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

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

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Speaker 1: and Va Loow in San Francisco.

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Speaker 2: This is Bloomberg Tech coming up.

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Speaker 3: All eyes on Nvidia earnings after the closing bell, Investors

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Speaker 3: expecting to learn more about where those billions of dollars

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Speaker 3: on AI spending are actually going.

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Speaker 4: Plus in Video CEO Jensen Wang and Tesla CEO Elil

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Speaker 4: Musk are speaking right now at the US Saudi Investment Forum.

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Speaker 5: We'll bring you the latest, and.

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Speaker 3: Brookfield Asset Management targets ten billion dollars of fund commitments

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Speaker 3: for a global AI infrastructure program.

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Speaker 2: In partnership with you guessed it in Video and.

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Speaker 5: In Vidia dictates trade.

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Speaker 4: Right now, we are seeing signs of stability in the

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Speaker 4: Nasdaq one hundred. More broadly, I remind you that almost

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Speaker 4: two trillion dollars have been wiped off of this benchmarks.

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Speaker 4: It's the end of October in large part because in

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Speaker 4: Video has been down, but all the key mag seven

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Speaker 4: names have been under pressure. But today's some reprieve and

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Speaker 4: video in the point's perspective helping within as that one

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Speaker 4: hundred crypto though still in the EI of the storm

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Speaker 4: or by two point eight percent, that anxiety driving it

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Speaker 4: below ninety thousand dollars, so still some risk A version.

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Speaker 3: Ed okay, in Vidia is up more than two and

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Speaker 3: a half percent, but off its session high. It is

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Speaker 3: a stock that's up almost forty percent year to date,

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Speaker 3: outperforming double the performance of what we've seen of the

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Speaker 3: NAZAQ one hundred in S and P five hundred. The

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Speaker 3: expectation is revenue growth above fifty percent, net income growth

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Speaker 3: above fifty percent. But all that matters is what CEO

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Speaker 3: Jensmong tells us about the future. Let's get with Bloomberg'ssey

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Speaker 3: and King, who leads our semiconductive coverage. I mean, that's

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Speaker 3: what it comes down to. Either they will beat consensus

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Speaker 3: or they won't. But expectations are really high for this quarter,

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Speaker 3: as is the skepticism of what's happening bigger picture with

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Speaker 3: data center instructure. Give us the things we need to

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Speaker 3: look out for, and what's in your preview of this

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Speaker 3: company's earnings?

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Speaker 6: Yeah, I mean the numbers speak for themselves, right. We're

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Speaker 6: looking for a prediction in the sixty billion range for revenue,

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Speaker 6: and just to give that context, that's ten x where

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Speaker 6: we were three years ago, ten x Okay on profit

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Speaker 6: for this year, we're going to be looking at one

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Speaker 6: hundred billion dollars of net income. That's more than Intel

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Speaker 6: and AMD get in revenue combined. So the numbers have

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Speaker 6: come off the charts. The key here is we all

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Speaker 6: know the numbers are going to be good. We know

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Speaker 6: the forecast is going to be But the key is, well,

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Speaker 6: do we really believe the basis for those numbers? And

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Speaker 6: that's going to be the key question he's going to face.

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Speaker 4: So in what can his response be that's more than

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Speaker 4: what are you already signals in GtC that he has

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Speaker 4: line of sight on half a trillion dollars worth of

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Speaker 4: orders not you black Bell, but Roobin into twenty twenty six?

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Speaker 4: How much more can he signal that they will remain

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Speaker 4: integral to inference as well as training.

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Speaker 6: Absolutely right, he's essentially played all his cards in that respect.

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Speaker 6: What's going to happen will be that the investment community

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Speaker 6: are getting their first chance to sort of pull that apart,

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Speaker 6: to ask him about the details, to ask him about

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Speaker 6: the new products, to ask about the margins, to ask

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Speaker 6: about when exactly these sales will kick in and we haven't.

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Speaker 6: They haven't really had that chance. So that's what they'll

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Speaker 6: dig into today. And his response is how precise he

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Speaker 6: is will condition how they feel.

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Speaker 3: There are some real concerns and there are some real

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Speaker 3: questions from videos. Some of those will get to pose

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Speaker 3: this evening to gentlemen self. There is the idea of

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Speaker 3: depreciation on older chips and circular financing that's just not going.

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Speaker 6: Away, absolutely not. I mean, we saw a deal announced

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Speaker 6: with Anthropic yesterday, big commitment to use a lot more

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Speaker 6: of in Video's chips. But guess what Invidea is putting

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Speaker 6: ten billion dollars to work in that company over time.

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Speaker 6: So yes, that concern is absolutely not going away. And

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Speaker 6: if anything is going to accelerate, who we get a

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Speaker 6: clear outcome.

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Speaker 4: Bloomg's inking, who will be across all those earnings after

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Speaker 4: the bell alongside ed We so appreciate it. Meanwhile, look,

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Speaker 4: we know Wall Street is eagerly awaiting in Vidia's results

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Speaker 4: for a clearer picture on AI spending because it impacts

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Speaker 4: the whole rest of the market. Bloomberg's US Equities report

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Speaker 4: a common rhyining keys here to just bring us the context.

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Speaker 4: We know the question is gonna be asked of Nvidia,

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Speaker 4: But what does it signal about the commitment of the

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Speaker 4: Magnificent seven of the key hyperscalers and more broadly the

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Speaker 4: rest of the AI trade.

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Speaker 7: Yeah, so this is a huge moment for the AI trade.

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Speaker 7: I think a lot of people are really looking to

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Speaker 7: Invidia and the stocks reaction to sort of decide the

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Speaker 7: next direction of where the market, the entire market is

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Speaker 7: going to go. You know, in Nvidia is the largest

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Speaker 7: waiting in the S and P five hundred and the biggest.

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Speaker 5: Name in AI.

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Speaker 7: And what we also know about in Vidia is that

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Speaker 7: its biggest four clients are some of the other MAGS

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Speaker 7: seven members. So we're going to see where they're spending

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Speaker 7: is flowing if it's still flowing to.

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Speaker 5: Nvidia how much.

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Speaker 3: When I was reading your your piece which you co

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Speaker 3: wrote with Rhyan Vla Seleca, the data is really interesting.

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Speaker 3: It's gonna be a big part of the Bluma tech

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Speaker 3: audience that don't know some of that. So you just

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Speaker 3: talked about waiting right in video is about eight percent

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Speaker 3: of the S and P five hundred. That's a factor

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Speaker 3: you also looked at in Nvidia's multiples relative to the

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Speaker 3: index level the Nasdaq one hundred, for example. What are

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Speaker 3: the other key data points that have us on such

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Speaker 3: edge ahead of the earnings report later this evening.

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Speaker 7: Well, you know, as Ian said, I think a lot

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Speaker 7: of people are really looking at the forward guidance. You know,

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Speaker 7: in video is expected to continue to grow revenue, even

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Speaker 7: though that growth is expected to slow in the coming years.

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Speaker 7: I think the other thing that people are really looking

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Speaker 7: for is what Jensen's going to say about what they're

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Speaker 7: seeing in the future. Right Also, is Ian pointed out

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Speaker 7: the guidance is very important here, and that's I mean,

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Speaker 7: probably even more important than the numbers that in Vidia

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Speaker 7: actually reports. We want to see the line of sight

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Speaker 7: into revenue growth going forward. There are also still some

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Speaker 7: questions about China, how much revenue can be expected there

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Speaker 7: or not, And so I think the sentiment here, what

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Speaker 7: investors take away about their confidence from the report, is

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Speaker 7: going to be maybe even more important than the actual numbers.

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Speaker 4: Pretty most common Rhino Keey, it's a great story. Thank

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Speaker 4: you very much indeed for bringing us the data as well.

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Speaker 4: And now we bring you the investment perspective. Martin Norton's

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Speaker 4: here with us chief investment strategistic and Power, which administers

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Speaker 4: more than one point six trillion dollars in assets, and

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Speaker 4: that's about the same amount that's been wiped off on

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Speaker 4: nude like one hundred since the end of October.

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Speaker 5: Yes, is there room to the.

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Speaker 4: Downside here or are you thinking there's some sort of

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Speaker 4: relief rally from any data we get from Jensen later?

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Speaker 8: Well, I don't think you can ever count out a

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Speaker 8: relief rally. But what I come back to when we

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Speaker 8: look at the price action that we've seen over the

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Speaker 8: past few weeks is that we have taken some froth

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Speaker 8: off the top, but we're not looking at really attractive

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Speaker 8: valuations at this point. We're still at elevated valuations for

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Speaker 8: a lot of these names. And what's interesting, and it

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Speaker 8: alluded to this, you know, the questions that people are

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Speaker 8: raising the current environment, I don't think they're necessarily going

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Speaker 8: to be conclusively answered within Video's report, right, the depreciation question,

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Speaker 8: the demand question, you know, extending to the rest of

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Speaker 8: the economy. I think those doubts are are with us,

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Speaker 8: which would mean potentially more volatility.

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Speaker 4: Well, if Jensen can't tell us how long his GPUs

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Speaker 4: are going to last and what the depreciation of them are.

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Speaker 4: I'm not sure anyone have, but you're so right that

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Speaker 4: that has been again an argument about why perhaps.

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Speaker 5: Evaluations are flush.

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Speaker 4: If you look at a video trenting about thirty times

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Speaker 4: future earnings, that's not that elevated. So is it the

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Speaker 4: rest of the trade, the palenteers, or perhaps some of

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Speaker 4: the energy stocks that have risen to extraordinary degrees.

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Speaker 8: Well, I think you raise a good point. It's not

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Speaker 8: that elevated, especially if you're focused on that right side

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Speaker 8: of the probability distribution, if you're looking at a full

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Speaker 8: probability distribution, and you come to the conclusion that I

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Speaker 8: don't know if I share, but some folks are raising, hey,

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Speaker 8: this isn't as transformative as people have suggested it is.

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Speaker 8: We're not going to see every application take off the

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Speaker 8: way people suggest it would. Then I think there's room

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Speaker 8: for some of these stacks to come down. Now, I'm

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Speaker 8: not sure I share that view. I tend to believe

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Speaker 8: that the AI supercycle is real, that it is going

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Speaker 8: to have a pretty profound impact on the economy, but

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Speaker 8: we have to make room for the full range of

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Speaker 8: outcomes when we price these things.

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Speaker 3: Marta, it's great to have you back on Bloomberg Tech.

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Speaker 3: As is quoted in that well read story on the

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Speaker 3: Bloomberg term on Bloomberg dot com from one investor, this

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Speaker 3: is a quote, so goes Nvidia, so goes the market

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Speaker 3: kind of report from your desk, and your perspective is

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Speaker 3: that the situation here.

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Speaker 8: Well, I think there's no question that this is a

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Speaker 8: Capstone report. It's a macro indicator, and I think has

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Speaker 8: the potential and you see that in pricing around it,

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Speaker 8: right this idea that you could have swings up to

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Speaker 8: seven percent either way. I think this is the kind

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Speaker 8: of thing that people are really going to key off of.

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Speaker 8: My question is whether this is going to answer all

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Speaker 8: the AI doubts that are out there or whether it's

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Speaker 8: just going to arrest them for now. But we're still

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Speaker 8: going to be wrestling with some of these questions going forward.

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Speaker 3: Depreciation is the most common concern or question that I've

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Speaker 3: received for Gensen so far. You know, get out to

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Speaker 3: people and said what would you ask? There are people

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Speaker 3: that look at the depreciation issue and say, how can

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Speaker 3: I model for that impact on the balance sheet of

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Speaker 3: those key customers of Nvidia, But others on social media

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Speaker 3: are talking about a different data set which is utilization.

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Speaker 3: If those older generation GPUs are running at one hundred percent,

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Speaker 3: that's a really good problem to have, right. This is

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Speaker 3: kind of very specific, but are those soft data sets

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Speaker 3: that your team are looking at to work out what

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Speaker 3: neof's going on?

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Speaker 8: Well, you know, I think the thing that we're focused

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Speaker 8: most on as we turn our gaze to twenty twenty

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Speaker 8: six is this question in particular about the capacity bill

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Speaker 8: that we expect in twenty twenty six and frankly in

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Speaker 8: twenty twenty seven. So our view is that, you know,

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Speaker 8: we're going to have these questions to your point about utilization,

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Speaker 8: about appreciation, but our view is that those questions are

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Speaker 8: going to linger as we build out the capacity for

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Speaker 8: AI in twenty twenty six, in twenty twenty seven, because

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Speaker 8: there is so much that you have to put in

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Speaker 8: place to be able to see the demand come through.

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Speaker 8: And so for us, as we go through twenty twenty six,

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Speaker 8: we're going to be watching, of course, like everybody else.

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Speaker 8: Are we seeing that capex is the conviction still there

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Speaker 8: to build out the massive infrastructure that you need for AI,

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Speaker 8: And I think that's the key question that we're wrestling with.

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Speaker 4: It was interesting that you said you don't align yourself

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Speaker 4: with the negativity and actually applications of AI, right, you

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Speaker 4: do think the supercycle's real?

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Speaker 5: What data set are you looking for that? Because we do.

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Speaker 4: Have the MIT pilots aren't working, there's always a counter

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Speaker 4: example for every time there's a negative, but Intelligent just

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Speaker 4: have some great analysis out showing that actually only ten

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Speaker 4: percent of companies at the moment or people surveyed are

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Speaker 4: saying that they're using generator of AI for revenue or

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Speaker 4: for new product.

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Speaker 8: Right, I think there's still an I mean, first of all,

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Speaker 8: if you take a look at kind of adoption rates

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Speaker 8: for the AI cycle relative to the Internet and things

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Speaker 8: like that, people are pointing out the Federal Reserve and

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Speaker 8: others that you've seen much faster acceleration of adoption. But

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Speaker 8: to your point, we're still very much early days, and

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Speaker 8: so I think one of the things that we look

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Speaker 8: at is just at the earnings season themselves and looking

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Speaker 8: at what companies are saying. And a lot of the

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Speaker 8: commentary around AI at this point is still very generic.

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Speaker 5: It's not very.

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Speaker 8: Specific in terms of how AI is actually transforming those businesses.

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Speaker 8: And I think also watching to see how earnings in

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Speaker 8: the broader economy are responding, Are you starting to see

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Speaker 8: those cost savings come through, which I think is of

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Speaker 8: course the first leg, and then ultimately you'd also want

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Speaker 8: to see the revenue which we're seeing from the hyperscalers.

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Speaker 8: But I think those, you know, that real world application

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Speaker 8: is really what we're going to be keying off of it,

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Speaker 8: and I think it's going to take time. I think

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Speaker 8: you still need to build that capacity to see the

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Speaker 8: earnings impact.

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Speaker 4: Broadly speaking, Macha, so it's great to have you in

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Speaker 4: good thank you for coming to this year. Mantulton close

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Speaker 4: side of empower. Meanwhile, in Vidia CEO and Hung it's

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Speaker 4: already on stage ahead of his earnings publication.

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Speaker 5: Of course, I mean while test to.

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Speaker 4: Alongside in DC as part of the US Saudi Investment

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Speaker 4: Forum in Washington, d C.

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Speaker 5: Take a listen to his.

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Speaker 9: Royal Highness announced the AI strategic Framework and partnership. Today

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Speaker 9: we're going big with Elon and Jensen.

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Speaker 10: So thank you for those opportunities.

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Speaker 9: Now they told me I have time for two last questions.

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Speaker 9: So last night at the dinner, I got a number

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Speaker 9: of questions because it seems that the schedule leaked and

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Speaker 9: everybody was giving me hints about the last two questions

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Speaker 9: I'm going to do. So the first one was for

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Speaker 9: you Elon, and there's a big one for you Jensen,

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Speaker 9: So prepare for that one.

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Speaker 10: AI in space Is that possible?

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Speaker 11: Yes, if civilization continues, which it probably will, then AI

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Speaker 11: in space is inevitable. You know, I always have to

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Speaker 11: like preface that, you know, we shouldn't take civilization for granted.

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Speaker 11: We need to make sure to take care to ensure

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Speaker 11: that civilization hasn't an upward arc. I mean, any student

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Speaker 11: of history knows that civilization does not always have an

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Speaker 11: upward arc, and in fact, civilizations have life life cycles.

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Speaker 11: So hopefully we are in a strong upward arc. I

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Speaker 11: think we are for now, but we don't want to

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Speaker 11: take that for granted or becomplacent. But in order to

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Speaker 11: the way to think of AI in space is that

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Speaker 11: in order to achieve any meaningful percentage of a Kotdashev

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Speaker 11: two scale civilization where you're using even a millionth of

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Speaker 11: a millionth of the Sun's energy, you must have solar

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Speaker 11: powered AI satellites in deep space, so that once you realize,

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Speaker 11: like once you think in terms of a Cottashev two

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Speaker 11: scale civilization, which is what percentage of the Sun's energy

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Speaker 11: are you turning into useful work? Then you then it

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Speaker 11: becomes obvious that space is overwhelmingly what matters.

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Speaker 12: Overwhelmingly.

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Speaker 11: So the Sun only receives one roughly one two billionth

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Speaker 11: of the Earth only receives roughly one two billionth of

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Speaker 11: the Sun's energy. So if you want to have something

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Speaker 11: that is, say a million times more energy than Earth

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Speaker 11: could possibly produce, you must.

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Speaker 12: Go into space. It's and so.

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Speaker 11: This is where it's kind of handy to have a

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Speaker 11: space company, I guess.

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Speaker 13: Sell the books cold chips in space too, Yes, easier

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Speaker 13: to cool chips in space.

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Speaker 11: Yes, there's definitely no water in space, so you're gonna

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Speaker 11: have to do something that doesn't involve water.

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Speaker 12: Well, it's just got to radiate, that's right.

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Speaker 11: So my estimate is that actually that that that the

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Speaker 11: cost of electricity, like the cost effectiveness of AI in

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Speaker 11: space will be overwhelmingly better than AI on the ground,

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Speaker 11: so far, long before you exhaust potential energy sources on Earth,

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Speaker 11: long before.

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Speaker 12: Meaning like I think even PEFs in.

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Speaker 11: The four or five year timeframe, the lowest cost way

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Speaker 11: to do AI compute will be with solar powered AI satellites.

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Speaker 12: So I'd say not more than five years from now.

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Speaker 13: Wow, And just look at the supercomputers we're building together.

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Speaker 13: Let's say each one of the racks is two tons.

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Speaker 13: Out of that two tons, one point nine to five

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Speaker 13: of it is probably for cooling.

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Speaker 8: Right.

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Speaker 13: Just imagine how tiny that little supercomputer is, right, each

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Speaker 13: one of these GB three hundred racks and.

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Speaker 14: Mull just be a little tiny thing.

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Speaker 11: And just electricity generation is already becoming a challenge. So

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Speaker 11: if you start doing any kind of scaling for both

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Speaker 11: electricity generation and cooling, you realize, okay, space is incredibly compelling.

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Speaker 11: So like, let's say you wanted to do I don't know,

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Speaker 11: two or three hundred gigawats per year of AI computed,

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Speaker 11: It's very difficult to do that on Earth. So the

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Speaker 11: US average electricity usage last time I checked it was

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Speaker 11: around four hundred and sixty gigawats per year average usage.

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Speaker 12: So so something like say you know three hundred, If

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Speaker 12: you're doing three.

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Speaker 11: Hundred giga what's a year, that would be like two

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Speaker 11: thirds of US electricity production per year. There's no way

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Speaker 11: you're building power plants at that level. And then if

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Speaker 11: you take it up to say a taro wat per

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Speaker 11: year impossible, Like you have to do that in space

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Speaker 11: there they're just is there just is.

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Speaker 12: No way to do a tarow per year on Earth.

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Speaker 11: And in space you've got tenuous solar you've got you

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Speaker 11: don't You actually don't need batteries because it's always sunny

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Speaker 11: in space, right exactly, And and the solar panels actually

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Speaker 11: become cheaper because you don't need glass or framing.

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Speaker 12: And the cooling is just radiative. So that's that's why

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Speaker 12: I think.

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Speaker 14: That's the dream. Yes, that's the dream.

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Speaker 9: So Jensen, everybody last night was asking me, and I'm

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Speaker 9: mindful it's Earning's call for you today. So I'm gonna

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Speaker 9: say this delicately. Everybody has been asking me to ask you.

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Speaker 9: Are we going to have an AI bubble?

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Speaker 14: That's the last question?

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Speaker 10: All right, let's not.

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Speaker 13: All right, let me see well, let me just tell

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Speaker 13: you what we see, okay. So, so I think it's

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Speaker 13: really important when you look at what's happening around the

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Speaker 13: world and go back to the first principles of what's

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Speaker 13: happening in computer science and computing. There are three things

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Speaker 13: that's happening. The first thing is that we all know

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Speaker 13: that Moore's laws run its course, and the ability the

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Speaker 13: amount of demand for computing, since the amount of computation

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Speaker 13: we can get out of general purpose computing is really challenging,

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Speaker 13: and so the world's been moving to accelerated computing for

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Speaker 13: some time. We've been pushing this now for some over

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Speaker 13: twenty years. Let me give you one statistic. I was

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Speaker 13: just at supercomputing six years ago. CPUs were ninety percent

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Speaker 13: of the world supercomputers top five hundred supercomputers. Six years ago.

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Speaker 13: This year less than fifteen percent. Went from ninety percent

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Speaker 13: to ten percent. And meanwhile accelerated computing went from the

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Speaker 13: other way ten percent to now ninety percent. Okay, so

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Speaker 13: you're seeing that inflection point, the transition in high performance

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Speaker 13: computing from general purpose of computing to accelerated computing. Well

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Speaker 13: of the one of the most data intensive, one of

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Speaker 13: the most intensive computation things that the world does in

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Speaker 13: cloud is data processing. Several hundred billion dollars of computation

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Speaker 13: is done on just raw data. Process has nothing to

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Speaker 13: do with AI, just CFL processing data frames. You know

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Speaker 13: everybody's names, address, their sex, their age, where do they live,

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Speaker 13: you know how much money they make. All of that

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Speaker 13: sits into a data frame, and that data frame drives

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Speaker 13: the world today, whether it's in banking or you know,

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Speaker 13: whether it's in credit cards or of course e commerce

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Speaker 13: and everything from ad recommendation and everything is driven off

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Speaker 13: of that data frame. That data frame costs hundreds of

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Speaker 13: billions are always to go compute. And so that's the

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Speaker 13: number one thing end of More's lows. The second thing

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Speaker 13: is generative AI. The most important application of the last

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Speaker 13: fifteen years is called rexis recommended systems. How do we

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Speaker 13: know what information to recommend to us in a social feed?

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Speaker 13: How do you know what ad to recommend to somebody,

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Speaker 13: what book to recommend, what movie to recommend? The world

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Speaker 13: is the Internet is so gigantic without a recommended system

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Speaker 13: that the little tiny phone of us will have no chance.

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Speaker 13: I've ever seen the right information that REXUS is the

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Speaker 13: engine of the Internet today. That's going generative AI. It

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Speaker 13: used to be running on CPUs, now it runs on GPS,

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Speaker 13: which then says the third thing. When if you just

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Speaker 13: look at those two applications, many of the Internet companies

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Speaker 13: can build an enormous number of GPUs supercomputers. Just doing that,

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Speaker 13: of course, then it creates this the third opportunity on.

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Speaker 14: Top of it, which is agentic AI.

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Speaker 13: This is Grock and this is open AI, this is

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Speaker 13: anthropic you know, this is Gemini.

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Speaker 14: Agentic AI sits on top of that.

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Speaker 13: But don't you know, don't forget to think about what

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Speaker 13: is happening above, underneath what everybody sees as AI today,

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Speaker 13: there's a whole movement of computing from jenniferpose computing to

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Speaker 13: accelerated computing, and that if you just if you take

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Speaker 13: that into consideration, you'll come to the conclusion that in fact,

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Speaker 13: what is left over to fuel that revolutionary agentic AI

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Speaker 13: is not only substantially less than your thought and all

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Speaker 13: of it justified.

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Speaker 9: Well, I was just informed by the team that my

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Speaker 9: boss and your bosses is going to talk next the

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Speaker 9: Honorable President and his Royal Highness to the com Prince,

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Speaker 9: and hence we ran out of time.

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Speaker 10: But in essence, this is ah.

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Speaker 9: Such so much love for you Elon and Jensen. But this,

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Speaker 9: in essence is a ninety two alliance that shifted from

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Speaker 9: energy to digital to the intelligence age, powered by pioneers

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Speaker 9: such as Elon and Jensen to serve humanity and create

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Speaker 9: on a net new basis, new economies, new jobs and

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Speaker 9: a better future for humanity powered by the Kingdom of

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Speaker 9: aud Arabia and the United States. Thank you for our

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Speaker 9: lifetime partnership and friendship. Thank you, Elon, thank you, Jensen.

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Speaker 12: Thank you.

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Speaker 3: That was Elon Musk speaking alongside and Vidio CEO Jensen

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Speaker 3: Huang at the US Saudi Investment Forum in Washington, d C. Probably, Caroline,

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Speaker 3: the biggest piece of music came out of it was

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Speaker 3: Elon Musk confirming that Xai is going to do a

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Speaker 3: five hundred megawat data center in the Kingo of Saudi

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Speaker 3: Arabia in partnership with Humane. That's a story that we

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Speaker 3: broke actually back in July, so we've had a sense

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Speaker 3: that it was coming for its confirmation.

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Speaker 4: Confirmation of course, all eyes on really the access that

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Speaker 4: Saudi Arabia has to the latest greatest chips and what

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Speaker 4: they're able to continue to export out in the United

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Speaker 4: States to Saudi Arabia, too Humane to be able to

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Speaker 4: use on the ground when it comes to Blackwell, and

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Speaker 4: of course we're going to hear so much with your

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Speaker 4: interview later today ed on details of an AI bubble,

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Speaker 4: the vindication there that we're starting to hear from jensens to.

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Speaker 5: Already the CPU, the GPU.

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Speaker 4: Necessities when it comes to just our social media desires,

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Speaker 4: let alone what's happening with the genta AI.

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Speaker 3: Yeah, we should probably point out the obvious to our

439
00:22:42,400 --> 00:22:45,640
Speaker 3: audience that in video reports earnings after the closing bell.

440
00:22:46,040 --> 00:22:49,080
Speaker 3: Jensen Wang is an experienced executive to say the least,

441
00:22:49,080 --> 00:22:52,080
Speaker 3: and he probably thinks to himself, Ah, what can I

442
00:22:52,280 --> 00:22:55,000
Speaker 3: or can I not say during the course of this conversation,

443
00:22:55,840 --> 00:22:58,199
Speaker 3: But a lot of his final answer there was repetition

444
00:22:58,520 --> 00:23:01,960
Speaker 3: about the idea that as we move from the balance

445
00:23:02,000 --> 00:23:06,439
Speaker 3: of workloads being inference having previously been training, that is

446
00:23:06,480 --> 00:23:11,040
Speaker 3: his evidence base for this big build out INAI infrastructure

447
00:23:11,080 --> 00:23:11,840
Speaker 3: to continue.

448
00:23:12,080 --> 00:23:15,119
Speaker 4: And I think more broadly as well, there were going

449
00:23:15,119 --> 00:23:18,560
Speaker 4: into the realms of imagination, a realm of imagination that

450
00:23:18,600 --> 00:23:20,200
Speaker 4: I've heard time and time again. We've heard it from

451
00:23:20,200 --> 00:23:21,919
Speaker 4: Sooner pitch I, We've heard it from Jeff Bezos. Now

452
00:23:21,920 --> 00:23:24,000
Speaker 4: we're hearing it from Elon Musk about the idea that

453
00:23:24,160 --> 00:23:27,439
Speaker 4: actually the energy limitations are far less in space.

454
00:23:27,720 --> 00:23:28,480
Speaker 5: And this is why.

455
00:23:28,320 --> 00:23:30,840
Speaker 4: Suddenly you're hearing a lot of these executives talking about

456
00:23:31,000 --> 00:23:34,080
Speaker 4: how we might be building data centers not on this Earth,

457
00:23:34,160 --> 00:23:37,920
Speaker 4: but outside of the world at the moment. This is

458
00:23:37,960 --> 00:23:41,560
Speaker 4: an interesting way diversion perhaps of talking about the cost

459
00:23:41,560 --> 00:23:43,080
Speaker 4: of energy that seems to be going up into the

460
00:23:43,119 --> 00:23:44,320
Speaker 4: right here in the United States.

461
00:23:44,600 --> 00:23:47,280
Speaker 3: Yeah, in space, you can put solar panels on satellites

462
00:23:47,560 --> 00:23:50,520
Speaker 3: and you can use radiation to do calling. That seemed

463
00:23:50,560 --> 00:23:53,440
Speaker 3: to be the point that Jensen Wong and Elon Musk

464
00:23:54,200 --> 00:23:56,159
Speaker 3: were making, as if by magic. In the time that

465
00:23:56,200 --> 00:24:00,000
Speaker 3: we've been speaking, Bloomberg Senior Tech Executive editor Tom Giles

466
00:24:00,040 --> 00:24:01,720
Speaker 3: has appeared on set. I mean, it's a big moment,

467
00:24:01,800 --> 00:24:04,840
Speaker 3: right if you have the world's richest man sat alongside,

468
00:24:04,880 --> 00:24:07,719
Speaker 3: let's be honest, probably the most important person in global technology,

469
00:24:07,840 --> 00:24:10,119
Speaker 3: you're kind of bracing for news. The news that I

470
00:24:10,160 --> 00:24:14,160
Speaker 3: saw was confirmation is something we reported. That's SAI doing

471
00:24:14,200 --> 00:24:15,480
Speaker 3: something in Saudi Arabia.

472
00:24:15,600 --> 00:24:16,080
Speaker 2: That's right.

473
00:24:16,640 --> 00:24:20,760
Speaker 15: It's yet again evidence of this huge need for data

474
00:24:20,840 --> 00:24:24,119
Speaker 15: centers and computing capacity, which is what they were talking

475
00:24:24,119 --> 00:24:27,640
Speaker 15: about from beginning to end. And this is how XAI

476
00:24:27,920 --> 00:24:28,440
Speaker 15: is going.

477
00:24:28,320 --> 00:24:29,199
Speaker 14: To take part in it.

478
00:24:29,240 --> 00:24:32,520
Speaker 15: This is how Elon Musk and his empire are going

479
00:24:32,600 --> 00:24:36,440
Speaker 15: to take part in ensuring that we have the capacity

480
00:24:36,640 --> 00:24:39,840
Speaker 15: that we need to fuel all of these services, especially

481
00:24:39,840 --> 00:24:43,080
Speaker 15: the ones that Xai is providing with GROC.

482
00:24:43,920 --> 00:24:48,600
Speaker 4: All of this Tom Hinges on access to compute and

483
00:24:48,720 --> 00:24:52,160
Speaker 4: to GPU, how we unfolding that story as a newsroom

484
00:24:52,200 --> 00:24:54,880
Speaker 4: at the moment of Humane's access to the latest in video,

485
00:24:54,920 --> 00:24:58,640
Speaker 4: Blackwell architecture, and more broadly, how we see the relationship

486
00:24:58,760 --> 00:25:01,320
Speaker 4: for the demand dainty centers to be built out in

487
00:25:01,320 --> 00:25:03,240
Speaker 4: Saudi Arabia rather than here in the United States.

488
00:25:03,920 --> 00:25:07,399
Speaker 15: Yeah, Caroline, I was just in Saudi a couple of

489
00:25:07,400 --> 00:25:10,320
Speaker 15: weeks ago, and the thing that one of the things

490
00:25:10,320 --> 00:25:15,200
Speaker 15: that I took away from that was this urgency to

491
00:25:15,240 --> 00:25:19,520
Speaker 15: find sovereign AI first of all, ensure that each region

492
00:25:19,520 --> 00:25:20,080
Speaker 15: of the world.

493
00:25:19,960 --> 00:25:21,359
Speaker 14: Has the computing that it needs.

494
00:25:21,520 --> 00:25:25,800
Speaker 15: I also saw the urgency of these relationships between US

495
00:25:25,840 --> 00:25:31,359
Speaker 15: based companies and sovereign wealth funds in partnership with the

496
00:25:31,400 --> 00:25:35,240
Speaker 15: Saudi government and other governments throughout that region. And the

497
00:25:35,320 --> 00:25:38,240
Speaker 15: idea is that there's going to be a lot more partnerships.

498
00:25:38,320 --> 00:25:41,320
Speaker 15: You're going to see a lot more collaboration. Humane is

499
00:25:41,359 --> 00:25:43,760
Speaker 15: this company that just came out of nowhere a few

500
00:25:43,760 --> 00:25:47,080
Speaker 15: months ago and really does seem to be wanting to

501
00:25:47,200 --> 00:25:51,800
Speaker 15: take play a big role in this data center build out.

502
00:25:52,040 --> 00:25:53,760
Speaker 3: Before we let you go there. There is also this

503
00:25:53,840 --> 00:25:57,199
Speaker 3: issue of reciprocity. So for example, there's all these projects

504
00:25:57,200 --> 00:26:00,240
Speaker 3: announced in Saudi and other Gulf states, but the United

505
00:26:00,240 --> 00:26:04,399
Speaker 3: States government, i think, is very hopeful that those titans

506
00:26:04,480 --> 00:26:07,359
Speaker 3: of Middle East finance will also put the equivalent number

507
00:26:07,359 --> 00:26:09,200
Speaker 3: of dollars into the United States itself.

508
00:26:09,480 --> 00:26:09,920
Speaker 2: That's right.

509
00:26:10,080 --> 00:26:12,879
Speaker 15: They want to see they want to jointly invest in

510
00:26:12,920 --> 00:26:16,520
Speaker 15: the region. They want to see countries from around the world,

511
00:26:16,600 --> 00:26:21,159
Speaker 15: particularly this region, the oil rich region, also making investments

512
00:26:21,280 --> 00:26:25,120
Speaker 15: and showing that the US is a place to invest.

513
00:26:25,680 --> 00:26:26,160
Speaker 14: You know, this.

514
00:26:26,160 --> 00:26:29,600
Speaker 15: Administration wants to send the message that we're open for

515
00:26:29,680 --> 00:26:31,680
Speaker 15: business and that we're here to build jobs, and we're

516
00:26:31,720 --> 00:26:36,439
Speaker 15: here to bring some sort of manufacturing and technology dominance

517
00:26:36,680 --> 00:26:38,000
Speaker 15: back to the United States.

518
00:26:38,200 --> 00:26:38,520
Speaker 12: Being both.

519
00:26:38,560 --> 00:26:41,240
Speaker 3: Senior Executive Editor, Tom Giles, thank you very much. Don't

520
00:26:41,240 --> 00:26:43,960
Speaker 3: forget to tune in. This afternoon, we have an exclusive

521
00:26:43,960 --> 00:26:47,800
Speaker 3: interview with Video CEO Jensen Wong following the company's earnings.

522
00:26:47,840 --> 00:26:51,760
Speaker 3: Print that conversation around six thirty pm Eastern time.

523
00:26:51,800 --> 00:26:52,439
Speaker 2: Okay, coming up.

524
00:26:52,440 --> 00:26:55,800
Speaker 3: Pooja Goyle from Carlisle joins us to talk about AI

525
00:26:55,840 --> 00:26:59,760
Speaker 3: infrastructure spending from a very different side of the table

526
00:27:00,040 --> 00:27:02,520
Speaker 3: very much looking forward to this one. This halftime, We'll

527
00:27:02,520 --> 00:27:13,000
Speaker 3: be right back. This is Bloomberg Tech. Welcome back to

528
00:27:13,040 --> 00:27:16,720
Speaker 3: Bloomberg Tech. Nvidia is the super Bowl moment today earnings

529
00:27:16,760 --> 00:27:18,480
Speaker 3: after the bell, the stock up more than two percent

530
00:27:18,520 --> 00:27:21,439
Speaker 3: off its session high. It is a stock that's up

531
00:27:21,480 --> 00:27:24,960
Speaker 3: almost forty percent year to date, and there are very

532
00:27:25,040 --> 00:27:29,119
Speaker 3: high expectations, but there is also very high skepticism about

533
00:27:29,119 --> 00:27:31,720
Speaker 3: what is happening in this AI infrastructure build out. I

534
00:27:31,800 --> 00:27:33,840
Speaker 3: will continue to track it throughout the hour. It is

535
00:27:33,880 --> 00:27:36,159
Speaker 3: the big one. But one big big move to the

536
00:27:36,240 --> 00:27:40,240
Speaker 3: upside is Alphabet, parent company of Google. Shares trading at

537
00:27:40,520 --> 00:27:43,040
Speaker 3: a record high, on track for their biggest jump since

538
00:27:43,040 --> 00:27:48,240
Speaker 3: about mid September. Yesterday, Gemini three was released, an executive

539
00:27:48,320 --> 00:27:51,920
Speaker 3: saying that this is a big jump in the model's

540
00:27:51,920 --> 00:27:56,399
Speaker 3: abilities for reasoning and coding. This seems to have been

541
00:27:56,440 --> 00:27:58,600
Speaker 3: some kind of delayed response in the stock. You know,

542
00:27:58,680 --> 00:28:01,600
Speaker 3: you did see others like am Outman of Open Ai,

543
00:28:01,720 --> 00:28:05,280
Speaker 3: even Elon Musk on social media congratulate Google what they've

544
00:28:05,280 --> 00:28:06,440
Speaker 3: achieved with Gemini free.

545
00:28:06,800 --> 00:28:09,240
Speaker 2: Now that's playing out in the shares character it is.

546
00:28:09,240 --> 00:28:10,800
Speaker 5: And well cool.

547
00:28:10,800 --> 00:28:13,520
Speaker 4: One hundred folty billion being added in terms of honey capitalization,

548
00:28:13,560 --> 00:28:15,040
Speaker 4: we're up at more than three and a half trillion

549
00:28:15,080 --> 00:28:17,399
Speaker 4: for Google. Now let's break down though, what this so

550
00:28:17,520 --> 00:28:20,000
Speaker 4: called Gemini means man deep singers with us being the

551
00:28:20,040 --> 00:28:22,640
Speaker 4: intelligent senior tech analyst joining us.

552
00:28:22,680 --> 00:28:23,360
Speaker 5: Is it a big leap?

553
00:28:24,000 --> 00:28:24,400
Speaker 14: It is?

554
00:28:24,520 --> 00:28:27,080
Speaker 16: And when you look at some of the benchmarks they

555
00:28:27,200 --> 00:28:30,320
Speaker 16: showed in the paper around visual reasoning, I mean everyone

556
00:28:30,320 --> 00:28:34,280
Speaker 16: has been focused on multimodality. This was the true kind

557
00:28:34,320 --> 00:28:37,879
Speaker 16: of model where you could see multimodality in action in

558
00:28:37,960 --> 00:28:41,239
Speaker 16: terms of okay, we can do code, the model can

559
00:28:41,280 --> 00:28:45,520
Speaker 16: also do image generation and visual reasoning, which is what

560
00:28:45,560 --> 00:28:47,720
Speaker 16: you see in Weymel. I mean when I think about

561
00:28:47,880 --> 00:28:50,840
Speaker 16: you know why they're so successful with Weamel. Yes, they've

562
00:28:50,880 --> 00:28:53,480
Speaker 16: been doing it for the longest, but also some of

563
00:28:53,560 --> 00:28:56,600
Speaker 16: it is AI that's coming from their models, and I

564
00:28:56,600 --> 00:28:58,880
Speaker 16: think that was reflected in the paper. And look at

565
00:28:58,880 --> 00:29:01,640
Speaker 16: how far they've come in the past two years from

566
00:29:01,640 --> 00:29:05,240
Speaker 16: that botch Bard launch to now Gemini three model being

567
00:29:05,240 --> 00:29:08,960
Speaker 16: a frontier model, so really well executed. And I think

568
00:29:09,520 --> 00:29:11,880
Speaker 16: it was all on TPUs. That's the other thing, right,

569
00:29:12,000 --> 00:29:16,240
Speaker 16: n ro N video GPUs used for training, which everyone

570
00:29:16,480 --> 00:29:18,480
Speaker 16: still relies on Nvidio for training.

571
00:29:18,560 --> 00:29:19,760
Speaker 14: So that's a big.

572
00:29:19,720 --> 00:29:23,120
Speaker 3: Le Mandy, this is interesting. We were reading your research

573
00:29:23,120 --> 00:29:24,400
Speaker 3: this morning. I think we're going to bring it up

574
00:29:24,400 --> 00:29:26,800
Speaker 3: on the screen. So you're basically saying that if this

575
00:29:26,960 --> 00:29:31,520
Speaker 3: is evidence of the success of the TPU, Google's custom chip,

576
00:29:31,920 --> 00:29:36,400
Speaker 3: that might free up Google Cloud or GCP to take

577
00:29:36,440 --> 00:29:39,040
Speaker 3: their n video allocation and then put it to work

578
00:29:39,040 --> 00:29:41,120
Speaker 3: for customers, which is a good thing for their cloud

579
00:29:41,160 --> 00:29:43,760
Speaker 3: business when it comes to external facing customers.

580
00:29:43,960 --> 00:29:44,440
Speaker 10: That's right.

581
00:29:44,520 --> 00:29:49,280
Speaker 16: And so look, Google is still buying in video chips.

582
00:29:49,280 --> 00:29:52,040
Speaker 16: In fact, they are one of the top three customers

583
00:29:52,040 --> 00:29:55,200
Speaker 16: for Nvidia. And so when I look at you know

584
00:29:55,800 --> 00:29:59,040
Speaker 16: how everyone is using their Nvidia allocation, some of the

585
00:29:59,080 --> 00:30:03,080
Speaker 16: workloads are in fact for Meta everything is being consumed

586
00:30:03,200 --> 00:30:06,360
Speaker 16: inside you know Meta with the family of apps for

587
00:30:06,440 --> 00:30:10,200
Speaker 16: training and for inferencing and recommendation systems in the case

588
00:30:10,240 --> 00:30:13,960
Speaker 16: of alphabet, I mean, given everything internal is running on TPUs,

589
00:30:14,320 --> 00:30:16,840
Speaker 16: Google Cloud is where they deploy a lot of the

590
00:30:16,960 --> 00:30:20,200
Speaker 16: Nvidia allocation, whether it's the latest Black veil or the

591
00:30:20,240 --> 00:30:22,920
Speaker 16: prior versions. And that's where you can rent it. You

592
00:30:22,960 --> 00:30:26,000
Speaker 16: can generate revenues same way as new clouds are doing it.

593
00:30:26,280 --> 00:30:29,080
Speaker 16: And so from that perspective, I do think that cloud

594
00:30:29,200 --> 00:30:31,800
Speaker 16: revenue could get a lift just because they are more

595
00:30:31,840 --> 00:30:34,000
Speaker 16: availability of in video GPUs over there.

596
00:30:35,000 --> 00:30:37,080
Speaker 3: I really recommend you go read that research if you're

597
00:30:37,080 --> 00:30:39,320
Speaker 3: a terminal client. If you're not, maybe I'll post it

598
00:30:39,320 --> 00:30:41,600
Speaker 3: on the social media's later. You just heard Man deep

599
00:30:41,600 --> 00:30:44,640
Speaker 3: Seeing of Bloomberg Intelligence break it down. Thank you very much.

600
00:30:44,960 --> 00:30:48,560
Speaker 3: AI infrastructure spending news keeps rolling in Brickfield Asset Management

601
00:30:48,640 --> 00:30:51,280
Speaker 3: is teaming up with in Video but also creates wealth

602
00:30:51,320 --> 00:30:54,800
Speaker 3: fund targeting ten billion dollars of commitments for a program

603
00:30:54,840 --> 00:30:57,680
Speaker 3: to build global AI infrastructure. The big plan is to

604
00:30:57,720 --> 00:31:00,800
Speaker 3: acquire up to one hundred billion dollars of data center,

605
00:31:00,920 --> 00:31:04,080
Speaker 3: energy and other assets. I want to discuss with Pooja Goyle,

606
00:31:04,160 --> 00:31:08,520
Speaker 3: she's the partner and chief investment officer for Carlisle's Infrastructure Group.

607
00:31:08,960 --> 00:31:11,400
Speaker 3: That's just a piece of news, but the structure of

608
00:31:11,440 --> 00:31:14,520
Speaker 3: it is a really interesting case study for what's happening

609
00:31:14,600 --> 00:31:19,120
Speaker 3: right now in AI infrastructure. A financing arm getting commitments

610
00:31:19,160 --> 00:31:21,440
Speaker 3: for a fund, partnering with some of the players in

611
00:31:21,520 --> 00:31:24,720
Speaker 3: video in the technology case, and then saying over course

612
00:31:24,760 --> 00:31:28,440
Speaker 3: of time, we're going to go out and buy these assets.

613
00:31:29,320 --> 00:31:31,280
Speaker 2: How do you make a response to that. How do

614
00:31:31,320 --> 00:31:32,000
Speaker 2: you react to that.

615
00:31:33,240 --> 00:31:35,680
Speaker 17: Well, first of all, Ed, thank you for having me

616
00:31:35,720 --> 00:31:40,800
Speaker 17: on your show. And look, from our perspective as infrastructure investors,

617
00:31:40,880 --> 00:31:45,080
Speaker 17: we have a pieces driven approach to investing in infrastructure

618
00:31:45,120 --> 00:31:48,040
Speaker 17: assets and a longer term time horizon, and we do

619
00:31:48,200 --> 00:31:53,080
Speaker 17: believe that AI infrastructure is a significant investment opportunity for us.

620
00:31:53,520 --> 00:31:58,040
Speaker 17: Now you're at Carla, we're developing over twenty gigawatts of

621
00:31:58,160 --> 00:32:02,480
Speaker 17: data center capacity, primarily scale data center capacity. But we

622
00:32:02,640 --> 00:32:06,280
Speaker 17: aren't just developing these assets in isolation. We have taken

623
00:32:06,320 --> 00:32:10,240
Speaker 17: a much more comprehensive view where we are looking across

624
00:32:10,280 --> 00:32:13,960
Speaker 17: the value chain for AI infrastructure and we are making

625
00:32:14,000 --> 00:32:17,640
Speaker 17: sure that we're developing these assets with that comprehensive lens.

626
00:32:17,920 --> 00:32:22,880
Speaker 17: So that means absolutely developing data centers, but also addressing

627
00:32:22,920 --> 00:32:25,760
Speaker 17: one of the most significant bottlenecks when it comes to

628
00:32:25,840 --> 00:32:29,360
Speaker 17: data center development, which is access to power. Look, you

629
00:32:29,560 --> 00:32:31,520
Speaker 17: had a little bit of a snapshot where you were

630
00:32:31,560 --> 00:32:35,360
Speaker 17: watching Elon talk about AI and he was talking about

631
00:32:35,400 --> 00:32:39,840
Speaker 17: access to energy being the one most significant bottleneck. The

632
00:32:39,880 --> 00:32:43,760
Speaker 17: way we are developing AI infrastructure is that we are

633
00:32:43,800 --> 00:32:48,320
Speaker 17: building these large scale energy campuses where we have power

634
00:32:48,360 --> 00:32:52,920
Speaker 17: generation capacity. That's co located next to this data center capacity.

635
00:32:54,040 --> 00:32:57,240
Speaker 5: That's Copier power. Am I right? This is actually something

636
00:32:57,240 --> 00:32:57,800
Speaker 5: that you formed.

637
00:32:57,800 --> 00:33:00,120
Speaker 4: I was reading about the release back in twenty twenty one,

638
00:33:00,240 --> 00:33:03,520
Speaker 4: a new portfolio company that's just building out in terms

639
00:33:03,560 --> 00:33:06,280
Speaker 4: of a platform nature these campuses.

640
00:33:06,680 --> 00:33:08,000
Speaker 5: The scale is extraordinary.

641
00:33:08,120 --> 00:33:09,880
Speaker 4: What was interesting was back in twenty twenty one, it

642
00:33:09,920 --> 00:33:12,320
Speaker 4: was all about sustainable infrastructure.

643
00:33:12,360 --> 00:33:14,560
Speaker 5: It was all about renewable power sources.

644
00:33:14,760 --> 00:33:18,640
Speaker 4: Is that realistic now when we think about the energy necessity?

645
00:33:19,400 --> 00:33:23,040
Speaker 17: Yeah, I mean, Caroline, You're absolutely right. From an energy perspective,

646
00:33:23,120 --> 00:33:25,680
Speaker 17: you need to take in all of the above approach.

647
00:33:25,880 --> 00:33:29,520
Speaker 17: So absolutely you need solar and storage, but gas is

648
00:33:29,560 --> 00:33:31,160
Speaker 17: a very important part of.

649
00:33:31,080 --> 00:33:32,280
Speaker 5: The solution as well.

650
00:33:32,680 --> 00:33:34,200
Speaker 2: Look at Copia for example.

651
00:33:34,320 --> 00:33:38,800
Speaker 17: We think of data center development as building large campuses,

652
00:33:39,440 --> 00:33:42,560
Speaker 17: and a campus is basically like a mini city that

653
00:33:42,760 --> 00:33:47,800
Speaker 17: has multiple gigawads of power generation capacity. This includes gas,

654
00:33:48,000 --> 00:33:52,200
Speaker 17: solar as well as storage. That capacity is connected onto

655
00:33:52,240 --> 00:33:56,240
Speaker 17: the grid and located right next to that power generation capacity.

656
00:33:56,560 --> 00:34:00,000
Speaker 17: A hyperscale data centers that are also connected to the grid.

657
00:34:00,400 --> 00:34:02,520
Speaker 2: So I'm not talking about building islands.

658
00:34:02,640 --> 00:34:07,280
Speaker 17: I'm talking about building a fully integrated city or a campus,

659
00:34:07,400 --> 00:34:10,080
Speaker 17: which is a better term for it, and by doing that,

660
00:34:10,200 --> 00:34:13,000
Speaker 17: you're making sure that data centers are getting access to

661
00:34:13,120 --> 00:34:17,520
Speaker 17: power in a more timely manner. Remember, timing is very

662
00:34:17,520 --> 00:34:21,440
Speaker 17: important here, and that power is actually cost effective. Cost

663
00:34:21,520 --> 00:34:25,160
Speaker 17: an economics matter a lot here, but more importantly, it's

664
00:34:25,200 --> 00:34:29,640
Speaker 17: also more reliable power. Everyone's talking about five nine reliability,

665
00:34:29,640 --> 00:34:32,400
Speaker 17: which is ninety nine point nine nine nine percent. In

666
00:34:32,520 --> 00:34:35,719
Speaker 17: order to do that and build long lived infrastructure assets,

667
00:34:36,040 --> 00:34:39,760
Speaker 17: that reliability is just a very important point. So at Kopia,

668
00:34:39,840 --> 00:34:43,279
Speaker 17: for example, we have a site that we're building in Arizona.

669
00:34:43,680 --> 00:34:46,680
Speaker 17: It is about three times the size of Manhattan. Once

670
00:34:46,719 --> 00:34:49,440
Speaker 17: that site is fully built out, you're talking about thirty

671
00:34:49,520 --> 00:34:54,160
Speaker 17: billion dollars in capital investment between the power generation assets

672
00:34:54,200 --> 00:34:56,880
Speaker 17: as well as the data center assets. And then Coopia

673
00:34:57,000 --> 00:35:01,600
Speaker 17: has another five campuses in the web behind that. So

674
00:35:01,640 --> 00:35:04,640
Speaker 17: you want to make sure these campuses are located close

675
00:35:04,680 --> 00:35:08,040
Speaker 17: to where there will be demand for compute power, but

676
00:35:08,080 --> 00:35:09,680
Speaker 17: you also want to make sure it's going to be

677
00:35:09,719 --> 00:35:12,960
Speaker 17: cost effective, delivered on time, and also reliable.

678
00:35:13,760 --> 00:35:16,280
Speaker 4: I wish we had more time, absolutely fascinating the size

679
00:35:16,280 --> 00:35:19,000
Speaker 4: and the scale. Pooja Goil, come back soon. Tell us

680
00:35:19,000 --> 00:35:22,000
Speaker 4: how these campuses are evolving. Chief Investment officer for Carlile's

681
00:35:22,040 --> 00:35:24,960
Speaker 4: Infrastructure group. We thank you. Let's just turn attention to

682
00:35:25,040 --> 00:35:27,880
Speaker 4: Nokia now, which is also streamlining its business to focus

683
00:35:27,920 --> 00:35:30,960
Speaker 4: on the networking infrastructure that can connect all of these

684
00:35:31,040 --> 00:35:31,720
Speaker 4: data centers.

685
00:35:31,880 --> 00:35:34,240
Speaker 5: Now, Ka CEO Justin Hotel spoke with us earlier.

686
00:35:35,120 --> 00:35:38,640
Speaker 18: With AI, the market is going to change dramatically. It's

687
00:35:38,680 --> 00:35:40,759
Speaker 18: already changing in the data center, which is a part

688
00:35:40,800 --> 00:35:44,040
Speaker 18: of our business. You know, we're building AI factories. Obviously

689
00:35:44,360 --> 00:35:46,200
Speaker 18: Jensen talks a lot about this, I know in video

690
00:35:46,200 --> 00:35:49,239
Speaker 18: has earnings later today. But we're connecting data centers to

691
00:35:49,280 --> 00:35:52,040
Speaker 18: each other and building massive AI factories. That's using our

692
00:35:52,040 --> 00:35:55,480
Speaker 18: optical technology, it's using our IP routing technology. And where's

693
00:35:55,520 --> 00:35:58,440
Speaker 18: the future headed. The futures headed to physical AI, robotics,

694
00:35:58,440 --> 00:36:04,840
Speaker 18: autonomous vehicles, delivery drones, ARVR, you glasses, more and more devices.

695
00:36:04,960 --> 00:36:07,360
Speaker 18: And fundamentally, that means our networks need to change to

696
00:36:07,400 --> 00:36:10,000
Speaker 18: handle that, and that's what we're planning for and anticipating

697
00:36:10,000 --> 00:36:11,040
Speaker 18: to seize that opportunity.

698
00:36:11,040 --> 00:36:14,399
Speaker 16: Can you briefly describe the growth opportunity here and how

699
00:36:14,400 --> 00:36:16,719
Speaker 16: the profile, the growth profile of your company will change

700
00:36:16,760 --> 00:36:17,600
Speaker 16: as you make the shift.

701
00:36:18,040 --> 00:36:18,279
Speaker 14: Yeah.

702
00:36:18,320 --> 00:36:20,680
Speaker 18: Look, I mean today you see the pockets of growth

703
00:36:20,680 --> 00:36:23,640
Speaker 18: that we have in the fixed infrastructure, and as we

704
00:36:23,680 --> 00:36:26,920
Speaker 18: see this build for AI native networks going into mobile,

705
00:36:27,200 --> 00:36:29,120
Speaker 18: we're going to see tremendous growth there. It's just not

706
00:36:29,200 --> 00:36:31,680
Speaker 18: coming in the next few years, or we believe it

707
00:36:31,719 --> 00:36:34,360
Speaker 18: will come over the longer term as the market invests

708
00:36:34,400 --> 00:36:36,000
Speaker 18: and builds, but it's not going to be in the

709
00:36:36,040 --> 00:36:37,840
Speaker 18: next couple of years. So really think of our business

710
00:36:37,920 --> 00:36:41,040
Speaker 18: in a couple of ways, capturing growth and fixed infrastructure today,

711
00:36:41,520 --> 00:36:44,840
Speaker 18: and then in mobile infrastructure, positioning the business for technology

712
00:36:44,880 --> 00:36:47,399
Speaker 18: innovation and longer term growth as that market picks up.

713
00:36:47,600 --> 00:36:50,440
Speaker 8: What industries do you expect to be most dominant in.

714
00:36:50,520 --> 00:36:53,160
Speaker 2: Are there particular industries that you think will.

715
00:36:52,960 --> 00:36:56,319
Speaker 8: Be fastest to adopt the AI connectivity that you're hoping

716
00:36:56,400 --> 00:36:57,160
Speaker 8: to provide.

717
00:36:57,320 --> 00:36:59,319
Speaker 18: Yeah, I think there's a few things. First of all,

718
00:37:00,120 --> 00:37:03,720
Speaker 18: you know, there's no question that the core tech industry

719
00:37:03,719 --> 00:37:05,799
Speaker 18: that we're in today is the engine of growth, right

720
00:37:05,880 --> 00:37:09,400
Speaker 18: AI and cloud customers, hyperscalers, cloud providers, and that's of

721
00:37:09,400 --> 00:37:12,400
Speaker 18: course serving the technologies we have today l l ms,

722
00:37:12,480 --> 00:37:14,960
Speaker 18: you know, AI agents. But when we look ahead, I

723
00:37:15,400 --> 00:37:18,920
Speaker 18: see it being in areas like transport and logistics and manufacturing,

724
00:37:19,320 --> 00:37:22,880
Speaker 18: physical AI also an area that we already serve that

725
00:37:22,920 --> 00:37:27,040
Speaker 18: we call mission critical enterprises. Think of public safety, uh,

726
00:37:27,120 --> 00:37:29,840
Speaker 18: you know, rail transport. These are these are places where

727
00:37:29,880 --> 00:37:35,520
Speaker 18: AI can add better reliability, better security, better outcomes for people.

728
00:37:35,520 --> 00:37:39,120
Speaker 18: If you think about public safety, for example, emergency services,

729
00:37:39,480 --> 00:37:41,040
Speaker 18: and those are areas where I think we'll see fast

730
00:37:41,040 --> 00:37:43,719
Speaker 18: you know, we'll see faster AI adoption. But I don't

731
00:37:43,719 --> 00:37:46,280
Speaker 18: think we can. You know, we can predict the future perfectly.

732
00:37:46,280 --> 00:37:48,520
Speaker 18: We need to just anticipate what are the use cases

733
00:37:48,520 --> 00:37:51,160
Speaker 18: and the needs. It may be that, you know, delivery drones,

734
00:37:51,200 --> 00:37:55,200
Speaker 18: other retail applications also accelerate and those are early users.

735
00:37:55,239 --> 00:37:58,120
Speaker 18: So for us, it's about building the plumbing and the

736
00:37:58,120 --> 00:37:59,560
Speaker 18: core capability.

737
00:37:58,960 --> 00:37:59,439
Speaker 14: That we need.

738
00:38:00,560 --> 00:38:03,880
Speaker 3: There was Nokia CEO Justin Hozard Okay coming up in

739
00:38:03,920 --> 00:38:08,239
Speaker 3: earnings reports, investor presentations, and company memos. His egitors have

740
00:38:08,280 --> 00:38:11,920
Speaker 3: been touting efficiency gains from AI and pointing to the

741
00:38:12,000 --> 00:38:15,080
Speaker 3: tech for shrinking or flat workforce as we are more

742
00:38:15,080 --> 00:38:16,160
Speaker 3: in that next carac.

743
00:38:16,440 --> 00:38:19,520
Speaker 4: Meanwhile, we're watching a deal that has sent sem Rush

744
00:38:19,520 --> 00:38:24,120
Speaker 4: holding shares skyrocketing seventy four percent, Adobe agreeing to buy

745
00:38:24,120 --> 00:38:27,000
Speaker 4: the marketing platform first take over announcement of course this

746
00:38:27,080 --> 00:38:30,200
Speaker 4: has failed acquisition of Figma. It's all cash deal, twelve

747
00:38:30,200 --> 00:38:30,879
Speaker 4: dollars for share.

748
00:38:31,719 --> 00:38:32,840
Speaker 5: This is a blue bag tech.

749
00:38:46,760 --> 00:38:51,279
Speaker 13: Looking ahead, however, layoffs and reduction and hiring plans due

750
00:38:51,280 --> 00:38:54,920
Speaker 13: to AI use are expected to increase.

751
00:38:55,120 --> 00:38:59,680
Speaker 19: You see a significant number of companies either announcing that

752
00:38:59,719 --> 00:39:01,800
Speaker 19: they are are not going to be doing much hiring.

753
00:39:01,840 --> 00:39:06,359
Speaker 19: We're actually doing layoffs and meant much of the time

754
00:39:06,440 --> 00:39:08,360
Speaker 19: they were talking about AI and what it can.

755
00:39:08,239 --> 00:39:11,440
Speaker 20: Do, the supporting cast of the soul crushing work is

756
00:39:11,520 --> 00:39:14,239
Speaker 20: now being done by agents. They work hard twenty four

757
00:39:14,320 --> 00:39:16,560
Speaker 20: x seven. You don't have to pay them, and they

758
00:39:16,560 --> 00:39:20,080
Speaker 20: don't need any lunch, and they don't have any healthcare benefits,

759
00:39:20,080 --> 00:39:24,160
Speaker 20: so they're very affordable and that really complements our workforce.

760
00:39:25,520 --> 00:39:28,160
Speaker 4: Just a few who have been hearing about AI's impact

761
00:39:28,200 --> 00:39:30,920
Speaker 4: on the workforce. Increasingly executives have been laying the blame

762
00:39:30,920 --> 00:39:34,360
Speaker 4: for job cuts at technology's feet. Nelu Meg's editor for

763
00:39:34,400 --> 00:39:37,640
Speaker 4: AIU Seth Figeman joins us now and Seth, I'm hearing

764
00:39:37,640 --> 00:39:41,320
Speaker 4: the term AI washing. Is it actually that AI is

765
00:39:41,360 --> 00:39:44,040
Speaker 4: to blame for these job cuts or it's a nice excuse,

766
00:39:44,080 --> 00:39:46,719
Speaker 4: but actually they overhearede in COVID and this is a

767
00:39:46,719 --> 00:39:48,040
Speaker 4: good way of announcing job cuts.

768
00:39:48,080 --> 00:39:48,279
Speaker 14: Yeah.

769
00:39:48,320 --> 00:39:49,799
Speaker 21: I think it's a little bit of both. I mean,

770
00:39:49,800 --> 00:39:52,520
Speaker 21: first off, the step back. We're seeing a real shifting

771
00:39:52,520 --> 00:39:54,680
Speaker 21: how we talk about this. Even six months ago, a

772
00:39:54,760 --> 00:39:57,440
Speaker 21: year ago, companies were pretty sheepish about saying AI had

773
00:39:57,480 --> 00:40:01,239
Speaker 21: anything whatsoever to do with cost cugging headcount reduction, because

774
00:40:01,239 --> 00:40:03,280
Speaker 21: I think no one wanted to be the poster child

775
00:40:03,480 --> 00:40:07,360
Speaker 21: for massive job displaced unemployment. Nobody wanted that back headline.

776
00:40:07,480 --> 00:40:09,480
Speaker 21: Something has shifted in the last six months, And I

777
00:40:09,520 --> 00:40:11,839
Speaker 21: think it comes down to one, we are in a

778
00:40:11,840 --> 00:40:16,160
Speaker 21: bit more of a difficult macroeconomic environment where companies one

779
00:40:16,239 --> 00:40:19,600
Speaker 21: have more reason to be cugging costs, and potentially AI

780
00:40:20,200 --> 00:40:22,240
Speaker 21: improving against that backdrop is going to be a perfect

781
00:40:22,239 --> 00:40:24,359
Speaker 21: storm for those cost cuts. But two, it's a little

782
00:40:24,400 --> 00:40:26,960
Speaker 21: bit easier probably for investors to say it's AI than

783
00:40:27,040 --> 00:40:29,759
Speaker 21: to say we over hired, we're a bloated workforce, we're

784
00:40:29,800 --> 00:40:31,080
Speaker 21: dealing with outdating technologies.

785
00:40:31,160 --> 00:40:31,880
Speaker 14: Let's just say AI.

786
00:40:33,000 --> 00:40:35,600
Speaker 3: So in terms of a post a child or maybe

787
00:40:35,640 --> 00:40:38,879
Speaker 3: a better phrase of case study, you have Amazon. Right,

788
00:40:39,000 --> 00:40:42,920
Speaker 3: So in June Andy Jesse signaled or said, you know

789
00:40:43,080 --> 00:40:45,680
Speaker 3: that this would happen long term because of AI, but

790
00:40:45,840 --> 00:40:49,440
Speaker 3: then more recently when Amazon actually did job cuts, that

791
00:40:49,600 --> 00:40:51,560
Speaker 3: was not the messaging that's right.

792
00:40:51,600 --> 00:40:54,239
Speaker 21: I think he came out there and said, well, you know,

793
00:40:54,400 --> 00:40:56,840
Speaker 21: not yet you know. And I think that the Amazon

794
00:40:56,840 --> 00:40:59,960
Speaker 21: cuts maybe speak to a different phenomenon that's very TEXTPASI,

795
00:41:00,200 --> 00:41:01,760
Speaker 21: which is that we're seeing a lot of tech companies

796
00:41:01,800 --> 00:41:04,560
Speaker 21: do significant content. Some of that is because of overhiring

797
00:41:04,600 --> 00:41:07,080
Speaker 21: during the pandemic, to be sure, but also these same

798
00:41:07,080 --> 00:41:10,840
Speaker 21: tech companies are reallocating substantial resources to compete in the

799
00:41:10,920 --> 00:41:13,120
Speaker 21: larger AI race, and as a result of that, they're

800
00:41:13,120 --> 00:41:15,959
Speaker 21: trying to trim and be more efficient in other parts

801
00:41:15,960 --> 00:41:18,120
Speaker 21: of their businesses. So AI is a part of it,

802
00:41:18,280 --> 00:41:20,680
Speaker 21: but it may not just be because chatbots are taking

803
00:41:20,680 --> 00:41:21,560
Speaker 21: people's jobs.

804
00:41:21,800 --> 00:41:23,200
Speaker 5: There is data we can reflect on.

805
00:41:23,440 --> 00:41:25,120
Speaker 4: I think the New York State is the first state

806
00:41:25,160 --> 00:41:27,000
Speaker 4: that said when you make big layoffs, you've got to

807
00:41:27,000 --> 00:41:30,080
Speaker 4: say whether it's AI automation related. We're seeing in the

808
00:41:30,239 --> 00:41:33,400
Speaker 4: Challenge of Gray and Christmas numbers, thirty one thousand jobs

809
00:41:33,560 --> 00:41:37,920
Speaker 4: was sort of AI sacrificed in just October elone.

810
00:41:38,000 --> 00:41:40,839
Speaker 21: That's right, but again the challenger stuff is also based

811
00:41:40,880 --> 00:41:44,640
Speaker 21: on how people how companies are representing that publicly. To

812
00:41:44,680 --> 00:41:46,279
Speaker 21: your point, though, I think other states are trying to

813
00:41:46,280 --> 00:41:48,560
Speaker 21: emulate the New York legislation. We would love nothing more

814
00:41:48,600 --> 00:41:51,000
Speaker 21: than greater transparency on this rund because I think there's

815
00:41:51,000 --> 00:41:52,840
Speaker 21: a lot of fear, there's a lot of misinformation, and

816
00:41:52,840 --> 00:41:54,800
Speaker 21: that would help us really set back from fiction.

817
00:41:56,400 --> 00:41:58,840
Speaker 3: Beg Seth Figman, Thank you very much. Let's get to

818
00:41:58,880 --> 00:42:02,280
Speaker 3: another top story. Meta has secured a key legal victory

819
00:42:02,360 --> 00:42:06,000
Speaker 3: against the Federal Trade Commission. The FTC alleged the company's

820
00:42:06,040 --> 00:42:10,359
Speaker 3: purchases of Instagram and WhatsApp violated antitrust law.

821
00:42:11,120 --> 00:42:12,160
Speaker 2: A judge didn't agree.

822
00:42:12,200 --> 00:42:14,920
Speaker 3: Bloomberg's Riley Griffin joins us with the details. I think

823
00:42:14,960 --> 00:42:17,919
Speaker 3: let's start with the basic legal reasoning that the judge

824
00:42:18,000 --> 00:42:21,839
Speaker 3: gave what was the decision based on what happens.

825
00:42:21,680 --> 00:42:23,600
Speaker 22: Well, and you have to remember that when the FDC

826
00:42:23,760 --> 00:42:27,160
Speaker 22: first launched this lawsuit, that was five years ago, that

827
00:42:27,320 --> 00:42:30,759
Speaker 22: was Trump one point zero. The social media landscape has

828
00:42:30,880 --> 00:42:34,040
Speaker 22: changed drastically since then, and that was really the reasoning

829
00:42:34,120 --> 00:42:38,600
Speaker 22: behind his ruling. The FDC had argued that me Wei

830
00:42:38,840 --> 00:42:41,000
Speaker 22: and Snap where it's only competitors, and you and I

831
00:42:41,080 --> 00:42:43,640
Speaker 22: have spoken plenty of times about what TikTok is doing

832
00:42:43,680 --> 00:42:46,919
Speaker 22: to Meta's market share. That's what Judge Boseberg said as.

833
00:42:46,840 --> 00:42:51,400
Speaker 4: Well, and then Meta gets up and says, see, we

834
00:42:51,520 --> 00:42:54,200
Speaker 4: have got so much competition, it's fierce out there, and

835
00:42:54,280 --> 00:42:58,000
Speaker 4: continued to tackle it. It's an interesting sort of argument

836
00:42:58,040 --> 00:43:00,799
Speaker 4: to have to make to your investor base employees that

837
00:43:00,880 --> 00:43:02,239
Speaker 4: you are under threat in some way.

838
00:43:03,120 --> 00:43:06,560
Speaker 22: It's such an important point, Caroline, because really this win

839
00:43:06,880 --> 00:43:10,560
Speaker 22: is also a warning. Looking forward, Meta is going to

840
00:43:10,600 --> 00:43:13,480
Speaker 22: have to grapple with Judge Boseberg's comments, which are that

841
00:43:14,080 --> 00:43:17,040
Speaker 22: it is not differentiated from its competitors and that TikTok

842
00:43:17,080 --> 00:43:18,239
Speaker 22: is eroding its market share.

843
00:43:18,880 --> 00:43:20,640
Speaker 2: This is probably the most difficult question.

844
00:43:20,719 --> 00:43:22,719
Speaker 3: But what happens next is that just it now, it's

845
00:43:22,760 --> 00:43:25,399
Speaker 3: all done, or there are some changes that Meta has

846
00:43:25,400 --> 00:43:27,440
Speaker 3: to make, or there will be more legal challenges down

847
00:43:27,480 --> 00:43:27,759
Speaker 3: the road.

848
00:43:28,239 --> 00:43:31,360
Speaker 22: So we've been speaking with analysts. Nobody expects the appeal

849
00:43:31,440 --> 00:43:34,320
Speaker 22: process to proceed, but we're going to wait and see.

850
00:43:35,200 --> 00:43:37,520
Speaker 22: Really this means that Meta doesn't have to spin off

851
00:43:37,560 --> 00:43:41,360
Speaker 22: Instagram or WhatsApp. That was the overhanging threat, but a

852
00:43:41,360 --> 00:43:44,200
Speaker 22: big win one that was really priced in. Analysts had

853
00:43:44,239 --> 00:43:46,960
Speaker 22: expected Meta to take the w here.

854
00:43:47,560 --> 00:43:50,200
Speaker 4: Bru Mags, Ridy Griffin has been alled across the story,

855
00:43:50,480 --> 00:43:52,319
Speaker 4: thank you so much. I mean, while coming up, we're

856
00:43:52,360 --> 00:43:54,120
Speaker 4: going to get back to the Nvidia earning So course

857
00:43:54,160 --> 00:43:56,840
Speaker 4: we are. Will Street is awaiting AI signals. We await

858
00:43:56,880 --> 00:43:58,680
Speaker 4: an exclusive conversation with Jensen Wang.

859
00:43:58,719 --> 00:44:00,759
Speaker 5: The ed will conduct is blut that tech.

860
00:44:15,000 --> 00:44:18,680
Speaker 3: Turning back to Nvidio ins investors awaiteds results after the

861
00:44:18,680 --> 00:44:22,120
Speaker 3: closing bell Conjensavanni Bloomberg Intelligence ci Alis wrote at the

862
00:44:22,200 --> 00:44:25,439
Speaker 3: end of October that in Video's partnerships could broaden its

863
00:44:25,440 --> 00:44:30,080
Speaker 3: revenue and quote. With China constrained expansion into quantum robotics

864
00:44:30,120 --> 00:44:35,480
Speaker 3: and networking reinforces a long term growth trajectory. Conjensavaranni joins

865
00:44:35,520 --> 00:44:39,239
Speaker 3: us now here in San Francisco. Whatever happens in the

866
00:44:39,280 --> 00:44:43,240
Speaker 3: quarter happens, It's all about this kind of long term story.

867
00:44:44,160 --> 00:44:47,360
Speaker 3: You wrote that note after GtC and DC, and you

868
00:44:47,400 --> 00:44:50,319
Speaker 3: seem to have seen enough to think that long term

869
00:44:50,360 --> 00:44:51,160
Speaker 3: story is intact.

870
00:44:52,280 --> 00:44:52,800
Speaker 12: Definitely.

871
00:44:52,840 --> 00:44:54,560
Speaker 23: I mean, it's not going to be able the numbers,

872
00:44:54,600 --> 00:44:57,080
Speaker 23: as you said this QUAD, but I think given the

873
00:44:57,239 --> 00:45:00,840
Speaker 23: macro and sentiment backdrop that have been rising concerns or

874
00:45:00,920 --> 00:45:06,520
Speaker 23: question regarding sustainability of these deals be double our customers

875
00:45:06,560 --> 00:45:10,160
Speaker 23: double ordering or double securing supply and see the final

876
00:45:10,520 --> 00:45:12,839
Speaker 23: can supply keep up even if the demand is true

877
00:45:12,880 --> 00:45:15,439
Speaker 23: and can sustain whether it's from the supply chip side,

878
00:45:15,440 --> 00:45:18,520
Speaker 23: from TSMC and packaging, or whether supply from the end

879
00:45:18,600 --> 00:45:20,879
Speaker 23: of data center build up that the customers are trying

880
00:45:20,880 --> 00:45:21,200
Speaker 23: to build.

881
00:45:21,239 --> 00:45:22,480
Speaker 2: Can they execute that fast?

882
00:45:23,040 --> 00:45:28,360
Speaker 5: Qinjin. When we heard a GtC from Jensen.

883
00:45:28,040 --> 00:45:30,839
Speaker 4: About the five hundred billion dollar half a trillion line

884
00:45:30,840 --> 00:45:33,880
Speaker 4: of sight, how real and tangible are those orders?

885
00:45:35,400 --> 00:45:37,360
Speaker 23: I mean when you look peel into the onion, the

886
00:45:37,680 --> 00:45:39,920
Speaker 23: timing of that was sort of over six quarters and

887
00:45:40,239 --> 00:45:43,319
Speaker 23: it did some analysis and that just suggests basically five

888
00:45:43,360 --> 00:45:47,279
Speaker 23: to ten percent above consensus. So a very very achievable

889
00:45:47,320 --> 00:45:49,720
Speaker 23: and very executable target that he laid out.

890
00:45:49,960 --> 00:45:53,200
Speaker 3: That's one reason why expectations is so high going in

891
00:45:53,280 --> 00:45:57,080
Speaker 3: since Aday that single slide behind him on stage, five

892
00:45:57,200 --> 00:46:01,320
Speaker 3: hundred billion dollars. If there are some questions to pose

893
00:46:01,520 --> 00:46:04,960
Speaker 3: to Jensen, Wang and Nvidia, not just today but on

894
00:46:05,000 --> 00:46:07,480
Speaker 3: an ongoing basis, what are the it's your minds conngent.

895
00:46:07,760 --> 00:46:10,359
Speaker 23: I think we need more clarity around these deals. Right,

896
00:46:10,400 --> 00:46:13,560
Speaker 23: there's deals when it comes to sort of media securing

897
00:46:13,600 --> 00:46:16,840
Speaker 23: revenue by these investments and sloan. There are questions around

898
00:46:16,840 --> 00:46:21,080
Speaker 23: depreciation schedule of their GPUs and again, finally there seems

899
00:46:21,120 --> 00:46:24,839
Speaker 23: to be or at least implies some sort of securing

900
00:46:25,040 --> 00:46:28,279
Speaker 23: scarcity of GPU securing from multiple different provider, whether it's

901
00:46:28,320 --> 00:46:31,400
Speaker 23: other merchant silicon providers or acy providers.

902
00:46:32,320 --> 00:46:33,000
Speaker 5: Can I ask.

903
00:46:33,280 --> 00:46:37,480
Speaker 4: About the depreciation of GPUs? Is Jensen the key person

904
00:46:37,520 --> 00:46:40,600
Speaker 4: to ask about this? He's obviously understanding exactly how they're

905
00:46:40,680 --> 00:46:43,040
Speaker 4: using and how they're appreciating within other those data centers.

906
00:46:43,040 --> 00:46:44,680
Speaker 5: Can he give a signal as to whether.

907
00:46:44,440 --> 00:46:48,960
Speaker 4: Companies on their own forward looking basis estimating right whether

908
00:46:49,000 --> 00:46:51,120
Speaker 4: it's three four years, four years, five years.

909
00:46:52,000 --> 00:46:54,839
Speaker 23: Well, definitely from a technology perspective, he can definitely give

910
00:46:54,880 --> 00:46:57,520
Speaker 23: that answer. But there are two input factors here. One

911
00:46:57,600 --> 00:47:00,759
Speaker 23: is realistically, how long can you use this chips, which

912
00:47:00,800 --> 00:47:03,760
Speaker 23: we believe of three to five year period seems fine,

913
00:47:03,840 --> 00:47:07,200
Speaker 23: But there's also business decisions that the customers are making

914
00:47:07,239 --> 00:47:10,600
Speaker 23: when they're evaluating lifetime of these chips, whether will they

915
00:47:10,719 --> 00:47:13,480
Speaker 23: upgrade to newer chips, will these chips be still valid

916
00:47:13,480 --> 00:47:16,480
Speaker 23: to use their models which are increasing at unprecedented rate.

917
00:47:17,000 --> 00:47:18,800
Speaker 3: There were people out there that say the kind of

918
00:47:18,880 --> 00:47:22,320
Speaker 3: micro barriers of this world are wrong because those older

919
00:47:22,320 --> 00:47:26,480
Speaker 3: generation chips are one hundred percent utilization. Is there a

920
00:47:26,520 --> 00:47:28,279
Speaker 3: Bloombag Intelligence house view on that.

921
00:47:28,719 --> 00:47:31,719
Speaker 23: Well, again, from a technology perspective, we think that useful life.

922
00:47:31,800 --> 00:47:34,880
Speaker 23: We're seeing most of the cases are correct and the

923
00:47:34,960 --> 00:47:38,400
Speaker 23: chips can be used that long. Whether from a business perspective,

924
00:47:38,400 --> 00:47:41,320
Speaker 23: whether from a GPU rental pricing perspective, that's valued or

925
00:47:41,360 --> 00:47:43,320
Speaker 23: not depends on the customer use cases.

926
00:47:44,000 --> 00:47:46,720
Speaker 5: Con Jin, you've got a busy day. We so appreciate

927
00:47:46,760 --> 00:47:48,120
Speaker 5: that you've come on to front Ron.

928
00:47:48,200 --> 00:47:51,040
Speaker 4: What is our super bowl? Kunjin, Sivanni and Bluembag Intelligence,

929
00:47:51,040 --> 00:47:53,239
Speaker 4: We thank you. Do not forget to tune in later.

930
00:47:53,840 --> 00:47:55,960
Speaker 4: You've got to tune in for an exclusive inefit with

931
00:47:56,080 --> 00:47:59,319
Speaker 4: Invidia CEO Jensen Wang following his earning.

932
00:47:59,040 --> 00:48:01,360
Speaker 5: Six thirty pm is going to be thirty pm Pacific.

933
00:48:01,520 --> 00:48:02,279
Speaker 5: Who's doing it?

934
00:48:02,960 --> 00:48:05,920
Speaker 3: You're doing it in Yeah, it's going to be an

935
00:48:05,920 --> 00:48:09,920
Speaker 3: interesting conversation. There are difficult questions for him, the depreciation factor,

936
00:48:10,400 --> 00:48:13,920
Speaker 3: circular financing, which they've already pushed at back up before.

937
00:48:14,160 --> 00:48:15,360
Speaker 2: But let's see what's in the print.

938
00:48:16,560 --> 00:48:18,680
Speaker 5: Yeah, that does it? From this edition of Bloomberg Tech.

939
00:48:19,760 --> 00:48:21,520
Speaker 3: Yeah, don't forget to check out the podcast. A new

940
00:48:21,560 --> 00:48:24,320
Speaker 3: way to find it online on all the Bloomberg platforms.

941
00:48:24,480 --> 00:48:25,479
Speaker 3: This is Bloomberg Tech.