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Speaker 1: Bloomberg Audio Studios, podcasts, radio news.

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Speaker 2: From Mahart where innovation, money and power Collie in Silicon

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Speaker 2: Vallet NBN.

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Speaker 3: This is Bloomberg Technology with Caroline Hyde and Ed.

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Speaker 4: Ludlow live from San Francisco to our TV and radio

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Speaker 4: audiences around the world. Welcome to a special edition of

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Speaker 4: Bloomberg Technology. I'm Ed Ludlow. In just a few moments

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Speaker 4: in video, CEO Jensen Wang will join us for a

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Speaker 4: live interview following their latest earnings report, the company posting

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Speaker 4: a revenue forecast that beat consensus that fell short to

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Speaker 4: some of the most optimistic estimates, stoking concern that the

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Speaker 4: explosive growth is waning. Let's get right to Bloomberg Semiconductor

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Speaker 4: correspondent Ian King, who joins me on set. Let's start

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Speaker 4: with the basics, the fiscal third quarter forecasts and what

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Speaker 4: we learned through it.

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Speaker 5: Yeah, that forecast was fine. If you compare it to consensus,

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Speaker 5: it was there or thereabouts, and for most of the

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Speaker 5: companies in the world, that would be great and everybody

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Speaker 5: would be happy.

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Speaker 3: But this is in video.

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Speaker 5: This is a company which beats pretty much every quarter

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Speaker 5: and by an order of magnitude, and it didn't do that,

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Speaker 5: and it didn't indicate it would do that, and that

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Speaker 5: raised a lot of questions. As we heard on the.

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Speaker 4: Call, there are many storylines. I think the demand from

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Speaker 4: the hyperscalas is clearly intact, but Blackwell was everything. I

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Speaker 4: want to play a SoundBite of what Jensen Wang said

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Speaker 4: about Blackwell. Listened to this.

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Speaker 6: The change to the mask is complete. There were no

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Speaker 6: functional changes necessary, and so we're sampling functional samples of Blackwell,

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Speaker 6: Grace Blackwell in a variety of system configurations as we speak.

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Speaker 4: The main point here is that there was not a

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Speaker 4: design issue with Blackwell itself as had been reported, but

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Speaker 4: based on what Nvidia said, this was about the production mechanism.

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Speaker 4: Blue Beazine King, you did a very good job in

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Speaker 4: the top Life blog of explaining a GPU mask. Could

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Speaker 4: you just try to give a short version of that

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Speaker 4: to our audience.

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Speaker 5: Yeah, the mask is basically the blueprint, which basically that

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Speaker 5: is used to burn in the circuit onto the surface

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Speaker 5: of the chip to give it its function. Right, is

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Speaker 5: a very important step. That's the fundamental blueprint, and they

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Speaker 5: were saying we didn't get it wrong, but when it

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Speaker 5: came to manufacturing, it didn't produce as many good chips

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Speaker 5: as we wanted, so we made some tweaks to that.

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Speaker 5: Didn't have to redesign it, but made some tweaks, and

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Speaker 5: that is helping us to get a better yield.

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Speaker 4: It's worth noting at this stage the stocks down almost

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Speaker 4: seven percent in after hours, had been down more at

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Speaker 4: the conclusion of the call. I think basically because we

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Speaker 4: didn't learn enough about Blackwell. What they said was in

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Speaker 4: the fiscal fourth quarter of this year twenty five, there

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Speaker 4: will be several billion dollars of sales through Blackwell. Why

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Speaker 4: does the market want more and what does it want?

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Speaker 5: They wanted a reassurance from in videos management, and they

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Speaker 5: wanted reassurance in the form of details. They wanted, you know,

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Speaker 5: gens to Puddy's arm around everybody and say, don't worry,

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Speaker 5: it'll be fine. This is how much I'm going to get.

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Speaker 5: They asked consistently, We're asked questions of how many billions

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Speaker 5: and when will those billions exactly come in and collect. Kretz,

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Speaker 5: the CFO and Jensen Wang essentially avoided that question and

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Speaker 5: refuse to give that precise reassurance.

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Speaker 4: We're showing some of the after ours reaction, not just

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Speaker 4: in in video itself, but some of its peers, both

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Speaker 4: on the chip making side the server equipment providers, and

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Speaker 4: I think AMD and ARM in particular are very noteworthy.

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Speaker 4: Bloombogt and King stay with us. I want to go

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Speaker 4: out to Chicago and Bloomberg's Ryan for Lostelka on I

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Speaker 4: Equities team and Ryan. That's the broad summary from me

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Speaker 4: about the names moving and after Hours there's probably a

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Speaker 4: bigger picture after ours movement in the markets as well.

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Speaker 4: I'll start with Nvidia and work outward from that.

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Speaker 1: Sure, well, one thing I would say about in videos

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Speaker 1: after hours decline is that it does come after a

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Speaker 1: very strong year to day performance. I think it closed

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Speaker 1: up more than one hundred and fifty percent this year,

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Speaker 1: So even though the re forecast was maybe a little

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Speaker 1: bit shy of some of the most optimistic expectations, it

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Speaker 1: did beat expectations. And it is, you know, coming off

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Speaker 1: such a huge gain. So it's not necessarily surprising to

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Speaker 1: see a little bit of a consolidation now. And I

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Speaker 1: think even the decline is a little bit less and

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Speaker 1: the options market was anticipating. So just some context there

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Speaker 1: for looking at the declient. But you're right that pretty

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Speaker 1: much we are seeing widespread weakness following this report. All

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Speaker 1: the megacaps are, you know, modestly lower. We are seeing

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Speaker 1: much more pronounced weakness in other chip makers and chip

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Speaker 1: design companies and so forth. So yeah, certainly it does

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Speaker 1: seem like the initial read through here is negative, but

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Speaker 1: again it does come after a very strong start to

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Speaker 1: the year.

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Speaker 4: We made a big deal about this earnings print for

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Speaker 4: quite a long time. I look at something like an

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Speaker 4: investco QQQ, the ETF that tracks the nast that one hundred,

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Speaker 4: and I think it's down around a percentage point, right.

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Speaker 4: But in truth, Ryan, in the market's reaction, was this

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Speaker 4: the macro level event that we thought it was going

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Speaker 4: to be.

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Speaker 1: That's a great question. I would say that it's kind

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Speaker 1: of close enough in line with expectations, even if it

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Speaker 1: is a little bit shy the most optimistic ones that

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Speaker 1: I don't think this is going to really cause people

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Speaker 1: to really change how they're allocating, change their opinions on

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Speaker 1: AIS as kind of fundamental secular driver. But maybe in

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Speaker 1: the near term we do see a little bit of weakness.

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Speaker 1: I mean, again, some of these stocks have been moving

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Speaker 1: up so much there have been sort of kind of

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Speaker 1: growing calls about their valuation some concerns about that. I

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Speaker 1: don't know if this is the kind of absolute blowout

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Speaker 1: that we'll just you know, cause people to continue piling

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Speaker 1: into the way that they were doing earlier this year.

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Speaker 4: There is a lot in the news cycle inclusive of

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Speaker 4: and outside of in video. I think one of the

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Speaker 4: names that you note in after ours is super Micro.

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Speaker 4: It's down significantly. There's an video relationship to that, and

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Speaker 4: then there's the news around that name in and of itself.

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Speaker 1: Yeah. Absolutely so. We saw yesterday a short report came

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Speaker 1: out today it's delaying the filing of the ten K.

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Speaker 1: You know, both of those caused us a weakness in

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Speaker 1: the stock. I think today it was down more than

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Speaker 1: double digits, so you know, a lot of reasons to

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Speaker 1: be concerned there in general. And it is one of

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Speaker 1: Nvidia's biggest customers. I think it's the third largest, so

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Speaker 1: you know, this is just you know, another reason for

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Speaker 1: people who might have been kind of souring on super

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Speaker 1: Micro to you know, maybe be pulling the cell button.

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Speaker 3: A little bit.

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Speaker 4: Bloomdo's Ryan for Selica with the after hours action out

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Speaker 4: of Chicago. Thank you. Let's get the reaction from the

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Speaker 4: cell side with one of the stocks relative bears. D A.

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Speaker 4: Davidson's GIL Luria had a neutral rating and a street

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Speaker 4: low ninety dollars price target on shares of Nvidia. He

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Speaker 4: joins US now and GIL. I guess does that change

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Speaker 4: your barished perspective on the stock and your main takeaway

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Speaker 4: from the analyst school, the quarter.

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Speaker 2: Was a very good quarter. There ability to continue to

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Speaker 2: grow delivering chips at this rate at this scale is

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Speaker 2: fantastic and unprecedented. I don't think there's much of a

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Speaker 2: revenue issue here, of a growth issue here. I think

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Speaker 2: a little bit of a pushback is probably more around

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Speaker 2: the margin situation. You had a good conversation with Ian

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Speaker 2: about Blackwell and some of the moving pieces part of

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Speaker 2: what's happening with Blackwell. It's a new product, it's got

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Speaker 2: a few hitches along the way that put pressure on

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Speaker 2: gross margins, and then operating margins grew more than anticipated,

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Speaker 2: so less of the top line upside flowed to the

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Speaker 2: bottom line. And that's probably where investors are getting a

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Speaker 2: little bit more cautious than they were before, because in

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Speaker 2: the last several quarters, the huge upside to revenue flowed

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Speaker 2: all the way to the bottom line, creating huge upside

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Speaker 2: there to believe in and nvideo right now is to

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Speaker 2: believe twenty twenty six calendar is going to be about

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Speaker 2: two hundred billion of revenue and about four and a

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Speaker 2: half to five dollars of earnings. That's only possible if

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Speaker 2: they continue to get this type of growth on the

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Speaker 2: top line and more leverage. And the second part of

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Speaker 2: that equation looks a little less certain right now.

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Speaker 3: Gil.

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Speaker 4: When you said that two hundred billion dollar figure, Bloombergsy

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Speaker 4: and King, who's covered semi conductors at this company since

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Speaker 4: nineteen ninety eight, sat next to me still for what

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Speaker 4: it's worth, gave a little smile. I mean, it's an

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Speaker 4: astonishing figure. I think there's a lot of emphasis here

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Speaker 4: on Blackwell. How did you react to the news of

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Speaker 4: several billion dollars in fiscal fourth with Blackwell and the

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Speaker 4: explanation that this was a production issue impacting the ramp,

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Speaker 4: not a sort of core design issue around the product itself.

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Speaker 2: So, first of all, our estimate is far less than

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Speaker 2: two hundred billion for twenty twenty six.

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Speaker 4: But that's you're a relative bear, Gill, and I got

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Speaker 4: that bit right.

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Speaker 2: I think in terms of Blackwell, what happened to in

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Speaker 2: n videos. They used to be on a two year

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Speaker 2: cycle for the data center product, and they decided to

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Speaker 2: aggressively move to a one year cycle and then put

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Speaker 2: a lot of new feature functionality and bundle into the

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Speaker 2: Blackwell platform. So that was a very aggressive agenda, and

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Speaker 2: it's not that surprising that there's some challenges that they

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Speaker 2: have to encounter. If they can still ship in their

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Speaker 2: fiscal fourth quarter, that's a very good sign. And by

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Speaker 2: the way, their customers don't really care that much. Their

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Speaker 2: customers are just going to continue to buy the latest

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Speaker 2: and greatest, and if that's the Age two hundred, they'll

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Speaker 2: just continue to buy h two hundred. They have made

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Speaker 2: that abundantly clear on their conference calls, Microsoft, Meta, Amazon, Google, Tesla.

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Speaker 2: Elon Musk made it abundantly clear they'll buy as much

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Speaker 2: GPUs as in Video can produce, at least this year,

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Speaker 2: because they're saying the demand signals are there. As long

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Speaker 2: as the demand signals are there, they'll buy whatever and

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Speaker 2: video is selling, and towards the end of the year,

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Speaker 2: a piece of that will be Blackwell.

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Speaker 4: Gil Jensen Wong was it pains and gave a detailed

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Speaker 4: answer that basically summarizing video as a systems vendor. The

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Speaker 4: discussion around Envy link, CPUs, everything beyond GPU. How much

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Speaker 4: credit do you assign the company for that, the sort

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Speaker 4: of complete systems that they sell.

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Speaker 2: A tremendous amount of credit. Let's start with the beginning.

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Speaker 2: When history is written, we'll talk about how Jensen Wangen

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Speaker 2: Nvidia created the capabilities that make generative AI possible today,

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Speaker 2: in a similar way that Elon Musk is responsible for

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Speaker 2: the electric vehicle. And part of the genius wasn't just

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Speaker 2: seeing that this is coming, that accelerate computers coming, AI

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Speaker 2: capabilities is coming well before anybody else did. It's also

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Speaker 2: realizing that in order to create a moat around the chip,

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Speaker 2: they need to do a couple of things. One is

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Speaker 2: to own the proprietary software around that KUDA, but the

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Speaker 2: other thing is to enhance the bundle as much as possible.

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Speaker 2: These GPUs work in a raise when they work together,

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Speaker 2: ten thousands or sometimes tens of thousands of GPUs together.

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Speaker 2: When you box those up in a box with multiple

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Speaker 2: g CPUs, wiring, cabling, and firmware, you make it harder

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Speaker 2: for your competitors to catch up. And they've done a

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Speaker 2: remarkable job of that.

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Speaker 4: I also wonder about all the rest. The story thus

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Speaker 4: far has been about the hyperscale is a meta five names,

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Speaker 4: essentially being the core customer of Nvidia. We got some

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Speaker 4: detail on low double digit billions sales this year in

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Speaker 4: Sovereign AI. Do you see evidence that the market is

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Speaker 4: broadening out beyond just the cloud providers?

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Speaker 3: Not really.

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Speaker 2: It seems like those five are still a substantial amount

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Speaker 2: of the revenue. And those five have in their most

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Speaker 2: recent calls said some interesting things. One of them is

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Speaker 2: that they consistently said they are over investing. So they

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Speaker 2: said they definitely want to invest more. We're seeing that

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Speaker 2: in results today. But they also use the term over investing.

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Speaker 2: Over investing is something you do before you do the

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Speaker 2: other thing, and they made it clear that they're only

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Speaker 2: going to do so when they have the demand signals.

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Speaker 2: If or whatever reason, demand for AI capacity goes down,

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Speaker 2: for instance, because compute doesn't continue to drive performance of

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Speaker 2: foundational models, you're gonna see those same five actors pull

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Speaker 2: back and say, you know, maybe we have enough data

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Speaker 2: center capacity, or maybe what we have in the pipeline

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Speaker 2: is enough. And that's the concern for twenty twenty five

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Speaker 2: and twenty six is that those very companies could do that.

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Speaker 2: Beyond that, these are the same companies that are developing

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Speaker 2: their own chips. They're at various stages of this, but

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Speaker 2: Google's TPUs are probably as good as in videos. So

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Speaker 2: in terms of their internal use and selling to customers

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Speaker 2: such as Apple, Google's already caught up, Amazon's catching up,

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Speaker 2: Meta and Microsoft are earlier stages. But these same companies

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Speaker 2: that are buying every GPU from video they can will

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Speaker 2: be the ones that will buy possibly less next year

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Speaker 2: in the year after that.

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Speaker 4: I just want to reflect for our TV audience there

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Speaker 4: are people tuned in around the world. My understanding is

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Speaker 4: there are watch parties in cities from New York to London.

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Speaker 4: For this earnings, We've hyped it up for weeks. Probably

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Speaker 4: this is the most unique earnings print that I've covered

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Speaker 4: in my career. What about for you in the semiconductor space,

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Speaker 4: Why is this so different?

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Speaker 2: Well, so, first of all, I tried to participate by

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Speaker 2: wearing in video green today. But importantly and video is

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Speaker 2: important because of how much they've added to the cumulative

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Speaker 2: earnings of this in p. Five hundred and the cumulative performance.

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Speaker 2: But I wouldn't necessarily extrapolate their success or sometimes setbacks

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Speaker 2: to the rest of the market because what they do

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Speaker 2: is very specific. The growth and data center construction and

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Speaker 2: data center a GPU fulfillment into those data centers is

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Speaker 2: a very small set of companies. I'd argue and Video

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Speaker 2: is sucking the oxygen for the room from a lot

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Speaker 2: of other technology companies. Well, we celebrate with Nvideo because

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Speaker 2: they've added so much value. I wouldn't necessarily read through

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Speaker 2: to the rest of technology.

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Speaker 4: Gilerya of DA Davidson, the relatively most bearish or perhaps

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Speaker 4: the least bullish name on the street, Thank you so

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Speaker 4: welcome to this special edition of Bloomberg Technology for our

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Speaker 4: TV and radio audience around the world. Joining me now

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Speaker 4: is Jensen One, CEO of Nvidia, straight from the analyst's call,

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Speaker 4: and Jensen, good evening, good afternoon to you. I think

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Speaker 4: the market wanted more on Blackwell, they wanted more specifics,

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Speaker 4: and I'm trying to go through all of the call

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Speaker 4: and the transcript. It seems like a very clearly this

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Speaker 4: was a production issue and not a fundamental design issue

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Speaker 4: with Blackwell, but the deployment in the real world, what

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Speaker 4: does that look like? Tangibly and is there a sort

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Speaker 4: of delay in the timeline of that deployment and thus

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Speaker 4: revenue from that product.

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Speaker 7: I let's see, that's just the fact that I was

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Speaker 7: so clear and it wasn't clear enough kind of tripped

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Speaker 7: me up there right away.

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Speaker 8: And so let's see, we made a mass change to

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Speaker 8: improve the yield. Functionality of Blackwell is wonderful. We're sampling

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Speaker 8: Blackwell all over the world today. We show people giving

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Speaker 8: tours to people of the Blackwall systems that we have

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Speaker 8: up and running. You could find pictures of Blackwall systems

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Speaker 8: all over the web. We have started volume production. Volume

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Speaker 8: production will ship in Q four. Q four, we will

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Speaker 8: have billions of dollars of Blackwell revenues and.

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Speaker 3: We will ramp from there. We will ramp from there.

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Speaker 8: The demand for Blackwell far exceeds its supply, of course

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Speaker 8: in the beginning, because the demand is so great, But

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Speaker 8: we're going to have lots and lots of supply and

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Speaker 8: we will be able to ramp. Starting in Q four,

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Speaker 8: we have billions of dollars of revenues and we'll ramp

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Speaker 8: from there into Q one, into Q two and two

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Speaker 8: next year. We're going to have a great next year

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Speaker 8: as well.

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Speaker 4: Jensen, what is the demand for accelerated computing beyond the

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Speaker 4: hyperscalers and meta.

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Speaker 8: Hyperscalers represent about forty five percent of our total data

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Speaker 8: center business. We're relatively diversified today. We have hyperscalers, we

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Speaker 8: have Internet service providers, we have sovereign AIS, we have

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Speaker 8: industries enterprises, so it's fairly fairly diversified. A site outside

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Speaker 8: of hyperscalers is the other fifty five percent. Now, the

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Speaker 8: application use across all of that, all of that data

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Speaker 8: center starts with accelerated computing. Accelerated computing does everything, of course,

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Speaker 8: from well the models the things that we know about,

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Speaker 8: which is generative AI, and that gets most of the attention.

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Speaker 8: But at the core we also do database processing, pre

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Speaker 8: and post processing of data before you use it for

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Speaker 8: generative AI, trans coding, scientific simulations, computer graphics of course,

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Speaker 8: image processing of course. And so there's tons of applications

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Speaker 8: that people use are accelerated computing for, and one of

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Speaker 8: them is generative AI. And so let's see what else

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Speaker 8: can I say?

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Speaker 3: I think that's the.

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Speaker 4: Lever jump in Jensen. Please on sovereign AI. You and

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Speaker 4: I've talked about that before and it was so interesting

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Speaker 4: to hear something behind it that in this fiscal year

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Speaker 4: there will be low double digit I think you said

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Speaker 4: billions of dollars in sovereign AI sales. But to the

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Speaker 4: lay person, what does that mean? It means deals with

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Speaker 4: specific governments, if so, where.

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Speaker 8: It's not necessarily Sometimes it's deals with particular regional service

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Speaker 8: provider that's been funded by the government. And oftentimes that's

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Speaker 8: the case in a case of in the case of Japan,

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Speaker 8: for example, the the Japanese government came out and UH

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Speaker 8: offered UH subsidies of a couple of billion dollars. I

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Speaker 8: think for several different internet companies and telcos to be

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Speaker 8: able to fund their AI infrastructure. UH India has a

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Speaker 8: sovereign AI initiative going and they're building their AI infrastructure. Canada,

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Speaker 8: the UK, France, Italy, missing somebody, Singapore, Malaysia, UH. You know,

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Speaker 8: a large number of countries are subsidizing their regional data

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Speaker 8: centers so that they could become able to build out

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Speaker 8: their AI infrastructure. They recognize that their countri's knowledge, their

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Speaker 8: countries data digital data is also their natural resource, not

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Speaker 8: just the land they're sitting on, not just the air

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Speaker 8: above them. But they realize now that their digital knowledge

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Speaker 8: is part of their natural and national resource, and they

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Speaker 8: are to harvest that and process that and transform it

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Speaker 8: into their national digital intelligence. And so this is what

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Speaker 8: we call sovereign AI. You could imagine almost every single

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Speaker 8: country in the world will eventually recognize this and build

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Speaker 8: out their AI infrastructure.

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Speaker 4: Jenson, you use the word resource, and that makes me

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Speaker 4: think about the energy requirements here. I think on the

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Speaker 4: cool you talk about how the next generation models will

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Speaker 4: have many orders of magnitude greater compute needs. But how

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Speaker 4: will the energy needs increase and what is the advantage

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Speaker 4: you feel in Vidia has in that sense, Well, the.

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Speaker 8: Most important thing that we do is is increase the

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Speaker 8: performance of and increase the performance and efficiency of our

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Speaker 8: next generation. So Blackwell is many times more performance than

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Speaker 8: Hopper at the same level of power used, and so

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Speaker 8: that's energy efficiency, more performance with the same amount of power,

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Speaker 8: or same performance at a lower power.

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Speaker 3: And that's number one.

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Speaker 8: And the second is using luca cooling. We support air

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Speaker 8: cool we support air cooling, we support liqual cooling, but

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Speaker 8: liqual cooling is a lot more energy efficient. And so

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Speaker 8: the combination of all of that, you're going to get

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Speaker 8: a pretty large, pretty large step up.

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Speaker 3: But the important thing to also.

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Speaker 8: Realize is that AI doesn't really care where it goes

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Speaker 8: to school, and so increasingly we're going to see AI

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Speaker 8: be trained somewhere else, have that model come back and

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Speaker 8: be used near the population, or even running on your

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Speaker 8: PC or your phone.

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Speaker 3: And so we're going.

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Speaker 8: To train large models, but the goal is not to

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Speaker 8: run the large models necessarily all the time. You can

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Speaker 8: surely do that for some of the premium services and

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Speaker 8: the very high value AIS, but it's very likely that

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Speaker 8: these large models would then help to train and teach

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Speaker 8: smaller models, and what we'll end up doing is have

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Speaker 8: one large, few large models that are able to train

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Speaker 8: a whole bunch of small models and they run everywhere.

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Speaker 4: Jensen, you explain clearly that demand to build generative AI

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Speaker 4: product on models or even at the GPU level is

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Speaker 4: greater than current supply. In black Vel's case in particular,

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Speaker 4: explain the supply dynamics to me for your products and

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Speaker 4: whether you see an improvement sequentially quarter on quarter or

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Speaker 4: at some point by the end of fiscal year into

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Speaker 4: next year.

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Speaker 8: Well, the fact that we're growing would suggest that our

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Speaker 8: supply is improving and our supply chain is quite large,

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Speaker 8: one of the largest supply chains in the world. We

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Speaker 8: have incredible partners and they're doing a great job supporting

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Speaker 8: us in our growth. As you know, we're one of

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Speaker 8: the fastest growing technology companies in history, and none of

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Speaker 8: that would have been possible without very strong demand but

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Speaker 8: also very strong supply. We're expecting Q three to have

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Speaker 8: more supply than Q two, We're expecting Q four to

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Speaker 8: have more supply than Q three, and we're expecting Q

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Speaker 8: one to have more supply than Q four, And so

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Speaker 8: I think our supply, our supply condition going into next

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Speaker 8: year will be in it will be a large improvement

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Speaker 8: over this last year with respect to demand. Blackwell is

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Speaker 8: just such a leap and there's several things that are happening,

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Speaker 8: you know, just the Foundation model makers themselves. The size

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Speaker 8: of the foundation models are growing from hundreds of billions

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Speaker 8: parameters to trillions of parameters. They're also learning more languages.

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Speaker 8: Instead of just learning human language, they're learning the language

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Speaker 8: of images and sounds and videos, and they're even learning

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Speaker 8: the language of three D graphics. And whenever they are

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Speaker 8: able to learn these languages, they can understand what they see,

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Speaker 8: but they can also generate what they're asked to generate,

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Speaker 8: and so they're learning the language of proteins and chemicals

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Speaker 8: and physics. You know, it could be fluids and it

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Speaker 8: could be particle physics, and so they're learning all kinds

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Speaker 8: of different languages, learn the meaning of what we call modalities,

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Speaker 8: but basically learning languages.

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Speaker 3: And so these models are growing in size.

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Speaker 8: They're learning from more data, and there are more model

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Speaker 8: makers then there was a year ago. And so the

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Speaker 8: number of model makers have grown substantially because of all

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Speaker 8: these different modalities. And so that's just one, just the

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Speaker 8: frontier model. The foundation model makers themselves haven't really grown tremendously.

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Speaker 8: And then the generative AI market has really diversified, you know,

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Speaker 8: beyond the Internet service makers to startups and now enterprises

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Speaker 8: are jumping in and different countries are jumping in, so

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Speaker 8: the demand is really growing.

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Speaker 4: Jensen, I'm sorry to cut you off. I will lose

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Speaker 4: your time soon. You've also diversified. And when I said

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Speaker 4: to our audience you were coming on, I got so

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Speaker 4: many questions. Probably the most common one is what is

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Speaker 4: in Nvidia. We talked about you as a systems vendor,

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Speaker 4: but so many points on in Vidia GPU cloud, and

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Speaker 4: I want to ask, finally, do you have plans to

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Speaker 4: become literally a cloud compute provider.

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Speaker 7: No.

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Speaker 8: Our GPU cloud was designed to be the best version

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Speaker 8: of Nvidia Cloud that's built within each cloud. Nvidio DGX

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Speaker 8: cloud is built inside GCP, inside Azure, inside AWS, inside OCI,

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Speaker 8: and so we build our clouds within THEIRS so that

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Speaker 8: we can implement our best version of our cloud, work

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Speaker 8: with them to make that cloud, that that infrastructure, that

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Speaker 8: AI infrastructure, in video infrastructure, as performance, as great TCO

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Speaker 8: as possible. And so that strategy has worked incredibly well.

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Speaker 8: And of course we are large consumers of it because

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Speaker 8: we create a lot of AI ourselves, because our chips

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Speaker 8: aren't possible to design without AI, our software is not

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Speaker 8: possible to write without AI, and so we use it

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Speaker 8: ourselves tremendous, you know, tremendous amount of it self driving cars,

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Speaker 8: the general robotics work that we're doing, the omniverse work

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Speaker 8: that we're doing. So we're using the DGX cloud for ourselves.

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Speaker 8: We also use it for an AI foundry. We make

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Speaker 8: AI models for companies that we'd like to have expertise

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Speaker 8: in doing so, and so we are AI.

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Speaker 3: We're an AI.

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Speaker 8: We're a foundry for AI like tsmcs a foundry for

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Speaker 8: our chips, and so there are three fundamental reasons why

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Speaker 8: we do it. One is to have the best version

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Speaker 8: of Nvidia inside all the clouds. Two because we're a

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Speaker 8: large consumer to ourselves, and third because we use it

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Speaker 8: for AI foundry for to help every other company.

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Speaker 4: Jensen one, CEO and Video. I want to thank you

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Speaker 4: for your time the extended conversation straight off the earnings call.

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Speaker 4: Thank you in videos earnings, Fiscal is good to see

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Speaker 4: you too. Fiscal second quarter done. I point out that

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Speaker 4: in after hours the stock is down still almost seven percent.

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Speaker 4: There'll be a lot of analysis to be done overnight

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Speaker 4: by the cell side, by the byside, and we'll bring

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Speaker 4: you the best from Bloomberg's reporters and editors from San Francisco.

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Speaker 4: This is Bloomberg Technology