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Speaker 1: From the heart where Innovation, money and power Collie in

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Speaker 1: Silicon Valley, NBN.

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Speaker 2: This is Bloomberg Technology with Caroline Hyde and Ed lud.

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Speaker 3: Love live from New York.

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Speaker 4: This is Bloomberg Technology coming up in video.

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Speaker 3: Says it's new.

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Speaker 4: Chipper on track, but the rush to get the product

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Speaker 4: lineup out the door is.

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Speaker 3: Proving more costly than expected.

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Speaker 4: Plus, Bitcoin rises to new heights as the Trump transition

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Speaker 4: team weighs the creation of a dedicated white house cryptopost

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Speaker 4: and the CEO of Palo Alto Networks joins on the

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Speaker 4: heels of its earnings.

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Speaker 3: We'll discuss how AI can.

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Speaker 4: Help stop the threats of the future, but first we

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Speaker 4: get straight to the world of in video and indeed

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Speaker 4: the points trag that it's having on the Nasdaq is

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Speaker 4: somewhat We're off by eighteen percent, but it's been a

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Speaker 4: volatile day of trade as people.

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Speaker 3: Try to pass.

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Speaker 4: Ultimately, how strong future growth still looks under in video

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Speaker 4: and indeed the supply side headaches that much has been discussed.

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Speaker 4: Let's get straight to within King and look, we'd all

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Speaker 4: been fretting about Blackwell the product lineup, how easily it

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Speaker 4: is to get into customers. Hans did Jensen Wong manage

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Speaker 4: to calm investors' nerves?

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Speaker 5: I mean, obviously to an extent. We haven't seen the

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Speaker 5: massive fall off that we've seen manifest previously with concerns.

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Speaker 5: He talked about, Yeah, we're getting it to market. We're

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Speaker 5: actually going to be probably getting more to market than

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Speaker 5: we had thought we would. At the same time, it's

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Speaker 5: going to cost us.

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Speaker 4: Yeah, let's just weigh in on the profit margins, because

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Speaker 4: you do such a great job at giving the context here. Ultimately,

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Speaker 4: this is a company that gave us ninety four percent

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Speaker 4: increase in revenue in the third courting guided to another

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Speaker 4: seventy percent increase, give or take plus or minus two percent.

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Speaker 4: But the profitability is what just coming down ever so

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Speaker 4: slightly to what seventy three to seventy four percent margin?

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Speaker 5: That's right, I mean, I think that's the right way

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Speaker 5: to look at it. They've set such high expectations, right,

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Speaker 5: they were at seventy five percent, which had narrowed slightly.

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Speaker 5: But seventy five percent is like software mhudgeons, Right, this

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Speaker 5: is a semiconductor company, right, you know, AMD's twenty points

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Speaker 5: south of that. You know, Intel isn't even in the

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Speaker 5: same zip code right now. So yes, they're losing a

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Speaker 5: few points because they're spending more in engineering to get

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Speaker 5: those chips out, get those systems in place quicker.

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Speaker 2: But really this is a very very high bar.

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Speaker 4: And then full production comes what the back end of

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Speaker 4: their fiscal twenty twenty five in. But I suppose they

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Speaker 4: still have to address the issue that. Sure they're getting

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Speaker 4: them out the door, but it's to the same kind

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Speaker 4: of companies. How are they managing to diversify their demand base?

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Speaker 5: Well, I mean that was one of the things that

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Speaker 5: we saw in the release. They're not right. The amount

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Speaker 5: of revenue coming from those big companies, the Microsoft's, the

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Speaker 5: Amazons has actually gone up from forty five percent to

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Speaker 5: fifty percent, So like half of the most important divisi

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Speaker 5: that they have is dependent upon just a few companies.

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Speaker 5: Investors are definitely concerned about that. Why they're concerned about

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Speaker 5: that because these companies are making their own chips, trying

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Speaker 5: in effect to replace in video and everybody else that

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Speaker 5: supplies them.

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Speaker 4: Is there any real competition here, because we've heard it

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Speaker 4: time and time again that as much as AMD tries,

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Speaker 4: as much as Intel tries, really in videos, the any

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Speaker 4: game in time for here.

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Speaker 5: Yeah, I mean that that definitely appears to be the case.

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Speaker 5: I mean that any questions about and demand, any questions

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Speaker 5: about losing market share were just irrelevant, right. I mean

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Speaker 5: they talked about incredible demand, demand that they cannot meet

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Speaker 5: right now. So you know that part of the quote,

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Speaker 5: the story is really in the future, if at all, Ian.

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Speaker 4: King very much in the present when it comes to

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Speaker 4: the market move, We really appreciate it.

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Speaker 3: Thank you.

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Speaker 4: Let's get you more contact and what it means to

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Speaker 4: the rest of the market. Epshkere is with US Senior

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Speaker 4: and a list at Swiss Quote and Ibet. We turned

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Speaker 4: to you for the market implications because we were anticipating

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Speaker 4: as much as a three hundred billion dollar market cap

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Speaker 4: swing up to eight percent move higher or lower. Actually,

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Speaker 4: a lack of holatility must be a slight sign of relief.

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Speaker 6: Well, actually it is because there has been a few

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Speaker 6: red flags in yesterday's report. As A was just talking

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Speaker 6: about the pressure on the productivity levels due to the

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Speaker 6: manufacturing challenges for the black Bell chip is one of them.

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Speaker 6: The fact that the big technology companies make up to

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Speaker 6: fifty percent of Nvidia's revenue is another sticky point. Yet

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Speaker 6: the results have been strong and despite these small red

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Speaker 6: flags that we got yesterday from the report, well, the aftermass,

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Speaker 6: the immediate selloff has been only twenty half percent, and

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Speaker 6: this morning we don't see that the Nvidia shares are

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Speaker 6: being sold at the level that we would expect them

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Speaker 6: to with such a disappointment, or if with the small

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Speaker 6: disappointment from investors, we would think that any miss that.

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Speaker 7: Would be a excel off.

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Speaker 6: Yet we just see that the market is quite resilient.

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Speaker 4: We've got a bit too used to him managing to

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Speaker 4: smash expectations.

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Speaker 3: That he had set and that of the market.

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Speaker 4: And look, you only beat them slightly when it comes

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Speaker 4: to their own internal guidance IPEC. The rest of the market, though,

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Speaker 4: had been anticipating, well, maybe a questioning around valuation. What

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Speaker 4: do you make of the fact that we're still up

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Speaker 4: let's call it two hundred percent over the course of

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Speaker 4: this year. It's an extraordinary run up for one particular company.

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Speaker 3: Can it be sustained?

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Speaker 6: Well, it is absolutely an extraordinary run up, and now

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Speaker 6: we are questioning the valuation levels, especially if.

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Speaker 7: The demand started to solve.

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Speaker 6: This is not the case right now, but at some

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Speaker 6: point we expect that the demand from the big technology

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Speaker 6: companies will start swing, and because this is a big

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Speaker 6: chunk of the company's revenue, that's going to be a headache.

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Speaker 7: There's another thing.

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Speaker 6: Right now, the competition is not a problem because Nvidia

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Speaker 6: has got the most expensive but the most premium chips.

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Speaker 6: They've got the best performance, and the big technology companies

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Speaker 6: are looking for those. But if the demand was to

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Speaker 6: slow for the big technology companies, then Nvidia will have

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Speaker 6: to find other customers. And these other customers may not

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Speaker 6: be looking for the premium solutions. They will be just

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Speaker 6: looking for the solutions to manage their day to day

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Speaker 6: businesses and increase their productivity and increase their costs. But

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Speaker 6: they would also up for maybe more cost efficient and

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Speaker 6: well slightly more affordable chip solutions than the Nvida offers.

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Speaker 6: And I think that that's going to be a major issue.

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Speaker 4: We're going to be talking exactly that later in the programming.

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Speaker 4: For now, when we think about the context of in video,

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Speaker 4: about twenty percent of all the run up in the

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Speaker 4: S and P five hundred is thanks to this one

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Speaker 4: particular name this year, about twenty five percent of all

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Speaker 4: earnings per share increase. How key man risk is in

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Speaker 4: video and how does one protect themselves against that.

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Speaker 6: Well, obviously, the AI rally and especially has been one

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Speaker 6: of the major pillars of the market rally in the

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Speaker 6: US that we have seen over the past two years.

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Speaker 6: So anything any lack of epissites for Nvidia from now

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Speaker 6: on is going to have a negative impact on the

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Speaker 6: market sentiment. Now, we have seen that AI rarely broaden

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Speaker 6: toward the other sectors, other non technology sectors. But it's

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Speaker 6: worth noting that this company has added so much upside

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Speaker 6: pressure to the SMP five hundred, So anything less than

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Speaker 6: any kind of loss of momentum here will obviously be

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Speaker 6: well quite negative for the SMP five hundred, especially knowing

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Speaker 6: that the SMP five hundred today is struggling near its

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Speaker 6: all time high levels.

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Speaker 4: So when you're sat ultimately in Europe and you're thinking

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Speaker 4: about a global perspective, it should people be broadening out

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Speaker 4: when it comes to just magnificent seven, let alone just

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Speaker 4: in video litlone just US bets on technology.

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Speaker 6: Well, when it comes to technology, I really believe that

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Speaker 6: the US is still in a very dominant position, So

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Speaker 6: you wouldn't be going to to Europe for technology solutions.

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Speaker 6: There's one place that we actually like, and that's Japan,

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Speaker 6: especially given that the government there is going to give

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Speaker 6: some more support to their technology industry.

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Speaker 7: So in terms of geographical.

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Speaker 6: Diversification, Japan could be a good solution. There is China

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Speaker 6: as well that we have been looking at, but we

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Speaker 6: are still not positive for China because well, the chipwoar

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Speaker 6: has weighed greatly on sentiment and on the progress of

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Speaker 6: the technology advancement there, So we're not really looking at

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Speaker 6: China right now. So one place that investors could be

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Speaker 6: looking at would be Japan, but other than that, the

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Speaker 6: US is still in a very dominant position. So if

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Speaker 6: you want to invest in tech, well you would just

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Speaker 6: the first place you would like to go is the

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Speaker 6: US because this is where you have massive upside potential.

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Speaker 4: So back the clients that are calling you, are they

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Speaker 4: saying they want to add to Invidia, for example, as

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Speaker 4: a name at these particular levels or do they just

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Speaker 4: keep their exposure where it is well.

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Speaker 6: To be perfectly honest with you, I don't have clients

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Speaker 6: who would like to sell Nvidia, but at the current

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Speaker 6: levels people are more skeptical about entering into.

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Speaker 7: The NVDA shares.

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Speaker 6: What I mostly here is what level would be a

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Speaker 6: good entry level if we start seeing a price pullback

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Speaker 6: from the actual level, So people will still be buying

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Speaker 6: MVDA shares from this point on, but they're still at

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Speaker 6: disvaluations at this point in time, they will be looking

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Speaker 6: for good entry levels.

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Speaker 4: Is there other names, smaller chip rivals that you've thought

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Speaker 4: would be a better entry point.

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Speaker 6: Well, I think that AMD is at a good position

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Speaker 6: right now. Obviously, they do not offer the same potential

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Speaker 6: and d do not offer the same margins than Nvidia does,

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Speaker 6: but it's share price have come down quite significantly to

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Speaker 6: offer an interesting entry point at the current levels. It's

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Speaker 6: also to be said that AMD is about to launch

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Speaker 6: two powerful chips, one for the end of this year

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Speaker 6: and one for next year. And what's interesting with AMD

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Speaker 6: is they may not be as powerful as premium as

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Speaker 6: Nvidia chips, but they're clearly going to be more cost

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Speaker 6: efficient and there are many many sectors and companies out

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Speaker 6: there that will be looking for more cost efficient and

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Speaker 6: more affordable solutions, and AMD could actually help fulfilling that gap.

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Speaker 4: MD currently trading at forty one times future earnings compared

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Speaker 4: to about a fifty times for a video effect Oskodeshka.

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Speaker 3: Thank you so much for your time.

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Speaker 4: Seenior analyst at Swiss Code Now coming up bitcoin ooh,

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Speaker 4: nearing one hundred thousand dollars. The Trump dream is looking

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Speaker 4: towards creating a new crypto role. Well that next is

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Speaker 4: the Bluebeg technology Bitcoin's massive run. Well, let's see the

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Speaker 4: digital asset edging now towards one.

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Speaker 3: Hundred thousand dollars.

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Speaker 4: This as the Trunk team is said to be mulling

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Speaker 4: over creating its first ever crypto role that would oversee

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Speaker 4: cryptocurrency policy making. Marlbrenberg's Kaylee lines chows us Now, so

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Speaker 4: just what is.

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Speaker 8: Being weighed right now? Well, a lot of it, Caroline

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Speaker 8: is still up in the area. This would be the

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Speaker 8: first crypto specific role in the White House, and in

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Speaker 8: many ways this would signal that Donald Trump was serious

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Speaker 8: about the crypto related promises he made on the campaign trail,

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Speaker 8: which of course included making the US the bitcoin capital

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Speaker 8: of the world, firing the current chair of the SEC,

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Speaker 8: Gary Gensler. He also had said he wanted to make

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Speaker 8: the first ever crypto presidential Advisory Council, So in some

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Speaker 8: ways it shouldn't be all too surprising that he is

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Speaker 8: looking at a role like this, But it's not abundantly

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Speaker 8: clear at this time what form this would actually take,

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Speaker 8: if this is going to be an actual White House

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Speaker 8: staff position or just someone who is more of a

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Speaker 8: crypto zar quote unquote if you will. I was speaking

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Speaker 8: to a source on this this morning who said, yes,

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Speaker 8: this is all very up in the air. They are

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Speaker 8: seriously looking at this, but it's just not clear what

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Speaker 8: kind of power would come win this role. If they

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Speaker 8: are going to have actual staff reporting into them, for example,

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Speaker 8: if they will just be advising, or if this really

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Speaker 8: ultimately is going to be about liaising and coordinating.

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Speaker 3: And by coordinating I mean all the.

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Speaker 8: Different parts of government that are involved in overseeing crypto here,

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Speaker 8: so between the White House and Congress, who would actually

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Speaker 8: write digital asset legislation, or the White House and other

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Speaker 8: agencies that have jurisdiction over this, like the CFTC and

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Speaker 8: the SEC. And I would just point you, Caroline to

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Speaker 8: the tweet from Kristin Smith, the CEO of the Blockchain Association,

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Speaker 8: yesterday she posted on x and she said, look, the

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Speaker 8: cryptos are idea quote unquote is a great idea, but

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Speaker 8: ultimately they can only actually do things if there is

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Speaker 8: a strong Secretary of the Treasury and SEC chair to

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Speaker 8: go along with that, and of course, Caroline, we have

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Speaker 8: not gotten picks for either of those roles from the

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Speaker 8: President elect yet, so a little.

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Speaker 4: Of this is signaling, Kaylee. But just remind us on

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Speaker 4: the campaign trail, some of the promises to make the

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Speaker 4: US the crypto capital of the planet, to make mining

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Speaker 4: of a mole focus to the United States, to have

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Speaker 4: even well reserve of bitcoin. Is that necessary to have

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Speaker 4: congressmate moves or can this be done from an executive order.

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Speaker 3: From a White House only led position.

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Speaker 8: Some of it can in that the president obviously has

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Speaker 8: the power of appointment, so he could put in place

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Speaker 8: a more crypto friendly SEC and CFTC chair who would

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Speaker 8: be more lenient with the industry perhaps than we have

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Speaker 8: seen in the Biden administration. But a lot of this

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Speaker 8: will come down to Congress as well. It was actually

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Speaker 8: part of not just Donald Trump's campaign platform, but it

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Speaker 8: was in the Republican Party platform that was adopted at

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Speaker 8: the convention in Milwaukee this summer that they want to

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Speaker 8: end what they call democrats Unamerican crackdown on the industry.

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Speaker 8: So that would signal that this is kind of more

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Speaker 8: of a government wide effort than just what's going to

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Speaker 8: be coming out of the White House in Congress. Will

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Speaker 8: be really important here is they have the law making

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Speaker 8: power in terms of actually delineating jurisdiction between these different

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Speaker 8: agencies that kind of have been jockeying for control over it.

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Speaker 3: And the chairmanship of.

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Speaker 8: The House Financial Services Committee is something we should pay

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Speaker 8: attention to. It's going to be decided in the next

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Speaker 8: few months is one hundred and nineteenth Congress comes in.

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Speaker 8: Chairman currently Patrick mckenry is going to be leaving the

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Speaker 8: House and there's kind of a three way battle going

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Speaker 8: on between Congress and Bill Barr or excuse me, Andy

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Speaker 8: Barr of Kentucky, Bill Hezenga of Michigan, and French Hill

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Speaker 8: of Arkansas, who currently leads the subcommittee specifically on digital assets,

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Speaker 8: and so when where it's chairing, the Financial Services Committee

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Speaker 8: will have a lot of control over the shape of

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Speaker 8: the legislation that ultimately could work its way through Congress.

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Speaker 3: And that's going to be.

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Speaker 8: What a lot of the industry's lobbying time is spent

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Speaker 8: specifically on on.

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Speaker 4: Industry lobbied going into these elections, and it works. I mean,

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Speaker 4: the money that was put to work. Backing crypto friendly

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Speaker 4: congress people is important.

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Speaker 3: Right, absolutely.

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Speaker 8: I mean you look at packs like fair Shape, for example,

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Speaker 8: that deployed tens of millions of dollars in some of

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Speaker 8: those key congressional races, including for example, a successful ousting

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Speaker 8: of the current chair of the Senate Banking Committee, Shared

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Speaker 8: Brown of Ohio. He was ousted not just by any

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Speaker 8: candidate but a crypto entrepreneur himself, and Bernie Marine the

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Speaker 8: incoming Republican Senate designate or Senate elect So that is

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Speaker 8: going to be really interesting to watch. As I mentioned

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Speaker 8: the House Financial Services Committee. The Senate, of course, is

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Speaker 8: going to be where a lot of the uphill climb

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Speaker 8: has been historically when it comes to advancing the interests

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Speaker 8: of the crypto industry. And Tim Scott will now be

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Speaker 8: taking over as the Senate Banking Chair.

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Speaker 3: I would just point out.

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Speaker 8: That the ranking member could very well be Democratic Senate

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Speaker 8: that Elizabeth Warren, who faced a crypto opponent of her own,

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Speaker 8: but one ultimately her reelection race, and she may have

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Speaker 8: a little bit of a role in at least effort

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Speaker 8: in attempt to pull back some of the efforts to

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Speaker 8: advance the industry's interest. But the crypto money that was

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Speaker 8: active in this election cycle.

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Speaker 3: Has no sign that it's going anywhere.

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Speaker 8: Now that they've proven that they actually can make a

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Speaker 8: difference in outcomes, here will We're watching to see how

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Speaker 8: this pack and other interest groups and the others individuals

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Speaker 8: who were contributing to it, think Mark and Dreson for example,

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Speaker 8: or Brian Armstrong, how active they will be in the

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Speaker 8: midterms in twenty twenty six and then ultimately in the

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Speaker 8: next presidential cycle in twenty twenty eight, always.

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Speaker 3: Pushing us forward. Kaylee me, thank you.

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Speaker 4: Now, let's talk about artificial super intelligence.

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Speaker 3: Perhaps it's not far off.

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Speaker 4: Last months off Bak CEO Masayoshi's son and predicted then

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Speaker 4: it would become a reality by twenty thirty five. Well,

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Speaker 4: former Google CEO Eric Schmidt, who is out with a

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Speaker 4: new book on artificial intelligence, says it might be coming

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Speaker 4: even sooner than we think.

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Speaker 3: Take a listen.

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Speaker 9: It's probable that we can build systems that are the

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Speaker 9: technical term is super intelligent, where you have a simple

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Speaker 9: system sarry a single system that is at the nineteh

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Speaker 9: percentile of physics, math, chemistry, and arts and so forth.

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Speaker 7: No human can do that.

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Speaker 9: It looks like these systems will be not only available

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Speaker 9: in the next five years because we already have examples

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Speaker 9: of passing these tests already, but also that they'll be

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Speaker 9: broadly available for all society.

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Speaker 4: A bit more on AI app because happening today in

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Speaker 4: New York the Evident AI Symposium, where some of the

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Speaker 4: most senior leaders of the world's banking and artificial intelligence

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Speaker 4: areas came together. They're discussing the AI adoption in the

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Speaker 4: banking sector based on the latest data from an AI index.

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Speaker 4: It's a global standard benchmark for aimaturity in banking. I

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Speaker 4: sat down with JP Morgan Chase chief Data and Analytics

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Speaker 4: Officer Teresa heitsen Rada and asked if look, the efficiencies

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Speaker 4: from AI are the number one return on investment.

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Speaker 3: Take listen.

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Speaker 1: The magic question is what's what's the return?

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Speaker 10: Right?

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Speaker 1: And the answer is very, very difficult to quantify, And

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Speaker 1: I think at this stage of the game, the return

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Speaker 1: is a little bit of efficiency for a lot of

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Speaker 1: people every day, but not something that you can actually

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Speaker 1: hang a number on. So I think that the next

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Speaker 1: generation of where we see this heading is, rather than

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Speaker 1: just using it to help you write your email or

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Speaker 1: summarize a document, is when we start to plug the

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Speaker 1: technology into knowledge bases, when we start to actually look

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Speaker 1: at the workflow of a particular person or group and

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Speaker 1: linked together the steps in that workflow with the tools

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Speaker 1: that they need to be able to do their So

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Speaker 1: you basically go from the five minutes of efficiency to

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Speaker 1: the five hours of efficiency. That's a road like that's

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Speaker 1: going to take some time to actually get there, but

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Speaker 1: I think that's where we see it evolving. So there

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Speaker 1: are near term benefits, but I think the most compelling

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Speaker 1: is just putting the technology in people's hands and letting

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Speaker 1: them start to experiment and understand how it actually works

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Speaker 1: and how it can be used.

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Speaker 4: JP Morgan Chase Chief Data and Analytics Officer Teresa heisen Weather.

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Speaker 4: There I mean, while coming up, it's official the DOJ

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Speaker 4: does want Google to divest its Chrome web browser.

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Speaker 3: All details next.

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Speaker 11: This is bring big technology.

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Speaker 4: The Justice Department and a group of states do indeed

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Speaker 4: suggest that Google may have to divest its popular Chrome

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Speaker 4: web browser and imposed limits on its Android operating system

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Speaker 4: in order to address anti trust concerns, all confirming earlier

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Speaker 4: reports by Bloomberg.

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Speaker 3: For Now, Bloombergsley and Nyland.

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Speaker 4: Joins US and it was your reporting that ultimately has

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Speaker 4: been confirmed. Just tell us through how realistic still this

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Speaker 4: divestment would be.

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Speaker 12: We'll see probably in early spring. The Justice Department has

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Speaker 12: said that they want Google to be forced to sell

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Speaker 12: off Chrome.

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Speaker 3: It's not clear who would buy it.

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Speaker 12: You know, the top two other browser makers, Microsoft and Apple,

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Speaker 12: who have the cash, the Justice Apartment probably wouldn't want

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Speaker 12: to let them, so the next you know, most obvious

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Speaker 12: candidates are probably people like open ai or Perplexity or

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Speaker 12: some of the other AI startups that would really love

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Speaker 12: to have this kind of an avenue to customers for

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Speaker 12: their AI search goals. Other things that the Justice Department

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Speaker 12: put in there is that they want Google to have

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Speaker 12: to license a lot of its data. That's probably a

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Speaker 12: more realistic thing that would have to happen, and and

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Speaker 12: it would definitely give a boost to other search engines

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Speaker 12: and possibly AI startups who would now.

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Speaker 3: Not have to scrape the web themselves.

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Speaker 4: Alphabet once again one of the key drags on the

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Speaker 4: NASTAC today. Investors worried about this alphabet itself, worried about

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Speaker 4: it coming out, and still once again trying to say

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Speaker 4: that this is a radical turn of events if imposed,

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Speaker 4: how complex is it to actually see a company sell

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Speaker 4: off such businesses? And how much do we have to

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Speaker 4: now wait for the legal maneuvering to play out? They

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Speaker 4: now have what a couple of months to respond.

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Speaker 12: They have until next month to respond, and then the

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Speaker 12: Trump administration in March gets to have another reply. So

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Speaker 12: if the Trump administration wants to change this proposal a

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Speaker 12: little bit, they are allowed to. We probably won't have

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Speaker 12: a decision from the court until next summer.

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Speaker 7: The judge has promised.

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Speaker 12: That he will rule by August twenty twenty five, but

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Speaker 12: then Google says it will appeal. That'll take another year,

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Speaker 12: So at the earliest we're looking at them having to

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Speaker 12: do this in probably twenty twenty six. But I think

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Speaker 12: people are recognizing that Chrome is actually pretty important to

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Speaker 12: Google's business. It is, you know, an avenue through which

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Speaker 12: a lot of people access it's a search engine, but

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Speaker 12: it also is how Google collects a lot of the

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Speaker 12: data that underlies its advertising. You know, whenever a user

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Speaker 12: logs in, it keeps a lot of information about the

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Speaker 12: browser history.

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Speaker 3: You know where.

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Speaker 12: People are going online, what they're looking at, and then

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Speaker 12: Google uses that data to help sell advertising. So this

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Speaker 12: could have knock on effects to how some of Google's

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Speaker 12: core businesses.

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Speaker 3: Me and Eilan appreciate the update. Thank you.

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Speaker 4: Now it's time for talking tech, and first up, we're

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Speaker 4: talking PDD chares as you see plunging after warning that

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Speaker 4: it's profitability will trend downwards over time because of intensifying

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Speaker 4: competition in its home market. Of course, of China, PDD,

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Speaker 4: which competes with Ali Barba and its team, is struggling

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Speaker 4: to catch up with unspecified rivals because of the lack

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Speaker 4: of expertise, speaking of which Ali Baba has a pointed

440
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Speaker 4: better and executive Jeng Fan to oversee its entire online

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Speaker 4: e commerce operation. Now Jiang will lead a newly created

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Speaker 4: department that consolidates Ali Baba's online shopping assets, making him

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Speaker 4: the most powerful person in the company after the CEO

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Speaker 4: and chairman and Baido. It recorded its biggest revenue drop

445
00:22:16,480 --> 00:22:19,120
Speaker 4: more than two years after China's economic malaise. Under mind

446
00:22:19,119 --> 00:22:21,160
Speaker 4: it's push into general to AI. Now the company's ernie

447
00:22:21,200 --> 00:22:25,200
Speaker 4: Bot has fallen behind Bite Dances d'albao in China usage,

448
00:22:25,320 --> 00:22:28,600
Speaker 4: while its core business is losing ground. Two newer social

449
00:22:28,640 --> 00:22:30,480
Speaker 4: platforms off by seven percents.

450
00:22:30,480 --> 00:22:31,520
Speaker 3: You'll see now coming up.

451
00:22:31,800 --> 00:22:35,520
Speaker 4: We're going back to the US earning story Nvidia the

452
00:22:35,600 --> 00:22:37,800
Speaker 4: impact on the chip sector at large. We're currently off

453
00:22:37,840 --> 00:22:40,439
Speaker 4: by one point three percent, one point two percent for

454
00:22:40,560 --> 00:22:43,359
Speaker 4: the world's most valuable publicly traded company. This is the

455
00:22:43,400 --> 00:22:57,800
Speaker 4: room Mate Technology. Welcome back to Blue meag Technology and

456
00:22:57,880 --> 00:23:00,080
Speaker 4: Karin Hider, New York. Let's get back to it. In

457
00:23:00,160 --> 00:23:02,879
Speaker 4: Video's earnings. Here is the reaction from just some of

458
00:23:02,920 --> 00:23:04,120
Speaker 4: our guest somebody Meg Television.

459
00:23:05,480 --> 00:23:08,240
Speaker 3: I have a feeling that we've reached peak Navidio. How

460
00:23:08,280 --> 00:23:09,640
Speaker 3: long can you stay perfect? Great?

461
00:23:09,720 --> 00:23:12,080
Speaker 13: So I think that, yeah, I mean, I do think

462
00:23:12,080 --> 00:23:15,680
Speaker 13: that a lot of investors are expecting a little bit

463
00:23:15,720 --> 00:23:16,160
Speaker 13: too much.

464
00:23:16,200 --> 00:23:17,920
Speaker 10: There's a little bit of this kind of fatigue.

465
00:23:18,240 --> 00:23:21,800
Speaker 7: But make a mistake. This was a remarkably good quarter.

466
00:23:22,080 --> 00:23:24,880
Speaker 3: When they're putting up in terms of Hopper, in terms

467
00:23:24,880 --> 00:23:28,800
Speaker 3: of overall growth, it's exact what the what the bulls want.

468
00:23:29,080 --> 00:23:32,719
Speaker 13: Never underestimate Wall Street's ability to miss the bigger picture.

469
00:23:32,960 --> 00:23:35,720
Speaker 13: This company has double the operating margin of any other

470
00:23:35,800 --> 00:23:40,119
Speaker 13: Meg seven outside of Microsoft. To nitpick in Video on

471
00:23:40,200 --> 00:23:42,520
Speaker 13: those margins is truly missing the big picture.

472
00:23:43,440 --> 00:23:44,680
Speaker 3: Let's nitpick some more.

473
00:23:44,960 --> 00:23:48,560
Speaker 4: Daniel Pelling, portfolio manager and senior technology research analyst at

474
00:23:48,560 --> 00:23:52,200
Speaker 4: Sans Capital joins us now and Daniel, Yeah, we're quibbling

475
00:23:52,320 --> 00:23:54,639
Speaker 4: on a margin of seventy five percent going to somewhere

476
00:23:54,640 --> 00:23:58,439
Speaker 4: to seventy three to seventy four percent, so much more

477
00:23:58,520 --> 00:23:59,880
Speaker 4: profitable than rivals.

478
00:24:00,480 --> 00:24:03,959
Speaker 14: Yes, Hi, good morning. Yeah, so well, I think actually

479
00:24:03,960 --> 00:24:08,000
Speaker 14: the gross Martin stuff is largely behind us. They've guided

480
00:24:08,000 --> 00:24:10,800
Speaker 14: for sort of seventy five percent exiting the year, And

481
00:24:10,880 --> 00:24:13,119
Speaker 14: I know this was a concern a while back, but

482
00:24:13,160 --> 00:24:16,600
Speaker 14: I would actually argue it's somewhat frankly irrelevant. I think

483
00:24:16,640 --> 00:24:20,440
Speaker 14: what was much more important was two things. First of all,

484
00:24:20,520 --> 00:24:23,320
Speaker 14: the company noted that the scaling laws are well and

485
00:24:23,400 --> 00:24:27,000
Speaker 14: alive and that we can both scale training and inference

486
00:24:27,080 --> 00:24:30,000
Speaker 14: going forward. So this entire discussion about how we're hitting

487
00:24:30,000 --> 00:24:32,159
Speaker 14: a wall is sort of probably off, and there's just

488
00:24:32,280 --> 00:24:35,359
Speaker 14: many walls and they're far away. And then the second

489
00:24:35,440 --> 00:24:37,679
Speaker 14: really important thing I thought that they mentioned was is

490
00:24:37,680 --> 00:24:40,399
Speaker 14: to say that data centers are becoming a little bit

491
00:24:40,480 --> 00:24:44,640
Speaker 14: like smartphones, i e. They are electricity constrained. And what

492
00:24:44,680 --> 00:24:46,920
Speaker 14: that means is just amazing, right, because that means every

493
00:24:47,000 --> 00:24:49,880
Speaker 14: year the hyperscalers have to buy a new chip because

494
00:24:49,920 --> 00:24:52,600
Speaker 14: the new chip is better in terms of electricity usage,

495
00:24:53,119 --> 00:24:55,120
Speaker 14: and we've never had that before in the data center.

496
00:24:55,200 --> 00:24:57,240
Speaker 14: So those two things I think are very positive for

497
00:24:57,320 --> 00:24:59,280
Speaker 14: the medium and long term of the company.

498
00:24:59,359 --> 00:25:01,359
Speaker 4: I want to go about to the scaling laws, Daniel,

499
00:25:01,440 --> 00:25:04,359
Speaker 4: because yes, we've had Jensen Wang actually back on the

500
00:25:04,359 --> 00:25:07,640
Speaker 4: conference trail just earlier this week talking about how compute

501
00:25:07,640 --> 00:25:10,159
Speaker 4: power is going to continue to extend, and indeed the

502
00:25:10,200 --> 00:25:13,159
Speaker 4: more data, the more compute you have, the more sophisticated.

503
00:25:12,680 --> 00:25:14,080
Speaker 3: The larger language models will become.

504
00:25:14,119 --> 00:25:16,720
Speaker 4: But then you listen to the reporting out of Bloomberg

505
00:25:16,760 --> 00:25:20,320
Speaker 4: around open ai and the latest iteration of large language models.

506
00:25:20,359 --> 00:25:23,040
Speaker 4: Just stuttering a little, Why are you so confident that

507
00:25:23,119 --> 00:25:24,040
Speaker 4: scaling laws.

508
00:25:23,800 --> 00:25:24,600
Speaker 3: Aresketden in place?

509
00:25:25,680 --> 00:25:27,760
Speaker 14: Yes, So, I think the key thing to note here

510
00:25:28,280 --> 00:25:30,919
Speaker 14: is that all that information that we have is based

511
00:25:30,960 --> 00:25:35,280
Speaker 14: off of smaller clusters. So now we for the first

512
00:25:35,320 --> 00:25:38,400
Speaker 14: time have a cluster that's something like one hundred or

513
00:25:37,520 --> 00:25:41,760
Speaker 14: two hundred thousand GPUs that is owned by Elon Musk

514
00:25:42,160 --> 00:25:43,879
Speaker 14: and we'll see what comes out of that in the

515
00:25:43,880 --> 00:25:47,240
Speaker 14: next twelve months. And secondly, and more importantly, maybe the

516
00:25:47,320 --> 00:25:51,879
Speaker 14: black clusters start at one hundred thousand GPUs. Hopper started

517
00:25:51,920 --> 00:25:54,480
Speaker 14: at ten thousand GPUs, so we'll have to wait the

518
00:25:54,480 --> 00:25:56,840
Speaker 14: next twelve months. What can we train with these much

519
00:25:56,840 --> 00:26:00,760
Speaker 14: better chips and much larger clusters and whatever out of that?

520
00:26:00,760 --> 00:26:02,879
Speaker 14: That is the crux. So I think most of the

521
00:26:02,880 --> 00:26:05,359
Speaker 14: stuff that's in these articles is kind of backward looking

522
00:26:05,400 --> 00:26:07,160
Speaker 14: because we just didn't have the chips nor these big

523
00:26:07,200 --> 00:26:08,200
Speaker 14: clusters yet.

524
00:26:08,560 --> 00:26:11,800
Speaker 4: That has been reporting about the latest valuation of Xai

525
00:26:11,960 --> 00:26:14,280
Speaker 4: and the fact that they're raising money simply to buy

526
00:26:14,760 --> 00:26:18,840
Speaker 4: more GPUs, But there is that worry about density of

527
00:26:18,920 --> 00:26:21,560
Speaker 4: customer for n video. Are we going to see a

528
00:26:21,560 --> 00:26:22,200
Speaker 4: broadening out?

529
00:26:23,119 --> 00:26:25,280
Speaker 14: Yeah, So I think the big question here is like this,

530
00:26:25,720 --> 00:26:28,640
Speaker 14: and I think you're right. So the big customers are hyperscalers,

531
00:26:28,720 --> 00:26:31,680
Speaker 14: venture finers, startups, and actually healthcare companies are using training

532
00:26:31,720 --> 00:26:34,520
Speaker 14: a lot too. My sense is that the most important

533
00:26:34,520 --> 00:26:36,080
Speaker 14: thing to look at here is for the next year

534
00:26:36,160 --> 00:26:39,760
Speaker 14: is agentic AI and this idea of that we're spending

535
00:26:39,840 --> 00:26:44,520
Speaker 14: much more compute on inference and letting inference think. And

536
00:26:44,600 --> 00:26:47,199
Speaker 14: I think it's starting just now, and if we can

537
00:26:47,240 --> 00:26:49,800
Speaker 14: see this, then companies can really start using this via

538
00:26:49,880 --> 00:26:53,320
Speaker 14: using service now, Workday, Microsoft, et cetera, et cetera. So

539
00:26:53,359 --> 00:26:55,040
Speaker 14: I think that might be a big tipping point and

540
00:26:55,080 --> 00:26:57,480
Speaker 14: we're literally at inning one of that. And just to

541
00:26:57,480 --> 00:27:00,239
Speaker 14: be clear, agentic AI is this sort of THISI they

542
00:27:00,240 --> 00:27:04,760
Speaker 14: can do things autonomously within an enterprise and help with workflows.

543
00:27:05,240 --> 00:27:07,159
Speaker 4: We've had a lot about agents from the likes you

544
00:27:07,200 --> 00:27:09,840
Speaker 4: mentioned service now we've also heard it from Salesforce.

545
00:27:09,400 --> 00:27:10,080
Speaker 3: And the light Daniel.

546
00:27:10,160 --> 00:27:13,560
Speaker 4: So when we look at your funds managed what the

547
00:27:13,680 --> 00:27:16,800
Speaker 4: top holding is in Nvidia, does that start to broaden

548
00:27:16,840 --> 00:27:19,720
Speaker 4: out for you or do you mean comfortable with your

549
00:27:19,760 --> 00:27:22,399
Speaker 4: alignment on in video even though it's at what the

550
00:27:22,440 --> 00:27:26,520
Speaker 4: most valuable market capitalization in the entire world.

551
00:27:27,600 --> 00:27:28,199
Speaker 7: Two things.

552
00:27:28,920 --> 00:27:32,000
Speaker 14: One, so I think there's a good scenario to be

553
00:27:32,040 --> 00:27:35,400
Speaker 14: made that Nvidia becomes the first ten trillion dollar market company.

554
00:27:36,040 --> 00:27:37,679
Speaker 14: And I'll tell you why. I mean, even if you

555
00:27:37,760 --> 00:27:41,040
Speaker 14: just look at the top four hyperscalers, they're estimated to

556
00:27:41,320 --> 00:27:43,880
Speaker 14: generate about three hundred billion dollars of free cash flow

557
00:27:44,400 --> 00:27:46,600
Speaker 14: next year alone. And what I'm trying to say is

558
00:27:46,640 --> 00:27:48,760
Speaker 14: there's a lot of dollars left to go to Nvidia

559
00:27:49,280 --> 00:27:50,560
Speaker 14: if the scaling.

560
00:27:50,200 --> 00:27:51,280
Speaker 7: Laws hold true.

561
00:27:51,640 --> 00:27:54,080
Speaker 14: So that's one, we're very bullish on video or continue

562
00:27:54,119 --> 00:27:56,439
Speaker 14: to be positive on video. But two, the trends are

563
00:27:56,480 --> 00:27:59,320
Speaker 14: really proliferating into other spaces, and maybe I'll make two

564
00:27:59,320 --> 00:28:02,119
Speaker 14: comments there. First, First of all, the software company Service

565
00:28:02,200 --> 00:28:05,120
Speaker 14: now Microsoft et Centa, they will benefit. But there's also

566
00:28:05,160 --> 00:28:08,480
Speaker 14: other companies you know that have significant competitive advantages and

567
00:28:08,520 --> 00:28:10,760
Speaker 14: it can really take AI and monetize it right. And

568
00:28:10,800 --> 00:28:13,960
Speaker 14: one great example might be a company called Axon that

569
00:28:14,359 --> 00:28:17,399
Speaker 14: you know, manufacturers tasers and cameras for the police force.

570
00:28:17,720 --> 00:28:21,639
Speaker 14: They've added an ability to transcribe you know, police when

571
00:28:21,680 --> 00:28:25,560
Speaker 14: they're engaged with people, and that says forty percent of

572
00:28:25,600 --> 00:28:27,960
Speaker 14: time for that police officer. That's a huge improvement, and

573
00:28:28,000 --> 00:28:29,800
Speaker 14: the stock has started to price them in as an

574
00:28:29,840 --> 00:28:31,520
Speaker 14: AI winner, and I think you'll see more of that

575
00:28:31,560 --> 00:28:32,240
Speaker 14: over time too.

576
00:28:32,800 --> 00:28:35,000
Speaker 4: Another company for us to focus in on for our

577
00:28:35,040 --> 00:28:37,280
Speaker 4: audience standing pelling. So great to have time with the

578
00:28:37,359 --> 00:28:41,600
Speaker 4: senior technology research analyst, Sam's Capital. How interesting that he

579
00:28:41,720 --> 00:28:44,479
Speaker 4: kept referencing inference. We're going to have a conversation on

580
00:28:44,520 --> 00:28:46,520
Speaker 4: that now because we have a perspective and someone in

581
00:28:46,520 --> 00:28:49,120
Speaker 4: the field. Rodrigo leac CEO of sa Manova is an

582
00:28:49,120 --> 00:28:52,560
Speaker 4: AI infrastructure company that delivers advanced AI solutions for enterprises.

583
00:28:52,600 --> 00:28:56,360
Speaker 4: You're kind of an Nvidia competitor upstart.

584
00:28:56,120 --> 00:28:57,960
Speaker 3: And I just want to articulate for our audience what

585
00:28:58,000 --> 00:29:00,000
Speaker 3: it is that you do. Yeah, no, thanks for having us.

586
00:29:00,080 --> 00:29:03,080
Speaker 15: So some of the is started as a startup coming

587
00:29:03,080 --> 00:29:07,600
Speaker 15: out of Stanford University and palto really focus on efficient computing.

588
00:29:07,640 --> 00:29:10,000
Speaker 15: And so as we're seeing in video scale and you

589
00:29:10,000 --> 00:29:13,000
Speaker 15: see all the all the great numbers that that people

590
00:29:13,000 --> 00:29:15,320
Speaker 15: are talking about with video or we're now seeing, is

591
00:29:15,400 --> 00:29:17,479
Speaker 15: what happens when the world wants to go into production

592
00:29:17,840 --> 00:29:19,880
Speaker 15: and going to scale. And one of the things that Franklin,

593
00:29:19,920 --> 00:29:22,560
Speaker 15: we're not talking enough about is the fact that power

594
00:29:22,600 --> 00:29:24,440
Speaker 15: and energy is going to be a big bottleneck, and

595
00:29:24,480 --> 00:29:26,640
Speaker 15: you know, people are talking about building nuclear power plants

596
00:29:26,640 --> 00:29:29,640
Speaker 15: and things like that. Somanova Te took the focus of

597
00:29:29,760 --> 00:29:34,520
Speaker 15: how do we actually take AI at scale power efficiency

598
00:29:34,560 --> 00:29:37,080
Speaker 15: and drive it down so that you can actually scale

599
00:29:37,280 --> 00:29:40,240
Speaker 15: without running into these limits that we start seeing with

600
00:29:40,600 --> 00:29:44,040
Speaker 15: both companies and countries running out of power and figuring

601
00:29:44,040 --> 00:29:48,440
Speaker 15: out how to actually provide enough energy to supply these GPUs.

602
00:29:49,080 --> 00:29:52,320
Speaker 4: So your view is you can't like there's going to

603
00:29:52,320 --> 00:29:56,360
Speaker 4: be a certain limitation on the amount of energy efficiency

604
00:29:56,400 --> 00:29:58,480
Speaker 4: that a company like and Video is ever going to hit.

605
00:29:58,520 --> 00:30:00,800
Speaker 4: With the GPU whereas you of a lot.

606
00:30:01,280 --> 00:30:04,120
Speaker 15: Well, so today, for example, you know, a single rack

607
00:30:04,160 --> 00:30:07,000
Speaker 15: of Nvidia is north of one hundred kilowatts, and just

608
00:30:07,040 --> 00:30:10,480
Speaker 15: to give your audience a sense, that's one hundred, it's

609
00:30:10,720 --> 00:30:13,560
Speaker 15: one hundred homes that it powers. And so if you

610
00:30:13,560 --> 00:30:15,920
Speaker 15: think about someone of technology, we're able to drive ten

611
00:30:16,000 --> 00:30:19,960
Speaker 15: next to performance at one tenth the power now because

612
00:30:19,960 --> 00:30:22,280
Speaker 15: of a completely different architecture. As you know with n video,

613
00:30:22,280 --> 00:30:25,040
Speaker 15: it's an architecture that's been around for several decades and

614
00:30:25,080 --> 00:30:28,760
Speaker 15: so we're trying to evolve this graphics GPU architecture into

615
00:30:28,840 --> 00:30:32,680
Speaker 15: AI enabled now for inferencing, there's so many new techniques

616
00:30:32,680 --> 00:30:35,360
Speaker 15: that you can actually apply because you don't need to

617
00:30:35,400 --> 00:30:38,280
Speaker 15: do all the heavy computing that training requires, and so

618
00:30:38,320 --> 00:30:41,520
Speaker 15: you're able to drive a much much lower footprint in

619
00:30:41,560 --> 00:30:44,240
Speaker 15: terms of power, in terms of costs, in terms of space,

620
00:30:44,520 --> 00:30:48,120
Speaker 15: which ultimately is required when you're actually going to scale.

621
00:30:48,320 --> 00:30:50,240
Speaker 7: And my belief is AI.

622
00:30:50,000 --> 00:30:52,520
Speaker 15: Inferencing is going to be ten times more of a

623
00:30:52,520 --> 00:30:54,720
Speaker 15: footprint than training, and so I think we're going to

624
00:30:54,800 --> 00:30:56,840
Speaker 15: have to think about how to be energy efficient, how

625
00:30:56,880 --> 00:30:59,280
Speaker 15: to be cost efficient before it gets to the scale

626
00:30:59,280 --> 00:31:00,160
Speaker 15: that everybody get.

627
00:31:00,160 --> 00:31:00,560
Speaker 2: To have it.

628
00:31:01,040 --> 00:31:03,520
Speaker 4: Can you for our audience that is not only an

629
00:31:03,560 --> 00:31:08,160
Speaker 4: investing audience, but really a technologically savvy audience. Describe to

630
00:31:08,280 --> 00:31:11,520
Speaker 4: us what it is that's different that enables this higher

631
00:31:12,000 --> 00:31:14,320
Speaker 4: energy efficiency within your particular.

632
00:31:13,960 --> 00:31:15,840
Speaker 3: Type of technology, because it's not GPU at all.

633
00:31:15,880 --> 00:31:17,920
Speaker 15: What is it, right, It's not graphics and so it's

634
00:31:17,960 --> 00:31:20,680
Speaker 15: something that was inventing researchers stand for University. It's called

635
00:31:20,760 --> 00:31:23,800
Speaker 15: data flow architecture, and what it does it allows you

636
00:31:23,880 --> 00:31:26,640
Speaker 15: to actually map the hardware in the way that a

637
00:31:26,760 --> 00:31:29,560
Speaker 15: neural net wants to operate. And so with n video today,

638
00:31:29,560 --> 00:31:31,960
Speaker 15: you're having to break up these neural nets into these

639
00:31:32,000 --> 00:31:34,880
Speaker 15: cores that exists inside the graphics chip, which turns out

640
00:31:34,880 --> 00:31:36,600
Speaker 15: to be a lot of work, a lot of energy,

641
00:31:36,640 --> 00:31:39,120
Speaker 15: a lot of power salmon over with the data flow

642
00:31:39,160 --> 00:31:42,000
Speaker 15: architecture allows you to avoid all of that extra power

643
00:31:42,040 --> 00:31:44,360
Speaker 15: that's be incurred when you actually break up these neural

644
00:31:44,440 --> 00:31:46,360
Speaker 15: nets and let it flow through the machine, and so

645
00:31:46,400 --> 00:31:51,200
Speaker 15: you're getting these order magnitude improvements in power efficiency, which

646
00:31:51,280 --> 00:31:53,640
Speaker 15: ultimately allows you to be able to deploy these in

647
00:31:53,800 --> 00:31:56,400
Speaker 15: existing data centers, which, by the way, is one of

648
00:31:56,400 --> 00:31:58,360
Speaker 15: the things that I think is not talked about. When

649
00:31:58,360 --> 00:32:01,560
Speaker 15: we actually acquire GPUs because you also have to upgrade

650
00:32:01,560 --> 00:32:04,560
Speaker 15: the data centers where for our technology going into existing

651
00:32:04,640 --> 00:32:09,560
Speaker 15: data centers with existing power grids, with existing cooling systems,

652
00:32:09,840 --> 00:32:12,320
Speaker 15: it becomes one of those things that we allows us

653
00:32:12,320 --> 00:32:14,960
Speaker 15: to generate value for enterprises really really quickly.

654
00:32:15,080 --> 00:32:17,200
Speaker 3: What enterprises you already locked in with?

655
00:32:17,480 --> 00:32:20,120
Speaker 4: How are you managing to sell and this narrative into

656
00:32:20,480 --> 00:32:21,920
Speaker 4: corporates and federal government?

657
00:32:22,160 --> 00:32:25,400
Speaker 15: Yeah, we're yeah, we're as a startup again and videos

658
00:32:25,440 --> 00:32:27,720
Speaker 15: at the top of the tier. We're a startup that's

659
00:32:28,000 --> 00:32:32,000
Speaker 15: you know, coming in and then providing disruptive technology. We're

660
00:32:32,040 --> 00:32:34,520
Speaker 15: the most deployed in the US government today as a startup.

661
00:32:34,640 --> 00:32:36,480
Speaker 3: You know, we're going to go into national abs.

662
00:32:36,200 --> 00:32:37,160
Speaker 7: And places like that.

663
00:32:37,240 --> 00:32:41,040
Speaker 15: And and today we power also Saudi Aramco's Metabrane, you know,

664
00:32:41,120 --> 00:32:44,000
Speaker 15: so they're internal GPT. This is a private GPT with

665
00:32:44,200 --> 00:32:47,520
Speaker 15: data trained into it and now operating air gap within

666
00:32:47,600 --> 00:32:50,320
Speaker 15: the company. And so companies that worried about their data

667
00:32:50,360 --> 00:32:54,040
Speaker 15: their secured their privacy can now train these models on

668
00:32:54,080 --> 00:32:57,800
Speaker 15: assemblent of platform completely securely and then deploy within their

669
00:32:57,840 --> 00:32:59,800
Speaker 15: own infrastructure because you don't have to expose the.

670
00:32:59,760 --> 00:33:01,719
Speaker 3: Data outside your firewalls.

671
00:33:02,720 --> 00:33:07,200
Speaker 4: There are other startups in this space, or indeed trying

672
00:33:07,240 --> 00:33:11,080
Speaker 4: to offer more energy efficient options and pay for example

673
00:33:11,120 --> 00:33:13,840
Speaker 4: also back by Oracle. You yourself used to work to Oracle.

674
00:33:14,360 --> 00:33:16,840
Speaker 4: I'm interested as to what your next step is to

675
00:33:16,880 --> 00:33:17,880
Speaker 4: really gain market share.

676
00:33:17,960 --> 00:33:18,920
Speaker 3: Is it about raising funds?

677
00:33:18,920 --> 00:33:20,800
Speaker 4: I know you've got big VC backing for example.

678
00:33:21,360 --> 00:33:22,040
Speaker 7: Yeah, I think you know.

679
00:33:22,080 --> 00:33:23,080
Speaker 3: It's really good point.

680
00:33:23,400 --> 00:33:26,720
Speaker 15: My belief when we started the company back seven years.

681
00:33:26,560 --> 00:33:27,800
Speaker 3: Ago is about access.

682
00:33:27,960 --> 00:33:30,280
Speaker 15: When the marketized AI, you got to make it available

683
00:33:30,320 --> 00:33:35,320
Speaker 15: to everybody. As your previous guests just said, is today

684
00:33:35,480 --> 00:33:37,960
Speaker 15: in video is really in large scale only to a

685
00:33:38,000 --> 00:33:40,520
Speaker 15: few customers. What we want to do by collapsing the

686
00:33:40,600 --> 00:33:43,480
Speaker 15: power is to be able to make it available to everybody,

687
00:33:43,520 --> 00:33:46,760
Speaker 15: to make it easily available, and that means lower cost,

688
00:33:47,640 --> 00:33:51,040
Speaker 15: much lower power and then able to deploy in very

689
00:33:51,120 --> 00:33:54,640
Speaker 15: tight spaces and not regarding needing and giggle what data center.

690
00:33:54,760 --> 00:33:57,880
Speaker 15: So some of that we focus very much on completely

691
00:33:58,000 --> 00:34:01,840
Speaker 15: restructuring the power, the power requirements of the AI infrastructure

692
00:34:02,080 --> 00:34:05,680
Speaker 15: so they can deploy racks at performance that much higher

693
00:34:05,680 --> 00:34:09,160
Speaker 15: than in VideA, not less than VideA, but much higher

694
00:34:09,160 --> 00:34:10,760
Speaker 15: than nvidea and much lower power.

695
00:34:11,320 --> 00:34:15,279
Speaker 4: Samonova Rodrigo Lean CEO talking about some of the alternatives

696
00:34:15,320 --> 00:34:17,720
Speaker 4: out there. John and video, We thank you for coming on. Meanwhile,

697
00:34:17,760 --> 00:34:21,000
Speaker 4: coming up Palo Alto Networks earning. They are looking at

698
00:34:21,040 --> 00:34:24,720
Speaker 4: the first quarter that is proving itself with this focus

699
00:34:24,760 --> 00:34:27,520
Speaker 4: on platformization. We'll dig into that word in a moment.

700
00:34:27,640 --> 00:34:45,800
Speaker 4: CEO Niqushaurra joins us. This is Blue Bag Technology, cybersecurity company,

701
00:34:45,800 --> 00:34:49,799
Speaker 4: Palo Alto Networks, releasing earnings after the market Wednesday. And look,

702
00:34:49,880 --> 00:34:53,600
Speaker 4: we're seeing revenue climbing fourteen percent, earnings climbing thirteen percent.

703
00:34:53,600 --> 00:34:57,320
Speaker 4: We're seeing a focus on platformization and also a stock split.

704
00:34:57,400 --> 00:34:59,640
Speaker 4: We're currently up one and a quarter percent. But let's

705
00:34:59,640 --> 00:35:02,200
Speaker 4: get to the details with the CEO Nikosh Aurora of

706
00:35:02,320 --> 00:35:05,240
Speaker 4: Palo Alto Networks without some volatile trading for the market

707
00:35:05,280 --> 00:35:07,840
Speaker 4: now pushing you higher. And I'm just interested on the

708
00:35:07,920 --> 00:35:09,480
Speaker 4: nu once you are trying to tell.

709
00:35:09,280 --> 00:35:10,600
Speaker 3: A different story to your investors.

710
00:35:10,640 --> 00:35:13,680
Speaker 4: You're talking about platformization, you're talking about bundling. But still

711
00:35:13,719 --> 00:35:16,279
Speaker 4: they want to turn to your billings number. How do

712
00:35:16,360 --> 00:35:18,320
Speaker 4: you manage to get that across to them that stopped

713
00:35:18,320 --> 00:35:19,560
Speaker 4: looking at it because there was a little bit of

714
00:35:19,600 --> 00:35:20,480
Speaker 4: worry about billings.

715
00:35:21,239 --> 00:35:23,120
Speaker 2: Well, first of all, thank you for having me, Carol,

716
00:35:23,160 --> 00:35:26,200
Speaker 2: and I appreciate you saying the word platformization, because this

717
00:35:26,280 --> 00:35:27,600
Speaker 2: is something we've been trying.

718
00:35:27,320 --> 00:35:28,240
Speaker 7: To tell the market.

719
00:35:28,480 --> 00:35:30,719
Speaker 2: I think we are seeing a c shift in the

720
00:35:30,760 --> 00:35:35,320
Speaker 2: cybersecurity industry where people are tired of buying point products

721
00:35:35,320 --> 00:35:38,200
Speaker 2: which are not solving the problem. Today, you have to

722
00:35:38,200 --> 00:35:40,719
Speaker 2: make these products work together. And I was listening to

723
00:35:40,719 --> 00:35:44,400
Speaker 2: the conversations before this. Everybody's talking about AI. AI in

724
00:35:44,480 --> 00:35:47,200
Speaker 2: our business means things are going to happen faster. Attacks

725
00:35:47,200 --> 00:35:48,480
Speaker 2: are going to happen faster. You're going to have to

726
00:35:48,520 --> 00:35:52,000
Speaker 2: resolve them faster, which means you need a common language,

727
00:35:52,000 --> 00:35:56,560
Speaker 2: you need common data which actually points towards integration and

728
00:35:56,600 --> 00:35:59,439
Speaker 2: stuff that works together, or points towards platformization.

729
00:36:00,000 --> 00:36:03,680
Speaker 4: Cloud platform strategy that seems to be earning you plenty

730
00:36:03,680 --> 00:36:04,200
Speaker 4: of companies.

731
00:36:04,239 --> 00:36:05,239
Speaker 3: What is it accumulative?

732
00:36:05,320 --> 00:36:08,560
Speaker 4: You've got one, one hundred platformizations?

733
00:36:09,120 --> 00:36:12,640
Speaker 3: Ultimately, is that more valuable? Is that more reven generating?

734
00:36:12,640 --> 00:36:14,960
Speaker 4: Because people are worried that maybe within this bundling there

735
00:36:15,000 --> 00:36:16,120
Speaker 4: is some discounting going on.

736
00:36:17,080 --> 00:36:20,320
Speaker 2: Well, I think it's important to understand once you deploy

737
00:36:20,320 --> 00:36:23,319
Speaker 2: a platform and it works well for you, you're not going

738
00:36:23,360 --> 00:36:25,759
Speaker 2: to replace it. So You're coming from an industry where

739
00:36:25,800 --> 00:36:27,560
Speaker 2: you look, we've had companies that have worked well for

740
00:36:27,600 --> 00:36:29,600
Speaker 2: eight or ten years, but they've been through a cycle,

741
00:36:29,800 --> 00:36:31,640
Speaker 2: and then you have to replace them and replace them

742
00:36:31,640 --> 00:36:34,000
Speaker 2: on a net new set of vendors because the last

743
00:36:34,040 --> 00:36:36,320
Speaker 2: ones didn't stay innovative enough. And what we're trying to

744
00:36:36,360 --> 00:36:38,080
Speaker 2: do is we're trying to stay evergreen. We're trying to

745
00:36:38,080 --> 00:36:40,319
Speaker 2: be innovative. We're trying to make sure we embed the

746
00:36:40,400 --> 00:36:44,320
Speaker 2: platform as part of your security solution that you logically

747
00:36:44,320 --> 00:36:46,360
Speaker 2: come to us for the next piece of innovation. And

748
00:36:46,440 --> 00:36:49,040
Speaker 2: you have to go through a huge process of constantly

749
00:36:49,360 --> 00:36:52,840
Speaker 2: transforming your security. In state a state today, more customers

750
00:36:52,880 --> 00:36:56,360
Speaker 2: have security transformation projects on tap than any other technology

751
00:36:56,360 --> 00:36:59,440
Speaker 2: transformation because the security landscape is constantly changing.

752
00:37:00,120 --> 00:37:03,200
Speaker 4: Talk about the cyber landscape and whether it changes with

753
00:37:03,680 --> 00:37:06,520
Speaker 4: a new administration. Is there going to be more consolidation,

754
00:37:06,719 --> 00:37:08,239
Speaker 4: and is there going to be more deal making from

755
00:37:08,280 --> 00:37:10,840
Speaker 4: you look, I get.

756
00:37:10,719 --> 00:37:13,440
Speaker 2: Past that in different ways. You know, I'm watching it

757
00:37:13,520 --> 00:37:15,880
Speaker 2: like everybody else and saying it sounds like we're going

758
00:37:15,920 --> 00:37:18,520
Speaker 2: to see the new doge go out and do a

759
00:37:18,560 --> 00:37:20,440
Speaker 2: lot of cost cutting and deploying a lot of technology.

760
00:37:20,480 --> 00:37:24,560
Speaker 2: Now Traditionally, the federal government in the United States has

761
00:37:24,719 --> 00:37:27,759
Speaker 2: been sloughed off new technology rightfully so, perhaps because they

762
00:37:27,760 --> 00:37:30,600
Speaker 2: want to sense their way through it. But as you

763
00:37:30,680 --> 00:37:33,920
Speaker 2: see efficiency, as you see technology investment happening, it has

764
00:37:33,960 --> 00:37:36,799
Speaker 2: to be secure. So I think security gets a knock

765
00:37:36,840 --> 00:37:39,759
Speaker 2: on benefit if we actually see a technology wave coming

766
00:37:39,800 --> 00:37:43,280
Speaker 2: from an administrative perspective, Do I expect regulations to get easier?

767
00:37:43,320 --> 00:37:45,840
Speaker 2: Possibly from an M and A perspective, Do I expect

768
00:37:45,840 --> 00:37:46,360
Speaker 2: that things.

769
00:37:46,160 --> 00:37:46,759
Speaker 7: Will move faster?

770
00:37:46,880 --> 00:37:49,320
Speaker 3: Hopefully? So, I think generally it should.

771
00:37:49,920 --> 00:37:53,440
Speaker 2: Sort of board well for all of us in technology,

772
00:37:53,680 --> 00:37:55,239
Speaker 2: But you know, we'll all find out.

773
00:37:55,280 --> 00:37:56,000
Speaker 1: We'll wait and watch.

774
00:37:56,400 --> 00:37:59,759
Speaker 4: Look as you innovate, as particularly within the world of

775
00:37:59,760 --> 00:38:04,240
Speaker 4: gender to AI, we managed to get more sophisticated cyber protection,

776
00:38:04,320 --> 00:38:07,680
Speaker 4: you're also getting more sophisticated hackers. The US government has

777
00:38:07,719 --> 00:38:11,600
Speaker 4: just flags and flaws with Palo Alto's owned products that might.

778
00:38:11,440 --> 00:38:12,600
Speaker 3: Have been exploited by hackers.

779
00:38:12,600 --> 00:38:15,040
Speaker 4: Can you just tell us a little bit about whether

780
00:38:15,080 --> 00:38:16,160
Speaker 4: that was a realistic one for.

781
00:38:16,160 --> 00:38:19,800
Speaker 3: You, what patches have been made? Well, like what.

782
00:38:19,680 --> 00:38:23,000
Speaker 2: Happens is because we're coming from a hardware industry where

783
00:38:23,000 --> 00:38:27,320
Speaker 2: we sell products, customers deploy them, there's a long product cycle,

784
00:38:27,320 --> 00:38:29,080
Speaker 2: and you've seen this across the industry. At any point

785
00:38:29,080 --> 00:38:31,840
Speaker 2: in time, you know, there's always some bug that is

786
00:38:31,880 --> 00:38:33,640
Speaker 2: out there that needs to be passed and fixed. So

787
00:38:33,680 --> 00:38:36,160
Speaker 2: we're very good about making sure we work with our customers.

788
00:38:36,200 --> 00:38:38,839
Speaker 2: We have full visibility into who has what product. We're

789
00:38:38,840 --> 00:38:40,480
Speaker 2: able to work with them to pass those things. I

790
00:38:40,480 --> 00:38:44,040
Speaker 2: think as we move from a hardware based solution business

791
00:38:44,080 --> 00:38:46,040
Speaker 2: to a software business, which the world is moving to,

792
00:38:46,320 --> 00:38:48,680
Speaker 2: that becomes a lot easier to fix. I think software

793
00:38:48,920 --> 00:38:52,760
Speaker 2: beats hardware from a security recency perspective all the time,

794
00:38:52,960 --> 00:38:55,239
Speaker 2: So as the world transforms there we'll see less than

795
00:38:55,320 --> 00:38:56,840
Speaker 2: less of that in the future.

796
00:38:57,280 --> 00:39:01,080
Speaker 4: As the world transforms, everyone becomes an investor retail community

797
00:39:01,239 --> 00:39:03,600
Speaker 4: as well. Talk to us about your two for one

798
00:39:03,600 --> 00:39:05,879
Speaker 4: stock split. You're already up what thirty percent on the year.

799
00:39:05,960 --> 00:39:07,680
Speaker 4: Is that why you spread it out? Is it about

800
00:39:07,719 --> 00:39:08,640
Speaker 4: giving more access?

801
00:39:09,320 --> 00:39:10,840
Speaker 2: Well, we want to make sure that there is ample

802
00:39:10,880 --> 00:39:13,799
Speaker 2: access to our stock to retailed investors. You know, five

803
00:39:13,920 --> 00:39:15,640
Speaker 2: years ago when I started, we were a sort of

804
00:39:15,640 --> 00:39:18,520
Speaker 2: a niche name from the cybersecurity industry. Or delighted that

805
00:39:18,560 --> 00:39:20,520
Speaker 2: we are now part of the general conversation. We want

806
00:39:20,520 --> 00:39:22,080
Speaker 2: to make sure everybody has access to our stock.

807
00:39:22,920 --> 00:39:25,640
Speaker 4: Well for now, we'll currently see it's going to be

808
00:39:25,760 --> 00:39:27,560
Speaker 4: action in a two for one stock split. We'll see

809
00:39:27,560 --> 00:39:29,680
Speaker 4: how much adoption there is around that. But in cash

810
00:39:29,760 --> 00:39:32,560
Speaker 4: Aurora on the fundamentals and some of the share moves.

811
00:39:32,560 --> 00:39:34,920
Speaker 4: CEO of pallel to Networks, we appreciate your time today.

812
00:39:35,040 --> 00:39:35,400
Speaker 3: Thank you.

813
00:39:36,040 --> 00:39:39,600
Speaker 4: Coming up more earnings, Snowflake giving a pretty strong outlook

814
00:39:39,640 --> 00:39:40,640
Speaker 4: for its product sales growth.

815
00:39:40,640 --> 00:39:41,440
Speaker 3: We dig in this is.

816
00:39:41,440 --> 00:39:50,080
Speaker 11: Blue meg Technology.

817
00:39:55,680 --> 00:39:56,560
Speaker 3: More earnings for you.

818
00:39:56,760 --> 00:40:00,400
Speaker 4: Snowflake shares absolutely jumping after the company go some pretty

819
00:40:00,400 --> 00:40:03,440
Speaker 4: solid sales outlooks for us, suggesting newly launched products are

820
00:40:03,440 --> 00:40:07,120
Speaker 4: receiving a strong reception from customers. Brady Ford has the

821
00:40:07,160 --> 00:40:10,880
Speaker 4: inside take, I mean extraordinary move to the upside from Snowflake.

822
00:40:10,920 --> 00:40:13,360
Speaker 4: We know it's a volatile stop, but what's managing to

823
00:40:13,400 --> 00:40:14,240
Speaker 4: win over customers?

824
00:40:14,239 --> 00:40:18,399
Speaker 10: Bernie in software right now, the big question is who

825
00:40:18,440 --> 00:40:19,280
Speaker 10: gets left.

826
00:40:19,080 --> 00:40:20,680
Speaker 7: Behind in the age of Genai.

827
00:40:21,239 --> 00:40:24,920
Speaker 10: For a long time, investors worried that Snowflake was focusing

828
00:40:25,000 --> 00:40:28,320
Speaker 10: on maybe a two narrow slice of the data market.

829
00:40:28,400 --> 00:40:31,640
Speaker 10: You know, the services used to kind of organize and

830
00:40:31,680 --> 00:40:35,400
Speaker 10: analyze all your data. And Snowflake is successfully making an

831
00:40:35,520 --> 00:40:38,920
Speaker 10: argument that no, in the era of Genai, when folks

832
00:40:38,960 --> 00:40:42,440
Speaker 10: need to understand such a wide array of data and

833
00:40:42,480 --> 00:40:46,319
Speaker 10: turn it into interesting applications, we will remain a platform

834
00:40:46,440 --> 00:40:48,600
Speaker 10: where they spend their money and.

835
00:40:48,560 --> 00:40:49,600
Speaker 7: Make their decisions.

836
00:40:49,600 --> 00:40:52,400
Speaker 10: And so that's what investors are reacting to in these results.

837
00:40:52,560 --> 00:40:54,719
Speaker 4: They organize, they analyze your data, but when it comes

838
00:40:54,760 --> 00:40:56,600
Speaker 4: to unstructured data, they're having.

839
00:40:56,400 --> 00:40:58,160
Speaker 3: To make purchases. Here, it's a bit of.

840
00:40:58,239 --> 00:41:00,120
Speaker 7: M and a Yes.

841
00:41:00,280 --> 00:41:03,759
Speaker 10: So many have heard of this famous rivalry between Snowflake

842
00:41:03,800 --> 00:41:06,480
Speaker 10: and Data Bricks for a long time. The argument was

843
00:41:06,520 --> 00:41:10,200
Speaker 10: that data Bricks handled the unstructured data better, and that's

844
00:41:10,200 --> 00:41:13,000
Speaker 10: the real you know, something that's not in a spreadsheet,

845
00:41:13,000 --> 00:41:15,760
Speaker 10: something that's real disorganized. Maybe it's a bunch of Google docs.

846
00:41:15,760 --> 00:41:17,839
Speaker 10: You want to be able to scan through and say, hey,

847
00:41:18,280 --> 00:41:21,160
Speaker 10: give me insights. And so Snowflake has worked to beef

848
00:41:21,280 --> 00:41:24,000
Speaker 10: up their ability to deal with that kind of hard

849
00:41:24,000 --> 00:41:26,960
Speaker 10: to access data. And yeah, last night we saw them

850
00:41:26,960 --> 00:41:30,439
Speaker 10: make an acquisition of a startup that focuses specifically on

851
00:41:31,080 --> 00:41:34,440
Speaker 10: ingesting this kind of messy, unstructured data you need for

852
00:41:34,760 --> 00:41:36,440
Speaker 10: a lot of Genai use cases.

853
00:41:37,400 --> 00:41:39,440
Speaker 4: I know that you've reported a lot on that rivalry.

854
00:41:39,480 --> 00:41:41,279
Speaker 4: So see if that M and A does indeed help.

855
00:41:41,320 --> 00:41:45,400
Speaker 4: But under the guidance of the new leadership in Ramaswami,

856
00:41:45,440 --> 00:41:49,400
Speaker 4: he's also trying to integrate large language models, right, how

857
00:41:49,480 --> 00:41:52,920
Speaker 4: is this being seen in the anthropic deal Exactly?

858
00:41:52,960 --> 00:41:55,960
Speaker 10: They want to bring more LLLM access right into the

859
00:41:56,000 --> 00:41:58,880
Speaker 10: Snowflake platform. So if you want to do things like

860
00:41:59,400 --> 00:42:03,080
Speaker 10: against stand through all your information and pull out insights,

861
00:42:03,239 --> 00:42:07,520
Speaker 10: make dashboards, make visualizations, you can do it right in Snowflake.

862
00:42:07,520 --> 00:42:09,640
Speaker 10: You don't have to go open up a new you know,

863
00:42:09,760 --> 00:42:14,440
Speaker 10: Azure instance or leave the platform. So bringing anthropic models

864
00:42:14,520 --> 00:42:17,880
Speaker 10: right into Snowflake is again hoping to make it a

865
00:42:17,920 --> 00:42:21,680
Speaker 10: platform where you remain where kind of your most important

866
00:42:21,719 --> 00:42:24,040
Speaker 10: data is. They really hope to be a center of

867
00:42:24,120 --> 00:42:27,680
Speaker 10: gravity for all your information and not just something you

868
00:42:27,680 --> 00:42:30,480
Speaker 10: you know, use when you want to make an analysis

869
00:42:30,480 --> 00:42:32,160
Speaker 10: of a spreadsheet somebody sent to you.

870
00:42:33,400 --> 00:42:36,520
Speaker 4: Brady Ford, across the Snowflake moves and indeed some of

871
00:42:36,560 --> 00:42:39,439
Speaker 4: the intricacies and the earnings we thank you. Look, let's

872
00:42:39,440 --> 00:42:42,080
Speaker 4: just get a border perspective of how snowflakes thirty percent

873
00:42:42,200 --> 00:42:44,400
Speaker 4: the upside fits into what's happening more broadly across the

874
00:42:44,480 --> 00:42:49,319
Speaker 4: NASDAK because look, it hasn't been an round out risk

875
00:42:49,400 --> 00:42:51,759
Speaker 4: on kind of a day. In fact, we've been lower pulled,

876
00:42:51,800 --> 00:42:54,680
Speaker 4: lowered by about two tens a percent, large part because

877
00:42:54,719 --> 00:42:57,080
Speaker 4: of Nvideos off by one point seven percent, Alphabet two

878
00:42:57,320 --> 00:43:00,000
Speaker 4: on the back of that formalization of Bloomberg reporting. Then

879
00:43:00,040 --> 00:43:02,440
Speaker 4: indeed the DOJ does want it to be selling off

880
00:43:02,480 --> 00:43:04,640
Speaker 4: Chrome for example, but in video of by one point

881
00:43:04,719 --> 00:43:08,000
Speaker 4: seven percent is important because it is the most valuable company.

882
00:43:08,120 --> 00:43:09,920
Speaker 4: It is going to have a huge points perspective when

883
00:43:09,920 --> 00:43:10,560
Speaker 4: it drags lower.

884
00:43:10,560 --> 00:43:11,520
Speaker 3: But look, we were.

885
00:43:11,440 --> 00:43:14,000
Speaker 4: Anticipating in the options market there's mure an eight percent

886
00:43:14,080 --> 00:43:17,799
Speaker 4: swing upside or downside. So this is relatively stable, shall

887
00:43:17,840 --> 00:43:20,760
Speaker 4: we say. After earnings did show a ninety four percent

888
00:43:20,800 --> 00:43:23,920
Speaker 4: increase in revenue, a seventy percent increase in revenue for

889
00:43:23,960 --> 00:43:26,680
Speaker 4: the Guide, and margins being pretty strong.

890
00:43:26,920 --> 00:43:28,520
Speaker 3: Interestingly, the chops that the.

891
00:43:28,520 --> 00:43:31,680
Speaker 4: Socks the chip sector is up some seven percent, so

892
00:43:31,760 --> 00:43:33,600
Speaker 4: the rest of the market doing well today. Have a

893
00:43:33,640 --> 00:43:36,000
Speaker 4: look at what's happening with Alphabet because the parent company

894
00:43:36,000 --> 00:43:39,640
Speaker 4: of Google off another six point eight percent today. Once again,

895
00:43:39,920 --> 00:43:44,120
Speaker 4: this anxiety surrounding antitrust and how it might have to

896
00:43:44,120 --> 00:43:46,319
Speaker 4: be split off weighing on the stock that does it.

897
00:43:46,680 --> 00:43:49,280
Speaker 4: In this edition of Bloomberg Technology, a lot to get through.

898
00:43:49,680 --> 00:43:51,560
Speaker 4: You want to check it all out again on the podcast.

899
00:43:51,719 --> 00:43:53,880
Speaker 4: You'll find it on the terminal, as well as online.

900
00:43:53,640 --> 00:43:57,280
Speaker 3: On Apple, Spotify, and iHeart. This is Bloomberg

901
00:44:00,239 --> 00:44:04,800
Speaker 7: Today, The Healthy Everyday, Healthy Everyday, The health of