WEBVTT - Amazon Debuts New AI Chips and Models at AWS

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

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

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<v Speaker 1>and Eva though in San Francisco.

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<v Speaker 2>This is Bloomberg Tech coming up. We go to AWS

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<v Speaker 2>we Invent in Las Vegas to discuss the cloud companies,

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<v Speaker 2>new chips, new models, and new AI agents. Becaus Michael

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<v Speaker 2>Dell donates an unprecedented six billion dollars aimed at jump

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<v Speaker 2>starting the investment accounts for twenty five million children in America,

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<v Speaker 2>and Warner Brothers Discovery receives a new round of bids,

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<v Speaker 2>with Netflix flashing mostly cash for its offer. We'll have

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<v Speaker 2>the details, but first let's check in on these markets

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<v Speaker 2>which are flashing green. We actually have a bit of

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<v Speaker 2>a reprieve after yesterday's sell off. We're back into risk

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<v Speaker 2>on mode tentatively. So across the benchmarks, we're looking at

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<v Speaker 2>the NaSTA one hundred nineteen percent of course in video

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<v Speaker 2>leading then in terms of points, but all of some

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<v Speaker 2>of the mag seven really dominating.

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<v Speaker 3>On the day.

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<v Speaker 2>You're looking at bitcoin, even getting a bit yesterday it

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<v Speaker 2>was woeful. Today we find some sort of stability where

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<v Speaker 2>up more than five percent in fact, largely Crypto is

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<v Speaker 2>in the green. We also turn our attention to some

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<v Speaker 2>of the key mags seven that we want to look

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<v Speaker 2>at in terms of their own events, their own announcements.

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<v Speaker 2>And I'm looking at what's happening with Amazon. We're currently

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<v Speaker 2>training up one point three percent. We're getting a little

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<v Speaker 2>nudge higher on some new news coming out about its

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<v Speaker 2>own large language models, about updates of course to its

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<v Speaker 2>own AI agentic focus. And key has got to be

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<v Speaker 2>Trainium three all about its chips.

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<v Speaker 3>Let's get straight over to Ed Ludlow.

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<v Speaker 2>You're in Las Vegas at the reinvent event.

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<v Speaker 4>Yeah, it's probably the Trainium three headlines that move the

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<v Speaker 4>needle right. This is a push forward acceleration of bringing

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<v Speaker 4>the latest generation accelerator to market called Trainium, but useful

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<v Speaker 4>in both the training and inference use case, and much

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<v Speaker 4>as Amazon has done in prygation generations of Trainium, it's

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<v Speaker 4>talking out the cost and performance metrics of it four

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<v Speaker 4>x the prior generation. On price performance, it was interesting

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<v Speaker 4>when the headline's hit, I mean, we're at one and

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<v Speaker 4>a half percent on Amazon, a small move lower or

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<v Speaker 4>pairing some of the gain on both Google and Nvidia.

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<v Speaker 4>And this is the story right. You know, Amazon wants

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<v Speaker 4>to expand the use of Trainium servers beyond one core customer,

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<v Speaker 4>which is anthropic, and they're giving evidence in this release.

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<v Speaker 4>We'll get to it later in the day when we

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<v Speaker 4>speak to Matt Garmon in particular of the savings that

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<v Speaker 4>those customers are made using it while also making sure

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<v Speaker 4>they have capacity that is based in video GPUs and

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<v Speaker 4>of course the open aideal, the AWS has thirty eight

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<v Speaker 4>billion dollars of it is predicated on availability of Nvidia.

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<v Speaker 2>I mean the eye the focus must be so trained

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<v Speaker 2>on what's just been seemingly a positive thrust for Google

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<v Speaker 2>when it all comes down to its own chips right now.

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<v Speaker 4>Yeah, in recent weeks and months when there have been

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<v Speaker 4>reports about Google TPU and the idea that Google Cloud

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<v Speaker 4>will have a big third party use it, it's moved

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<v Speaker 4>the needle on those shares. What universally right now, the

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<v Speaker 4>hyperscalers are experience the supply constraints right comply supply constraints

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<v Speaker 4>where they're basically saying, this is out there in the

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<v Speaker 4>real on Trainium three. It is out there in the

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<v Speaker 4>real world. By the way, general availability from today and

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<v Speaker 4>they've also talked about the pipeline through the Trainium four.

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<v Speaker 2>But you're right, it's all about TPU, it's all about chips.

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<v Speaker 2>It's also about their own innovations when it comes to

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<v Speaker 2>large language models lest we forget. Yes, they've got an

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<v Speaker 2>integration of open AI and anthropic Claude, but they've also

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<v Speaker 2>been movieving the needle on over two.

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<v Speaker 4>Yeah, an over two and updated year on version of

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<v Speaker 4>its first kind of in house foundation model, a variant

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<v Speaker 4>of which OMNI can take inputs MTI model inputs of text, video,

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<v Speaker 4>image responding kind. But that's going to be the question

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<v Speaker 4>the story today. You know, AWS is number one in

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<v Speaker 4>cloud and cloud computing through scale, through leveraging infrastructure, but

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<v Speaker 4>when does it become number one in AI? You know,

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<v Speaker 4>not just being a place that hosts others models, but

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<v Speaker 4>has evidence that its own foundation model is resulting in

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<v Speaker 4>something tangibly useful, particularly for enterprise customers. And I'm pretty

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<v Speaker 4>sure that that's what those that have joined the show

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<v Speaker 4>today want to talk about.

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<v Speaker 2>Yeah, we've got so many conversations come up in this show,

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<v Speaker 2>but you've got to be sticking around because we thank you, ed.

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<v Speaker 2>We're coming back to you because Throughout the show we'll

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<v Speaker 2>have interviews, but at the end we'll have a sit

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<v Speaker 2>down interview with AWSCO Matt Garmon at three pm Eastern time. Meanwhile, look,

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<v Speaker 2>this is all a story of infrastructure, about big tech

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<v Speaker 2>companies such as Amazon, but also Alphabet, Meta and Microsoft

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<v Speaker 2>spending heavily on AI with expected capital expenditure forever three

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<v Speaker 2>round eighty billion dollars combined in their current fiscal years. Now,

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<v Speaker 2>that's going against big tech's mantra for the past two decades.

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<v Speaker 3>Let's call it.

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<v Speaker 2>We are just more about delivering growth while keeping really

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<v Speaker 2>tight little spending.

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<v Speaker 3>It's a real flip reverse.

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<v Speaker 2>Let's talk about Oblimbug's tech equity reporter Carmen ryani Key,

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<v Speaker 2>and you've all been assessing how investor is tight. The

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<v Speaker 2>sudden wild capital expenditure.

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<v Speaker 5>Yeah, I mean, it has been the talking point and

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<v Speaker 5>the thing that we've been focusing on the most with

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<v Speaker 5>some of these big companies, and as you said, it

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<v Speaker 5>really represents a change from how they've operated over the

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<v Speaker 5>last two decades. They really had very capital light businesses

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<v Speaker 5>and were able to be very profitable, grow exponentially in

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<v Speaker 5>some ways because of that, and so now having this shift.

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<v Speaker 5>We're seeing investors rewarded on summons and then really punish

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<v Speaker 5>it on others. So, you know, Microsoft, we saw you

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<v Speaker 5>jump after its latest starting support, but Meta sort of

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<v Speaker 5>got hit because Zuckerberg, you know, didn't explain enough about

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<v Speaker 5>the return on investment of this spending. The other thing

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<v Speaker 5>that's interesting is that this capital expenditures is a portion

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<v Speaker 5>of revenue have jumped, which to a level that's not

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<v Speaker 5>normal for tech companies. So for Microsoft, it's capital expenditures

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<v Speaker 5>are now twenty percent of its revenue. And in addition,

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<v Speaker 5>you know, Alphabet and Amazon, these spending to sales ratios

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<v Speaker 5>are some of the high in the market.

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<v Speaker 2>Okay, so we now have to perhaps digest a period

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<v Speaker 2>of higher capital expenditure. Were meanwhile worrying about some of

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<v Speaker 2>the financing that goes on within it, some of the

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<v Speaker 2>circular financing that's been the narrative. But today the market jumps.

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<v Speaker 3>Why what are you hearing from the investors you're talking to?

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<v Speaker 5>Yeah, I mean, I think that last week was very interesting.

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<v Speaker 5>I think we're seeing a little bit of buying, some

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<v Speaker 5>more optimism and enthusiasm coming back into the market. There's

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<v Speaker 5>a lot you know, in videos CFO was speaking this morning.

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<v Speaker 5>There's a there's just a lot of tech news sort

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<v Speaker 5>of coming back into the market that people are getting

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<v Speaker 5>excited about.

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<v Speaker 3>Well, i'll see if it holds.

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<v Speaker 2>Santa rally upon us potentially come and Ranicky, We so

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<v Speaker 2>appreciate your time. Let's talk about the crypto markets, because

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<v Speaker 2>they too are recovering today off that a heavy sell

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<v Speaker 2>of yesterday when bitcoin still as much as eight percent.

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<v Speaker 2>Let's take a look at strategy as well. The artist

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<v Speaker 2>formerly known as MicroStrategy, heavy investor in bitcoin gaining some

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<v Speaker 2>support after scrambling to calm its own investors, creating a

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<v Speaker 2>one point four billion dollar reserve to fund dividend and

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<v Speaker 2>interest payments rather than having to potentially sell bitcoin as

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<v Speaker 2>a last resort. And if I've seen a digital finance editor,

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<v Speaker 2>Anna Erera is joining us. What's more from London? Anna,

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<v Speaker 2>why today the did buying.

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<v Speaker 6>It's hard to know with crypto, as I'm sure you're

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<v Speaker 6>aware of, and it's crazy to think that yesterday we

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<v Speaker 6>were down eight percent and thinking of all the macro

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<v Speaker 6>condition that might have been leading to that drop. So

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<v Speaker 6>you know, we were speaking to traders today and investors,

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<v Speaker 6>and it seemed that although the price is up, the

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<v Speaker 6>sentiment is still a bit cautious, and volumes aren't as

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<v Speaker 6>up as they were before. I think people are sort

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<v Speaker 6>of assessing that crypto in reality has been on a

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<v Speaker 6>downward spiral of bit since October twelfth, the eleventh, when

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<v Speaker 6>the price crashed, And so while today we're a bit up,

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<v Speaker 6>in reality, you know, we're still pretty lower than we

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<v Speaker 6>were around a month ago when bitcoin reached its record highs.

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<v Speaker 2>I mean, negative bitcoin funding rate is still an anxiety,

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<v Speaker 2>extreme fear levels with coin market caps, fear and greed.

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<v Speaker 2>There's still a lot of comfort needed to an investor

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<v Speaker 2>base that's been beaten up, particularly the retail trade who

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<v Speaker 2>have tried to gain exposure not just by owning crypto,

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<v Speaker 2>but by owning crypto related assets such as what leveraged

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<v Speaker 2>bets on MicroStrategy now called Strategy and it talked to

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<v Speaker 2>us about how much retail of hurt here as well

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<v Speaker 2>as institutional.

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<v Speaker 6>Yes, obviously we know that people were trying to retail

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<v Speaker 6>investor are trying to get exposure into bitcoin and crypto

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<v Speaker 6>through buying ETFs. Thatt, we're supposed to strategy or strategy

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<v Speaker 6>stock itself, and that's fallen dramatically over the past year,

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<v Speaker 6>and so you know they've been hurting. But at the

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<v Speaker 6>same time we've been trying to wait to see if

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<v Speaker 6>more institutional buyers will come in and offset that. But

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<v Speaker 6>it seems to be from what we're hearing, a sort

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<v Speaker 6>of weight and see mode, partly waiting until the FED

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<v Speaker 6>decision next week. So it's kind of in a way,

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<v Speaker 6>same old with crypto. One day it goes ninety percent,

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<v Speaker 6>the next thaying it's a five percent. The asset class

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<v Speaker 6>seems to be maturing and there's more ways to invest

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<v Speaker 6>in it, but maybe not much has changed.

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<v Speaker 3>In Bergana or Era.

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<v Speaker 2>Always great to have you, Thank you very much, and

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<v Speaker 2>stay Withloomberg because there's a conversation coming up you won't

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<v Speaker 2>want to miss if you're in crypto Fondly Strategy President

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<v Speaker 2>CEO coming up in the next hour on Bloomberg Crypto.

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<v Speaker 2>Elsewhere in that ecosystem, not all is gaining support. We're

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<v Speaker 2>watching shares of American Bitcoin Corp. Look at that forty

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<v Speaker 2>four percent at one point down fifty percent. A bitcoin

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<v Speaker 2>minor which counts Eric Trump as a co founder and

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<v Speaker 2>chief strategy officer, seeing multiple trading halts amid the volatility

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<v Speaker 2>now coming up, we'll be joined by Colleen Aubrey, Senior

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<v Speaker 2>and Vice President of Applied AI Solutions over at AWS.

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<v Speaker 2>We are live from the Companies we Invent conference in

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<v Speaker 2>Las Vegas.

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<v Speaker 3>This is Bloomberg Tech.

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<v Speaker 4>Good morning from Las Vegas. So let's talk about some

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<v Speaker 4>of the announcements out from AWS. Colleen Aubrey is Senior

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<v Speaker 4>vice president of AWS, Applied AI Solutions and clean You

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<v Speaker 4>basically work with the full spectrum of the smallest startups

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<v Speaker 4>to the biggest enterprise customers that AWS has and the

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<v Speaker 4>public sector, and it's distilling it as far down as

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<v Speaker 4>I can. You basically help them to take technology and

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<v Speaker 4>innovate make whatever they do better.

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<v Speaker 7>Yeah, the mission I'm on is really to put AI

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<v Speaker 7>into the hands of the business on a day to

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<v Speaker 7>day basis and really make it work for their business

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<v Speaker 7>in production every day, helping them to deliver better experiences

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<v Speaker 7>to their customers.

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<v Speaker 4>We have a new generation of AWS Silicon and server design,

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<v Speaker 4>We have a new generation of Nova model, and we

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<v Speaker 4>have frontier agents. Right and I think you probably heard

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<v Speaker 4>me at the top of the show, but I think

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<v Speaker 4>the big question about AWS Reinvent is what is it

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<v Speaker 4>in this year's offering that takes aws beyond being the

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<v Speaker 4>sort of number one cloud computing platform for capacity to

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<v Speaker 4>giving something tangible, useful that can improve a company's own technology.

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<v Speaker 7>Yeah, two areas that I'd like to talk about specifically

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<v Speaker 7>for me personally, one of my products is Amazon Connect

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<v Speaker 7>and this started as a contact center application, but today

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<v Speaker 7>you know, I see it really as an agentic product

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<v Speaker 7>that's actually looking at whole customer experience. And so for me,

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<v Speaker 7>this is where we're putting our infrastructure, our silicon, our models,

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<v Speaker 7>and now today like bringing that all together in a

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<v Speaker 7>product that can work for businesses. So we launched twenty

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<v Speaker 7>nine new features on Sunday we announced that, and I

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<v Speaker 7>would say there's four key gentic capabilities that come with that.

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<v Speaker 7>The first is actually AI Voice and allowing customers to

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<v Speaker 7>interact in a very natural way using Novasonic and having

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<v Speaker 7>agents actually resolve issues for them on the behalf in

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<v Speaker 7>the background. The second is actually putting AI to work

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<v Speaker 7>as a teammate next to customer service representatives, helping them

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<v Speaker 7>to actually get tasked on complete the paperwork the processes,

0:11:50.960 --> 0:11:53.720
<v Speaker 7>provide recommendations, and help them really have a better view

0:11:53.720 --> 0:11:56.200
<v Speaker 7>of the customer so that the conversation they have with

0:11:56.320 --> 0:12:00.000
<v Speaker 7>customers is much much richer. The third area is actually

0:12:00.200 --> 0:12:03.240
<v Speaker 7>combining what I call clickstream, which is the path that

0:12:03.280 --> 0:12:07.000
<v Speaker 7>customers will take through websites and profiles, to be able

0:12:07.040 --> 0:12:10.400
<v Speaker 7>to present a much more specific recommendation and what a

0:12:10.480 --> 0:12:13.079
<v Speaker 7>next step won't be for a customer. And finally, you know,

0:12:13.120 --> 0:12:15.720
<v Speaker 7>one of the big issues for companies is sort of

0:12:15.920 --> 0:12:18.760
<v Speaker 7>confidently putting AI to work in their business. And in

0:12:18.760 --> 0:12:22.240
<v Speaker 7>this case, we've added observability where you can expect, how

0:12:22.640 --> 0:12:25.599
<v Speaker 7>inspect how an AI is reasoning, how it's thinking, in

0:12:25.760 --> 0:12:29.199
<v Speaker 7>what tools it's using, so companies can really observe AI

0:12:29.240 --> 0:12:31.200
<v Speaker 7>in the same way they would think about the people

0:12:31.240 --> 0:12:33.040
<v Speaker 7>and their business working with customers.

0:12:33.040 --> 0:12:34.520
<v Speaker 4>I want to go to point number two. You use

0:12:34.559 --> 0:12:37.720
<v Speaker 4>the word teammates, yes, but you've in the past talked

0:12:37.760 --> 0:12:42.600
<v Speaker 4>about a hybrid workforce, people and agents. A lot of

0:12:42.640 --> 0:12:45.560
<v Speaker 4>what's come out since Sunday Night seems to reflect that

0:12:46.200 --> 0:12:50.439
<v Speaker 4>you want to increase the number of useful agents. That also, internally,

0:12:50.480 --> 0:12:52.680
<v Speaker 4>we can talk a little bit about what you're encouraging

0:12:52.720 --> 0:12:55.440
<v Speaker 4>Amazon teams to do, but the idea is that that

0:12:55.559 --> 0:12:59.760
<v Speaker 4>workforces will type should get comfortable working alongside agentic AI.

0:13:00.080 --> 0:13:00.320
<v Speaker 8>Yeah.

0:13:00.760 --> 0:13:03.720
<v Speaker 7>I see a future where actually everyone is managing a

0:13:03.760 --> 0:13:06.640
<v Speaker 7>team of AI agents. And I think our frontier agents

0:13:07.000 --> 0:13:11.160
<v Speaker 7>with software developments, sec ops and so DevOps and security

0:13:11.400 --> 0:13:13.480
<v Speaker 7>is sort of one of our first moves in that direction.

0:13:13.520 --> 0:13:17.959
<v Speaker 7>Where you actually have a teammate with a developer who's

0:13:18.000 --> 0:13:21.439
<v Speaker 7>able to address complex problems, able to work over hours and.

0:13:21.440 --> 0:13:23.360
<v Speaker 9>Days, be able to solve for whole.

0:13:23.160 --> 0:13:27.000
<v Speaker 7>Objectives, and to scale with that person, that the role changes.

0:13:27.080 --> 0:13:30.360
<v Speaker 7>I think we all are managing AI teammates, a team

0:13:30.840 --> 0:13:33.040
<v Speaker 7>of people that are all of AI, people that are

0:13:33.080 --> 0:13:36.520
<v Speaker 7>out able to we can delegate to, we can inspect

0:13:36.520 --> 0:13:39.079
<v Speaker 7>what they're doing, we can iterate with them, provide feedback,

0:13:39.360 --> 0:13:41.800
<v Speaker 7>and for me, that's where I think we end up going.

0:13:42.120 --> 0:13:44.320
<v Speaker 7>I think we're clearly moving that way in the customer

0:13:44.400 --> 0:13:47.520
<v Speaker 7>service direction, and I think the developer experience is also

0:13:47.559 --> 0:13:48.480
<v Speaker 7>clearly moving that direction.

0:13:48.559 --> 0:13:51.000
<v Speaker 4>You're in a leadership position at AWS, but you have

0:13:51.080 --> 0:13:54.400
<v Speaker 4>been with Amazon broadly entering your twenty first year. You're

0:13:54.440 --> 0:13:58.199
<v Speaker 4>part of the S team, the leadership team generally internally

0:13:58.240 --> 0:14:02.640
<v Speaker 4>within Amazon's many arms. Is this like, particularly in the

0:14:02.640 --> 0:14:05.920
<v Speaker 4>context of the technology you've released here Reinvent twenty twenty five,

0:14:06.000 --> 0:14:08.280
<v Speaker 4>Is this at a stage where Amazon teams are dog

0:14:08.320 --> 0:14:11.960
<v Speaker 4>fooding this or it's just inherent the use of technology.

0:14:12.040 --> 0:14:14.440
<v Speaker 4>You want your own teams to be. I think AI native.

0:14:14.600 --> 0:14:17.360
<v Speaker 7>Yeah, that's true, and I think what I observe within

0:14:17.440 --> 0:14:19.920
<v Speaker 7>Amazon certainly we're all very keen to get our hands

0:14:19.960 --> 0:14:22.320
<v Speaker 7>on our new frontier agents, and that is something we've

0:14:22.720 --> 0:14:24.800
<v Speaker 7>had in beta internally, and I think teams are very

0:14:24.800 --> 0:14:28.720
<v Speaker 7>excited to use that in production. We use connect within

0:14:28.760 --> 0:14:31.160
<v Speaker 7>our own customer service teams, our seller support teams, our

0:14:31.200 --> 0:14:34.160
<v Speaker 7>AWS support teams, for example. So we continue like to

0:14:34.160 --> 0:14:36.880
<v Speaker 7>iterate and learn from that. And I would say broadly

0:14:36.880 --> 0:14:42.240
<v Speaker 7>across Amazon, we've really tried to sort of embrace the chaos,

0:14:42.440 --> 0:14:45.320
<v Speaker 7>to put AI in the hands of every person in

0:14:45.360 --> 0:14:48.080
<v Speaker 7>the company and to see what they can do, how

0:14:48.160 --> 0:14:50.720
<v Speaker 7>they can transform how they work, what they learn. And

0:14:50.760 --> 0:14:53.880
<v Speaker 7>so you have a lot of teams sort of grassroots,

0:14:54.240 --> 0:14:56.240
<v Speaker 7>sort of trying to solve for what is the new

0:14:56.280 --> 0:14:59.359
<v Speaker 7>way of working using AI, and some of those experiments

0:14:59.480 --> 0:15:02.320
<v Speaker 7>don't lead in newhere, and some lead and a place

0:15:02.360 --> 0:15:04.040
<v Speaker 7>that's really meaningful. And then you have sort of this

0:15:04.120 --> 0:15:06.520
<v Speaker 7>social contagion that happens that people learn from each other.

0:15:06.560 --> 0:15:09.920
<v Speaker 4>Okay, aws number one in cloud computing and in the

0:15:09.960 --> 0:15:14.480
<v Speaker 4>sense of scale infrastructure deployments, is it number one in AI?

0:15:15.280 --> 0:15:18.080
<v Speaker 7>I think we have all the pieces in place and

0:15:18.120 --> 0:15:20.680
<v Speaker 7>we're well on our way. We're very, very focused, and

0:15:20.760 --> 0:15:22.960
<v Speaker 7>what I really like about our strategy is that we're

0:15:23.000 --> 0:15:25.480
<v Speaker 7>working all the way, all the way, the full stack,

0:15:25.720 --> 0:15:30.560
<v Speaker 7>you know, from clean energy, chips, data centers around the world,

0:15:31.560 --> 0:15:34.680
<v Speaker 7>bedrock with real great selection, including our own models. And

0:15:34.720 --> 0:15:37.320
<v Speaker 7>I think Roehet's making really fast progress on that. And

0:15:37.320 --> 0:15:39.360
<v Speaker 7>I think for Swami and I really trying to put

0:15:39.400 --> 0:15:43.280
<v Speaker 7>those all of those pieces to work where out of

0:15:43.320 --> 0:15:45.560
<v Speaker 7>the box a business can get value.

0:15:45.600 --> 0:15:47.480
<v Speaker 4>We've got a conversation with Swami coming up in a bit.

0:15:47.560 --> 0:15:51.400
<v Speaker 4>Colleen Aubrey, SVP of Applied AI Solutions to AWS, Thank

0:15:51.480 --> 0:15:52.720
<v Speaker 4>you so much, Carrot, what.

0:15:52.800 --> 0:15:56.160
<v Speaker 2>A great conversation. Let's stick with Amazon as well, because

0:15:56.240 --> 0:15:58.920
<v Speaker 2>the company, which is vast it plans to offer deliveries

0:15:58.920 --> 0:16:02.440
<v Speaker 2>of hundreds of house items, including some fresh groceries or

0:16:02.440 --> 0:16:05.880
<v Speaker 2>over the counter medicines within just thirty minutes and test program.

0:16:05.920 --> 0:16:08.840
<v Speaker 2>But it's begun in Philadelphia, we stand will begin there

0:16:08.880 --> 0:16:12.480
<v Speaker 2>and its home city of Seattle now coming up the

0:16:12.600 --> 0:16:16.640
<v Speaker 2>Dells make an unprecedented six point twenty five billion dollar

0:16:16.680 --> 0:16:18.600
<v Speaker 2>donation to the Kids of America or on that.

0:16:18.640 --> 0:16:20.000
<v Speaker 3>Next, this is Bloomberg Tech.

0:16:34.920 --> 0:16:38.120
<v Speaker 2>Michael and Susan Dell are donating six point twenty five

0:16:38.160 --> 0:16:41.360
<v Speaker 2>billion dollars, gifting twenty five million children in America two

0:16:41.400 --> 0:16:43.760
<v Speaker 2>hundred and fifty dollars each in an effort to jumpstart

0:16:43.800 --> 0:16:47.000
<v Speaker 2>their investment accounts for the future. The proceeds build on

0:16:47.040 --> 0:16:50.600
<v Speaker 2>the Invest America Initiative or Trump accounts as they're known,

0:16:50.720 --> 0:16:52.880
<v Speaker 2>which we'll see one thousand dollars for every child born

0:16:52.920 --> 0:16:55.200
<v Speaker 2>from twenty twenty five to twenty twenty eight. Let's talk

0:16:55.200 --> 0:16:57.920
<v Speaker 2>about all of it with bost Tom Maloney and how

0:16:58.000 --> 0:17:01.240
<v Speaker 2>unprecedented is this? This SI is a philanthropic gift.

0:17:02.280 --> 0:17:06.119
<v Speaker 10>It's pretty unprecedtentded I mean, there've been big philanthropic gifts before,

0:17:06.200 --> 0:17:09.320
<v Speaker 10>but something going to cover twenty five million children in

0:17:09.359 --> 0:17:12.840
<v Speaker 10>the cover really is pretty unheard of. And a gift

0:17:12.920 --> 0:17:15.040
<v Speaker 10>to go through the federal government. I mean, he's donating

0:17:15.080 --> 0:17:18.040
<v Speaker 10>the money to the US Department of Treasury. That's pretty

0:17:18.119 --> 0:17:21.440
<v Speaker 10>unusual too, So it's quite an unprecedented gift.

0:17:21.880 --> 0:17:23.680
<v Speaker 2>Let's go back to what all of this is sort

0:17:23.680 --> 0:17:27.280
<v Speaker 2>of sitting alongside, which is invest in America. We sort

0:17:27.280 --> 0:17:29.879
<v Speaker 2>of saw that roundtable alongside other CEOs, a lot of

0:17:29.880 --> 0:17:32.640
<v Speaker 2>them tech who have been thinking about how to get

0:17:32.680 --> 0:17:35.399
<v Speaker 2>money into the hands of children to invest in the

0:17:35.480 --> 0:17:35.960
<v Speaker 2>longer term.

0:17:36.000 --> 0:17:38.120
<v Speaker 4>Tom, that's right.

0:17:38.200 --> 0:17:40.120
<v Speaker 10>Yeah, those were created as part of the One Big

0:17:40.160 --> 0:17:43.800
<v Speaker 10>Beautiful Bill, as you mentioned, one thousand dollars for kids

0:17:43.800 --> 0:17:46.040
<v Speaker 10>born between twenty twenty five and twenty twenty eight. And

0:17:46.119 --> 0:17:48.920
<v Speaker 10>what Della is doing is kind of covering the gap

0:17:49.000 --> 0:17:52.280
<v Speaker 10>for children ten under who aren't covered under that program

0:17:52.440 --> 0:17:55.520
<v Speaker 10>and giving them two hundred and fifty dollars to be

0:17:55.640 --> 0:17:59.000
<v Speaker 10>used for things like college or startup, starting up their

0:17:59.040 --> 0:18:02.760
<v Speaker 10>own business, or home deposit when they're eighteen or older.

0:18:03.320 --> 0:18:06.520
<v Speaker 2>Now, Michael Dell and his family and he's the eleventh

0:18:06.840 --> 0:18:11.200
<v Speaker 2>wealthiest person in the world, but it's other tech moneyed

0:18:11.560 --> 0:18:13.679
<v Speaker 2>people who have been putting this initiative to work. And

0:18:13.720 --> 0:18:16.520
<v Speaker 2>I think about really Brad Gerstner, who has been the

0:18:16.600 --> 0:18:19.920
<v Speaker 2>driving force initially behind what is this sort of invest

0:18:20.000 --> 0:18:22.760
<v Speaker 2>America outreach. He set it up back in twenty twenty three.

0:18:22.800 --> 0:18:24.360
<v Speaker 2>Of course we know him from Ultimeter.

0:18:25.080 --> 0:18:27.040
<v Speaker 10>Yeah, that's right. He's been working on this for a

0:18:27.080 --> 0:18:30.879
<v Speaker 10>couple of years. As you mentioned it predates Trump obviously,

0:18:32.160 --> 0:18:36.680
<v Speaker 10>you know, the name Trump Accounts certainly got his attention

0:18:37.000 --> 0:18:39.480
<v Speaker 10>and I think helped put it into the One Big

0:18:39.520 --> 0:18:43.159
<v Speaker 10>Beautiful Bill. But yeah, it's kind of an issue that

0:18:44.880 --> 0:18:47.560
<v Speaker 10>Democrats and Republicans have been interested in something like this,

0:18:47.680 --> 0:18:51.280
<v Speaker 10>a product like this that gets everyday Americans invested in

0:18:51.359 --> 0:18:54.159
<v Speaker 10>the stock market from a very young age. It's kind

0:18:54.200 --> 0:18:55.879
<v Speaker 10>of a bipartisan issue.

0:18:56.320 --> 0:18:58.800
<v Speaker 2>Yeah, And Brad over at Altameter, he's been saying that

0:18:58.880 --> 0:19:01.520
<v Speaker 2>this is a platform for every company in America to

0:19:01.560 --> 0:19:04.000
<v Speaker 2>think about how they reward employee's own children.

0:19:04.040 --> 0:19:05.439
<v Speaker 3>But it doesn't need to just be a company.

0:19:05.480 --> 0:19:08.520
<v Speaker 2>It could be mums and dad's, churches, synagogue's. Many to

0:19:08.600 --> 0:19:10.920
<v Speaker 2>be putting it aside in a sort of tax efficient way.

0:19:10.960 --> 0:19:11.240
<v Speaker 9>Tom.

0:19:11.480 --> 0:19:12.480
<v Speaker 3>What's interesting is, of.

0:19:12.400 --> 0:19:15.919
<v Speaker 2>Course Dell Stock has moved on all of this, and

0:19:15.960 --> 0:19:18.439
<v Speaker 2>this comes after a truth social post.

0:19:18.680 --> 0:19:19.360
<v Speaker 3>Yeah, that's right.

0:19:19.640 --> 0:19:22.159
<v Speaker 10>Dell stock was up about four percent last time I looked.

0:19:22.880 --> 0:19:26.280
<v Speaker 10>Trump obviously singling out Michael and Susan Dell for their

0:19:26.280 --> 0:19:29.600
<v Speaker 10>donation this morning, he was seemingly very happy about it.

0:19:29.640 --> 0:19:33.800
<v Speaker 10>And you know, when Trump tweets about something, it tends

0:19:33.840 --> 0:19:35.760
<v Speaker 10>to move stuff in the market. So we're kind of

0:19:35.760 --> 0:19:37.520
<v Speaker 10>saying that I don't know if it's going to last,

0:19:37.880 --> 0:19:38.840
<v Speaker 10>but there you go.

0:19:39.440 --> 0:19:41.760
<v Speaker 2>And of course that contributes to the networth of the

0:19:41.800 --> 0:19:44.080
<v Speaker 2>Dell family. On the back of that, Bloomberg's Tom Maloney,

0:19:44.280 --> 0:19:46.679
<v Speaker 2>it's been fascinating talking to you, Thank you very much. Indeed,

0:19:46.960 --> 0:19:49.600
<v Speaker 2>let's turn now to Warner Brothers Discovery. The company has

0:19:49.600 --> 0:19:52.680
<v Speaker 2>received a new round of bids, with one from Netflix

0:19:52.720 --> 0:19:56.080
<v Speaker 2>consisting of a mostly cash offers, according to sources. For more,

0:19:56.119 --> 0:19:59.520
<v Speaker 2>Bloomberg's Michelle Davis, who covers M and A Immediate joins us.

0:19:59.560 --> 0:20:00.880
<v Speaker 3>Now, so can we.

0:20:00.840 --> 0:20:03.120
<v Speaker 2>Break down how these is starting to note because they're

0:20:03.119 --> 0:20:03.840
<v Speaker 2>going to be sweetened.

0:20:04.560 --> 0:20:07.600
<v Speaker 11>They've been sweetened, yes, so the bidding is heating up.

0:20:07.720 --> 0:20:10.840
<v Speaker 11>Yesterday Warner Brothers had asked for sweetened offers from all

0:20:10.840 --> 0:20:12.960
<v Speaker 11>of the companies, so we know they received offers from

0:20:13.119 --> 0:20:16.080
<v Speaker 11>Netflix mostly cash. That was a big surprise because people

0:20:16.080 --> 0:20:18.720
<v Speaker 11>were expecting it to be stock. You know, Netflix's stock

0:20:18.720 --> 0:20:21.159
<v Speaker 11>has been doing pretty well. They have great currency, but

0:20:21.160 --> 0:20:24.000
<v Speaker 11>they're also an investment grade company, so they can afford

0:20:24.040 --> 0:20:26.240
<v Speaker 11>to raise a lot of money. We hear they're in

0:20:26.280 --> 0:20:28.520
<v Speaker 11>the market talking to banks about tens of billions of

0:20:28.560 --> 0:20:31.640
<v Speaker 11>dollars for a bridgeland. So there's Netflix, there's Comcast which

0:20:31.680 --> 0:20:34.520
<v Speaker 11>has also bid it only wants to streaming, and the

0:20:34.560 --> 0:20:37.879
<v Speaker 11>studios of Warner Brothers similar to Netflix. We're still trying

0:20:37.920 --> 0:20:40.439
<v Speaker 11>to glean the details of that offer, but we have

0:20:40.600 --> 0:20:43.080
<v Speaker 11>heard that it likely includes a little more stock than

0:20:43.119 --> 0:20:45.639
<v Speaker 11>the cash offered by Netflix. And finally, Paramount, the one

0:20:45.640 --> 0:20:47.800
<v Speaker 11>who kicked all of this off. They're the only bidders

0:20:47.800 --> 0:20:50.160
<v Speaker 11>who have actually made a play for the whole company.

0:20:50.640 --> 0:20:53.560
<v Speaker 11>We're hearing that their offers also is also completely cash.

0:20:53.680 --> 0:20:56.919
<v Speaker 11>They have backing from Larry Ellison, David Ellison, you know,

0:20:57.040 --> 0:20:59.760
<v Speaker 11>some of the rich, wealthiest people on Earth and Middle

0:20:59.800 --> 0:21:01.680
<v Speaker 11>East earn money as well as Apollo er putting in

0:21:01.800 --> 0:21:03.160
<v Speaker 11>some of the financing for their offer.

0:21:03.240 --> 0:21:05.600
<v Speaker 2>And it feels as though that's been the narrative that

0:21:06.000 --> 0:21:08.960
<v Speaker 2>the Edison bid might be one that is more approved

0:21:09.000 --> 0:21:11.560
<v Speaker 2>of by the administration. But are there any blocks? Are

0:21:11.600 --> 0:21:14.680
<v Speaker 2>there any issues from a regulatory perspective for Paramounts guide

0:21:14.680 --> 0:21:16.640
<v Speaker 2>downs just getting any big O or any of these

0:21:16.640 --> 0:21:18.119
<v Speaker 2>players getting any BIGA.

0:21:18.280 --> 0:21:21.200
<v Speaker 11>Yes, So the Warner Brothers board is really I mean,

0:21:21.280 --> 0:21:23.440
<v Speaker 11>they are in a position where they have lots to assess,

0:21:23.440 --> 0:21:27.400
<v Speaker 11>but it's not apples to apples, because they're evaluating, you know,

0:21:27.840 --> 0:21:29.680
<v Speaker 11>a bid for the whole company in cash, a bid

0:21:29.720 --> 0:21:32.280
<v Speaker 11>that might include stock and cash but only for part

0:21:32.320 --> 0:21:35.679
<v Speaker 11>and then you know other structures that are unclear for

0:21:35.760 --> 0:21:38.600
<v Speaker 11>Paramount and comcasts. There's clearly lots of synergies that they

0:21:38.640 --> 0:21:41.040
<v Speaker 11>could achieve by combining the companies, but there's a view

0:21:41.040 --> 0:21:43.359
<v Speaker 11>that doing that, you know, to achieve those synergies, they

0:21:43.400 --> 0:21:45.840
<v Speaker 11>would cut a lot of jobs. I mean, maybe thousands

0:21:45.840 --> 0:21:49.240
<v Speaker 11>of jobs at the studios by combining these studios Netflix

0:21:49.760 --> 0:21:51.920
<v Speaker 11>Their argument might be, we're not going to do something

0:21:51.960 --> 0:21:53.639
<v Speaker 11>like that, and so it'll play better, you know, with

0:21:53.760 --> 0:21:58.240
<v Speaker 11>labor unions and regulators. But Netflix also already has a

0:21:58.240 --> 0:22:00.880
<v Speaker 11>lot of power. So it's a bit wildcard to see

0:22:00.880 --> 0:22:02.200
<v Speaker 11>how the regulators are going to view that.

0:22:02.359 --> 0:22:05.000
<v Speaker 2>Yea, many are going to say, is YouTube count being

0:22:05.040 --> 0:22:06.760
<v Speaker 2>counted in the numbers? How we think about this from

0:22:06.800 --> 0:22:11.280
<v Speaker 2>a regulatory perspective, from a state and federal perspective, timeline, any.

0:22:11.080 --> 0:22:12.080
<v Speaker 3>Sort of idea.

0:22:12.240 --> 0:22:14.639
<v Speaker 11>We're hearing that they want to get this sorted before

0:22:14.640 --> 0:22:16.800
<v Speaker 11>the end of the year, and that makes some sense

0:22:16.840 --> 0:22:19.800
<v Speaker 11>because for the banks who are underwriting the loans, they

0:22:19.800 --> 0:22:22.080
<v Speaker 11>don't want the loans on their books at your end,

0:22:22.160 --> 0:22:23.720
<v Speaker 11>because it's going to cost them a lot more money,

0:22:23.720 --> 0:22:25.280
<v Speaker 11>which means the bidders won't be able to pay as

0:22:25.320 --> 0:22:27.399
<v Speaker 11>much for these assets. So we've heard that there's a

0:22:27.440 --> 0:22:30.640
<v Speaker 11>deadline of before the holidays. A decision could be made

0:22:30.680 --> 0:22:32.800
<v Speaker 11>as early as this week. You know, the binding offers

0:22:32.800 --> 0:22:34.720
<v Speaker 11>are in, so that means Warner Brothers could move quickly,

0:22:34.960 --> 0:22:37.560
<v Speaker 11>but they're also giving themselves room to ask for more bids.

0:22:38.200 --> 0:22:40.200
<v Speaker 11>You know, later on this month, CHERL.

0:22:40.320 --> 0:22:42.560
<v Speaker 2>Davis will be across the story. We appreciate it.

0:22:42.840 --> 0:22:43.040
<v Speaker 9>Now.

0:22:43.040 --> 0:22:44.919
<v Speaker 3>Coming up, we're going to be going back to.

0:22:44.920 --> 0:22:48.120
<v Speaker 2>AWS Reinvent the conferences on in Las Vegas. We're gonna

0:22:48.119 --> 0:22:50.919
<v Speaker 2>sit down with Swami sieve us of Romanian, vice president

0:22:50.960 --> 0:22:52.399
<v Speaker 2>of a Gentki for that company.

0:22:52.520 --> 0:23:02.880
<v Speaker 3>This is pretty big tech. Welcome back to Bloomberg Tech.

0:23:02.880 --> 0:23:04.439
<v Speaker 2>We check in on these markets that are losing a

0:23:04.480 --> 0:23:06.480
<v Speaker 2>bit of their earlier esteem. We're still holding on to

0:23:06.560 --> 0:23:08.959
<v Speaker 2>three ten percent higher, but remember we're up more than

0:23:08.960 --> 0:23:11.480
<v Speaker 2>a percentage point in earlier trading. Is in video perhaps

0:23:11.520 --> 0:23:14.000
<v Speaker 2>loses some of its edge in terms of points perspective.

0:23:14.200 --> 0:23:16.720
<v Speaker 2>Apple leading the charge right now, Intel as well. I'm

0:23:16.720 --> 0:23:19.320
<v Speaker 2>looking at Bitcoin though, still holding onto its games. Best

0:23:19.440 --> 0:23:23.359
<v Speaker 2>day since May. We're now at the highest level and

0:23:23.400 --> 0:23:26.320
<v Speaker 2>ninety nine since the end of November, so a few

0:23:26.400 --> 0:23:28.639
<v Speaker 2>days we're up four point percent as we see a

0:23:28.720 --> 0:23:30.720
<v Speaker 2>little bit of shifting in sentiment. Let's look at a

0:23:30.760 --> 0:23:32.720
<v Speaker 2>key name that has shifted in sentiment. It was beaten

0:23:32.800 --> 0:23:35.520
<v Speaker 2>up in November. Now we're still up two percent. Let's

0:23:35.520 --> 0:23:38.480
<v Speaker 2>call it. We're rolling over somewhat. But Pan Andeer, in

0:23:38.600 --> 0:23:41.600
<v Speaker 2>another AI names, have seen a bit of love today.

0:23:41.640 --> 0:23:43.920
<v Speaker 2>It seems as though Pan Andeer could be a trillion

0:23:43.960 --> 0:23:46.480
<v Speaker 2>dollar company. That's what Dan I've been saying on the day.

0:23:46.520 --> 0:23:48.639
<v Speaker 2>But really he's looking at big Tech and MAG seven

0:23:48.880 --> 0:23:51.040
<v Speaker 2>to add twenty to twenty five percent in terms of

0:23:51.040 --> 0:23:53.000
<v Speaker 2>share growth for twenty twenty six.

0:23:53.359 --> 0:23:54.800
<v Speaker 3>But for now, we return to one of.

0:23:54.760 --> 0:23:56.919
<v Speaker 2>The key MAG seven in that pile, and it is

0:23:56.960 --> 0:23:59.560
<v Speaker 2>Amazon and it's Aws. Even is upon us in Las

0:23:59.640 --> 0:24:00.920
<v Speaker 2>Vegas and take it away.

0:24:03.359 --> 0:24:04.200
<v Speaker 9>Yeah, thank you very much.

0:24:04.280 --> 0:24:08.919
<v Speaker 4>Karas talk more about Amazon's agentic plans with Swami Siva Supermanian.

0:24:08.960 --> 0:24:12.359
<v Speaker 4>He's vice president for a Genta ki at AWS and

0:24:12.400 --> 0:24:15.760
<v Speaker 4>you've basically brought out and released three from what you

0:24:15.800 --> 0:24:16.920
<v Speaker 4>call frontier agents.

0:24:17.000 --> 0:24:19.160
<v Speaker 9>That's right, across.

0:24:19.200 --> 0:24:24.960
<v Speaker 4>An autonomous agent security DevOps and considering where aw sits

0:24:24.960 --> 0:24:27.800
<v Speaker 4>in the market and everyone that attends reinvent, probably the

0:24:28.520 --> 0:24:30.840
<v Speaker 4>question most people would have is if I'm a large

0:24:30.960 --> 0:24:35.040
<v Speaker 4>enterprise and I have multiple teams, multiple deaths, doing multiple things,

0:24:35.320 --> 0:24:38.320
<v Speaker 4>how do I deploy three of these frontier agents?

0:24:38.400 --> 0:24:39.240
<v Speaker 9>Yeah?

0:24:39.560 --> 0:24:43.320
<v Speaker 8>Father MEAs out of it, saying here at daw As

0:24:43.320 --> 0:24:46.480
<v Speaker 8>we are of building the foundation for billions of agents,

0:24:46.920 --> 0:24:49.879
<v Speaker 8>and that's an understatement. So what do we launch with

0:24:49.920 --> 0:24:54.360
<v Speaker 8>these frontier agents? Is essentially a new category of agents,

0:24:54.480 --> 0:24:58.199
<v Speaker 8>because while so much our activity is happening in agents,

0:24:58.560 --> 0:25:02.880
<v Speaker 8>there are historically being more assistance to humans. And while

0:25:02.920 --> 0:25:05.760
<v Speaker 8>we deploy it, even with an Amazon Christen sew thousand

0:25:05.800 --> 0:25:09.720
<v Speaker 8>set developers to help with software development, we were actually

0:25:09.760 --> 0:25:13.280
<v Speaker 8>wanting to push the envelopement being able to actually do

0:25:13.400 --> 0:25:17.439
<v Speaker 8>a lot more where they are actually autonomous, where you

0:25:17.520 --> 0:25:20.560
<v Speaker 8>don't need humans to constantly steer them, and they are

0:25:20.600 --> 0:25:22.720
<v Speaker 8>massively scalable and they can run along.

0:25:22.800 --> 0:25:26.280
<v Speaker 4>To be autonomous, they need to have an end goal,

0:25:26.520 --> 0:25:28.560
<v Speaker 4>that's right, How does that work? So it's a case

0:25:28.560 --> 0:25:32.240
<v Speaker 4>of saying, by the way you use the term AI assistant, Yeah,

0:25:32.280 --> 0:25:34.720
<v Speaker 4>what we're now really talking about is AI co worker.

0:25:34.880 --> 0:25:37.320
<v Speaker 9>That's right, and that is the key thing here.

0:25:37.560 --> 0:25:40.560
<v Speaker 8>It's just like what these frontier agents are doing is

0:25:40.600 --> 0:25:44.680
<v Speaker 8>completely transforming how software is getting done in these teams.

0:25:45.440 --> 0:25:49.119
<v Speaker 8>So much activity happens primarily in like software development, but

0:25:49.400 --> 0:25:53.840
<v Speaker 8>now these agentic teammates they show up as another teammate

0:25:55.000 --> 0:25:59.439
<v Speaker 8>for software development. They pull up actually jobs from like

0:25:59.520 --> 0:26:03.879
<v Speaker 8>guitar task and start coding and clearing backlog. And if

0:26:03.920 --> 0:26:06.040
<v Speaker 8>you're in the middle of the night actually getting page

0:26:06.080 --> 0:26:10.040
<v Speaker 8>to deal with an issue happening with your website, this

0:26:10.119 --> 0:26:12.880
<v Speaker 8>agent first takes the first page and says, oh, let

0:26:12.880 --> 0:26:15.920
<v Speaker 8>me look into what's happening and unpack and then find

0:26:15.920 --> 0:26:18.199
<v Speaker 8>out the root costs so that you can actually quickly

0:26:18.280 --> 0:26:20.480
<v Speaker 8>resolve it and go back to bed, like I've been

0:26:20.520 --> 0:26:22.560
<v Speaker 8>on call and I know I could use that help

0:26:22.960 --> 0:26:26.479
<v Speaker 8>and be proactive so that those issues don't happen. And

0:26:26.680 --> 0:26:30.280
<v Speaker 8>third is actually a security teammate, so it shows up again.

0:26:30.600 --> 0:26:34.040
<v Speaker 8>And most of security has always been about afterthought. One

0:26:34.080 --> 0:26:36.160
<v Speaker 8>of the things we are changing the game here is

0:26:36.280 --> 0:26:40.560
<v Speaker 8>actually meeting developers even before they write a single line

0:26:40.560 --> 0:26:43.800
<v Speaker 8>of code when they're designing software. We catch them right

0:26:43.840 --> 0:26:45.960
<v Speaker 8>there and say here are the things you should be

0:26:46.000 --> 0:26:48.560
<v Speaker 8>aware of. And when they write code, we say, oh,

0:26:48.640 --> 0:26:52.400
<v Speaker 8>don't do that, do this and actually do penetration testing.

0:26:52.080 --> 0:26:53.160
<v Speaker 9>All before shipping.

0:26:53.320 --> 0:26:56.280
<v Speaker 8>So now you can view it as like you have

0:26:56.400 --> 0:27:00.960
<v Speaker 8>the best in class developer, OPS and security engineers showing

0:27:01.040 --> 0:27:03.040
<v Speaker 8>up as virtual teammates for every.

0:27:02.800 --> 0:27:06.720
<v Speaker 4>Software you say best in class? What evidence does AWS

0:27:06.760 --> 0:27:10.360
<v Speaker 4>have that in real world deployments? Running these three agents

0:27:10.400 --> 0:27:15.639
<v Speaker 4>in parallel autonomously has a clear benefit. Yeah, their efficiency, productivity.

0:27:16.000 --> 0:27:16.960
<v Speaker 4>What data can you share?

0:27:17.040 --> 0:27:19.159
<v Speaker 9>Yeah, I'll give actually a couple of examples.

0:27:19.200 --> 0:27:23.240
<v Speaker 8>One is in the DevOps agent that we launched, we

0:27:23.400 --> 0:27:28.760
<v Speaker 8>ran it across thousands of escalations that happened this year alone,

0:27:28.840 --> 0:27:33.399
<v Speaker 8>on incidents and so forth. These agents actually correctly identified

0:27:33.400 --> 0:27:35.440
<v Speaker 8>the root cost eighty six percent of time.

0:27:35.960 --> 0:27:39.600
<v Speaker 9>That is like really impresses eighty six eighty six eighty

0:27:39.640 --> 0:27:40.320
<v Speaker 9>six percent.

0:27:40.520 --> 0:27:43.800
<v Speaker 8>And the second one is Commonwealth Bank of Australia.

0:27:43.920 --> 0:27:46.480
<v Speaker 9>They actually have a huge cloud.

0:27:46.200 --> 0:27:50.600
<v Speaker 8>Infrastructure running across thousand, seven hundred accounts and they put

0:27:50.600 --> 0:27:54.600
<v Speaker 8>this DevOps agent test to test and for a very

0:27:54.640 --> 0:27:58.119
<v Speaker 8>complex networking stack to debug it. And what they said,

0:27:58.160 --> 0:28:01.159
<v Speaker 8>what did to take them? Like our Diba this was

0:28:01.240 --> 0:28:04.000
<v Speaker 8>able to do it in minutes and these are just

0:28:04.359 --> 0:28:08.240
<v Speaker 8>few simple examples, same thing. What's going on at security agent.

0:28:08.359 --> 0:28:13.320
<v Speaker 8>Smart Bug is a great photo company and there I'm

0:28:13.359 --> 0:28:17.400
<v Speaker 8>a big customer, and they are completely automating their security

0:28:18.280 --> 0:28:22.480
<v Speaker 8>operations altogether using like AWS Security Agent. And these are

0:28:22.520 --> 0:28:25.320
<v Speaker 8>all the beginning of how this is going to change

0:28:25.320 --> 0:28:26.280
<v Speaker 8>the game in.

0:28:26.240 --> 0:28:26.800
<v Speaker 9>A big way.

0:28:26.920 --> 0:28:29.800
<v Speaker 8>Because what do you want these agentic teammates to be

0:28:30.040 --> 0:28:34.040
<v Speaker 8>is always be there and you delegates on the boring

0:28:34.160 --> 0:28:39.080
<v Speaker 8>drudgery associated with like ops and security and backlog on

0:28:39.200 --> 0:28:43.560
<v Speaker 8>developments so that developers are empowered to be extremely creative

0:28:43.720 --> 0:28:47.240
<v Speaker 8>and you will start seeing fighter ten x improvement in

0:28:47.320 --> 0:28:48.560
<v Speaker 8>productivity in a big way.

0:28:48.920 --> 0:28:51.880
<v Speaker 4>To reach out its three agents, Kero is software development.

0:28:52.000 --> 0:28:55.440
<v Speaker 4>You have the AWS Security Agent, the AWS DevOps agent.

0:28:55.560 --> 0:28:59.680
<v Speaker 4>They are designed to run for hours autonomously. To sum

0:28:59.760 --> 0:29:02.880
<v Speaker 4>that disconcerting, right, And I go back to my other question,

0:29:03.800 --> 0:29:07.680
<v Speaker 4>how much concern is there in the last segment Clean

0:29:08.080 --> 0:29:12.400
<v Speaker 4>talk to me about observability, understanding in real time what's doing.

0:29:12.400 --> 0:29:14.880
<v Speaker 4>But that sounds to me like it's human supervision.

0:29:15.480 --> 0:29:16.320
<v Speaker 9>It's a great question.

0:29:16.440 --> 0:29:19.840
<v Speaker 8>I mean, this is the nuance and the art of balance.

0:29:19.840 --> 0:29:22.200
<v Speaker 8>We had to do one. You want these agents to

0:29:22.280 --> 0:29:26.240
<v Speaker 8>be autonomous and massively scalable, but you also want them

0:29:26.280 --> 0:29:29.239
<v Speaker 8>to not go off the rails. So first we are

0:29:29.240 --> 0:29:32.080
<v Speaker 8>built it with the right guard rails. And second even

0:29:32.120 --> 0:29:35.440
<v Speaker 8>a spart a development workflow, these agents can go actually

0:29:35.840 --> 0:29:39.200
<v Speaker 8>take a goal from a developer to say, hey, go

0:29:39.240 --> 0:29:40.600
<v Speaker 8>work on upgrading.

0:29:40.160 --> 0:29:43.280
<v Speaker 9>This piece of code to the latest systyk and then

0:29:43.320 --> 0:29:44.160
<v Speaker 9>start upgrading.

0:29:44.440 --> 0:29:47.320
<v Speaker 8>But then as a final step, it might send it

0:29:47.360 --> 0:29:49.560
<v Speaker 8>for a code review to the human and say take

0:29:49.560 --> 0:29:51.960
<v Speaker 8>a look and make sure I'm okay. And once they

0:29:51.960 --> 0:29:54.240
<v Speaker 8>say I'm okay, I think it's fine, or they can

0:29:54.360 --> 0:29:56.239
<v Speaker 8>even configure it to say if it pass us all

0:29:56.280 --> 0:29:58.880
<v Speaker 8>these tests, you're good to ship it. So that you

0:29:59.120 --> 0:30:02.760
<v Speaker 8>have built in s either through human or automated tests.

0:30:02.800 --> 0:30:05.760
<v Speaker 8>And these are the kind of things now we are

0:30:05.920 --> 0:30:09.479
<v Speaker 8>able to actually innovate with the customers as well. And

0:30:09.520 --> 0:30:13.360
<v Speaker 8>even there we have done some pure innovation like introduced

0:30:13.400 --> 0:30:19.800
<v Speaker 8>automated mathematically provable property based testing, so that means now

0:30:20.120 --> 0:30:24.080
<v Speaker 8>developers can specify their goal and we can mathematically prove

0:30:24.160 --> 0:30:28.040
<v Speaker 8>with things like kiro that whatever it is generating can

0:30:28.120 --> 0:30:30.800
<v Speaker 8>be tested exactly that way and it's accurate. So that

0:30:30.960 --> 0:30:32.280
<v Speaker 8>is like another game changer.

0:30:33.200 --> 0:30:35.920
<v Speaker 4>SWAM we see the supermanian vice president for a gens

0:30:36.040 --> 0:30:39.840
<v Speaker 4>Ki at aws three new Frontier agents carry We're talking

0:30:39.840 --> 0:30:41.200
<v Speaker 4>about here in Las Vegas, back.

0:30:41.040 --> 0:30:43.600
<v Speaker 2>To you, loving it back here in New York. Ed's

0:30:43.680 --> 0:30:46.880
<v Speaker 2>time for talking tech. First up, Apple's head of AI

0:30:47.120 --> 0:30:50.080
<v Speaker 2>John Jian Andrea, Well, he's coming down with mans to

0:30:50.160 --> 0:30:52.680
<v Speaker 2>lead the company entirely in the spring. The move caps

0:30:52.720 --> 0:30:55.840
<v Speaker 2>are pretty tumultuous tenure that included a fumbled entry into

0:30:55.880 --> 0:31:00.480
<v Speaker 2>General's AI. Apple won't be directly replacing Jian Andrea, instead

0:31:00.680 --> 0:31:04.120
<v Speaker 2>opting to break up the AIT plus Samsung It's unveiled

0:31:04.160 --> 0:31:08.920
<v Speaker 2>its first trifle smartphone. It's called Galaxy Z Trifle and

0:31:08.960 --> 0:31:12.320
<v Speaker 2>the device contains two hinges allow it to transform into

0:31:12.360 --> 0:31:14.760
<v Speaker 2>a larger tablet like device. The phone is set to

0:31:14.800 --> 0:31:17.120
<v Speaker 2>be released first in South Korea on December the twelfth,

0:31:17.160 --> 0:31:20.600
<v Speaker 2>with a price of about four hundred and fifty dollars,

0:31:20.760 --> 0:31:23.640
<v Speaker 2>and the US Commerce Department has agreed to invest as

0:31:23.720 --> 0:31:26.720
<v Speaker 2>much as one hundred and fifty million dollars into x Light.

0:31:27.000 --> 0:31:30.360
<v Speaker 2>It's a chip startup where former Intel CEO Pat Gelsinger

0:31:30.560 --> 0:31:33.080
<v Speaker 2>serves as executive chairman. So the latest moved by the

0:31:33.080 --> 0:31:36.520
<v Speaker 2>Trump administration to bring chip making capabilities back to the

0:31:36.600 --> 0:31:39.840
<v Speaker 2>United States. Now coming up, guess while we go back

0:31:39.880 --> 0:31:43.160
<v Speaker 2>to AWS in Vegas. This time it's a conversation.

0:31:42.720 --> 0:31:44.560
<v Speaker 3>With Sanjay Back to Cone.

0:31:44.680 --> 0:31:47.840
<v Speaker 2>Nas chief Product and Technology Officer is a blueber tech.

0:32:04.480 --> 0:32:07.720
<v Speaker 2>Luma AI, known for its flagship creative product, dream Machine

0:32:07.800 --> 0:32:11.160
<v Speaker 2>video generator, taking a step forward in global expansion with

0:32:11.200 --> 0:32:14.719
<v Speaker 2>opening its first international office and it's in London. This

0:32:14.800 --> 0:32:17.720
<v Speaker 2>follows its recent nine hundred million dollar Series C investment,

0:32:17.720 --> 0:32:20.240
<v Speaker 2>which was led by Humane and Met Jane Is with

0:32:20.320 --> 0:32:23.000
<v Speaker 2>US co founder CEO of Luma AI and so.

0:32:23.000 --> 0:32:23.680
<v Speaker 3>Pleased to join you.

0:32:23.680 --> 0:32:25.840
<v Speaker 2>I can't wait to get into reasoning video models, into

0:32:25.840 --> 0:32:26.960
<v Speaker 2>the future of world models.

0:32:27.000 --> 0:32:28.719
<v Speaker 3>But first am it? Why London?

0:32:31.120 --> 0:32:33.120
<v Speaker 12>We have a huge pipeline. Actually, by the way, thanks

0:32:33.160 --> 0:32:36.600
<v Speaker 12>for having me. I'm very excited to be here. We

0:32:36.640 --> 0:32:42.240
<v Speaker 12>have a huge pipeline of researchers, engineers from Europe and

0:32:42.520 --> 0:32:46.840
<v Speaker 12>also deep Mind that want to join luma And also

0:32:47.080 --> 0:32:50.400
<v Speaker 12>London is the gateway for the business in Europe as

0:32:50.440 --> 0:32:53.200
<v Speaker 12>well as Middle East, so it seems to be the

0:32:53.320 --> 0:32:56.280
<v Speaker 12>right place to have our second office outside of Palo

0:32:56.320 --> 0:32:58.760
<v Speaker 12>Alto and Bay Area. So yeah, today is our launch

0:32:58.800 --> 0:32:59.760
<v Speaker 12>of the London office.

0:33:00.000 --> 0:33:02.560
<v Speaker 2>I have a feeling demos hasibus over at deep Mind

0:33:02.600 --> 0:33:05.560
<v Speaker 2>and the AI labs at Google we'll be having is

0:33:05.560 --> 0:33:08.800
<v Speaker 2>is pricked by that, amit? So why would people currently

0:33:08.840 --> 0:33:11.080
<v Speaker 2>working or having been trained at deep Mind and the

0:33:11.280 --> 0:33:13.720
<v Speaker 2>like want a jump ship to help build Ray three

0:33:13.760 --> 0:33:14.880
<v Speaker 2>your reasoning video model.

0:33:15.880 --> 0:33:18.360
<v Speaker 12>Yeah, I think that's a great question. So Luma already

0:33:18.400 --> 0:33:22.080
<v Speaker 12>has actually a deep bench of researchers from from n

0:33:22.160 --> 0:33:26.440
<v Speaker 12>video from from deep Mind, from you know, schools like MIT,

0:33:26.840 --> 0:33:32.040
<v Speaker 12>Stanford and Berkeley. And the reason that these incredibly exceptional

0:33:32.080 --> 0:33:36.120
<v Speaker 12>people actually join Luma is because, one, while it is

0:33:36.200 --> 0:33:39.960
<v Speaker 12>a very very well resourced AGI lab, we are only

0:33:39.960 --> 0:33:42.440
<v Speaker 12>about one hundred and fifty people, so we get to

0:33:42.600 --> 0:33:45.520
<v Speaker 12>do and get to have resources per person that, like,

0:33:45.560 --> 0:33:47.200
<v Speaker 12>you know, it's unheard of in the rest of the

0:33:47.200 --> 0:33:51.640
<v Speaker 12>industry actually. And second, Luma is so ultra aligned on

0:33:51.680 --> 0:33:54.640
<v Speaker 12>this one goal, which is to build multi model AGI.

0:33:54.720 --> 0:33:58.360
<v Speaker 12>There's actually no second There is no second project happening

0:33:58.360 --> 0:33:59.080
<v Speaker 12>at Luma.

0:33:59.160 --> 0:33:59.959
<v Speaker 9>This is what we do.

0:34:00.320 --> 0:34:06.680
<v Speaker 12>So people researchers, engineers and product people who believe strongly

0:34:07.200 --> 0:34:09.880
<v Speaker 12>in this mission, Luma is the best place in the

0:34:09.920 --> 0:34:10.680
<v Speaker 12>world for them to be.

0:34:11.400 --> 0:34:14.680
<v Speaker 2>Looking at some of the amazing video that you create

0:34:15.239 --> 0:34:17.200
<v Speaker 2>I can see how it goes into the creative sphere,

0:34:17.239 --> 0:34:20.279
<v Speaker 2>into advertising and the like, but it feels as though

0:34:20.360 --> 0:34:23.960
<v Speaker 2>this model, these world models, has moved to perhaps more

0:34:23.960 --> 0:34:27.120
<v Speaker 2>physical AI applications must open up different industries.

0:34:28.120 --> 0:34:32.480
<v Speaker 12>Yeah so, I mean video and video is actually the

0:34:32.520 --> 0:34:34.640
<v Speaker 12>path to AGI. And let me tell you just very

0:34:34.640 --> 0:34:37.480
<v Speaker 12>briefly why that is the case. Language gives us reasoning,

0:34:37.640 --> 0:34:40.360
<v Speaker 12>and language gives us the human abstraction that is necessary.

0:34:40.680 --> 0:34:43.120
<v Speaker 12>Video shows us the entire universe. Right, you know how

0:34:43.400 --> 0:34:46.480
<v Speaker 12>things behave, how water behaves, how the laws of physics work.

0:34:46.719 --> 0:34:51.320
<v Speaker 12>Then you combine them together. Then video, audio and language

0:34:51.360 --> 0:34:55.080
<v Speaker 12>together is basically a chance to build a universal simulator.

0:34:55.520 --> 0:34:57.800
<v Speaker 12>So one application of that, as you point out, is

0:34:58.000 --> 0:35:01.080
<v Speaker 12>obviously for entertainment and to generate video to automate or

0:35:01.160 --> 0:35:04.240
<v Speaker 12>or to to make digital the act of creating video.

0:35:04.880 --> 0:35:07.759
<v Speaker 12>But in addition to that, it is the gateway to

0:35:07.800 --> 0:35:11.960
<v Speaker 12>be able to build actually general purpose robotics. That is

0:35:12.000 --> 0:35:14.160
<v Speaker 12>the second area that we are very deeply focused on

0:35:14.200 --> 0:35:16.360
<v Speaker 12>and now starting to build out a team and the

0:35:16.400 --> 0:35:20.600
<v Speaker 12>research area of that, and we have if we want

0:35:20.640 --> 0:35:24.319
<v Speaker 12>to have any shot at building general purpose robots, then

0:35:24.320 --> 0:35:26.960
<v Speaker 12>the only way that happens is by giving them this

0:35:27.120 --> 0:35:30.040
<v Speaker 12>level of general understanding of the universe so that they can,

0:35:30.440 --> 0:35:32.640
<v Speaker 12>you know, reason in their head. They can actually simulate

0:35:32.680 --> 0:35:35.000
<v Speaker 12>every scenario right in their head, like, Okay, what would

0:35:35.000 --> 0:35:36.480
<v Speaker 12>happen if I do this? What would happen if I

0:35:36.480 --> 0:35:40.480
<v Speaker 12>do something else? This level of learning and reasoning is essential,

0:35:40.480 --> 0:35:43.320
<v Speaker 12>so video as it advances and as we scale these models,

0:35:43.440 --> 0:35:45.440
<v Speaker 12>they will not only get better and better, they will

0:35:45.440 --> 0:35:48.440
<v Speaker 12>get more accurate in simulating physics. And this is the

0:35:48.480 --> 0:35:51.359
<v Speaker 12>path to building physical intelligence. And that's why Luma's model

0:35:52.120 --> 0:35:56.520
<v Speaker 12>mission is to build multimodel agi that can generate, understand,

0:35:56.760 --> 0:35:59.160
<v Speaker 12>and operate in the physical world. And that is the

0:35:59.239 --> 0:35:59.680
<v Speaker 12>end goal.

0:36:00.600 --> 0:36:02.359
<v Speaker 2>What is the biggest blocker to that or the thing

0:36:02.400 --> 0:36:03.759
<v Speaker 2>that keeps you up Because at the moment, you've got

0:36:03.800 --> 0:36:05.440
<v Speaker 2>the money, Boy, do you have the money nine hundred

0:36:05.480 --> 0:36:08.080
<v Speaker 2>million raised, But we've also got promise of compute coming

0:36:08.080 --> 0:36:08.880
<v Speaker 2>from Saudi Arabia.

0:36:09.000 --> 0:36:09.880
<v Speaker 3>Is it about compute?

0:36:09.920 --> 0:36:12.560
<v Speaker 2>Is it more about the talent that is what you

0:36:12.600 --> 0:36:14.360
<v Speaker 2>need to push through to get to your mission.

0:36:15.280 --> 0:36:18.359
<v Speaker 12>Yeah, So there are two really important things for LUMA.

0:36:19.400 --> 0:36:22.560
<v Speaker 12>Over the last couple of years, we worked tirelessly to

0:36:23.000 --> 0:36:25.080
<v Speaker 12>solve the research problem for like you know, really really

0:36:25.120 --> 0:36:27.920
<v Speaker 12>great video. Now the next frontier for us are these

0:36:27.960 --> 0:36:30.080
<v Speaker 12>omni models that are able to like you know, reason

0:36:30.120 --> 0:36:33.759
<v Speaker 12>an audio, video, language text together. So the research is

0:36:34.440 --> 0:36:36.439
<v Speaker 12>like you know, the first one that we pay mental

0:36:36.400 --> 0:36:38.879
<v Speaker 12>amount of attention to now to solve the research of course,

0:36:38.920 --> 0:36:42.160
<v Speaker 12>you know, people and really really brilliant people is one

0:36:42.160 --> 0:36:44.120
<v Speaker 12>of those bottlencks. So we hire from some of the

0:36:44.160 --> 0:36:46.880
<v Speaker 12>best places. See, we don't need thousands of people. We

0:36:46.960 --> 0:36:48.960
<v Speaker 12>need like, you know, maybe two hundred or three hundred

0:36:49.040 --> 0:36:52.719
<v Speaker 12>really really brilliant people to work on these problems. So yeah,

0:36:52.760 --> 0:36:57.240
<v Speaker 12>that's that's the first one. The talent and great aligned people,

0:36:57.320 --> 0:36:59.719
<v Speaker 12>that is the first one. Second is obviously compute. So

0:36:59.760 --> 0:37:02.719
<v Speaker 12>alongside with the SERIOC announcement, we announced that we will

0:37:02.760 --> 0:37:07.280
<v Speaker 12>build with Humane and collaboration with Humane, a two gigawat

0:37:07.440 --> 0:37:11.799
<v Speaker 12>compute cluster that is the largest compute build out in

0:37:11.840 --> 0:37:14.279
<v Speaker 12>our space of world models and video models and like

0:37:14.280 --> 0:37:16.960
<v Speaker 12>you know, this kind of physical AI. And it is

0:37:17.000 --> 0:37:20.400
<v Speaker 12>also one of the largest compute built out in AI period, right,

0:37:20.560 --> 0:37:23.160
<v Speaker 12>So compute is one of the other things that is

0:37:23.239 --> 0:37:27.920
<v Speaker 12>a big limitation. Ultimately, multi model AI will be a

0:37:27.960 --> 0:37:30.839
<v Speaker 12>superset of llms, will be a superset of the AI

0:37:30.880 --> 0:37:33.640
<v Speaker 12>we have today, it will require more compute than llms

0:37:33.719 --> 0:37:36.960
<v Speaker 12>do right now. So compute is the second very important

0:37:36.960 --> 0:37:39.200
<v Speaker 12>input to our business that, like you know, we are

0:37:39.280 --> 0:37:41.759
<v Speaker 12>working to not only shore up but solve in a

0:37:41.760 --> 0:37:44.359
<v Speaker 12>way we can serve these models economically to a lot

0:37:44.400 --> 0:37:44.800
<v Speaker 12>of people.

0:37:45.160 --> 0:37:47.440
<v Speaker 2>I mean, Jane, come back and tell us how you'll

0:37:47.440 --> 0:37:48.400
<v Speaker 2>continuing on that mission.

0:37:48.520 --> 0:37:50.560
<v Speaker 3>Go found a CEO of Luma AI.

0:37:51.000 --> 0:37:51.360
<v Speaker 9>Thank you.

0:37:52.040 --> 0:37:55.440
<v Speaker 2>Coming up, we're talking more about AI and infrastructure. We're

0:37:55.440 --> 0:37:58.640
<v Speaker 2>going to aws's event in Vegas. Were a conversation with

0:37:58.760 --> 0:38:02.400
<v Speaker 2>Sanjay Blackta Conde, NAS chief product and technology officer is

0:38:02.400 --> 0:38:03.040
<v Speaker 2>a blue big.

0:38:02.920 --> 0:38:21.759
<v Speaker 4>Tech Sanday back to Conde, NAS chief Product and Technology

0:38:21.800 --> 0:38:25.239
<v Speaker 4>officer joins us from AWS reinvent in Las Vegas. You're

0:38:25.320 --> 0:38:27.640
<v Speaker 4>kind of bucking a trend that we've been talking about

0:38:28.120 --> 0:38:31.160
<v Speaker 4>throughout the year in the context of AI. Actually on

0:38:31.320 --> 0:38:34.319
<v Speaker 4>prem has kind of been back when AI is being

0:38:34.400 --> 0:38:36.959
<v Speaker 4>run at the edge. You're going the other way from

0:38:36.960 --> 0:38:39.560
<v Speaker 4>on prem, relying heavily on AWS.

0:38:39.640 --> 0:38:41.240
<v Speaker 9>The rationale, well.

0:38:41.080 --> 0:38:43.440
<v Speaker 13>We are actually users of AI. We are not like

0:38:43.480 --> 0:38:47.120
<v Speaker 13>some of these LLLM companies that are creating and training

0:38:47.160 --> 0:38:50.040
<v Speaker 13>their own models. Although we have trained models in the past,

0:38:50.080 --> 0:38:53.000
<v Speaker 13>but at a much smaller scale. So for us, investing

0:38:53.000 --> 0:38:55.719
<v Speaker 13>in infrastructure and building out data centers for the size

0:38:55.760 --> 0:38:57.280
<v Speaker 13>of our operation doesn't make sense.

0:38:57.640 --> 0:39:01.799
<v Speaker 4>The work with AWS is focused on deli ring personalized content, right,

0:39:02.320 --> 0:39:04.799
<v Speaker 4>And is that at the foundation model level you know

0:39:04.840 --> 0:39:07.800
<v Speaker 4>AWS out with Nova to today, or it's just simply

0:39:07.880 --> 0:39:11.480
<v Speaker 4>that you're leveraging their scale the multitude of data platforms

0:39:11.480 --> 0:39:11.719
<v Speaker 4>they have.

0:39:11.880 --> 0:39:14.600
<v Speaker 13>Yeah, for that particular use case, we're actually just leveraging

0:39:14.600 --> 0:39:17.680
<v Speaker 13>their infrastructure and scale. We've built all the personalization models

0:39:17.680 --> 0:39:20.759
<v Speaker 13>ourselves and we've trained them in house, and we've been

0:39:20.760 --> 0:39:24.759
<v Speaker 13>doing that for the last probably three four years, long

0:39:24.800 --> 0:39:27.759
<v Speaker 13>before LLMS became a thing. We've had our own data

0:39:27.760 --> 0:39:30.520
<v Speaker 13>science team and we've been training models. So we use

0:39:30.560 --> 0:39:33.480
<v Speaker 13>our own homegrown models for most of the personalization and

0:39:33.560 --> 0:39:34.600
<v Speaker 13>recommendation workload.

0:39:34.680 --> 0:39:38.440
<v Speaker 4>So here in lies the question of AWS reinvent AWS

0:39:38.640 --> 0:39:42.200
<v Speaker 4>number one in cloud computing, but they want to do more,

0:39:42.400 --> 0:39:44.560
<v Speaker 4>They want to be number one in AI. You know,

0:39:44.640 --> 0:39:48.440
<v Speaker 4>their own foundation models, the agentic tools that they release today.

0:39:48.960 --> 0:39:51.680
<v Speaker 4>What would it take from Amazon in terms of the

0:39:51.760 --> 0:39:54.400
<v Speaker 4>utility of that technology for you to rely on them

0:39:54.400 --> 0:39:54.880
<v Speaker 4>more heavily.

0:39:55.200 --> 0:39:59.600
<v Speaker 13>Well, actually, for some of the generative use cases you

0:39:59.600 --> 0:40:02.320
<v Speaker 13>know that are LLLM based. We are using Amazon Bedrock.

0:40:03.080 --> 0:40:05.800
<v Speaker 13>You know, we have a contracts management, rights clearance system

0:40:05.840 --> 0:40:09.799
<v Speaker 13>that we just launched. We are using Amazon's Bedrock capability

0:40:09.880 --> 0:40:13.560
<v Speaker 13>for some of our moderation AI based moderation of user

0:40:13.600 --> 0:40:17.000
<v Speaker 13>generated content. So we're looking more and more now to

0:40:17.000 --> 0:40:21.280
<v Speaker 13>towards using out of the box capabilities that Amazon provides

0:40:21.440 --> 0:40:23.560
<v Speaker 13>rather than build and train our own models, which we.

0:40:23.640 --> 0:40:24.560
<v Speaker 9>Used to do in the past.

0:40:24.600 --> 0:40:27.600
<v Speaker 13>I think the need for that is becoming less and less.

0:40:28.160 --> 0:40:31.279
<v Speaker 4>Conde has a relationship with open AI. Condy was one

0:40:31.320 --> 0:40:33.160
<v Speaker 4>of the first to make a deal with open Ai

0:40:33.719 --> 0:40:36.080
<v Speaker 4>early days, it seems. But how is that progressing?

0:40:36.440 --> 0:40:37.120
<v Speaker 9>That's going well.

0:40:37.160 --> 0:40:40.680
<v Speaker 13>Actually, we're starting to use chat GPT quite widely within

0:40:40.760 --> 0:40:46.080
<v Speaker 13>the enterprise internally, yes, and for external use case. We've

0:40:46.080 --> 0:40:50.840
<v Speaker 13>actually launched an AI based recipe search on bond Appetite

0:40:52.000 --> 0:40:53.960
<v Speaker 13>on our on our website, and also it's going to

0:40:54.000 --> 0:40:56.279
<v Speaker 13>come out in our app which allows customers to go

0:40:56.360 --> 0:40:58.680
<v Speaker 13>in and do natural language search and also be able

0:40:58.760 --> 0:41:01.840
<v Speaker 13>to modify the it has peace according to their taste.

0:41:02.160 --> 0:41:03.960
<v Speaker 13>So that's the first use case we are looking at

0:41:03.960 --> 0:41:05.399
<v Speaker 13>others with opening I as well.

0:41:06.120 --> 0:41:09.680
<v Speaker 4>The broad theme of the program today here at AWS

0:41:09.760 --> 0:41:12.759
<v Speaker 4>has been about companies of all sizes moving from using

0:41:12.840 --> 0:41:17.040
<v Speaker 4>AI assistance internally to what awso call the AI co worker,

0:41:17.520 --> 0:41:20.600
<v Speaker 4>you know, a hybrid workforce of people and energentic AI.

0:41:21.239 --> 0:41:25.640
<v Speaker 4>Conde has done layoffs in the past two years, financial years.

0:41:25.960 --> 0:41:30.120
<v Speaker 4>How much has that been about AI tools changing productivity,

0:41:30.560 --> 0:41:33.560
<v Speaker 4>eliminating certain roles, changing certain roles.

0:41:33.800 --> 0:41:35.680
<v Speaker 13>Well, I don't think it's most of it has been

0:41:35.719 --> 0:41:38.279
<v Speaker 13>about AI. Actually, some of it, you know, especially in tech.

0:41:38.360 --> 0:41:41.520
<v Speaker 13>You know, we've had we use extensively, We use AI

0:41:41.600 --> 0:41:43.480
<v Speaker 13>for our work work on a day to day basis,

0:41:43.520 --> 0:41:46.640
<v Speaker 13>which eliminates the need for some roles. We can do

0:41:46.719 --> 0:41:50.520
<v Speaker 13>more things with fewer people. But other than that, across

0:41:50.520 --> 0:41:53.400
<v Speaker 13>the organization, we've not really had any AI based impacts.

0:41:54.160 --> 0:41:58.320
<v Speaker 4>Got to ask in a different part of the Amazon universe.

0:41:58.960 --> 0:42:02.440
<v Speaker 4>I've been experimenting at home with Alexa and Alexa plus.

0:42:02.840 --> 0:42:05.719
<v Speaker 4>Do we get some kind of Conde nas Alexa integration.

0:42:06.120 --> 0:42:07.520
<v Speaker 9>Well, that's work going on there.

0:42:07.880 --> 0:42:10.640
<v Speaker 13>We have actually already integrated with Amazon Alexa.

0:42:10.640 --> 0:42:12.440
<v Speaker 9>So how does that work available?

0:42:12.760 --> 0:42:15.280
<v Speaker 13>So you know, you can query and you can get content,

0:42:15.360 --> 0:42:17.040
<v Speaker 13>you know, read out to you from some of our

0:42:17.040 --> 0:42:20.120
<v Speaker 13>publications not all so, but I think it's pretty cool.

0:42:20.120 --> 0:42:20.799
<v Speaker 13>We should try it out.

0:42:21.200 --> 0:42:22.600
<v Speaker 4>At the heart of that question is something a bit

0:42:22.600 --> 0:42:28.000
<v Speaker 4>more existential. Conday sees itself more as maybe entertainment, and

0:42:28.040 --> 0:42:30.399
<v Speaker 4>if you look at the revenue streams very different from

0:42:30.400 --> 0:42:33.640
<v Speaker 4>saying you're a news organization. Just explain how you see

0:42:33.680 --> 0:42:35.239
<v Speaker 4>this company transitioning right now?

0:42:35.320 --> 0:42:37.759
<v Speaker 13>Yeah, I think they're definitely in the entertainment space because

0:42:37.760 --> 0:42:39.760
<v Speaker 13>if you look at our brands, you know we mostly

0:42:39.840 --> 0:42:43.960
<v Speaker 13>cover leisure, fashion, lifestyle. We're not a daily news outlet,

0:42:44.000 --> 0:42:46.160
<v Speaker 13>so people don't come to us on an everyday basis.

0:42:46.239 --> 0:42:49.960
<v Speaker 13>So we are competing with other entertainment outlets, whether it

0:42:50.040 --> 0:42:53.200
<v Speaker 13>is you know, streaming media like Netflix or Hulu or

0:42:53.719 --> 0:42:57.960
<v Speaker 13>Amazon Prime, or social media you know TikTok and Instagram

0:42:58.000 --> 0:43:00.640
<v Speaker 13>and others. So people have limited spare time, so we

0:43:00.680 --> 0:43:03.200
<v Speaker 13>are competing for a slice of that. So we definitely

0:43:03.239 --> 0:43:06.319
<v Speaker 13>have to do way better in terms of our personalization

0:43:06.400 --> 0:43:09.640
<v Speaker 13>and user experience and also our great journalism that we have,

0:43:09.800 --> 0:43:11.000
<v Speaker 13>I think is what is going to take.

0:43:10.960 --> 0:43:13.080
<v Speaker 4>Us forward, Sunjay, in the year ahead, what's the one

0:43:13.120 --> 0:43:15.279
<v Speaker 4>big change you want to make on the technology side,

0:43:15.280 --> 0:43:17.360
<v Speaker 4>the one thing you're still yet to do be AI

0:43:17.560 --> 0:43:18.160
<v Speaker 4>or something else?

0:43:18.360 --> 0:43:22.480
<v Speaker 13>Yes, I think it's mostly definitely around AI. We have

0:43:22.560 --> 0:43:25.520
<v Speaker 13>a pretty solid data infrastructure that we've built on Amazon

0:43:25.560 --> 0:43:28.239
<v Speaker 13>with data bricks, and our next task is really to

0:43:28.239 --> 0:43:30.600
<v Speaker 13>figure out how do we vectorize all of our content

0:43:31.120 --> 0:43:34.440
<v Speaker 13>and make it readily available in real time to lllams.

0:43:34.920 --> 0:43:36.840
<v Speaker 13>I think that is probably our number one challenge.

0:43:37.000 --> 0:43:39.560
<v Speaker 4>Sanjay Bakta Conde Nas, thank you so much for joining

0:43:39.640 --> 0:43:40.600
<v Speaker 4>us here in Las Vegas.

0:43:40.640 --> 0:43:41.399
<v Speaker 9>Carry back to you.

0:43:41.760 --> 0:43:45.040
<v Speaker 2>Fascinating set of conversations across the gamut of all things

0:43:45.080 --> 0:43:46.560
<v Speaker 2>AI and entertainment edge.

0:43:46.600 --> 0:43:47.160
<v Speaker 3>We thank you.

0:43:47.160 --> 0:43:49.080
<v Speaker 2>You got so much more coming up that does it

0:43:49.239 --> 0:43:52.160
<v Speaker 2>this edition of Bloomberg Tech, But do stick around. It

0:43:52.239 --> 0:43:54.759
<v Speaker 2>is going to sit down interview with AWS CEO Matt

0:43:54.800 --> 0:43:59.160
<v Speaker 2>Garman's three pm Eastern twelve pm Pacific first right here

0:43:59.160 --> 0:44:00.000
<v Speaker 2>on Boomog Television.

0:44:00.080 --> 0:44:02.359
<v Speaker 3>And who all, don't forget to check out a podcast you're.

0:44:02.239 --> 0:44:04.600
<v Speaker 2>Finding on the terminal as well as online on Apples, Spotify,

0:44:04.640 --> 0:44:06.600
<v Speaker 2>and iHeart from New York from Las Vegas.

0:44:07.040 --> 0:44:07.919
<v Speaker 3>This is a blue Bag Tech