WEBVTT - Figma CEO Dylan Field on Strong Earnings

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

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

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<v Speaker 1>and Ed Lovelow in sentences, go.

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<v Speaker 2>This is Bloomberg Tech coming up.

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<v Speaker 3>President Trump says he discussed in video's eighty two hundred

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<v Speaker 3>chips with Jijingping.

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<v Speaker 2>Will break down the latest.

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<v Speaker 4>Plus we speak with the CEO of Figma after its

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<v Speaker 4>earnings results, the five fears that AI would disrupt the design.

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<v Speaker 3>Stack, and our conversation with open Ai CFO Sarah Fryer,

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<v Speaker 3>who says the startup may raise more capital after completing

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<v Speaker 3>its recent fundraising round.

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<v Speaker 2>Carry has got the markets.

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<v Speaker 5>I have, let's check in on them.

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<v Speaker 4>We're currently seeing the n AS that one hundred actually

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<v Speaker 4>having its worst day since March the twenty seventh. At

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<v Speaker 4>the moment, edre by one point three percent. Look over

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<v Speaker 4>the course of the week, it's not nearly so ugly.

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<v Speaker 5>We're actually inning down about a tenth percent.

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<v Speaker 4>But on this day it is the chip stocks in particular,

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<v Speaker 4>and I'm looking at Micron on the downside.

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<v Speaker 5>That's a key point drag. You're looking at Invidia as well.

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<v Speaker 4>Look, this is the day in which we digest what

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<v Speaker 4>happened over in Beijing.

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<v Speaker 3>Yes, and that is reflected in semiconductors in part. So

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<v Speaker 3>the Philadelphia Semiconductor Index or SOCKS is the main gauge

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<v Speaker 3>of chip makers, chip equipment makers. It's down almost four percent.

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<v Speaker 3>You have to take into account the astonishing rally in

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<v Speaker 3>chip stocks year to date seventy percent or something prior

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<v Speaker 3>to yesterday's close.

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<v Speaker 2>In video at one point in the session, it's off.

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<v Speaker 3>Session lows was down on track for its biggest decline

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<v Speaker 3>since February. In part, as I say, because the market's digesting,

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<v Speaker 3>what was the net outcome of the meeting between President

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<v Speaker 3>Trump and President G four Invidia And for chips, his

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<v Speaker 3>President Trump talking about in video's eight two hundred chips

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<v Speaker 3>on Air Force one earlier today.

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<v Speaker 2>As you know, Jensen was, there's amazing and the video and.

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<v Speaker 6>He would be a fla.

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<v Speaker 7>You know, they have much higher level than the HQO hundred,

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<v Speaker 7>but the H two hundred is good Chinadja And so yeah.

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<v Speaker 3>President Trump there on Air Force one. Let's get the

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<v Speaker 3>latest with Bloomberg's Tyler Kendall, who joins us once again

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<v Speaker 3>from Beijing.

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<v Speaker 2>Chips in focus for US.

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<v Speaker 3>On Bloomberg Tech Taiwan in focus between the President and

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<v Speaker 3>President g What else do we need to take away

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<v Speaker 3>from this historic meeting?

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<v Speaker 8>Well ed? At this point, we're actually getting some breaking

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<v Speaker 8>news on how China views how this visit went between

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<v Speaker 8>President Trump and Chinese President Jijingping. Chinese state media reporting

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<v Speaker 8>moments ago that China feels that Taiwan is that number

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<v Speaker 8>one issue for US China relations now. President Trump and

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<v Speaker 8>US officials here on the ground maintained that the US

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<v Speaker 8>policy regarding Taiwan has not changed, though the President Trump

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<v Speaker 8>told Fox News earlier today that he would like to

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<v Speaker 8>see tensions cooled down, in his words, between China and Taiwan.

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<v Speaker 8>When pressed about future US weapons sales, the President was

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<v Speaker 8>also noncommittal as Congress waits for him to prove a

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<v Speaker 8>fourteen billion dollar package that's been queued up. That was

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<v Speaker 8>clearly the biggest point of contention amid a summit that

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<v Speaker 8>was rather cordial, and both sides said that they were

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<v Speaker 8>prioritizing stability. But we didn't really get a lot of

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<v Speaker 8>tangibles and deliverables in terms of progress. Right We're still

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<v Speaker 8>waiting on some key details regarding planned commitments. When it

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<v Speaker 8>comes to purchase agreements or new investment deals. Though we

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<v Speaker 8>can confirm that Boeing officials were still here on the

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<v Speaker 8>ground in Beijing over the last few hours meeting with

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<v Speaker 8>Chinese officials and what was considered a positive sign for

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<v Speaker 8>that deal.

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<v Speaker 5>But as you.

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<v Speaker 8>Mentioned, there there was a big expectation that perhaps there

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<v Speaker 8>could be a deal when it came to in Vidia's

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<v Speaker 8>H two hundred chips, as the Nvidia CEO Dnsen Wong

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<v Speaker 8>was a last minute addition to the travel here to Beijing.

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<v Speaker 8>President Trump did confirm that they spoke about the chips,

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<v Speaker 8>but a timately said that China wants to develop their

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<v Speaker 8>own and that's why we hadn't seen any purchases go

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<v Speaker 8>through Ed and Caroline. The President also mentioned that the

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<v Speaker 8>two sides did discuss the future of artificial intelligence and

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<v Speaker 8>where we could see some collaboration in terms of guardrails

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<v Speaker 8>related to the technology.

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<v Speaker 4>For the most Tyler Kendall extraordinary work throughout the past

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<v Speaker 4>few days over in Beijing. We so appreciated. Look, we've

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<v Speaker 4>got to get there. For the broader chip industry perspective,

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<v Speaker 4>what does all of this mean in terms of the

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<v Speaker 4>United States and its ability to drive up manufacturing capacity

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<v Speaker 4>and its workforce right here.

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<v Speaker 5>Sharry List is with us.

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<v Speaker 4>It's a vice president of Global Workforce Development Initiatives at SEMI.

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<v Speaker 4>It's a global industry association connection professionals worldwide across the

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<v Speaker 4>chip and electronics design and manufacturing supply chain.

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<v Speaker 5>It is wonderful, Chary, to have you here with us.

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<v Speaker 5>Thank you so much for having so.

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<v Speaker 4>If we do see tensions rise, and if we do

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<v Speaker 4>focus therefore even more on a manufacturing footprint brought back

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<v Speaker 4>to America where Chips have manufactured their design rather than

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<v Speaker 4>make so reliant on Taiwan, are we able to do it?

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<v Speaker 2>We are?

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<v Speaker 9>I mean, I think I speak on behalf of the

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<v Speaker 9>workforce component involved this. It is one of the bottlenecks

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<v Speaker 9>for this industry in the US, for sure, but there

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<v Speaker 9>are a remarkable set of programs that are being built

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<v Speaker 9>all around the country to meet that need. The Chips

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<v Speaker 9>investments here in the US are launching an incredible growth

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<v Speaker 9>here for us on our soil here in this country,

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<v Speaker 9>and we're going to need another one hundred and fifty

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<v Speaker 9>years one thousand or so people in this work environment.

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<v Speaker 9>So we're building programs all over the.

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<v Speaker 3>Country away from the geopolitics. You know, the news of

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<v Speaker 3>the week highlights just capacity reliance. Right, So the one

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<v Speaker 3>thing that Taiwan's really good at is the brutal economics

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<v Speaker 3>of semiconductor manufacturing, and talent's a key component of that.

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<v Speaker 3>Of course, there's all this plan on paper to build

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<v Speaker 3>more fabs, have more foundry capacity in the United States,

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<v Speaker 3>Do we have the people with the skills to make

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<v Speaker 3>them run and not just make them run, make them

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<v Speaker 3>hum right, brutal economics.

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<v Speaker 9>Yes, I mean I think that's what we're all trying

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<v Speaker 9>to work towards. Absolutely, had we didn't have as many

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<v Speaker 9>programs established in the US anymore because we weren't manufacturing here.

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<v Speaker 9>With the investments that are happening here, programs are launching

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<v Speaker 9>all over the country. In fact, under the Chips investment,

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<v Speaker 9>there's a two hundred million dollar workforce This.

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<v Speaker 2>Is the Chips Act. A result of the Ships actually, sorry.

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<v Speaker 9>As a result of the Chips Act, there is a

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<v Speaker 9>two hundred million dollar investment in workforce through the National

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<v Speaker 9>Science Foundation in concert with the Department of Commerce, to

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<v Speaker 9>invest in building a national infrastructure around workforce development so

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<v Speaker 9>that we can fund regional nodes around the country to

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<v Speaker 9>build what's needed regionally in workforce and how to feed

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<v Speaker 9>that into a national infrastructure.

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<v Speaker 2>Not just jump in ofs.

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<v Speaker 3>I mean Tim Cook very famously said, didn't he that

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<v Speaker 3>China could fill a sports stadium with tooling engineers and

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<v Speaker 3>that type of talent in America would struggle to fill

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<v Speaker 3>a meeting room. What kind of roles are we talking

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<v Speaker 3>about here? Show that you're trying to skill up.

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<v Speaker 9>This nation on Yeah, I mean, I think we need

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<v Speaker 9>everything across the industry, from technicians and operators to fill

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<v Speaker 9>the fabs, to be on the fab floors, to all

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<v Speaker 9>sorts of engineers across electrical engineering, mechanical engineering, chemical engineering,

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<v Speaker 9>to our researchers, our PhDs. We need everybody. We need

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<v Speaker 9>marketing talent, we need finance talent. So I think the

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<v Speaker 9>challenge right now in the US is actually the image

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<v Speaker 9>and awareness of our industry with students. So we do

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<v Speaker 9>a lot of work in the space of getting students

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<v Speaker 9>excited or passionate about this industry or.

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<v Speaker 5>Even to know it.

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<v Speaker 9>Because kids walk around all day every day with their phones,

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<v Speaker 9>the phones, their iPads, their computers were in cars that

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<v Speaker 9>are driven through chips. We are using appliances, everything we

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<v Speaker 9>use all day long, and most people don't know that,

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<v Speaker 9>parents don't all know you know, So it's it's an

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<v Speaker 9>educating of the country. Really.

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<v Speaker 4>Well, it's been so interesting is the method by which

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<v Speaker 4>we start to up our fabrication footprint. Now, in some

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<v Speaker 4>ways it's about leaning into intel, into local plays, but

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<v Speaker 4>a lot of it's been about how drawing TSMC and

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<v Speaker 4>saying please build here in Arizona. How much in the

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<v Speaker 4>past have we relied on sort of TSMC talent coming here.

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<v Speaker 4>Is that a way that's been reskilled and we've learned

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<v Speaker 4>from others, or is it really about teaching them in

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<v Speaker 4>our own.

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<v Speaker 1>Education system, seeing others being brought up through the STEM

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<v Speaker 1>education perspective, rather than learning from talent abroad.

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<v Speaker 9>There's certainly been a mix of both. I think we have,

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<v Speaker 9>of course, relied on talent abroad. As an industry. This

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<v Speaker 9>is a global industry. This is a really intense, intricate,

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<v Speaker 9>complicated industry, and we need talent from everywhere, right, that's clear.

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<v Speaker 9>What we're trying to do now is make sure that

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<v Speaker 9>we can build the workforce in the US with US

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<v Speaker 9>citizens to get US jobs and to fill all of

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<v Speaker 9>these roles. So we are still learning of course, we're

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<v Speaker 9>all learning from each other. We all have different strengths

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<v Speaker 9>across the world, so you know, hopefully we'll be able

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<v Speaker 9>to meet those needs here in the US with all

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<v Speaker 9>the programs that are being established.

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<v Speaker 2>We're out of time.

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<v Speaker 3>But actually Caroen I didn't even think about that, right.

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<v Speaker 3>What did not come out of the meeting again between

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<v Speaker 3>the two presidents probably the issue of visas right and

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<v Speaker 3>talent here in Silicon Valley, San Francisco. How long has

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<v Speaker 3>that been a story, you know, talent from China coming

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<v Speaker 3>to universities, particularly in the world of AI, particularly in

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<v Speaker 3>the world of AI. Shery list is Semi. It's great

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<v Speaker 3>to have you on Bloomberg Tech. Thank you very much. Listen, guys,

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<v Speaker 3>tune in Monday for an exclusive interview with the CEOs

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<v Speaker 3>of Dell and Nvidia. From the sidelines of Dell Technology's world.

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<v Speaker 3>Will be jennying over to Las Vegas for what is

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<v Speaker 3>could not be a more timely conversation. Coming up, Cerebras

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<v Speaker 3>surges in its trading debut, turning summer Silicon Valley's earliest

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<v Speaker 3>backers into billion dollar winners.

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<v Speaker 6>US.

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<v Speaker 2>Next, this is Bloomberg Tech.

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<v Speaker 5>We have got to check in on Cerebras.

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<v Speaker 4>What a debut yesterday extraordinary sixty eight percent higher close

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<v Speaker 4>at one point eighty nine percent. Unsurprisingly, there's been a

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<v Speaker 4>profit taking company today more by some four point eight percent,

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<v Speaker 4>let's call it on the day as we see the

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<v Speaker 4>rest of the industry being under some pressure across the

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<v Speaker 4>AI in chip spectrum because of concerns about really where

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<v Speaker 4>the US China relationship goes. But three of the chip

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<v Speaker 4>makers earliest venture capital backers al cerebra said Benchmark, Eclipse

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<v Speaker 4>Foundation Capital, they're poised to make billions from their bets

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<v Speaker 4>following Cerebras's IPO here for more has been a bouth

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<v Speaker 4>VC and startups reporter Rebecca Times, So how early do

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<v Speaker 4>they back this company and what sort of rewards are

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<v Speaker 4>they going to be able to give LPs?

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<v Speaker 10>Yeah, so two of the are sorry, three of the

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<v Speaker 10>four biggest backers in Cerebras of their biggest outside backers

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<v Speaker 10>came in a decade ago. In twenty sixteen, they invested

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<v Speaker 10>in Cerebras's earliest round on the order of twenty five

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<v Speaker 10>million dollars, and they now stand to make billions of

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<v Speaker 10>dollars each at the IPO. Benchmark is among them inter

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<v Speaker 10>firms with the biggest steak now around eight percent, and

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<v Speaker 10>this would have you know, this was its first hardware

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<v Speaker 10>investment in over a decade at the time, and that

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<v Speaker 10>big swing has really paid off for them in spades. Obviously,

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<v Speaker 10>the stock trading up massively from its IPO price of

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<v Speaker 10>one hundred and eighty five dollars per share rebackgau.

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<v Speaker 3>I don't know if you noticed yesterday, but when Andrew Feldman,

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<v Speaker 3>the Cerebris CEO, was on the show, le Or Susan,

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<v Speaker 3>the CEO of venture firm Eclipse, was hanging over his

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<v Speaker 3>right shoulder every couple of seconds gave a little cheeky

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<v Speaker 3>glance down into the camera lens. Eclipse another firm you know,

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<v Speaker 3>regulars on this show that made a lot of money.

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<v Speaker 7>Here.

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<v Speaker 3>It's really fun reporting on this with you. It's like,

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<v Speaker 3>you know, it's a really important IPO day. Look what

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<v Speaker 3>else do we need to know? And just reflect what

0:11:36.760 --> 0:11:39.400
<v Speaker 3>your week was like covering this blockbuster listing.

0:11:40.040 --> 0:11:40.720
<v Speaker 2>Absolutely so.

0:11:40.880 --> 0:11:42.960
<v Speaker 10>We also learned in the days leading up to the

0:11:43.040 --> 0:11:47.880
<v Speaker 10>IPO that Semikin Doctor Company Arm and its majority backer SoftBank,

0:11:48.480 --> 0:11:52.480
<v Speaker 10>had made an attempt to acquire Cereubris in the weeks

0:11:52.480 --> 0:11:56.600
<v Speaker 10>before it's listing. Those offers were ultimately rebuffed, but there's

0:11:56.720 --> 0:11:59.560
<v Speaker 10>huge competition in this market. There's obviously tons of interest

0:12:00.080 --> 0:12:02.240
<v Speaker 10>in the private markets and now the public markets as well.

0:12:03.240 --> 0:12:08.400
<v Speaker 10>In aichip companies an Area Infrastructure, Eclipse and Foundation Capital,

0:12:08.440 --> 0:12:11.839
<v Speaker 10>one of the other largest free risk backers. This sort

0:12:11.880 --> 0:12:15.200
<v Speaker 10>of hardware, this infrastructure is very much their bread and butter,

0:12:16.240 --> 0:12:18.360
<v Speaker 10>and so they've got, you know, some of the things

0:12:18.360 --> 0:12:21.240
<v Speaker 10>in the pipeline sort of that follow this general theme.

0:12:22.000 --> 0:12:24.920
<v Speaker 10>And certainly there are more investments being made all across

0:12:24.960 --> 0:12:27.440
<v Speaker 10>stages in the private markets, very much in line with

0:12:27.480 --> 0:12:29.800
<v Speaker 10>this theme. So expecting to see much more activity here.

0:12:30.760 --> 0:12:33.800
<v Speaker 3>Invoted for Rebecca Torrance, big week, thank you very much.

0:12:34.120 --> 0:12:37.560
<v Speaker 3>From the excitement around Srebris's debut to the massive AI

0:12:37.640 --> 0:12:41.839
<v Speaker 3>spending that's still ongoing at the hyperscale, is investors increasingly

0:12:41.880 --> 0:12:46.079
<v Speaker 3>focused on whether companies like Alphabet, Meta and Amazon can

0:12:46.320 --> 0:12:50.160
<v Speaker 3>justify the soaring capex with sustained revenue growth. On the

0:12:50.160 --> 0:12:52.400
<v Speaker 3>other side, let's get more on that with Eric Sherid

0:12:52.440 --> 0:12:55.360
<v Speaker 3>and Goldman sax Co Business Unit, leader of the Technology,

0:12:55.400 --> 0:13:00.960
<v Speaker 3>Media and Telecommunications Group in Global Investment Research. Some weeker,

0:13:01.400 --> 0:13:05.319
<v Speaker 3>I mean that's the formula. You look at the hyperscalers,

0:13:05.320 --> 0:13:07.720
<v Speaker 3>you get the capital expenditures number. We get to the

0:13:07.840 --> 0:13:12.000
<v Speaker 3>end of earning season and video reports next Wednesday. Probably

0:13:12.000 --> 0:13:14.719
<v Speaker 3>the largest beneficiary of that capital expendit show. If we're

0:13:14.760 --> 0:13:17.320
<v Speaker 3>being honest, you sit here on a Friday morning, Eric,

0:13:17.400 --> 0:13:19.719
<v Speaker 3>what's your conclusion of the week's news flow and how

0:13:19.720 --> 0:13:22.880
<v Speaker 3>it impacts those bigger names that you cover well.

0:13:22.920 --> 0:13:25.240
<v Speaker 11>I think the main takeaways for us is that we

0:13:25.320 --> 0:13:29.960
<v Speaker 11>remain in an infrastructure led cycle, so CAPEX continues to

0:13:30.000 --> 0:13:34.240
<v Speaker 11>have an upward bias, albeit the bias to the upside

0:13:34.440 --> 0:13:37.160
<v Speaker 11>was more muted this quarter than it was last quarter,

0:13:37.840 --> 0:13:42.040
<v Speaker 11>which we think investors generally received positively. The second element

0:13:42.080 --> 0:13:45.280
<v Speaker 11>would be that the revenue backlogs, or the future revenue

0:13:45.280 --> 0:13:48.360
<v Speaker 11>that could come from this capex is now over nine

0:13:48.520 --> 0:13:53.480
<v Speaker 11>hundred billion dollars combined, spread across both Alphabet and Amazon's

0:13:53.600 --> 0:13:57.840
<v Speaker 11>cloud computing divisions. That is giving investors increased confidence that

0:13:57.880 --> 0:14:01.880
<v Speaker 11>there's revenue that will follow allbeit one, two, three years

0:14:01.920 --> 0:14:06.320
<v Speaker 11>after the capex is spent. And interestingly, the margins in

0:14:06.360 --> 0:14:10.240
<v Speaker 11>these cloud segments also surprised to the upside because non

0:14:10.280 --> 0:14:15.640
<v Speaker 11>AI workloads are accelerating structuring data is accelerating, so therefore

0:14:15.800 --> 0:14:19.520
<v Speaker 11>the long term view of earning a return on a

0:14:19.600 --> 0:14:23.200
<v Speaker 11>larger revenue base gave people more confidence. And that's why

0:14:23.200 --> 0:14:26.040
<v Speaker 11>you've seen Amazon and the Alphabet over the last one

0:14:26.680 --> 0:14:29.960
<v Speaker 11>three months act very very well as stocks as there's

0:14:30.000 --> 0:14:31.480
<v Speaker 11>been a greater appreciation for that.

0:14:32.120 --> 0:14:33.880
<v Speaker 4>I mean, Eric at one point this week we wondered

0:14:33.880 --> 0:14:36.000
<v Speaker 4>if Alphabet was going to eclipse in video as the

0:14:36.000 --> 0:14:39.440
<v Speaker 4>world's most valuable company. And we think about that vertical

0:14:39.480 --> 0:14:42.720
<v Speaker 4>integration that is just helping with this flywheel, the fact

0:14:42.760 --> 0:14:44.880
<v Speaker 4>that they are able to have such prowess when it

0:14:44.880 --> 0:14:47.440
<v Speaker 4>comes to TPU and the amount that they're able to

0:14:47.480 --> 0:14:51.000
<v Speaker 4>begain inefficiencies. And I think if Amazon actually using cerebras

0:14:51.240 --> 0:14:56.920
<v Speaker 4>share stock. More broadly, they've been using Cerebris hardware alongside

0:14:56.960 --> 0:14:59.880
<v Speaker 4>some of their own in house chips. How are you

0:15:00.120 --> 0:15:04.280
<v Speaker 4>seeing this world of benefit from using your own self

0:15:04.280 --> 0:15:06.360
<v Speaker 4>made chips alongside those from others.

0:15:06.960 --> 0:15:11.280
<v Speaker 11>Custom Silicon or TPUs from Alphabet and Amazon, we think

0:15:11.360 --> 0:15:15.840
<v Speaker 11>continues to be one of the most underappreciated narratives in

0:15:15.880 --> 0:15:20.840
<v Speaker 11>the market. Firstly, it continues to drive workloads into these

0:15:20.840 --> 0:15:25.360
<v Speaker 11>cloud ecosystems so they capture more revenue overall. Secondarily, because

0:15:25.400 --> 0:15:29.120
<v Speaker 11>they designed the custom silicon, they garner more of the

0:15:29.240 --> 0:15:33.280
<v Speaker 11>margin by doing it. And the performance of TPUs, while

0:15:33.320 --> 0:15:37.200
<v Speaker 11>not quite where GPUs are on an absolute performance basis,

0:15:37.440 --> 0:15:40.360
<v Speaker 11>if you measure it on price to performance, they're actually

0:15:40.480 --> 0:15:43.280
<v Speaker 11>quite competitive. So this is something where you can go

0:15:43.360 --> 0:15:47.160
<v Speaker 11>to your customers offer a price to performance ratio that

0:15:47.200 --> 0:15:51.000
<v Speaker 11>looks very attractive and they benefit from garnering more revenue

0:15:51.040 --> 0:15:54.440
<v Speaker 11>and more incremental margin. That's another theme that we think

0:15:54.520 --> 0:15:57.640
<v Speaker 11>is gaining in prominence across this landscape.

0:15:58.920 --> 0:16:03.480
<v Speaker 3>Eric, you lead the team at that is covering Amazon

0:16:03.520 --> 0:16:06.280
<v Speaker 3>and Alphabet right, and I'm just saying that's point out

0:16:06.280 --> 0:16:10.200
<v Speaker 3>the obvious. That's your focus, but you must track Anthropic

0:16:10.320 --> 0:16:15.320
<v Speaker 3>so closely. Both companies have significant financial interests in anthropic.

0:16:16.080 --> 0:16:20.720
<v Speaker 3>Both have some kind of competition with Anthropic at the

0:16:20.800 --> 0:16:25.680
<v Speaker 3>model level. In Amazon's case, Bedrock is the marketplace for Claude.

0:16:26.080 --> 0:16:29.240
<v Speaker 3>That's very tangled as a web. How do you untangle it?

0:16:30.240 --> 0:16:30.400
<v Speaker 12>Well?

0:16:30.400 --> 0:16:32.680
<v Speaker 11>I think there's a lot without getting into any one

0:16:32.760 --> 0:16:35.960
<v Speaker 11>company in their relationship with another. I think the world

0:16:36.000 --> 0:16:39.160
<v Speaker 11>overall is becoming more interdependent. When it comes to AI,

0:16:39.280 --> 0:16:42.200
<v Speaker 11>you're going to have foundational model companies that need compute,

0:16:42.360 --> 0:16:45.119
<v Speaker 11>You're going to have hyper scalers that can deliver that compute.

0:16:45.280 --> 0:16:48.080
<v Speaker 11>You increasingly are going to have hyperscalers who are effective

0:16:48.120 --> 0:16:51.440
<v Speaker 11>partners that allow the foundational model companies to come to

0:16:51.600 --> 0:16:56.520
<v Speaker 11>market and connect with enterprises like Goldman Sachs, and there

0:16:56.640 --> 0:16:59.360
<v Speaker 11>is going to be a lot of scale that benefits

0:17:00.040 --> 0:17:04.359
<v Speaker 11>driving incremental growth in this landscape. The truest measure of

0:17:04.640 --> 0:17:08.440
<v Speaker 11>technology computing shifts in my career has been that only

0:17:08.480 --> 0:17:11.240
<v Speaker 11>a handful of companies on both the infrastructure and the

0:17:11.280 --> 0:17:15.840
<v Speaker 11>platform layer earn excess returns on capital. So there's only

0:17:15.920 --> 0:17:17.800
<v Speaker 11>going to be a handful of companies that are enterprise

0:17:17.840 --> 0:17:19.800
<v Speaker 11>platform companies. There's only going to be a handful of

0:17:19.800 --> 0:17:22.520
<v Speaker 11>companies that are consumer platform companies. And if you come

0:17:22.520 --> 0:17:25.320
<v Speaker 11>back to the earlier point that you led with, which

0:17:25.359 --> 0:17:28.639
<v Speaker 11>is the capital need for this entire cycle, there's only

0:17:28.640 --> 0:17:30.800
<v Speaker 11>a handful of companies that have the access to capital

0:17:31.080 --> 0:17:32.879
<v Speaker 11>to be able to build to this. So there is

0:17:32.920 --> 0:17:35.639
<v Speaker 11>going to be an interdependence that comes from just a

0:17:35.640 --> 0:17:38.040
<v Speaker 11>handful of companies that can actually build at this level

0:17:38.080 --> 0:17:38.520
<v Speaker 11>of scale.

0:17:39.480 --> 0:17:41.159
<v Speaker 5>Alek Sheridan fascinating.

0:17:41.280 --> 0:17:43.120
<v Speaker 4>Not having you on the show has always come back

0:17:43.160 --> 0:17:44.520
<v Speaker 4>soon of Goldman sachs.

0:17:44.800 --> 0:17:45.480
<v Speaker 5>Now coming up.

0:17:45.800 --> 0:17:49.240
<v Speaker 4>Tensions rise between Apple and Open Ai. Why the AI

0:17:49.280 --> 0:17:52.000
<v Speaker 4>startup is weighing possible legal action against the iPhone maker.

0:17:52.359 --> 0:17:53.440
<v Speaker 5>This is Blomberg Tech.

0:18:02.600 --> 0:18:04.880
<v Speaker 4>And it's time now for talking tech and first up.

0:18:05.160 --> 0:18:08.320
<v Speaker 4>Samsung management is making a rare eleventh hour visit to

0:18:08.440 --> 0:18:11.560
<v Speaker 4>union leaders to a massive chip factory strike, but the

0:18:11.600 --> 0:18:14.320
<v Speaker 4>world stop memory maker is facing an eighteen day walkout

0:18:14.320 --> 0:18:16.680
<v Speaker 4>that could cost the company seven hundred million dollars a

0:18:16.800 --> 0:18:20.720
<v Speaker 4>day and good stall critical AI chip production. Plus Bill

0:18:20.760 --> 0:18:24.000
<v Speaker 4>Ackman's Pershing Square has taken a new course take in Microsoft,

0:18:24.119 --> 0:18:26.800
<v Speaker 4>with Ackman arguing that the market is unestimating the tech

0:18:26.880 --> 0:18:28.400
<v Speaker 4>chanswer resilience now.

0:18:28.480 --> 0:18:30.320
<v Speaker 5>The move comes as Microsoft.

0:18:29.800 --> 0:18:33.280
<v Speaker 4>Continues its aggressive push into AI, reclaiming its status in

0:18:33.400 --> 0:18:37.400
<v Speaker 4>Agwin's portfolio and Elon Musk's XAI, and is officially entering

0:18:37.440 --> 0:18:40.159
<v Speaker 4>the coding agent race with the launch of grock Build

0:18:40.480 --> 0:18:42.560
<v Speaker 4>in an attempt to catch up with down public explaud

0:18:42.880 --> 0:18:46.200
<v Speaker 4>Musk is racing to close that gap in the developer market.

0:18:46.200 --> 0:18:49.320
<v Speaker 4>It's coding agents become the next multi billion dollar frontier

0:18:49.640 --> 0:18:50.200
<v Speaker 4>in AI.

0:18:50.600 --> 0:18:53.840
<v Speaker 3>Ed Let's chat about Open AI. You sat down with

0:18:53.960 --> 0:19:00.439
<v Speaker 3>CFO Sarah Fryer last night, and she you had the

0:19:00.480 --> 0:19:06.520
<v Speaker 3>opportunity to basically say, good timing. Sam Altman's under away

0:19:06.560 --> 0:19:09.280
<v Speaker 3>in a very big case against Elon Musk. And then

0:19:09.320 --> 0:19:11.560
<v Speaker 3>there's the breaking news last night about Apple and the

0:19:11.560 --> 0:19:12.440
<v Speaker 3>relationship flu part.

0:19:12.560 --> 0:19:13.640
<v Speaker 2>Just reflect on.

0:19:13.600 --> 0:19:16.360
<v Speaker 4>It, I mean an extraordinary not saying planfo.

0:19:16.600 --> 0:19:17.760
<v Speaker 2>Great timing though.

0:19:17.800 --> 0:19:20.720
<v Speaker 4>A joy a bounty of news when you're sitting down

0:19:20.720 --> 0:19:24.920
<v Speaker 4>with a key executive. And Sarah fry is very clear

0:19:24.960 --> 0:19:27.119
<v Speaker 4>about what she needs at the moment. She needs money

0:19:27.160 --> 0:19:29.119
<v Speaker 4>for compute that continues. He has already got more than

0:19:29.119 --> 0:19:31.080
<v Speaker 4>one hundred and twenty billion dollars of it, and she's

0:19:31.080 --> 0:19:32.520
<v Speaker 4>got plenty of optionality.

0:19:32.840 --> 0:19:34.760
<v Speaker 5>We talked about her relationship with Sam Altman.

0:19:34.880 --> 0:19:38.560
<v Speaker 4>She's very What's so interesting is she's saying, Look, if

0:19:38.600 --> 0:19:41.480
<v Speaker 4>you want your CEO and CFO to be, you know,

0:19:41.800 --> 0:19:43.560
<v Speaker 4>always agreeing on absolutely.

0:19:43.080 --> 0:19:45.600
<v Speaker 5>Everything, you're sort of getting her wrong steer. That shouldn't

0:19:45.600 --> 0:19:47.320
<v Speaker 5>be how it works. But they have a really good

0:19:47.359 --> 0:19:48.280
<v Speaker 5>working relationship.

0:19:48.280 --> 0:19:50.879
<v Speaker 4>Look, she was just in California at his ranch on

0:19:50.920 --> 0:19:53.760
<v Speaker 4>the weekend because they're working extra time on things like Compute.

0:19:53.880 --> 0:19:55.120
<v Speaker 5>Here is a close relationship.

0:19:55.160 --> 0:19:57.080
<v Speaker 4>But yes, at times they have to disagree, so that

0:19:57.160 --> 0:20:00.400
<v Speaker 4>was an interesting discussion, particularly when we've had some heman's

0:20:00.440 --> 0:20:04.000
<v Speaker 4>own sort of way in which he presents himself and

0:20:04.040 --> 0:20:06.879
<v Speaker 4>the bill in the business being under the coals and

0:20:06.960 --> 0:20:07.680
<v Speaker 4>under the limelight.

0:20:07.880 --> 0:20:08.720
<v Speaker 5>But I think was notable.

0:20:08.720 --> 0:20:11.040
<v Speaker 4>I asked her about the open Ai and Apple relationship,

0:20:11.040 --> 0:20:12.720
<v Speaker 4>and of course she couldn't comment. But this is a

0:20:12.760 --> 0:20:16.200
<v Speaker 4>company that depends on partnerships to get their technology into

0:20:16.200 --> 0:20:16.959
<v Speaker 4>people's hands.

0:20:17.080 --> 0:20:18.080
<v Speaker 5>Apple is key for.

0:20:18.080 --> 0:20:20.480
<v Speaker 4>The CHATCHBT, but it was meant to be better integrated

0:20:20.520 --> 0:20:22.240
<v Speaker 4>into the overall Apple Intelligence experience.

0:20:22.280 --> 0:20:23.880
<v Speaker 2>We're going to get the detail on that just a set.

0:20:23.920 --> 0:20:25.960
<v Speaker 3>We're going to hear more from Kara's conversation with Sarah

0:20:25.960 --> 0:20:28.800
<v Speaker 3>Fryer later this hour. Stick around for that. Here's the

0:20:28.840 --> 0:20:31.880
<v Speaker 3>details Apple and Open Area's relationship is framed. Open Ai

0:20:32.000 --> 0:20:36.159
<v Speaker 3>is weighing possible legal action against the iPhone maker, arguing

0:20:36.240 --> 0:20:39.720
<v Speaker 3>it hasn't seen the expected benefits from the partnership and

0:20:39.760 --> 0:20:42.560
<v Speaker 3>it's Apple's use of its technology remains limited and hard

0:20:42.560 --> 0:20:44.640
<v Speaker 3>for users. Define let's get out to Bloomberg Senior Tech

0:20:44.720 --> 0:20:48.680
<v Speaker 3>editor Dana Wolman incredibly detailed report from the team. I

0:20:48.720 --> 0:20:51.240
<v Speaker 3>suppose what was that agreement initially?

0:20:52.320 --> 0:20:55.280
<v Speaker 13>So the two companies teamed up and really open Aiyes,

0:20:55.280 --> 0:20:57.760
<v Speaker 13>saw this as an opportunity to, as you said, get

0:20:57.840 --> 0:21:00.320
<v Speaker 13>its product as big of a name as it is

0:21:00.320 --> 0:21:03.240
<v Speaker 13>in front of even more people Apple's huge user base.

0:21:03.880 --> 0:21:07.240
<v Speaker 13>So open Air's technology would be built into Apple's platforms,

0:21:07.240 --> 0:21:09.879
<v Speaker 13>and for a lot of users, especially users who are

0:21:09.880 --> 0:21:13.240
<v Speaker 13>perhaps less tech savvy, it might have been their first

0:21:13.280 --> 0:21:16.560
<v Speaker 13>exposure to open AI's technology and open air. I was

0:21:16.600 --> 0:21:19.240
<v Speaker 13>hoping that this would result in billions of dollars worth

0:21:19.320 --> 0:21:21.040
<v Speaker 13>annually in new subscribers.

0:21:22.520 --> 0:21:24.639
<v Speaker 4>Let's talk about whether or not they have to be

0:21:24.680 --> 0:21:27.520
<v Speaker 4>forced with some legal action to get recompense here, but

0:21:27.600 --> 0:21:31.119
<v Speaker 4>more broadly, it's a time where Apple's own Apple Intelligence

0:21:31.160 --> 0:21:33.000
<v Speaker 4>is not working as they hope, having to depend on

0:21:33.040 --> 0:21:34.280
<v Speaker 4>Google to help build siry.

0:21:34.320 --> 0:21:36.479
<v Speaker 5>More broadly, are they just having to go to more

0:21:36.480 --> 0:21:37.560
<v Speaker 5>players in the ecosystem?

0:21:38.480 --> 0:21:40.879
<v Speaker 13>I'm sorry, can you repeat the question? I don't know

0:21:40.880 --> 0:21:41.840
<v Speaker 13>if I fully heard you.

0:21:42.480 --> 0:21:46.240
<v Speaker 4>How are we thinking about Apple's own dependence on other

0:21:46.359 --> 0:21:49.520
<v Speaker 4>offerings for its own Apple Intelligence that isn't working?

0:21:50.320 --> 0:21:52.800
<v Speaker 13>So Apple is going to be opening up its own

0:21:52.800 --> 0:21:56.520
<v Speaker 13>platforms to other developers as well, which surely is not

0:21:56.800 --> 0:21:59.560
<v Speaker 13>helping with the dynamic with open Ai. It's something that

0:21:59.600 --> 0:22:04.439
<v Speaker 13>Marker mentioned in his report. That said, and this has

0:22:04.440 --> 0:22:07.680
<v Speaker 13>come up in other of Bloomberg Technology's reports as well,

0:22:08.160 --> 0:22:12.040
<v Speaker 13>is that Apple is sort of benefiting from the investment

0:22:12.119 --> 0:22:15.120
<v Speaker 13>that all of these other AI developers have invested over

0:22:15.160 --> 0:22:17.600
<v Speaker 13>the years in AI. Now it's getting to sort of

0:22:17.640 --> 0:22:22.400
<v Speaker 13>integrate this menu of different increasingly advanced tools, as you said,

0:22:22.440 --> 0:22:26.280
<v Speaker 13>as it continues to build out its own much delayed

0:22:26.280 --> 0:22:28.640
<v Speaker 13>product of its own data.

0:22:28.680 --> 0:22:31.359
<v Speaker 4>Wollman, we so appreciate you, thank you for coming on

0:22:31.640 --> 0:22:33.399
<v Speaker 4>regarding open AI and Apple.

0:22:33.640 --> 0:22:36.800
<v Speaker 5>Coming right up, we're discussing AI in another area of Figma.

0:22:37.160 --> 0:22:39.679
<v Speaker 4>It is flipping the script on the AI disruption narrative.

0:22:39.800 --> 0:22:43.000
<v Speaker 4>CEO Dylan Fields joining us next after earnings were a blowout,

0:22:43.359 --> 0:22:46.480
<v Speaker 4>much better than expected, and so much for that disruption

0:22:46.720 --> 0:23:05.720
<v Speaker 4>vis Blue meg Tech. Welcome back to Bloomberg Tech, and

0:23:05.760 --> 0:23:08.359
<v Speaker 4>we've got to focus in on the company Figma. It

0:23:08.400 --> 0:23:11.560
<v Speaker 4>is defying those fears that AI would disrupt the design stack.

0:23:11.800 --> 0:23:14.159
<v Speaker 4>The company has just reported a massive first quarter with

0:23:14.200 --> 0:23:17.200
<v Speaker 4>revenue growth accelerating to forty six percent. It successfully begins

0:23:17.560 --> 0:23:21.159
<v Speaker 4>to monetize new AI features. Joining us now Figma founder

0:23:21.240 --> 0:23:26.240
<v Speaker 4>CEO Dylan Field. Dylan, Look, there has been many many

0:23:26.280 --> 0:23:29.520
<v Speaker 4>a question on software providers about the disruption that will

0:23:29.560 --> 0:23:32.760
<v Speaker 4>come from the likes of large language model frontim makers,

0:23:33.080 --> 0:23:36.359
<v Speaker 4>anthropic being one. What is driving your revenue growth and

0:23:36.400 --> 0:23:40.040
<v Speaker 4>the ability to exceed guidance as well well?

0:23:40.040 --> 0:23:41.640
<v Speaker 14>First of all, thank you for having me and good

0:23:41.640 --> 0:23:45.200
<v Speaker 14>to see you. And yeah, a strong quarter. We had.

0:23:45.200 --> 0:23:49.080
<v Speaker 14>Revenue accelerates forty six percent year every year, and our

0:23:49.160 --> 0:23:52.600
<v Speaker 14>net dour attention for customers that are over ten k

0:23:53.200 --> 0:23:56.480
<v Speaker 14>of ARR is now at one hundred and thirty nine percent.

0:23:56.800 --> 0:24:01.119
<v Speaker 14>And also strong cash flow with non up margin of

0:24:01.200 --> 0:24:04.040
<v Speaker 14>sixteen percent in the quarter in free cash flow of

0:24:04.040 --> 0:24:05.159
<v Speaker 14>twenty seven percent.

0:24:06.840 --> 0:24:09.760
<v Speaker 2>We also raise our guidance. So we're very glad with

0:24:09.800 --> 0:24:10.400
<v Speaker 2>the results.

0:24:10.960 --> 0:24:15.000
<v Speaker 14>And I think in terms of taking a step back

0:24:15.040 --> 0:24:19.119
<v Speaker 14>around what is behind the quarter and also the moment

0:24:19.160 --> 0:24:24.320
<v Speaker 14>you're mentioning as AI commanitizers code, it makes it so

0:24:24.400 --> 0:24:27.840
<v Speaker 14>that code is easier than ever to write, you know,

0:24:27.840 --> 0:24:31.520
<v Speaker 14>the layer above code as that gets commoitized is fine.

0:24:33.359 --> 0:24:35.080
<v Speaker 3>I got an audience question for you on that in

0:24:35.160 --> 0:24:37.760
<v Speaker 3>just a moment. But for me like that, I don't

0:24:37.760 --> 0:24:39.560
<v Speaker 3>know what you would call it, Like the credit caps

0:24:39.760 --> 0:24:42.720
<v Speaker 3>or the way you charge on usage was so fascinating.

0:24:42.760 --> 0:24:46.359
<v Speaker 3>So in March you started charging customers a fee to

0:24:46.560 --> 0:24:50.040
<v Speaker 3>use AI, in particular beyond a certain limit, and the

0:24:50.080 --> 0:24:54.399
<v Speaker 3>response is varied. Right when people hit that cap, many

0:24:54.440 --> 0:24:57.280
<v Speaker 3>were willing to pay for more credits. There was some

0:24:57.400 --> 0:24:59.439
<v Speaker 3>drop off though, like a small group was saying, well,

0:24:59.480 --> 0:25:03.000
<v Speaker 3>if that's the case, I won't use a Figma product anymore.

0:25:03.560 --> 0:25:05.320
<v Speaker 3>Where do you see that netting out?

0:25:06.000 --> 0:25:09.080
<v Speaker 14>Well, we have many products and you're always welcome to

0:25:09.720 --> 0:25:12.120
<v Speaker 14>you know, as a free user use one of our

0:25:12.119 --> 0:25:14.560
<v Speaker 14>free surfaces, or as a paid user of the seat,

0:25:15.560 --> 0:25:15.960
<v Speaker 14>use our.

0:25:15.880 --> 0:25:16.800
<v Speaker 2>Traditional design tool.

0:25:16.840 --> 0:25:18.919
<v Speaker 14>But yes, if you want to use the product we

0:25:19.000 --> 0:25:22.320
<v Speaker 14>have Figma make, we want to use AI features in

0:25:22.359 --> 0:25:27.199
<v Speaker 14>Figma design. What we did was we essentially added some

0:25:27.359 --> 0:25:30.600
<v Speaker 14>number of free credits to paid seats to make sure

0:25:30.600 --> 0:25:32.600
<v Speaker 14>that people had a way to try these features out.

0:25:33.440 --> 0:25:35.560
<v Speaker 14>And then we also made it to that if you

0:25:35.600 --> 0:25:38.280
<v Speaker 14>want to buy additional credits you can, And for a

0:25:38.280 --> 0:25:40.920
<v Speaker 14>long time we actually made it said everything was free.

0:25:40.960 --> 0:25:43.159
<v Speaker 14>But you know that's not exactly the move that us

0:25:43.359 --> 0:25:46.359
<v Speaker 14>do that forever and it does cost real money.

0:25:46.960 --> 0:25:47.399
<v Speaker 2>We also have.

0:25:47.400 --> 0:25:51.879
<v Speaker 14>Thima Weave, and weave is extremely exciting and with weave,

0:25:51.960 --> 0:25:56.240
<v Speaker 14>what you can do is essentially create a workflow, which

0:25:56.280 --> 0:25:59.360
<v Speaker 14>is a note through notebased editing tool where you connect

0:25:59.560 --> 0:26:06.760
<v Speaker 14>up different outputs from models I think images, videos, three

0:26:06.840 --> 0:26:09.880
<v Speaker 14>D models and more, and then you can push them

0:26:09.920 --> 0:26:12.480
<v Speaker 14>through a workflow so that you're able to really mold

0:26:12.480 --> 0:26:18.720
<v Speaker 14>those model outputs like clay. So for example, NBBJ, a

0:26:18.880 --> 0:26:22.880
<v Speaker 14>architecture firm. One of the customers we mentioned Durens Call.

0:26:23.480 --> 0:26:26.920
<v Speaker 14>They used to do these very extensive customer shoots where.

0:26:26.680 --> 0:26:29.160
<v Speaker 2>They would go out to site and they would.

0:26:29.040 --> 0:26:33.400
<v Speaker 14>Really understand what is the different lagging at different times

0:26:33.880 --> 0:26:36.600
<v Speaker 14>and they would then superimpose the three D model of

0:26:36.640 --> 0:26:39.879
<v Speaker 14>the building. And with Thigma weave they can do that

0:26:39.880 --> 0:26:42.160
<v Speaker 14>all in a workflow where they can just really easily

0:26:42.160 --> 0:26:45.560
<v Speaker 14>control all sorts of different parameters and it saves them

0:26:45.600 --> 0:26:48.399
<v Speaker 14>a ton of time and gets better results for the client.

0:26:48.880 --> 0:26:51.000
<v Speaker 14>So that's another one where we also see AA friend

0:26:51.000 --> 0:26:51.639
<v Speaker 14>SCIPT kick in.

0:26:53.640 --> 0:26:57.200
<v Speaker 4>You bring real anecdotal evidence to bear, and the anecdotical

0:26:57.240 --> 0:26:59.600
<v Speaker 4>elevidence we hear is like like if you ever try

0:26:59.600 --> 0:27:01.880
<v Speaker 4>and st wave figma from those that use it when in.

0:27:01.800 --> 0:27:03.280
<v Speaker 5>The workforce, they'll not leave.

0:27:03.320 --> 0:27:05.480
<v Speaker 4>Employees will like march out the building because they so

0:27:05.640 --> 0:27:06.320
<v Speaker 4>love the product.

0:27:06.400 --> 0:27:08.520
<v Speaker 5>But how do you then fight this narrative?

0:27:08.760 --> 0:27:11.960
<v Speaker 4>Then investors just want to sell first, ask questions later,

0:27:12.119 --> 0:27:14.560
<v Speaker 4>and has put your stock under pressure since the IPO.

0:27:16.960 --> 0:27:20.800
<v Speaker 14>I mean, I think we control the inputs and we

0:27:20.880 --> 0:27:23.440
<v Speaker 14>need to deliver for our customers, as simple as that.

0:27:24.119 --> 0:27:28.439
<v Speaker 14>And so we're working very hard always on making sure

0:27:28.600 --> 0:27:31.119
<v Speaker 14>that we're doing the right thing for the long term.

0:27:32.320 --> 0:27:38.960
<v Speaker 14>And I think that the long term is thankfully aligned

0:27:39.000 --> 0:27:41.520
<v Speaker 14>with our strategy. We're in the best position we think

0:27:41.560 --> 0:27:43.760
<v Speaker 14>we are basically can be. And you know, is I

0:27:43.920 --> 0:27:47.600
<v Speaker 14>becoming more important than ever? And I think that one

0:27:47.880 --> 0:27:50.520
<v Speaker 14>area when it comes to maybe the market or the world,

0:27:51.560 --> 0:27:54.360
<v Speaker 14>as we're seeing design go more broad in these companies

0:27:54.400 --> 0:27:57.840
<v Speaker 14>and also be more appreciated as the way that you win,

0:27:58.040 --> 0:28:00.439
<v Speaker 14>but also what you break through a very head of

0:28:00.480 --> 0:28:01.720
<v Speaker 14>information landscape.

0:28:02.520 --> 0:28:04.119
<v Speaker 2>You have to really people what design is.

0:28:04.240 --> 0:28:07.960
<v Speaker 14>It's not just you know, creating something that you know

0:28:08.080 --> 0:28:10.800
<v Speaker 14>you think is beautiful because you know, many people have

0:28:10.800 --> 0:28:14.960
<v Speaker 14>different aesthetics. It's how it works, it's ux, it's forum,

0:28:14.960 --> 0:28:15.439
<v Speaker 14>its function.

0:28:15.720 --> 0:28:17.640
<v Speaker 2>Yeah, and we have.

0:28:17.560 --> 0:28:21.639
<v Speaker 14>To I think, really help people understand the many different

0:28:21.640 --> 0:28:25.359
<v Speaker 14>facets of design and Enfigma, design is not always just

0:28:25.960 --> 0:28:28.280
<v Speaker 14>you know, how it looks, how it works. It's also

0:28:28.320 --> 0:28:33.639
<v Speaker 14>the thinking process out there, and I think in this world, Dyaling, Yes, sorry,

0:28:33.680 --> 0:28:34.399
<v Speaker 14>I don't.

0:28:34.280 --> 0:28:34.800
<v Speaker 2>Mean to cut you off.

0:28:34.880 --> 0:28:36.240
<v Speaker 3>We're running out of time, and I want to get

0:28:36.240 --> 0:28:38.000
<v Speaker 3>that audience question to you because, as you know, I

0:28:38.000 --> 0:28:41.040
<v Speaker 3>think it's really important regular of the show, Ben, how

0:28:41.080 --> 0:28:44.840
<v Speaker 3>do you see inference and token costs affecting your margins

0:28:44.840 --> 0:28:45.479
<v Speaker 3>going forward?

0:28:45.680 --> 0:28:47.040
<v Speaker 2>You kind of alluded to it earlier.

0:28:47.680 --> 0:28:49.600
<v Speaker 14>Yeah, we talked about in our Orange call yesterday and

0:28:49.600 --> 0:28:53.240
<v Speaker 14>how if we see an opportunity to go really big

0:28:53.400 --> 0:28:55.840
<v Speaker 14>and have a ton of growth, we will take it

0:28:55.920 --> 0:28:58.600
<v Speaker 14>and we will push hard. But I think there's a

0:28:58.640 --> 0:29:02.760
<v Speaker 14>short term, there's the long term, and sometimes there's ways

0:29:02.800 --> 0:29:05.840
<v Speaker 14>to push hard on short term and that might create

0:29:05.920 --> 0:29:08.840
<v Speaker 14>downward pressure and margins. But if you're going for a

0:29:08.920 --> 0:29:11.840
<v Speaker 14>massive TAM in the long term, I think that's the

0:29:11.880 --> 0:29:13.880
<v Speaker 14>right move in what our investors should be cheering us

0:29:13.880 --> 0:29:18.200
<v Speaker 14>on to do. And the TAM is very large both

0:29:18.240 --> 0:29:25.880
<v Speaker 14>for design, for sculpting, you know, advertising marketing and breaking

0:29:25.880 --> 0:29:29.480
<v Speaker 14>through noise, and I think that in general, if we're

0:29:29.560 --> 0:29:32.080
<v Speaker 14>able to deliver on that and able to win this

0:29:32.320 --> 0:29:37.640
<v Speaker 14>increasingly competitive landscape which is growing so fast of design

0:29:37.720 --> 0:29:41.360
<v Speaker 14>when it's the new code. I think that puts us

0:29:41.360 --> 0:29:44.200
<v Speaker 14>in an amazing edition for the future. So very excited

0:29:44.200 --> 0:29:46.320
<v Speaker 14>to bringing more people into the design processes. What we're

0:29:46.320 --> 0:29:48.840
<v Speaker 14>seeing with our customers, it's not just designers, it's many

0:29:48.880 --> 0:29:51.440
<v Speaker 14>others as well, but also level them up on design.

0:29:52.560 --> 0:29:54.880
<v Speaker 3>Figmacia didn't feel back on Bloomberg Tech.

0:29:55.080 --> 0:29:56.520
<v Speaker 2>Thank you very much for joining us.

0:29:56.520 --> 0:29:59.959
<v Speaker 3>Now coming up, Figure is putting their humanoid robots out

0:30:00.200 --> 0:30:03.240
<v Speaker 3>the lab and onto the live stream. CEO Brett Adcock

0:30:03.280 --> 0:30:14.520
<v Speaker 3>with us next. This is Bloomberg Tech. Figure says its

0:30:14.600 --> 0:30:17.600
<v Speaker 3>humanoid robots just completed more than twenty four hours of

0:30:17.680 --> 0:30:22.320
<v Speaker 3>continuous package sorting autonomously, a live stream watched by millions

0:30:22.560 --> 0:30:27.600
<v Speaker 3>across YouTube and X three FO three robots worked in shifts, scanning, flipping,

0:30:27.640 --> 0:30:32.040
<v Speaker 3>sorting packages at roughly human speed, all powered by its

0:30:32.080 --> 0:30:34.959
<v Speaker 3>in house AI software running directly on board the robots.

0:30:35.000 --> 0:30:38.400
<v Speaker 3>But that demonstration did spark some skepticism was it real?

0:30:39.040 --> 0:30:42.640
<v Speaker 3>Joining us now, as Brett Adcock founder CEO A Figure,

0:30:42.680 --> 0:30:47.080
<v Speaker 3>that's where we start, you can say, Brett definitively. Over

0:30:47.120 --> 0:30:50.120
<v Speaker 3>the twenty four hours or more, there was no teleoperation.

0:30:50.440 --> 0:30:52.239
<v Speaker 3>A lot of people in the comments, as you know,

0:30:52.880 --> 0:30:54.720
<v Speaker 3>pointed to the idea, and I think we had video

0:30:54.760 --> 0:30:59.640
<v Speaker 3>a bit that the three on shift kept gesturing to

0:30:59.680 --> 0:31:03.280
<v Speaker 3>the head, which is a tailtale sign in robotics of teleoperation.

0:31:04.000 --> 0:31:05.479
<v Speaker 2>Your pledge that there was none.

0:31:06.240 --> 0:31:09.280
<v Speaker 12>There's absolutely no telly operation into this. The robots are

0:31:09.280 --> 0:31:13.200
<v Speaker 12>all operating fully autonomously using an onboard neural network redesign

0:31:13.280 --> 0:31:17.000
<v Speaker 12>called Helix two. Sometimes when the robot takes a turn

0:31:17.080 --> 0:31:19.560
<v Speaker 12>to left to grab packages, it moves its left hand

0:31:19.560 --> 0:31:22.160
<v Speaker 12>out of the way upwards. You'll see this behavior happen

0:31:22.240 --> 0:31:24.280
<v Speaker 12>every single time the robot turns for packages.

0:31:24.960 --> 0:31:25.360
<v Speaker 7>But we've been.

0:31:25.320 --> 0:31:29.600
<v Speaker 12>Running autonomously now for close to fifty hours, the robots

0:31:29.640 --> 0:31:33.280
<v Speaker 12>operating shifts. There's been basically almost no downtime on the belt.

0:31:34.240 --> 0:31:38.880
<v Speaker 12>We've pushed over close to sixty thousand packages and we're

0:31:38.920 --> 0:31:40.560
<v Speaker 12>just going to keep going now and see how far

0:31:40.640 --> 0:31:41.080
<v Speaker 12>this can go.

0:31:41.960 --> 0:31:44.120
<v Speaker 3>So this was live streams, right, and that was one

0:31:44.160 --> 0:31:45.760
<v Speaker 3>reason I really wanted you to come on the program,

0:31:45.800 --> 0:31:49.680
<v Speaker 3>because there's the bit people don't see what's happening behind

0:31:49.720 --> 0:31:53.760
<v Speaker 3>the scenes, like in shift changes, where does the robot go?

0:31:54.200 --> 0:31:55.480
<v Speaker 2>Does it need maintenance?

0:31:57.560 --> 0:31:59.680
<v Speaker 12>For the most part, the robots operate on a four

0:31:59.680 --> 0:32:03.800
<v Speaker 12>hour battery life. After the battery is low, the robot

0:32:03.840 --> 0:32:06.640
<v Speaker 12>messages another robot to come out to take its place.

0:32:07.040 --> 0:32:09.960
<v Speaker 12>The robots didn't do a swap. The robot has just left.

0:32:09.960 --> 0:32:13.280
<v Speaker 12>The conveyor system is going to go charge wirelessly, understand

0:32:13.680 --> 0:32:16.600
<v Speaker 12>while the other robot continues to do work. If there

0:32:16.640 --> 0:32:20.440
<v Speaker 12>are issues, say we have hardware software issues, the robots

0:32:20.480 --> 0:32:22.720
<v Speaker 12>can basically walk off into maintenance and call another robot

0:32:22.760 --> 0:32:24.840
<v Speaker 12>to take its place. The goal is to be able

0:32:24.880 --> 0:32:29.160
<v Speaker 12>to enlistit twenty four to seven operations with basically no

0:32:29.360 --> 0:32:31.760
<v Speaker 12>like no failures on the on the use case itself,

0:32:32.080 --> 0:32:35.000
<v Speaker 12>which we haven't had today. So the robots basically the

0:32:35.040 --> 0:32:37.440
<v Speaker 12>conveyor system has been running twenty four to seven since

0:32:37.560 --> 0:32:40.160
<v Speaker 12>like middle of this week. I think we're now we're

0:32:40.160 --> 0:32:43.560
<v Speaker 12>approaching fifty hours of just full like every single hour

0:32:43.680 --> 0:32:46.880
<v Speaker 12>since since we've launched, the robots have been basically I've

0:32:46.920 --> 0:32:49.640
<v Speaker 12>been doing work now on this line, which.

0:32:49.440 --> 0:32:51.520
<v Speaker 2>I think is like wow, which I think is crazy.

0:32:51.560 --> 0:32:54.120
<v Speaker 12>You know, Figure wants to build like you know, we

0:32:54.120 --> 0:32:55.800
<v Speaker 12>want to build like I robot. You know, we want

0:32:55.880 --> 0:32:58.800
<v Speaker 12>robots everywhere in the world in the commercial market, Like

0:32:59.200 --> 0:33:01.720
<v Speaker 12>this is like the first large step to doing that.

0:33:02.920 --> 0:33:07.880
<v Speaker 4>Okay, So what's harder now robots getting faster or making

0:33:07.880 --> 0:33:09.640
<v Speaker 4>them even more reliable, because at the moment you seem

0:33:09.640 --> 0:33:10.280
<v Speaker 4>to be doing both.

0:33:12.160 --> 0:33:14.800
<v Speaker 12>The robot that you're seeing here is roughly operating around

0:33:14.840 --> 0:33:17.200
<v Speaker 12>human speech just about three seconds of package. That's the

0:33:17.240 --> 0:33:20.160
<v Speaker 12>requirement to operate on this logistics line. So we're at

0:33:20.240 --> 0:33:23.440
<v Speaker 12>like we're at human parity and speed. The goal is

0:33:23.480 --> 0:33:26.760
<v Speaker 12>also to have like ninety percent success rate on the

0:33:26.800 --> 0:33:30.680
<v Speaker 12>bark the package flips for barcode scanning. We're in that

0:33:31.000 --> 0:33:35.000
<v Speaker 12>as well. In that requirement, the robots are also like

0:33:35.400 --> 0:33:38.640
<v Speaker 12>getting extremely reliable. Like part of this whole process of

0:33:38.680 --> 0:33:40.800
<v Speaker 12>running this twenty four to seven with no downtime is

0:33:40.840 --> 0:33:43.920
<v Speaker 12>to show how reliable humanoid robots are. And four years

0:33:43.920 --> 0:33:47.240
<v Speaker 12>ago when I started the company, humanoid robots were falling.

0:33:47.440 --> 0:33:51.320
<v Speaker 12>They were extremely unreliable systems. We've designed the systems and

0:33:51.360 --> 0:33:53.360
<v Speaker 12>engineered this now to a point where the robots are

0:33:53.800 --> 0:33:56.040
<v Speaker 12>I think extremely reliable. I think we're showing that now

0:33:56.040 --> 0:33:59.120
<v Speaker 12>in the live stream to the entire world. The big

0:33:59.160 --> 0:34:01.040
<v Speaker 12>focus for us is is like how do we solve

0:34:01.080 --> 0:34:04.280
<v Speaker 12>for a truly general purpose machine, and then how do

0:34:04.320 --> 0:34:08.400
<v Speaker 12>we manufacture unprecedented volume similar to cell phones? Today, so like,

0:34:09.160 --> 0:34:12.760
<v Speaker 12>so this week back que our manufacturing facility will manufacture

0:34:12.760 --> 0:34:16.879
<v Speaker 12>anywhere between like sixty and seventy humanoid robots just this week, right,

0:34:16.920 --> 0:34:20.000
<v Speaker 12>and we do it right next door on the Figure campus.

0:34:20.600 --> 0:34:22.120
<v Speaker 3>So brat, I want to get into the idea, this

0:34:22.160 --> 0:34:25.799
<v Speaker 3>is full stack. I've been hearing a lot growing speculation

0:34:25.960 --> 0:34:29.319
<v Speaker 3>that open ai could get back into robotics. And you

0:34:29.360 --> 0:34:31.640
<v Speaker 3>have a history white Way, you had a partnership, you

0:34:31.719 --> 0:34:34.480
<v Speaker 3>decided on the software side you could do better yourself.

0:34:34.960 --> 0:34:36.440
<v Speaker 2>You know, what do you make of that?

0:34:36.440 --> 0:34:40.920
<v Speaker 3>That idea that ultimately a big party is going to

0:34:40.960 --> 0:34:44.399
<v Speaker 3>want to own the entire stack that powers the humanoid robot.

0:34:46.360 --> 0:34:48.319
<v Speaker 12>To really do this right, like if we want to

0:34:48.360 --> 0:34:52.799
<v Speaker 12>really build like I robot like the movie, yes, you

0:34:52.840 --> 0:34:56.040
<v Speaker 12>know we basically you have to design the entire hardware

0:34:56.080 --> 0:35:01.239
<v Speaker 12>system almost yourself, like motors like stater rotors, the electromagnetics work, like,

0:35:01.280 --> 0:35:03.000
<v Speaker 12>you have to do all the battery systems work. You

0:35:03.000 --> 0:35:06.600
<v Speaker 12>have to do all the actuator design, sensor design, kinematics

0:35:06.719 --> 0:35:09.440
<v Speaker 12>like structures like which we do all in house now

0:35:09.600 --> 0:35:13.200
<v Speaker 12>here at Figure. We also manufacture the robots, so we're

0:35:13.200 --> 0:35:15.360
<v Speaker 12>like and then we also test them and we do

0:35:15.440 --> 0:35:17.400
<v Speaker 12>all the AI data collection and all the AI and

0:35:17.400 --> 0:35:21.160
<v Speaker 12>neural net training ourselves here in house. So basically, this

0:35:21.200 --> 0:35:24.480
<v Speaker 12>is a full end to end vertically integrated system that

0:35:24.520 --> 0:35:27.440
<v Speaker 12>we now have out doing real use case work that

0:35:27.680 --> 0:35:30.760
<v Speaker 12>humans do, and we can do this at human speeds.

0:35:31.440 --> 0:35:34.120
<v Speaker 12>And we're doing this now for like you know this example,

0:35:34.680 --> 0:35:36.719
<v Speaker 12>most of these shifts run like this eight hours a day.

0:35:37.960 --> 0:35:40.880
<v Speaker 12>We're doing this like twenty four to seven, just to

0:35:40.920 --> 0:35:43.960
<v Speaker 12>show how reliable the systems are and how like mission

0:35:43.960 --> 0:35:47.120
<v Speaker 12>ready these things are to get out at scale. So

0:35:47.200 --> 0:35:50.080
<v Speaker 12>anyway to solve this you have to have like a

0:35:50.120 --> 0:35:53.600
<v Speaker 12>truly vertically integrated approach from top to bottom.

0:35:54.080 --> 0:35:56.440
<v Speaker 4>What about the bottlenecks? View is in our money do

0:35:56.480 --> 0:35:57.280
<v Speaker 4>you want to go public?

0:35:57.320 --> 0:35:57.600
<v Speaker 5>Brat?

0:35:57.600 --> 0:35:57.960
<v Speaker 8>What is?

0:35:58.080 --> 0:35:59.440
<v Speaker 5>What are you needing to get this out?

0:35:59.520 --> 0:36:04.799
<v Speaker 12>More broadly, our largest two bottlenecks are like data for

0:36:04.880 --> 0:36:08.600
<v Speaker 12>pre training, our helix, neural net and UH in manufacturing,

0:36:09.520 --> 0:36:14.080
<v Speaker 12>we're spinning up manufacturing here our manufacturing facilities called BATQ.

0:36:15.480 --> 0:36:18.719
<v Speaker 12>We've we're now doing we're now out of several like

0:36:18.800 --> 0:36:22.880
<v Speaker 12>thousands of run rate annually production that is continuing to

0:36:22.880 --> 0:36:23.400
<v Speaker 12>scale up.

0:36:24.000 --> 0:36:24.160
<v Speaker 5>UH.

0:36:24.680 --> 0:36:27.960
<v Speaker 12>You know, I think UH on the data side, we're

0:36:28.000 --> 0:36:34.840
<v Speaker 12>collecting and training, uh, kind of unprecedented models for like

0:36:34.880 --> 0:36:37.319
<v Speaker 12>our AI stack here internally that we've ever done.

0:36:38.000 --> 0:36:38.200
<v Speaker 7>Uh.

0:36:38.239 --> 0:36:40.520
<v Speaker 12>So I think we're like we're and we have you know,

0:36:40.600 --> 0:36:42.160
<v Speaker 12>we have well over a billion dollars of cash in

0:36:42.200 --> 0:36:44.759
<v Speaker 12>the balance sheet today. So I think from a from

0:36:44.800 --> 0:36:47.560
<v Speaker 12>a financial perspective, we're in a good spot. We're manufacturing

0:36:47.560 --> 0:36:50.719
<v Speaker 12>at pretty much unprecedented volumes for ourselves, and we're we're

0:36:50.719 --> 0:36:53.840
<v Speaker 12>building like next generation AI models that I think are uh,

0:36:53.920 --> 0:36:56.560
<v Speaker 12>to be honest, are just completely mind blowing. And so

0:36:56.880 --> 0:36:58.879
<v Speaker 12>the goal is like the goals to solve the data

0:36:58.880 --> 0:37:01.359
<v Speaker 12>problem and the manufacturing problem, to get humiliar robots out

0:37:01.360 --> 0:37:02.400
<v Speaker 12>of scale.

0:37:02.640 --> 0:37:04.640
<v Speaker 4>And maybe come up with even more names for these

0:37:04.840 --> 0:37:08.080
<v Speaker 4>robots currently getting them from online fans. I think one's

0:37:08.120 --> 0:37:11.200
<v Speaker 4>called Bob and Gary Brett Gadcock. We so appreciate your

0:37:11.239 --> 0:37:13.680
<v Speaker 4>time today, Founder and CEO of Figure.

0:37:14.400 --> 0:37:16.440
<v Speaker 5>Now let's send our attention to Annie Jasse. It was

0:37:16.440 --> 0:37:17.760
<v Speaker 5>once Jeff Bezos's deputy.

0:37:17.800 --> 0:37:20.279
<v Speaker 4>Remember, now five years into his tenure as CEO, Jesse

0:37:20.440 --> 0:37:21.799
<v Speaker 4>is steering the company through some of.

0:37:21.760 --> 0:37:25.319
<v Speaker 5>Its greatest changes. That's the focus of today's big take most.

0:37:25.360 --> 0:37:26.480
<v Speaker 5>Matt Day joins us.

0:37:26.520 --> 0:37:29.640
<v Speaker 4>With more You went to see sort of the new

0:37:29.760 --> 0:37:33.279
<v Speaker 4>real focus point for Annie Jasse. It's data centers, and

0:37:33.719 --> 0:37:36.440
<v Speaker 4>this is a company that is now just scaling so

0:37:36.560 --> 0:37:39.120
<v Speaker 4>much in a vertically integrated manner. Matt, what did you

0:37:39.239 --> 0:37:41.680
<v Speaker 4>learn from visiting the hubs and what it says about Andy?

0:37:42.640 --> 0:37:45.120
<v Speaker 15>But it says that there's just such sprawl to Amazon

0:37:45.120 --> 0:37:46.920
<v Speaker 15>these days. You think you know Amazon from the package

0:37:46.920 --> 0:37:49.080
<v Speaker 15>is showing up at your doorstep. So much of what

0:37:49.120 --> 0:37:51.680
<v Speaker 15>they're spending money on it's data centers. It's a supply

0:37:51.760 --> 0:37:54.800
<v Speaker 15>chain behind data centers. That means chips, that means hardware engineering,

0:37:54.840 --> 0:37:57.200
<v Speaker 15>that means software at the large language model level.

0:37:57.400 --> 0:37:59.120
<v Speaker 2>They're really just all over the place.

0:37:58.880 --> 0:38:01.400
<v Speaker 15>And it's kind of an unfathomable sort of things they

0:38:01.440 --> 0:38:03.759
<v Speaker 15>got going on, and really the sort of single organizational

0:38:03.800 --> 0:38:05.799
<v Speaker 15>principle behind it is Andy at the top of it.

0:38:06.000 --> 0:38:07.560
<v Speaker 15>He's making all these calls on where they got to

0:38:07.560 --> 0:38:09.000
<v Speaker 15>shift money with the comple and stuff out of what

0:38:09.000 --> 0:38:11.440
<v Speaker 15>they're putting into. It's really really an impressive machine.

0:38:12.120 --> 0:38:14.279
<v Speaker 3>So Matt, knowing that he's watching right now and he

0:38:14.360 --> 0:38:15.839
<v Speaker 3>Jesse is glued to Bloomberg Tech.

0:38:15.840 --> 0:38:17.360
<v Speaker 2>There is a good chance that's true.

0:38:17.719 --> 0:38:20.920
<v Speaker 3>What do we learn about him is the Amazon CEO

0:38:21.120 --> 0:38:22.880
<v Speaker 3>versus Jeff Like you and I've talked about this in

0:38:22.960 --> 0:38:25.680
<v Speaker 3>the past, But what's different about him? Does he lean

0:38:25.719 --> 0:38:27.239
<v Speaker 3>hard into the AWS thing?

0:38:28.480 --> 0:38:29.160
<v Speaker 2>You definitely did.

0:38:29.160 --> 0:38:30.800
<v Speaker 15>And if you look at where their bets are today,

0:38:31.360 --> 0:38:32.719
<v Speaker 15>you know a lot of them are, and what can

0:38:32.760 --> 0:38:35.600
<v Speaker 15>Amazon do uniquely and what can they do differently when

0:38:35.600 --> 0:38:37.279
<v Speaker 15>it comes to their core retail business. So they've left

0:38:37.280 --> 0:38:40.120
<v Speaker 15>some opportunities on the table there. That's not the case

0:38:40.160 --> 0:38:42.239
<v Speaker 15>in AI. They want to be everywhere in AI. They

0:38:42.239 --> 0:38:44.640
<v Speaker 15>want to sprinkle AI through all of their product lines,

0:38:45.000 --> 0:38:46.520
<v Speaker 15>and that is where they're spending a ton of their

0:38:46.600 --> 0:38:48.680
<v Speaker 15>their capex right now. So he's definitely brought some of

0:38:48.719 --> 0:38:51.759
<v Speaker 15>that AWS pedigree with him. We've also just learned he

0:38:51.840 --> 0:38:54.080
<v Speaker 15>is he is an extreme detail guy. It's not that

0:38:54.080 --> 0:38:56.040
<v Speaker 15>that was not a Jeff Bezos trade, but for the

0:38:56.120 --> 0:38:59.040
<v Speaker 15>last few years of his leadership at Amazon, Jeff was

0:38:59.120 --> 0:39:01.240
<v Speaker 15>checked out and some of the business. You know, Andy

0:39:01.280 --> 0:39:03.480
<v Speaker 15>hasn't let go of anything since he showed up. He's

0:39:03.480 --> 0:39:04.960
<v Speaker 15>all over the details of this business.

0:39:06.160 --> 0:39:09.040
<v Speaker 3>Blue Most Matt Day with the big take on Andy

0:39:09.120 --> 0:39:13.880
<v Speaker 3>jase Era at AWS and Amazon must coming up arguments

0:39:13.880 --> 0:39:16.440
<v Speaker 3>in the trial between Open AI and Elon Musk wrapped

0:39:16.520 --> 0:39:18.920
<v Speaker 3>up yesterday. We're going to get to the latest and

0:39:18.920 --> 0:39:20.879
<v Speaker 3>some more of the conversation we've been having with open

0:39:20.920 --> 0:39:23.960
<v Speaker 3>Ai the past twenty four hours. This is Bloomberg Tech.

0:39:31.400 --> 0:39:34.719
<v Speaker 4>Opening Eyes chief financial Officer Sir Fran talked about the

0:39:34.800 --> 0:39:37.759
<v Speaker 4>demand the company is seeing and the compute needs it's

0:39:37.840 --> 0:39:40.040
<v Speaker 4>up against. She sat down with me at the Cancello

0:39:40.320 --> 0:39:41.600
<v Speaker 4>Spark summit in Napa.

0:39:41.840 --> 0:39:42.400
<v Speaker 5>Take a listen.

0:39:43.080 --> 0:39:46.680
<v Speaker 16>Compute itself has clearly been a bottleneck. I don't think

0:39:46.719 --> 0:39:48.640
<v Speaker 16>any of us, even Sam and Greg, who I think

0:39:48.680 --> 0:39:52.800
<v Speaker 16>we're incredibly prescient overall, in terms of the need for compute,

0:39:52.920 --> 0:39:55.120
<v Speaker 16>I don't think they could have foreseen just the sheer

0:39:55.160 --> 0:39:57.959
<v Speaker 16>demand that we have right now in twenty twenty six,

0:39:58.440 --> 0:40:01.279
<v Speaker 16>and so that it's the energy that sits behind it.

0:40:01.520 --> 0:40:04.799
<v Speaker 16>We're seeing memory out of Southeast Asia where there's all

0:40:04.800 --> 0:40:07.200
<v Speaker 16>of these chok points in the supply chain, and this

0:40:07.239 --> 0:40:09.000
<v Speaker 16>is why it behooves us to get ahead of that,

0:40:09.320 --> 0:40:12.920
<v Speaker 16>strike those partnerships earlier when people are maybe not seeing

0:40:13.040 --> 0:40:14.040
<v Speaker 16>just the sheer scale.

0:40:14.640 --> 0:40:17.799
<v Speaker 4>Let's talk about partnership and about some of the people

0:40:17.840 --> 0:40:21.440
<v Speaker 4>you just talked about, Quite Brockman and Sam Altman.

0:40:22.160 --> 0:40:23.520
<v Speaker 5>They've had a tough week.

0:40:23.520 --> 0:40:27.120
<v Speaker 4>In terms of being in trial, having their relationships sort

0:40:27.160 --> 0:40:27.960
<v Speaker 4>of cast.

0:40:27.680 --> 0:40:31.440
<v Speaker 5>Into the limelight. What is this like working with Sam?

0:40:31.480 --> 0:40:34.879
<v Speaker 4>There's been some questioning as to how you work alongside him,

0:40:35.080 --> 0:40:35.920
<v Speaker 4>Do you work well?

0:40:36.040 --> 0:40:37.960
<v Speaker 5>Is it a strong bond? Is he trustworthy?

0:40:38.120 --> 0:40:38.440
<v Speaker 8>Is it?

0:40:38.520 --> 0:40:41.319
<v Speaker 4>Seemed to have been grilled by certain lawyers of Elon

0:40:41.440 --> 0:40:42.200
<v Speaker 4>Musk this week.

0:40:42.360 --> 0:40:44.759
<v Speaker 16>So I think mister Musk is very much out to

0:40:44.880 --> 0:40:48.480
<v Speaker 16>just distract, and we are just staying the course, like

0:40:48.640 --> 0:40:52.080
<v Speaker 16>building our technology, putting it into customers' hands, and really

0:40:52.120 --> 0:40:55.200
<v Speaker 16>creating agi for the benefit of humanity. Working with Sam

0:40:55.280 --> 0:40:58.120
<v Speaker 16>is great. We get on incredibly well. I think we

0:40:58.200 --> 0:41:02.120
<v Speaker 16>have a wonderful partnership, super good ying and Yang Sam

0:41:02.200 --> 0:41:02.640
<v Speaker 16>push us.

0:41:02.719 --> 0:41:03.760
<v Speaker 5>He's super curious.

0:41:04.080 --> 0:41:07.040
<v Speaker 16>My job is to take that curiosity and create optionality.

0:41:07.280 --> 0:41:09.239
<v Speaker 16>So just as we talked about whether it's making sure

0:41:09.280 --> 0:41:11.600
<v Speaker 16>we have enough compute, making sure we have the funds

0:41:11.680 --> 0:41:13.800
<v Speaker 16>to do it, makeing sure we build the business strongly.

0:41:14.160 --> 0:41:16.320
<v Speaker 16>And I spend an an ordinate amount of time with customers,

0:41:16.400 --> 0:41:18.359
<v Speaker 16>as does Sam, and we get to compare a lot

0:41:18.360 --> 0:41:21.080
<v Speaker 16>of notes on that front, as well as sometimes our

0:41:21.160 --> 0:41:26.160
<v Speaker 16>nuts on parenting. So it goes the whole gamut frankly Open.

0:41:25.960 --> 0:41:29.600
<v Speaker 3>AI CFO Sarah Fry speaking with Caroline about her relationship

0:41:29.640 --> 0:41:32.880
<v Speaker 3>with the company's CEO, Sam Altman. The relationships and choices

0:41:33.239 --> 0:41:36.160
<v Speaker 3>of open ais founders have been front and center of

0:41:36.200 --> 0:41:39.520
<v Speaker 3>the lawsuit brought by Elon Musk over the charitable status

0:41:39.520 --> 0:41:42.320
<v Speaker 3>of the company. Arguments in the trial wrapped up yesterday.

0:41:42.680 --> 0:41:46.080
<v Speaker 3>Bloombergs Madelin Meckelberg has been there every step of the way,

0:41:46.360 --> 0:41:49.279
<v Speaker 3>and so this is where we're at. Closing arguments we

0:41:49.320 --> 0:41:52.880
<v Speaker 3>wait for. I think jury deliberation Monday give us everything

0:41:52.920 --> 0:41:54.160
<v Speaker 3>that we need to know at this point.

0:41:55.400 --> 0:41:57.759
<v Speaker 6>That's right. I think one thing that we learned from

0:41:57.760 --> 0:42:00.920
<v Speaker 6>closing arguments, it's really relevant to the conversation you just

0:42:00.960 --> 0:42:04.440
<v Speaker 6>had with Sarah, is that this case, for both parties,

0:42:04.680 --> 0:42:08.040
<v Speaker 6>really boils down to credibility. That's the message that they

0:42:08.160 --> 0:42:11.640
<v Speaker 6>left jurors with on Thursday when they had closing arguments.

0:42:12.080 --> 0:42:15.000
<v Speaker 6>Musk's attorneys are trying to cast Sam Altman as someone

0:42:15.000 --> 0:42:19.480
<v Speaker 6>who's deceptive, who's untrustworthy. Meanwhile, lawyers for Open Ai were

0:42:19.520 --> 0:42:21.920
<v Speaker 6>trying to point to the evidence that's been presented so

0:42:22.040 --> 0:42:25.319
<v Speaker 6>far in the case, saying Musk's story doesn't add up

0:42:25.400 --> 0:42:27.399
<v Speaker 6>with what we've seen in the documents, what you've heard

0:42:27.400 --> 0:42:30.320
<v Speaker 6>from witnesses, and so now it's up to the jury

0:42:30.360 --> 0:42:33.840
<v Speaker 6>to decide whose version of events they believe about this

0:42:34.040 --> 0:42:38.000
<v Speaker 6>saga of the formation of open Ai, the dissolution of

0:42:38.080 --> 0:42:42.560
<v Speaker 6>this relationship and the transition into rivalry between Musk and

0:42:42.600 --> 0:42:44.360
<v Speaker 6>Altman that we see playing out today.

0:42:45.680 --> 0:42:48.040
<v Speaker 5>What happens if Musk wins.

0:42:49.600 --> 0:42:52.800
<v Speaker 6>That's a big question. So at this point, the jury

0:42:52.840 --> 0:42:55.040
<v Speaker 6>is going to start deliberating and they're going to issue

0:42:55.080 --> 0:42:59.640
<v Speaker 6>a verdict that's advisory, so it's a non binding recommendation

0:42:59.719 --> 0:43:01.719
<v Speaker 6>to the judge in this case, who's going to have

0:43:01.800 --> 0:43:04.960
<v Speaker 6>the final say on whether or not Musk has enough

0:43:05.000 --> 0:43:08.080
<v Speaker 6>to prove his claims here. And Musk is asking for

0:43:08.120 --> 0:43:10.359
<v Speaker 6>a lot. He's asking for one hundred and thirty four

0:43:10.360 --> 0:43:12.879
<v Speaker 6>billion dollars in d images that he says he's going

0:43:12.920 --> 0:43:16.000
<v Speaker 6>to donate to the Open Ai Foundation. He's asking for

0:43:16.040 --> 0:43:19.080
<v Speaker 6>Altman and Brockman to be removed from their jobs, and

0:43:19.120 --> 0:43:22.000
<v Speaker 6>he's asking for the open Ai come up company that's

0:43:22.000 --> 0:43:25.120
<v Speaker 6>been formed to be transitioned back to a nonprofit. These

0:43:25.120 --> 0:43:27.640
<v Speaker 6>are huge asks. The judge is also going to hear

0:43:27.760 --> 0:43:31.359
<v Speaker 6>arguments on Wednesday about which one of those might be realistic,

0:43:32.040 --> 0:43:34.359
<v Speaker 6>and we're just going to kind of have to wait

0:43:34.400 --> 0:43:36.440
<v Speaker 6>and see how she comes down on the main question

0:43:36.560 --> 0:43:38.600
<v Speaker 6>before we get to that. But the stakes are huge

0:43:38.640 --> 0:43:40.640
<v Speaker 6>and ex essential essentially for open.

0:43:40.480 --> 0:43:44.680
<v Speaker 4>Ai b mags Mann, Mackelberg, fascinating.

0:43:44.800 --> 0:43:45.120
<v Speaker 5>Thank you.

0:43:45.280 --> 0:43:47.160
<v Speaker 4>I mean, we wait with bated breath for the rest

0:43:47.200 --> 0:43:49.919
<v Speaker 4>of the week and what indeed happens in that court case.

0:43:49.920 --> 0:43:51.160
<v Speaker 5>But he'say, there's some.

0:43:51.080 --> 0:43:53.319
<v Speaker 3>Confidence world is part about it is that court case

0:43:53.320 --> 0:43:55.600
<v Speaker 3>happened in the context of the week, all the other

0:43:55.680 --> 0:43:57.600
<v Speaker 3>news flowing. It's almost kind of I don't want to

0:43:57.640 --> 0:44:00.279
<v Speaker 3>say buried, but we do wait for it. It for

0:44:00.280 --> 0:44:03.360
<v Speaker 3>this sedestion of Bloomberg Tech on Monday, speaking with the

0:44:03.400 --> 0:44:05.680
<v Speaker 3>CEOs of Dell and the video two thirty pm Eastern

0:44:05.719 --> 0:44:08.080
<v Speaker 3>eleven thirty m Pacific. If you do not want to

0:44:08.080 --> 0:44:10.440
<v Speaker 3>miss it, check out the pod for the recap. What

0:44:11.000 --> 0:44:13.040
<v Speaker 3>a week this is Bloomberg Tech