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Speaker 1: Bloomberg Audio Studios, podcasts, radio news. Bloomberg Tech is alive

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Speaker 1: from coast to coast with Caroline Hyde in New York

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Speaker 1: and Ed Lovelow in sentences, go.

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Speaker 2: This is Bloomberg Tech coming up.

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Speaker 3: President Trump says he discussed in video's eighty two hundred

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Speaker 3: chips with Jijingping.

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Speaker 2: Will break down the latest.

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Speaker 4: Plus we speak with the CEO of Figma after its

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Speaker 4: earnings results, the five fears that AI would disrupt the design.

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Speaker 3: Stack, and our conversation with open Ai CFO Sarah Fryer,

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Speaker 3: who says the startup may raise more capital after completing

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Speaker 3: its recent fundraising round.

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Speaker 2: Carry has got the markets.

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Speaker 5: I have, let's check in on them.

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Speaker 4: We're currently seeing the n AS that one hundred actually

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Speaker 4: having its worst day since March the twenty seventh. At

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Speaker 4: the moment, edre by one point three percent. Look over

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Speaker 4: the course of the week, it's not nearly so ugly.

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Speaker 5: We're actually inning down about a tenth percent.

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Speaker 4: But on this day it is the chip stocks in particular,

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Speaker 4: and I'm looking at Micron on the downside.

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

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Speaker 4: Look, this is the day in which we digest what

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Speaker 4: happened over in Beijing.

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Speaker 3: Yes, and that is reflected in semiconductors in part. So

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Speaker 3: the Philadelphia Semiconductor Index or SOCKS is the main gauge

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Speaker 3: of chip makers, chip equipment makers. It's down almost four percent.

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Speaker 3: You have to take into account the astonishing rally in

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Speaker 3: chip stocks year to date seventy percent or something prior

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Speaker 3: to yesterday's close.

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

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Speaker 3: Session lows was down on track for its biggest decline

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

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Speaker 3: what was the net outcome of the meeting between President

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Speaker 3: Trump and President G four Invidia And for chips, his

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Speaker 3: President Trump talking about in video's eight two hundred chips

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Speaker 3: on Air Force one earlier today.

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

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Speaker 6: He would be a fla.

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Speaker 7: You know, they have much higher level than the HQO hundred,

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Speaker 7: but the H two hundred is good Chinadja And so yeah.

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Speaker 3: President Trump there on Air Force one. Let's get the

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Speaker 3: latest with Bloomberg's Tyler Kendall, who joins us once again

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Speaker 3: from Beijing.

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Speaker 2: Chips in focus for US.

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Speaker 3: On Bloomberg Tech Taiwan in focus between the President and

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Speaker 3: President g What else do we need to take away

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Speaker 3: from this historic meeting?

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

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Speaker 8: news on how China views how this visit went between

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Speaker 8: President Trump and Chinese President Jijingping. Chinese state media reporting

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Speaker 8: moments ago that China feels that Taiwan is that number

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Speaker 8: one issue for US China relations now. President Trump and

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Speaker 8: US officials here on the ground maintained that the US

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Speaker 8: policy regarding Taiwan has not changed, though the President Trump

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Speaker 8: told Fox News earlier today that he would like to

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Speaker 8: see tensions cooled down, in his words, between China and Taiwan.

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Speaker 8: When pressed about future US weapons sales, the President was

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Speaker 8: also noncommittal as Congress waits for him to prove a

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Speaker 8: fourteen billion dollar package that's been queued up. That was

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Speaker 8: clearly the biggest point of contention amid a summit that

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Speaker 8: was rather cordial, and both sides said that they were

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Speaker 8: prioritizing stability. But we didn't really get a lot of

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Speaker 8: tangibles and deliverables in terms of progress. Right We're still

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Speaker 8: waiting on some key details regarding planned commitments. When it

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Speaker 8: comes to purchase agreements or new investment deals. Though we

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Speaker 8: can confirm that Boeing officials were still here on the

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Speaker 8: ground in Beijing over the last few hours meeting with

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Speaker 8: Chinese officials and what was considered a positive sign for

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Speaker 8: that deal.

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Speaker 5: But as you.

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Speaker 8: Mentioned, there there was a big expectation that perhaps there

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Speaker 8: could be a deal when it came to in Vidia's

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Speaker 8: H two hundred chips, as the Nvidia CEO Dnsen Wong

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Speaker 8: was a last minute addition to the travel here to Beijing.

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Speaker 8: President Trump did confirm that they spoke about the chips,

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Speaker 8: but a timately said that China wants to develop their

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Speaker 8: own and that's why we hadn't seen any purchases go

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Speaker 8: through Ed and Caroline. The President also mentioned that the

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Speaker 8: two sides did discuss the future of artificial intelligence and

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Speaker 8: where we could see some collaboration in terms of guardrails

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Speaker 8: related to the technology.

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Speaker 4: For the most Tyler Kendall extraordinary work throughout the past

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

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Speaker 4: got to get there. For the broader chip industry perspective,

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Speaker 4: what does all of this mean in terms of the

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Speaker 4: United States and its ability to drive up manufacturing capacity

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Speaker 4: and its workforce right here.

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Speaker 5: Sharry List is with us.

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

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Speaker 4: It's a global industry association connection professionals worldwide across the

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Speaker 4: chip and electronics design and manufacturing supply chain.

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Speaker 5: It is wonderful, Chary, to have you here with us.

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Speaker 5: Thank you so much for having so.

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Speaker 4: If we do see tensions rise, and if we do

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Speaker 4: focus therefore even more on a manufacturing footprint brought back

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Speaker 4: to America where Chips have manufactured their design rather than

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Speaker 4: make so reliant on Taiwan, are we able to do it?

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Speaker 2: We are?

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Speaker 9: I mean, I think I speak on behalf of the

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Speaker 9: workforce component involved this. It is one of the bottlenecks

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Speaker 9: for this industry in the US, for sure, but there

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Speaker 9: are a remarkable set of programs that are being built

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Speaker 9: all around the country to meet that need. The Chips

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Speaker 9: investments here in the US are launching an incredible growth

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Speaker 9: here for us on our soil here in this country,

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Speaker 9: and we're going to need another one hundred and fifty

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Speaker 9: years one thousand or so people in this work environment.

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Speaker 9: So we're building programs all over the.

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Speaker 3: Country away from the geopolitics. You know, the news of

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Speaker 3: the week highlights just capacity reliance. Right, So the one

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Speaker 3: thing that Taiwan's really good at is the brutal economics

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Speaker 3: of semiconductor manufacturing, and talent's a key component of that.

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Speaker 3: Of course, there's all this plan on paper to build

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Speaker 3: more fabs, have more foundry capacity in the United States,

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Speaker 3: Do we have the people with the skills to make

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Speaker 3: them run and not just make them run, make them

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Speaker 3: hum right, brutal economics.

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Speaker 9: Yes, I mean I think that's what we're all trying

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

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

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Speaker 9: With the investments that are happening here, programs are launching

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Speaker 9: all over the country. In fact, under the Chips investment,

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Speaker 9: there's a two hundred million dollar workforce This.

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Speaker 2: Is the Chips Act. A result of the Ships actually, sorry.

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Speaker 9: As a result of the Chips Act, there is a

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Speaker 9: two hundred million dollar investment in workforce through the National

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Speaker 9: Science Foundation in concert with the Department of Commerce, to

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Speaker 9: invest in building a national infrastructure around workforce development so

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Speaker 9: that we can fund regional nodes around the country to

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Speaker 9: build what's needed regionally in workforce and how to feed

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Speaker 9: that into a national infrastructure.

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Speaker 2: Not just jump in ofs.

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Speaker 3: I mean Tim Cook very famously said, didn't he that

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Speaker 3: China could fill a sports stadium with tooling engineers and

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Speaker 3: that type of talent in America would struggle to fill

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Speaker 3: a meeting room. What kind of roles are we talking

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Speaker 3: about here? Show that you're trying to skill up.

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Speaker 9: This nation on Yeah, I mean, I think we need

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Speaker 9: everything across the industry, from technicians and operators to fill

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Speaker 9: the fabs, to be on the fab floors, to all

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Speaker 9: sorts of engineers across electrical engineering, mechanical engineering, chemical engineering,

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Speaker 9: to our researchers, our PhDs. We need everybody. We need

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Speaker 9: marketing talent, we need finance talent. So I think the

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Speaker 9: challenge right now in the US is actually the image

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Speaker 9: and awareness of our industry with students. So we do

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Speaker 9: a lot of work in the space of getting students

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Speaker 9: excited or passionate about this industry or.

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Speaker 5: Even to know it.

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Speaker 9: Because kids walk around all day every day with their phones,

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Speaker 9: the phones, their iPads, their computers were in cars that

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Speaker 9: are driven through chips. We are using appliances, everything we

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Speaker 9: use all day long, and most people don't know that,

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Speaker 9: parents don't all know you know, So it's it's an

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Speaker 9: educating of the country. Really.

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Speaker 4: Well, it's been so interesting is the method by which

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Speaker 4: we start to up our fabrication footprint. Now, in some

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Speaker 4: ways it's about leaning into intel, into local plays, but

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Speaker 4: a lot of it's been about how drawing TSMC and

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Speaker 4: saying please build here in Arizona. How much in the

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Speaker 4: past have we relied on sort of TSMC talent coming here.

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Speaker 4: Is that a way that's been reskilled and we've learned

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Speaker 4: from others, or is it really about teaching them in

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Speaker 4: our own.

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Speaker 1: Education system, seeing others being brought up through the STEM

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Speaker 1: education perspective, rather than learning from talent abroad.

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

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Speaker 9: of course, relied on talent abroad. As an industry. This

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Speaker 9: is a global industry. This is a really intense, intricate,

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

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Speaker 9: What we're trying to do now is make sure that

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Speaker 9: we can build the workforce in the US with US

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Speaker 9: citizens to get US jobs and to fill all of

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Speaker 9: these roles. So we are still learning of course, we're

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Speaker 9: all learning from each other. We all have different strengths

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Speaker 9: across the world, so you know, hopefully we'll be able

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Speaker 9: to meet those needs here in the US with all

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Speaker 9: the programs that are being established.

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Speaker 2: We're out of time.

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

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Speaker 3: What did not come out of the meeting again between

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Speaker 3: the two presidents probably the issue of visas right and

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Speaker 3: talent here in Silicon Valley, San Francisco. How long has

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Speaker 3: that been a story, you know, talent from China coming

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Speaker 3: to universities, particularly in the world of AI, particularly in

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Speaker 3: the world of AI. Shery list is Semi. It's great

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Speaker 3: to have you on Bloomberg Tech. Thank you very much. Listen, guys,

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Speaker 3: tune in Monday for an exclusive interview with the CEOs

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Speaker 3: of Dell and Nvidia. From the sidelines of Dell Technology's world.

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Speaker 3: Will be jennying over to Las Vegas for what is

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Speaker 3: could not be a more timely conversation. Coming up, Cerebras

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Speaker 3: surges in its trading debut, turning summer Silicon Valley's earliest

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Speaker 3: backers into billion dollar winners.

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Speaker 6: US.

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Speaker 2: Next, this is Bloomberg Tech.

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Speaker 5: We have got to check in on Cerebras.

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Speaker 4: What a debut yesterday extraordinary sixty eight percent higher close

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

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Speaker 4: profit taking company today more by some four point eight percent,

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Speaker 4: let's call it on the day as we see the

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Speaker 4: rest of the industry being under some pressure across the

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Speaker 4: AI in chip spectrum because of concerns about really where

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Speaker 4: the US China relationship goes. But three of the chip

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Speaker 4: makers earliest venture capital backers al cerebra said Benchmark, Eclipse

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Speaker 4: Foundation Capital, they're poised to make billions from their bets

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Speaker 4: following Cerebras's IPO here for more has been a bouth

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Speaker 4: VC and startups reporter Rebecca Times, So how early do

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Speaker 4: they back this company and what sort of rewards are

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Speaker 4: they going to be able to give LPs?

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Speaker 10: Yeah, so two of the are sorry, three of the

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Speaker 10: four biggest backers in Cerebras of their biggest outside backers

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Speaker 10: came in a decade ago. In twenty sixteen, they invested

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Speaker 10: in Cerebras's earliest round on the order of twenty five

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Speaker 10: million dollars, and they now stand to make billions of

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Speaker 10: dollars each at the IPO. Benchmark is among them inter

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Speaker 10: firms with the biggest steak now around eight percent, and

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Speaker 10: this would have you know, this was its first hardware

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Speaker 10: investment in over a decade at the time, and that

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Speaker 10: big swing has really paid off for them in spades. Obviously,

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Speaker 10: the stock trading up massively from its IPO price of

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Speaker 10: one hundred and eighty five dollars per share rebackgau.

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

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Speaker 3: the Cerebris CEO, was on the show, le Or Susan,

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Speaker 3: the CEO of venture firm Eclipse, was hanging over his

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Speaker 3: right shoulder every couple of seconds gave a little cheeky

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Speaker 3: glance down into the camera lens. Eclipse another firm you know,

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Speaker 3: regulars on this show that made a lot of money.

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Speaker 7: Here.

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Speaker 3: It's really fun reporting on this with you. It's like,

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

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Speaker 3: else do we need to know? And just reflect what

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Speaker 3: your week was like covering this blockbuster listing.

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Speaker 2: Absolutely so.

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Speaker 10: We also learned in the days leading up to the

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Speaker 10: IPO that Semikin Doctor Company Arm and its majority backer SoftBank,

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Speaker 10: had made an attempt to acquire Cereubris in the weeks

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Speaker 10: before it's listing. Those offers were ultimately rebuffed, but there's

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Speaker 10: huge competition in this market. There's obviously tons of interest

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Speaker 10: in the private markets and now the public markets as well.

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Speaker 10: In aichip companies an Area Infrastructure, Eclipse and Foundation Capital,

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Speaker 10: one of the other largest free risk backers. This sort

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Speaker 10: of hardware, this infrastructure is very much their bread and butter,

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Speaker 10: and so they've got, you know, some of the things

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Speaker 10: in the pipeline sort of that follow this general theme.

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Speaker 10: And certainly there are more investments being made all across

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Speaker 10: stages in the private markets, very much in line with

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Speaker 10: this theme. So expecting to see much more activity here.

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Speaker 3: Invoted for Rebecca Torrance, big week, thank you very much.

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Speaker 3: From the excitement around Srebris's debut to the massive AI

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Speaker 3: spending that's still ongoing at the hyperscale, is investors increasingly

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Speaker 3: focused on whether companies like Alphabet, Meta and Amazon can

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Speaker 3: justify the soaring capex with sustained revenue growth. On the

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Speaker 3: other side, let's get more on that with Eric Sherid

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Speaker 3: and Goldman sax Co Business Unit, leader of the Technology,

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Speaker 3: Media and Telecommunications Group in Global Investment Research. Some weeker,

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Speaker 3: I mean that's the formula. You look at the hyperscalers,

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Speaker 3: you get the capital expenditures number. We get to the

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Speaker 3: end of earning season and video reports next Wednesday. Probably

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Speaker 3: the largest beneficiary of that capital expendit show. If we're

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Speaker 3: being honest, you sit here on a Friday morning, Eric,

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Speaker 3: what's your conclusion of the week's news flow and how

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Speaker 3: it impacts those bigger names that you cover well.

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Speaker 11: I think the main takeaways for us is that we

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Speaker 11: remain in an infrastructure led cycle, so CAPEX continues to

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Speaker 11: have an upward bias, albeit the bias to the upside

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Speaker 11: was more muted this quarter than it was last quarter,

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Speaker 11: which we think investors generally received positively. The second element

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Speaker 11: would be that the revenue backlogs, or the future revenue

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Speaker 11: that could come from this capex is now over nine

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Speaker 11: hundred billion dollars combined, spread across both Alphabet and Amazon's

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Speaker 11: cloud computing divisions. That is giving investors increased confidence that

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Speaker 11: there's revenue that will follow allbeit one, two, three years

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Speaker 11: after the capex is spent. And interestingly, the margins in

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Speaker 11: these cloud segments also surprised to the upside because non

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Speaker 11: AI workloads are accelerating structuring data is accelerating, so therefore

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Speaker 11: the long term view of earning a return on a

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Speaker 11: larger revenue base gave people more confidence. And that's why

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Speaker 11: you've seen Amazon and the Alphabet over the last one

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Speaker 11: three months act very very well as stocks as there's

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Speaker 11: been a greater appreciation for that.

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Speaker 4: I mean, Eric at one point this week we wondered

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Speaker 4: if Alphabet was going to eclipse in video as the

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Speaker 4: world's most valuable company. And we think about that vertical

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Speaker 4: integration that is just helping with this flywheel, the fact

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Speaker 4: that they are able to have such prowess when it

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Speaker 4: comes to TPU and the amount that they're able to

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Speaker 4: begain inefficiencies. And I think if Amazon actually using cerebras

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Speaker 4: share stock. More broadly, they've been using Cerebris hardware alongside

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Speaker 4: some of their own in house chips. How are you

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Speaker 4: seeing this world of benefit from using your own self

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Speaker 4: made chips alongside those from others.

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Speaker 11: Custom Silicon or TPUs from Alphabet and Amazon, we think

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Speaker 11: continues to be one of the most underappreciated narratives in

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Speaker 11: the market. Firstly, it continues to drive workloads into these

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Speaker 11: cloud ecosystems so they capture more revenue overall. Secondarily, because

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Speaker 11: they designed the custom silicon, they garner more of the

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Speaker 11: margin by doing it. And the performance of TPUs, while

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Speaker 11: not quite where GPUs are on an absolute performance basis,

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Speaker 11: if you measure it on price to performance, they're actually

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Speaker 11: quite competitive. So this is something where you can go

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Speaker 11: to your customers offer a price to performance ratio that

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Speaker 11: looks very attractive and they benefit from garnering more revenue

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Speaker 11: and more incremental margin. That's another theme that we think

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Speaker 11: is gaining in prominence across this landscape.

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Speaker 3: Eric, you lead the team at that is covering Amazon

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Speaker 3: and Alphabet right, and I'm just saying that's point out

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Speaker 3: the obvious. That's your focus, but you must track Anthropic

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Speaker 3: so closely. Both companies have significant financial interests in anthropic.

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Speaker 3: Both have some kind of competition with Anthropic at the

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Speaker 3: model level. In Amazon's case, Bedrock is the marketplace for Claude.

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Speaker 3: That's very tangled as a web. How do you untangle it?

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Speaker 12: Well?

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Speaker 11: I think there's a lot without getting into any one

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Speaker 11: company in their relationship with another. I think the world

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Speaker 11: overall is becoming more interdependent. When it comes to AI,

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Speaker 11: you're going to have foundational model companies that need compute,

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Speaker 11: You're going to have hyper scalers that can deliver that compute.

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Speaker 11: You increasingly are going to have hyperscalers who are effective

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Speaker 11: partners that allow the foundational model companies to come to

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Speaker 11: market and connect with enterprises like Goldman Sachs, and there

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Speaker 11: is going to be a lot of scale that benefits

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Speaker 11: driving incremental growth in this landscape. The truest measure of

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Speaker 11: technology computing shifts in my career has been that only

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Speaker 11: a handful of companies on both the infrastructure and the

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Speaker 11: platform layer earn excess returns on capital. So there's only

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Speaker 11: going to be a handful of companies that are enterprise

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Speaker 11: platform companies. There's only going to be a handful of

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Speaker 11: companies that are consumer platform companies. And if you come

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Speaker 11: back to the earlier point that you led with, which

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Speaker 11: is the capital need for this entire cycle, there's only

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Speaker 11: a handful of companies that have the access to capital

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Speaker 11: to be able to build to this. So there is

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Speaker 11: going to be an interdependence that comes from just a

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Speaker 11: handful of companies that can actually build at this level

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Speaker 11: of scale.

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Speaker 5: Alek Sheridan fascinating.

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Speaker 4: Not having you on the show has always come back

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Speaker 4: soon of Goldman sachs.

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Speaker 5: Now coming up.

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Speaker 4: Tensions rise between Apple and Open Ai. Why the AI

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Speaker 4: startup is weighing possible legal action against the iPhone maker.

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Speaker 5: This is Blomberg Tech.

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Speaker 4: And it's time now for talking tech and first up.

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Speaker 4: Samsung management is making a rare eleventh hour visit to

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Speaker 4: union leaders to a massive chip factory strike, but the

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Speaker 4: world stop memory maker is facing an eighteen day walkout

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Speaker 4: that could cost the company seven hundred million dollars a

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Speaker 4: day and good stall critical AI chip production. Plus Bill

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Speaker 4: Ackman's Pershing Square has taken a new course take in Microsoft,

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Speaker 4: with Ackman arguing that the market is unestimating the tech

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Speaker 4: chanswer resilience now.

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Speaker 5: The move comes as Microsoft.

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Speaker 4: Continues its aggressive push into AI, reclaiming its status in

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Speaker 4: Agwin's portfolio and Elon Musk's XAI, and is officially entering

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Speaker 4: the coding agent race with the launch of grock Build

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Speaker 4: in an attempt to catch up with down public explaud

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Speaker 4: Musk is racing to close that gap in the developer market.

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Speaker 4: It's coding agents become the next multi billion dollar frontier

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Speaker 4: in AI.

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Speaker 3: Ed Let's chat about Open AI. You sat down with

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Speaker 3: CFO Sarah Fryer last night, and she you had the

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Speaker 3: opportunity to basically say, good timing. Sam Altman's under away

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Speaker 3: in a very big case against Elon Musk. And then

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Speaker 3: there's the breaking news last night about Apple and the

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Speaker 3: relationship flu part.

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Speaker 2: Just reflect on.

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Speaker 4: It, I mean an extraordinary not saying planfo.

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Speaker 2: Great timing though.

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Speaker 4: A joy a bounty of news when you're sitting down

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Speaker 4: with a key executive. And Sarah fry is very clear

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Speaker 4: about what she needs at the moment. She needs money

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Speaker 4: for compute that continues. He has already got more than

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Speaker 4: one hundred and twenty billion dollars of it, and she's

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Speaker 4: got plenty of optionality.

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Speaker 5: We talked about her relationship with Sam Altman.

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Speaker 4: She's very What's so interesting is she's saying, Look, if

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Speaker 4: you want your CEO and CFO to be, you know,

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Speaker 4: always agreeing on absolutely.

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Speaker 5: Everything, you're sort of getting her wrong steer. That shouldn't

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Speaker 5: be how it works. But they have a really good

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Speaker 5: working relationship.

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Speaker 4: Look, she was just in California at his ranch on

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Speaker 4: the weekend because they're working extra time on things like Compute.

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Speaker 5: Here is a close relationship.

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Speaker 4: But yes, at times they have to disagree, so that

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Speaker 4: was an interesting discussion, particularly when we've had some heman's

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Speaker 4: own sort of way in which he presents himself and

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Speaker 4: the bill in the business being under the coals and

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Speaker 4: under the limelight.

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Speaker 5: But I think was notable.

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Speaker 4: I asked her about the open Ai and Apple relationship,

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Speaker 4: and of course she couldn't comment. But this is a

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Speaker 4: company that depends on partnerships to get their technology into

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Speaker 4: people's hands.

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Speaker 5: Apple is key for.

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Speaker 4: The CHATCHBT, but it was meant to be better integrated

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Speaker 4: into the overall Apple Intelligence experience.

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Speaker 2: We're going to get the detail on that just a set.

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Speaker 3: We're going to hear more from Kara's conversation with Sarah

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Speaker 3: Fryer later this hour. Stick around for that. Here's the

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Speaker 3: details Apple and Open Area's relationship is framed. Open Ai

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Speaker 3: is weighing possible legal action against the iPhone maker, arguing

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Speaker 3: it hasn't seen the expected benefits from the partnership and

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Speaker 3: it's Apple's use of its technology remains limited and hard

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Speaker 3: for users. Define let's get out to Bloomberg Senior Tech

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Speaker 3: editor Dana Wolman incredibly detailed report from the team. I

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Speaker 3: suppose what was that agreement initially?

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Speaker 13: So the two companies teamed up and really open Aiyes,

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Speaker 13: saw this as an opportunity to, as you said, get

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Speaker 13: its product as big of a name as it is

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Speaker 13: in front of even more people Apple's huge user base.

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Speaker 13: So open Air's technology would be built into Apple's platforms,

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Speaker 13: and for a lot of users, especially users who are

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Speaker 13: perhaps less tech savvy, it might have been their first

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Speaker 13: exposure to open AI's technology and open air. I was

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Speaker 13: hoping that this would result in billions of dollars worth

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Speaker 13: annually in new subscribers.

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Speaker 4: Let's talk about whether or not they have to be

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Speaker 4: forced with some legal action to get recompense here, but

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Speaker 4: more broadly, it's a time where Apple's own Apple Intelligence

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Speaker 4: is not working as they hope, having to depend on

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Speaker 4: Google to help build siry.

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Speaker 5: More broadly, are they just having to go to more

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Speaker 5: players in the ecosystem?

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Speaker 13: I'm sorry, can you repeat the question? I don't know

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Speaker 13: if I fully heard you.

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Speaker 4: How are we thinking about Apple's own dependence on other

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Speaker 4: offerings for its own Apple Intelligence that isn't working?

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Speaker 13: So Apple is going to be opening up its own

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Speaker 13: platforms to other developers as well, which surely is not

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Speaker 13: helping with the dynamic with open Ai. It's something that

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Speaker 13: Marker mentioned in his report. That said, and this has

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Speaker 13: come up in other of Bloomberg Technology's reports as well,

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Speaker 13: is that Apple is sort of benefiting from the investment

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Speaker 13: that all of these other AI developers have invested over

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Speaker 13: the years in AI. Now it's getting to sort of

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Speaker 13: integrate this menu of different increasingly advanced tools, as you said,

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Speaker 13: as it continues to build out its own much delayed

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Speaker 13: product of its own data.

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Speaker 4: Wollman, we so appreciate you, thank you for coming on

441
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Speaker 4: regarding open AI and Apple.

442
00:22:33,640 --> 00:22:36,800
Speaker 5: Coming right up, we're discussing AI in another area of Figma.

443
00:22:37,160 --> 00:22:39,679
Speaker 4: It is flipping the script on the AI disruption narrative.

444
00:22:39,800 --> 00:22:43,000
Speaker 4: CEO Dylan Fields joining us next after earnings were a blowout,

445
00:22:43,359 --> 00:22:46,480
Speaker 4: much better than expected, and so much for that disruption

446
00:22:46,720 --> 00:23:05,720
Speaker 4: vis Blue meg Tech. Welcome back to Bloomberg Tech, and

447
00:23:05,760 --> 00:23:08,359
Speaker 4: we've got to focus in on the company Figma. It

448
00:23:08,400 --> 00:23:11,560
Speaker 4: is defying those fears that AI would disrupt the design stack.

449
00:23:11,800 --> 00:23:14,159
Speaker 4: The company has just reported a massive first quarter with

450
00:23:14,200 --> 00:23:17,200
Speaker 4: revenue growth accelerating to forty six percent. It successfully begins

451
00:23:17,560 --> 00:23:21,159
Speaker 4: to monetize new AI features. Joining us now Figma founder

452
00:23:21,240 --> 00:23:26,240
Speaker 4: CEO Dylan Field. Dylan, Look, there has been many many

453
00:23:26,280 --> 00:23:29,520
Speaker 4: a question on software providers about the disruption that will

454
00:23:29,560 --> 00:23:32,760
Speaker 4: come from the likes of large language model frontim makers,

455
00:23:33,080 --> 00:23:36,359
Speaker 4: anthropic being one. What is driving your revenue growth and

456
00:23:36,400 --> 00:23:40,040
Speaker 4: the ability to exceed guidance as well well?

457
00:23:40,040 --> 00:23:41,640
Speaker 14: First of all, thank you for having me and good

458
00:23:41,640 --> 00:23:45,200
Speaker 14: to see you. And yeah, a strong quarter. We had.

459
00:23:45,200 --> 00:23:49,080
Speaker 14: Revenue accelerates forty six percent year every year, and our

460
00:23:49,160 --> 00:23:52,600
Speaker 14: net dour attention for customers that are over ten k

461
00:23:53,200 --> 00:23:56,480
Speaker 14: of ARR is now at one hundred and thirty nine percent.

462
00:23:56,800 --> 00:24:01,119
Speaker 14: And also strong cash flow with non up margin of

463
00:24:01,200 --> 00:24:04,040
Speaker 14: sixteen percent in the quarter in free cash flow of

464
00:24:04,040 --> 00:24:05,159
Speaker 14: twenty seven percent.

465
00:24:06,840 --> 00:24:09,760
Speaker 2: We also raise our guidance. So we're very glad with

466
00:24:09,800 --> 00:24:10,400
Speaker 2: the results.

467
00:24:10,960 --> 00:24:15,000
Speaker 14: And I think in terms of taking a step back

468
00:24:15,040 --> 00:24:19,119
Speaker 14: around what is behind the quarter and also the moment

469
00:24:19,160 --> 00:24:24,320
Speaker 14: you're mentioning as AI commanitizers code, it makes it so

470
00:24:24,400 --> 00:24:27,840
Speaker 14: that code is easier than ever to write, you know,

471
00:24:27,840 --> 00:24:31,520
Speaker 14: the layer above code as that gets commoitized is fine.

472
00:24:33,359 --> 00:24:35,080
Speaker 3: I got an audience question for you on that in

473
00:24:35,160 --> 00:24:37,760
Speaker 3: just a moment. But for me like that, I don't

474
00:24:37,760 --> 00:24:39,560
Speaker 3: know what you would call it, Like the credit caps

475
00:24:39,760 --> 00:24:42,720
Speaker 3: or the way you charge on usage was so fascinating.

476
00:24:42,760 --> 00:24:46,359
Speaker 3: So in March you started charging customers a fee to

477
00:24:46,560 --> 00:24:50,040
Speaker 3: use AI, in particular beyond a certain limit, and the

478
00:24:50,080 --> 00:24:54,399
Speaker 3: response is varied. Right when people hit that cap, many

479
00:24:54,440 --> 00:24:57,280
Speaker 3: were willing to pay for more credits. There was some

480
00:24:57,400 --> 00:24:59,439
Speaker 3: drop off though, like a small group was saying, well,

481
00:24:59,480 --> 00:25:03,000
Speaker 3: if that's the case, I won't use a Figma product anymore.

482
00:25:03,560 --> 00:25:05,320
Speaker 3: Where do you see that netting out?

483
00:25:06,000 --> 00:25:09,080
Speaker 14: Well, we have many products and you're always welcome to

484
00:25:09,720 --> 00:25:12,120
Speaker 14: you know, as a free user use one of our

485
00:25:12,119 --> 00:25:14,560
Speaker 14: free surfaces, or as a paid user of the seat,

486
00:25:15,560 --> 00:25:15,960
Speaker 14: use our.

487
00:25:15,880 --> 00:25:16,800
Speaker 2: Traditional design tool.

488
00:25:16,840 --> 00:25:18,919
Speaker 14: But yes, if you want to use the product we

489
00:25:19,000 --> 00:25:22,320
Speaker 14: have Figma make, we want to use AI features in

490
00:25:22,359 --> 00:25:27,199
Speaker 14: Figma design. What we did was we essentially added some

491
00:25:27,359 --> 00:25:30,600
Speaker 14: number of free credits to paid seats to make sure

492
00:25:30,600 --> 00:25:32,600
Speaker 14: that people had a way to try these features out.

493
00:25:33,440 --> 00:25:35,560
Speaker 14: And then we also made it to that if you

494
00:25:35,600 --> 00:25:38,280
Speaker 14: want to buy additional credits you can, And for a

495
00:25:38,280 --> 00:25:40,920
Speaker 14: long time we actually made it said everything was free.

496
00:25:40,960 --> 00:25:43,159
Speaker 14: But you know that's not exactly the move that us

497
00:25:43,359 --> 00:25:46,359
Speaker 14: do that forever and it does cost real money.

498
00:25:46,960 --> 00:25:47,399
Speaker 2: We also have.

499
00:25:47,400 --> 00:25:51,879
Speaker 14: Thima Weave, and weave is extremely exciting and with weave,

500
00:25:51,960 --> 00:25:56,240
Speaker 14: what you can do is essentially create a workflow, which

501
00:25:56,280 --> 00:25:59,360
Speaker 14: is a note through notebased editing tool where you connect

502
00:25:59,560 --> 00:26:06,760
Speaker 14: up different outputs from models I think images, videos, three

503
00:26:06,840 --> 00:26:09,880
Speaker 14: D models and more, and then you can push them

504
00:26:09,920 --> 00:26:12,480
Speaker 14: through a workflow so that you're able to really mold

505
00:26:12,480 --> 00:26:18,720
Speaker 14: those model outputs like clay. So for example, NBBJ, a

506
00:26:18,880 --> 00:26:22,880
Speaker 14: architecture firm. One of the customers we mentioned Durens Call.

507
00:26:23,480 --> 00:26:26,920
Speaker 14: They used to do these very extensive customer shoots where.

508
00:26:26,680 --> 00:26:29,160
Speaker 2: They would go out to site and they would.

509
00:26:29,040 --> 00:26:33,400
Speaker 14: Really understand what is the different lagging at different times

510
00:26:33,880 --> 00:26:36,600
Speaker 14: and they would then superimpose the three D model of

511
00:26:36,640 --> 00:26:39,879
Speaker 14: the building. And with Thigma weave they can do that

512
00:26:39,880 --> 00:26:42,160
Speaker 14: all in a workflow where they can just really easily

513
00:26:42,160 --> 00:26:45,560
Speaker 14: control all sorts of different parameters and it saves them

514
00:26:45,600 --> 00:26:48,399
Speaker 14: a ton of time and gets better results for the client.

515
00:26:48,880 --> 00:26:51,000
Speaker 14: So that's another one where we also see AA friend

516
00:26:51,000 --> 00:26:51,639
Speaker 14: SCIPT kick in.

517
00:26:53,640 --> 00:26:57,200
Speaker 4: You bring real anecdotal evidence to bear, and the anecdotical

518
00:26:57,240 --> 00:26:59,600
Speaker 4: elevidence we hear is like like if you ever try

519
00:26:59,600 --> 00:27:01,880
Speaker 4: and st wave figma from those that use it when in.

520
00:27:01,800 --> 00:27:03,280
Speaker 5: The workforce, they'll not leave.

521
00:27:03,320 --> 00:27:05,480
Speaker 4: Employees will like march out the building because they so

522
00:27:05,640 --> 00:27:06,320
Speaker 4: love the product.

523
00:27:06,400 --> 00:27:08,520
Speaker 5: But how do you then fight this narrative?

524
00:27:08,760 --> 00:27:11,960
Speaker 4: Then investors just want to sell first, ask questions later,

525
00:27:12,119 --> 00:27:14,560
Speaker 4: and has put your stock under pressure since the IPO.

526
00:27:16,960 --> 00:27:20,800
Speaker 14: I mean, I think we control the inputs and we

527
00:27:20,880 --> 00:27:23,440
Speaker 14: need to deliver for our customers, as simple as that.

528
00:27:24,119 --> 00:27:28,439
Speaker 14: And so we're working very hard always on making sure

529
00:27:28,600 --> 00:27:31,119
Speaker 14: that we're doing the right thing for the long term.

530
00:27:32,320 --> 00:27:38,960
Speaker 14: And I think that the long term is thankfully aligned

531
00:27:39,000 --> 00:27:41,520
Speaker 14: with our strategy. We're in the best position we think

532
00:27:41,560 --> 00:27:43,760
Speaker 14: we are basically can be. And you know, is I

533
00:27:43,920 --> 00:27:47,600
Speaker 14: becoming more important than ever? And I think that one

534
00:27:47,880 --> 00:27:50,520
Speaker 14: area when it comes to maybe the market or the world,

535
00:27:51,560 --> 00:27:54,360
Speaker 14: as we're seeing design go more broad in these companies

536
00:27:54,400 --> 00:27:57,840
Speaker 14: and also be more appreciated as the way that you win,

537
00:27:58,040 --> 00:28:00,439
Speaker 14: but also what you break through a very head of

538
00:28:00,480 --> 00:28:01,720
Speaker 14: information landscape.

539
00:28:02,520 --> 00:28:04,119
Speaker 2: You have to really people what design is.

540
00:28:04,240 --> 00:28:07,960
Speaker 14: It's not just you know, creating something that you know

541
00:28:08,080 --> 00:28:10,800
Speaker 14: you think is beautiful because you know, many people have

542
00:28:10,800 --> 00:28:14,960
Speaker 14: different aesthetics. It's how it works, it's ux, it's forum,

543
00:28:14,960 --> 00:28:15,439
Speaker 14: its function.

544
00:28:15,720 --> 00:28:17,640
Speaker 2: Yeah, and we have.

545
00:28:17,560 --> 00:28:21,639
Speaker 14: To I think, really help people understand the many different

546
00:28:21,640 --> 00:28:25,359
Speaker 14: facets of design and Enfigma, design is not always just

547
00:28:25,960 --> 00:28:28,280
Speaker 14: you know, how it looks, how it works. It's also

548
00:28:28,320 --> 00:28:33,639
Speaker 14: the thinking process out there, and I think in this world, Dyaling, Yes, sorry,

549
00:28:33,680 --> 00:28:34,399
Speaker 14: I don't.

550
00:28:34,280 --> 00:28:34,800
Speaker 2: Mean to cut you off.

551
00:28:34,880 --> 00:28:36,240
Speaker 3: We're running out of time, and I want to get

552
00:28:36,240 --> 00:28:38,000
Speaker 3: that audience question to you because, as you know, I

553
00:28:38,000 --> 00:28:41,040
Speaker 3: think it's really important regular of the show, Ben, how

554
00:28:41,080 --> 00:28:44,840
Speaker 3: do you see inference and token costs affecting your margins

555
00:28:44,840 --> 00:28:45,479
Speaker 3: going forward?

556
00:28:45,680 --> 00:28:47,040
Speaker 2: You kind of alluded to it earlier.

557
00:28:47,680 --> 00:28:49,600
Speaker 14: Yeah, we talked about in our Orange call yesterday and

558
00:28:49,600 --> 00:28:53,240
Speaker 14: how if we see an opportunity to go really big

559
00:28:53,400 --> 00:28:55,840
Speaker 14: and have a ton of growth, we will take it

560
00:28:55,920 --> 00:28:58,600
Speaker 14: and we will push hard. But I think there's a

561
00:28:58,640 --> 00:29:02,760
Speaker 14: short term, there's the long term, and sometimes there's ways

562
00:29:02,800 --> 00:29:05,840
Speaker 14: to push hard on short term and that might create

563
00:29:05,920 --> 00:29:08,840
Speaker 14: downward pressure and margins. But if you're going for a

564
00:29:08,920 --> 00:29:11,840
Speaker 14: massive TAM in the long term, I think that's the

565
00:29:11,880 --> 00:29:13,880
Speaker 14: right move in what our investors should be cheering us

566
00:29:13,880 --> 00:29:18,200
Speaker 14: on to do. And the TAM is very large both

567
00:29:18,240 --> 00:29:25,880
Speaker 14: for design, for sculpting, you know, advertising marketing and breaking

568
00:29:25,880 --> 00:29:29,480
Speaker 14: through noise, and I think that in general, if we're

569
00:29:29,560 --> 00:29:32,080
Speaker 14: able to deliver on that and able to win this

570
00:29:32,320 --> 00:29:37,640
Speaker 14: increasingly competitive landscape which is growing so fast of design

571
00:29:37,720 --> 00:29:41,360
Speaker 14: when it's the new code. I think that puts us

572
00:29:41,360 --> 00:29:44,200
Speaker 14: in an amazing edition for the future. So very excited

573
00:29:44,200 --> 00:29:46,320
Speaker 14: to bringing more people into the design processes. What we're

574
00:29:46,320 --> 00:29:48,840
Speaker 14: seeing with our customers, it's not just designers, it's many

575
00:29:48,880 --> 00:29:51,440
Speaker 14: others as well, but also level them up on design.

576
00:29:52,560 --> 00:29:54,880
Speaker 3: Figmacia didn't feel back on Bloomberg Tech.

577
00:29:55,080 --> 00:29:56,520
Speaker 2: Thank you very much for joining us.

578
00:29:56,520 --> 00:29:59,959
Speaker 3: Now coming up, Figure is putting their humanoid robots out

579
00:30:00,200 --> 00:30:03,240
Speaker 3: the lab and onto the live stream. CEO Brett Adcock

580
00:30:03,280 --> 00:30:14,520
Speaker 3: with us next. This is Bloomberg Tech. Figure says its

581
00:30:14,600 --> 00:30:17,600
Speaker 3: humanoid robots just completed more than twenty four hours of

582
00:30:17,680 --> 00:30:22,320
Speaker 3: continuous package sorting autonomously, a live stream watched by millions

583
00:30:22,560 --> 00:30:27,600
Speaker 3: across YouTube and X three FO three robots worked in shifts, scanning, flipping,

584
00:30:27,640 --> 00:30:32,040
Speaker 3: sorting packages at roughly human speed, all powered by its

585
00:30:32,080 --> 00:30:34,959
Speaker 3: in house AI software running directly on board the robots.

586
00:30:35,000 --> 00:30:38,400
Speaker 3: But that demonstration did spark some skepticism was it real?

587
00:30:39,040 --> 00:30:42,640
Speaker 3: Joining us now, as Brett Adcock founder CEO A Figure,

588
00:30:42,680 --> 00:30:47,080
Speaker 3: that's where we start, you can say, Brett definitively. Over

589
00:30:47,120 --> 00:30:50,120
Speaker 3: the twenty four hours or more, there was no teleoperation.

590
00:30:50,440 --> 00:30:52,239
Speaker 3: A lot of people in the comments, as you know,

591
00:30:52,880 --> 00:30:54,720
Speaker 3: pointed to the idea, and I think we had video

592
00:30:54,760 --> 00:30:59,640
Speaker 3: a bit that the three on shift kept gesturing to

593
00:30:59,680 --> 00:31:03,280
Speaker 3: the head, which is a tailtale sign in robotics of teleoperation.

594
00:31:04,000 --> 00:31:05,479
Speaker 2: Your pledge that there was none.

595
00:31:06,240 --> 00:31:09,280
Speaker 12: There's absolutely no telly operation into this. The robots are

596
00:31:09,280 --> 00:31:13,200
Speaker 12: all operating fully autonomously using an onboard neural network redesign

597
00:31:13,280 --> 00:31:17,000
Speaker 12: called Helix two. Sometimes when the robot takes a turn

598
00:31:17,080 --> 00:31:19,560
Speaker 12: to left to grab packages, it moves its left hand

599
00:31:19,560 --> 00:31:22,160
Speaker 12: out of the way upwards. You'll see this behavior happen

600
00:31:22,240 --> 00:31:24,280
Speaker 12: every single time the robot turns for packages.

601
00:31:24,960 --> 00:31:25,360
Speaker 7: But we've been.

602
00:31:25,320 --> 00:31:29,600
Speaker 12: Running autonomously now for close to fifty hours, the robots

603
00:31:29,640 --> 00:31:33,280
Speaker 12: operating shifts. There's been basically almost no downtime on the belt.

604
00:31:34,240 --> 00:31:38,880
Speaker 12: We've pushed over close to sixty thousand packages and we're

605
00:31:38,920 --> 00:31:40,560
Speaker 12: just going to keep going now and see how far

606
00:31:40,640 --> 00:31:41,080
Speaker 12: this can go.

607
00:31:41,960 --> 00:31:44,120
Speaker 3: So this was live streams, right, and that was one

608
00:31:44,160 --> 00:31:45,760
Speaker 3: reason I really wanted you to come on the program,

609
00:31:45,800 --> 00:31:49,680
Speaker 3: because there's the bit people don't see what's happening behind

610
00:31:49,720 --> 00:31:53,760
Speaker 3: the scenes, like in shift changes, where does the robot go?

611
00:31:54,200 --> 00:31:55,480
Speaker 2: Does it need maintenance?

612
00:31:57,560 --> 00:31:59,680
Speaker 12: For the most part, the robots operate on a four

613
00:31:59,680 --> 00:32:03,800
Speaker 12: hour battery life. After the battery is low, the robot

614
00:32:03,840 --> 00:32:06,640
Speaker 12: messages another robot to come out to take its place.

615
00:32:07,040 --> 00:32:09,960
Speaker 12: The robots didn't do a swap. The robot has just left.

616
00:32:09,960 --> 00:32:13,280
Speaker 12: The conveyor system is going to go charge wirelessly, understand

617
00:32:13,680 --> 00:32:16,600
Speaker 12: while the other robot continues to do work. If there

618
00:32:16,640 --> 00:32:20,440
Speaker 12: are issues, say we have hardware software issues, the robots

619
00:32:20,480 --> 00:32:22,720
Speaker 12: can basically walk off into maintenance and call another robot

620
00:32:22,760 --> 00:32:24,840
Speaker 12: to take its place. The goal is to be able

621
00:32:24,880 --> 00:32:29,160
Speaker 12: to enlistit twenty four to seven operations with basically no

622
00:32:29,360 --> 00:32:31,760
Speaker 12: like no failures on the on the use case itself,

623
00:32:32,080 --> 00:32:35,000
Speaker 12: which we haven't had today. So the robots basically the

624
00:32:35,040 --> 00:32:37,440
Speaker 12: conveyor system has been running twenty four to seven since

625
00:32:37,560 --> 00:32:40,160
Speaker 12: like middle of this week. I think we're now we're

626
00:32:40,160 --> 00:32:43,560
Speaker 12: approaching fifty hours of just full like every single hour

627
00:32:43,680 --> 00:32:46,880
Speaker 12: since since we've launched, the robots have been basically I've

628
00:32:46,920 --> 00:32:49,640
Speaker 12: been doing work now on this line, which.

629
00:32:49,440 --> 00:32:51,520
Speaker 2: I think is like wow, which I think is crazy.

630
00:32:51,560 --> 00:32:54,120
Speaker 12: You know, Figure wants to build like you know, we

631
00:32:54,120 --> 00:32:55,800
Speaker 12: want to build like I robot. You know, we want

632
00:32:55,880 --> 00:32:58,800
Speaker 12: robots everywhere in the world in the commercial market, Like

633
00:32:59,200 --> 00:33:01,720
Speaker 12: this is like the first large step to doing that.

634
00:33:02,920 --> 00:33:07,880
Speaker 4: Okay, So what's harder now robots getting faster or making

635
00:33:07,880 --> 00:33:09,640
Speaker 4: them even more reliable, because at the moment you seem

636
00:33:09,640 --> 00:33:10,280
Speaker 4: to be doing both.

637
00:33:12,160 --> 00:33:14,800
Speaker 12: The robot that you're seeing here is roughly operating around

638
00:33:14,840 --> 00:33:17,200
Speaker 12: human speech just about three seconds of package. That's the

639
00:33:17,240 --> 00:33:20,160
Speaker 12: requirement to operate on this logistics line. So we're at

640
00:33:20,240 --> 00:33:23,440
Speaker 12: like we're at human parity and speed. The goal is

641
00:33:23,480 --> 00:33:26,760
Speaker 12: also to have like ninety percent success rate on the

642
00:33:26,800 --> 00:33:30,680
Speaker 12: bark the package flips for barcode scanning. We're in that

643
00:33:31,000 --> 00:33:35,000
Speaker 12: as well. In that requirement, the robots are also like

644
00:33:35,400 --> 00:33:38,640
Speaker 12: getting extremely reliable. Like part of this whole process of

645
00:33:38,680 --> 00:33:40,800
Speaker 12: running this twenty four to seven with no downtime is

646
00:33:40,840 --> 00:33:43,920
Speaker 12: to show how reliable humanoid robots are. And four years

647
00:33:43,920 --> 00:33:47,240
Speaker 12: ago when I started the company, humanoid robots were falling.

648
00:33:47,440 --> 00:33:51,320
Speaker 12: They were extremely unreliable systems. We've designed the systems and

649
00:33:51,360 --> 00:33:53,360
Speaker 12: engineered this now to a point where the robots are

650
00:33:53,800 --> 00:33:56,040
Speaker 12: I think extremely reliable. I think we're showing that now

651
00:33:56,040 --> 00:33:59,120
Speaker 12: in the live stream to the entire world. The big

652
00:33:59,160 --> 00:34:01,040
Speaker 12: focus for us is is like how do we solve

653
00:34:01,080 --> 00:34:04,280
Speaker 12: for a truly general purpose machine, and then how do

654
00:34:04,320 --> 00:34:08,400
Speaker 12: we manufacture unprecedented volume similar to cell phones? Today, so like,

655
00:34:09,160 --> 00:34:12,760
Speaker 12: so this week back que our manufacturing facility will manufacture

656
00:34:12,760 --> 00:34:16,879
Speaker 12: anywhere between like sixty and seventy humanoid robots just this week, right,

657
00:34:16,920 --> 00:34:20,000
Speaker 12: and we do it right next door on the Figure campus.

658
00:34:20,600 --> 00:34:22,120
Speaker 3: So brat, I want to get into the idea, this

659
00:34:22,160 --> 00:34:25,799
Speaker 3: is full stack. I've been hearing a lot growing speculation

660
00:34:25,960 --> 00:34:29,319
Speaker 3: that open ai could get back into robotics. And you

661
00:34:29,360 --> 00:34:31,640
Speaker 3: have a history white Way, you had a partnership, you

662
00:34:31,719 --> 00:34:34,480
Speaker 3: decided on the software side you could do better yourself.

663
00:34:34,960 --> 00:34:36,440
Speaker 2: You know, what do you make of that?

664
00:34:36,440 --> 00:34:40,920
Speaker 3: That idea that ultimately a big party is going to

665
00:34:40,960 --> 00:34:44,399
Speaker 3: want to own the entire stack that powers the humanoid robot.

666
00:34:46,360 --> 00:34:48,319
Speaker 12: To really do this right, like if we want to

667
00:34:48,360 --> 00:34:52,799
Speaker 12: really build like I robot like the movie, yes, you

668
00:34:52,840 --> 00:34:56,040
Speaker 12: know we basically you have to design the entire hardware

669
00:34:56,080 --> 00:35:01,239
Speaker 12: system almost yourself, like motors like stater rotors, the electromagnetics work, like,

670
00:35:01,280 --> 00:35:03,000
Speaker 12: you have to do all the battery systems work. You

671
00:35:03,000 --> 00:35:06,600
Speaker 12: have to do all the actuator design, sensor design, kinematics

672
00:35:06,719 --> 00:35:09,440
Speaker 12: like structures like which we do all in house now

673
00:35:09,600 --> 00:35:13,200
Speaker 12: here at Figure. We also manufacture the robots, so we're

674
00:35:13,200 --> 00:35:15,360
Speaker 12: like and then we also test them and we do

675
00:35:15,440 --> 00:35:17,400
Speaker 12: all the AI data collection and all the AI and

676
00:35:17,400 --> 00:35:21,160
Speaker 12: neural net training ourselves here in house. So basically, this

677
00:35:21,200 --> 00:35:24,480
Speaker 12: is a full end to end vertically integrated system that

678
00:35:24,520 --> 00:35:27,440
Speaker 12: we now have out doing real use case work that

679
00:35:27,680 --> 00:35:30,760
Speaker 12: humans do, and we can do this at human speeds.

680
00:35:31,440 --> 00:35:34,120
Speaker 12: And we're doing this now for like you know this example,

681
00:35:34,680 --> 00:35:36,719
Speaker 12: most of these shifts run like this eight hours a day.

682
00:35:37,960 --> 00:35:40,880
Speaker 12: We're doing this like twenty four to seven, just to

683
00:35:40,920 --> 00:35:43,960
Speaker 12: show how reliable the systems are and how like mission

684
00:35:43,960 --> 00:35:47,120
Speaker 12: ready these things are to get out at scale. So

685
00:35:47,200 --> 00:35:50,080
Speaker 12: anyway to solve this you have to have like a

686
00:35:50,120 --> 00:35:53,600
Speaker 12: truly vertically integrated approach from top to bottom.

687
00:35:54,080 --> 00:35:56,440
Speaker 4: What about the bottlenecks? View is in our money do

688
00:35:56,480 --> 00:35:57,280
Speaker 4: you want to go public?

689
00:35:57,320 --> 00:35:57,600
Speaker 5: Brat?

690
00:35:57,600 --> 00:35:57,960
Speaker 8: What is?

691
00:35:58,080 --> 00:35:59,440
Speaker 5: What are you needing to get this out?

692
00:35:59,520 --> 00:36:04,799
Speaker 12: More broadly, our largest two bottlenecks are like data for

693
00:36:04,880 --> 00:36:08,600
Speaker 12: pre training, our helix, neural net and UH in manufacturing,

694
00:36:09,520 --> 00:36:14,080
Speaker 12: we're spinning up manufacturing here our manufacturing facilities called BATQ.

695
00:36:15,480 --> 00:36:18,719
Speaker 12: We've we're now doing we're now out of several like

696
00:36:18,800 --> 00:36:22,880
Speaker 12: thousands of run rate annually production that is continuing to

697
00:36:22,880 --> 00:36:23,400
Speaker 12: scale up.

698
00:36:24,000 --> 00:36:24,160
Speaker 5: UH.

699
00:36:24,680 --> 00:36:27,960
Speaker 12: You know, I think UH on the data side, we're

700
00:36:28,000 --> 00:36:34,840
Speaker 12: collecting and training, uh, kind of unprecedented models for like

701
00:36:34,880 --> 00:36:37,319
Speaker 12: our AI stack here internally that we've ever done.

702
00:36:38,000 --> 00:36:38,200
Speaker 7: Uh.

703
00:36:38,239 --> 00:36:40,520
Speaker 12: So I think we're like we're and we have you know,

704
00:36:40,600 --> 00:36:42,160
Speaker 12: we have well over a billion dollars of cash in

705
00:36:42,200 --> 00:36:44,759
Speaker 12: the balance sheet today. So I think from a from

706
00:36:44,800 --> 00:36:47,560
Speaker 12: a financial perspective, we're in a good spot. We're manufacturing

707
00:36:47,560 --> 00:36:50,719
Speaker 12: at pretty much unprecedented volumes for ourselves, and we're we're

708
00:36:50,719 --> 00:36:53,840
Speaker 12: building like next generation AI models that I think are uh,

709
00:36:53,920 --> 00:36:56,560
Speaker 12: to be honest, are just completely mind blowing. And so

710
00:36:56,880 --> 00:36:58,879
Speaker 12: the goal is like the goals to solve the data

711
00:36:58,880 --> 00:37:01,359
Speaker 12: problem and the manufacturing problem, to get humiliar robots out

712
00:37:01,360 --> 00:37:02,400
Speaker 12: of scale.

713
00:37:02,640 --> 00:37:04,640
Speaker 4: And maybe come up with even more names for these

714
00:37:04,840 --> 00:37:08,080
Speaker 4: robots currently getting them from online fans. I think one's

715
00:37:08,120 --> 00:37:11,200
Speaker 4: called Bob and Gary Brett Gadcock. We so appreciate your

716
00:37:11,239 --> 00:37:13,680
Speaker 4: time today, Founder and CEO of Figure.

717
00:37:14,400 --> 00:37:16,440
Speaker 5: Now let's send our attention to Annie Jasse. It was

718
00:37:16,440 --> 00:37:17,760
Speaker 5: once Jeff Bezos's deputy.

719
00:37:17,800 --> 00:37:20,279
Speaker 4: Remember, now five years into his tenure as CEO, Jesse

720
00:37:20,440 --> 00:37:21,799
Speaker 4: is steering the company through some of.

721
00:37:21,760 --> 00:37:25,319
Speaker 5: Its greatest changes. That's the focus of today's big take most.

722
00:37:25,360 --> 00:37:26,480
Speaker 5: Matt Day joins us.

723
00:37:26,520 --> 00:37:29,640
Speaker 4: With more You went to see sort of the new

724
00:37:29,760 --> 00:37:33,279
Speaker 4: real focus point for Annie Jasse. It's data centers, and

725
00:37:33,719 --> 00:37:36,440
Speaker 4: this is a company that is now just scaling so

726
00:37:36,560 --> 00:37:39,120
Speaker 4: much in a vertically integrated manner. Matt, what did you

727
00:37:39,239 --> 00:37:41,680
Speaker 4: learn from visiting the hubs and what it says about Andy?

728
00:37:42,640 --> 00:37:45,120
Speaker 15: But it says that there's just such sprawl to Amazon

729
00:37:45,120 --> 00:37:46,920
Speaker 15: these days. You think you know Amazon from the package

730
00:37:46,920 --> 00:37:49,080
Speaker 15: is showing up at your doorstep. So much of what

731
00:37:49,120 --> 00:37:51,680
Speaker 15: they're spending money on it's data centers. It's a supply

732
00:37:51,760 --> 00:37:54,800
Speaker 15: chain behind data centers. That means chips, that means hardware engineering,

733
00:37:54,840 --> 00:37:57,200
Speaker 15: that means software at the large language model level.

734
00:37:57,400 --> 00:37:59,120
Speaker 2: They're really just all over the place.

735
00:37:58,880 --> 00:38:01,400
Speaker 15: And it's kind of an unfathomable sort of things they

736
00:38:01,440 --> 00:38:03,759
Speaker 15: got going on, and really the sort of single organizational

737
00:38:03,800 --> 00:38:05,799
Speaker 15: principle behind it is Andy at the top of it.

738
00:38:06,000 --> 00:38:07,560
Speaker 15: He's making all these calls on where they got to

739
00:38:07,560 --> 00:38:09,000
Speaker 15: shift money with the comple and stuff out of what

740
00:38:09,000 --> 00:38:11,440
Speaker 15: they're putting into. It's really really an impressive machine.

741
00:38:12,120 --> 00:38:14,279
Speaker 3: So Matt, knowing that he's watching right now and he

742
00:38:14,360 --> 00:38:15,839
Speaker 3: Jesse is glued to Bloomberg Tech.

743
00:38:15,840 --> 00:38:17,360
Speaker 2: There is a good chance that's true.

744
00:38:17,719 --> 00:38:20,920
Speaker 3: What do we learn about him is the Amazon CEO

745
00:38:21,120 --> 00:38:22,880
Speaker 3: versus Jeff Like you and I've talked about this in

746
00:38:22,960 --> 00:38:25,680
Speaker 3: the past, But what's different about him? Does he lean

747
00:38:25,719 --> 00:38:27,239
Speaker 3: hard into the AWS thing?

748
00:38:28,480 --> 00:38:29,160
Speaker 2: You definitely did.

749
00:38:29,160 --> 00:38:30,800
Speaker 15: And if you look at where their bets are today,

750
00:38:31,360 --> 00:38:32,719
Speaker 15: you know a lot of them are, and what can

751
00:38:32,760 --> 00:38:35,600
Speaker 15: Amazon do uniquely and what can they do differently when

752
00:38:35,600 --> 00:38:37,279
Speaker 15: it comes to their core retail business. So they've left

753
00:38:37,280 --> 00:38:40,120
Speaker 15: some opportunities on the table there. That's not the case

754
00:38:40,160 --> 00:38:42,239
Speaker 15: in AI. They want to be everywhere in AI. They

755
00:38:42,239 --> 00:38:44,640
Speaker 15: want to sprinkle AI through all of their product lines,

756
00:38:45,000 --> 00:38:46,520
Speaker 15: and that is where they're spending a ton of their

757
00:38:46,600 --> 00:38:48,680
Speaker 15: their capex right now. So he's definitely brought some of

758
00:38:48,719 --> 00:38:51,759
Speaker 15: that AWS pedigree with him. We've also just learned he

759
00:38:51,840 --> 00:38:54,080
Speaker 15: is he is an extreme detail guy. It's not that

760
00:38:54,080 --> 00:38:56,040
Speaker 15: that was not a Jeff Bezos trade, but for the

761
00:38:56,120 --> 00:38:59,040
Speaker 15: last few years of his leadership at Amazon, Jeff was

762
00:38:59,120 --> 00:39:01,240
Speaker 15: checked out and some of the business. You know, Andy

763
00:39:01,280 --> 00:39:03,480
Speaker 15: hasn't let go of anything since he showed up. He's

764
00:39:03,480 --> 00:39:04,960
Speaker 15: all over the details of this business.

765
00:39:06,160 --> 00:39:09,040
Speaker 3: Blue Most Matt Day with the big take on Andy

766
00:39:09,120 --> 00:39:13,880
Speaker 3: jase Era at AWS and Amazon must coming up arguments

767
00:39:13,880 --> 00:39:16,440
Speaker 3: in the trial between Open AI and Elon Musk wrapped

768
00:39:16,520 --> 00:39:18,920
Speaker 3: up yesterday. We're going to get to the latest and

769
00:39:18,920 --> 00:39:20,879
Speaker 3: some more of the conversation we've been having with open

770
00:39:20,920 --> 00:39:23,960
Speaker 3: Ai the past twenty four hours. This is Bloomberg Tech.

771
00:39:31,400 --> 00:39:34,719
Speaker 4: Opening Eyes chief financial Officer Sir Fran talked about the

772
00:39:34,800 --> 00:39:37,759
Speaker 4: demand the company is seeing and the compute needs it's

773
00:39:37,840 --> 00:39:40,040
Speaker 4: up against. She sat down with me at the Cancello

774
00:39:40,320 --> 00:39:41,600
Speaker 4: Spark summit in Napa.

775
00:39:41,840 --> 00:39:42,400
Speaker 5: Take a listen.

776
00:39:43,080 --> 00:39:46,680
Speaker 16: Compute itself has clearly been a bottleneck. I don't think

777
00:39:46,719 --> 00:39:48,640
Speaker 16: any of us, even Sam and Greg, who I think

778
00:39:48,680 --> 00:39:52,800
Speaker 16: we're incredibly prescient overall, in terms of the need for compute,

779
00:39:52,920 --> 00:39:55,120
Speaker 16: I don't think they could have foreseen just the sheer

780
00:39:55,160 --> 00:39:57,959
Speaker 16: demand that we have right now in twenty twenty six,

781
00:39:58,440 --> 00:40:01,279
Speaker 16: and so that it's the energy that sits behind it.

782
00:40:01,520 --> 00:40:04,799
Speaker 16: We're seeing memory out of Southeast Asia where there's all

783
00:40:04,800 --> 00:40:07,200
Speaker 16: of these chok points in the supply chain, and this

784
00:40:07,239 --> 00:40:09,000
Speaker 16: is why it behooves us to get ahead of that,

785
00:40:09,320 --> 00:40:12,920
Speaker 16: strike those partnerships earlier when people are maybe not seeing

786
00:40:13,040 --> 00:40:14,040
Speaker 16: just the sheer scale.

787
00:40:14,640 --> 00:40:17,799
Speaker 4: Let's talk about partnership and about some of the people

788
00:40:17,840 --> 00:40:21,440
Speaker 4: you just talked about, Quite Brockman and Sam Altman.

789
00:40:22,160 --> 00:40:23,520
Speaker 5: They've had a tough week.

790
00:40:23,520 --> 00:40:27,120
Speaker 4: In terms of being in trial, having their relationships sort

791
00:40:27,160 --> 00:40:27,960
Speaker 4: of cast.

792
00:40:27,680 --> 00:40:31,440
Speaker 5: Into the limelight. What is this like working with Sam?

793
00:40:31,480 --> 00:40:34,879
Speaker 4: There's been some questioning as to how you work alongside him,

794
00:40:35,080 --> 00:40:35,920
Speaker 4: Do you work well?

795
00:40:36,040 --> 00:40:37,960
Speaker 5: Is it a strong bond? Is he trustworthy?

796
00:40:38,120 --> 00:40:38,440
Speaker 8: Is it?

797
00:40:38,520 --> 00:40:41,319
Speaker 4: Seemed to have been grilled by certain lawyers of Elon

798
00:40:41,440 --> 00:40:42,200
Speaker 4: Musk this week.

799
00:40:42,360 --> 00:40:44,759
Speaker 16: So I think mister Musk is very much out to

800
00:40:44,880 --> 00:40:48,480
Speaker 16: just distract, and we are just staying the course, like

801
00:40:48,640 --> 00:40:52,080
Speaker 16: building our technology, putting it into customers' hands, and really

802
00:40:52,120 --> 00:40:55,200
Speaker 16: creating agi for the benefit of humanity. Working with Sam

803
00:40:55,280 --> 00:40:58,120
Speaker 16: is great. We get on incredibly well. I think we

804
00:40:58,200 --> 00:41:02,120
Speaker 16: have a wonderful partnership, super good ying and Yang Sam

805
00:41:02,200 --> 00:41:02,640
Speaker 16: push us.

806
00:41:02,719 --> 00:41:03,760
Speaker 5: He's super curious.

807
00:41:04,080 --> 00:41:07,040
Speaker 16: My job is to take that curiosity and create optionality.

808
00:41:07,280 --> 00:41:09,239
Speaker 16: So just as we talked about whether it's making sure

809
00:41:09,280 --> 00:41:11,600
Speaker 16: we have enough compute, making sure we have the funds

810
00:41:11,680 --> 00:41:13,800
Speaker 16: to do it, makeing sure we build the business strongly.

811
00:41:14,160 --> 00:41:16,320
Speaker 16: And I spend an an ordinate amount of time with customers,

812
00:41:16,400 --> 00:41:18,359
Speaker 16: as does Sam, and we get to compare a lot

813
00:41:18,360 --> 00:41:21,080
Speaker 16: of notes on that front, as well as sometimes our

814
00:41:21,160 --> 00:41:26,160
Speaker 16: nuts on parenting. So it goes the whole gamut frankly Open.

815
00:41:25,960 --> 00:41:29,600
Speaker 3: AI CFO Sarah Fry speaking with Caroline about her relationship

816
00:41:29,640 --> 00:41:32,880
Speaker 3: with the company's CEO, Sam Altman. The relationships and choices

817
00:41:33,239 --> 00:41:36,160
Speaker 3: of open ais founders have been front and center of

818
00:41:36,200 --> 00:41:39,520
Speaker 3: the lawsuit brought by Elon Musk over the charitable status

819
00:41:39,520 --> 00:41:42,320
Speaker 3: of the company. Arguments in the trial wrapped up yesterday.

820
00:41:42,680 --> 00:41:46,080
Speaker 3: Bloombergs Madelin Meckelberg has been there every step of the way,

821
00:41:46,360 --> 00:41:49,279
Speaker 3: and so this is where we're at. Closing arguments we

822
00:41:49,320 --> 00:41:52,880
Speaker 3: wait for. I think jury deliberation Monday give us everything

823
00:41:52,920 --> 00:41:54,160
Speaker 3: that we need to know at this point.

824
00:41:55,400 --> 00:41:57,759
Speaker 6: That's right. I think one thing that we learned from

825
00:41:57,760 --> 00:42:00,920
Speaker 6: closing arguments, it's really relevant to the conversation you just

826
00:42:00,960 --> 00:42:04,440
Speaker 6: had with Sarah, is that this case, for both parties,

827
00:42:04,680 --> 00:42:08,040
Speaker 6: really boils down to credibility. That's the message that they

828
00:42:08,160 --> 00:42:11,640
Speaker 6: left jurors with on Thursday when they had closing arguments.

829
00:42:12,080 --> 00:42:15,000
Speaker 6: Musk's attorneys are trying to cast Sam Altman as someone

830
00:42:15,000 --> 00:42:19,480
Speaker 6: who's deceptive, who's untrustworthy. Meanwhile, lawyers for Open Ai were

831
00:42:19,520 --> 00:42:21,920
Speaker 6: trying to point to the evidence that's been presented so

832
00:42:22,040 --> 00:42:25,319
Speaker 6: far in the case, saying Musk's story doesn't add up

833
00:42:25,400 --> 00:42:27,399
Speaker 6: with what we've seen in the documents, what you've heard

834
00:42:27,400 --> 00:42:30,320
Speaker 6: from witnesses, and so now it's up to the jury

835
00:42:30,360 --> 00:42:33,840
Speaker 6: to decide whose version of events they believe about this

836
00:42:34,040 --> 00:42:38,000
Speaker 6: saga of the formation of open Ai, the dissolution of

837
00:42:38,080 --> 00:42:42,560
Speaker 6: this relationship and the transition into rivalry between Musk and

838
00:42:42,600 --> 00:42:44,360
Speaker 6: Altman that we see playing out today.

839
00:42:45,680 --> 00:42:48,040
Speaker 5: What happens if Musk wins.

840
00:42:49,600 --> 00:42:52,800
Speaker 6: That's a big question. So at this point, the jury

841
00:42:52,840 --> 00:42:55,040
Speaker 6: is going to start deliberating and they're going to issue

842
00:42:55,080 --> 00:42:59,640
Speaker 6: a verdict that's advisory, so it's a non binding recommendation

843
00:42:59,719 --> 00:43:01,719
Speaker 6: to the judge in this case, who's going to have

844
00:43:01,800 --> 00:43:04,960
Speaker 6: the final say on whether or not Musk has enough

845
00:43:05,000 --> 00:43:08,080
Speaker 6: to prove his claims here. And Musk is asking for

846
00:43:08,120 --> 00:43:10,359
Speaker 6: a lot. He's asking for one hundred and thirty four

847
00:43:10,360 --> 00:43:12,879
Speaker 6: billion dollars in d images that he says he's going

848
00:43:12,920 --> 00:43:16,000
Speaker 6: to donate to the Open Ai Foundation. He's asking for

849
00:43:16,040 --> 00:43:19,080
Speaker 6: Altman and Brockman to be removed from their jobs, and

850
00:43:19,120 --> 00:43:22,000
Speaker 6: he's asking for the open Ai come up company that's

851
00:43:22,000 --> 00:43:25,120
Speaker 6: been formed to be transitioned back to a nonprofit. These

852
00:43:25,120 --> 00:43:27,640
Speaker 6: are huge asks. The judge is also going to hear

853
00:43:27,760 --> 00:43:31,359
Speaker 6: arguments on Wednesday about which one of those might be realistic,

854
00:43:32,040 --> 00:43:34,359
Speaker 6: and we're just going to kind of have to wait

855
00:43:34,400 --> 00:43:36,440
Speaker 6: and see how she comes down on the main question

856
00:43:36,560 --> 00:43:38,600
Speaker 6: before we get to that. But the stakes are huge

857
00:43:38,640 --> 00:43:40,640
Speaker 6: and ex essential essentially for open.

858
00:43:40,480 --> 00:43:44,680
Speaker 4: Ai b mags Mann, Mackelberg, fascinating.

859
00:43:44,800 --> 00:43:45,120
Speaker 5: Thank you.

860
00:43:45,280 --> 00:43:47,160
Speaker 4: I mean, we wait with bated breath for the rest

861
00:43:47,200 --> 00:43:49,919
Speaker 4: of the week and what indeed happens in that court case.

862
00:43:49,920 --> 00:43:51,160
Speaker 5: But he'say, there's some.

863
00:43:51,080 --> 00:43:53,319
Speaker 3: Confidence world is part about it is that court case

864
00:43:53,320 --> 00:43:55,600
Speaker 3: happened in the context of the week, all the other

865
00:43:55,680 --> 00:43:57,600
Speaker 3: news flowing. It's almost kind of I don't want to

866
00:43:57,640 --> 00:44:00,279
Speaker 3: say buried, but we do wait for it. It for

867
00:44:00,280 --> 00:44:03,360
Speaker 3: this sedestion of Bloomberg Tech on Monday, speaking with the

868
00:44:03,400 --> 00:44:05,680
Speaker 3: CEOs of Dell and the video two thirty pm Eastern

869
00:44:05,719 --> 00:44:08,080
Speaker 3: eleven thirty m Pacific. If you do not want to

870
00:44:08,080 --> 00:44:10,440
Speaker 3: miss it, check out the pod for the recap. What

871
00:44:11,000 --> 00:44:13,040
Speaker 3: a week this is Bloomberg Tech