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

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Speaker 2: This is Bloomberg Tech coming up.

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Speaker 3: Meta agrees to a series of electricity deals to power

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Speaker 3: data centers, making it the biggest fire of nuclear power

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Speaker 3: among the hyperscalers plus Minimax, one of China's largest generative

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Speaker 3: AI startups.

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Speaker 2: Goes public in Hong Kong.

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Speaker 3: For that means with China's AI ecosystem and Snowflake plans

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Speaker 3: to buy Observe, an AI powered observability platform. We're going

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Speaker 3: to discuss the move with Snowflake's c EO. Let's get

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Speaker 3: right to it and what's happening in the markets. Later

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Speaker 3: in the program, we're going to go big on Intel. Lipbutan,

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Speaker 3: Intel CEO has spent the last twenty four hours in Washington,

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Speaker 3: d C. In the White House with the President and

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Speaker 3: with Howard Lutnik, the Commerce Secretary. Glowing remarks from President

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Speaker 3: Trump about Litbutan's leadership of Intel, but also the progress

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Speaker 3: that the US government's made in taking a steak in

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Speaker 3: the chip maker.

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Speaker 2: More to come.

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Speaker 3: Then there's the big one investing in a multi gigawatt

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Speaker 3: deal with some big names in nuclear energy. Opkloat up

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Speaker 3: fourteen percent on that deal. Of course backed by some

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Speaker 3: outman district corps. Up thirteen percent, Meta up eight ten

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Speaker 3: to one percent. We said at the top of the program.

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Speaker 3: This puts them among the biggest energy buyers, among the hyperscalers,

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Speaker 3: and we remind ourselves Meta is not technically a hyperscaler.

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Speaker 3: That's get the blue most Riley Griffin details here really

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Speaker 3: important size of the deal, how many gigawatts, but also

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Speaker 3: the structure.

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Speaker 2: Please.

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Speaker 4: Yeah, So three different agreements and they actually are different

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Speaker 4: in form and functions. So this is about supporting up

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Speaker 4: to six point six gigawatts in nuclear energy, but it

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Speaker 4: is both about ensuring that existing nuclear power plants continue

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Speaker 4: to thrive and investing in future nuclear power. Meta's head

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Speaker 4: of global energy told me last night that they heard

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Speaker 4: that there's real concern about the amount of energy that's

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Speaker 4: out there. As you know ed, there's an insatiable demand

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Speaker 4: for energy, and this cements their low carbon play.

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Speaker 3: I'll find this fascinating because while Meta is not a

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Speaker 3: hyperscale or a hyperscale of being a cloud computing company

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Speaker 3: that basically leases capacity, Meta is doing this on its

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Speaker 3: own behalf right. It has been aggressive in in securing

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Speaker 3: the supply of energy it needs for its own data centers,

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Speaker 3: which principally I use for training and inference of its

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Speaker 3: own activity in AI. What else has it done and

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Speaker 3: where does this bring them to date?

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Speaker 4: Well, I want to note that you made a really

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Speaker 4: important important point there. Prometheus and Hyperion. These are two

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Speaker 4: of their biggest AI plays. Prometheus actually is in Ohio

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Speaker 4: and these agreements are in Ohio and Pennsylvania. So some

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Speaker 4: of this energy presumably is going to go to the

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Speaker 4: Prometheus data center cluster, which is one of their biggest plays.

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Speaker 4: But we're also seeing Meta turn with Hyperion to natural gas.

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Speaker 4: At least three natural gas plants are being fired up

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Speaker 4: just for that single facility, So it's not like nuclear

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Speaker 4: is the only strategy that Meta is employing here.

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Speaker 3: We're going to get to the sort of available energy

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Speaker 3: sources around the world in a little bit. There is

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Speaker 3: an interesting point you co reported this with Will Wade,

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Speaker 3: who's just been really on top of the nuclear side

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Speaker 3: of the story. There does seem to be some anxiety

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Speaker 3: from the technology companies that the existing NUCLEME infrastructure that

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Speaker 3: does exist in America, limited as it may be, is

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Speaker 3: also at risk at being shut down. Is this kind

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Speaker 3: of future proofing a little bit.

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Speaker 4: Absolutely, that is what I'm hearing from metas head of

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Speaker 4: Global Energy. They started back a December two years ago

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Speaker 4: and they were looking into what nuclear needed to keep going,

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Speaker 4: and they heard we need investment now into plants that

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Speaker 4: could potentially shutter. So this is a future proofing, as

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Speaker 4: you say.

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Speaker 3: Bloomberg's Riley Griffin leading our coverage of Meta with a

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Speaker 3: must read, Thank you very much. Access to power has

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Speaker 3: been top of mind in AI. We spoke about that

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Speaker 3: with Nvidia CEO Jensenmong earlier this week.

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Speaker 5: In order for our new industry to emerge, you need energy,

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Speaker 5: and so I think it's safe to say that we

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Speaker 5: wish we had more energy in the United States. Your

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Speaker 5: wis should have more energy. I think the world all

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Speaker 5: wish we have more energy, and so we have to

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Speaker 5: invest in all sorts of different forms of energy.

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Speaker 2: Let's bring in Paul Meeks for more.

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Speaker 3: He's managing director and head of Technology research of Freedom

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Speaker 3: Capital Markets. And when I was reading your research last night, Paul,

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Speaker 3: A little bit relieved coming up this morning when I

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Speaker 3: saw the Meta headline, because you're very focused on the

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Speaker 3: real terms footprint of data centers. That's what we're talking

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Speaker 3: about here. You heard Riley's reporting on the specifics of

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Speaker 3: what Meta has done to secure future capacity. How much

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Speaker 3: of a bottleneck is that to your mind right now?

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Speaker 2: For this sector?

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Speaker 6: It is absolutely critical of all the bottomnecks. It is

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Speaker 6: the most important. I like what Meta is doing here,

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Speaker 6: but folks need to realize that if you ramp up

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Speaker 6: nuclear capacity, we're not going to really see power generation

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Speaker 6: until twenty thirty earliest, maybe even not until twenty thirty two.

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Speaker 6: So what do we do for the next four to

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Speaker 6: six years? So the immediate draw is probably that gas.

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Speaker 6: But when I look at all of these companies and

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Speaker 6: we cover the AI hyperscalers and the neo clouds and

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Speaker 6: everybody adjacent to them, we are looking for their availability

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Speaker 6: for power, their capacity to find power, because it's not

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Speaker 6: a demand issue, it is a supply issue focused on

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Speaker 6: this particular metric.

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Speaker 2: You know, pool.

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Speaker 3: I played you just just some of the interview that

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Speaker 3: we conducted with Jensen Wong earlier this week, and when

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Speaker 3: I was sitting in front of him, you don't really

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Speaker 3: get any sense of anxiety from him, right Think about

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Speaker 3: the mechanics of how this transaction works and video brings

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Speaker 3: GPUs actually that's probably not fair. Increasingly they bring more

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Speaker 3: of the content of the server, but there is an

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Speaker 3: acknowledgment that something needs to change, principally in America. He

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Speaker 3: did go on to talk about the differences between Europe,

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Speaker 3: Asia and the United States and where we're sourcing energy.

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Speaker 3: Do you get a sense from your research that the

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Speaker 3: companies you cover acknowledge the severity of that deficit right now?

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Speaker 6: So when they are public facing, particularly somebody like Jensen Wong,

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Speaker 6: who is essentially the spokesperson for the entire industry, they

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Speaker 6: have to exude optimism. However, behind the scenes, particularly when

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Speaker 6: I'm talking to companies about my financial models, which of

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Speaker 6: course the revenues and then the follow on cash flow

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Speaker 6: and earnings come from.

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Speaker 7: Do you have the power?

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Speaker 6: Yes, they are anxious, and I think they're actually very anxious.

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Speaker 3: There will be people watching Bloomberg Tech that work at

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Speaker 3: Meta or in the AI industry. There will be Meta

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Speaker 3: investors watching this program, and some of these people may

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Speaker 3: never have heard of an outclow.

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Speaker 2: Have you had to.

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Speaker 3: Change the remit and breadth of your coverage if you

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Speaker 3: say I cover AI data center to include future sources

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Speaker 3: of electricity supply, Yeah.

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Speaker 6: It's really interesting, ed that years ago, you know, nobody

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Speaker 6: in my line of work covering tech sector like I

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Speaker 6: have for decades, would be interested in any utility company.

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Speaker 6: It's the antithesis of tech. They are boring, regulated, slow

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Speaker 6: growth businesses. However, now they've come to the four and

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Speaker 6: of course these are not just your regular utilities, you know,

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Speaker 6: they're your new era utilities. But yes, if you are

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Speaker 6: a good analyst, you have to cover the entire supply chain,

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Speaker 6: the entire ecosystem. And because demand is so robust, we're

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Speaker 6: focusing on supply absolutely critical. And it's not just the power.

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Speaker 6: There are physical constraints here, like simply pouring the cement

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Speaker 6: to build the next data center, which can't be a bottleneck,

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Speaker 6: even though it sounds, you know, so old school in

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Speaker 6: a new school business. But yes, these are some factors

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Speaker 6: that are very, very interesting that people didn't even conceive

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Speaker 6: of a couple of years ago.

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Speaker 3: Pol is there an adequate federal level framework regulation to

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Speaker 3: support the energy requirements of the AI industry in this country?

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Speaker 6: You know, there is not. And here is my problem.

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Speaker 6: We are still suffering from a lack of solid regulation

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Speaker 6: for the Internet because even today in the US the

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Speaker 6: Internet is operating under nineteen ninety six legislation when Bill

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Speaker 6: Clinton was president, and so I'm hoping that we're not

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Speaker 6: too restrictive.

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Speaker 1: Right.

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Speaker 6: We need to embrace our entrepreneurial companies and give them

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Speaker 6: room to roll, but we need to have some guards

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Speaker 6: else on everything AI because what happened last time is

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Speaker 6: we end up abdicating the legislation to the EU because

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Speaker 6: we didn't have the will to do it ourselves. So

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Speaker 6: we missed that opportunity in the Internet. Here is the

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Speaker 6: next wave. We need to embrace it and at least

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Speaker 6: come up with some guardrails that are going to guide

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Speaker 6: all this because there are some important decisions on the

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Speaker 6: federal level to be made.

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Speaker 3: In your research pro it's very interesting to see you

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Speaker 3: approach data center from the perspective of yes, you know,

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Speaker 3: energy supply, but also real estate. The reats right, and

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Speaker 3: what you want to ask you is away from the GPUs,

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Speaker 3: where are the frailties in that industry? Is it literally

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Speaker 3: the folks building the tin can around the outside of

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Speaker 3: the data center, the labor, the concrete.

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Speaker 6: Yeah, we've seen some hit in short term revenues because

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Speaker 6: a couple of the number or a few of the

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Speaker 6: players have subcontractors. Now this is not them, this is

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Speaker 6: their subcontractors.

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Speaker 7: That are delayed.

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Speaker 6: What we call building a powered shell. So it's more

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Speaker 6: than a quandset hut that you might find the army in.

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Speaker 6: These powered shells are a little bit more sophisticated. But

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Speaker 6: one company I cover had a one hundred and fifty

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Speaker 6: million dollars revenue deferral from twenty five to twenty six

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Speaker 6: because at one of their forty one data centers someone

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Speaker 6: couldn't pour concrete because it was raining. And so yes,

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Speaker 6: all these things come into play, and it's interesting that

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Speaker 6: they may not necessarily be technical bottlenecks, but old school

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Speaker 6: physical including construction bottlenecks. In addition to the power.

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Speaker 2: I just go back from Vegas.

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Speaker 3: With all the attention on artificial intelligence in the digital world,

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Speaker 3: we have an industry that's getting hit by a rainstorm.

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Speaker 3: Pool meeks, buckets, unbelievable. It's great to have you on

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Speaker 3: Bloomberg Tech. A lot more to come and coming out

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Speaker 3: an exclusive interview with the co founder and COO of

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Speaker 3: Minimax after the company just went public in Hong Kong,

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Speaker 3: and that's the debut trade.

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Speaker 2: And this is Bloomberg Tech.

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Speaker 3: Minimax, one of China's largest generative AI startups, backed by

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Speaker 3: Ali Barber and Abadavi Sovereign Well Fund, is now public.

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Speaker 3: Shares surge in Hong Kong after an IPO that raised

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Speaker 3: six hundred and nineteen million dollars, closing up one hundred

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Speaker 3: and nine percent in its Friday debut trading. Bloomberg Stephen

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Speaker 3: Engel spoke exclusively with co founder and COO Yee Yun

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Speaker 3: about growth and expansion plans as Minimax faces hot AI

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Speaker 3: competition both locally and globally.

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Speaker 8: We really focus out that capital efficiency caused the efficiency,

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Speaker 8: so we had always spent a round five hundred media

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Speaker 8: USC in the in total, probably only one or two

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Speaker 8: percent of the biggen suspending, So we did all the organization,

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Speaker 8: mad creativity innovations on.

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Speaker 9: The h How do you compete though, if you're going

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Speaker 9: to go global, how do you compete with an open

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Speaker 9: AI or others that are throwing lots of capital at

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Speaker 9: their business?

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Speaker 2: Is it viable and what is the.

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Speaker 9: Technology gap looking like in the next few years.

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Speaker 8: Actually, I don't think it's a competition. It's more about

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Speaker 8: all the top talents across all the world. They bring

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Speaker 8: the technology break through to the society, to the end users.

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Speaker 8: So you will see each models have pros and accounts,

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Speaker 8: so we focus more resources on building the models, building

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Speaker 8: the best product experience to a more and more uses

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Speaker 8: from all the world.

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Speaker 4: To use it.

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Speaker 9: We're hearing as well, the Chinese government might approve in

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Speaker 9: Vidia's H two hundred for China in this quarter. Perhaps

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Speaker 9: is that something you need to look at importing more

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Speaker 9: advanced accelerators for your training.

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Speaker 1: And the like.

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Speaker 8: I think in every industry with a really high growth

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Speaker 8: when the new technology comes here, so you will see

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Speaker 8: every startups, all the companies. This please resources comestring probably

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Speaker 8: chief constring computing constring is only one of the challenges.

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Speaker 8: So in the past you will see our model performers

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Speaker 8: and our product performers, so our resources is stable. We

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Speaker 8: can get access to all of them, so we focus

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Speaker 8: more on our R and D and foundation model. So

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Speaker 8: we just use whatever chiefs who make sense for us

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Speaker 8: at that specific stage.

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Speaker 9: Is your goal to stay indigenously with your chips locally

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Speaker 9: made chips or would you buy nvidio chips as well?

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Speaker 8: It doesn't matter what kinds of chiefs it is. It's

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Speaker 8: more about which chiefs can give us best ROI, which

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Speaker 8: can help us to achieve our mission to make the

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Speaker 8: best technology too accessible to all of the users across

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Speaker 8: the world.

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Speaker 9: So when does the emphasis turn from efficiency to profitability

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Speaker 9: and also getting that steady revenue growth that will then

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Speaker 9: justify a higher stock price valuation.

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Speaker 8: So we put loss of R and D resources and

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Speaker 8: innovations on the efficiency part. You will see our API

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Speaker 8: we have the enterprise business and you will see the

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Speaker 8: gross profit margin of our API is more than sixty

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Speaker 8: five percent, which is probably already one of the highest globally.

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Speaker 8: So we focus more on make the best technology and

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Speaker 8: the best the user experience that people would love to

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Speaker 8: pay for the best performers models for their use cases.

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Speaker 9: You've also come out of a price war essentially a

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Speaker 9: race to the bottom some have called, because of you're

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Speaker 9: trying to keep costs down and you want to survive

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Speaker 9: as well. There was a battle of survival and you

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Speaker 9: have been one of the survivors. But at the same time,

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Speaker 9: the startups are not the only ones. The big players

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Speaker 9: Ali Baba, by Dads, tens and others. How do you

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Speaker 9: compete with them at a low cost basis?

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Speaker 8: Actually it's not. I don't think that's a press word.

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Speaker 8: It's more about the performers the competition, So you need

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Speaker 8: to make your model best. And the people I mean

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Speaker 8: and the user are interpress customers. They don't choose your

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Speaker 8: model because of its cheap or expensive. It's more about

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Speaker 8: its performers. So that's the key. As you said, I

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Speaker 8: think it's not about the competition between like all these

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Speaker 8: big chenz, but it's more about some of them. They

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Speaker 8: are like our cloud providers and some of their products

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Speaker 8: you see our models, So it's more about the collaborations.

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Speaker 8: All this company brings the foundation models to all the world.

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Speaker 3: Many Max co founder and COO Yaun speaking exclusively with

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Speaker 3: Bloomberg Steven Engel. Public listings in Hong Kong are hot

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Speaker 3: right now. Chinese industrial robot maker in Events is now

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Speaker 3: also considering a second listing in Hong Kong, according to sources.

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Speaker 3: Let's get out to Bloomberg Executive Tech editor Peter Elstrom.

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Speaker 2: To discuss this.

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Speaker 3: But also like this growing trend of more of China's

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Speaker 3: sort of post chat gbtai firms going public. The team

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Speaker 3: that you lead out in Asia busy right now. In

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Speaker 3: Events's case, what are the details in our reporting that

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Speaker 3: we need to know about.

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Speaker 10: Yeah, there are a lot of these IPOs that we're seeing,

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Speaker 10: and we're seeing a much more forward move into the

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Speaker 10: public markets for a bunch of these companies and events

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Speaker 10: has already gone public in the mainland and Shenjen now

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Speaker 10: it's looking at an IPO in Hong Kong.

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Speaker 11: Two.

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Speaker 10: That's partly a reflection of how hot demand is from

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Speaker 10: investors for these kinds of companies. When you're talking about

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Speaker 10: Minimax in particular, they went public, as you mentioned, their

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Speaker 10: shares more than doubled in the debut. The founder of

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Speaker 10: the company is very fascinating. These are some people called

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Speaker 10: them the AI tigers, some people the AI dragons. But

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Speaker 10: these are startups that really have very very different strategies

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Speaker 10: for how they're approaching the market. Compared with some of

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Speaker 10: the US competitors, Like in open Ai, they tend to

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Speaker 10: be much lower cost models. They tend to be much

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Speaker 10: more aggressive about implementing the technology with their customers, with

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Speaker 10: corporate customers, and giving them very low cost, very low,

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Speaker 10: very efficient models that they can use and they can

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Speaker 10: actually deploy more quickly than some of the US counterparts.

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Speaker 3: PETE in the world of technology or in financial market's

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Speaker 3: like a term like an AI tiger or AI dragon.

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Speaker 2: You know, it's kind of commonplace. We bandy it around.

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Speaker 3: But for those watching Bloomberg Tech that are saying, what

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Speaker 3: are you talking about? How do we define the AI tiger?

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Speaker 3: What are we talking about here?

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Speaker 10: Well, we're talking about a whole series of companies coming

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Speaker 10: out of China. In Stevens interview with the Minimax COO,

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Speaker 10: they talked about the competition there. We've of course heard

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Speaker 10: a lot about Deep Seek and how good their model is,

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Speaker 10: how technically good it is, but also how low cost

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Speaker 10: it is. Behind Deep Seek, you have a whole bunch

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Speaker 10: of other companies like Minimax, like Jipou, which just went

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Speaker 10: public on Thursday. So yeah, the IPO of Geepoo on Thursday,

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Speaker 10: mini Max on Friday investors showed a lot of demand

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Speaker 10: for those shares. And what these companies have been able

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Speaker 10: to show is that with a very low cost model,

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Speaker 10: they're out there in the market competing. It's not clear

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Speaker 10: that they're going to be able to compete directly with

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Speaker 10: Open AI or Anthropic, but they have very innovative models.

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Speaker 10: I mean, first of all, the US models are not

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Speaker 10: going to be let into the China market. But these

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Speaker 10: companies are also going out very aggressively and competing in

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Speaker 10: some other parts of the world. We've written about how

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Speaker 10: much traction Deep Seek is getting in Africa in particular,

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Speaker 10: because their companies are sensitive to costs. They're sensitive to

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Speaker 10: how much power, how much computer resources they need to

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Speaker 10: be able to deploy these models. So they are having

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Speaker 10: some successes. We're going to see a lot of competitive

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Speaker 10: clashes in the months ahead.

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Speaker 2: Bloomberg's executive editive for Tech, Peter Alstrom. Great to catch

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Speaker 2: up with you, Thank you so much. Now coming up

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Speaker 2: on the.

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Speaker 3: Program, Love Musks Grock image generation tool faces backlash after

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Speaker 3: reports that's generated thousands of undressed women and children.

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Speaker 2: Go more on that data. Next, this is Bloomberg Tech.

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Speaker 3: Elon Musk's AI startup Xai is burning cash quickly. The

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Speaker 3: company spent almost eight billion dollars in the first nine

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Speaker 3: months of the year. That's according to internal docs that

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Speaker 3: Bloomberg's viewed. Though revenue has nearly doubled quarter over quarter

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Speaker 3: to one hundred and seven million dollars, Xai's losses have

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Speaker 3: been mounting on costs to build data centers, recruit talent

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Speaker 3: developed software and to build AI that is self sufficient,

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Speaker 3: and according to Bloomberg's reporting, the plan is to use

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Speaker 3: that AI to eventually power Tesla's humanoid.

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Speaker 2: Robots in the Optimus program.

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Speaker 3: Right Sticking with XAI, the company has limited access to

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Speaker 3: Grock's image generation tool for most users, following widespread criticism

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Speaker 3: that the feature was producing thousands of explicit images of

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Speaker 3: women and children. Bloomberg reporter Cecilia Deannastasio broke, the story

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Speaker 3: has been tracking the developments, Cecilia, that we just summarized

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Speaker 3: the issue, right think, let's start with the methodology, how

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Speaker 3: we went about looking at the data, how we got

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Speaker 3: the data, and we'll go from that.

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Speaker 1: Thank you for having me. Bloomberg worked as a researcher

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Speaker 1: who scraped thousands of images produced by groc and published

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Speaker 1: to the platform X between the period January fifth and

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Speaker 1: January sixth. The researcher analyzes images to determine what percent

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Speaker 1: of them were sexualized and neudifying, and found that every

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Speaker 1: hour during that twenty four hour period, X was publishing

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Speaker 1: about six thousand, seven hundred images identified as such, and

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Speaker 1: just for a comparison, websites that are dedafied dedicated to

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Speaker 1: publishing deep faked images AI generated images of women who

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Speaker 1: are sexualized neudified. Those top five websites out there published

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Speaker 1: together eighty images per hour. So GROC is one of

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Speaker 1: the biggest deep fake producers on the Internet today.

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Speaker 2: Where are these images showing up?

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Speaker 3: You know, to the uninitiated people may not know the

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Speaker 3: sort of interconnects between XAI the AI company, and x

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Speaker 3: the social media platform, for example, but they are now

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Speaker 3: kind of one entity as they combine last year.

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Speaker 1: Well, that connection is exactly why x has become one

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Speaker 1: of the biggest deep fake websites on the internet. Anybody

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Speaker 1: can well not anybody now, now you have to pay

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Speaker 1: to do this, But over the last couple of days,

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Speaker 1: anybody could just at rock on the X platform and say,

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Speaker 1: put this woman in a bikini under an image that

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Speaker 1: a woman had post on the internet. And a lot

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Speaker 1: of women women primarily are the victims of this, took

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Speaker 1: to complaining to GROC directly and arguing with GROC in

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Speaker 1: their comments.

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Speaker 7: GROC would apologize for posting.

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Speaker 1: These images, but then continue posting them and not take

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Speaker 1: down Many of the images that the women felt were violating.

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Speaker 3: Succeed That did X or XAI respond to our reporting,

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Speaker 3: engage with us on it.

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Speaker 1: X has not responded to our numerous requests for comment

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Speaker 1: over the last couple of days. Elon Musk himself, in

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Speaker 1: a reply to a post on X said anybody using

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Speaker 1: groc to make illegal con tent will suffer the same

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Speaker 1: consequences as if they upload a legal content, and Musk

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Speaker 1: there was referring to allegations we believe of child sexual

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Speaker 1: abuse material that's been posted to X using the croc app.

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Speaker 3: Okay, Bloomberg's the CID Danna Slasio important reporting.

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Speaker 2: Read the story on the Bloomberg terminal on bloombuw dot com.

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Speaker 2: Thank you.

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Speaker 3: Now, coming up from the program, we're going to speak

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Speaker 3: with Snowflake CEO Shrida Ramaswami. Is the company enters into

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Speaker 3: an agreement to buy AI powered platform observe. We'll get

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Speaker 3: his observations on that piece of M and A that's

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Speaker 3: coming up next. You're halfway through the program, and this

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Speaker 3: is Bloomberg Tech. Welcome back to Bloomberg Tech. I've been

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Speaker 3: using the power of my Bloomberg terminal. It's been a

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Speaker 3: long week with cs and Las Vegas. The power of

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Speaker 3: my brain is dwindling. But it tells me that Apple

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Speaker 3: is down for an eighth straight session, which matches the

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Speaker 3: run of declines that Apple saw in May of twenty

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Speaker 3: twenty five.

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Speaker 2: Now using more of the power of the Bloomberg terminal.

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Speaker 3: If Apple was full for a ninth day, we think

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Speaker 3: that would be the longest run of decline since the

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Speaker 3: early nineties. And obviously we are only nine days into

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Speaker 3: twenty twenty six. But so far year to date, this

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Speaker 3: is a stock that's down five and a half percent.

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Speaker 3: You saw the letter from Tim Cook to shareholders talking

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Speaker 3: up the company's progress of late and last year, but

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Speaker 3: right now that stock heading down and will continue to

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Speaker 3: track it. We had some M and A in the

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Speaker 3: last twenty four hours, but it's actually in the AI

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Speaker 3: and software space. Snowflake has agreed to buy Observe, an

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Speaker 3: observability platform and AI powered observability platform. Observability something that's

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Speaker 3: come up a lot recently, particularly in the context of AGENTICAI.

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Speaker 3: When I've been around the table, particularly on the engineering side.

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Speaker 3: Why this piece of M and A? Why now that's

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Speaker 3: sorts of Snowflake CEO Treda Ramaswami. Those are the reasonable questions,

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Speaker 3: right SHREDA I do want to get to defining observability

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Speaker 3: and why it's important.

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Speaker 2: But it's an interesting deal. Why do you do it?

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Speaker 12: Hey?

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Speaker 7: Great to see you.

441
00:24:15,960 --> 00:24:19,560
Speaker 12: Snowflake's acquisition of Observe is a game changer for our customers.

442
00:24:19,800 --> 00:24:24,960
Speaker 12: As you pointed out, observability is basically looking at applications, websites,

443
00:24:25,080 --> 00:24:28,320
Speaker 12: AIA agents to make sure that they are functioning properly.

444
00:24:28,600 --> 00:24:31,479
Speaker 7: It's a diverse and messy problem. It's a big data problem.

445
00:24:32,240 --> 00:24:35,920
Speaker 12: And because observer is built right on top of Snowflake,

446
00:24:36,400 --> 00:24:39,399
Speaker 12: they are able to use our very efficient storage as

447
00:24:39,440 --> 00:24:43,120
Speaker 12: well as computer platform to help customers find problems ten

448
00:24:43,240 --> 00:24:48,040
Speaker 12: times faster, often at three to four x less cost.

449
00:24:49,000 --> 00:24:52,720
Speaker 12: Take a company like Top Golf, it's a customer of

450
00:24:52,760 --> 00:24:56,000
Speaker 12: a Observe. I go to Top Golf all the time

451
00:24:56,160 --> 00:24:58,040
Speaker 12: with my team, not much of a golfer, so I'm

452
00:24:58,040 --> 00:25:00,240
Speaker 12: playing an angry Bird's game or something like that. Top

453
00:25:00,280 --> 00:25:03,359
Speaker 12: Golf used to struggle when these games went down because

454
00:25:03,359 --> 00:25:05,800
Speaker 12: they collected everything in a central place but could not

455
00:25:06,040 --> 00:25:09,480
Speaker 12: identify that a particular booth had problems. They're able to

456
00:25:09,560 --> 00:25:13,080
Speaker 12: do that with Observe in a matter of a few seconds,

457
00:25:13,359 --> 00:25:15,479
Speaker 12: send out a tech team to go fix that problem

458
00:25:16,000 --> 00:25:17,520
Speaker 12: and have happy customers.

459
00:25:17,960 --> 00:25:20,040
Speaker 7: That's the power of this acquisition.

460
00:25:20,560 --> 00:25:25,240
Speaker 12: Increasingly, capabilities like observability are going to be a core

461
00:25:25,480 --> 00:25:29,640
Speaker 12: part of a platform like Snowflake. And we have great

462
00:25:29,680 --> 00:25:34,320
Speaker 12: customers folks like Barclays, Capital One, Commonwealth Bank of America,

463
00:25:34,600 --> 00:25:36,679
Speaker 12: and we're super excited to bring this to over ten

464
00:25:36,720 --> 00:25:40,040
Speaker 12: thousand plus customers on Snowflake SHREDA.

465
00:25:40,160 --> 00:25:41,960
Speaker 3: Let's get it out the way if we can. The

466
00:25:42,080 --> 00:25:45,159
Speaker 3: report saw that it's about a billion dollar deal. What

467
00:25:45,359 --> 00:25:47,800
Speaker 3: was the structure of the deal and the financials on it?

468
00:25:47,880 --> 00:25:48,120
Speaker 2: Please?

469
00:25:49,400 --> 00:25:52,680
Speaker 12: We can comment on the structure of the deal it is,

470
00:25:53,520 --> 00:25:55,840
Speaker 12: you know, some of it will come out in the

471
00:25:56,440 --> 00:25:58,160
Speaker 12: some of it will come out in the filing.

472
00:26:00,080 --> 00:26:03,359
Speaker 3: Signal that there might be some more pieces of M

473
00:26:03,400 --> 00:26:05,520
Speaker 3: and A, particularly on that layer that you sit on

474
00:26:05,680 --> 00:26:07,679
Speaker 3: right you know, as you know very well you were

475
00:26:07,720 --> 00:26:10,440
Speaker 3: there on Monday at the video event Jensen talks about

476
00:26:10,440 --> 00:26:14,480
Speaker 3: the five layer cake and one of the layers you occupy.

477
00:26:15,040 --> 00:26:18,920
Speaker 3: But as observability evidence is, there might be pieces of

478
00:26:19,000 --> 00:26:21,359
Speaker 3: competency missing. How are you going to use M and

479
00:26:21,480 --> 00:26:23,439
Speaker 3: A to plug those gaps.

480
00:26:24,960 --> 00:26:27,640
Speaker 12: We've been pretty open that we want to be an

481
00:26:27,880 --> 00:26:31,600
Speaker 12: end to end data platform for our customers from the moment.

482
00:26:31,720 --> 00:26:34,119
Speaker 12: Data is barn when you interact with an application, for

483
00:26:34,320 --> 00:26:39,320
Speaker 12: example two, bringing it in for analysis, analyzing it, and

484
00:26:39,480 --> 00:26:43,639
Speaker 12: then acting on it increasingly with AI agents. We've been

485
00:26:43,720 --> 00:26:47,800
Speaker 12: systematically going through this process of both acquiring customers and

486
00:26:47,960 --> 00:26:48,600
Speaker 12: building up.

487
00:26:48,480 --> 00:26:50,399
Speaker 7: Functionality from within.

488
00:26:51,040 --> 00:26:55,720
Speaker 12: Things like observability or data clean rooms are adjacent functionality

489
00:26:55,840 --> 00:26:58,720
Speaker 12: that sit on top of a data layer. You will

490
00:26:58,760 --> 00:27:01,520
Speaker 12: continue to be super act about both spinning up new

491
00:27:01,600 --> 00:27:06,560
Speaker 12: projects internally and acquiring companies. I think the environment is

492
00:27:06,680 --> 00:27:09,280
Speaker 12: right for that, and honestly, our customers want that because

493
00:27:09,280 --> 00:27:12,760
Speaker 12: they're spending entirely too much time just stitching things together,

494
00:27:13,240 --> 00:27:15,320
Speaker 12: and we make it seamless and easy to use for that.

495
00:27:16,600 --> 00:27:18,959
Speaker 2: How competitive is that m and a environment right now?

496
00:27:19,040 --> 00:27:22,200
Speaker 3: Because when I look at Observe and what it's offered, you,

497
00:27:22,400 --> 00:27:25,040
Speaker 3: you know, the analysts looked at that deal and said

498
00:27:25,119 --> 00:27:28,160
Speaker 3: completely logical that Snowflake would do it. But one would

499
00:27:28,160 --> 00:27:30,240
Speaker 3: imagine that there would be others in the market for

500
00:27:30,280 --> 00:27:32,400
Speaker 3: a name like Observe as well.

501
00:27:34,400 --> 00:27:34,560
Speaker 2: Well.

502
00:27:34,560 --> 00:27:38,520
Speaker 12: There are many things that we're going for this particular deal.

503
00:27:38,720 --> 00:27:42,359
Speaker 12: As I said earlier, Observer is built on top of Snowflake,

504
00:27:42,440 --> 00:27:44,720
Speaker 12: which means that we didn't have to deal with a

505
00:27:45,160 --> 00:27:49,080
Speaker 12: long integration cycle. The products are super tight. We've been

506
00:27:49,160 --> 00:27:52,760
Speaker 12: collaborating closely with the team as partners for a very

507
00:27:52,800 --> 00:27:56,040
Speaker 12: long time. I've actually gone and visited the Observed team

508
00:27:56,160 --> 00:28:00,400
Speaker 12: multiple times in their location in San Mateo. We felt

509
00:28:00,480 --> 00:28:03,480
Speaker 12: like this was a natural extension of who we were

510
00:28:03,760 --> 00:28:09,280
Speaker 12: as a platform, and we think that Observed, by using

511
00:28:09,480 --> 00:28:13,240
Speaker 12: both their technical capabilities and now also being part of Snowflake,

512
00:28:13,560 --> 00:28:17,639
Speaker 12: can offer an incredibly cost competitive solution. Cost has been

513
00:28:17,680 --> 00:28:20,760
Speaker 12: a big factor in the world of observability, especially with

514
00:28:20,880 --> 00:28:23,920
Speaker 12: things like AI agents, which are complicated pieces of software

515
00:28:24,160 --> 00:28:28,360
Speaker 12: that generate enormous amounts of essentially telemetry information.

516
00:28:28,480 --> 00:28:31,320
Speaker 7: We think this is a good combination that's.

517
00:28:31,240 --> 00:28:34,600
Speaker 3: Worth some academic debate, you know, should I'm not a

518
00:28:34,720 --> 00:28:37,440
Speaker 3: computer scientist or an engineer, right, so so take that

519
00:28:37,520 --> 00:28:42,520
Speaker 3: into account. But the thing that we argue about with

520
00:28:42,640 --> 00:28:45,640
Speaker 3: a truly autonomous AI agent is that it's exactly that

521
00:28:46,160 --> 00:28:51,160
Speaker 3: it is supposed to act. With autonomy and observability raises

522
00:28:51,200 --> 00:28:53,800
Speaker 3: the question of if a human is required at some

523
00:28:53,960 --> 00:28:56,200
Speaker 3: point in the process to check.

524
00:28:56,040 --> 00:28:57,520
Speaker 2: In, what's your point?

525
00:28:58,200 --> 00:29:02,600
Speaker 12: You know, well, it becomes a matter of how many

526
00:29:02,720 --> 00:29:06,560
Speaker 12: people you need in order to accomplish a problem. So

527
00:29:06,920 --> 00:29:09,760
Speaker 12: I live and breathe agent Kai. I mean, I really

528
00:29:09,840 --> 00:29:12,640
Speaker 12: mean that I use these products every single day. And

529
00:29:12,800 --> 00:29:16,240
Speaker 12: the leverage that you get is the game changer. Used

530
00:29:16,240 --> 00:29:18,640
Speaker 12: to be that a support ticket would come and a

531
00:29:18,720 --> 00:29:21,320
Speaker 12: person had to go read the text of what that

532
00:29:21,520 --> 00:29:24,680
Speaker 12: case was, copy and paste that text into multiple tools,

533
00:29:24,840 --> 00:29:27,240
Speaker 12: figure out what is going on, and then perhaps send

534
00:29:27,280 --> 00:29:29,960
Speaker 12: a Slack message over to somebody in order to answer

535
00:29:30,040 --> 00:29:30,520
Speaker 12: a problem.

536
00:29:30,720 --> 00:29:33,560
Speaker 7: A lot of this, as you know, is low value work.

537
00:29:33,760 --> 00:29:37,000
Speaker 12: Copying information from one screen or one app to another

538
00:29:37,440 --> 00:29:40,360
Speaker 12: is just tedious, it's no fun. I think the power

539
00:29:40,440 --> 00:29:43,400
Speaker 12: here with Agentic systems is going to be all that

540
00:29:43,560 --> 00:29:46,160
Speaker 12: boring stuff is going to be automated, so that when

541
00:29:46,160 --> 00:29:48,880
Speaker 12: a problem comes in, you go reach out to the

542
00:29:48,960 --> 00:29:51,280
Speaker 12: ten tools that you need to collect information from, you

543
00:29:51,520 --> 00:29:54,640
Speaker 12: analyze it, and then you show that somebody to that

544
00:29:54,880 --> 00:29:57,360
Speaker 12: person who decides what action to take.

545
00:29:57,800 --> 00:29:59,960
Speaker 7: But furthermore, if they have to go write.

546
00:29:59,760 --> 00:30:02,280
Speaker 12: Some custom gode to solve a new problem, they'll do

547
00:30:02,320 --> 00:30:05,000
Speaker 12: it two three times and turn that into a skill

548
00:30:05,080 --> 00:30:07,480
Speaker 12: that can be used by everybody else. I think it

549
00:30:07,800 --> 00:30:11,440
Speaker 12: leverages people enormously. I can tell you again from personal

550
00:30:11,520 --> 00:30:13,960
Speaker 12: experience that I can get stuff done in a matter

551
00:30:14,000 --> 00:30:16,560
Speaker 12: of a couple of hours on top of Snowflake building

552
00:30:16,680 --> 00:30:19,080
Speaker 12: things that would honestly have taken me two to three

553
00:30:19,160 --> 00:30:21,200
Speaker 12: weeks just last year to get done.

554
00:30:21,480 --> 00:30:26,000
Speaker 3: That's the game changer that's here treat us. Snowflake rose

555
00:30:26,080 --> 00:30:29,200
Speaker 3: forty two percent last year. What are the goals that

556
00:30:29,280 --> 00:30:31,600
Speaker 3: you're holding you and the team to for twenty twenty

557
00:30:31,720 --> 00:30:34,360
Speaker 3: six and the things that you want to achieve this

558
00:30:34,520 --> 00:30:35,040
Speaker 3: year in AI?

559
00:30:37,720 --> 00:30:39,880
Speaker 12: As you know ed, it's important to focus on the

560
00:30:40,000 --> 00:30:44,320
Speaker 12: things that you can control. What we're super excited for

561
00:30:44,480 --> 00:30:47,760
Speaker 12: twenty twenty six is making agent ki.

562
00:30:47,680 --> 00:30:49,840
Speaker 7: Come alive for all our customers.

563
00:30:50,120 --> 00:30:55,520
Speaker 12: Snowflake Intelligence is our agentic data product and it's been

564
00:30:55,560 --> 00:30:58,160
Speaker 12: a game changer again for me to do things like

565
00:30:58,280 --> 00:31:01,560
Speaker 12: do research on customers before I meet with them. This

566
00:31:01,800 --> 00:31:05,720
Speaker 12: is the fastest product in terms of customer adoption and

567
00:31:05,920 --> 00:31:09,400
Speaker 12: revenue in Snowflake's history. We want to make sure that

568
00:31:09,720 --> 00:31:13,080
Speaker 12: we drive adoption of these products, and in turn, what

569
00:31:13,280 --> 00:31:17,480
Speaker 12: this does is it enhances and makes the power of

570
00:31:17,600 --> 00:31:21,360
Speaker 12: data more and more visible to future customers. I expect

571
00:31:21,600 --> 00:31:25,680
Speaker 12: this AI to be a strong pull that makes data

572
00:31:25,800 --> 00:31:29,800
Speaker 12: modernization much much more relevant part every customer and future

573
00:31:29,880 --> 00:31:30,920
Speaker 12: customer of Snowflake.

574
00:31:31,200 --> 00:31:33,280
Speaker 3: It is really that in and yang that I'm super

575
00:31:33,360 --> 00:31:36,840
Speaker 3: excited about. We started the week together in Las Vegas

576
00:31:36,920 --> 00:31:39,440
Speaker 3: at the Nvidia keynote. We end the week together talking

577
00:31:39,520 --> 00:31:42,640
Speaker 3: about the roadback from part four the Snowflake. Snowfake CEO

578
00:31:42,720 --> 00:31:45,280
Speaker 3: Shreeta Ramaswami. Great to have you back on Bloomberg Tech.

579
00:31:45,360 --> 00:31:45,600
Speaker 2: Thank you.

580
00:31:45,720 --> 00:31:47,280
Speaker 3: Now coming up, we're going to take a look at

581
00:31:47,280 --> 00:31:50,200
Speaker 3: the state of media mergers as one of brows sticks

582
00:31:50,240 --> 00:31:53,440
Speaker 3: with its buyout by Netflix and it's trying to shake

583
00:31:53,560 --> 00:31:55,320
Speaker 3: off that paramount takeover.

584
00:31:55,520 --> 00:31:57,920
Speaker 2: We have the details. Next, this is Bloomberg Tech.

585
00:32:05,520 --> 00:32:08,920
Speaker 3: Lairs of Netflix have tumbled nearly twenty eight percent since October,

586
00:32:09,120 --> 00:32:11,880
Speaker 3: but the streaming giant stock still appears to be too

587
00:32:12,040 --> 00:32:16,959
Speaker 3: expensive to many investors. Here of more is Bloomberg Stock Reporter,

588
00:32:17,240 --> 00:32:20,240
Speaker 3: All Things Terminal, authoring Squawk Police Morantz.

589
00:32:20,680 --> 00:32:21,960
Speaker 2: This is a really important write up.

590
00:32:22,080 --> 00:32:23,960
Speaker 3: Right there is so much hype and a lot of

591
00:32:24,000 --> 00:32:27,200
Speaker 3: headlines around what's happening with Warner Brothers Discovery.

592
00:32:27,320 --> 00:32:28,880
Speaker 2: And you do what you needed it.

593
00:32:29,000 --> 00:32:31,840
Speaker 3: You go back to basics, look at the fundamentals, look

594
00:32:31,880 --> 00:32:34,280
Speaker 3: at the stock to must read on the Bloomberg Tamila

595
00:32:34,320 --> 00:32:36,000
Speaker 3: dot com. But just I guess give us the top

596
00:32:36,040 --> 00:32:37,000
Speaker 3: line of the reporting.

597
00:32:37,520 --> 00:32:40,560
Speaker 13: The top line is that this stock has really plunged

598
00:32:41,400 --> 00:32:44,280
Speaker 13: since investors started questioning whether this deal was a good

599
00:32:44,360 --> 00:32:44,960
Speaker 13: idea or not.

600
00:32:45,640 --> 00:32:49,320
Speaker 7: You have questions about cost, You have integration risk as.

601
00:32:49,320 --> 00:32:53,520
Speaker 13: Netflix doesn't have much experienced swallowing large deals, and you

602
00:32:53,640 --> 00:32:58,320
Speaker 13: also have a big regulatory fight that's looming. So there's

603
00:32:58,400 --> 00:33:03,600
Speaker 13: not a lot of extreme enthusiasm about the stock right now.

604
00:33:04,000 --> 00:33:04,880
Speaker 7: In fact, it's down.

605
00:33:05,240 --> 00:33:07,200
Speaker 13: You can see it's down about two percent today on

606
00:33:07,320 --> 00:33:08,280
Speaker 13: an update for the S.

607
00:33:08,320 --> 00:33:09,200
Speaker 7: And P five hundred.

608
00:33:10,240 --> 00:33:11,840
Speaker 3: The people you speak to it in the piece, you know,

609
00:33:12,480 --> 00:33:14,960
Speaker 3: one of the anecdotes is like Netflix is not screaming

610
00:33:15,120 --> 00:33:18,000
Speaker 3: by right now. Right But you also, I guess, look

611
00:33:18,040 --> 00:33:22,040
Speaker 3: at multiples, current multiples and historic multiples and compare them.

612
00:33:22,320 --> 00:33:23,200
Speaker 2: What would that tell us?

613
00:33:24,360 --> 00:33:27,240
Speaker 7: So there are various different kinds of multiples.

614
00:33:27,520 --> 00:33:30,959
Speaker 13: But if you look at a basic price to earnings ratio,

615
00:33:31,480 --> 00:33:35,600
Speaker 13: the stock is not expensive historically, but Nora is it

616
00:33:35,800 --> 00:33:36,360
Speaker 13: very cheap.

617
00:33:36,680 --> 00:33:37,720
Speaker 7: It's training a.

618
00:33:37,760 --> 00:33:41,920
Speaker 13: Bit below its historic norm, but not enough to really

619
00:33:42,080 --> 00:33:42,800
Speaker 13: grab people.

620
00:33:44,520 --> 00:33:47,280
Speaker 3: Bloomberg's police morans is awesome and it's been great to

621
00:33:47,320 --> 00:33:49,040
Speaker 3: have you back on bloombog take this Friday.

622
00:33:49,360 --> 00:33:52,000
Speaker 2: Thank you very much. I want to get deep. You're welcome.

623
00:33:52,680 --> 00:33:54,080
Speaker 2: Let's get deeper into that deal.

624
00:33:54,240 --> 00:33:57,600
Speaker 3: Part the future of streaming the Warner Brothers Discovery Saga

625
00:33:57,960 --> 00:34:00,320
Speaker 3: and then the two bids Matthew Dolgan, morning Star Senior

626
00:34:00,320 --> 00:34:03,000
Speaker 3: around equity and this covering communication services.

627
00:34:03,600 --> 00:34:06,080
Speaker 2: Felice did a good job right going over the stock.

628
00:34:06,200 --> 00:34:08,520
Speaker 3: But there's a piece of the deal that I asked

629
00:34:08,560 --> 00:34:12,400
Speaker 3: the team to find someone to dig in with us on,

630
00:34:12,680 --> 00:34:17,279
Speaker 3: which is do we value the cable networks part of

631
00:34:17,320 --> 00:34:19,000
Speaker 3: Warner Brothers Discovery at zero?

632
00:34:19,719 --> 00:34:20,560
Speaker 2: Which paramount?

633
00:34:20,560 --> 00:34:23,000
Speaker 3: When it reaffirmed it's thirty dollars a share bid came

634
00:34:23,040 --> 00:34:24,920
Speaker 3: out with your take on that.

635
00:34:25,840 --> 00:34:29,719
Speaker 14: No, we don't think it should be valued at zero. However, well,

636
00:34:29,760 --> 00:34:31,719
Speaker 14: first of all, there is a risk that it could be.

637
00:34:31,960 --> 00:34:33,520
Speaker 14: We don't think that that's right. But this is going

638
00:34:33,600 --> 00:34:36,560
Speaker 14: to be a very heavily debt laiden company and that

639
00:34:36,800 --> 00:34:39,439
Speaker 14: leads to more risk, and so it's possible that zero

640
00:34:39,960 --> 00:34:41,960
Speaker 14: it would be in the future. We don't think that's

641
00:34:41,960 --> 00:34:44,839
Speaker 14: the most likely thing, but there is question of whether

642
00:34:44,920 --> 00:34:47,279
Speaker 14: it's worth two dollars a share or three dollars a

643
00:34:47,400 --> 00:34:52,520
Speaker 14: share or more and that matters because that is what's

644
00:34:52,600 --> 00:34:55,640
Speaker 14: making up the difference between what Netflix offered for the

645
00:34:55,680 --> 00:34:58,239
Speaker 14: portion of Warner Brothers Discovery that it wants versus what

646
00:34:58,360 --> 00:35:01,520
Speaker 14: Paramount offered for the your company. And so there are

647
00:35:01,520 --> 00:35:03,640
Speaker 14: a few different moving parts as you compare the deals,

648
00:35:03,640 --> 00:35:06,480
Speaker 14: which are not apples to apples. But it doesn't have

649
00:35:06,560 --> 00:35:09,399
Speaker 14: to be zero. If it's one dollar, like Paramount has

650
00:35:10,400 --> 00:35:13,439
Speaker 14: said previously, and this week with versants trading, that adds

651
00:35:13,480 --> 00:35:18,920
Speaker 14: another potential new data point, but it can be definitely

652
00:35:19,040 --> 00:35:20,800
Speaker 14: less than what the thirty is combined.

653
00:35:21,040 --> 00:35:23,120
Speaker 3: So I'm just going to recap for the Bloomberg Tech audience.

654
00:35:23,120 --> 00:35:26,600
Speaker 3: Here's where we stand. Netflix has offered twenty dollars twenty

655
00:35:26,640 --> 00:35:30,200
Speaker 3: seven dollars a share for the streaming and the studios

656
00:35:30,320 --> 00:35:33,120
Speaker 3: and would plan to spin out the legacy cable networks.

657
00:35:33,440 --> 00:35:35,520
Speaker 3: And as we've said for five days in a row

658
00:35:35,600 --> 00:35:39,560
Speaker 3: on Bloomberg Tech, Paramounts Guidance want the whole enchilada, all

659
00:35:39,640 --> 00:35:42,360
Speaker 3: of it, but at thirty dollars a share, saying that

660
00:35:42,480 --> 00:35:48,040
Speaker 3: they assume a zero dollar value for that cable network.

661
00:35:49,280 --> 00:35:51,920
Speaker 3: You're covering the space, and you're covering these names, and

662
00:35:51,920 --> 00:35:54,600
Speaker 3: you're covering the deal. It's actually a very reasonable question

663
00:35:54,680 --> 00:35:59,000
Speaker 3: of what happens next. Because Warner Brothers board sent a

664
00:35:59,120 --> 00:36:02,879
Speaker 3: letter reject the Paramount offer and explained why. The next

665
00:36:03,000 --> 00:36:07,320
Speaker 3: day Paramounts guidence reaffirm that offer. We just continue in

666
00:36:07,360 --> 00:36:07,880
Speaker 3: that cycle.

667
00:36:08,520 --> 00:36:10,759
Speaker 2: Well, we continue in that cycle for a little bit.

668
00:36:10,920 --> 00:36:14,320
Speaker 14: Yes, January twenty first is a critical date that we

669
00:36:14,360 --> 00:36:17,160
Speaker 14: should circle on our calendars. That's when the tender offer

670
00:36:17,960 --> 00:36:21,040
Speaker 14: for the Warner Brothers Discovery shares by Paramount is currently

671
00:36:21,160 --> 00:36:23,879
Speaker 14: set to expire. So we'll see what happens at that point.

672
00:36:24,080 --> 00:36:27,640
Speaker 14: It doesn't appear that right now Paramount is likely to

673
00:36:29,080 --> 00:36:31,480
Speaker 14: get a sufficient number of shares by that date, but

674
00:36:31,600 --> 00:36:34,279
Speaker 14: they can extend it, extend the date, and they can

675
00:36:34,360 --> 00:36:36,919
Speaker 14: also well at any time. But that's when we would

676
00:36:36,920 --> 00:36:40,200
Speaker 14: expect potentially they would look at increasing their offer to

677
00:36:40,280 --> 00:36:43,080
Speaker 14: beyond thirty dollars a share. And so, yes, we do

678
00:36:43,239 --> 00:36:45,640
Speaker 14: kind of wait and see the next development. I don't

679
00:36:45,920 --> 00:36:48,120
Speaker 14: really expect anything until close to the twenty first as

680
00:36:48,160 --> 00:36:52,480
Speaker 14: far as new information. But at this point we'll see

681
00:36:52,480 --> 00:36:54,480
Speaker 14: where Paramount is on the shares that are being tendered,

682
00:36:54,560 --> 00:36:57,360
Speaker 14: and then the ball is kind of it's kind of

683
00:36:57,520 --> 00:37:00,080
Speaker 14: in its court as far as whether it wants to

684
00:37:00,120 --> 00:37:04,120
Speaker 14: do something else in the interim before later this spring

685
00:37:04,239 --> 00:37:08,479
Speaker 14: or summer, when Warner Brothers Discovery shareholders are likely to vote,

686
00:37:08,480 --> 00:37:12,040
Speaker 14: and that Netflix deal that the Warner board has recommended.

687
00:37:13,080 --> 00:37:15,240
Speaker 3: We spend a lot of time this week talking about

688
00:37:15,760 --> 00:37:19,000
Speaker 3: the Warner Brothers Discovery rationale for rejecting Paramoun's guide on

689
00:37:19,080 --> 00:37:23,960
Speaker 3: so we included debt. It included a discussion either negative

690
00:37:24,040 --> 00:37:26,680
Speaker 3: or positive about Larry Allison being the bank stop on

691
00:37:26,719 --> 00:37:29,320
Speaker 3: the deal, and it talks about the restrictive covenants on

692
00:37:29,600 --> 00:37:33,400
Speaker 3: Warner Brothers not being able to make major investments what

693
00:37:33,640 --> 00:37:36,520
Speaker 3: in this interim period. But the question actually I have

694
00:37:36,760 --> 00:37:40,840
Speaker 3: is who would get the most out of those assets HBO, Max,

695
00:37:40,960 --> 00:37:43,959
Speaker 3: the catalog and then the studios, Like which company would

696
00:37:44,000 --> 00:37:46,439
Speaker 3: be better at getting them out into the real world

697
00:37:46,520 --> 00:37:47,040
Speaker 3: to your mind?

698
00:37:48,440 --> 00:37:51,480
Speaker 14: Well, we think Paramount is the company that I guess

699
00:37:51,719 --> 00:37:54,200
Speaker 14: needs them more and probably would benefit from them more.

700
00:37:54,280 --> 00:37:58,000
Speaker 14: As far as getting them out into the world, that well,

701
00:37:58,239 --> 00:38:02,760
Speaker 14: arguably would be more Netflix a side with the business

702
00:38:02,800 --> 00:38:04,719
Speaker 14: they already have in place in the streaming service and

703
00:38:04,760 --> 00:38:07,600
Speaker 14: subscribers they already have in place. But if you're looking

704
00:38:07,680 --> 00:38:11,200
Speaker 14: from a business and an operating point of view, we

705
00:38:11,320 --> 00:38:14,560
Speaker 14: think Netflix has far less to gain as it does

706
00:38:14,640 --> 00:38:17,600
Speaker 14: that than Paramount would, which really needs the scale and

707
00:38:17,800 --> 00:38:22,360
Speaker 14: has I think a better combined type of offering, whereas

708
00:38:22,400 --> 00:38:26,239
Speaker 14: Netflix incrementally is sure it adds quite a bit to

709
00:38:26,360 --> 00:38:28,560
Speaker 14: it as far as incrementally on how much more revenue

710
00:38:28,600 --> 00:38:31,640
Speaker 14: that bring in, whether they're cannibalizing some of the profits

711
00:38:31,680 --> 00:38:34,200
Speaker 14: that Warner takes in with some of its licensing. The

712
00:38:34,320 --> 00:38:36,680
Speaker 14: reasons why we think that from a business standpoint, Netflix

713
00:38:36,680 --> 00:38:40,520
Speaker 14: doesn't benefit quite as much as Paramount would. That's of

714
00:38:40,600 --> 00:38:44,560
Speaker 14: course a maybe different question than what the consuming public

715
00:38:44,880 --> 00:38:47,600
Speaker 14: and how they benefit from the content and the availability

716
00:38:47,640 --> 00:38:48,440
Speaker 14: and the pricing for that.

717
00:38:48,560 --> 00:38:49,080
Speaker 2: It's out there.

718
00:38:50,040 --> 00:38:54,720
Speaker 3: You heard Felice's reporting on the stock why very quickly

719
00:38:54,760 --> 00:38:57,520
Speaker 3: we just have thirty seconds? Has that sentiment with Netflix

720
00:38:57,719 --> 00:39:01,280
Speaker 3: soured since the deal first kind of got announced.

721
00:39:02,400 --> 00:39:05,120
Speaker 14: As far as the deal goes, I think there's been

722
00:39:05,200 --> 00:39:08,359
Speaker 14: some fear that Netflix would even pay more. There's also

723
00:39:08,440 --> 00:39:10,920
Speaker 14: some fear they've overpaid with what they've agreed to already,

724
00:39:11,120 --> 00:39:13,759
Speaker 14: so that may not be worth it. I also think

725
00:39:13,800 --> 00:39:16,239
Speaker 14: it's maybe just brought to light some things that we

726
00:39:16,400 --> 00:39:19,640
Speaker 14: had been thinking about for some time, even apart from

727
00:39:19,719 --> 00:39:22,880
Speaker 14: what's going on with Warner, which is the growth is

728
00:39:22,920 --> 00:39:25,560
Speaker 14: set to slow at Netflix, and therefore the multiples that

729
00:39:25,600 --> 00:39:28,680
Speaker 14: it had historically aren't necessarily justified today. And so if

730
00:39:28,719 --> 00:39:30,920
Speaker 14: it is at lower motiles now, which it certainly is

731
00:39:31,080 --> 00:39:34,480
Speaker 14: where compared where it's been historically, that's probably justified. Being

732
00:39:34,520 --> 00:39:36,680
Speaker 14: the growth outlook now versus in the past.

733
00:39:37,360 --> 00:39:38,239
Speaker 2: We got to leave it there.

734
00:39:38,280 --> 00:39:40,040
Speaker 3: But that brings us a nicey full circle to what

735
00:39:40,120 --> 00:39:41,799
Speaker 3: police was talking about at the start of the block.

736
00:39:41,840 --> 00:39:43,839
Speaker 2: Matthew Dog in the morning, so thank you very much.

737
00:39:43,920 --> 00:39:46,399
Speaker 3: Now coming up, Intel CEO was at the White House

738
00:39:46,719 --> 00:39:50,399
Speaker 3: to deliver a progress report on its activities. The US

739
00:39:50,520 --> 00:39:54,160
Speaker 3: government is its big shareholder that it's being accounting to.

740
00:39:54,760 --> 00:40:03,320
Speaker 3: Bit more on that next, as the Bloomberg Tech for

741
00:40:03,440 --> 00:40:08,000
Speaker 3: Talking Tech first up, Vodafone Idea is considering raising debt

742
00:40:08,080 --> 00:40:09,720
Speaker 3: financing to accelerate growth.

743
00:40:10,120 --> 00:40:11,200
Speaker 2: That's according to sources.

744
00:40:11,480 --> 00:40:14,800
Speaker 3: This comes after Delhi decided to cap annual payouts for

745
00:40:14,920 --> 00:40:19,000
Speaker 3: past spectrum fees, forerring a lifeline to the country's third

746
00:40:19,120 --> 00:40:22,760
Speaker 3: place carrier that's out in India. Plus compensation for Apple

747
00:40:22,840 --> 00:40:26,319
Speaker 3: CEO Tim Cook held steady at a cool seventy four

748
00:40:26,440 --> 00:40:29,319
Speaker 3: million dollars last year. Just three million of that comes

749
00:40:29,360 --> 00:40:31,640
Speaker 3: from salary, though the rest is in the form of

750
00:40:31,719 --> 00:40:34,120
Speaker 3: stock awards. You may remember a few years ago Cook

751
00:40:34,280 --> 00:40:37,839
Speaker 3: was criticized for pay packages close to one hundred million

752
00:40:37,840 --> 00:40:42,719
Speaker 3: dollars apiece, prompting him to request a compensation reduction, and

753
00:40:42,840 --> 00:40:46,400
Speaker 3: TSMC provided an upbeat sales forecast. The company reported a

754
00:40:46,520 --> 00:40:49,680
Speaker 3: roughly twenty percent rise in the December quarter revenues based

755
00:40:49,719 --> 00:40:53,520
Speaker 3: on calculations off monthly figures. The company reports full quarterly

756
00:40:53,520 --> 00:40:56,760
Speaker 3: earnings next week. All this coming after chip makers projected

757
00:40:56,800 --> 00:41:01,320
Speaker 3: optimism for AI demand at cs this week. Now, sticking

758
00:41:01,360 --> 00:41:04,440
Speaker 3: with Chips, shares of Intel up today in a big way.

759
00:41:04,520 --> 00:41:07,719
Speaker 3: Of the company's CEO delivered a progress report on its

760
00:41:07,760 --> 00:41:11,320
Speaker 3: company's U line of processors. At the White House, Libutan

761
00:41:11,400 --> 00:41:14,560
Speaker 3: met with President Trump and Commerce Secretary Howard Lutnik. After

762
00:41:14,640 --> 00:41:18,279
Speaker 3: the meeting, Trump praised Intel and said the government was

763
00:41:18,400 --> 00:41:19,880
Speaker 3: proud to be a shareholder.

764
00:41:20,239 --> 00:41:20,600
Speaker 2: For more.

765
00:41:20,840 --> 00:41:23,520
Speaker 3: Let's get out to Bloomberg's Ian King, who of course

766
00:41:23,640 --> 00:41:26,040
Speaker 3: leads our coverage of Chips. Actually, where I wanted to

767
00:41:26,040 --> 00:41:29,880
Speaker 3: start in is the President in his post talked about

768
00:41:30,000 --> 00:41:34,000
Speaker 3: the result of the government's investment in Intel, yielding I

769
00:41:34,040 --> 00:41:37,080
Speaker 3: think said tens of billions of dollars for the American people.

770
00:41:37,640 --> 00:41:39,840
Speaker 3: It's important probably to do the math on what the

771
00:41:39,960 --> 00:41:41,640
Speaker 3: actual return would be at this stage.

772
00:41:41,680 --> 00:41:41,960
Speaker 2: Please.

773
00:41:43,000 --> 00:41:49,520
Speaker 11: Yeah, no, I mean, as taxpayers, we own currently about

774
00:41:49,560 --> 00:41:52,880
Speaker 11: eleven billion dollars worth of Intel stock, and that's a

775
00:41:53,000 --> 00:41:56,480
Speaker 11: nice return, roughly twice what we owned when we made

776
00:41:56,840 --> 00:42:00,400
Speaker 11: the investment as a nation last year, and not in

777
00:42:00,440 --> 00:42:03,520
Speaker 11: the tens of billions. For that to happen, obviously Intel's

778
00:42:03,520 --> 00:42:05,120
Speaker 11: share price would have to go up a lot more,

779
00:42:05,640 --> 00:42:11,400
Speaker 11: and or some ex extra kind of arrangements that exists

780
00:42:11,400 --> 00:42:12,360
Speaker 11: would have to be bigger.

781
00:42:14,120 --> 00:42:16,760
Speaker 3: All the same that there has been some stock performance

782
00:42:16,840 --> 00:42:20,960
Speaker 3: here since lit Bhutan kind of had this improvement in

783
00:42:21,000 --> 00:42:23,719
Speaker 3: relationship with the president. What do we know about yesterday's

784
00:42:23,760 --> 00:42:27,719
Speaker 3: meeting and kind of where Intel's focus right now in

785
00:42:27,800 --> 00:42:29,640
Speaker 3: what it's trying to tell the US government.

786
00:42:30,360 --> 00:42:32,719
Speaker 11: Yeah, I mean, you know, the fact that he's in

787
00:42:32,800 --> 00:42:36,239
Speaker 11: the White House is important because that is basically drawing

788
00:42:36,280 --> 00:42:39,120
Speaker 11: attention to the fact that this investment that we saw

789
00:42:39,200 --> 00:42:42,600
Speaker 11: last year from the US government from in Nvidia, from

790
00:42:42,719 --> 00:42:47,120
Speaker 11: SoftBank has really stabilized Intel's kind of balance sheet, and

791
00:42:47,239 --> 00:42:49,840
Speaker 11: so it's in a much stronger position than it was.

792
00:42:50,680 --> 00:42:53,200
Speaker 11: But where we are and where we really need to

793
00:42:53,239 --> 00:42:56,160
Speaker 11: be is to have much better operations and much better

794
00:42:56,239 --> 00:42:58,400
Speaker 11: performance from the company, and that is going to take

795
00:42:58,480 --> 00:43:01,600
Speaker 11: new products. You and I were at CES this week

796
00:43:01,680 --> 00:43:03,400
Speaker 11: and we saw Intel come out and say, hey, here

797
00:43:03,440 --> 00:43:05,640
Speaker 11: are our new chips. These are the ones we promised.

798
00:43:05,680 --> 00:43:08,160
Speaker 11: These are the ones that they are going to deliver. Obviously,

799
00:43:08,360 --> 00:43:10,360
Speaker 11: three or four days into that launch, we don't know

800
00:43:10,480 --> 00:43:12,239
Speaker 11: how well they're going to do yet, but at least

801
00:43:12,320 --> 00:43:15,600
Speaker 11: in terms of the specs, way better than the position

802
00:43:15,719 --> 00:43:16,800
Speaker 11: that Intel has been in.

803
00:43:17,719 --> 00:43:20,360
Speaker 3: And again a large portion of the government's ownership of

804
00:43:20,440 --> 00:43:24,040
Speaker 3: Intel is contingent on performance of those goals. Bloomberg Z

805
00:43:24,160 --> 00:43:26,399
Speaker 3: and King, Happy Friday to you and thank you very much.

806
00:43:26,960 --> 00:43:29,120
Speaker 3: That does it for this edition of Bloomberg Tech in

807
00:43:29,200 --> 00:43:31,719
Speaker 3: what's been a monster week. Frankly, just back from Las

808
00:43:31,800 --> 00:43:34,280
Speaker 3: Vegas and see yes and the headlines keep coming, particularly

809
00:43:34,600 --> 00:43:37,600
Speaker 3: in the world of AI. Great place to recap the show,

810
00:43:37,880 --> 00:43:40,640
Speaker 3: the week, the interviews and the reporting is on the podcast.

811
00:43:40,920 --> 00:43:43,320
Speaker 3: You know where to find it online, Apple, Spotify, iHeart

812
00:43:43,480 --> 00:43:44,720
Speaker 3: and all the Bloomberg platforms.

813
00:43:44,760 --> 00:43:46,840
Speaker 2: Happy Friday. This is Bloomberg