WEBVTT - SpaceX Eyes Borrowing for Nvidia Chips, Apple Plans Smart Home Push

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<v Speaker 1>Bloomberg Audio Studios. Podcasts.

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<v Speaker 2>Radio. News. Bloomberg Tech is live from the heart of

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<v Speaker 2>Silicon Valley with Ed Ludlow in San Francisco.

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<v Speaker 1>This is Bloomberg Tech. Coming up, the AI build-out keeps

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<v Speaker 1>driving record borrowing, with SpaceX in talks to borrow $ 40

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<v Speaker 1>billion to buy chips from NVIDIA. Plus, Apple plans its

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<v Speaker 1>entry into the smart home space with a doorbell, thermostat,

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<v Speaker 1>and other accessories developed with LG. And we'll discuss the

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<v Speaker 1>mega AI IPO pipeline with J.D. Moriarty, vice chair and

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<v Speaker 1>head of global tech equity capital markets at Bank of America.

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<v Speaker 1>of America Securities. Hello from the world of tech. So

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<v Speaker 1>stocks closed at records Tuesday, including the Nasdaq 100, very

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<v Speaker 1>tech heavy index. We're now hitting pause on that, largely

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<v Speaker 1>because of concerns about inflation. Oil is pushing higher. Inflation

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<v Speaker 1>is still the concern of the day. But there's a

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<v Speaker 1>lot in the news cycle. So let's get to our

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<v Speaker 1>top story and take a look at today's big number.

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<v Speaker 1>$ 40 billion. Bloomberg's reporting that SpaceX is in talks with

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<v Speaker 1>banks and investors to borrow $ 40 billion to buy chips

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<v Speaker 1>from NVIDIA, all according to sources. I want to get

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<v Speaker 1>the details with Bloomberg's chief Wall Street correspondent, Sridhar Nadarajan.

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<v Speaker 1>Let's start with the structure, right? There is a split

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<v Speaker 1>between loan and some debt and the players involved based

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<v Speaker 1>on Bloomberg's reporting.

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<v Speaker 3>Look, every other morning we seem to be waking up

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<v Speaker 3>to these big, unfathomable numbers almost. It's $ 60 billion one week,

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<v Speaker 3>$ 40 billion next week. It's in tens of billions of dollars.

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<v Speaker 3>This latest one involves SpaceX trying to finance these Nvidia

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<v Speaker 3>chips with a $ 40 billion package, $ 10 billion of it

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<v Speaker 3>to be syndicated through banks. $ 30 billion of it will

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<v Speaker 3>likely go through private market players led by Apollo. PIMCO

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<v Speaker 3>is possibly involved. They will syndicate it to the rest.

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<v Speaker 3>But it is very clear that the tech story today

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<v Speaker 3>is the debt story. And there is going to be

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<v Speaker 3>this increasing confluence between some of the biggest private market players,

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<v Speaker 3>some of the biggest firms with the deepest balance sheets,

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<v Speaker 3>and the tech companies that so desperately need to invest

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<v Speaker 3>and borrow so many hundreds of billions of dollars to

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<v Speaker 3>fund and finance this AI infrastructure build-out.

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<v Speaker 1>Sri, this morning, Bloomberg's Danny Berger had the opportunity to

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<v Speaker 1>speak to John Cortese, who's the co-head of corporate credit

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<v Speaker 1>at Apollo, one of the parties named in our reporting.

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<v Speaker 1>And she asked about this scoop.

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<v Speaker 4>Can't talk about SpaceX, but I think if you look at.

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<v Speaker 4>All of these companies that are the best companies in

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<v Speaker 4>the world, the largest companies in the world, the largest

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<v Speaker 4>parts of equity portfolios. It's not surprising that the largest

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<v Speaker 4>companies are looking at this marketplace and saying, we'd like

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<v Speaker 4>to find structured solutions for complicated build-outs or project build-outs,

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<v Speaker 4>AI being one of them, chip financing being one of them.

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<v Speaker 4>So it's a huge amount of that happening. I would

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<v Speaker 4>expect more of it. It's been very clearly stated to

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<v Speaker 4>the marketplace what the CapEx needs are to build that AI.

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<v Speaker 4>So I think you're going to see more and more.

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<v Speaker 1>We also got a warning signal through the CDS, right?

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<v Speaker 1>Five-year credit default swaps jumping to their highest level since

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<v Speaker 1>they started trading in June. But listening to that Apollo

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<v Speaker 1>non-response and the parties on the Wall Street side we're

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<v Speaker 1>talking about, there's an appetite to back these kinds of deals, right?

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<v Speaker 1>There's no real concern that the capital can't be found.

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<v Speaker 3>And the numbers show that. If you look at the

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<v Speaker 3>amount raised for data centers, for stacking these data centers

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<v Speaker 3>with chips, for the power required to power these data centers,

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<v Speaker 3>we've raised about $ 360 billion so far this year. That's

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<v Speaker 3>about three times the pace last year And if you

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<v Speaker 3>talk to analysts across the street, that number is only

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<v Speaker 3>going to keep going up in the next year or two.

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<v Speaker 3>We are talking about trillions of dollars in requirements, and

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<v Speaker 3>these companies will keep coming back to the market. Yes,

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<v Speaker 3>like you point out, with what is happening in the

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<v Speaker 3>broader rate environment, with some of the concerns starting to

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<v Speaker 3>seep in, if there is too much ebullience and too

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<v Speaker 3>much enthusiasm out there, you will see some of these

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<v Speaker 3>risk gauges starting to go up. SpaceX, you can see

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<v Speaker 3>that in the CDS a couple of weeks ago. We

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<v Speaker 3>talked about Oracle and we saw the moves in Oracle CDS.

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<v Speaker 3>But for the large part, these are not crazy blowouts.

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<v Speaker 3>These are contained moves. And for the biggest, most credit

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<v Speaker 3>worthy players, there will be demand. Because when you listen

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<v Speaker 3>to what else people like the senior executives at Apollo

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<v Speaker 3>have to say, they're not really trying to make a

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<v Speaker 3>bet on whether these chips will depreciate in value or

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<v Speaker 3>hold their value. They are basically betting on the underlying

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<v Speaker 3>credit worthiness of the borrower. And as long as that

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<v Speaker 3>is solid, they are willing to extend their balance sheet

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<v Speaker 3>to them.

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<v Speaker 1>Bloomberg Shridhar Natarajan, thank you very much. Now, staying with SpaceX,

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<v Speaker 1>its Crew 12 is heading home. The four-person crew undocked

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<v Speaker 1>from the International Space Station this morning after spending nearly

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<v Speaker 1>eight months in orbit. Their Dragon capsule is now on

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<v Speaker 1>a 27-hour journey back to Earth. Splashdown is set for

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<v Speaker 1>Thursday evening. in the Pacific, and we will track the

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<v Speaker 1>progress of that mission. Another Musk story. Elon Musk setting

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<v Speaker 1>the record straight on his chip-making ambition, saying there should be, quote,

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<v Speaker 1>zero doubt about who's in charge of that initiative. Earlier

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<v Speaker 1>this week, Musk confirmed discussions with TSMC over his planned

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<v Speaker 1>terror fab in Texas, which raised questions about Intel's role

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<v Speaker 1>and if they'd be displaced. Now, Intel CEO Lit Bhutan,

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<v Speaker 1>speaking overnight in Tokyo, said, says his company is still

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<v Speaker 1>there as a technology partner, while Musk is saying TSMC

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<v Speaker 1>won't be running that fab. Bloomberg's global tech editor, Peter Elstrom,

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<v Speaker 1>is with us. The twists and turns. Try and summarize

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<v Speaker 1>where we've landed.

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<v Speaker 5>Yeah, let's pause for a second. Musk talked about TerraFab

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<v Speaker 5>back in March. And at the time, it was unclear

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<v Speaker 5>exactly where he was going to go with this venture.

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<v Speaker 5>He talked about it as an epic adventure in chip building.

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<v Speaker 5>He said that there was such a shortage in the

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<v Speaker 5>chip industry that he was going to get into the

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<v Speaker 5>business of making the chips himself. He said it was

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<v Speaker 5>important enough that they wanted to get into the kind

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<v Speaker 5>of logic chips that TSMC makes, and they're going to

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<v Speaker 5>make memory chips at the same time. And the vision

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<v Speaker 5>he laid out was really quite exceptional at that point.

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<v Speaker 5>It was called a fever dream by some people. And

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<v Speaker 5>one analyst estimated that it would cost $ 5 trillion to

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<v Speaker 5>$ 13 trillion to be able to build out this whole project.

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<v Speaker 5>So early on, they brought Intel on as the partner

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<v Speaker 5>to help them. If you don't have this chip expertise,

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<v Speaker 5>you need to be able to use the IP from someplace. Intel,

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<v Speaker 5>of course, has that kind of expertise. They began working together.

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<v Speaker 5>But then, as you mentioned, there was this report that

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<v Speaker 5>TSMC was somehow involved. In fact, Musk confirmed that there

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<v Speaker 5>were talks at that point without going into any kind

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<v Speaker 5>of detail. So, as you mentioned, we talked with Luke Buten,

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<v Speaker 5>the CEO of Intel Overnight in Tokyo. He confirmed they're

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<v Speaker 5>still working with Musk and Terafab at this point. They

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<v Speaker 5>plan on continuing that work. That's a positive for Intel.

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<v Speaker 5>And then also Musk responded to something that suggested TerraFab

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<v Speaker 5>would never actually own the fabs. In fact, it would

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<v Speaker 5>be TSMC that would build the fabs and own the fabs.

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<v Speaker 5>And he said, that's not at all the case. TerraFab,

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<v Speaker 5>Musk and his team are going to build these fabs themselves.

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<v Speaker 5>TSMC may be involved somehow, but they're not going to

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<v Speaker 5>own the property.

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<v Speaker 1>I think it's really useful to go back to the

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<v Speaker 1>basics of what TerraFab is, right? It's a joint venture

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<v Speaker 1>between SpaceX and and Tesla to currently separate companies. But

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<v Speaker 1>I think Musk has always talked about the idea that

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<v Speaker 1>there isn't enough chip supply for the things that both

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<v Speaker 1>those companies want to do. So, Musk's solution, build it themselves.

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<v Speaker 1>Is that kind of a fair sum?

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<v Speaker 5>Yeah, that's exactly it.

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<v Speaker 6>Yeah.

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<v Speaker 5>And he said at the time back in March, he

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<v Speaker 5>said in order to accomplish what he wants to accomplish

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<v Speaker 5>with rockets and robots and autonomous cars, They need chips.

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<v Speaker 5>And he said if the chip industry is not gonna

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<v Speaker 5>be able to supply those chips, and he thinks it

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<v Speaker 5>doesn't have that capacity right now, then they have to

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<v Speaker 5>build it themselves. So it sounded like they were pushing

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<v Speaker 5>into this business partly to motivate the chip industry, to

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<v Speaker 5>move more quickly, to expand more quickly. Now, the other

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<v Speaker 5>side of this, of course, is the chip industry is

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<v Speaker 5>terribly cyclical. These companies invest billions and billions of dollars,

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<v Speaker 5>and then they find themselves with too much capacity. They've

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<v Speaker 5>done this again and again over the years. So they've

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<v Speaker 5>been a little bit cautious about how quickly they've expanded,

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<v Speaker 5>especially on the memory chip front. But now Musk is

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<v Speaker 5>trying to push that a little bit faster. Talk about

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<v Speaker 5>capital needs. I mean, they may need to raise billions

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<v Speaker 5>of dollars, tens of billions of dollars, if not trillions

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<v Speaker 5>of dollars, to be able to accomplish some of the

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<v Speaker 5>goals he has for TerraFab.

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<v Speaker 1>Bloomberg's Peter Ahlstrom. Thank you. SpaceX is pushing the Trump

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<v Speaker 1>administration to make it easier for rocket companies to get

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<v Speaker 1>access to bands of spectrum, a move that could help

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<v Speaker 1>increase launches. But those efforts are facing pushback from rivals

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<v Speaker 1>in the aerospace industry. So Boeing, Lockheed Martin and GE

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<v Speaker 1>Healthcare are among those warning the changes could disrupt everything

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<v Speaker 1>from flight tests of commercial jets and missiles to hospital

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<v Speaker 1>equipment and and even TV broadcasts. The Federal Communications Commission,

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<v Speaker 1>which is being asked to scrap a third-party entity that

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<v Speaker 1>controls how spectrum is shared, did not respond to requests

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<v Speaker 1>for comment. Coming up here on Bloomberg Tech, Apple's push

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<v Speaker 1>into smart home devices includes an unusual partnership. We've got

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<v Speaker 1>the details next. This is Bloomberg Tech. Apple's upcoming push

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<v Speaker 1>into smart home devices will include a doorbell, thermostat, and

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<v Speaker 1>other accessories developed through an unusual partnership with LG Electronics.

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<v Speaker 1>The products will be part of an ecosystem that works

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<v Speaker 1>with Apple's new smart home hub, according to Bloomberg sources.

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<v Speaker 1>Let's get the details with Bloomberg's Mark Gurman, who leads

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<v Speaker 1>our coverage of consumer technology. So interesting to read this one.

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<v Speaker 1>And we'll bring the diagrams back up that were in

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<v Speaker 1>your story. But Apple is partnering on this range of devices.

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<v Speaker 7>So I've been reporting for years now that Apple has several...

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<v Speaker 7>first party in-house smart home devices coming. This is part

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<v Speaker 7>of a gigantic reboot to its home strategy. HomeKit launched

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<v Speaker 7>in 2014 has been an unmitigated disaster compared to what

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<v Speaker 7>you're seeing from Ring, from what you're seeing from Google,

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<v Speaker 7>from what you're seeing from others. So Apple is starting over.

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<v Speaker 7>And so they'll announce a new HomePod mini, a smart

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<v Speaker 7>home hub with a display that's like an Amazon Echo Show,

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<v Speaker 7>and a new Apple TV. But yesterday, I revealed there's more.

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<v Speaker 7>In addition to the flagship devices, Apple's basically coming out

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<v Speaker 7>with what I'm calling a smart home starter kit. If

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<v Speaker 7>you're building a smart home, what would you want? You

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<v Speaker 7>would want a smart lock. You'd want a smart thermostat.

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<v Speaker 7>You'd want a smart doorbell. And you would want smart

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<v Speaker 7>security cameras inside, outside. It may be a floodlight camera.

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<v Speaker 7>So they're coming out with all of that. And they're

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<v Speaker 7>partnering with LG on it. according to my sources. And

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<v Speaker 7>I searched to the FCC database and found that these

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<v Speaker 7>LG branded devices are in midst of getting regulatory approval

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<v Speaker 7>in the United States.

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<v Speaker 1>Okay. So how unusual is the step for Apple to say,

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<v Speaker 1>go to another company and be like, you guys are

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<v Speaker 1>good at this and we're good at this and we

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<v Speaker 1>can work together. And then the big picture of why

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<v Speaker 1>a lock and a doorbell and a thermostat works with

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<v Speaker 1>what Apple's trying to do in the home.

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<v Speaker 7>It's extremely unusual, but it's not unprecedented. They've done this

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<v Speaker 7>a couple times. They've built devices with Belkin and put

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<v Speaker 7>them under the Belkin brand before. They've built some iPad

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<v Speaker 7>accessories with Logitech and put it under the Logitech brand before.

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<v Speaker 7>But this LG one is kind of crazy because LG

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<v Speaker 7>doesn't make smart home devices, right? Like this is new

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<v Speaker 7>territory completely for LG. Belkin has made iPhone accessories for years.

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<v Speaker 7>Logitech has made iPad accessories for years. I mean, LG

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<v Speaker 7>is basically completely out of left field and completely random.

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<v Speaker 7>This is why this news was so shocking. They don't

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<v Speaker 7>make a smart home devices. They don't make a smart

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<v Speaker 7>cameras for the home. They don't make door bells. They

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<v Speaker 7>don't make locks. They don't make any of this stuff. Uh,

0:12:00.490 --> 0:12:04.050
<v Speaker 7>so it's fascinating. And Apple and LG have a big relationship, right? LG,

0:12:04.530 --> 0:12:07.610
<v Speaker 7>the sister companies, LG electronics, LG display, make screens for

0:12:07.650 --> 0:12:10.410
<v Speaker 7>Apple devices. Uh, one of the pieces of the LG

0:12:10.429 --> 0:12:14.440
<v Speaker 7>conglomerate make camera modules. So there is some relationship there. And, uh,

0:12:15.350 --> 0:12:17.750
<v Speaker 7>To me, these look pretty nice. And like I said earlier,

0:12:17.950 --> 0:12:21.210
<v Speaker 7>if you're wanting to build a smart home as a consumer,

0:12:21.390 --> 0:12:23.120
<v Speaker 7>you're going to start with a doorbell. You're going to

0:12:23.130 --> 0:12:24.820
<v Speaker 7>start with a lock. You're going to start with a thermostat.

0:12:25.200 --> 0:12:26.980
<v Speaker 7>And so that's why Apple's going after these products.

0:12:28.080 --> 0:12:30.580
<v Speaker 1>Bloomberg's Mark Gurman. And don't forget to read Power On,

0:12:30.820 --> 0:12:33.959
<v Speaker 1>his newsletter, and listen to the new podcast, Power On.

0:12:34.890 --> 0:12:37.189
<v Speaker 1>Thank you very much. Meanwhile, the leader in the smart

0:12:37.230 --> 0:12:40.570
<v Speaker 1>doorbell market, Ring, is out with its own new products.

0:12:40.610 --> 0:12:44.580
<v Speaker 1>The Amazon-owned home security company releasing its first smart lock

0:12:44.929 --> 0:12:48.360
<v Speaker 1>and new default security encryption for users' data. Delighted to

0:12:48.400 --> 0:12:51.459
<v Speaker 1>say that Ring founder, Jamie Siminoff, joins us now. Let's

0:12:51.480 --> 0:12:54.100
<v Speaker 1>start with what's new here. Lots of the audience familiar

0:12:54.140 --> 0:12:57.370
<v Speaker 1>with Ring, the doorbell. Goes beyond that now with a

0:12:57.429 --> 0:12:58.030
<v Speaker 1>range of things.

0:12:58.890 --> 0:13:01.750
<v Speaker 8>Yeah, so we have a large range of products from

0:13:01.890 --> 0:13:05.230
<v Speaker 8>outdoor cameras, indoor cameras, alarm system. We started with the doorbell,

0:13:05.730 --> 0:13:07.490
<v Speaker 8>and now I call it the peanut butter and jelly.

0:13:07.550 --> 0:13:09.830
<v Speaker 8>I mean, the lock and the doorbell have always, we thought,

0:13:09.910 --> 0:13:12.510
<v Speaker 8>come together, but we didn't have an angle on the

0:13:12.550 --> 0:13:14.800
<v Speaker 8>lock or an innovation on the lock that we thought

0:13:14.860 --> 0:13:17.300
<v Speaker 8>warranted us putting our brand on it until now.

0:13:18.740 --> 0:13:22.709
<v Speaker 1>Jamie, let's talk about that innovation. There are, as Mark's

0:13:22.750 --> 0:13:27.570
<v Speaker 1>kind of outlined, an increasing number of options of smart

0:13:27.590 --> 0:13:29.810
<v Speaker 1>home devices. What is it that you're good at? What

0:13:29.850 --> 0:13:31.460
<v Speaker 1>is it that you think you're better at on the

0:13:31.500 --> 0:13:34.900
<v Speaker 1>hardware side, the software side relative to the competition?

0:13:35.780 --> 0:13:38.239
<v Speaker 8>First of all, we start everything around a mission to

0:13:38.260 --> 0:13:41.240
<v Speaker 8>make neighborhoods safer. I think with that North Star that

0:13:41.260 --> 0:13:44.620
<v Speaker 8>really drives our business into a direction that so far

0:13:44.980 --> 0:13:47.400
<v Speaker 8>no one has been able to follow. Secondly, I'm an inventor.

0:13:47.660 --> 0:13:51.420
<v Speaker 8>I've invented the smart doorbell, the floodlight camera, and many

0:13:51.540 --> 0:13:54.890
<v Speaker 8>other innovations around the home. including today with the smart

0:13:54.929 --> 0:13:59.429
<v Speaker 8>lock that now has a rechargeable power.

0:13:59.210 --> 0:13:59.680
<v Speaker 1>Dial on it.

0:13:59.730 --> 0:14:01.470
<v Speaker 8>So that if the smart, I mean, you know, when

0:14:01.490 --> 0:14:03.449
<v Speaker 8>a smart lock loses its batteries, it becomes a not

0:14:03.470 --> 0:14:04.670
<v Speaker 8>smart lock or a dumb lock.

0:14:04.850 --> 0:14:05.050
<v Speaker 1>Right.

0:14:05.130 --> 0:14:07.540
<v Speaker 8>And so, you know, now instead of having to have

0:14:07.559 --> 0:14:09.540
<v Speaker 8>a backup key that's hidden in the area where the

0:14:09.580 --> 0:14:12.360
<v Speaker 8>spiders are around your house, you literally just twist this

0:14:12.420 --> 0:14:15.080
<v Speaker 8>power dial. It comes back on and now you get in.

0:14:15.100 --> 0:14:17.160
<v Speaker 8>And so that's, you know, Ring really mission to make

0:14:17.179 --> 0:14:20.970
<v Speaker 8>neighborhoods safer. We're very innovative. I consider myself an inventor.

0:14:21.680 --> 0:14:23.310
<v Speaker 8>And that's what we've been able to do so far,

0:14:23.350 --> 0:14:25.330
<v Speaker 8>which is, I think, how we've become what I believe

0:14:25.370 --> 0:14:26.930
<v Speaker 8>is the largest home security company in the world.

0:14:27.530 --> 0:14:27.790
<v Speaker 9>Right.

0:14:28.250 --> 0:14:31.790
<v Speaker 1>Jamie, I have a smart doorbell from from another brand.

0:14:31.890 --> 0:14:34.850
<v Speaker 1>But the main point I'm getting at is that it's

0:14:34.910 --> 0:14:38.470
<v Speaker 1>part of an integrated system of multiple devices of different types.

0:14:38.550 --> 0:14:38.770
<v Speaker 3>Right.

0:14:39.510 --> 0:14:41.750
<v Speaker 1>What kind of data do you see about a consumer

0:14:41.770 --> 0:14:46.440
<v Speaker 1>that buys one Ring product in aggregate with lots of others, right?

0:14:46.480 --> 0:14:48.820
<v Speaker 1>They're kind of bulk buyers of systems rather than just

0:14:48.900 --> 0:14:49.530
<v Speaker 1>one piece of it.

0:14:49.800 --> 0:14:52.220
<v Speaker 8>Yeah, certainly it's the rings of security and everything you

0:14:52.260 --> 0:14:54.740
<v Speaker 8>can buy from Ring works on its own or works

0:14:54.780 --> 0:14:57.000
<v Speaker 8>together sort of on the app and in the ecosystem.

0:14:57.040 --> 0:14:58.240
<v Speaker 1>It also works with Alexa Plus.

0:14:59.220 --> 0:15:00.980
<v Speaker 8>So we do see that people, you know, they certainly

0:15:01.440 --> 0:15:03.920
<v Speaker 8>starting at the doorbell as your first ring of security,

0:15:04.390 --> 0:15:08.710
<v Speaker 8>working your way around alarm system, outdoor cameras, some indoor cameras.

0:15:09.170 --> 0:15:11.890
<v Speaker 8>smart lock now, you know, we really help people to

0:15:11.950 --> 0:15:14.200
<v Speaker 8>build the security they need and also, you know, choose

0:15:14.220 --> 0:15:15.760
<v Speaker 8>your power. I mean, one of the big things is,

0:15:16.040 --> 0:15:19.830
<v Speaker 8>you know, we offer batteries, solar, and powered options in

0:15:19.890 --> 0:15:21.470
<v Speaker 8>basically every camera that we have.

0:15:23.140 --> 0:15:26.920
<v Speaker 1>Jamie, you heard Mark Gurman's reporting there on Apple's entry

0:15:26.980 --> 0:15:30.960
<v Speaker 1>into this category, which includes a smart lock and thermostat,

0:15:31.000 --> 0:15:34.600
<v Speaker 1>et cetera, based on his reporting. How do you react

0:15:34.640 --> 0:15:37.640
<v Speaker 1>to that? What's your feeling about Apple coming into the space?

0:15:37.980 --> 0:15:40.160
<v Speaker 8>I've had competitors since day one. I've stuck to my

0:15:40.180 --> 0:15:42.460
<v Speaker 8>mission of making neighborhoods safer, and so far that's paid

0:15:42.500 --> 0:15:44.400
<v Speaker 8>dividends for us. So we're just going to keep focusing

0:15:44.440 --> 0:15:45.000
<v Speaker 8>on our neighbors.

0:15:45.320 --> 0:15:46.500
<v Speaker 1>Which is what we call our customers.

0:15:46.520 --> 0:15:50.150
<v Speaker 8>I'm going to keep innovating, bringing new-to-world products, and I

0:15:50.190 --> 0:15:52.470
<v Speaker 8>feel like if I do that, I'll remain successful and

0:15:52.490 --> 0:15:54.230
<v Speaker 8>keep being the leader in the market.

0:15:55.890 --> 0:16:00.830
<v Speaker 1>Here. I'm genuinely interested in the economics of consumer electronics, right?

0:16:01.250 --> 0:16:04.530
<v Speaker 1>Especially when you push into getting electronics getting smaller and

0:16:04.550 --> 0:16:09.020
<v Speaker 1>smaller and smaller. And Ring within Amazon, we showed... In fact, Tim,

0:16:09.040 --> 0:16:10.980
<v Speaker 1>put the table back up with the price points for

0:16:11.000 --> 0:16:13.440
<v Speaker 1>this generation of Ring gear.

0:16:13.840 --> 0:16:14.020
<v Speaker 9>Like.

0:16:15.020 --> 0:16:17.440
<v Speaker 1>How do you get that right, the pricing? Is it

0:16:17.480 --> 0:16:20.140
<v Speaker 1>a benefit to have Amazon scale that you can price

0:16:20.180 --> 0:16:23.210
<v Speaker 1>such as you do? Is this a profitable enterprise?

0:16:24.130 --> 0:16:27.650
<v Speaker 8>Certainly Ring is very profitable at this point. And we also,

0:16:27.770 --> 0:16:29.930
<v Speaker 8>we are big enough now in terms of scale that yes,

0:16:30.250 --> 0:16:33.710
<v Speaker 8>certainly being at Amazon is not a detriment, but we're

0:16:33.730 --> 0:16:35.850
<v Speaker 8>big enough now that we can get scale in the

0:16:35.890 --> 0:16:38.280
<v Speaker 8>pieces and the things that we buy. And we're very

0:16:38.320 --> 0:16:41.800
<v Speaker 8>innovative in how we are able to choose the chips,

0:16:41.880 --> 0:16:43.780
<v Speaker 8>build the products, build them from scratch, try to use

0:16:43.800 --> 0:16:46.380
<v Speaker 8>first principles effectively. and really build the best products at

0:16:46.400 --> 0:16:49.040
<v Speaker 8>the best cost to democratize home security for everyone.

0:16:50.060 --> 0:16:53.320
<v Speaker 1>Jamie, what happens next in this field? How does the

0:16:53.360 --> 0:16:55.540
<v Speaker 1>smart home change in the future, to your mind?

0:16:56.180 --> 0:16:59.950
<v Speaker 8>I mean, with AI and superintelligence, certainly the smart home

0:16:59.970 --> 0:17:03.210
<v Speaker 8>is going to continue to change. I believe we're the

0:17:03.230 --> 0:17:07.109
<v Speaker 8>appendages for AI and superintelligence. It's going to need to

0:17:07.150 --> 0:17:09.210
<v Speaker 8>have the physical, it's going to need to have a lock,

0:17:09.230 --> 0:17:10.290
<v Speaker 8>it's going to need to have a doorbell, it's going

0:17:10.310 --> 0:17:11.389
<v Speaker 8>to need to have cameras, it's going to need to

0:17:11.430 --> 0:17:13.149
<v Speaker 8>be able to see and sense things And when you

0:17:13.170 --> 0:17:15.570
<v Speaker 8>put all of that into a superintelligence, I do believe

0:17:15.590 --> 0:17:20.060
<v Speaker 8>that that is going to superpower our ability in the home,

0:17:20.119 --> 0:17:22.840
<v Speaker 8>our peace of mind in the home, and doing things

0:17:22.960 --> 0:17:24.160
<v Speaker 8>for us. I mean, in the end of the day,

0:17:24.180 --> 0:17:27.180
<v Speaker 8>it doesn't matter the technology. It's how is it better

0:17:27.220 --> 0:17:28.900
<v Speaker 8>for us to live in our homes when we have

0:17:28.940 --> 0:17:31.159
<v Speaker 8>all this stuff? And I do believe the age of

0:17:31.200 --> 0:17:32.720
<v Speaker 8>abundance is ahead of us, and we're going to have,

0:17:32.740 --> 0:17:35.200
<v Speaker 8>you know, around our area, we're going to be living

0:17:35.240 --> 0:17:37.500
<v Speaker 8>in our homes safer, more secure, and it's going to

0:17:37.540 --> 0:17:38.610
<v Speaker 8>be more peace of mind.

0:17:39.940 --> 0:17:42.950
<v Speaker 1>Ring founder, Jamie Siminoff. Thank you very much for your time.

0:17:42.810 --> 0:17:43.190
<v Speaker 5>On the show.

0:17:44.270 --> 0:17:47.949
<v Speaker 1>Okay, coming up, the hidden risks of open source AI.

0:17:48.550 --> 0:17:53.260
<v Speaker 1>Why powerful models running offline are becoming harder to police.

0:17:53.340 --> 0:17:55.879
<v Speaker 1>We have an important piece of Bloomberg reporting next. This

0:17:55.940 --> 0:18:08.810
<v Speaker 1>is Bloomberg Tech. The rise of open source AI is

0:18:08.890 --> 0:18:12.330
<v Speaker 1>creating a new challenge for law enforcement. Once powerful models

0:18:12.450 --> 0:18:16.230
<v Speaker 1>are downloaded and run privately, their safeguards can be stripped away.

0:18:16.430 --> 0:18:20.550
<v Speaker 1>And investigators say they're increasingly being used to create illegal

0:18:20.609 --> 0:18:24.520
<v Speaker 1>child sexual abuse material that can be nearly impossible to track.

0:18:25.080 --> 0:18:27.280
<v Speaker 1>It's the topic of today's Big Take. And Bloomberg's Big

0:18:27.320 --> 0:18:31.139
<v Speaker 1>Tech editor, Sarah Fryer, joins us now. Sarah, you edited

0:18:31.200 --> 0:18:34.620
<v Speaker 1>this story with the team. We talk a lot about

0:18:34.640 --> 0:18:36.960
<v Speaker 1>the risk of open source AI. And the benefits of

0:18:37.000 --> 0:18:40.060
<v Speaker 1>open source also on this program, but very simply, this

0:18:40.160 --> 0:18:45.909
<v Speaker 1>story explains how open source tech makes illegal activity easier

0:18:46.310 --> 0:18:48.990
<v Speaker 1>for human users. Can you explain why?

0:18:49.700 --> 0:18:53.340
<v Speaker 10>Well, once it's on somebody's personal device, there's no commercial

0:18:53.660 --> 0:18:56.690
<v Speaker 10>entity that it's interacting with. You can train it for

0:18:56.730 --> 0:19:00.610
<v Speaker 10>whatever purpose you have, and then it can just run locally.

0:19:01.050 --> 0:19:04.879
<v Speaker 10>And so that makes it very difficult for investigators to

0:19:04.980 --> 0:19:08.619
<v Speaker 10>find this illegal activity and stop it. And it also

0:19:09.320 --> 0:19:13.649
<v Speaker 10>means that these communities of people who engage in child

0:19:13.710 --> 0:19:18.010
<v Speaker 10>exploitative imagery, they're passing notes with each other on forums

0:19:18.270 --> 0:19:21.290
<v Speaker 10>talking about how to use open source tech to make

0:19:21.330 --> 0:19:25.660
<v Speaker 10>this content and sharing their models for people to download

0:19:25.780 --> 0:19:30.980
<v Speaker 10>on their own devices and create unlimited amounts, which is

0:19:31.140 --> 0:19:32.260
<v Speaker 10>quite concerning.

0:19:33.580 --> 0:19:37.560
<v Speaker 1>The story details case studies of child exploitation crimes, right?

0:19:37.740 --> 0:19:42.280
<v Speaker 1>And in the reporting, in talking to investigators, what have

0:19:42.359 --> 0:19:46.730
<v Speaker 1>those investigators, and I guess the industry, noticed about how

0:19:46.810 --> 0:19:51.990
<v Speaker 1>AI is changing and accelerating, frankly, what is harm to miners?

0:19:52.950 --> 0:19:57.780
<v Speaker 10>The limit to what can be created And the amount

0:19:57.800 --> 0:20:04.030
<v Speaker 10>of content is just the perpetrator's imagination. And they can

0:20:04.210 --> 0:20:07.470
<v Speaker 10>ask AI to take an image of a child that

0:20:07.650 --> 0:20:12.730
<v Speaker 10>is just a normal photo and decide to create whatever

0:20:12.790 --> 0:20:17.129
<v Speaker 10>kind of results they want, possibly an illegal result in

0:20:17.410 --> 0:20:21.870
<v Speaker 10>sexual positions. So what we're seeing here is that for investigators,

0:20:22.190 --> 0:20:26.620
<v Speaker 10>this flood of images, the dramatic increase in the amount

0:20:26.700 --> 0:20:28.899
<v Speaker 10>of illegal content that is being created is making it

0:20:28.960 --> 0:20:32.100
<v Speaker 10>very difficult for them to figure out of these children

0:20:32.140 --> 0:20:34.360
<v Speaker 10>we see. First of all, who are the real children?

0:20:34.840 --> 0:20:39.400
<v Speaker 10>Who are the generated images of real children? And then

0:20:39.880 --> 0:20:42.460
<v Speaker 10>who might be in danger today? Who might be in

0:20:42.660 --> 0:20:46.379
<v Speaker 10>physical danger in their communities that we need to help save?

0:20:46.820 --> 0:20:48.700
<v Speaker 10>And it's just becoming more difficult than ever.

0:20:50.000 --> 0:20:52.470
<v Speaker 1>Sarah, can anything be done to remedy it?

0:20:53.220 --> 0:20:57.260
<v Speaker 10>Well, it's very difficult to fix the problem in this space,

0:20:57.300 --> 0:21:00.860
<v Speaker 10>in part because as models are doing their red teaming

0:21:01.220 --> 0:21:04.730
<v Speaker 10>and trying to figure out how these tools could be

0:21:04.869 --> 0:21:09.170
<v Speaker 10>used for illegal activity, they cannot themselves create this kind

0:21:09.210 --> 0:21:13.230
<v Speaker 10>of content. It's just too illegal to do.

0:21:13.090 --> 0:21:14.710
<v Speaker 1>If you're a company. However.

0:21:15.430 --> 0:21:17.760
<v Speaker 10>There are some investigators that are trying to figure out

0:21:17.840 --> 0:21:21.680
<v Speaker 10>if they can determine which models have been fine-tuned for

0:21:21.720 --> 0:21:25.730
<v Speaker 10>this purpose and trained for this purpose to prevent them

0:21:25.770 --> 0:21:30.290
<v Speaker 10>from coming on to the model of sharing groups like

0:21:30.330 --> 0:21:33.140
<v Speaker 10>Hugging Face so they never get uploaded so that other

0:21:33.180 --> 0:21:33.919
<v Speaker 10>people can't use them.

0:21:34.930 --> 0:21:37.290
<v Speaker 1>And what Sarah was just talking about, red teaming, so interesting.

0:21:37.330 --> 0:21:40.810
<v Speaker 1>That's the sort of adversarial testing process where humans or

0:21:40.869 --> 0:21:45.210
<v Speaker 1>sometimes AI agents try to expose those vulnerabilities in the

0:21:45.250 --> 0:21:48.190
<v Speaker 1>underlying model. All of that detailed in the big take.

0:21:48.230 --> 0:21:51.320
<v Speaker 1>Bloomberg's Sarah Farrar, who edited the big take and led

0:21:51.359 --> 0:21:55.340
<v Speaker 1>the team that reported it out. Thank you very much indeed. Okay,

0:21:55.359 --> 0:21:57.659
<v Speaker 1>coming up in the show, we'll get the credit reaction

0:21:57.720 --> 0:22:01.090
<v Speaker 1>from Bloomberg Intelligence on our top story, which was SpaceX's

0:22:01.130 --> 0:22:05.550
<v Speaker 1>potential $ 40 billion Nvidia chip financing package split across loan

0:22:05.590 --> 0:22:07.680
<v Speaker 1>and debt. It's a big one in the market. See

0:22:07.700 --> 0:22:11.860
<v Speaker 1>the equities involved in that story. SpaceX, but also Nvidia

0:22:11.900 --> 0:22:16.359
<v Speaker 1>kind of under pressure. Remember Tuesday, technology stocks, but stocks

0:22:16.400 --> 0:22:18.870
<v Speaker 1>in general closed at record highs on the S &amp;

0:22:18.400 --> 0:22:22.470
<v Speaker 1>P 500 and NASDAQ 100. We're hitting the pause button

0:22:22.490 --> 0:22:26.250
<v Speaker 1>on that a little bit. Particular underperformance in semiconductors, the

0:22:26.290 --> 0:22:29.689
<v Speaker 1>SOX or Philadelphia Semiconductor Index down 2%. And again, we're

0:22:29.710 --> 0:22:33.669
<v Speaker 1>going to go back into that story. about SpaceX. Also

0:22:34.210 --> 0:22:36.909
<v Speaker 1>in the background, the world of IPOs. There's a lot

0:22:36.930 --> 0:22:38.990
<v Speaker 1>more to come. It's halftime here in the city of

0:22:39.040 --> 0:22:42.040
<v Speaker 1>San Francisco. Thanks for being with us. You're watching Bloomberg Tech.

0:23:01.270 --> 0:23:03.270
<v Speaker 1>Welcome back to Bloomberg Tech. Let's get back to today's

0:23:03.310 --> 0:23:06.940
<v Speaker 1>top story, which is SpaceX in talks to borrow $ 40

0:23:06.619 --> 0:23:10.359
<v Speaker 1>billion to buy NVIDIA chips. From the equity part of

0:23:10.400 --> 0:23:13.699
<v Speaker 1>the story, the stock under pressure down about 2%. Bloomberg

0:23:13.720 --> 0:23:16.200
<v Speaker 1>Intelligence already out with a credit react saying the deal

0:23:16.280 --> 0:23:20.540
<v Speaker 1>underscores the substantial pools of capital available to fund a

0:23:20.560 --> 0:23:24.180
<v Speaker 1>broader AI build out. Bloomberg Intelligence analyst Robert Schiffman joins

0:23:24.260 --> 0:23:28.619
<v Speaker 1>us with that react. I saw the CDS chart, right?

0:23:28.920 --> 0:23:30.350
<v Speaker 1>But at the end of the day, you and I

0:23:30.350 --> 0:23:33.610
<v Speaker 1>have been talking regularly and for some time now that

0:23:33.630 --> 0:23:36.899
<v Speaker 1>the capital is there and there's a willingness from capital

0:23:36.940 --> 0:23:39.720
<v Speaker 1>markets to support what's going on. Just take me through

0:23:39.740 --> 0:23:40.119
<v Speaker 1>the react.

0:23:41.770 --> 0:23:44.879
<v Speaker 6>Yeah, listen, quite frankly, CDS has barely budged and bonds

0:23:44.890 --> 0:23:48.359
<v Speaker 6>have barely budged. We have told people to buckle up.

0:23:48.800 --> 0:23:51.240
<v Speaker 6>There is a lot of this to come. And quite frankly,

0:23:51.300 --> 0:23:54.300
<v Speaker 6>today's announcement, or at least last night's announcement, I think

0:23:54.380 --> 0:23:58.070
<v Speaker 6>is wildly positive. Coming up with creative ways to finance

0:23:58.130 --> 0:24:02.290
<v Speaker 6>balance sheets that are just not straight, unsecured debt. Make

0:24:02.310 --> 0:24:06.650
<v Speaker 6>a ton of sense. And I think having partners like Apollo, NVIDIA,

0:24:06.859 --> 0:24:11.480
<v Speaker 6>PIMCO helping potentially to securitize chips to help NVIDIA raise

0:24:11.480 --> 0:24:15.280
<v Speaker 6>$ 40 billion is fantastic. Quite frankly, they may have a

0:24:15.380 --> 0:24:17.899
<v Speaker 6>couple hundred billion dollars to raise over the next few years.

0:24:18.720 --> 0:24:20.929
<v Speaker 6>So I think this sort of creativity is good for

0:24:20.950 --> 0:24:23.490
<v Speaker 6>the market. And I think demand for bonds like this

0:24:23.830 --> 0:24:24.630
<v Speaker 6>are going to be through the roof.

0:24:26.210 --> 0:24:28.750
<v Speaker 1>Robert Schiffman of Bloomberg Intelligence. Check out his react on

0:24:28.770 --> 0:24:31.090
<v Speaker 1>the Bloomberg terminal. Thank you very much. Okay, two of

0:24:31.109 --> 0:24:33.629
<v Speaker 1>the biggest names in artificial intelligence are moving toward the

0:24:33.670 --> 0:24:37.570
<v Speaker 1>public markets, but in different timelines. And the potential scale

0:24:37.609 --> 0:24:41.080
<v Speaker 1>of open AI and anthropic could test investor appetite for

0:24:41.119 --> 0:24:42.720
<v Speaker 1>AI like never before.

0:24:42.840 --> 0:24:43.060
<v Speaker 9>J.D.

0:24:43.100 --> 0:24:45.920
<v Speaker 1>Moriarty is Vice Chair and Head of Global Technology, Equity

0:24:45.960 --> 0:24:48.780
<v Speaker 1>Capital Markets at Bank of America Securities. He's in San

0:24:48.800 --> 0:24:53.520
<v Speaker 1>Francisco for Bank of America's 15th Annual Technology Innovation Summit, where...

0:24:53.800 --> 0:24:57.580
<v Speaker 1>AI is the anchor of these conversations, one would suspect. JD,

0:24:57.619 --> 0:25:00.800
<v Speaker 1>what do you think everyone's going to be talking about

0:25:00.820 --> 0:25:01.640
<v Speaker 1>in town this week?

0:25:01.780 --> 0:25:03.180
<v Speaker 2>I think you just told us, and I think you're

0:25:03.200 --> 0:25:06.700
<v Speaker 2>probably right. The majority of the discussion will be certainly

0:25:06.740 --> 0:25:09.879
<v Speaker 2>around AI. The conference has been going on for, this

0:25:09.960 --> 0:25:12.040
<v Speaker 2>is the 15th year that we've done it, and the

0:25:12.100 --> 0:25:16.840
<v Speaker 2>Tech Innovation Summit is intended to give companies an opportunity

0:25:16.859 --> 0:25:20.400
<v Speaker 2>to talk to our technology department, to better understand our needs,

0:25:20.480 --> 0:25:23.940
<v Speaker 2>and for us to discover interesting companies along the way, right?

0:25:23.980 --> 0:25:26.220
<v Speaker 2>So it's really, this is a conference not put on

0:25:26.260 --> 0:25:28.790
<v Speaker 2>by investment banking, but put on by our technology department.

0:25:30.130 --> 0:25:33.389
<v Speaker 2>And there's been an unbelievable track record of many companies

0:25:33.430 --> 0:25:35.510
<v Speaker 2>that you're aware of today that, you know, 10 years

0:25:35.550 --> 0:25:38.570
<v Speaker 2>ago were at this conference and became partners of ours.

0:25:38.869 --> 0:25:42.080
<v Speaker 1>Just real quick on the... corporate side, the use of

0:25:42.140 --> 0:25:46.310
<v Speaker 1>AI in your industry, is there a good economic proposition?

0:25:46.619 --> 0:25:48.970
<v Speaker 1>When you're offered different tools, do you think the banks

0:25:48.990 --> 0:25:51.810
<v Speaker 1>look at it and say, yes, it's worth spending money on?

0:25:52.130 --> 0:25:53.370
<v Speaker 1>We see a benefit for this.

0:25:53.450 --> 0:25:53.870
<v Speaker 2>Yeah.

0:25:53.910 --> 0:25:55.930
<v Speaker 1>Listen, I think that's evolving.

0:25:56.670 --> 0:26:00.530
<v Speaker 2>I think the definition of that economic proposition will continue

0:26:00.590 --> 0:26:04.490
<v Speaker 2>to evolve. Certainly in terms of You can't afford to

0:26:04.590 --> 0:26:08.220
<v Speaker 2>wait for that economic proposition to be totally clear given

0:26:08.240 --> 0:26:10.220
<v Speaker 2>the rate of change and given what all of our

0:26:10.240 --> 0:26:13.350
<v Speaker 2>competitors are doing. But even just this year, in terms

0:26:13.380 --> 0:26:20.520
<v Speaker 2>of our internal utilization, we've seen that play out. The

0:26:20.560 --> 0:26:24.100
<v Speaker 2>narrative is going to evolve in terms of the use

0:26:24.160 --> 0:26:26.300
<v Speaker 2>case for all the major guys that you talk about

0:26:26.340 --> 0:26:29.479
<v Speaker 2>in the context of the prospective IPOs and then how

0:26:29.540 --> 0:26:32.140
<v Speaker 2>vertically oriented it will be as well. But it's certainly

0:26:32.180 --> 0:26:34.080
<v Speaker 2>helping us in terms of internal efficiency.

0:26:34.300 --> 0:26:39.469
<v Speaker 1>Okay, so those prospective IPOs, Bloomberg's reporting is that Anthropic's

0:26:39.670 --> 0:26:42.929
<v Speaker 1>timeline is to try and go before Thanksgiving. But as

0:26:42.970 --> 0:26:47.230
<v Speaker 1>a case study, right, Anthropic or OpenAI, how is it different?

0:26:47.910 --> 0:26:50.410
<v Speaker 1>to IPOs in the past in terms of what those

0:26:50.470 --> 0:26:54.669
<v Speaker 1>companies need to say to investors to help them understand

0:26:55.080 --> 0:26:58.480
<v Speaker 1>their futures. You know, I'm not necessarily even talking about financials.

0:26:58.520 --> 0:27:02.419
<v Speaker 1>I'm talking about, yes, their capital needs in the future,

0:27:02.660 --> 0:27:05.480
<v Speaker 1>but also like this idea of the risk disclosures being

0:27:06.020 --> 0:27:09.770
<v Speaker 1>completely novel, you know, a threat to human life existentially.

0:27:09.790 --> 0:27:09.950
<v Speaker 6>Yeah.

0:27:10.450 --> 0:27:13.109
<v Speaker 1>That's a hard one to put into a document.

0:27:13.250 --> 0:27:15.810
<v Speaker 2>It is rare that we're having a conversation in the

0:27:15.869 --> 0:27:17.909
<v Speaker 2>media about risk factors, for sure.

0:27:20.910 --> 0:27:23.220
<v Speaker 1>I wouldn't look at those two.

0:27:23.140 --> 0:27:26.919
<v Speaker 2>Prospective IPOs as indicative of the broader IPO market. Obviously,

0:27:27.040 --> 0:27:29.700
<v Speaker 2>we're dealing with something that's, whether it's SpaceX or Anthropic

0:27:29.740 --> 0:27:33.870
<v Speaker 2>or OpenAI, very different with respect to just a sheer scale, right?

0:27:33.880 --> 0:27:34.070
<v Speaker 9>Yes.

0:27:34.390 --> 0:27:37.170
<v Speaker 2>And recognize that the IPO market has evolved a great

0:27:37.190 --> 0:27:41.300
<v Speaker 2>deal because of the abundance of private capital. And crossover

0:27:41.320 --> 0:27:46.800
<v Speaker 2>investors that are already in each of SpaceX before its IPO,

0:27:46.940 --> 0:27:51.770
<v Speaker 2>in OpenAI already, Anthropic already. So recognize that institutionally, those

0:27:51.830 --> 0:27:54.830
<v Speaker 2>companies are very well known by the buy side long

0:27:54.890 --> 0:27:56.030
<v Speaker 2>before the IPO process.

0:27:56.970 --> 0:27:58.109
<v Speaker 1>May I give you a case study?

0:27:58.250 --> 0:27:58.510
<v Speaker 9>Sure.

0:27:58.530 --> 0:28:02.350
<v Speaker 1>Emma Norchette from T. Rowe Price was on the show yesterday.

0:28:02.390 --> 0:28:07.640
<v Speaker 1>She does private investments. She explained they have multibillion-dollar exposure

0:28:07.700 --> 0:28:10.340
<v Speaker 1>to both open AI and anthropic, but that part of

0:28:10.359 --> 0:28:12.620
<v Speaker 1>their underlying thesis is that there is room for both

0:28:12.680 --> 0:28:15.840
<v Speaker 1>of them at the frontier in the world in the end. Now,

0:28:15.940 --> 0:28:19.960
<v Speaker 1>from the public market investor's side, from an IPO story's perspective,

0:28:20.480 --> 0:28:23.200
<v Speaker 1>is there room for two of these things to happen

0:28:23.240 --> 0:28:26.770
<v Speaker 1>in proximity? Three, if you go back to June and SpaceX.

0:28:27.030 --> 0:28:30.870
<v Speaker 2>Well, we certainly started the year, as we talked about SpaceX,

0:28:30.970 --> 0:28:35.790
<v Speaker 2>and everybody in our world was questioning exactly that. What

0:28:35.810 --> 0:28:39.010
<v Speaker 2>would be the impact on the market with the SpaceX IPO?

0:28:39.310 --> 0:28:41.430
<v Speaker 2>We were looking at how much cash was on the sidelines.

0:28:41.770 --> 0:28:43.790
<v Speaker 2>What would need to be sold to buy that IPO?

0:28:44.870 --> 0:28:48.050
<v Speaker 2>And as a reminder, just as that IPO went on

0:28:48.070 --> 0:28:51.570
<v Speaker 2>the road, Alphabet came and raised a huge amount of

0:28:51.610 --> 0:28:53.110
<v Speaker 2>capital in the public market.

0:28:53.130 --> 0:28:53.250
<v Speaker 5>Right.

0:28:53.920 --> 0:28:55.700
<v Speaker 1>And it was a blip, right?

0:28:56.240 --> 0:28:58.820
<v Speaker 2>The market absorbed it quickly, just as the market has

0:28:58.880 --> 0:29:03.200
<v Speaker 2>absorbed and rewarded SpaceX, right? So there's less conversation about

0:29:03.280 --> 0:29:04.200
<v Speaker 2>capacity today.

0:29:04.640 --> 0:29:09.250
<v Speaker 1>That's so interesting because you heard what Robert was discussing,

0:29:09.290 --> 0:29:14.010
<v Speaker 1>this Bloomberg report around SpaceX in talks to borrow $ 40 billion.

0:29:14.470 --> 0:29:18.380
<v Speaker 1>But what was amazing about the period post-SpaceX IPO was

0:29:18.730 --> 0:29:24.200
<v Speaker 1>the speed at which they returned to markets. Again, Anthropic

0:29:24.320 --> 0:29:28.000
<v Speaker 1>and OpenAI, they go next. But do investors accept that

0:29:28.200 --> 0:29:30.100
<v Speaker 1>that's the operating environment?

0:29:30.140 --> 0:29:35.710
<v Speaker 2>I think investors certainly accept that the traditional construct of

0:29:35.730 --> 0:29:37.670
<v Speaker 2>you go public and you don't do anything for six

0:29:37.710 --> 0:29:39.910
<v Speaker 2>months for these companies is irrelevant. Right.

0:29:40.200 --> 0:29:40.840
<v Speaker 1>Serve them well.

0:29:49.450 --> 0:29:51.830
<v Speaker 2>In fact, just this morning, we were having a discussion

0:29:51.870 --> 0:29:55.210
<v Speaker 2>with our colleagues in our investment grade area about the

0:29:55.250 --> 0:29:58.890
<v Speaker 2>amount of CapEx that the hyperscalers project. And if you

0:29:58.950 --> 0:30:03.050
<v Speaker 2>look at their behavior in 25 and 26, where essentially

0:30:03.070 --> 0:30:06.270
<v Speaker 2>their CapEx projections went up by 20% in 25 and

0:30:06.630 --> 0:30:09.490
<v Speaker 2>remains to be seen on 26 final numbers, but if

0:30:09.510 --> 0:30:13.800
<v Speaker 2>you project that out to 2030, it projects a staggering

0:30:13.840 --> 0:30:16.800
<v Speaker 2>amount of CapEx that will be supported by the investment

0:30:16.820 --> 0:30:21.350
<v Speaker 2>grade market And historically, about half of that capex has

0:30:21.370 --> 0:30:26.570
<v Speaker 2>been there. That will have huge implications for the high-grade index.

0:30:27.470 --> 0:30:31.010
<v Speaker 2>So additional pools of capital are actually very good for

0:30:31.070 --> 0:30:33.900
<v Speaker 2>all of those companies and for the functioning of the market.

0:30:34.020 --> 0:30:37.640
<v Speaker 2>So if you just look at it, it's timely that

0:30:37.660 --> 0:30:42.420
<v Speaker 2>you're seeing that announcement because what is going on in

0:30:42.480 --> 0:30:47.320
<v Speaker 2>that market to fund the hyperscaler capex has implications for

0:30:47.400 --> 0:30:49.219
<v Speaker 2>all of our markets. Our markets are more connected than

0:30:49.240 --> 0:30:49.780
<v Speaker 2>they've ever been.

0:30:49.800 --> 0:30:53.150
<v Speaker 1>I think the challenge of understanding what's going on is,

0:30:53.310 --> 0:30:55.990
<v Speaker 1>you know, Daniela Amodei said herself at a Bloomberg event

0:30:56.030 --> 0:30:59.330
<v Speaker 1>in June, the IPO, the rationale is that's the mechanism

0:30:59.370 --> 0:31:03.570
<v Speaker 1>to raise capital at the levels you need. But then Alphabet,

0:31:03.610 --> 0:31:06.090
<v Speaker 1>as you said, was able to do it in the

0:31:06.170 --> 0:31:09.060
<v Speaker 1>equity markets quite willingly. I guess let's end our conversation

0:31:09.080 --> 0:31:12.560
<v Speaker 1>with like, what was the lesson from SpaceX's IPO, the

0:31:12.600 --> 0:31:16.500
<v Speaker 1>scale of it? And what it foreshadows, foreshadows is a

0:31:16.540 --> 0:31:20.260
<v Speaker 1>harsh term, what it sets us up for in markets.

0:31:20.680 --> 0:31:24.160
<v Speaker 2>The lesson is that the capacity is absolutely there in

0:31:24.200 --> 0:31:28.910
<v Speaker 2>the equity market. The lesson is that they are able

0:31:28.950 --> 0:31:30.850
<v Speaker 2>to tap other markets as well.

0:31:31.110 --> 0:31:31.370
<v Speaker 5>Right.

0:31:32.190 --> 0:31:34.930
<v Speaker 2>The lesson is that retail investors very much want to participate.

0:31:34.950 --> 0:31:37.490
<v Speaker 1>So you, therefore, in response, prioritize AI deals?

0:31:37.510 --> 0:31:37.580
<v Speaker 6>Yes.

0:31:38.340 --> 0:31:39.600
<v Speaker 1>Because you see the demand there.

0:31:39.660 --> 0:31:40.180
<v Speaker 9>Oh, of course.

0:31:40.220 --> 0:31:42.180
<v Speaker 2>The demand is there both institutionally and from retail.

0:31:42.220 --> 0:31:44.320
<v Speaker 1>Yeah, so for your job, I mean.

0:31:44.460 --> 0:31:44.940
<v Speaker 2>Oh, for sure.

0:31:44.960 --> 0:31:47.610
<v Speaker 1>Can I just ask you what the first half of

0:31:47.650 --> 0:31:51.090
<v Speaker 1>next year looks like to you if you had to

0:31:51.150 --> 0:31:55.230
<v Speaker 1>project something today and maybe how your thinking's changed?

0:31:55.350 --> 0:31:59.250
<v Speaker 2>Well, listen, it's been a wonderful market, but not for everyone, right?

0:31:59.430 --> 0:32:03.670
<v Speaker 2>So it is a market that is, we've exceeded 2021

0:32:03.670 --> 0:32:06.680
<v Speaker 2>levels of productivity in the IPO market on a proceeds basis.

0:32:07.190 --> 0:32:12.040
<v Speaker 2>with many fewer deals, much larger deals, right? Software has

0:32:12.060 --> 0:32:16.420
<v Speaker 2>been absent because of the potential disruption of AI. And

0:32:16.540 --> 0:32:19.640
<v Speaker 2>so if we fast forward to next year, that will

0:32:19.660 --> 0:32:22.050
<v Speaker 2>be interesting to see whether there's a viable market for

0:32:22.150 --> 0:32:23.510
<v Speaker 2>more traditional technology.

0:32:25.000 --> 0:32:27.380
<v Speaker 1>Are you hiring people for those specialisms?

0:32:27.900 --> 0:32:28.140
<v Speaker 7>Sure.

0:32:28.590 --> 0:32:32.310
<v Speaker 1>Yeah, to try and accommodate that changing opportunity.

0:32:32.330 --> 0:32:34.430
<v Speaker 2>But I would say hiring on a go-forward basis is

0:32:34.490 --> 0:32:38.469
<v Speaker 2>probably less specialized, right? You have to be more nimble

0:32:38.570 --> 0:32:42.340
<v Speaker 2>across business model. And so to just say software, AI

0:32:42.420 --> 0:32:45.140
<v Speaker 2>is changing the definition of a number of these companies

0:32:45.200 --> 0:32:45.800
<v Speaker 2>and subsectors.

0:32:46.430 --> 0:32:49.130
<v Speaker 1>JD Moriarty of Bank of America. It's really great to

0:32:49.150 --> 0:32:50.990
<v Speaker 1>have you here. Great to meet you. Thank you very

0:32:51.030 --> 0:32:54.270
<v Speaker 1>much indeed. Coming up, Mecca, a startup working to build

0:32:54.290 --> 0:32:58.020
<v Speaker 1>the foundation layer of deploying physical AI, just raised $ 60 million.

0:32:58.120 --> 0:33:00.700
<v Speaker 1>We're going to speak with CEO Josh Gao. That's next.

0:33:01.080 --> 0:33:13.030
<v Speaker 1>This is Bloomberg Tech. Mecha AI, a startup that designs

0:33:13.140 --> 0:33:17.010
<v Speaker 1>systems to help robots understand and operate in the physical world.

0:33:17.350 --> 0:33:19.870
<v Speaker 1>There's already $ 60 million in new funding. The Series B round,

0:33:20.190 --> 0:33:24.350
<v Speaker 1>led by Sequoia Capital, we're backing from NVIDIA, Microsoft's M12,

0:33:24.630 --> 0:33:28.380
<v Speaker 1>and Qualcomm, among others. Mecha co-founder and CEO Josh Gow

0:33:28.420 --> 0:33:30.880
<v Speaker 1>joins us here in San Francisco. I want to start

0:33:30.900 --> 0:33:33.540
<v Speaker 1>with what Mecha AI is and does. You know, we

0:33:33.560 --> 0:33:36.560
<v Speaker 1>do a lot on this show about world models. that

0:33:36.840 --> 0:33:41.140
<v Speaker 1>underpin physical AI deployments, but MechEd's doing something slightly different.

0:33:41.900 --> 0:33:43.000
<v Speaker 1>Thanks so much for having me, Ed.

0:33:43.400 --> 0:33:45.180
<v Speaker 9>Yeah, I think a big part of this is that

0:33:45.340 --> 0:33:49.240
<v Speaker 9>robotics is entering this deep learning era. A big challenge

0:33:49.280 --> 0:33:51.600
<v Speaker 9>for a lot of traditional robotics has been they weren't

0:33:51.720 --> 0:33:52.719
<v Speaker 9>able to generalize.

0:33:53.240 --> 0:33:55.100
<v Speaker 1>The world is incredibly.

0:33:54.480 --> 0:33:58.390
<v Speaker 9>Chaotic in the amount of noise, light changes, even the

0:33:58.430 --> 0:34:00.630
<v Speaker 9>dexterity of a lot of motions that we have to

0:34:00.670 --> 0:34:03.970
<v Speaker 9>go through. You can imagine just understanding physics is a

0:34:04.010 --> 0:34:05.170
<v Speaker 9>very challenging problem.

0:34:05.390 --> 0:34:05.630
<v Speaker 8>Yes.

0:34:05.850 --> 0:34:08.310
<v Speaker 9>And so for us, when we think about models, ultimately

0:34:08.350 --> 0:34:10.489
<v Speaker 9>it comes down to three things in this new deep

0:34:10.510 --> 0:34:13.279
<v Speaker 9>learning era for us. And that is, one, we have

0:34:13.300 --> 0:34:15.900
<v Speaker 9>to have compute algorithms and data.

0:34:16.540 --> 0:34:17.660
<v Speaker 1>And so some of the work that.

0:34:17.580 --> 0:34:20.299
<v Speaker 9>We do is how can we digitize a lot of

0:34:20.320 --> 0:34:24.280
<v Speaker 9>the physical world? There is incredible detail in the world

0:34:24.660 --> 0:34:27.030
<v Speaker 9>about how much pressure.

0:34:26.670 --> 0:34:28.470
<v Speaker 1>It takes to interact with objects.

0:34:29.130 --> 0:34:31.510
<v Speaker 9>What happens if I drop a cup and it shatters

0:34:31.570 --> 0:34:33.110
<v Speaker 9>on the floor? How do I know where the pieces

0:34:33.170 --> 0:34:35.669
<v Speaker 9>are going to be? This imagination is really the data

0:34:35.690 --> 0:34:37.070
<v Speaker 9>that we're trying to digitize.

0:34:37.310 --> 0:34:41.489
<v Speaker 1>And also how that data originates, right? So I think

0:34:41.810 --> 0:34:44.049
<v Speaker 1>a case study I always think about is me watching

0:34:44.650 --> 0:34:49.300
<v Speaker 1>engineers control a robotic arm repetitively to generate data of

0:34:49.360 --> 0:34:52.080
<v Speaker 1>moving an object from point A to point B. But

0:34:52.320 --> 0:34:55.359
<v Speaker 1>there is now a huge role for synthetic data. Getting

0:34:55.400 --> 0:34:59.040
<v Speaker 1>lots of real-world data is hard. How are you taking

0:34:59.080 --> 0:34:59.480
<v Speaker 1>that on?

0:35:00.120 --> 0:35:02.880
<v Speaker 9>I think a great example is if you look at AlphaGo,

0:35:02.920 --> 0:35:07.790
<v Speaker 9>for example. AlphaGo had behavior cloning with millions of different

0:35:07.810 --> 0:35:10.730
<v Speaker 9>Go games played over time. And then ultimately, through the

0:35:10.790 --> 0:35:13.299
<v Speaker 9>rules of the game, we were able to have self-play.

0:35:14.280 --> 0:35:17.020
<v Speaker 9>And the big difference here is that just the rules

0:35:17.060 --> 0:35:20.029
<v Speaker 9>of the game of Go is the entire world. In

0:35:20.230 --> 0:35:24.750
<v Speaker 9>our situation, the entire physical world can't be digitized right

0:35:24.790 --> 0:35:28.430
<v Speaker 9>into rules right away. And so the gap for us

0:35:28.450 --> 0:35:30.570
<v Speaker 9>to be able to create what is a perfect world

0:35:30.590 --> 0:35:33.380
<v Speaker 9>model or a perfect simulator for us to have synthetic

0:35:33.400 --> 0:35:35.700
<v Speaker 9>data is a long ways away. And hopefully we can

0:35:35.719 --> 0:35:39.020
<v Speaker 9>fill that gap across many different modalities and domains.

0:35:39.440 --> 0:35:41.879
<v Speaker 1>How does Mecca's business run? What is it that you

0:35:41.940 --> 0:35:45.680
<v Speaker 1>sell or what is your product? There's ultimately three stacks

0:35:45.920 --> 0:35:49.069
<v Speaker 1>in robotics. Can we handle the hardware, the software, and

0:35:49.090 --> 0:35:51.730
<v Speaker 1>the operations really well? So the first piece of the

0:35:51.770 --> 0:35:56.390
<v Speaker 1>business is ultimately, does the hardware exist to sensorize.

0:35:55.750 --> 0:35:58.450
<v Speaker 9>This information in the world? And so a great example

0:35:58.550 --> 0:36:01.839
<v Speaker 9>actually is force data. Typically when I'm grabbing a cup,

0:36:02.280 --> 0:36:05.260
<v Speaker 9>when I'm opening a door handle, when I'm twisting a bottle,

0:36:05.640 --> 0:36:09.060
<v Speaker 9>how many newtons does it take to apply the pressure

0:36:09.080 --> 0:36:12.120
<v Speaker 9>to And so our work is first, can we create

0:36:12.160 --> 0:36:15.330
<v Speaker 9>those sensors if they don't exist? We manufacture, we design,

0:36:15.370 --> 0:36:19.090
<v Speaker 9>we produce them at scale. And the second is ultimately operations.

0:36:19.610 --> 0:36:23.180
<v Speaker 9>We distribute and run logistics internationally so that we can

0:36:23.219 --> 0:36:26.620
<v Speaker 9>go into different scenarios, all the edge cases, different types

0:36:26.660 --> 0:36:30.680
<v Speaker 9>of businesses. An excellent example in this case is we've

0:36:30.700 --> 0:36:34.390
<v Speaker 9>also had Tony Hsu backing us. The founder of Doge

0:36:34.510 --> 0:36:38.410
<v Speaker 9>is probably the best operator or top operator in the

0:36:38.450 --> 0:36:40.390
<v Speaker 9>world at this point. And so we've had to go

0:36:40.430 --> 0:36:42.880
<v Speaker 9>through this muscle building of what does it take to

0:36:42.940 --> 0:36:45.200
<v Speaker 9>actually get the physical.

0:36:45.520 --> 0:36:50.760
<v Speaker 1>Intelligence into the world ultimately. Before we started the segment

0:36:50.780 --> 0:36:52.799
<v Speaker 1>with you, you said to me during the commercial break,

0:36:52.860 --> 0:36:56.730
<v Speaker 1>we are just getting started. But you've raised significant amount

0:36:56.750 --> 0:36:58.850
<v Speaker 1>of capital. You know, what are the priorities for you

0:36:59.090 --> 0:37:01.800
<v Speaker 1>where you need to hire people and what it is

0:37:02.340 --> 0:37:03.460
<v Speaker 1>you're going to do to scale?

0:37:04.680 --> 0:37:07.640
<v Speaker 9>I think a big piece of even thinking about the

0:37:07.700 --> 0:37:09.620
<v Speaker 9>scale that we need to be at, you can look

0:37:09.660 --> 0:37:12.190
<v Speaker 9>at Tesla in terms of spending a decade to solve

0:37:12.270 --> 0:37:15.450
<v Speaker 9>autonomous driving. They solve for two degrees of freedom of

0:37:15.530 --> 0:37:19.770
<v Speaker 9>acceleration and then direction. In robotics, we have many dozens

0:37:19.810 --> 0:37:23.070
<v Speaker 9>of degrees of freedom, even in our hands, every joint movement,

0:37:23.110 --> 0:37:26.850
<v Speaker 9>every body movement. And that scale of data is quite exponential.

0:37:27.300 --> 0:37:28.779
<v Speaker 9>So a lot of this funding and what we're trying

0:37:28.800 --> 0:37:31.380
<v Speaker 9>to do is we're trying to lay the infrastructure to

0:37:31.420 --> 0:37:32.220
<v Speaker 9>make this possible.

0:37:32.700 --> 0:37:34.500
<v Speaker 1>In many regions, even to support this data.

0:37:34.520 --> 0:37:37.080
<v Speaker 9>We have to set up hundreds of gigabytes of upload

0:37:37.100 --> 0:37:40.770
<v Speaker 9>bandwidth that doesn't exist before. And so set up the infrastructure,

0:37:40.989 --> 0:37:46.550
<v Speaker 9>scale in terms of hiring, attracting top-tier robotics talent, infrastructure, operations, hardware,

0:37:47.489 --> 0:37:48.719
<v Speaker 9>and ultimately be able to scale up.

0:37:49.219 --> 0:37:51.320
<v Speaker 1>Josh Gow, CEO and co-founder of Mecco. It's great to

0:37:51.340 --> 0:37:53.980
<v Speaker 1>have you on the show. Thank you very much indeed. Okay,

0:37:54.100 --> 0:37:58.500
<v Speaker 1>coming up, pictures, not prompts. Marissa Mayer is back with

0:37:58.540 --> 0:38:01.520
<v Speaker 1>a new AI assistant that turns your camera roll into

0:38:01.560 --> 0:38:04.480
<v Speaker 1>a tool for getting things done. Really looking forward to

0:38:04.500 --> 0:38:10.629
<v Speaker 1>this one. That's next. This is Bloomberg Tech. Most AI

0:38:10.670 --> 0:38:14.120
<v Speaker 1>assistants start with a prompt. Marissa Meyer is betting yours

0:38:14.180 --> 0:38:16.980
<v Speaker 1>should start with your camera roll. The former Yahoo CEO

0:38:17.020 --> 0:38:20.200
<v Speaker 1>and longtime Google exec is launching Dazzle, a personal AI

0:38:20.320 --> 0:38:24.319
<v Speaker 1>system that uses your photos and screenshots to learn what

0:38:24.400 --> 0:38:27.339
<v Speaker 1>matters to you. Marissa Merge joins us now here in SF.

0:38:27.700 --> 0:38:31.620
<v Speaker 1>This is so interesting. Again, the basics, you know, we're

0:38:31.680 --> 0:38:33.980
<v Speaker 1>used to saying, do this for me or through the voice,

0:38:34.020 --> 0:38:37.050
<v Speaker 1>do this for me or and the agent acts. This

0:38:37.070 --> 0:38:40.879
<v Speaker 1>is live real time monitoring of camera roll. Yes.

0:38:40.960 --> 0:38:44.149
<v Speaker 11>So what Dazzle does is a personal AI assistant that

0:38:44.190 --> 0:38:46.890
<v Speaker 11>learns from your camera roll. And it does two things. One,

0:38:46.910 --> 0:38:49.770
<v Speaker 11>it looks over your history of your photos and it

0:38:49.810 --> 0:38:52.149
<v Speaker 11>tries to understand you. Who's important in your life?

0:38:52.350 --> 0:38:54.029
<v Speaker 1>What are your hobbies? Where do you like to go?

0:38:54.050 --> 0:38:56.420
<v Speaker 11>How do you like to spend your time? And then

0:38:56.760 --> 0:38:59.500
<v Speaker 11>we also have an AI agent that monitors your camera roll.

0:38:59.820 --> 0:39:02.220
<v Speaker 11>So when you take a new photo or a new screenshot,

0:39:03.120 --> 0:39:05.680
<v Speaker 11>it monitors and says, wait, why did Ed take this

0:39:05.719 --> 0:39:06.219
<v Speaker 11>picture today?

0:39:06.560 --> 0:39:09.060
<v Speaker 1>What can I do to help him? Give me a

0:39:09.100 --> 0:39:11.279
<v Speaker 1>case study. I have one for you, but you give

0:39:11.300 --> 0:39:13.250
<v Speaker 1>me one first. A case study that helps the audience

0:39:13.270 --> 0:39:16.030
<v Speaker 1>understand why this is a good idea, like why it works.

0:39:16.350 --> 0:39:16.610
<v Speaker 7>Sure.

0:39:16.810 --> 0:39:18.910
<v Speaker 11>Well, a couple of different case studies. So one is,

0:39:19.250 --> 0:39:20.930
<v Speaker 11>the idea is like if I see something I like,

0:39:21.190 --> 0:39:24.009
<v Speaker 11>I'm shopping online, I'm busy, and so I can take

0:39:24.030 --> 0:39:26.029
<v Speaker 11>a screenshot, or I could, if I saw something, I

0:39:26.050 --> 0:39:28.609
<v Speaker 11>could take a picture of it. And Dazzle actually comes

0:39:28.670 --> 0:39:30.549
<v Speaker 11>back to me and says, did you want to buy this?

0:39:31.950 --> 0:39:35.640
<v Speaker 11>And it can overall make it a seamless purchase.

0:39:35.660 --> 0:39:37.780
<v Speaker 1>We're showing that kind of case study on the screen

0:39:37.820 --> 0:39:39.000
<v Speaker 1>right now. Exactly.

0:39:39.300 --> 0:39:41.980
<v Speaker 11>And the other thing that it can do is it

0:39:42.360 --> 0:39:46.390
<v Speaker 11>really understands at a very deep level Things that you're into,

0:39:46.450 --> 0:39:48.410
<v Speaker 11>things that your kids are into. My kids are off

0:39:48.450 --> 0:39:51.009
<v Speaker 11>of school on Friday. I asked Dazzle what should they

0:39:51.050 --> 0:39:53.830
<v Speaker 11>do on Friday. And I said, you know, Marielle's been

0:39:53.850 --> 0:39:58.500
<v Speaker 11>really into magic lately. Why doesn't she and her friend

0:39:59.660 --> 0:40:03.000
<v Speaker 11>go to a magic shop? And they picked a specific

0:40:03.080 --> 0:40:07.020
<v Speaker 11>one here in San Francisco, Misperceptions Magic Shop, where they

0:40:07.060 --> 0:40:10.240
<v Speaker 11>can go buy tricks, learn tricks. and spend the afternoon.

0:40:10.380 --> 0:40:12.859
<v Speaker 11>And so the fact that I could actually understand that

0:40:12.920 --> 0:40:15.620
<v Speaker 11>my daughter is going through a phase where she's really

0:40:15.680 --> 0:40:20.310
<v Speaker 11>entranced with magic is pretty fun and pretty interesting. Same thing,

0:40:20.350 --> 0:40:23.509
<v Speaker 11>I asked to tell my mother to buy skis. And

0:40:23.570 --> 0:40:25.710
<v Speaker 11>I said, well, because she's 76 years old, and because

0:40:25.730 --> 0:40:27.759
<v Speaker 11>this is where she skis, and roughly her stature, and

0:40:27.780 --> 0:40:30.200
<v Speaker 11>she loves the Rossignol brand, why.

0:40:30.080 --> 0:40:32.100
<v Speaker 1>Doesn't she buy this pair of skis? Based on photos.

0:40:32.160 --> 0:40:34.120
<v Speaker 11>Based on photos, because it's got pictures of her skiing.

0:40:34.180 --> 0:40:39.870
<v Speaker 1>Of those experiences. Let's talk about technology. How did you

0:40:39.969 --> 0:40:42.910
<v Speaker 1>build this? How did you get to what we're showing

0:40:42.989 --> 0:40:46.620
<v Speaker 1>is Dazzle AI today? And tell me a little bit

0:40:46.640 --> 0:40:49.839
<v Speaker 1>about the underlying technologies that support the agent. Sure.

0:40:49.980 --> 0:40:53.819
<v Speaker 11>Well, we obviously are using some frontier models to handle users'

0:40:53.880 --> 0:40:56.509
<v Speaker 11>requests and chats. But what we're doing is we're also building...

0:40:56.570 --> 0:40:59.870
<v Speaker 11>a deep context layer. So we try to understand who

0:40:59.910 --> 0:41:03.850
<v Speaker 11>you are and add that context as relevant to the

0:41:03.890 --> 0:41:06.989
<v Speaker 11>queries that we see. So when you're chatting with Dazzle,

0:41:07.290 --> 0:41:10.660
<v Speaker 11>we're using that context to augment and basically make it

0:41:10.680 --> 0:41:14.310
<v Speaker 11>a more personal form of AI. But in addition, we're

0:41:14.390 --> 0:41:18.670
<v Speaker 11>also using an agent to screen your photos, new photos

0:41:18.690 --> 0:41:21.279
<v Speaker 11>that you're taking, to understand how can we help, how

0:41:21.300 --> 0:41:24.000
<v Speaker 11>can we turn these into tasks that aren't necessarily a

0:41:24.020 --> 0:41:26.360
<v Speaker 11>to-do list for you, they're a to-do list for our agent,

0:41:26.460 --> 0:41:28.810
<v Speaker 11>because then our agents can kick in. and say, how

0:41:28.830 --> 0:41:30.590
<v Speaker 11>can we buy it? How can we book this repair?

0:41:30.870 --> 0:41:31.930
<v Speaker 11>How can we add this to your calendar?

0:41:31.950 --> 0:41:33.870
<v Speaker 1>So that goes to the definition of an agent. Is

0:41:33.910 --> 0:41:37.270
<v Speaker 1>that agent genuinely autonomous? It's able to make a transaction

0:41:37.350 --> 0:41:40.950
<v Speaker 1>or an action that's real. In Dazzle AI's case, it is.

0:41:40.969 --> 0:41:41.489
<v Speaker 1>That's right.

0:41:41.700 --> 0:41:43.939
<v Speaker 11>So our agents do go ahead and they are able

0:41:43.960 --> 0:41:46.480
<v Speaker 11>to do most of those tasks. We also think it's

0:41:46.520 --> 0:41:50.719
<v Speaker 11>important because some tasks are either more efficient or are

0:41:50.780 --> 0:41:53.240
<v Speaker 11>high value enough that it makes sense to have humans

0:41:53.280 --> 0:41:54.920
<v Speaker 11>in the loop. And that's another thing that we do

0:41:54.980 --> 0:41:57.930
<v Speaker 11>at Dazzle. So most of the work is done by agents.

0:41:58.460 --> 0:42:01.589
<v Speaker 11>But for the more complicated and intricate tasks, we actually

0:42:01.610 --> 0:42:04.109
<v Speaker 11>have humans in the loop that make sure that we've

0:42:04.170 --> 0:42:08.030
<v Speaker 11>got high accuracy and high fidelity. Last week, someone actually

0:42:08.330 --> 0:42:12.200
<v Speaker 11>went about replacing their roof using Dazzle. Took pictures of it,

0:42:12.510 --> 0:42:15.610
<v Speaker 11>got three quote people to come out and quote it,

0:42:15.900 --> 0:42:18.779
<v Speaker 11>picked it, and booked it through Dazzle. And for that,

0:42:18.880 --> 0:42:21.060
<v Speaker 11>I would argue that I think at this particular moment

0:42:21.080 --> 0:42:24.260
<v Speaker 11>in AI, maybe not forever, don't know what happens next year,

0:42:24.550 --> 0:42:26.710
<v Speaker 11>but for this moment in AI, it's actually reassuring that

0:42:26.969 --> 0:42:29.529
<v Speaker 11>to have a human in the loop guiding that process,

0:42:29.810 --> 0:42:31.790
<v Speaker 11>even if the agents are doing the majority of the work.

0:42:32.010 --> 0:42:34.489
<v Speaker 1>So you're taking us to this idea that in Dazzle's case,

0:42:34.530 --> 0:42:36.810
<v Speaker 1>this is for the individual, the consumer. Last week when

0:42:36.830 --> 0:42:39.730
<v Speaker 1>I was with Sam Altman at OpenAI Dev Day, you know,

0:42:39.850 --> 0:42:43.790
<v Speaker 1>dots is targeted at work for the enterprise. You're very

0:42:43.830 --> 0:42:47.430
<v Speaker 1>experienced in the world of technology. Did you arrive pretty

0:42:47.469 --> 0:42:49.530
<v Speaker 1>quickly at the idea this is consumer first?

0:42:50.270 --> 0:42:50.489
<v Speaker 2>Yes.

0:42:50.550 --> 0:42:52.430
<v Speaker 11>Well, I think that, you know, photos, I would make

0:42:52.450 --> 0:42:54.960
<v Speaker 11>the argument, If I had to be your executive assistant,

0:42:55.280 --> 0:42:59.360
<v Speaker 11>work assistant for a week, email is what you would

0:42:59.380 --> 0:43:01.640
<v Speaker 11>want though. I can figure out what to do from

0:43:01.660 --> 0:43:04.870
<v Speaker 11>your email. For your personal assistant, Right. Where are you

0:43:04.910 --> 0:43:06.930
<v Speaker 11>going to dinner tonight? What are you doing this weekend?

0:43:07.010 --> 0:43:09.489
<v Speaker 11>Buying you clothes, buying gifts for people that you care

0:43:09.530 --> 0:43:13.290
<v Speaker 11>about your the visuals of your life. Your camera roll

0:43:13.530 --> 0:43:17.170
<v Speaker 11>is a much richer source of context for me to

0:43:17.230 --> 0:43:19.780
<v Speaker 11>do that work in a really excellent way. That's why

0:43:19.840 --> 0:43:21.680
<v Speaker 11>we said, you know, look, we really want to affect

0:43:21.719 --> 0:43:25.000
<v Speaker 11>someone's personal life and be a good personal assistant. We

0:43:25.020 --> 0:43:27.520
<v Speaker 11>think photos, you know, maybe we add email over time

0:43:27.560 --> 0:43:29.220
<v Speaker 11>as well. But photos are a great.

0:43:29.080 --> 0:43:29.739
<v Speaker 1>Place to start.

0:43:29.760 --> 0:43:29.840
<v Speaker 6>Yeah.

0:43:30.440 --> 0:43:32.480
<v Speaker 1>Marissa Mayer-Abdallah, it's great to have you back with us

0:43:32.540 --> 0:43:35.100
<v Speaker 1>on the show. Thank you very much indeed. That does

0:43:35.180 --> 0:43:38.120
<v Speaker 1>it for this edition of Bloomberg Tech. There are so

0:43:38.140 --> 0:43:40.320
<v Speaker 1>many big stories out there, lots of them related to

0:43:40.360 --> 0:43:43.310
<v Speaker 1>the world of AI. Recap all of the conversations that

0:43:43.330 --> 0:43:46.210
<v Speaker 1>we've had and the reporting from Bloomberg News on the podcast.

0:43:46.230 --> 0:43:47.990
<v Speaker 1>You know where to find it, on the Bloomberg Terminal,

0:43:48.310 --> 0:43:52.090
<v Speaker 1>as well as online on Apple, Spotify, and iHeart, just

0:43:52.170 --> 0:43:54.830
<v Speaker 1>halfway through the working week here in the city of

0:43:54.870 --> 0:43:58.319
<v Speaker 1>San Francisco. Thanks for being with us. This is Bloomberg Tech.

0:44:03.580 --> 0:44:04.520
<v Speaker 3>Thank you.