WEBVTT - Adobe Calls off Figma Deal, Apple Halts Watch Sales, AI Deep Dive

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<v Speaker 1>From Marhart where Innovation, Money and Power Collie in Silicon Valley, NBN.

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<v Speaker 2>This is Bloomberg Technology with Caroline Hyde and Ed loved Love.

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<v Speaker 3>I'm Karine Heinde of Bloomberg's World head quarters in New York.

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<v Speaker 4>Then i am at Lovelow in San Francisco. This is

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<v Speaker 4>Bloomberg Technology.

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<v Speaker 3>Adobe Figma, they call off their twenty billion dollar deal

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<v Speaker 3>after clashing with regulators in Europe.

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<v Speaker 5>And the UK. Will bring you everything you need to know.

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<v Speaker 4>Plus a Rod's black Chech company plans a merger with

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<v Speaker 4>satellite Communications provide a link global. We hear from the

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<v Speaker 4>former New York Yankees. All start on the deal and.

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<v Speaker 3>We'll take a deep dive into the world of you

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<v Speaker 3>guessed it, artificial intelligence, from investing in the space, how

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<v Speaker 3>businesses are utilizing the technology, and the ethical concerns attached

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<v Speaker 3>to AI or break it all down.

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<v Speaker 5>Let's first talk to us about these deals.

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<v Speaker 4>Yeah, well, the deal that's dead, Adobe and Figma mutually

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<v Speaker 4>agreeing to terminate Adobe's acquisition of Figma, a twenty billion

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<v Speaker 4>dollar deal. As you said, equity market reaction Adobe up

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<v Speaker 4>one point nine percent. And it's interesting because the question

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<v Speaker 4>is would this have ever pass muster with the regulators.

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<v Speaker 4>We'll get to that in just a second. The news

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<v Speaker 4>being that Adobe pays Figma a one billion dollar termination

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<v Speaker 4>or breakup fee. Now that the deal has failed, lots

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<v Speaker 4>of opinions out there on social media about whether this

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<v Speaker 4>was a good or bad deal. Let's get the details

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<v Speaker 4>and bring in Bloomberg's Brody Ford. I guess this is

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<v Speaker 4>one of those deals and one of those stories, Brody,

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<v Speaker 4>where you start with the why why did both parties

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<v Speaker 4>decide to terminate this agreement?

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<v Speaker 6>Yeah, I mean imagine getting paid one billion dollars to

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<v Speaker 6>break up The reason, the big one is just that

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<v Speaker 6>it was taking so long. Right fifteen months ago, I

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<v Speaker 6>think we sat here and spoke about why Adobe was

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<v Speaker 6>going to buy Pigma, because it wanted to expand its

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<v Speaker 6>kind of creative dominance to look at the app designers,

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<v Speaker 6>the UI designers, and also just invest in a more

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<v Speaker 6>lightweight product. But of course regulators have been very secheptical

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<v Speaker 6>of these deals where a big incumbent tech player will

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<v Speaker 6>kind of scoop up a younger startup that maybe one

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<v Speaker 6>day could threaten it. The sense is that regulators felt

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<v Speaker 6>like they really missed the ball and not stopping Facebook

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<v Speaker 6>from buying WhatsApp or Instagram, and they really wanted to

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<v Speaker 6>kind of make an example here, and so they faced

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<v Speaker 6>severe pushback in.

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<v Speaker 1>The UK EU.

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<v Speaker 7>The US regulators were expected to sue as well, and

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<v Speaker 7>so there was a certain point where both Adobe and

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<v Speaker 7>Figma said, we do not see a way forward here.

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<v Speaker 7>I mean, they could have litigated this for another six

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<v Speaker 7>twelve months and it might have still not worked.

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<v Speaker 6>So they decided to walk away.

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<v Speaker 3>I mean Dylanfield, co founder CEO of Figma, putting out

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<v Speaker 3>a blob today really talking about what was it that

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<v Speaker 3>thousands of hours spent with regulators they still couldn't get

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<v Speaker 3>this deal away. We can see that Adobe's rallying a

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<v Speaker 3>bit on the news, but what is the future of Figma.

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<v Speaker 5>This is a big VC backed company.

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<v Speaker 3>There's a lot of money to be made on this

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<v Speaker 3>particular bet, and I'm interested that they still seem pretty

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<v Speaker 3>positive about what they managed to basically put to market

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<v Speaker 3>during this time.

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<v Speaker 6>Yeah, the big question for US now is are Adobe

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<v Speaker 6>and Figma still on a collision course?

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<v Speaker 8>Right?

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<v Speaker 6>Figma is growing rapidly. For those who are super familiar,

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<v Speaker 6>Figma's really core market is the software that helps you

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<v Speaker 6>design the way that apps and websites look. But they're

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<v Speaker 6>expanding elsewhere. They've looked into more productivity software suite kind

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<v Speaker 6>of think about like a Google workspaces kind of thing.

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<v Speaker 6>They've looked a bit into image editing, right, and so

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<v Speaker 6>you figure that this is a capable company, a capable leadership.

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<v Speaker 6>They've hired five hundred people in the last year. It's

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<v Speaker 6>not hard to imagine a scenario where they grow to

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<v Speaker 6>challenge some of these larger software companies like Adobe.

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<v Speaker 3>It's interesting they talk about the huge advancewers in AI

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<v Speaker 3>as well, and how that is an area of focus

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<v Speaker 3>for the business. I mean, look, we're all waiting for

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<v Speaker 3>IPOs and exits, and this is an exit that's put

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<v Speaker 3>on ice. Do you think this company will be going

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<v Speaker 3>in alone and looking to build in a public market.

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<v Speaker 6>Brody, I think it'll be a while before we see

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<v Speaker 6>an IPO. I think again that Adobe was buying a

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<v Speaker 6>fast growing but somewhat still nascent competitor. I don't remember

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<v Speaker 6>their revenue numbers, but you know they are rapidly growing,

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<v Speaker 6>they're scaling. I think it'd be a while before we

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<v Speaker 6>see an IPO. I mean I've already seen those some

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<v Speaker 6>theories out there, like is fig mug going to merge

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<v Speaker 6>with CANBA? Is fig mug going to get acquired by

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<v Speaker 6>these other large software companies? So I think you're right

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<v Speaker 6>to ask what are the next path what are the exits?

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<v Speaker 6>Because I think there are many scenarios for Figma here

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<v Speaker 6>all right?

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<v Speaker 4>Bloomberg's Brady Ford on the collapse of the ad Pigma deal.

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<v Speaker 4>Thank you. Staying in the world of deals. A blank

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<v Speaker 4>check company set up by former New York Yankees all

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<v Speaker 4>star Alex Rodriguez, is planning to merge with Satellite Communications

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<v Speaker 4>provide a Link Global. Earlier I sat down with the

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<v Speaker 4>CEOs of Link and slam Court to discuss have a listen.

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<v Speaker 8>Link invented this technology. You go back to twenty fifteen

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<v Speaker 8>when the original idea was created. Many people thought this

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<v Speaker 8>is impossible, and we then invented all the core technology

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<v Speaker 8>to connect to the standard phone already in your pocket

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<v Speaker 8>with no change to the phone. People thought that was

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<v Speaker 8>crazy thinking. Not only did we invent the tech, we

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<v Speaker 8>then proved it and we have three operational commercially license

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<v Speaker 8>satellites in orbit today and we're operational in five countries.

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<v Speaker 8>We proved it on all seven continents in over two

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<v Speaker 8>dozen countries, and so it puts us in a great

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<v Speaker 8>position to bring this service to the world. There's huge

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<v Speaker 8>demand for this. This is a life saving service with

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<v Speaker 8>over five billion customers that have a phone today.

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<v Speaker 4>Charles, you have the consolation well, a small constellation up

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<v Speaker 4>and running, you have the commercial deals in place. This

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<v Speaker 4>is an area just last week we learned that SpaceX

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<v Speaker 4>wants to get into as well. Is there enough room

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<v Speaker 4>for you and the likes of SpaceX to build out

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<v Speaker 4>the SAT to sell network.

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<v Speaker 8>Well, absolutely, so let me explain why. First of all,

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<v Speaker 8>is completely validating that SpaceX wants to follow link right.

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<v Speaker 8>We're the category creators. We inmitted this, we proved it,

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<v Speaker 8>and there's a bunch of companies jumping in now that

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<v Speaker 8>we've done it. But this is the key thing to understand.

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<v Speaker 8>There's no one's going to get a monopoly in the

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<v Speaker 8>mobile wireless industry. There's at least two or three winners.

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<v Speaker 8>They don't let anybody get a monopoly. They didn't let

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<v Speaker 8>Apple get a monopoly when they invented the modern smartphone.

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<v Speaker 8>They pulled into existence Android within eighteen months.

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<v Speaker 1>So there's going to be at least two.

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<v Speaker 8>Maybe three winners in satellite direct the standard phone, it's

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<v Speaker 8>going to be Link and one or two other companies.

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<v Speaker 8>So that's a natural, it's best for the consumer, and

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<v Speaker 8>it just completely validates what we're doing.

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<v Speaker 4>Yeah, Alex, really similar question for you. In a lot

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<v Speaker 4>of the SPACs that I've covered over the last three

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<v Speaker 4>or four years, the ones that have gone wrong, is

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<v Speaker 4>where due diligence was a problem. And I wondered how

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<v Speaker 4>much of a concern entering a market with Link with

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<v Speaker 4>a big competitor or potential competitor like SpaceX, how much

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<v Speaker 4>of that was a consideration for you?

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<v Speaker 1>Yeah, it was.

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<v Speaker 9>And look, we are super disciplined that we'd rather not

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<v Speaker 9>do as fact and take something in public unless we

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<v Speaker 9>felt really passionate about it, where long term thinkers and

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<v Speaker 9>our core values are aligned. I mean, it's such an

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<v Speaker 9>exciting space to think that this is a trillion dollar

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<v Speaker 9>business annually opportunity and to connect the unconnected, to think

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<v Speaker 9>that Charles and Link can connect over a billion people

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<v Speaker 9>more than we have today. And if you think at Amazon,

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<v Speaker 9>if you think of Amazon ed how they've disrupted the

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<v Speaker 9>retail business, what Link is is the disrupting business where

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<v Speaker 9>you have these sale towers where you would need a

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<v Speaker 9>football field or two to provide. Charles has created one

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<v Speaker 9>that his satellites go in the size of a pizza

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<v Speaker 9>box and then you set them to orbit and over

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<v Speaker 9>the next five years there should be hundreds, if not

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<v Speaker 9>thousands of these up at orbit alex rates.

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<v Speaker 4>Is a good point, which is the industries you're trying

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<v Speaker 4>to disrupt. But if you think about the end user,

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<v Speaker 4>right me the consumer with the cell phone, it seems

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<v Speaker 4>to be the carriers childs that you want to do

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<v Speaker 4>business with.

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<v Speaker 8>That is a key part of our strategy. We're partner

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<v Speaker 8>with mobile phone companies like many people here in the

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<v Speaker 8>United States, no Verizon AT and T AT T Mobile,

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<v Speaker 8>but there's eight hundred around the world, and we are

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<v Speaker 8>going to be a partner with mobile network operators around

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<v Speaker 8>the world. It goes directly to our business model. We

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<v Speaker 8>are going set up as a trusted partner of mobile

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<v Speaker 8>network operators and it has huge implications for our economics.

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<v Speaker 8>So we don't have to go sign up subscribers. We

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<v Speaker 8>cut a deal with a mobile network operator and we

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<v Speaker 8>instantly get a million to maybe one hundred million subscribers.

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<v Speaker 8>So our subscriber acquisition cost because it uses the phone

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<v Speaker 8>in your pocket, basically almost goes to zero.

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<v Speaker 4>A rod Spack taking Linked Global and its CEO Charles

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<v Speaker 4>Miller Public Caroline will being well valuation around eight hundred

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<v Speaker 4>million dollars and they literally want to take on SpaceX.

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<v Speaker 4>It's not a new story I thought i'd wake up

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<v Speaker 4>to on a Monday morning.

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<v Speaker 3>There's been quite a few stories to wake up to

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<v Speaker 3>on this Monday morning. And one of them, in fact,

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<v Speaker 3>another market moving story for US was the fact that

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<v Speaker 3>Apple is holding sales of its flagship Apple Watch models in.

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<v Speaker 5>The United States.

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<v Speaker 3>I'm going to bring you yet another sort of antitrust

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<v Speaker 3>story here as well.

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<v Speaker 4>And what if you go right and overnight in China

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<v Speaker 4>are colleagues reporting that the government crack down on the

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<v Speaker 4>use of foreign handsets continues across government and state backed enterprise.

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<v Speaker 4>And look at some of Apple's key suppliers. Broad Coom

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<v Speaker 4>interestingly now up one point two percent, have been lower

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<v Speaker 4>early in the session. Other key suppliers seem to be

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<v Speaker 4>moving lower. On that news, we all have the details. Next,

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<v Speaker 4>this is Bloomberg Technology. Okay, time for talking tech. And

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<v Speaker 4>in the news, IBM has agreed to buy data integration

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<v Speaker 4>platforms stream sets and web methods from Software ag for

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<v Speaker 4>two point three billion dollars in cash. The deals expected

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<v Speaker 4>to complete in the second quarter of twenty twenty four

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<v Speaker 4>and is an effort to strengthen its AI and cloud capabilities.

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<v Speaker 4>And since Time plunged the most in more than a

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<v Speaker 4>year after its co founder's death, spooked investors already grappling

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<v Speaker 4>with the fallout from slowing growth and US sanctioned since

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<v Speaker 4>Time disclosed that its co founder, m major shareholder, died

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<v Speaker 4>on Friday after an illness. The MIT graduate and Hong

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<v Speaker 4>Kong professor was regarded as a pioneer in China's AI sector,

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<v Speaker 4>helping create one of the nation's leaders in facial and

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<v Speaker 4>image recognition. Plus more, Chinese agencies and government backed firms

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<v Speaker 4>across the country have ordered staff to stop bringing iPhones

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<v Speaker 4>and other foreign devices to work work. The formal directors

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<v Speaker 4>follow a general mandate from months ago and sets in

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<v Speaker 4>motion an unpre precedented prohibition that's likely to block Apple

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<v Speaker 4>and Samsung from parts of the world's biggest smartphone market. Caroline.

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<v Speaker 3>And there is so much to chew on when it

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<v Speaker 3>comes to Apple today, ed, So let's stick with the

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<v Speaker 3>company that's also confirming it's soon going to be halting

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<v Speaker 3>sales of its flagship Apple Watch models right.

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<v Speaker 5>Here in the United States.

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<v Speaker 1>Now.

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<v Speaker 3>The move comes following an ITC ruling as part of

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<v Speaker 3>a long running pattern dispute between Apple and the medical

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<v Speaker 3>technology company Massimo around the Apple Watches, blood oxygen sensor technology.

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<v Speaker 3>Let's get our tape from our Bloomberg Intelligence and our

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<v Speaker 3>Grana and.

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<v Speaker 5>First the watches.

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<v Speaker 3>This is quite a major step, but basically they're anticipating

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<v Speaker 3>an import ban here.

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<v Speaker 10>Yeah, Nie, we were expecting, you know, my colleague Hamlin

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<v Speaker 10>Stern know a lot of work for this, that there

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<v Speaker 10>would be a settlement by now between the two companies.

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<v Speaker 10>And you know, we still have a few days. Let's

0:11:56.240 --> 0:11:58.920
<v Speaker 10>see if this is just arm stronging by both the companies.

0:11:58.920 --> 0:12:01.520
<v Speaker 10>But let's see what how happens. But we are hopeful

0:12:01.559 --> 0:12:04.400
<v Speaker 10>that there'll be a settlement between them.

0:12:04.520 --> 0:12:08.600
<v Speaker 4>The other story we're reported overnight amarag According to Bloomberg sources,

0:12:08.640 --> 0:12:12.200
<v Speaker 4>this kind of government or public sector clamp down on

0:12:12.480 --> 0:12:16.000
<v Speaker 4>foreign handsets in China. Right it started with I think

0:12:16.040 --> 0:12:19.880
<v Speaker 4>Beijing and Chianjen. Now there are eight other provinces where

0:12:20.360 --> 0:12:24.800
<v Speaker 4>state backed entities or companies government entities are saying leave

0:12:24.840 --> 0:12:28.240
<v Speaker 4>your foreign handset at work by domestic Does that really

0:12:28.360 --> 0:12:32.080
<v Speaker 4>impact Apple sales grow for or potential in China?

0:12:33.400 --> 0:12:35.880
<v Speaker 10>Yeah, I think there isn't that much good news for

0:12:35.920 --> 0:12:39.120
<v Speaker 10>Apple in the last several days. I think the iPhone

0:12:39.160 --> 0:12:42.040
<v Speaker 10>news is something that you know, I mean all frankness,

0:12:42.200 --> 0:12:44.960
<v Speaker 10>you don't expect the government employees to showcase their iPhones

0:12:44.960 --> 0:12:47.120
<v Speaker 10>anyway in the public. That's just been you know, this

0:12:47.280 --> 0:12:49.720
<v Speaker 10>is something you don't do for four years. But I

0:12:49.720 --> 0:12:51.920
<v Speaker 10>think the bigger issue at that time right now is

0:12:52.280 --> 0:12:55.440
<v Speaker 10>Huawei has a new forum with five G capabilities and

0:12:55.520 --> 0:12:58.240
<v Speaker 10>that's really doing well in China right now. And you know,

0:12:58.320 --> 0:13:01.040
<v Speaker 10>one reason could be that's a first stop graded many years.

0:13:01.320 --> 0:13:04.200
<v Speaker 10>But I think iPhone really is on a back you know,

0:13:04.200 --> 0:13:05.400
<v Speaker 10>backfoot at this point.

0:13:05.559 --> 0:13:06.960
<v Speaker 1>So you know, when when they.

0:13:06.840 --> 0:13:10.199
<v Speaker 10>Report results over the next couple of months, I think

0:13:10.640 --> 0:13:12.760
<v Speaker 10>this is going to be the single most important question

0:13:13.559 --> 0:13:16.120
<v Speaker 10>asked to Tim Cokz to you know, what's really happening

0:13:16.160 --> 0:13:18.880
<v Speaker 10>in China and how should we expect because it is

0:13:18.880 --> 0:13:21.480
<v Speaker 10>the biggest growth market for Apple in.

0:13:21.480 --> 0:13:26.040
<v Speaker 1>Terms of you know, our phones, and Apple's.

0:13:25.640 --> 0:13:29.160
<v Speaker 4>Up fifty percent year today the years always out fell

0:13:29.240 --> 0:13:32.680
<v Speaker 4>twenty six percent in twenty twenty two. What's the story

0:13:32.720 --> 0:13:34.400
<v Speaker 4>going to be for Apple in twenty twenty four.

0:13:35.640 --> 0:13:37.360
<v Speaker 10>So, as far as we can see right now, I

0:13:37.400 --> 0:13:40.559
<v Speaker 10>think the growth prospects are pretty tepid at this point.

0:13:40.600 --> 0:13:42.800
<v Speaker 10>I mean, growth rate of three four five percent is

0:13:42.800 --> 0:13:44.680
<v Speaker 10>nothing to really write home about.

0:13:44.720 --> 0:13:47.160
<v Speaker 1>But you know, one of the reasons.

0:13:46.720 --> 0:13:50.120
<v Speaker 10>People look at Apple is because of it's it's you know,

0:13:50.160 --> 0:13:52.280
<v Speaker 10>what we call as a flight to quality, and with

0:13:52.360 --> 0:13:54.719
<v Speaker 10>interest rates going down, that has helped some of the

0:13:54.800 --> 0:13:55.800
<v Speaker 10>larger tech naus.

0:13:55.960 --> 0:13:57.880
<v Speaker 1>But fundamentally, I think Apple.

0:13:57.640 --> 0:13:59.280
<v Speaker 10>Still needs to do a little bit of work to

0:13:59.320 --> 0:14:02.480
<v Speaker 10>showcase that they can get back to that high single

0:14:02.520 --> 0:14:05.320
<v Speaker 10>digit to load double digit growth rate. But we don't

0:14:05.320 --> 0:14:07.199
<v Speaker 10>think that's going to happen next year, next twelve months.

0:14:07.240 --> 0:14:08.280
<v Speaker 10>It's going to be tough for them.

0:14:08.960 --> 0:14:10.839
<v Speaker 3>We of course, have seen that there's been a slight

0:14:10.960 --> 0:14:14.600
<v Speaker 3>pivot from a supply chain perspective looking towards India and

0:14:14.640 --> 0:14:17.520
<v Speaker 3>indeed looking towards India as a demand driver as well

0:14:17.520 --> 0:14:19.240
<v Speaker 3>an rug Is that going to be some sort of

0:14:19.240 --> 0:14:22.400
<v Speaker 3>a silver lining amid the pullback in China and a

0:14:22.440 --> 0:14:24.400
<v Speaker 3>more difficult outlook for twenty twenty four.

0:14:25.520 --> 0:14:27.200
<v Speaker 10>Yeah, I mean, in the long term, India is a

0:14:27.280 --> 0:14:29.800
<v Speaker 10>very big growth market for them, that's no doubt about it.

0:14:29.960 --> 0:14:31.600
<v Speaker 10>But that's something I think it's going to play it

0:14:31.640 --> 0:14:33.120
<v Speaker 10>into three to five years range.

0:14:33.280 --> 0:14:34.760
<v Speaker 1>Over the next two to three years.

0:14:34.880 --> 0:14:37.480
<v Speaker 10>China remains one of the most important things because the

0:14:37.520 --> 0:14:41.000
<v Speaker 10>purchasing power of consumers in China still is a lot.

0:14:40.840 --> 0:14:42.520
<v Speaker 1>More than what it is in India.

0:14:42.600 --> 0:14:45.000
<v Speaker 10>In India, if you look at the smartphone market, you know,

0:14:45.040 --> 0:14:48.040
<v Speaker 10>Apple doesn't even qualify for ninety five percent of the

0:14:48.120 --> 0:14:50.440
<v Speaker 10>unit shipments because they are below three hundred dollars.

0:14:50.560 --> 0:14:52.080
<v Speaker 1>Apple doesn't play in that market.

0:14:52.320 --> 0:14:58.040
<v Speaker 10>So while the population size is very attractive and very appetizing,

0:14:58.120 --> 0:15:01.560
<v Speaker 10>but the smartphone growth is not there just from units

0:15:01.600 --> 0:15:02.760
<v Speaker 10>at this point, all.

0:15:02.720 --> 0:15:05.680
<v Speaker 4>Right, Bloomberg Xander Egran or Bloomberg Intelligence, great to get

0:15:05.720 --> 0:15:07.520
<v Speaker 4>your analysis on a lot of Apple news.

0:15:14.920 --> 0:15:17.600
<v Speaker 3>Let's turn our attention to Nvidia now and then really

0:15:17.600 --> 0:15:21.000
<v Speaker 3>fascinating deep dive by Bloomberg highlighting the threat that AI

0:15:21.080 --> 0:15:24.920
<v Speaker 3>poses to minorities. This is highlighted by two former employees

0:15:24.960 --> 0:15:28.200
<v Speaker 3>of Invidia who warn the CEO of the consequences of

0:15:28.280 --> 0:15:32.040
<v Speaker 3>artificial intelligence on marginalized communities. Thus far, and Video has

0:15:32.080 --> 0:15:34.400
<v Speaker 3>declined to comment on the specifics of the meeting that

0:15:34.480 --> 0:15:35.240
<v Speaker 3>was conducted, but.

0:15:35.200 --> 0:15:37.680
<v Speaker 5>The company said it could quote continues to devote.

0:15:37.400 --> 0:15:42.240
<v Speaker 3>Tremendous resources to ensuring that AI benefits everyone. Let's get

0:15:42.240 --> 0:15:45.520
<v Speaker 3>to the reporter behind this incredible piece, Sindujo Rangarajan is

0:15:45.520 --> 0:15:48.760
<v Speaker 3>with us and Sinduja it goes back several years to

0:15:48.800 --> 0:15:52.440
<v Speaker 3>a meeting that they had with Jensen the CEO of Nvidia,

0:15:52.800 --> 0:15:55.400
<v Speaker 3>and these two employees basically walked out feeling that they

0:15:55.400 --> 0:15:56.440
<v Speaker 3>weren't hurt, right.

0:15:58.080 --> 0:16:04.520
<v Speaker 11>Correct, got all good and alex Alexander Sodo, you know,

0:16:04.640 --> 0:16:08.840
<v Speaker 11>they were both presidents of the Black Black Employees Group.

0:16:09.320 --> 0:16:12.200
<v Speaker 11>But they work with executives and people from across the

0:16:12.240 --> 0:16:18.400
<v Speaker 11>company to put together presentation for Jensen Huang, the CEO

0:16:18.400 --> 0:16:21.760
<v Speaker 11>of f and video about the risks AI can post,

0:16:21.760 --> 0:16:24.000
<v Speaker 11>how can we make AI safe and the general topic

0:16:24.040 --> 0:16:27.760
<v Speaker 11>about AI ethics, and they wanted to make sure that

0:16:27.840 --> 0:16:31.040
<v Speaker 11>you know, the company which is supported which which they

0:16:31.080 --> 0:16:33.760
<v Speaker 11>claim is you know, going to be a major AI

0:16:33.840 --> 0:16:37.560
<v Speaker 11>superpower in the future, should do something about this. Have guardrails,

0:16:37.640 --> 0:16:39.480
<v Speaker 11>have processes and frameworks that.

0:16:39.480 --> 0:16:41.479
<v Speaker 5>Make sure that AI is safe.

0:16:42.920 --> 0:16:46.080
<v Speaker 11>And they and then they they you know, walked out

0:16:46.080 --> 0:16:50.560
<v Speaker 11>of the meetings not feeling heard and they quit shortly after.

0:16:51.040 --> 0:16:53.480
<v Speaker 4>A part of this is not just the not feeling

0:16:53.560 --> 0:16:57.280
<v Speaker 4>heard or basically an inaction by video from their point

0:16:57.320 --> 0:17:01.080
<v Speaker 4>of view, was the idea that it's the people building

0:17:01.120 --> 0:17:04.560
<v Speaker 4>the technology. It's important that there's diversity within it, right,

0:17:04.600 --> 0:17:09.360
<v Speaker 4>And you dug down into in videos workforce, essentially it's

0:17:09.440 --> 0:17:12.399
<v Speaker 4>hiring relative to some of its peers. What did you

0:17:12.480 --> 0:17:13.920
<v Speaker 4>find and why is that important?

0:17:14.680 --> 0:17:17.520
<v Speaker 11>You know, there's a lot of research that says that

0:17:17.720 --> 0:17:21.760
<v Speaker 11>you know, particularly for AI, but technology as a whole,

0:17:22.080 --> 0:17:26.280
<v Speaker 11>that as an industry, it's so it has such few

0:17:26.359 --> 0:17:30.080
<v Speaker 11>underrepersent and minorities that it's likely to miss blind spots

0:17:30.320 --> 0:17:32.879
<v Speaker 11>when it comes to building these tools that you know

0:17:33.400 --> 0:17:37.920
<v Speaker 11>are supposed to be the future of technology. And when

0:17:37.960 --> 0:17:42.640
<v Speaker 11>I looked into in videos, you know, diversity numbers, they

0:17:42.720 --> 0:17:45.399
<v Speaker 11>were in the bottom two for sm P one hundred

0:17:45.480 --> 0:17:50.480
<v Speaker 11>four underrepersent and minorities, Black, Hispanic, mixed races of people.

0:17:51.080 --> 0:17:58.440
<v Speaker 11>And you know, it's important because research says that if

0:17:59.040 --> 0:18:02.760
<v Speaker 11>if you don't have these people involved in discussions as

0:18:02.760 --> 0:18:05.720
<v Speaker 11>you're building products, then you know you're likely to miss

0:18:05.760 --> 0:18:06.680
<v Speaker 11>by blind spots.

0:18:07.000 --> 0:18:10.040
<v Speaker 4>We read in Vidia's statement in response to the story

0:18:10.119 --> 0:18:12.159
<v Speaker 4>at the beginning of the segment. But what is the

0:18:12.200 --> 0:18:14.600
<v Speaker 4>net result of this reporting, Ben, what kind of action

0:18:14.800 --> 0:18:18.320
<v Speaker 4>is in video taking and has anything improved?

0:18:19.600 --> 0:18:21.920
<v Speaker 11>They, you know, the meeting happened a while ago in

0:18:22.040 --> 0:18:25.080
<v Speaker 11>video claims that it's done a tremendous amount of work

0:18:25.280 --> 0:18:29.399
<v Speaker 11>in thinking about this issue, and they've hired you know,

0:18:29.520 --> 0:18:34.679
<v Speaker 11>Nikki Pope to run their trustworthy AI division. They've you know,

0:18:34.720 --> 0:18:37.720
<v Speaker 11>made sure that their data sets that the models are

0:18:37.760 --> 0:18:41.560
<v Speaker 11>trained on are debiased, and they've put in a lot

0:18:41.560 --> 0:18:44.000
<v Speaker 11>of work into making their models are transparent and not

0:18:44.080 --> 0:18:48.399
<v Speaker 11>as black foxy. They have released something called this openly

0:18:48.520 --> 0:18:52.560
<v Speaker 11>more Guardrails, which is supposed to keep chatbots safe and

0:18:52.600 --> 0:18:56.320
<v Speaker 11>on topic and appropriate. So they claim to have done

0:18:56.480 --> 0:18:59.480
<v Speaker 11>a lot of work since that meeting and since that

0:18:59.640 --> 0:19:02.320
<v Speaker 11>time to make sure that you know AI is safe

0:19:02.320 --> 0:19:02.960
<v Speaker 11>and transparent.

0:19:03.160 --> 0:19:06.040
<v Speaker 3>Because it's worth reminding people that these two employees when

0:19:06.040 --> 0:19:08.080
<v Speaker 3>they first went to the CEO Jensen, it was back

0:19:08.080 --> 0:19:10.680
<v Speaker 3>in twenty twenty. A lot has changed, a lot of

0:19:11.080 --> 0:19:14.280
<v Speaker 3>soul searching among corporate America has occurred since, of course,

0:19:14.320 --> 0:19:17.120
<v Speaker 3>the tragic death of George Floyd as well. I'm interested

0:19:17.160 --> 0:19:20.159
<v Speaker 3>in ultimately the real part of AI they seem to

0:19:20.160 --> 0:19:20.919
<v Speaker 3>be worried about.

0:19:21.359 --> 0:19:23.399
<v Speaker 5>Is facial recognition, correct.

0:19:24.359 --> 0:19:27.080
<v Speaker 11>That's one of the big pieces, and that was one

0:19:27.080 --> 0:19:29.479
<v Speaker 11>of the things that they raised during that meeting. You know,

0:19:29.960 --> 0:19:32.800
<v Speaker 11>we'll there's a lot of research that says already that

0:19:32.920 --> 0:19:38.720
<v Speaker 11>facial recognition technologies in general, you know, don't identify people

0:19:38.760 --> 0:19:42.800
<v Speaker 11>of color quite well. So this was just just generally raised.

0:19:42.840 --> 0:19:45.600
<v Speaker 11>You know, if that's going to be powering self driving cars,

0:19:45.760 --> 0:19:48.040
<v Speaker 11>then you know what's going to happen, Like what if

0:19:48.040 --> 0:19:52.160
<v Speaker 11>they don't recognize people with darker skins or different kinds

0:19:52.160 --> 0:19:53.600
<v Speaker 11>of people in general.

0:19:54.520 --> 0:19:58.119
<v Speaker 4>A right, Bloomberg's Induja and Garage and just really important

0:19:58.160 --> 0:20:01.320
<v Speaker 4>and deep reporting on video company that everyone has become

0:20:01.400 --> 0:20:12.920
<v Speaker 4>so obsessed with throughout twenty twenty three. Welcome back to

0:20:12.920 --> 0:20:15.959
<v Speaker 4>Bloomberg Technology. Ed love Low here in San Francisco.

0:20:15.600 --> 0:20:16.520
<v Speaker 5>Carolin Hired in New York.

0:20:16.560 --> 0:20:18.760
<v Speaker 3>Let's get a check on these markets, because we're managing

0:20:18.760 --> 0:20:21.040
<v Speaker 3>to hold firm in the NASDAQ stock still managing to

0:20:21.080 --> 0:20:23.840
<v Speaker 3>power on hire. Remember we've had seven straight weeks of

0:20:23.840 --> 0:20:25.720
<v Speaker 3>gains in the S and P five hundred, but still

0:20:25.720 --> 0:20:27.760
<v Speaker 3>the moon music is positive inequities even though in the

0:20:27.760 --> 0:20:29.560
<v Speaker 3>bob market we're selling off a little bit. Is we

0:20:29.600 --> 0:20:31.600
<v Speaker 3>get a little bit more caution coming from some of

0:20:31.640 --> 0:20:33.840
<v Speaker 3>those fed voices. We look to a voj whether or

0:20:33.920 --> 0:20:37.000
<v Speaker 3>not they'll be amping up some discussion of central bank

0:20:37.080 --> 0:20:38.840
<v Speaker 3>policy as the ECB tries to say, look, you are

0:20:38.880 --> 0:20:40.720
<v Speaker 3>front running us on the idea that we're going to

0:20:40.720 --> 0:20:43.879
<v Speaker 3>be cutting rates. Here US ten year yield currently up

0:20:43.920 --> 0:20:46.080
<v Speaker 3>some four basis points. Bitcoin selling off a little bit

0:20:46.119 --> 0:20:48.440
<v Speaker 3>after has been a rapid run up in recent months.

0:20:48.480 --> 0:20:50.600
<v Speaker 5>Let's move on to look at the individual companies. They're

0:20:50.640 --> 0:20:51.000
<v Speaker 5>on the move.

0:20:51.119 --> 0:20:54.560
<v Speaker 3>There is so much winding and unwinding of MNA is extraordinary.

0:20:54.600 --> 0:20:56.719
<v Speaker 3>Today you see Vodaphone up four point two percent. These

0:20:56.760 --> 0:20:59.240
<v Speaker 3>are the ADRs traded here in the United States of

0:20:59.320 --> 0:21:02.359
<v Speaker 3>what is European telecom juggernaut that's looking to tie up

0:21:02.359 --> 0:21:05.200
<v Speaker 3>its Italian unit with that of Iliad in France as well.

0:21:05.280 --> 0:21:07.880
<v Speaker 3>To keep an eye on that particular coming together and

0:21:08.480 --> 0:21:11.880
<v Speaker 3>Italian coordination. I'm looking at Coupang, South Korean e commerce

0:21:11.880 --> 0:21:15.360
<v Speaker 3>company looking to basically save a down and out UK

0:21:15.520 --> 0:21:17.520
<v Speaker 3>one far Fetch. It was one of the pin ups

0:21:17.560 --> 0:21:20.159
<v Speaker 3>of the UK space at IPO five years ago. It

0:21:20.240 --> 0:21:23.560
<v Speaker 3>did incredibly well during COVID and then well didn't manage

0:21:23.600 --> 0:21:25.680
<v Speaker 3>to make the jump. And it seems that as though

0:21:25.920 --> 0:21:28.560
<v Speaker 3>those that are holders of far Fetch are going to

0:21:28.600 --> 0:21:29.480
<v Speaker 3>be wiped out by.

0:21:29.359 --> 0:21:30.160
<v Speaker 5>This particular deal.

0:21:30.200 --> 0:21:32.959
<v Speaker 3>Five hundred million dollars of bridge loans going in Coupang

0:21:33.000 --> 0:21:35.159
<v Speaker 3>basically teaming up with another company to be able to

0:21:35.320 --> 0:21:37.960
<v Speaker 3>buy this company out of an administration. It's down some four

0:21:38.000 --> 0:21:42.120
<v Speaker 3>percent on the purchase news Lumina one point eight percent higher.

0:21:42.240 --> 0:21:45.119
<v Speaker 3>This is as they are forced to divest of its grail.

0:21:45.440 --> 0:21:48.080
<v Speaker 3>This has called a cancer technology company that it bought.

0:21:48.119 --> 0:21:51.359
<v Speaker 3>And look, once again, US really feeling that they had

0:21:51.440 --> 0:21:53.760
<v Speaker 3>to not go forward with this particular bit of M

0:21:53.760 --> 0:21:55.040
<v Speaker 3>and A even though they fought hard.

0:21:55.080 --> 0:21:57.200
<v Speaker 5>They are divesting of that particular acid ed.

0:21:58.200 --> 0:21:59.879
<v Speaker 4>All right, let's get it a deep dive now on

0:22:00.080 --> 0:22:03.040
<v Speaker 4>generative AI and what's actually happened so far in the

0:22:03.040 --> 0:22:05.880
<v Speaker 4>world of AI. Joining us now is Mayland Thompson, Big Vic,

0:22:05.960 --> 0:22:08.919
<v Speaker 4>Amazon Web Services vice President. It's great to have you

0:22:08.960 --> 0:22:11.720
<v Speaker 4>back on the program. And what I want to get

0:22:11.880 --> 0:22:14.359
<v Speaker 4>into is detail. You know, I talked a lot this

0:22:14.440 --> 0:22:19.600
<v Speaker 4>year about Amazon Aws Bedrock. What's being offered now that

0:22:19.640 --> 0:22:22.600
<v Speaker 4>the year is coming to close, myl what is the

0:22:22.640 --> 0:22:25.199
<v Speaker 4>one thing that most customers have come to you and

0:22:25.240 --> 0:22:25.680
<v Speaker 4>asked for.

0:22:27.840 --> 0:22:31.600
<v Speaker 12>Customers all over the world, as you know, are looking

0:22:31.680 --> 0:22:34.840
<v Speaker 12>to deploy generative AI at scale, but they want to

0:22:34.840 --> 0:22:38.000
<v Speaker 12>do it responsibly, they want to do it safely, and

0:22:38.080 --> 0:22:39.760
<v Speaker 12>so that really comes.

0:22:39.480 --> 0:22:40.879
<v Speaker 5>Down to your data.

0:22:41.359 --> 0:22:44.000
<v Speaker 12>What is the data driving your business today and how

0:22:44.000 --> 0:22:46.760
<v Speaker 12>can you put that data to work as well as

0:22:46.800 --> 0:22:51.119
<v Speaker 12>how can you do it securely responsibly by default. And

0:22:51.160 --> 0:22:54.360
<v Speaker 12>that is just top of mind for CEOs all over

0:22:54.400 --> 0:22:56.280
<v Speaker 12>the world, no matter what industry they're in.

0:22:57.280 --> 0:22:59.600
<v Speaker 4>But is that specifically them saying, Okay, we want to

0:22:59.680 --> 0:23:04.359
<v Speaker 4>use because we can access all kinds of third party models,

0:23:04.480 --> 0:23:06.480
<v Speaker 4>or because it's a place they can build their own.

0:23:08.320 --> 0:23:13.040
<v Speaker 12>I think the first reason why customers come to Amazon Bedrock.

0:23:12.720 --> 0:23:13.680
<v Speaker 1>Is about choice.

0:23:14.000 --> 0:23:16.439
<v Speaker 12>There's not going to be one model that every single

0:23:16.520 --> 0:23:18.760
<v Speaker 12>customer is going to use. In fact, what we're seeing

0:23:19.280 --> 0:23:21.800
<v Speaker 12>that customers are doing today is they're actually using a

0:23:21.800 --> 0:23:24.000
<v Speaker 12>lot of different models for different purposes.

0:23:24.240 --> 0:23:25.320
<v Speaker 5>I'll give you an example.

0:23:25.840 --> 0:23:29.800
<v Speaker 12>Adobe launched a generative pill in Photoshop back in May,

0:23:30.400 --> 0:23:34.200
<v Speaker 12>and generative phil is normously popular with creatives. That's actually

0:23:34.240 --> 0:23:37.920
<v Speaker 12>the most used feature of Photoshop. In fact, it's been

0:23:37.960 --> 0:23:41.919
<v Speaker 12>adopted ten times faster than any other feature in Photoshop history.

0:23:42.280 --> 0:23:44.840
<v Speaker 12>And Adobe built that on AWLS, and they built it

0:23:44.960 --> 0:23:48.280
<v Speaker 12>using a variety of different models, large language models and

0:23:48.320 --> 0:23:51.879
<v Speaker 12>foundation models, one of which they built themselves. And so

0:23:51.960 --> 0:23:55.080
<v Speaker 12>when customers come and they use Amazon Bedrock, they're coming

0:23:55.119 --> 0:23:59.080
<v Speaker 12>to Amazon Bedrop for choice, the ability to use the

0:23:59.160 --> 0:24:02.840
<v Speaker 12>latest language and foundation models. The ones that we added

0:24:02.920 --> 0:24:06.480
<v Speaker 12>just recently are Anthropics claud two point one as well

0:24:06.480 --> 0:24:09.439
<v Speaker 12>as med Islama two and be able to take those

0:24:10.000 --> 0:24:14.080
<v Speaker 12>leading models and use the tooling in Amazon Bedrock to

0:24:14.160 --> 0:24:18.960
<v Speaker 12>deploy generative AI applications really quickly and securely on as

0:24:19.000 --> 0:24:22.760
<v Speaker 12>you say, and their own custom data, their data sets

0:24:22.880 --> 0:24:25.080
<v Speaker 12>that are at work today for their enterprises.

0:24:25.440 --> 0:24:29.359
<v Speaker 3>How price sensitive are these customers at the moment, Minn

0:24:29.520 --> 0:24:32.679
<v Speaker 3>Because we enter this period of AI exuberance at a

0:24:32.680 --> 0:24:36.320
<v Speaker 3>time of economic realities where companies need to cut costs

0:24:36.359 --> 0:24:37.000
<v Speaker 3>in many ways.

0:24:38.480 --> 0:24:39.240
<v Speaker 1>Well, I think.

0:24:39.040 --> 0:24:41.800
<v Speaker 12>Cost is always going to be front and center of

0:24:41.840 --> 0:24:44.720
<v Speaker 12>any large scale deployment, and that's what customers are really

0:24:44.800 --> 0:24:47.560
<v Speaker 12>trying to do with generative AI. They're trying to use

0:24:47.640 --> 0:24:52.600
<v Speaker 12>generative AI not just to transform their customer experiences. For example,

0:24:52.640 --> 0:24:55.880
<v Speaker 12>into it, which has over one hundred million small business

0:24:55.920 --> 0:25:01.640
<v Speaker 12>and consumer customers today, into It rolled out something called

0:25:01.680 --> 0:25:04.840
<v Speaker 12>assist in all of their into it portfolio. So if

0:25:04.840 --> 0:25:07.520
<v Speaker 12>you're using quick books, you can use assist. If you're

0:25:07.560 --> 0:25:10.159
<v Speaker 12>turning your mind to taxes right after the holiday season

0:25:10.200 --> 0:25:13.720
<v Speaker 12>with turbo tax, you can use a cyst uh. And

0:25:13.920 --> 0:25:17.520
<v Speaker 12>when you have customers that want to transform their customer

0:25:17.560 --> 0:25:21.040
<v Speaker 12>experiences or use it to improve their internal productivity, they're

0:25:21.040 --> 0:25:24.000
<v Speaker 12>gonna think about cost. And I think that is one

0:25:24.040 --> 0:25:26.560
<v Speaker 12>of the reasons why, you know, when we build our

0:25:26.600 --> 0:25:30.520
<v Speaker 12>generative AI capabilities, we're also building at all of our

0:25:30.560 --> 0:25:33.840
<v Speaker 12>three layers, the foundational layer, that middle layer that we

0:25:33.880 --> 0:25:38.160
<v Speaker 12>call Amazon bedrock, which is image AI services, and we're

0:25:38.160 --> 0:25:42.399
<v Speaker 12>building capabilities called like EC two capacity blocks and easy

0:25:42.440 --> 0:25:45.720
<v Speaker 12>two capacity blocks work much the way that you reserve

0:25:45.760 --> 0:25:48.800
<v Speaker 12>a hotel room with different beds, except with EACY two

0:25:48.840 --> 0:25:53.280
<v Speaker 12>capacity blocks, you're reserving s uh GPUs. You're reserving hundreds

0:25:53.280 --> 0:25:56.680
<v Speaker 12>of GPUs for when you need it in the future,

0:25:57.119 --> 0:25:59.440
<v Speaker 12>and then once you're done using it, you can dial

0:25:59.440 --> 0:26:03.080
<v Speaker 12>all those back because you want to save on cost.

0:26:03.720 --> 0:26:06.399
<v Speaker 3>Man, I know you're a thoughtful person when it comes

0:26:06.400 --> 0:26:09.000
<v Speaker 3>to ultimately humanity. At the same time as working within

0:26:09.320 --> 0:26:11.560
<v Speaker 3>technology or someone who thinks a lot about Asians and

0:26:11.560 --> 0:26:14.240
<v Speaker 3>Amazon you're sponsoring women within technology, and therefore, I'm sure

0:26:14.240 --> 0:26:17.199
<v Speaker 3>you're thinking about the implications of AI II more broadly.

0:26:17.240 --> 0:26:19.199
<v Speaker 3>And to that end, when you are thinking about companies

0:26:19.240 --> 0:26:22.160
<v Speaker 3>cutting costs, how do you feel about the fact that

0:26:22.240 --> 0:26:24.560
<v Speaker 3>we're going to either be or menting humanity or indeed

0:26:24.640 --> 0:26:26.800
<v Speaker 3>perhaps putting a few out of jobs.

0:26:26.840 --> 0:26:28.280
<v Speaker 5>Is that something that you talk about a lot within

0:26:28.280 --> 0:26:28.760
<v Speaker 5>the business.

0:26:29.840 --> 0:26:31.520
<v Speaker 1>Well, you know, I think.

0:26:31.359 --> 0:26:35.159
<v Speaker 12>The world wants to be responsible in how we're using AI.

0:26:36.280 --> 0:26:39.840
<v Speaker 12>For me personally, Carolin, I'm very excited about generative AI

0:26:39.960 --> 0:26:44.119
<v Speaker 12>because for me, it levels the playing field for data literacy.

0:26:44.600 --> 0:26:48.760
<v Speaker 12>It brings everybody, as it were, to the data table today.

0:26:48.920 --> 0:26:50.479
<v Speaker 12>You know, if you think about it, the way we

0:26:50.560 --> 0:26:53.719
<v Speaker 12>make sense of our world is often with numbers, and

0:26:54.640 --> 0:26:57.480
<v Speaker 12>we use spreadsheets, and we use data queries. But there's

0:26:57.560 --> 0:27:00.879
<v Speaker 12>a lot of people out there who don't have exposure

0:27:00.920 --> 0:27:04.639
<v Speaker 12>to those technologies early on. And with generative AI, you

0:27:04.720 --> 0:27:06.960
<v Speaker 12>can take a query and you can turn it into

0:27:06.960 --> 0:27:10.199
<v Speaker 12>a question, spoken question, and then to a spoken conversation.

0:27:10.840 --> 0:27:13.680
<v Speaker 12>And so for me, I am very excited about the

0:27:13.680 --> 0:27:17.119
<v Speaker 12>idea of generative AI rolling out kind of unleashing the

0:27:17.200 --> 0:27:21.440
<v Speaker 12>human potential that is everywhere by making the s the

0:27:21.520 --> 0:27:26.600
<v Speaker 12>native intelligence of people and unlocking that uh, you know,

0:27:26.640 --> 0:27:28.840
<v Speaker 12>frankly with the data using generative AI.

0:27:29.040 --> 0:27:29.200
<v Speaker 1>Now.

0:27:29.520 --> 0:27:32.719
<v Speaker 12>In order to do this at scale, AWS is all

0:27:32.720 --> 0:27:36.400
<v Speaker 12>about being at scale. We've been doing AWS cloud services

0:27:36.440 --> 0:27:37.960
<v Speaker 12>now for just about eighteen years.

0:27:38.520 --> 0:27:38.680
<v Speaker 11>Uh.

0:27:38.720 --> 0:27:41.800
<v Speaker 12>We have to be able to do it responsibly. We

0:27:41.920 --> 0:27:43.560
<v Speaker 12>have to be able to do it in a way

0:27:43.640 --> 0:27:46.480
<v Speaker 12>where you know, one of the top three data challenges

0:27:46.560 --> 0:27:49.359
<v Speaker 12>that you know I I hear from customers when I

0:27:49.400 --> 0:27:52.919
<v Speaker 12>talk to them, is you know, like, how can I

0:27:53.080 --> 0:27:57.000
<v Speaker 12>be my own best auditor today? This is a major

0:27:57.080 --> 0:27:59.800
<v Speaker 12>initiative right now, right like the world of regulation has

0:27:59.880 --> 0:28:03.160
<v Speaker 12>no yet arrived and generative AI, but you know it will,

0:28:03.720 --> 0:28:04.440
<v Speaker 12>you know it will.

0:28:05.040 --> 0:28:05.359
<v Speaker 4>Uh.

0:28:05.400 --> 0:28:08.160
<v Speaker 12>And so what we do is we try to think about,

0:28:08.240 --> 0:28:10.919
<v Speaker 12>you know, how can we build that capability that audit

0:28:11.000 --> 0:28:16.920
<v Speaker 12>trail into our AWS UH managed AI solutions like Amazon

0:28:16.960 --> 0:28:21.040
<v Speaker 12>Bedrock by default. So if you're using Amazon Bedrock, as

0:28:21.200 --> 0:28:25.919
<v Speaker 12>over ten thousand customers are, uh, you are able to

0:28:26.040 --> 0:28:29.480
<v Speaker 12>get an audit trail for not just your user interactions

0:28:29.520 --> 0:28:33.360
<v Speaker 12>with the model, but also how is the model making decisions?

0:28:33.760 --> 0:28:36.680
<v Speaker 12>And that audit trail is logged and available for your

0:28:36.800 --> 0:28:40.160
<v Speaker 12>use for any type of compliance you need, and it's

0:28:40.280 --> 0:28:44.760
<v Speaker 12>using the same services that customers like Finrara use. They

0:28:44.840 --> 0:28:48.880
<v Speaker 12>use AWS cloud Trail, which is UH for for audit logging,

0:28:49.280 --> 0:28:52.040
<v Speaker 12>and they use it for their own compliance. That is

0:28:52.400 --> 0:28:57.160
<v Speaker 12>built by default into Amazon Bedrock. Because we know the

0:28:57.200 --> 0:29:00.760
<v Speaker 12>world wants to be responsible. But Caroline is one thing

0:29:00.800 --> 0:29:04.800
<v Speaker 12>to want to be responsible, another thing to be responsible

0:29:04.800 --> 0:29:07.720
<v Speaker 12>once and twice, once or twice, but it's the third thing.

0:29:07.760 --> 0:29:08.880
<v Speaker 1>It's the next level to.

0:29:08.840 --> 0:29:13.960
<v Speaker 12>Be responsible by default, to be responsible in an automated way,

0:29:14.040 --> 0:29:18.400
<v Speaker 12>and that's what we're building into our AI services my lan.

0:29:18.640 --> 0:29:21.240
<v Speaker 4>Last month, Adam joined me on the program to talk

0:29:21.280 --> 0:29:25.240
<v Speaker 4>about next gen trainingum Trainium two, but also the deeper

0:29:25.280 --> 0:29:29.000
<v Speaker 4>relationship with Nvidia. In the three weeks that have followed,

0:29:29.080 --> 0:29:32.440
<v Speaker 4>can you give us any granular detail on how selective

0:29:32.520 --> 0:29:35.760
<v Speaker 4>customers have been and how deeply they think about which

0:29:35.800 --> 0:29:38.160
<v Speaker 4>silicon their workloads are being trained on.

0:29:39.640 --> 0:29:46.280
<v Speaker 12>Well, fundamentally, customers want choice, and for us, we've had

0:29:46.400 --> 0:29:49.160
<v Speaker 12>over thirteen years of a deep partnership with Nvidia, which

0:29:49.200 --> 0:29:52.320
<v Speaker 12>is going to continue into the next year. We've announced

0:29:52.800 --> 0:29:56.680
<v Speaker 12>how the Nvidia Grace Happer superchip is going to be

0:29:57.960 --> 0:30:00.440
<v Speaker 12>offered through AWS. It's gonna be in a graded with

0:30:00.440 --> 0:30:05.400
<v Speaker 12>our AWS Nitro security and virtualization system. But customers are

0:30:05.440 --> 0:30:08.240
<v Speaker 12>about choice, and we have been building our own custom

0:30:08.320 --> 0:30:12.920
<v Speaker 12>silicon now since twenty eighteen. We have four generations of

0:30:12.960 --> 0:30:17.120
<v Speaker 12>Graviton chips. We have two generations of inferance and machine

0:30:17.160 --> 0:30:19.400
<v Speaker 12>learning trips as well, and.

0:30:19.320 --> 0:30:20.640
<v Speaker 1>So our goal is.

0:30:20.560 --> 0:30:24.320
<v Speaker 12>To give AWS customers whatever choice they want, whether it's

0:30:24.440 --> 0:30:28.160
<v Speaker 12>using our partnership that we've had with an Nvidia for

0:30:28.240 --> 0:30:32.560
<v Speaker 12>their choices, or to use our custom silicon, which we

0:30:32.600 --> 0:30:35.960
<v Speaker 12>think gives the best price for performance as well as

0:30:35.960 --> 0:30:39.480
<v Speaker 12>the right energy savings energy efficiency that we know the

0:30:39.480 --> 0:30:40.760
<v Speaker 12>world wants as well.

0:30:41.040 --> 0:30:44.040
<v Speaker 3>Just at the forefront of everything we've been discussing throughout

0:30:44.080 --> 0:30:44.880
<v Speaker 3>twenty twenty three.

0:30:45.000 --> 0:30:46.440
<v Speaker 5>It's a joy to catch up with you. Thank you

0:30:46.440 --> 0:30:47.080
<v Speaker 5>for joining the show.

0:30:47.120 --> 0:30:51.040
<v Speaker 3>Maylan Thompson Quick of course of Amazon Web Services. Meanwhile

0:30:51.040 --> 0:30:52.840
<v Speaker 3>coming up that we're going to be continuing the conversation

0:30:52.960 --> 0:30:55.520
<v Speaker 3>on AI and discuss some of the ethical implications the

0:30:55.560 --> 0:30:58.680
<v Speaker 3>ethical concerns of the technology. Alex Hannan, director Research of

0:30:58.720 --> 0:31:01.880
<v Speaker 3>the Distributed AI Research, going to be joining the show

0:31:02.360 --> 0:31:04.080
<v Speaker 3>as the Bloomberg Technology.

0:31:12.800 --> 0:31:16.440
<v Speaker 4>Earlier in the program, we discussed concerns regarding AI's impact

0:31:16.520 --> 0:31:19.600
<v Speaker 4>on minorities and marginalized communities. I want to continue that

0:31:19.680 --> 0:31:23.000
<v Speaker 4>conversation and bring in Alex Hanner, director of research at

0:31:23.000 --> 0:31:27.480
<v Speaker 4>the Distributed Ai Research Institute, whose work centers on the

0:31:27.560 --> 0:31:30.320
<v Speaker 4>data use in new computational technologies and the way in

0:31:30.360 --> 0:31:35.400
<v Speaker 4>which these datas exacerbate racial, gender, and class inequality. That

0:31:35.800 --> 0:31:38.960
<v Speaker 4>Bloomberg report looked at in Nvidia, and one element of

0:31:39.000 --> 0:31:43.160
<v Speaker 4>that was an analysis of Nvidia's workforce seven point eight

0:31:43.160 --> 0:31:47.360
<v Speaker 4>percent of the workforce being Black, Hispanic or other races

0:31:47.360 --> 0:31:50.040
<v Speaker 4>as a percentage of their totals workforce, but that ranks

0:31:50.080 --> 0:31:54.080
<v Speaker 4>them very low in Vidia, as Caroline caused the picks

0:31:54.120 --> 0:31:58.120
<v Speaker 4>and shovels of the AI story this year, But basically

0:31:58.200 --> 0:32:01.360
<v Speaker 4>the compute behind everything that's happen and what do you

0:32:01.440 --> 0:32:04.760
<v Speaker 4>make therefore that data analysis that Bloomberg put together.

0:32:04.880 --> 0:32:07.240
<v Speaker 2>Yeah, I mean, that was a fascinating story, and it's

0:32:07.240 --> 0:32:11.520
<v Speaker 2>something that's prevalent within the industry, especially in companies that

0:32:11.520 --> 0:32:15.520
<v Speaker 2>are doing work in AI. Google, Microsoft, Meta, all the

0:32:16.000 --> 0:32:20.800
<v Speaker 2>big tech firms focusing on AI development. There is a

0:32:20.880 --> 0:32:26.160
<v Speaker 2>complete and epidemic of just the inability to really hire

0:32:26.280 --> 0:32:31.680
<v Speaker 2>and retain Black, Latin X and minority talent, and that

0:32:31.920 --> 0:32:36.080
<v Speaker 2>has big repercussions for the way that these things are developed.

0:32:36.520 --> 0:32:40.400
<v Speaker 2>As a story that was mentioned earlier mentioned there are

0:32:40.600 --> 0:32:45.000
<v Speaker 2>huge problems and disparities on facial recognition technologies, but also

0:32:45.120 --> 0:32:51.360
<v Speaker 2>things like voice recognition technologies, how particular minorities are represented.

0:32:51.440 --> 0:32:56.320
<v Speaker 2>When we ask centertive AI particular things the biases in

0:32:56.560 --> 0:33:01.160
<v Speaker 2>text image generation, they are really through the technologies, so

0:33:01.240 --> 0:33:05.200
<v Speaker 2>that lack of diversity in the workforce gets reflected into

0:33:05.440 --> 0:33:09.440
<v Speaker 2>how these models actually are used in what they produce.

0:33:09.920 --> 0:33:12.520
<v Speaker 4>One of the questions I posted to our reporter is, well,

0:33:12.520 --> 0:33:14.120
<v Speaker 4>what was the result of this reporting?

0:33:14.160 --> 0:33:15.120
<v Speaker 1>What did Nvidia do?

0:33:15.720 --> 0:33:19.040
<v Speaker 4>And we read in video's statement on the story several

0:33:19.040 --> 0:33:21.600
<v Speaker 4>times earlier, but one of the answers was to de

0:33:21.840 --> 0:33:26.160
<v Speaker 4>bias some of the models that they worked on. Does

0:33:26.200 --> 0:33:28.960
<v Speaker 4>that work if they've already been trained and are in

0:33:29.040 --> 0:33:31.840
<v Speaker 4>the public domain to go back and de bias them.

0:33:32.440 --> 0:33:35.120
<v Speaker 4>How do you respond to that as a sort of fix.

0:33:35.680 --> 0:33:39.600
<v Speaker 2>There's no actual way to completely de bias a model.

0:33:40.080 --> 0:33:43.080
<v Speaker 2>People can test for it, and there are methods for auditing,

0:33:43.200 --> 0:33:46.960
<v Speaker 2>for red teaming, for ensuring that it doesn't have sort

0:33:46.960 --> 0:33:50.920
<v Speaker 2>of catastrophic sort of results, but there are always going

0:33:51.000 --> 0:33:54.920
<v Speaker 2>to be biases. For instance, more recently there was some

0:33:55.040 --> 0:34:01.000
<v Speaker 2>biases revealed in chat GPT and in metas stickered and

0:34:01.120 --> 0:34:05.560
<v Speaker 2>AI where there is biases against Palestinians, where Mona Challabe

0:34:05.720 --> 0:34:07.800
<v Speaker 2>the journalists for The Guardian that used to be for

0:34:07.880 --> 0:34:11.960
<v Speaker 2>the working for the Guardian ask do Palestinians deserve freedom?

0:34:12.040 --> 0:34:16.080
<v Speaker 2>And it said it's complicated, But when asking do Israelis

0:34:16.080 --> 0:34:19.880
<v Speaker 2>deserve freedom said yes, everybody deserves freedom. So these biases

0:34:20.280 --> 0:34:22.200
<v Speaker 2>can have a lot of testing, but they're always going

0:34:22.239 --> 0:34:24.520
<v Speaker 2>to have problems down the line.

0:34:24.840 --> 0:34:26.880
<v Speaker 3>I'm interested that we were just having a conversation with

0:34:26.880 --> 0:34:30.839
<v Speaker 3>my line I've wrote aws really saying her perspective is

0:34:30.880 --> 0:34:34.640
<v Speaker 3>that her clients are thinking, trying to think ahead of regulation,

0:34:34.840 --> 0:34:38.839
<v Speaker 3>trying to think about compliance, governance, auditing, And you were

0:34:38.880 --> 0:34:40.959
<v Speaker 3>just talking about red teaming in the way in which

0:34:40.960 --> 0:34:43.280
<v Speaker 3>you can really stress test some of the underlying foundational

0:34:43.360 --> 0:34:46.400
<v Speaker 3>models and indeed their application. Alex, are you finding that

0:34:46.400 --> 0:34:48.000
<v Speaker 3>Corporate America cares enough about this?

0:34:49.320 --> 0:34:52.760
<v Speaker 2>It's really hard to say, because the way that Corporate

0:34:52.800 --> 0:34:57.160
<v Speaker 2>America focuses on it, they tend to really not release

0:34:57.560 --> 0:35:00.799
<v Speaker 2>what the results of those stress tests are the kind

0:35:00.800 --> 0:35:04.320
<v Speaker 2>of transparency that we have into data, into the red teaming,

0:35:04.400 --> 0:35:07.960
<v Speaker 2>into the auditing that we're doing. That they're doing is

0:35:08.360 --> 0:35:10.840
<v Speaker 2>pretty much a black box. We don't know what's actually

0:35:10.880 --> 0:35:14.919
<v Speaker 2>going on behind the curtain. We see reports that Microsoft,

0:35:15.120 --> 0:35:20.400
<v Speaker 2>Meta and different huge companies have disbanded their responsible AI teams,

0:35:20.719 --> 0:35:23.080
<v Speaker 2>even though that they're turning around and also say they

0:35:23.200 --> 0:35:26.960
<v Speaker 2>have a commitment to AI ethics, and if they don't

0:35:27.000 --> 0:35:30.480
<v Speaker 2>have any kind of transparency into what's happening at those companies,

0:35:30.880 --> 0:35:34.600
<v Speaker 2>it's hard for other people, people like there or other

0:35:34.680 --> 0:35:35.719
<v Speaker 2>types of auditors.

0:35:36.120 --> 0:35:37.440
<v Speaker 1>We're done an auditor, but.

0:35:37.280 --> 0:35:40.040
<v Speaker 2>There are many different types of auditors out there. There's

0:35:40.040 --> 0:35:42.319
<v Speaker 2>no way to actually get in there and actually see

0:35:42.360 --> 0:35:45.719
<v Speaker 2>what they're doing in any kind of transparent way. So

0:35:45.800 --> 0:35:49.160
<v Speaker 2>the research community doesn't have the visibility. External auditors don't

0:35:49.200 --> 0:35:52.240
<v Speaker 2>have this visibility, and so it's really hard to see

0:35:52.440 --> 0:35:53.719
<v Speaker 2>if we can take them at their word.

0:35:54.239 --> 0:35:56.520
<v Speaker 3>The question keeps something one that we will have to

0:35:56.560 --> 0:35:58.560
<v Speaker 3>address in twenty twenty four, and for now, we thank

0:35:58.600 --> 0:36:01.160
<v Speaker 3>you for helping us address a somewhat twenty three. Alex Hannah,

0:36:01.239 --> 0:36:02.640
<v Speaker 3>great to have you back on the show. Director of

0:36:02.640 --> 0:36:18.440
<v Speaker 3>Research at the distributed AI research institute.

0:36:13.480 --> 0:36:14.680
<v Speaker 5>Bluestone Equity Partners.

0:36:14.719 --> 0:36:17.040
<v Speaker 3>You know it for its investments in sports media technology

0:36:17.080 --> 0:36:20.279
<v Speaker 3>the Intersection, and its latest investment is going to be

0:36:20.320 --> 0:36:22.960
<v Speaker 3>delivering growth in the world of sports entertainment. How you

0:36:23.040 --> 0:36:26.760
<v Speaker 3>consume it? With its aipowered video editing software, heitch talkers

0:36:26.760 --> 0:36:29.640
<v Speaker 3>through it. Bobby Sharma is founding and managing partner at

0:36:29.680 --> 0:36:34.359
<v Speaker 3>Bluestone Equity Partners. Bobby, You've got plenty of expertise having

0:36:34.480 --> 0:36:37.680
<v Speaker 3>sort of help steer the growth and fortunes of plenty

0:36:37.719 --> 0:36:41.080
<v Speaker 3>of sports outlook and develop the world's of cricket, basketball,

0:36:41.080 --> 0:36:45.080
<v Speaker 3>and many others. What about this video editing is it

0:36:45.160 --> 0:36:47.200
<v Speaker 3>video Verse? How are they going to be making our

0:36:47.239 --> 0:36:48.640
<v Speaker 3>consumption of it totally different?

0:36:49.239 --> 0:36:49.319
<v Speaker 1>So?

0:36:49.880 --> 0:36:54.360
<v Speaker 13>Video Verse is the company. It's founded in India and

0:36:54.520 --> 0:36:57.840
<v Speaker 13>based here in the US, California. Their flagship product is

0:36:57.880 --> 0:37:02.640
<v Speaker 13>called Magnify, and what it does is it leverages artificial intelligence,

0:37:02.680 --> 0:37:07.040
<v Speaker 13>computer vision, and machine learning to automate in real time

0:37:07.239 --> 0:37:10.880
<v Speaker 13>the development of I should say, the curation of short

0:37:10.880 --> 0:37:14.360
<v Speaker 13>form content and highlights from live and long form content

0:37:14.520 --> 0:37:16.080
<v Speaker 13>like sports and even news.

0:37:16.760 --> 0:37:19.239
<v Speaker 3>I can see the application for our particular industry, the

0:37:19.280 --> 0:37:22.120
<v Speaker 3>amount that we need to be putting out long form conversations,

0:37:22.160 --> 0:37:24.799
<v Speaker 3>finding the most pithy bit, putting it onto our social media,

0:37:24.800 --> 0:37:27.120
<v Speaker 3>and they're like, where is the uptic coming from? When

0:37:27.120 --> 0:37:29.400
<v Speaker 3>it's born out of India in America, where is the

0:37:29.440 --> 0:37:30.360
<v Speaker 3>client engagement?

0:37:31.000 --> 0:37:35.000
<v Speaker 13>So video Verse and works with clients such as Champions League,

0:37:35.480 --> 0:37:38.120
<v Speaker 13>the Indian Premier League, the massive success story that is

0:37:38.160 --> 0:37:41.799
<v Speaker 13>something my former colleagues at IMG where I spent four

0:37:41.880 --> 0:37:46.680
<v Speaker 13>years building professional sports leagues around the world, architected for

0:37:46.719 --> 0:37:50.400
<v Speaker 13>the Cricket Federation. They work with Wimbledon, the US Open,

0:37:50.520 --> 0:37:54.520
<v Speaker 13>the Australian Open, even NCAA basketball and large Division one

0:37:54.800 --> 0:37:58.279
<v Speaker 13>athletic conferences at schools here in the US. Well, what

0:37:58.320 --> 0:38:01.400
<v Speaker 13>they do is essentially bring down the cost basis for

0:38:01.480 --> 0:38:07.440
<v Speaker 13>something that historically required a tremendous amount of human labor

0:38:07.480 --> 0:38:11.400
<v Speaker 13>and time and something that required otherwise from their nearest

0:38:11.400 --> 0:38:15.360
<v Speaker 13>competitor presently expensive data feeds, so they're able to cost

0:38:15.360 --> 0:38:19.919
<v Speaker 13>effectively and time efficiently put out highlights in real time

0:38:19.960 --> 0:38:24.560
<v Speaker 13>to social media. The AI technology also gives an ability

0:38:24.640 --> 0:38:36.160
<v Speaker 13>to search content, video and demand type content for objects, images, plays, sounds, actions,

0:38:36.560 --> 0:38:39.960
<v Speaker 13>So there's a whole host of video editing functions that

0:38:40.000 --> 0:38:44.120
<v Speaker 13>are otherwise time and cost inefficient at this point that

0:38:44.520 --> 0:38:48.280
<v Speaker 13>an applied AI technology like this will help solve.

0:38:49.840 --> 0:38:50.120
<v Speaker 1>Bobby.

0:38:50.239 --> 0:38:53.399
<v Speaker 4>There is no greater agony when you're a sports fan

0:38:53.920 --> 0:38:56.560
<v Speaker 4>than when you are not watching a game. You're somewhere,

0:38:56.600 --> 0:38:58.560
<v Speaker 4>it doesn't matter where, maybe your mother in law's for

0:38:58.640 --> 0:39:03.600
<v Speaker 4>dinner or a show, and you know what's happened, but

0:39:03.680 --> 0:39:05.040
<v Speaker 4>you can't see the highlight.

0:39:05.560 --> 0:39:06.719
<v Speaker 1>But here's my question for you.

0:39:07.719 --> 0:39:11.319
<v Speaker 4>Does the platform choose what's important in the highlight or

0:39:11.360 --> 0:39:14.880
<v Speaker 4>is it still a human saying this is important, but

0:39:14.920 --> 0:39:17.400
<v Speaker 4>the process of editing is automated.

0:39:18.000 --> 0:39:21.560
<v Speaker 13>I think that's the biggest differentiator between video Verse and

0:39:21.600 --> 0:39:26.840
<v Speaker 13>its flagship Magnify and its nearest competitor. There's a human

0:39:26.920 --> 0:39:33.480
<v Speaker 13>element involved with other existing cutting edge technology that this circumvents.

0:39:33.480 --> 0:39:36.879
<v Speaker 13>It's fully automated and through the machine learning that AI

0:39:37.000 --> 0:39:42.040
<v Speaker 13>technology can drive the automatic curation and distribution of those highlights.

0:39:42.080 --> 0:39:45.280
<v Speaker 13>So instead of waiting minutes or hours for that highlight,

0:39:45.800 --> 0:39:48.920
<v Speaker 13>it will happen within seconds. So and again you can

0:39:48.960 --> 0:39:52.920
<v Speaker 13>imagine the joy that can bring not just to you,

0:39:53.160 --> 0:39:56.880
<v Speaker 13>but generationally. Gen Z gen Z Plus, which has increasingly

0:39:56.920 --> 0:40:00.720
<v Speaker 13>becoming a larger and larger segment of the act audience

0:40:00.760 --> 0:40:04.719
<v Speaker 13>in a way for sports properties to stay relevant as

0:40:04.840 --> 0:40:08.600
<v Speaker 13>their traditional fan bases are starting the age. That's the

0:40:08.640 --> 0:40:11.120
<v Speaker 13>only way they consume a lot of sports and entertainment.

0:40:11.200 --> 0:40:14.120
<v Speaker 13>So this is important as a matter of fan engagement

0:40:14.160 --> 0:40:18.760
<v Speaker 13>as much as it is to inform you about what happened.

0:40:19.080 --> 0:40:23.840
<v Speaker 13>And in some cases it'll be creating fans and monetizing

0:40:24.239 --> 0:40:26.360
<v Speaker 13>different avenues that didn't even exist before.

0:40:26.920 --> 0:40:28.800
<v Speaker 3>Well, there an investment coming out of that three hundred

0:40:28.800 --> 0:40:29.640
<v Speaker 3>million fund.

0:40:29.480 --> 0:40:30.319
<v Speaker 5>I love talking about it.

0:40:30.360 --> 0:40:33.560
<v Speaker 3>Abishama, founder and managing partner over at Bluestone Equity Partners.

0:40:33.600 --> 0:40:34.279
<v Speaker 5>You thank you for your time.

0:40:34.280 --> 0:40:36.200
<v Speaker 3>I mean, and I'm sure you weren't trying to insinuate

0:40:36.600 --> 0:40:37.680
<v Speaker 3>your wife and your mother in law.

0:40:37.719 --> 0:40:39.200
<v Speaker 5>Don't minu watching some sort.

0:40:39.080 --> 0:40:41.440
<v Speaker 4>Of for example that this week, of all weeks, I

0:40:41.440 --> 0:40:44.040
<v Speaker 4>will live to regret. That does it For this edition

0:40:44.480 --> 0:40:47.799
<v Speaker 4>of Bloomberg Technology Massive, Massive week Ahead, don't forget to

0:40:47.800 --> 0:40:51.120
<v Speaker 4>recap on the pod. Thanks for listening from San Francisco,

0:40:51.280 --> 0:40:53.000
<v Speaker 4>New York City. This is Bloomberg