --> 00:28:02,520
Speaker 3: people we orientate their own businesses. You came from salesforce

603
00:28:02,560 --> 00:28:04,320
Speaker 3: and they're busy trying to get rid of some talent

604
00:28:04,400 --> 00:28:05,560
Speaker 3: to bring in more AI talent?

605
00:28:05,680 --> 00:28:07,400
Speaker 2: Is it face to get hold of the people you need.

606
00:28:08,119 --> 00:28:10,399
Speaker 4: We are hiring at Lattice and we have a great

607
00:28:10,440 --> 00:28:12,840
Speaker 4: talent pool that's coming in and we invest in our people.

608
00:28:12,920 --> 00:28:15,520
Speaker 4: And this is something which I'm proud of as a

609
00:28:15,600 --> 00:28:18,600
Speaker 4: CEO that I know that my company trusts me, and

610
00:28:18,640 --> 00:28:20,880
Speaker 4: I know that they know that I will be transparent

611
00:28:20,960 --> 00:28:23,800
Speaker 4: and accountable to the decisions that we make and yes,

612
00:28:24,119 --> 00:28:27,480
Speaker 4: investing in the people. We need to also as leaders

613
00:28:27,560 --> 00:28:30,520
Speaker 4: invest in ourselves. Like we are not immune to this.

614
00:28:30,600 --> 00:28:33,680
Speaker 4: I mean, who's to say that we'll have AI salespeople

615
00:28:33,920 --> 00:28:35,240
Speaker 4: not or AI CEOs.

616
00:28:35,520 --> 00:28:36,879
Speaker 2: Who knows what the future will be.

617
00:28:37,400 --> 00:28:40,080
Speaker 4: But we all have to embrace this change, and we

618
00:28:40,200 --> 00:28:42,920
Speaker 4: all have to embrace AI in a way that it

619
00:28:43,000 --> 00:28:45,520
Speaker 4: is just a reality. It is in the workforce and

620
00:28:45,560 --> 00:28:48,520
Speaker 4: the question is how do we meet people the primary benefactor?

621
00:28:48,960 --> 00:28:52,680
Speaker 3: What about globally speaking, is there as much adoption and

622
00:28:53,240 --> 00:28:56,720
Speaker 3: willingness to bring it into the HR space in Europe,

623
00:28:56,880 --> 00:28:58,640
Speaker 3: in Asia, is there in the United States.

624
00:28:59,360 --> 00:29:01,000
Speaker 2: You know, again it's early days.

625
00:29:01,040 --> 00:29:03,800
Speaker 4: What I see is from my vantage point as a

626
00:29:03,840 --> 00:29:09,560
Speaker 4: tech CEO, is that the globalization that AI provides us,

627
00:29:09,760 --> 00:29:14,560
Speaker 4: it really helps us to battle the misunderstanding that we

628
00:29:14,680 --> 00:29:17,560
Speaker 4: have when language is a barrier, when culture is a barrier.

629
00:29:18,080 --> 00:29:21,040
Speaker 4: And that's something that I personally am optimistic about is

630
00:29:21,080 --> 00:29:22,920
Speaker 4: if we can use this technology in a way to

631
00:29:22,920 --> 00:29:27,680
Speaker 4: help us collaborate better, remove misunderstanding, understand nuance better.

632
00:29:28,160 --> 00:29:29,720
Speaker 2: And that's something I'm hopeful that.

633
00:29:29,680 --> 00:29:32,280
Speaker 4: Bringing AI into the workforce, it can help us have

634
00:29:32,360 --> 00:29:33,320
Speaker 4: better understanding.

635
00:29:33,680 --> 00:29:35,680
Speaker 3: You've been ranked to one of the falsest growing companies

636
00:29:35,800 --> 00:29:39,120
Speaker 3: private companies out that what is holding back growth at

637
00:29:39,160 --> 00:29:41,480
Speaker 3: the moment. If you could ask anything of the administration

638
00:29:41,600 --> 00:29:43,040
Speaker 3: of the markets, of the environment, what.

639
00:29:43,000 --> 00:29:46,560
Speaker 4: Would it be uncertainty. We are in a very uncertain

640
00:29:46,600 --> 00:29:50,520
Speaker 4: time right now. And so the way that we get

641
00:29:50,640 --> 00:29:53,920
Speaker 4: everyone together and go to growth is by being very

642
00:29:53,960 --> 00:29:58,480
Speaker 4: certain in our pathway and just qualming the fears that

643
00:29:58,520 --> 00:30:01,360
Speaker 4: we are going to eliminate people from all jobs, say

644
00:30:01,440 --> 00:30:04,000
Speaker 4: we are committed to a people powered future and we're

645
00:30:04,000 --> 00:30:07,840
Speaker 4: committed to building your success. Yes, it's a change. It's

646
00:30:07,880 --> 00:30:11,240
Speaker 4: happening fast. We can't be living in that movie. Don't

647
00:30:11,280 --> 00:30:13,160
Speaker 4: look up, you know. We have to know that this

648
00:30:13,280 --> 00:30:16,000
Speaker 4: is going to happen, embrace it, and have the courage

649
00:30:16,200 --> 00:30:18,480
Speaker 4: and the support of each other to together go into

650
00:30:18,480 --> 00:30:22,200
Speaker 4: this future and be responsible, accountable and transparent because trust

651
00:30:22,280 --> 00:30:24,520
Speaker 4: is really the currency of the AI economy.

652
00:30:24,800 --> 00:30:26,560
Speaker 3: Sarah, it's great to catch up with you here. Great

653
00:30:26,560 --> 00:30:28,640
Speaker 3: to have a wonderful time at human X, Sarah Franklin.

654
00:30:28,960 --> 00:30:32,040
Speaker 3: That is CEO here in Las Vegas. The music is

655
00:30:32,080 --> 00:30:35,440
Speaker 3: pumping because we've got a mixture of your foia around

656
00:30:35,520 --> 00:30:38,760
Speaker 3: artificial intelligence. We've also got a healthy dose of anxiety

657
00:30:38,800 --> 00:30:42,560
Speaker 3: around these public markets. Andrew Felman joins us now Cerebra CEO,

658
00:30:42,800 --> 00:30:47,520
Speaker 3: who is in the market of updating ultimately access to compute,

659
00:30:47,640 --> 00:30:50,040
Speaker 3: but the whole new way of thinking about bringing the

660
00:30:50,120 --> 00:30:51,200
Speaker 3: data center to life.

661
00:30:51,280 --> 00:30:52,400
Speaker 2: Andrew, I ask.

662
00:30:52,280 --> 00:30:54,800
Speaker 3: You, in this time where we're worried about an electricity

663
00:30:55,280 --> 00:30:58,400
Speaker 3: state of emergency, you need power for your data centers.

664
00:30:58,440 --> 00:31:00,720
Speaker 3: I know you've got efficient data centres, a whole new

665
00:31:00,760 --> 00:31:03,719
Speaker 3: way of powering that means less power is necessary than

666
00:31:03,760 --> 00:31:06,600
Speaker 3: the usual architecture and GPUs, but does that worry you

667
00:31:06,640 --> 00:31:08,240
Speaker 3: an electricity state of an emergency.

668
00:31:08,520 --> 00:31:12,400
Speaker 10: Look, I think as a nation, we've underinvested in our infrastructure,

669
00:31:12,440 --> 00:31:17,080
Speaker 10: including electricity infrastructure, and AI uses a fair bit of power,

670
00:31:17,600 --> 00:31:21,880
Speaker 10: and even companies like Cerebraus, who have the most efficient

671
00:31:21,960 --> 00:31:23,680
Speaker 10: compute in the.

672
00:31:23,640 --> 00:31:25,800
Speaker 5: Market, we use a fair bit of power.

673
00:31:25,840 --> 00:31:30,480
Speaker 10: And so it's not unreasonable that the administration recognizes that

674
00:31:30,680 --> 00:31:34,640
Speaker 10: power is fundamental. AI is fundamental, and that they have

675
00:31:34,760 --> 00:31:38,400
Speaker 10: taken some steps to I think, to eliminate some of

676
00:31:38,640 --> 00:31:40,680
Speaker 10: the red tape. It takes a long time to bring

677
00:31:40,760 --> 00:31:43,320
Speaker 10: up new power plants and to deliver those to new

678
00:31:43,360 --> 00:31:43,960
Speaker 10: data centers.

679
00:31:44,080 --> 00:31:44,680
Speaker 5: Has it been.

680
00:31:44,560 --> 00:31:46,000
Speaker 2: Holding your growth back on you?

681
00:31:46,000 --> 00:31:48,800
Speaker 3: You've just been deploying in places like Oklahoma City in Montreal,

682
00:31:48,880 --> 00:31:50,920
Speaker 3: But has the power infrastructure.

683
00:31:50,400 --> 00:31:50,880
Speaker 2: Of been an issue?

684
00:31:51,080 --> 00:31:51,640
Speaker 7: Absolutely?

685
00:31:52,600 --> 00:31:57,040
Speaker 10: Where these facilities are and how limited and how scarce

686
00:31:57,120 --> 00:32:00,840
Speaker 10: they are is limiting everybody in the day right now.

687
00:32:01,200 --> 00:32:04,520
Speaker 10: These are hard to find, they're expensive, they're driving up

688
00:32:04,600 --> 00:32:08,840
Speaker 10: the cost of AI. About half of the cost of

689
00:32:08,880 --> 00:32:09,440
Speaker 10: AI is.

690
00:32:09,440 --> 00:32:10,640
Speaker 2: Attributed straight to power.

691
00:32:11,680 --> 00:32:13,520
Speaker 10: And so I think if we can do a better

692
00:32:13,600 --> 00:32:16,560
Speaker 10: job at the infrastructure level than as we provide more

693
00:32:16,600 --> 00:32:22,480
Speaker 10: compute right across the country, we will benefit from from

694
00:32:22,520 --> 00:32:25,440
Speaker 10: AI as its price drops, Well, let's.

695
00:32:25,200 --> 00:32:28,560
Speaker 3: Just remind people of your CS three system. Ultimately, this

696
00:32:28,640 --> 00:32:32,040
Speaker 3: is a way of bringing compute in a different way

697
00:32:32,160 --> 00:32:35,800
Speaker 3: with a much smaller surface area ultimately than usual tech

698
00:32:35,800 --> 00:32:37,360
Speaker 3: stack and the usual center GPUs.

699
00:32:38,040 --> 00:32:39,920
Speaker 2: How is that selling into this narrative?

700
00:32:40,000 --> 00:32:42,280
Speaker 3: How are you able to distinguish yourselves because you need

701
00:32:42,440 --> 00:32:44,840
Speaker 3: less power, because you need less space as well within

702
00:32:44,840 --> 00:32:45,480
Speaker 3: a data center.

703
00:32:45,680 --> 00:32:47,720
Speaker 5: That's right, So we chose a very different strategy.

704
00:32:47,840 --> 00:32:52,280
Speaker 10: We chose to build the world's largest chip chip fifty

705
00:32:52,280 --> 00:32:55,360
Speaker 10: six times larger than any previous chip. That meant we

706
00:32:55,440 --> 00:32:58,760
Speaker 10: had to move less information and that allowed us to

707
00:32:58,920 --> 00:33:04,240
Speaker 10: use less energy. And we are a washing demand and yeah,

708
00:33:04,280 --> 00:33:08,160
Speaker 10: we're buried in demand right now. Well, we just announced

709
00:33:08,240 --> 00:33:12,760
Speaker 10: large customers like Hugging Face, like Alpha Sense, like Perplexity,

710
00:33:13,280 --> 00:33:18,719
Speaker 10: like Mistral. That remember AI is made with training and

711
00:33:18,840 --> 00:33:21,360
Speaker 10: used in inference, and right now everybody wants to use it,

712
00:33:22,080 --> 00:33:27,720
Speaker 10: and so there's tremendous demand for blazing fast, low power

713
00:33:27,800 --> 00:33:30,320
Speaker 10: inference and that's what we're bringing to market right now.

714
00:33:30,520 --> 00:33:33,360
Speaker 3: Mistral a global player over in France. You have had

715
00:33:33,360 --> 00:33:35,520
Speaker 3: a lot of exposure to I mean least and player

716
00:33:35,600 --> 00:33:38,800
Speaker 3: G forty two. How are you changing that exposure because

717
00:33:38,840 --> 00:33:40,880
Speaker 3: there have been some anxiety too dependent on them as

718
00:33:40,880 --> 00:33:41,400
Speaker 3: a customer.

719
00:33:41,720 --> 00:33:44,840
Speaker 10: Well, I think the way you catch three very large

720
00:33:44,840 --> 00:33:47,440
Speaker 10: customers is to begin with one very large customer. And

721
00:33:47,760 --> 00:33:51,000
Speaker 10: we begin with a strategic partnership with G forty two

722
00:33:51,040 --> 00:33:54,320
Speaker 10: and that's been an extraordinary partnership and they've been everything

723
00:33:54,320 --> 00:33:56,640
Speaker 10: we could have hoped for in a partnership. We began

724
00:33:56,680 --> 00:34:01,520
Speaker 10: deploying with them in the US and they through this deployment,

725
00:34:01,560 --> 00:34:03,120
Speaker 10: we proved to the world that we could build some

726
00:34:03,120 --> 00:34:05,959
Speaker 10: of the largest data centers, train some of the largest models,

727
00:34:05,960 --> 00:34:08,160
Speaker 10: and deliver some of the fastest inference in the world.

728
00:34:08,680 --> 00:34:11,640
Speaker 10: And that's opened the door for others, and now we're

729
00:34:11,640 --> 00:34:15,200
Speaker 10: in conversations with the largest players in the world. It's

730
00:34:15,560 --> 00:34:17,799
Speaker 10: that they were a door opener for us and have

731
00:34:17,840 --> 00:34:18,880
Speaker 10: been an excellent partner.

732
00:34:19,239 --> 00:34:21,880
Speaker 3: Is that door wide open when you've got such rules

733
00:34:21,880 --> 00:34:24,920
Speaker 3: as the AI diffusion rule, which potentially limits you selling

734
00:34:24,960 --> 00:34:29,040
Speaker 3: into allies as well as so called adversary countries.

735
00:34:29,320 --> 00:34:32,360
Speaker 10: Look, we've been a big, big proponent and work closely

736
00:34:32,400 --> 00:34:36,160
Speaker 10: with commerce. That's a confusing rule, to be honest, and

737
00:34:36,920 --> 00:34:39,359
Speaker 10: it's if I understand correctly, it's not quite a rule.

738
00:34:39,440 --> 00:34:41,279
Speaker 10: Yet it's a recommendation to be a rule, and it

739
00:34:41,360 --> 00:34:43,919
Speaker 10: will come in in June. I think as a rule,

740
00:34:45,360 --> 00:34:47,920
Speaker 10: I think there are other ways that we could have

741
00:34:47,960 --> 00:34:52,960
Speaker 10: achieved the same goals, but we support would commerce. Department

742
00:34:52,960 --> 00:34:54,960
Speaker 10: of Commerce says, and if that's the path, then that's the.

743
00:34:54,960 --> 00:34:55,560
Speaker 2: Playbook we use.

744
00:34:55,600 --> 00:34:56,680
Speaker 3: Think it will be or do you think that they'll

745
00:34:56,719 --> 00:34:58,040
Speaker 3: navigate away all of it?

746
00:34:58,560 --> 00:35:00,879
Speaker 10: My view is, I think we can achieve the same

747
00:35:00,920 --> 00:35:04,640
Speaker 10: objectives with slightly less intrusive approach, and I hope they

748
00:35:04,680 --> 00:35:09,319
Speaker 10: navigate away. But we weren't asrebrase selling to China long

749
00:35:09,400 --> 00:35:12,080
Speaker 10: before the rules said we couldn't. We thought that was

750
00:35:12,120 --> 00:35:13,960
Speaker 10: the right thing to do, and so we chose to

751
00:35:15,040 --> 00:35:15,880
Speaker 10: follow our ethics.

752
00:35:17,040 --> 00:35:19,319
Speaker 2: So we will do whatever's right.

753
00:35:20,400 --> 00:35:23,160
Speaker 3: How will you do what Seva's right within these public markets, who,

754
00:35:23,200 --> 00:35:24,920
Speaker 3: of course are eyeing an IPO.

755
00:35:25,560 --> 00:35:26,480
Speaker 2: Is now the right time?

756
00:35:27,800 --> 00:35:30,120
Speaker 10: You know? I think there's a lot of things when

757
00:35:30,160 --> 00:35:30,760
Speaker 10: you go public.

758
00:35:30,800 --> 00:35:31,640
Speaker 2: You can't control.

759
00:35:32,600 --> 00:35:36,160
Speaker 10: You can't control what the president says, you can't control sentiment.

760
00:35:36,440 --> 00:35:38,640
Speaker 10: What you can control is how you run your company

761
00:35:38,680 --> 00:35:41,960
Speaker 10: every day. You can build extraordinary technology. You can treat

762
00:35:42,000 --> 00:35:44,560
Speaker 10: your people well so they want to stay. You can

763
00:35:45,040 --> 00:35:47,160
Speaker 10: hire some of the best people from around the world.

764
00:35:47,440 --> 00:35:50,239
Speaker 10: You can deliver and keep your customers extremely happy. These

765
00:35:50,280 --> 00:35:52,719
Speaker 10: are things we can control, and whether you go out

766
00:35:52,800 --> 00:35:55,800
Speaker 10: in a month or a year, those are things I

767
00:35:55,840 --> 00:35:58,560
Speaker 10: think that fall into place if you build an extraordinary business,

768
00:35:58,640 --> 00:35:59,840
Speaker 10: and that's what we can focus.

769
00:36:00,080 --> 00:36:03,040
Speaker 3: You can't control others innovation, we can, and I'm interested

770
00:36:03,080 --> 00:36:05,720
Speaker 3: in the innovation that Deep Set brought and the idea

771
00:36:05,760 --> 00:36:08,520
Speaker 3: that maybe we'll have more and more powerful models and less.

772
00:36:08,400 --> 00:36:10,120
Speaker 2: And less in compute need.

773
00:36:10,560 --> 00:36:12,600
Speaker 3: Where do you are we ultimately just going to scale

774
00:36:12,600 --> 00:36:13,359
Speaker 3: the applications.

775
00:36:13,440 --> 00:36:17,040
Speaker 10: I I think the h that wasn't exactly what Deep

776
00:36:17,080 --> 00:36:20,000
Speaker 10: Deep Seek showed. I I think what deep Sea showed

777
00:36:20,280 --> 00:36:24,360
Speaker 10: was that several hundred people and a fair bit of compute,

778
00:36:24,360 --> 00:36:26,319
Speaker 10: maybe less than others had used, but a lot of

779
00:36:26,360 --> 00:36:29,200
Speaker 10: compute could produce a very interesting result in the open

780
00:36:29,200 --> 00:36:32,560
Speaker 10: source community. So building on the work of others, right

781
00:36:32,680 --> 00:36:37,080
Speaker 10: work could be moved forward, innovations added. And I think

782
00:36:37,080 --> 00:36:40,680
Speaker 10: that the public markets have almost always taken the wrong

783
00:36:40,800 --> 00:36:43,640
Speaker 10: first step when the cost of compute comes down, right,

784
00:36:43,800 --> 00:36:46,160
Speaker 10: the cost of When the cost of compute comes.

785
00:36:45,960 --> 00:36:47,240
Speaker 5: Down, our market grows.

786
00:36:47,760 --> 00:36:50,399
Speaker 10: And it's done that every single time for the last

787
00:36:50,400 --> 00:36:53,120
Speaker 10: fifty or sixty years, and every single time the public

788
00:36:53,120 --> 00:36:55,799
Speaker 10: markets have thought, oh, the market's getting smaller, but that's

789
00:36:55,880 --> 00:36:58,640
Speaker 10: not actually what happens when the cost of compute, when

790
00:36:58,640 --> 00:37:02,480
Speaker 10: we can do more for less new applications urgeon.

791
00:37:02,760 --> 00:37:05,640
Speaker 5: And this is making the market bigger, not smaller.

792
00:37:06,120 --> 00:37:08,560
Speaker 3: And you'll be there with the offering of influencing compute.

793
00:37:08,680 --> 00:37:12,120
Speaker 3: We will Andrew Fellman cerebra CEO. Delight to have him here,

794
00:37:12,280 --> 00:37:15,880
Speaker 3: Thank you. Now coming up so much more poolside, CEO

795
00:37:16,120 --> 00:37:18,839
Speaker 3: going to be discussing the future of his business as

796
00:37:18,840 --> 00:37:21,520
Speaker 3: he focuses in on coding with generator of AI. Jason

797
00:37:21,520 --> 00:37:24,440
Speaker 3: Warner joins us. Now we return to what's happening in

798
00:37:24,440 --> 00:37:26,440
Speaker 3: the nasac and the socks right now. As we know,

799
00:37:26,920 --> 00:37:29,960
Speaker 3: tumult has been upon the tech sector. We're now up

800
00:37:30,000 --> 00:37:31,520
Speaker 3: about a tenth of a percent and the Nazak one

801
00:37:31,560 --> 00:37:34,320
Speaker 3: hundred after its worst day since twenty twenty two. Yesterday

802
00:37:34,520 --> 00:37:36,880
Speaker 3: we lost almost four percent. We wiped out a trillion

803
00:37:36,880 --> 00:37:39,719
Speaker 3: dollars worth of market cap. We see a slight recovery,

804
00:37:40,000 --> 00:37:43,600
Speaker 3: but still worries there. The semiconductor index down six tenths percent.

805
00:37:43,960 --> 00:38:00,120
Speaker 11: This is of bluembog tenology.

806
00:38:02,280 --> 00:38:05,440
Speaker 3: Welcome back to this special edition of Bloomberg Technology. I'm

807
00:38:05,480 --> 00:38:07,920
Speaker 3: Carin Hyde in Las Vegas. We are live at the

808
00:38:08,000 --> 00:38:11,839
Speaker 3: Human XAI conference, and there is a tone of anxiety

809
00:38:12,120 --> 00:38:14,920
Speaker 3: in the public markets that maybe plays into the private

810
00:38:14,920 --> 00:38:16,640
Speaker 3: sector a little bit when you look at a magnificence

811
00:38:16,640 --> 00:38:19,479
Speaker 3: seven actually managing to be now up a percentage point

812
00:38:19,520 --> 00:38:22,120
Speaker 3: having been in the red in earlier trade. We tried

813
00:38:22,120 --> 00:38:25,880
Speaker 3: to understand what the impact of an energy state of

814
00:38:25,960 --> 00:38:28,600
Speaker 3: crisis is for the United States state of emergency, how

815
00:38:28,600 --> 00:38:31,239
Speaker 3: that implicates data center demand. We try to understand what

816
00:38:31,280 --> 00:38:34,720
Speaker 3: the latest tariffs on aluminium on metals coming from Canada

817
00:38:34,800 --> 00:38:37,960
Speaker 3: means for broader risk sentiment. But for now some reprieve

818
00:38:38,000 --> 00:38:40,720
Speaker 3: after a harsh sell off yesterday as people sold their winners,

819
00:38:40,760 --> 00:38:42,680
Speaker 3: and those winners are twenty twenty three and twenty twenty

820
00:38:42,719 --> 00:38:45,399
Speaker 3: four with the Magnificent seven names. We want to think

821
00:38:45,400 --> 00:38:48,640
Speaker 3: about the impact on the private state and indeed what's

822
00:38:48,680 --> 00:38:49,760
Speaker 3: happening in terms of innovation.

823
00:38:50,040 --> 00:38:51,640
Speaker 2: It's not stopping, it's not cooling down.

824
00:38:51,719 --> 00:38:54,800
Speaker 3: Jason Warner is here Pulside CEO of course, previously CTO

825
00:38:54,800 --> 00:38:58,920
Speaker 3: of GitHub, which is now so embodied within the area

826
00:38:59,000 --> 00:39:03,040
Speaker 3: of helping coder is helping engineers. You are building basically

827
00:39:03,040 --> 00:39:04,959
Speaker 3: superintelligence for engineers going forward.

828
00:39:05,000 --> 00:39:05,640
Speaker 2: How are you doing that?

829
00:39:06,480 --> 00:39:08,520
Speaker 7: Well, first, great to be here, thanks for having me.

830
00:39:09,239 --> 00:39:12,640
Speaker 7: And yes, we're building towards a human intelligence future where

831
00:39:12,680 --> 00:39:15,560
Speaker 7: artificial intelligence the humans live side by side, but artificial

832
00:39:15,600 --> 00:39:17,960
Speaker 7: intelligence can do most of the things that humans can do.

833
00:39:18,560 --> 00:39:19,880
Speaker 7: The way in which we do that is we have

834
00:39:19,920 --> 00:39:23,399
Speaker 7: some proprietary research techniques we have dubbed them reinforcement learning

835
00:39:23,480 --> 00:39:27,040
Speaker 7: via code execution feedback. But for the lay person, what

836
00:39:27,040 --> 00:39:29,239
Speaker 7: they need to understand is we're just going to give

837
00:39:29,239 --> 00:39:31,759
Speaker 7: them the superpower to write software in the future, and

838
00:39:31,760 --> 00:39:34,239
Speaker 7: anyone who currently knows it will feel like being augmented

839
00:39:34,239 --> 00:39:35,080
Speaker 7: by one hundred acts.

840
00:39:36,280 --> 00:39:38,920
Speaker 3: Will that displace the number of engineers we need or

841
00:39:39,000 --> 00:39:41,120
Speaker 3: ultimately free them up to do more? How are you

842
00:39:41,200 --> 00:39:43,040
Speaker 3: thinking about augmenting versus replacing?

843
00:39:43,200 --> 00:39:45,839
Speaker 7: Yeah, I mean, I think the conversation of replacement is

844
00:39:45,880 --> 00:39:48,640
Speaker 7: always one that pops up. But I tend to look

845
00:39:48,640 --> 00:39:50,279
Speaker 7: at these things about what we're going to be able

846
00:39:50,280 --> 00:39:52,640
Speaker 7: to do more of, what we'll be able to do faster.

847
00:39:52,960 --> 00:39:54,839
Speaker 7: And this is where I get excited, because I think

848
00:39:54,840 --> 00:39:58,000
Speaker 7: about what software does currently in twenty twenty five, It

849
00:39:58,080 --> 00:40:01,279
Speaker 7: uderwrates almost the entire economy we have. But if you

850
00:40:01,280 --> 00:40:02,920
Speaker 7: think about what it can do in the future, is

851
00:40:02,920 --> 00:40:05,120
Speaker 7: it could take something that might be five or six

852
00:40:05,560 --> 00:40:07,880
Speaker 7: people months of ten humans to do this, and it

853
00:40:07,920 --> 00:40:10,200
Speaker 7: can reduce that to compute hours. And that's great for

854
00:40:10,280 --> 00:40:13,759
Speaker 7: someone like biologist or the physicists or the person doing

855
00:40:13,760 --> 00:40:16,080
Speaker 7: cancer research. And this is the thing I think about

856
00:40:16,120 --> 00:40:18,800
Speaker 7: compressing human knowledge learning into compute hours.

857
00:40:19,680 --> 00:40:23,080
Speaker 3: You're not the only CEO we've recently spoken to who's

858
00:40:23,120 --> 00:40:26,080
Speaker 3: thinking about giving super intelligence and superpowers to an engineer.

859
00:40:26,120 --> 00:40:28,879
Speaker 3: And look, we just have Reflection AI on the store

860
00:40:29,000 --> 00:40:30,080
Speaker 3: on the show yesterday.

861
00:40:30,880 --> 00:40:31,760
Speaker 2: How is the space?

862
00:40:32,000 --> 00:40:34,279
Speaker 3: How is there an awful lot of startups trying to

863
00:40:34,360 --> 00:40:34,759
Speaker 3: do the.

864
00:40:34,680 --> 00:40:35,880
Speaker 2: Same thing or different things.

865
00:40:36,160 --> 00:40:37,880
Speaker 3: Distinguish yourselves from the competition.

866
00:40:37,920 --> 00:40:42,080
Speaker 7: They're all slightly different. The idea here with Poolside is

867
00:40:42,200 --> 00:40:44,360
Speaker 7: more akin to open AI and Anthropic in that we

868
00:40:44,400 --> 00:40:47,560
Speaker 7: are going after the thing that we might call AGI

869
00:40:47,640 --> 00:40:51,200
Speaker 7: and eventually ASI. And so what we are at our

870
00:40:51,239 --> 00:40:54,560
Speaker 7: heart is a frontier AI company similar to open ai Anthropic,

871
00:40:54,840 --> 00:40:57,120
Speaker 7: and we are pushing the boundaries on that. We're going

872
00:40:57,280 --> 00:40:59,600
Speaker 7: via software For technical reasons, we don't need to get

873
00:40:59,600 --> 00:41:02,279
Speaker 7: into that. It's very technical reasons, and then many other

874
00:41:02,320 --> 00:41:04,200
Speaker 7: folks end up in what I would call the AI

875
00:41:04,320 --> 00:41:07,560
Speaker 7: consumer camp. There will be five or six very large

876
00:41:07,920 --> 00:41:12,120
Speaker 7: winners who build out the core infrastructure of artificial intelligence,

877
00:41:12,239 --> 00:41:14,600
Speaker 7: and the rest of the folks will consume the artificial

878
00:41:14,600 --> 00:41:16,000
Speaker 7: intelligence produced by those companies.

879
00:41:16,280 --> 00:41:18,480
Speaker 3: So you see yourself as an infrastructure player.

880
00:41:18,239 --> 00:41:20,600
Speaker 7: Well, I see myself just right next to open AI and.

881
00:41:20,600 --> 00:41:23,920
Speaker 3: Anthropic Okay, do you see yourself as a valuation perspective

882
00:41:24,000 --> 00:41:26,320
Speaker 3: right next to them, because tell us about who you

883
00:41:26,400 --> 00:41:28,160
Speaker 3: are and your funding needs and whether you're out there

884
00:41:28,200 --> 00:41:28,879
Speaker 3: raising money to.

885
00:41:28,840 --> 00:41:32,440
Speaker 7: Do this well. Building artificial intelligence models of the capital

886
00:41:32,480 --> 00:41:35,239
Speaker 7: intensive game though, so at some point we always have

887
00:41:35,280 --> 00:41:38,239
Speaker 7: to consider how much compute capacity we have, and that

888
00:41:38,280 --> 00:41:40,840
Speaker 7: is a proxy for how fast you can go or

889
00:41:40,880 --> 00:41:42,960
Speaker 7: how big you can go. So that's always going to

890
00:41:42,960 --> 00:41:44,640
Speaker 7: be a topic. But right now, we're really happy where

891
00:41:44,719 --> 00:41:45,160
Speaker 7: where we sit.

892
00:41:45,320 --> 00:41:46,160
Speaker 2: So you're not raising money.

893
00:41:46,160 --> 00:41:47,239
Speaker 7: We're not raising money right now.

894
00:41:47,400 --> 00:41:50,719
Speaker 2: What about compute? And is what is holding you back

895
00:41:50,760 --> 00:41:51,120
Speaker 2: right now?

896
00:41:51,200 --> 00:41:54,640
Speaker 3: Is it energy? Infrastructure? Is it electricity? Is it access

897
00:41:54,640 --> 00:41:55,400
Speaker 3: to data centers?

898
00:41:55,440 --> 00:41:55,919
Speaker 2: What is it?

899
00:41:57,480 --> 00:41:59,719
Speaker 7: I would love more time, Yeah, I would love more

900
00:41:59,760 --> 00:42:02,880
Speaker 7: time time for us to go do more experiments But

901
00:42:03,000 --> 00:42:04,759
Speaker 7: for the thing that I think about holding us back

902
00:42:04,920 --> 00:42:07,360
Speaker 7: is really where we are in the world. If I

903
00:42:07,360 --> 00:42:09,719
Speaker 7: cannet this out into a different way, which is, if

904
00:42:09,760 --> 00:42:12,200
Speaker 7: we were in a world of infinites infinite access to energy,

905
00:42:12,239 --> 00:42:14,600
Speaker 7: infinite access to compute, and infinite access to data, we

906
00:42:14,640 --> 00:42:17,960
Speaker 7: would be at AGI or ASI already already. But we

907
00:42:17,960 --> 00:42:19,839
Speaker 7: don't have an infinite world. We have a constrained world,

908
00:42:19,880 --> 00:42:23,040
Speaker 7: so we must make different choices. So my main choice

909
00:42:23,040 --> 00:42:24,400
Speaker 7: that I have to make is how I spend my

910
00:42:24,440 --> 00:42:26,920
Speaker 7: compute budget. So if you were to ask, my primary

911
00:42:26,920 --> 00:42:28,839
Speaker 7: concern is always access to more compute.

912
00:42:29,800 --> 00:42:31,960
Speaker 2: At the moment, where do you get your compute from?

913
00:42:32,280 --> 00:42:35,600
Speaker 7: We have partnerships across a wide variety of folks, although

914
00:42:35,600 --> 00:42:38,600
Speaker 7: our primary clusters are the Amazon Aws web.

915
00:42:38,440 --> 00:42:40,880
Speaker 2: Services, and is that largely US placed.

916
00:42:41,040 --> 00:42:43,320
Speaker 3: What's interesting about you is perhaps people got you modeled

917
00:42:43,360 --> 00:42:45,040
Speaker 3: up with a French company at one point. I know

918
00:42:45,080 --> 00:42:48,000
Speaker 3: that you had team members. There are US domiciled. Where

919
00:42:48,000 --> 00:42:49,279
Speaker 3: are you thinking about your future?

920
00:42:49,120 --> 00:42:52,200
Speaker 7: We've always we are and always have been a US company,

921
00:42:53,040 --> 00:42:56,200
Speaker 7: though we have a presence in France, we have primarily

922
00:42:56,239 --> 00:42:59,759
Speaker 7: hired our AI researchers across Europe and in the UK.

923
00:43:00,120 --> 00:43:02,440
Speaker 7: And also we have an office in Paris, which the

924
00:43:02,560 --> 00:43:04,360
Speaker 7: entirety it happens to be right now because they're doing

925
00:43:04,400 --> 00:43:06,439
Speaker 7: an on site which we do once a month.

926
00:43:06,880 --> 00:43:10,799
Speaker 3: But we're a US company, US company and wishing for

927
00:43:10,840 --> 00:43:13,720
Speaker 3: more compute alongside a lot of other US companies. Jason

928
00:43:13,719 --> 00:43:16,080
Speaker 3: Warner has been great to have him the poolside CEO,

929
00:43:16,400 --> 00:43:18,800
Speaker 3: keep a track of that company in the private sector. Meanwhile,

930
00:43:18,800 --> 00:43:21,120
Speaker 3: back in the public sector, we are looking at shares

931
00:43:21,239 --> 00:43:23,120
Speaker 3: the NASDAK stocks that.

932
00:43:23,080 --> 00:43:25,160
Speaker 2: Are in the chip sector in VideA as well. A

933
00:43:25,239 --> 00:43:27,600
Speaker 2: very volatile day. We're currently Apple quarter percent and then

934
00:43:27,600 --> 00:43:28,560
Speaker 2: Asset one hundred.

935
00:43:28,360 --> 00:43:30,400
Speaker 3: After a brutal sell off yesterday, the worst that we've

936
00:43:30,400 --> 00:43:32,719
Speaker 3: seen since twenty twenty two. Some we can have to

937
00:43:32,800 --> 00:43:35,000
Speaker 3: index is still underwater by some four tenths percent. Remember

938
00:43:35,040 --> 00:43:36,840
Speaker 3: Oracle numbers as well, perhaps giving a bit of a

939
00:43:36,920 --> 00:43:40,399
Speaker 3: dampling spirit to ultimately the demand for AI and how

940
00:43:40,480 --> 00:43:43,640
Speaker 3: quickly we can get data centers on track. Interestingly, they're

941
00:43:43,640 --> 00:43:45,920
Speaker 3: still pointing towards fifteen to twenty percent revenue growth for

942
00:43:45,960 --> 00:43:48,840
Speaker 3: the next fiscal years, so pointing that really the demand

943
00:43:48,880 --> 00:43:51,960
Speaker 3: still remains insatiable when it comes to infrastructure. But thus

944
00:43:52,000 --> 00:43:54,080
Speaker 3: why it didn't live up to expectations in video, though,

945
00:43:54,160 --> 00:43:57,400
Speaker 3: rebounding two point six percent after it's lost more than

946
00:43:57,440 --> 00:44:01,040
Speaker 3: a trillion dollars from its market capt.

947
00:44:00,160 --> 00:44:01,120
Speaker 2: Couple of months.

948
00:44:01,440 --> 00:44:04,080
Speaker 3: Now, that does it for this edition of Bloomberg Technology