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

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

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Speaker 1: and ev Loow in San Francisco.

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Speaker 2: This is Bloomberg Tech coming up.

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Speaker 3: Amazon kicks off what is likely to be one of

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Speaker 3: the biggest corporate bond offerings ever.

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Speaker 2: In a bid to pay for the AI boom.

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Speaker 4: Plus, Google introduces AI agents across the Pentagon's workforce to

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Speaker 4: automate routine jobs. This is the Defense Department's fight with

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Speaker 4: anthropics drags.

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Speaker 3: On, and Hewlett Packard Enterprise projects sales topping estimates on

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Speaker 3: AI hardware demand. We'll break it all down with the CEO,

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Speaker 3: but now we break.

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Speaker 5: Down volatility in the market.

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Speaker 4: We once more focus in on the Middle East, the

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Speaker 4: conflict with Iran and the fact that it has been

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Speaker 4: upped in terms of aggressive force coming from the US

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Speaker 4: to Iran today, but also aggressive moves in oil markets.

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Speaker 5: Just drag down Brent.

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Speaker 4: Down ten percent, significant volatility in the oil contract, but

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Speaker 4: it means we're actually seeing some push higher in the

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Speaker 4: stock market, a regain on the NASAQ one hundred. In fact,

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Speaker 4: that a second straight day of games ed all of

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Speaker 4: this as we think about really the end goal and

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Speaker 4: whether this will be a short term or long term conflict.

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Speaker 3: I'm looking at the latest and what's been a wave

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Speaker 3: of AI related bond sales and it's Amazon. So this

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Speaker 3: is what Bloomberg's reporting, both US dollars denominated and Euro

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Speaker 3: denominated bonds in US eleven tranches of high grade the

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Speaker 3: longest duration I think twenty fifty six with a one

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Speaker 3: point five to five percent yield over treasuries.

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Speaker 2: But the goal is to raise up to forty.

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Speaker 3: Two billion dollars, maybe up to ten billion euros. And

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Speaker 3: we know the story, right, capital expenditures this year six

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Speaker 3: hundred and fifty billion dollars or so across the hyperscalers,

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Speaker 3: and many of them have looked to debt markets, looked

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Speaker 3: to raise money through bond saleales to finance that capex.

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Speaker 2: There's a lot to dig into here, there is, and.

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Speaker 4: We're going to continue the conversation. Therefore, Edward Robert Schiffman

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Speaker 4: and bretting Bag Intelligence, he's out with the reaction to

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Speaker 4: this morning, saying that the bond cell is quote likely

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Speaker 4: to draw a considerable demand despite a growing absolute debt

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Speaker 4: load that's poised to increase again next year as spending rises.

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Speaker 4: That the stock had been punished when they told us

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Speaker 4: that they were going to be spending up to two

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Speaker 4: hundred billion on capital expenditures.

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Speaker 5: But the bond market is going to lap this up.

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Speaker 6: It feels like, yeah, you know, My first thought was, Wow,

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Speaker 6: they must be sewing.

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Speaker 7: A lot of books.

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Speaker 6: My second thought is, this is so wildly bullish for

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Speaker 6: the credit markets and in particular for tech, impossibly the

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Speaker 6: worst potential macro environment. Bondholders are coming in saying we

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Speaker 6: want to own you. We want to own you in size,

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Speaker 6: and we're confident that your future growth plans are going

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Speaker 6: to play out.

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Speaker 5: And even though.

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Speaker 6: There's so much spending and we know more bonds are

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Speaker 6: likely to becoming, people are going to be lining up

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Speaker 6: for this deal.

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Speaker 3: I actually just misspoke at the board. I said twenty

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Speaker 3: fifty six, Actually twenty seventy six, the longest maturity that

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Speaker 3: Bloomberg's reporting Amazon's thinking about, the one that would yield

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Speaker 3: one point five five percent over treasuries. How does this

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Speaker 3: kind of stack up in the history of corporate debt markets,

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Speaker 3: recent and historic? Right, we talked a little bit about

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Speaker 3: what Alphabet's done most recently. Oracle is a slightly different

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Speaker 3: credit profile, but like a lot of focus on that too.

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Speaker 6: Well, this is going to be the biggest bond deal

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Speaker 6: of the year, it looks like by far, and I'll

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Speaker 6: tell you, I think people just need to get used

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Speaker 6: to this. These hyperscalers are effectively becoming serial issuers. The

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Speaker 6: one hundred billion dollar plus debt club is growing. So

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Speaker 6: not only do we have new current larger needs, but

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Speaker 6: just think about it, over the next handful of years,

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Speaker 6: as bonds mature, there's always going to be a constant

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Speaker 6: need to refinance and push out the curve. Amazon itself

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Speaker 6: is twenty billion dollars in debt maturing over the next

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Speaker 6: three years, so this is going to be a constant

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Speaker 6: and look for more of this action. Again, I think

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Speaker 6: bondholders can take huge advantage of this. The type of

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Speaker 6: concessions that are being offered here seem wildly wide to me.

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Speaker 6: I actually think the demand is going to be so

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Speaker 6: strong that they're going to come in dramatically, maybe twenty

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Speaker 6: thirty plus basis points, and.

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Speaker 5: They're tapping just every type of part of the market.

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Speaker 5: You've got like nineteen.

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Speaker 4: Different tranches, so different inspiration dates. Ultimately of when the

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Speaker 4: debt is going to becoming jew but also different currencies

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Speaker 4: as well. How are they managing to satiate all the

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Speaker 4: type of investor out there?

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Speaker 6: You know, we talked about this when Alphabet came that

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Speaker 6: they were doing a small tranches of Swiss francs and

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Speaker 6: British pounds and why they weren't tapping the Euro market.

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Speaker 6: You know, the r market is widely I think underinvested

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Speaker 6: in technology, and they're somewhat probably price agnostic. They're willing

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Speaker 6: to take down any Amazon bonds across the curve almost

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Speaker 6: at any price because they're so underweighted. So I think

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Speaker 6: there's a huge on tap market there that we're going

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Speaker 6: to see other companies come and grab as well.

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Speaker 3: That's the Bloomberg Intelligence react from Robert Schiffman. I would

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Speaker 3: say the details of Amazon's bond sale are according to

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Speaker 3: Bloomberg sources, and Bloomberg saying that the US side at

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Speaker 3: least mike price. Today a separate story, a court ruled

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Speaker 3: that Perplexity must stop using its Comet web browser agent

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Speaker 3: to make purchases on Amazon's marketplace. Amazon sued, accusing Perplexity

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Speaker 3: of computer fraud for not disclosing when Comet was shopping

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Speaker 3: for a user and refusing to stop when asked. The

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Speaker 3: order also bars access to password protected parts of Amazon

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Speaker 3: systems and requires destruction of Amazon data, with enforcement pause

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Speaker 3: for a week to allow for an appeal. Perplexity has

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Speaker 3: yet to respond to a messages seeking comment.

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Speaker 4: Cara, it's avas company. Then, of course it's about e

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Speaker 4: commerce and about cloud. Let's talk about Oracle now, because

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Speaker 4: it's about cloud and software and its earnings are after

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Speaker 4: the bell. It comes after recent Bloomberg reporting that Oracle

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Speaker 4: and OpenAI have ended plans to expand that flagship data

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Speaker 4: center project in Texas. While will investors be looking for

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Speaker 4: in the company's results, We want to talk it all

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Speaker 4: through with Gabrielle Borge's software sector analysts at Goldman Sachs Research.

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Speaker 4: How much of this is a test of Oracles all

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Speaker 4: bread and butter software or a test of how much

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Speaker 4: investors are willing to well stom at the capex of

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Speaker 4: getting into the cloud and furthering the AI focus.

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Speaker 8: Yeah, Hi, it's great to be back. Good morning. I

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Speaker 8: think you touched on it perfectly there where the capex

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Speaker 8: makes sense if you can justify the unit economics that

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Speaker 8: are attached to the AI infrastructure projects, and so Oracle's

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Speaker 8: given us a couple of nuggets in this regard. They've

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Speaker 8: taught us that they expect to be able to spend

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Speaker 8: less than one hundred billion in debt raised in order

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Speaker 8: to be able to fulfill north of five hundred billion

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Speaker 8: in cloud backlog that's now on their balance sheet. Those

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Speaker 8: are commitments from costomers for the next five six years.

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Speaker 8: They've also taught us that the gross margin on these

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Speaker 8: projects is thirty to forty percent over the lifetime of

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Speaker 8: a contract, so that's typically five to six years. And

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Speaker 8: so if you put those two data points together, whatever

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Speaker 8: incremental information we can get from the management team tonight

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Speaker 8: that helps make this less of a black box, I

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Speaker 8: think we'll go along way.

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Speaker 4: Because we are worried about turning cash flow negative, We

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Speaker 4: are worried about maybe some of the ability to drive

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Speaker 4: revenue growth to substantiate the capital expenditure spend. What do

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Speaker 4: you need to see in terms of growth and pointing

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Speaker 4: to growth because the stock is actually pretty cheap now.

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Speaker 8: Yeah, So when you actually break down the phases of

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Speaker 8: the different facilities coming online, so Abilene, for example in Texas,

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Speaker 8: that's a one point to big our capacity facility where

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Speaker 8: Oracle has already said that the four point five gigawats

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Speaker 8: that will be coming online to fulfill their existing commitments.

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Speaker 8: They already have all the leasing agreements for that already

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Speaker 8: signed and ready to go. What happens next is as

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Speaker 8: the revenue comes online, we expect to see is really

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Speaker 8: nice gross profit acceleration in Oracle's four co business that

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Speaker 8: includes the big part of OCI that's tied to some

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Speaker 8: of these contracts, that's Oracle Cloud. We've spect to see

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Speaker 8: that as we go through the year. And so what

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Speaker 8: we've noticed over the last three or four quarters with

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Speaker 8: our core is that it's less about the specific numbers

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Speaker 8: and some of the short term dynamics in the given

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Speaker 8: quarter and more about what they say about their ability

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Speaker 8: to fulfill some of these contracts over a longer time frame,

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Speaker 8: which is why just taking a step back, I do

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Speaker 8: think you'll see BREST profit acceleration and US as we'll

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Speaker 8: be looking for that as we go towards the end

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Speaker 8: of this year.

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Speaker 3: Gabriella, I hear you on that, but you know, the

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Speaker 3: streets expecting the infrastructure unit to grow almost eighty percent

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Speaker 3: in the quarter, you know what if they're off by

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Speaker 3: a few percentage points, because you know, in general that's

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Speaker 3: been the formula, right, a very high bar for tangible

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Speaker 3: AI related growth in those infrastructure divisions.

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Speaker 8: Yeah, I think you're making a really good point here,

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Speaker 8: because we have seen for a couple of quarters now

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Speaker 8: that the OCI estimate intra quarter can move around, and

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Speaker 8: part of that is the timing of any one piece

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Speaker 8: of facility coming online. I do think though, what we're

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Speaker 8: seeing from a land signal standpoint is still very strong.

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Speaker 8: And coming back to Abiline, Abiline has been rapping really

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Speaker 8: nice over the last three months. The one other thing

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Speaker 8: I'll say is ourcle stock does react to apros our

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Speaker 8: potential delays for example, so as we've gotten more headlines

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Speaker 8: out out of the new flow over the last two

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Speaker 8: three months now, I think expectations do move around. All

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Speaker 8: of which is to say what we've seen during software

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Speaker 8: earning season has got to have a clean print for

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Speaker 8: the stuff to work on the print, and so and

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Speaker 8: I hear you there, it's got to be a clean

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Speaker 8: print for the stock to work on the print.

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Speaker 3: What I reported with the team in the case of

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Speaker 3: Abilene was that simply Oracle and Crusoe decided not to

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Speaker 3: proceed from with an expansion from one point two gig

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Speaker 3: watts to two Google watts, and that Crusoe was negotiating

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Speaker 3: with Meta to come in and take that additional capacity,

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Speaker 3: which many of your colleagues on the cell side pointed

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Speaker 3: out might not have been reflected in RPO anyway. But

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Speaker 3: what the story does highlight, and you made the point

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Speaker 3: a moment ago on the four point five gigawatts agreed,

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Speaker 3: is concentration risk one customer, which is open AI.

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Speaker 2: Is that something you model for, Yeah, we.

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Speaker 8: Do actually in a couple of different ways. So we

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Speaker 8: take a discount to our open AI forecast and to

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Speaker 8: what ourcle has said their open AI backlog is to

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Speaker 8: account for dynamics like the one you're talking about, the

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Speaker 8: extreme customer concentration risk. The other thing I'll mention here though,

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Speaker 8: is Oracle will tell you that they can have fungibility

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Speaker 8: of CAPEX. What does that mean? That means you can

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Speaker 8: actually reconfigure a training cluster to be able to serve

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Speaker 8: inference use cases. It means you can reconfigure for one

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Speaker 8: customer to serve another customer, so there is some fungibility there.

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Speaker 8: What that means is that if AI demand remains strong

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Speaker 8: and that internalizes on enterprise AI option, which we've spoken

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Speaker 8: about before. Then someone should be able to come in

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Speaker 8: and pick up that capex in the event that it

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Speaker 8: needs to be moved around and it comes back to

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Speaker 8: the same air of fungibility.

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Speaker 3: Gary Ellavorg is Golm and Zach's research analyst looking ahead

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Speaker 3: to Oracle, which is after the bell.

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Speaker 2: Thank you very much.

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Speaker 3: Like coming up, we can to have the latest on

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Speaker 3: each expanding use of AI by the Pentagon.

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Speaker 2: That's next. This is Bloomberg Tech.

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Speaker 9: As President Trump declared yesterday, we're crushing the enemy in

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Speaker 9: an overwhelming display of technical skill and military force. We

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Speaker 9: will not relent until the enemy is totally and decisively defeated.

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Speaker 3: That was US Defense Secretary Pete Hegseef weighing in on

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Speaker 3: the war in Iran during a briefing. The Pentagon says

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Speaker 3: it's conducting the most intense day of attacks yet. This

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Speaker 3: is the Islamic Republic has been firing drones and missiles

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Speaker 3: at targets across the Middle East. In return, let's bring

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Speaker 3: in Bloomberg's Washington correspondent, Tyler kendo I saw a lot

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Speaker 3: of the headlines on the terminal this morning, for example,

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Speaker 3: around drone activity in the Gulf the UAE. We continue

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Speaker 3: to hear from this administration on or at least field

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Speaker 3: questions on the timeline for this war.

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Speaker 2: What's the latest we need to know?

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Speaker 8: Yeah, hey, and good morning.

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Speaker 10: Well, as you heard, their secretary Hexcess said that the

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Speaker 10: US will quote not relent until all of its objectives

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Speaker 10: are achieved. After yesterday, President Trump appeared to suggest that

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Speaker 10: the US was ahead of schedule when it comes to

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Speaker 10: maintaining its goals in the region. But then we heard

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Speaker 10: from Iran this morning and a new statement from the

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Speaker 10: country's parliament speaker saying that Iran is quote not at

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Speaker 10: all interested in a potential ceasefire deal, and the IRGC

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Speaker 10: released a statement overnight vowing to continue to block oil exports.

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Speaker 3: Now.

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Speaker 10: President Trump yesterday had threatened to widen the list of

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Speaker 10: US targets to electricity infrastructure if attacks in the Strait

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Speaker 10: of her moves and disruptions do continue. I will point

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Speaker 10: out that the President reiterated that he is prepared to

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Speaker 10: send a naval escort into the Strait to help those

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Speaker 10: tankers through. But when the Joint chiefs of Staffed Chair

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Speaker 10: Dan Kane was asked about this earlier this morning. It

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Speaker 10: does not appear that any of those plans have been

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Speaker 10: put into motion. Bloomberg News is also reporting that electronic

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Speaker 10: warfare is now jamming signals in the Critical Waterway, which

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Speaker 10: at this point really does remain effectively closed, and in Caroline.

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Speaker 10: Within the last hour, we got the headline that G

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Speaker 10: seven nations are now asking the International Energy Agency to

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Speaker 10: prepare scenarios for the potential release of emergency stockpiles as

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Speaker 10: these oil flows continue to be impacted in the region.

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Speaker 4: And the oil market certainly moves and despite the newsflow,

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Speaker 4: Tyler Kendall, we so appreciate it. Let's stick with Defense

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Speaker 4: News because Bloomberg has learned that the Pentagon plans to

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Speaker 4: add Google's AI agents to handle unclassified work and as

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Speaker 4: in talks to bring those tools to the classified and

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Speaker 4: top secret cloud. Bloomber's Katrina Manson has been importing on

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Speaker 4: this and in fact continues to report at the very

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Speaker 4: edge of AI adoption within the Pentagon. Look, what is

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Speaker 4: Emil Michael talking about in terms of Google.

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Speaker 5: Is this because they're having to.

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Speaker 11: Force out anthropic This I think is fair to see

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Speaker 11: as separate where Emil Michael came in in this senior

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Speaker 11: role very focused on AI. Back in August, he told

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Speaker 11: me he was shocked that the Pentagon had adopted so

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Speaker 11: few AI tools. It's brought in chatbots, that's thanks to Gemini.

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Speaker 11: It's had those for the last three months. One point

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Speaker 11: two million people in the Pentagon have used those or

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Speaker 11: in the Defense Department writ large. Now they're adding agents,

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Speaker 11: so that's going to automate some tasks that corporations, of

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Speaker 11: course across the country are already trying.

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Speaker 2: But it is.

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Speaker 11: Interesting timing because at this very moment they're bringing Gemini

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Speaker 11: agents onto unclassified networks and really quite pleased that they

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Speaker 11: have this partner in Google at a time that clearly

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Speaker 11: Anthropic has been cut.

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Speaker 3: Twenty four hours ago on this program, we broke the

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Speaker 3: news of Anthropic suit against the Defense Department. Late yesterday

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Speaker 3: evening we got more of a detailed response. What is

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Speaker 3: the Pentagon's official position and what does it see in

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Speaker 3: this suit from Anthropic against it.

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Speaker 11: Emil Michael told me that he was expecting this. He

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Speaker 11: doesn't think that bringing this to the courts is a

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Speaker 11: way to resolve this problem, and he said, call me

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Speaker 11: quite simply.

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Speaker 5: We're moving on.

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Speaker 11: So I do think that in this news today the

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Speaker 11: focus on Google. Of course, they've recently struck deals with

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Speaker 11: Xai and Open Ai to start operating on the classified cloud.

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Speaker 11: The Pentagon is really saying, we have three partners now

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Speaker 11: that we feel comfortable with and we are not expecting

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Speaker 11: to go back to anthropic.

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Speaker 4: Can you give us the context of just how much

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Speaker 4: AI is playing a role within the very here and

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Speaker 4: now geopolitical issue of Iran and the conflict there.

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Speaker 11: From our reporting, we understand the scent the central US

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Speaker 11: central commander conducting the operations in Iran is relying on

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Speaker 11: a variety of AI tools, and from separate reporting we

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Speaker 11: understand that means mayben smart system. This comes out of

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Speaker 11: Project Maven, an AI effort started in twenty seventeen.

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Speaker 5: You have a book coming out on I Do Yes,

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Speaker 5: I Do Yeah.

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Speaker 11: Thanks, and that helps narrow down time targets. Of course,

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Speaker 11: SKom is hitting I think they've hit five thousand targets

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Speaker 11: in ten days now, so an extraordinary number for the

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Speaker 11: US to be churning through. And at the same time

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Speaker 11: AI is helping with processors that enable fast movement.

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Speaker 3: Bloomers Katrina Manson with the latest reporting deep on what

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Speaker 3: the Pentagon is doing with AI.

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Speaker 2: Thank you very much.

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Speaker 3: Now coming up, AI start up Legora raises five hundred

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Speaker 3: and fifty million dollars, boosting its valuation to five point

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Speaker 3: five to five billion. We speak with the CEO, Max Unistrand. Next,

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Speaker 3: this is Bloomberg tech.

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Speaker 4: Logora, an AI platform built for lawyers, has just raised

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Speaker 4: a massive five hundred and fifty million dollars in a

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Speaker 4: series defunding round, pushing its valuation to an excess of

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Speaker 4: five and a half billion. Joining us now to talk

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Speaker 4: about the raise, the company's growth in the future of

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Speaker 4: AI and laws Lagora CEO Max Unistrand, Max, this is

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Speaker 4: only a few months after you just raised your previous series.

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Speaker 4: I mean, what are you going to be doing this

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Speaker 4: using this money for?

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Speaker 12: We are investing in the United States. We landed in

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Speaker 12: New York a year ago. This was the first week

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Speaker 12: in twenty twenty five New York Legal Week when Lagoire

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Speaker 12: launched in the United States. Now we've opened in New York,

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Speaker 12: We've opened in Denver, Chicago, Houston. We're investing in the

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Speaker 12: product and infrastructure, in customer support teams to support the

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Speaker 12: astronomic demand that we're seeing from the US, and that is.

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Speaker 4: What the vcs are responding to here, the fact that

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Speaker 4: you are signing more and more legal professionals to come

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Speaker 4: and join your platform. But yesterday we just had Harvey

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Speaker 4: AI on and what has it that they're a competitive

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Speaker 4: force out there? How are you seeing your space in

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Speaker 4: the world? How are you unique versus a Harvey for example.

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Speaker 12: So I think legal tech has for a long time

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Speaker 12: been sort of stuck in the pre digital era.

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Speaker 2: With llms. We're seeing a wave of.

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Speaker 12: Companies come and automated tasks, build new systems, and it's

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Speaker 12: a Renner songs for the industry. So there's lots of

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Speaker 12: companies going very quickly. There's a lot of green field opportunity.

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Speaker 12: We started in Stockholm, Sweden, just three years ago, and

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Speaker 12: now we've landed here. We're expanding with big law firms,

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Speaker 12: with big enterprises, and I think the reality is that

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Speaker 12: this market has only done zero to one. Now we

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Speaker 12: are on the journey of one to ten and ten

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Speaker 12: to one hundred, and there's so much left to build

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Speaker 12: and that's our opportunity and our challenge.

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Speaker 2: Max.

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Speaker 3: What's interesting about what you just outlined is different jurisdictions. Right,

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Speaker 3: You're talking about American law, Nordic law, European law.

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Speaker 2: Take us inside building.

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Speaker 3: Legora and the necessary data sets to be able to

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Speaker 3: service the legal profession in those different markets where laws

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Speaker 3: and fightings are completely different.

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Speaker 12: So in order to build systems and agents that perform

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Speaker 12: real work, there's really two different types of data that

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Speaker 12: you need to work with. As you said, it's the

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Speaker 12: local jurisdictional data, the case law, the legislation, and then

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Speaker 12: it's the firms and the enterprises own data. And it's

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Speaker 12: the combination of the two within a platform like Ligora

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Speaker 12: that becomes incredibly valuable because I think these systems used

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Speaker 12: to kind of feel like co pilots. You were working

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Speaker 12: together with them on small tasks, but now you're actually

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Speaker 12: able to offload entire end to end I mean agentic workflows,

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Speaker 12: and the systems are performing and to end work for you.

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Speaker 2: So when we started.

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Speaker 12: Out in Stockholm, Sweden, I mean that market is smaller

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Speaker 12: than one of the big law firms here in New York.

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Speaker 12: So we really practice that muscle. And now that we're here,

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Speaker 12: I'm an engineer, we really lid with a product.

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Speaker 3: Max I suffered through law school and I'm the son

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Speaker 3: of two lawyers, and I brought that up yesterday, so

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Speaker 3: I'll be consistent. And I know from that context the

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Speaker 3: lawyers build by the minute, sometimes scanning barcodes for five

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Speaker 3: minutes of work fifteen minutes work. How do you charge

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Speaker 3: and structure your revenues for the platform?

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Speaker 2: So which charge on a set basis? Today?

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Speaker 12: I think that that is the easiest way for some

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Speaker 12: of these customers to buy. But as we move towards

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Speaker 12: this world where leguore is performing more end to end work,

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Speaker 12: I think both consumption and outcome based pricing models will

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Speaker 12: make a lot of sense for our clients and also

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Speaker 12: in turn their clients. Right the way that law firms

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Speaker 12: are charging for tasks that AI can perform together with

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Speaker 12: them will have to move from a buildable hour model

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Speaker 12: to more of a fix thier outcome based system.

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Speaker 4: Can you just set the scene here, because you're building

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Speaker 4: upon in many ways the innovations of Anthropic, but Anthropic

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Speaker 4: is also doing its own claw plug in for law.

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Speaker 4: How does this competitive environment unfold for the future businesses

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Speaker 4: you want to sign on? How do they decide between

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Speaker 4: you and Thropic harvey what they already have as point solutions?

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Speaker 7: Yeah?

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Speaker 12: So I mean Legorre wouldn't exist without the llms. We

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Speaker 12: were founded in late spring of twenty twenty three off

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Speaker 12: the back of the release of GPT three point five.

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Speaker 12: That was the first time the models became good enough

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Speaker 12: to build any real software. In the beginning, there were

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Speaker 12: so many issues around the models. You had to reduce

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Speaker 12: the hallucinations, you had to guardrail them, We had to

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Speaker 12: build a lot of systems around them. Today we are

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Speaker 12: very focused on bringing the capabilities of those models into

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Speaker 12: an enterprise legal setting. So when we work with the

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Speaker 12: law firm like White and Case or Clearly Gottlib, they

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Speaker 12: are deploying Laguera and they're turning over, you know, billions

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Speaker 12: of dollars using our system. And the interesting thing for

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Speaker 12: us is every quarter the ground shakes a little bit.

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Speaker 12: The new models are capable of doing new things, which

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Speaker 12: needs to be reflected in our product roadmap. We cannot

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Speaker 12: plan for a year ahead because three months, in six

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Speaker 12: months the capabilities might have changed, so our product has

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Speaker 12: to change.

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Speaker 2: Max.

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Speaker 3: We just had twenty seconds. But I think what Caroline's

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Speaker 3: question is, what is your modes? Do you have a mote?

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Speaker 2: I think we have a real mold.

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Speaker 12: Of course, I mean the way that we're deployed within

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Speaker 12: this enterprise is connected to their data, the way we've

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Speaker 12: set up the permissions, whether we work with delivering work.

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Speaker 12: For instance, Deba Boys just launched their client portal on

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Speaker 12: the World where they are connecting their work to their

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Speaker 12: external clients. So there's a real network effect rates in

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Speaker 12: our product tool.

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Speaker 3: Max and Strand of Legora. Thank you very much for

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Speaker 3: your time here on Bloomberg Tech. Now coming up, Apple's

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Speaker 3: latest product is delayed because it waits the debut of

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Speaker 3: its new AI seri. We have more on that Bloomberg

442
00:22:25,080 --> 00:22:27,920
Speaker 3: reporting and some supply chain coverage as well with what's

443
00:22:27,960 --> 00:22:31,120
Speaker 3: going on with Apple. It's halftime. Don't go anywhere, We'll

444
00:22:31,160 --> 00:22:31,800
Speaker 3: be right back.

445
00:22:32,119 --> 00:22:42,800
Speaker 13: This is Bloomberg Tech. Welcome back to Bloomberg Tech.

446
00:22:42,880 --> 00:22:47,120
Speaker 3: Technology stocks actually in particular now pushing higher having seen

447
00:22:47,160 --> 00:22:49,399
Speaker 3: then as that one hundred lower earlier in the session.

448
00:22:49,400 --> 00:22:52,000
Speaker 3: Actually that chart tells the story pretty well, but a

449
00:22:52,040 --> 00:22:55,159
Speaker 3: lot of the story is still what's happening with the

450
00:22:55,200 --> 00:22:58,760
Speaker 3: war in Iran, the movement in oil, general risk sentiment

451
00:22:59,119 --> 00:23:03,000
Speaker 3: around geo politics. After the bell we get Oracle earnings,

452
00:23:03,040 --> 00:23:04,879
Speaker 3: and so once again we kind of focused on the

453
00:23:04,920 --> 00:23:09,200
Speaker 3: AI story. And capital expenditures. Amazon is up by almost

454
00:23:09,240 --> 00:23:11,280
Speaker 3: half percentage point. The big story of the day that

455
00:23:11,320 --> 00:23:14,159
Speaker 3: we hit earlier in the program is them looking at

456
00:23:14,200 --> 00:23:18,399
Speaker 3: a bond sale US dollar denominated and Euro denominated of

457
00:23:18,480 --> 00:23:20,399
Speaker 3: up to forty two billion dollars. And we had the

458
00:23:20,440 --> 00:23:23,640
Speaker 3: details on the program earlier. Oracles treading water of course

459
00:23:23,680 --> 00:23:26,480
Speaker 3: ahead of its print later today. But that is the

460
00:23:26,520 --> 00:23:31,000
Speaker 3: AI story once again, the credit conditions, the capital expenditures,

461
00:23:31,040 --> 00:23:32,919
Speaker 3: and whether this build out is intact Carrott.

462
00:23:33,119 --> 00:23:35,439
Speaker 4: We've got plenty more on the AI story and supply

463
00:23:35,520 --> 00:23:38,680
Speaker 4: chain stories now because Apple's manufacturing pivot to India and

464
00:23:38,680 --> 00:23:41,919
Speaker 4: away from China is picking up momentum. I've phoned production

465
00:23:42,080 --> 00:23:45,080
Speaker 4: in the country SAG fifty twenty five and now accounts

466
00:23:45,240 --> 00:23:46,880
Speaker 4: roughly a quarter of all.

467
00:23:46,760 --> 00:23:48,399
Speaker 5: iPhones made globally. For more.

468
00:23:48,480 --> 00:23:51,520
Speaker 4: Blomberg's Consumer Tech and Apple Managing editor Mark German joins

469
00:23:51,560 --> 00:23:54,120
Speaker 4: us now, and Mark, let's start with the supply chain

470
00:23:54,160 --> 00:23:56,840
Speaker 4: element of things. They've done this swiftly and effectively.

471
00:24:00,040 --> 00:24:02,440
Speaker 14: Yeah, our colleagues in India reporting that now about one

472
00:24:02,480 --> 00:24:06,080
Speaker 14: in four iPhones are produced in India. This is a

473
00:24:06,080 --> 00:24:08,679
Speaker 14: bit of an acceleration. About a year ago it was

474
00:24:08,800 --> 00:24:12,560
Speaker 14: one in five iPhones and a lot of the phone

475
00:24:12,600 --> 00:24:17,480
Speaker 14: production in India. That pivot happened because of tariffs about

476
00:24:17,480 --> 00:24:19,639
Speaker 14: a year ago. Apple looking for ways to produce the

477
00:24:19,680 --> 00:24:23,119
Speaker 14: phones without that potential tariff hit for devices built in

478
00:24:23,240 --> 00:24:25,080
Speaker 14: China and then imported.

479
00:24:26,960 --> 00:24:28,240
Speaker 2: Into the United States.

480
00:24:28,840 --> 00:24:30,840
Speaker 14: And they're going to continue doing more in India. They've

481
00:24:30,880 --> 00:24:35,400
Speaker 14: got AirPod development happening there at some point, more accessory

482
00:24:35,400 --> 00:24:39,240
Speaker 14: development happening there, more underline component development happening there. So

483
00:24:39,480 --> 00:24:42,480
Speaker 14: India not only big for Apple in terms of growth

484
00:24:42,600 --> 00:24:46,040
Speaker 14: in retail, but also for underlying manufacturing, not only in

485
00:24:46,040 --> 00:24:48,359
Speaker 14: India but in other parts of the world as well.

486
00:24:49,920 --> 00:24:52,080
Speaker 3: The other piece of reporting you have out this morning,

487
00:24:52,160 --> 00:24:56,119
Speaker 3: Mark is about the Apple Smart Home Display. Now in

488
00:24:56,160 --> 00:24:59,760
Speaker 3: my house, I right now have a lot of Amazon

489
00:24:59,760 --> 00:25:03,000
Speaker 3: ex devices, and so my understanding is that the Apples

490
00:25:03,040 --> 00:25:07,920
Speaker 3: Smart Home Display is like most akin to Amazon's Echo Show.

491
00:25:09,040 --> 00:25:12,320
Speaker 3: What are you learning? Why delayed? What's behind it?

492
00:25:15,800 --> 00:25:18,960
Speaker 14: Yeah, this device very much based on the new SERI

493
00:25:19,040 --> 00:25:21,160
Speaker 14: and the new AI features Apple has been working on.

494
00:25:21,800 --> 00:25:24,320
Speaker 14: Those features have yet to come to market, those continue

495
00:25:24,320 --> 00:25:26,879
Speaker 14: to be delayed, and as those features get delayed, the

496
00:25:26,920 --> 00:25:29,680
Speaker 14: hardware gets delayed. Because, as you know, Apple's very much

497
00:25:29,720 --> 00:25:33,359
Speaker 14: a hardware and software and services integrated company. It's all

498
00:25:33,600 --> 00:25:36,240
Speaker 14: one kit. It all comes together, so if one piece

499
00:25:36,280 --> 00:25:39,040
Speaker 14: is not working, you can't release the entire thing. It's

500
00:25:39,080 --> 00:25:41,120
Speaker 14: a pretty nifty device. You can walk up to it

501
00:25:41,440 --> 00:25:42,920
Speaker 14: and it can know who you are and give you

502
00:25:43,000 --> 00:25:46,440
Speaker 14: personalized content. And that very much relies on those new

503
00:25:46,520 --> 00:25:48,880
Speaker 14: serie features that the company announced a couple of years ago.

504
00:25:49,119 --> 00:25:50,879
Speaker 14: They're now aiming to roll them out before the end

505
00:25:50,920 --> 00:25:53,240
Speaker 14: of the year. And so this device, instead of launching

506
00:25:53,240 --> 00:25:55,359
Speaker 14: this month, likely to launch around September.

507
00:25:56,800 --> 00:25:59,560
Speaker 3: The famous Mark German and all things Apple, the Apple summary,

508
00:25:59,600 --> 00:26:02,200
Speaker 3: Thank you very much. There's a lot more news out there, Carrot.

509
00:26:02,280 --> 00:26:04,240
Speaker 4: There is ed and it's time now for talking tech

510
00:26:04,280 --> 00:26:07,040
Speaker 4: and first st up by d is exploring options to

511
00:26:07,200 --> 00:26:10,200
Speaker 4: enter the world of F one racing. According to sources,

512
00:26:10,200 --> 00:26:12,560
Speaker 4: it's a bid to boost the ev makers global appeal.

513
00:26:12,920 --> 00:26:14,800
Speaker 4: Now the move would mark a rare attempt by a

514
00:26:14,880 --> 00:26:18,120
Speaker 4: Chinese manufacturer to take on a sport dominated by European

515
00:26:18,119 --> 00:26:18,919
Speaker 4: and US teams.

516
00:26:19,359 --> 00:26:21,440
Speaker 5: Plus China's enthusiasm.

517
00:26:20,840 --> 00:26:23,639
Speaker 4: For all things open, Claw is igniting a stock rally

518
00:26:23,760 --> 00:26:26,480
Speaker 4: among local tech firms moving swiftly to embrace the open

519
00:26:26,480 --> 00:26:29,200
Speaker 4: source AI program, and I remember Openklaw is an agent

520
00:26:29,280 --> 00:26:33,480
Speaker 4: that leverages llms including anthropics Claude to perform daily functions

521
00:26:33,560 --> 00:26:37,440
Speaker 4: and the tool has garnered a cult like status in China.

522
00:26:37,680 --> 00:26:41,440
Speaker 4: And Polymarket is enlisting firms including Pan Andeer to help

523
00:26:41,480 --> 00:26:45,480
Speaker 4: police its force contracts. This is a prediction market faces

524
00:26:45,520 --> 00:26:48,440
Speaker 4: really intense scrutiny over inside of training. It's all according

525
00:26:48,440 --> 00:26:50,439
Speaker 4: to the sources, who say that the firms will prevent

526
00:26:50,600 --> 00:26:54,560
Speaker 4: and report suspicious activity with a monitoring system. The Polymarket

527
00:26:54,640 --> 00:26:55,440
Speaker 4: is building.

528
00:26:55,080 --> 00:26:59,000
Speaker 3: Out ed Okay, coming up, a three month old startup

529
00:26:59,040 --> 00:27:02,760
Speaker 3: founded by AI pioneer Yan Lacoon has raised over a

530
00:27:02,840 --> 00:27:05,720
Speaker 3: billion dollars in seed funding. I'll tell you about the

531
00:27:05,720 --> 00:27:10,680
Speaker 3: company called Advanced Machine Intelligence as next. This is Bloomberg Tech,

532
00:27:19,560 --> 00:27:22,560
Speaker 3: a startup founded by French AI pioneer Yan Lacun has

533
00:27:22,640 --> 00:27:25,720
Speaker 3: raised over a billion dollars in its initial funding round.

534
00:27:25,720 --> 00:27:29,960
Speaker 3: Advanced Machine Intelligence or AMI, is focused on building world

535
00:27:30,040 --> 00:27:33,320
Speaker 3: models that use visual and spatial data. Bloomberg's venture reporter

536
00:27:33,600 --> 00:27:37,119
Speaker 3: Natasha Mascarinus broke the story and joins us, Now the

537
00:27:37,200 --> 00:27:40,560
Speaker 3: Internet is kind of reacted by another one and what

538
00:27:40,680 --> 00:27:42,280
Speaker 3: is it that they do? But you know, there's a

539
00:27:42,400 --> 00:27:46,080
Speaker 3: history with Yandlercun and his work with Meta in particular.

540
00:27:46,280 --> 00:27:48,520
Speaker 3: Let's start with the vision for AMI. What is it

541
00:27:48,560 --> 00:27:50,040
Speaker 3: they're seeking to break through on?

542
00:27:50,400 --> 00:27:53,240
Speaker 15: Sure? I mean Yan Lacoon has been a long critic

543
00:27:53,320 --> 00:27:57,040
Speaker 15: of LM's being the best way to advance AI. So

544
00:27:57,240 --> 00:28:01,400
Speaker 15: a ME is a new company that'socused on world models.

545
00:28:02,280 --> 00:28:04,800
Speaker 15: The idea is that they are going to be able

546
00:28:04,800 --> 00:28:09,960
Speaker 15: to integrate with enterprises like robotics companies or car manufacturers

547
00:28:10,080 --> 00:28:12,440
Speaker 15: and help build AI that can operate in the real

548
00:28:12,440 --> 00:28:17,800
Speaker 15: world versus being something that consumers everyday. Consumers interact with

549
00:28:18,359 --> 00:28:20,960
Speaker 15: via chatbots, and so let's focus on text and think

550
00:28:21,040 --> 00:28:25,600
Speaker 15: more around spatial data, visual data and trying to operate

551
00:28:25,640 --> 00:28:28,280
Speaker 15: again in real world and real industries.

552
00:28:27,960 --> 00:28:30,080
Speaker 4: And maybe even the future of defense. There were so

553
00:28:30,080 --> 00:28:32,600
Speaker 4: many interesting parts to this story, Natasha that I want

554
00:28:32,640 --> 00:28:34,560
Speaker 4: to dig in on, but won't call my eye as

555
00:28:34,600 --> 00:28:36,680
Speaker 4: someone who sat in New York, they're not going to

556
00:28:36,840 --> 00:28:39,280
Speaker 4: Silicon Valley. This is because they really feel that there's

557
00:28:39,640 --> 00:28:42,000
Speaker 4: what did he call it, that they've been LLLM pilled there.

558
00:28:42,120 --> 00:28:44,280
Speaker 4: This is a European story at it's hot, but it's

559
00:28:44,320 --> 00:28:46,120
Speaker 4: also a non Silicon Valley one.

560
00:28:47,360 --> 00:28:48,280
Speaker 5: You're exactly right.

561
00:28:48,360 --> 00:28:51,000
Speaker 15: I mean, this is one of the largest venture rounds

562
00:28:51,200 --> 00:28:55,000
Speaker 15: raised ever in Europe and it is actively one that

563
00:28:55,040 --> 00:28:58,800
Speaker 15: has not included a lot of the traditional Silicon Valley investors.

564
00:28:59,160 --> 00:29:01,880
Speaker 15: When I asked you about that decision, he said that

565
00:29:02,080 --> 00:29:05,760
Speaker 15: Silicon Valley investors were looking for really high ownership in

566
00:29:05,880 --> 00:29:08,840
Speaker 15: the business, and he believed that it was important to

567
00:29:09,600 --> 00:29:12,200
Speaker 15: try and to check talent that wasn't just focused on

568
00:29:12,360 --> 00:29:15,640
Speaker 15: building within the large language models, which means not going

569
00:29:15,720 --> 00:29:19,280
Speaker 15: to where people are working for Anthropic Open AI, even

570
00:29:19,320 --> 00:29:23,400
Speaker 15: his former employer Meta, and trying to go to different geographies. Now,

571
00:29:23,680 --> 00:29:25,719
Speaker 15: I did ask if he will have to raise from

572
00:29:25,760 --> 00:29:28,640
Speaker 15: Silicon Valley eventually, and he said he's definitely open to

573
00:29:28,680 --> 00:29:29,600
Speaker 15: that for his next round.

574
00:29:31,120 --> 00:29:34,800
Speaker 3: Lakuma is at Meta for almost fifteen years, more than

575
00:29:34,880 --> 00:29:38,280
Speaker 3: ten years at least. Is there any evidence of reporting

576
00:29:38,440 --> 00:29:41,080
Speaker 3: that that that's the way they go, some kind of

577
00:29:41,160 --> 00:29:45,000
Speaker 3: relationship with Meta, some collaborative work something like that.

578
00:29:45,400 --> 00:29:45,560
Speaker 2: Yeah.

579
00:29:45,640 --> 00:29:48,200
Speaker 15: So at this point, Meta was not listed as an

580
00:29:48,280 --> 00:29:51,400
Speaker 15: investor in the new business, but we did ask him

581
00:29:51,440 --> 00:29:55,080
Speaker 15: about possible partnerships, which he did confirm. So while the

582
00:29:55,280 --> 00:29:58,960
Speaker 15: average consumer may not interact with his new company through

583
00:29:59,160 --> 00:30:02,200
Speaker 15: a chatbot, there's not a too far of a world

584
00:30:02,200 --> 00:30:04,800
Speaker 15: where we could see it showing up in the meta

585
00:30:04,880 --> 00:30:07,760
Speaker 15: ray bands or other AI ware of all the meta's

586
00:30:07,840 --> 00:30:10,680
Speaker 15: working on. So we're definitely tracking the possible partnership and

587
00:30:10,760 --> 00:30:12,880
Speaker 15: you can birm that there are ongoing discussions there.

588
00:30:13,600 --> 00:30:16,960
Speaker 4: Natasha Mascarinis just scoop off to scoot. We so appreciate

589
00:30:17,080 --> 00:30:20,680
Speaker 4: coming on without reporting. Let's not talk about another funding

590
00:30:20,960 --> 00:30:21,920
Speaker 4: or indeed relationship.

591
00:30:21,960 --> 00:30:24,240
Speaker 5: In Video is investing in Thinking Machines Lab.

592
00:30:24,720 --> 00:30:27,520
Speaker 4: It's an AI startup founded by opening Ey executive mirror

593
00:30:27,560 --> 00:30:29,760
Speaker 4: Marati now and Video is set to provide it's Vera

594
00:30:29,800 --> 00:30:32,960
Speaker 4: Ruben systems to power Thinking Machines AI models as part

595
00:30:33,000 --> 00:30:36,040
Speaker 4: of a multi year agreement. Blomberg AI reporter Rachel Metz

596
00:30:36,120 --> 00:30:39,280
Speaker 4: joins us some more so, what is Thinking Machines Lab doing?

597
00:30:40,760 --> 00:30:44,800
Speaker 16: Yeah, so this company, they are building their own AI

598
00:30:44,960 --> 00:30:48,320
Speaker 16: models and they have a deal that they just announced

599
00:30:48,360 --> 00:30:51,480
Speaker 16: with in Video. They're getting investment from the Video and

600
00:30:51,600 --> 00:30:55,320
Speaker 16: they are also going to be using quite a lot

601
00:30:55,480 --> 00:30:59,560
Speaker 16: of in Vidia's chip power to train and run their

602
00:30:59,600 --> 00:31:00,440
Speaker 16: AI models.

603
00:31:01,440 --> 00:31:04,080
Speaker 3: I posted your story on X and you know noting

604
00:31:04,160 --> 00:31:06,200
Speaker 3: that up to a gig or what ver Rubin or

605
00:31:06,280 --> 00:31:09,120
Speaker 3: a minimum of a giga what sorry, ver Rubin systems like,

606
00:31:09,200 --> 00:31:10,320
Speaker 3: that's a lot of compute.

607
00:31:10,720 --> 00:31:11,880
Speaker 2: But the unknowns are.

608
00:31:11,960 --> 00:31:14,640
Speaker 3: Is in video making this investment in cash or chips

609
00:31:14,680 --> 00:31:15,280
Speaker 3: in lieu of cash.

610
00:31:15,360 --> 00:31:16,000
Speaker 2: We just don't know.

611
00:31:16,440 --> 00:31:18,880
Speaker 3: And the response that everyone gets back is, so, what

612
00:31:19,000 --> 00:31:22,480
Speaker 3: does thinking Machines do? What is thinking machines other than AI?

613
00:31:23,800 --> 00:31:27,800
Speaker 16: Okay, so so far, Thinking Machines has released one product,

614
00:31:28,040 --> 00:31:30,960
Speaker 16: and it is called Tinker, and it came out last year,

615
00:31:31,400 --> 00:31:36,120
Speaker 16: and that is a software that companies can use to

616
00:31:36,280 --> 00:31:39,760
Speaker 16: fine tune AI models. What they're working on as well

617
00:31:40,120 --> 00:31:43,200
Speaker 16: is some AI models. It's not quite known yet what

618
00:31:43,320 --> 00:31:46,120
Speaker 16: the details of those are. It sounds like they will

619
00:31:46,160 --> 00:31:49,640
Speaker 16: be things that will be useful to a range of enterprises,

620
00:31:50,240 --> 00:31:52,920
Speaker 16: but it's not entirely clear yet if they're going to

621
00:31:52,960 --> 00:31:55,680
Speaker 16: be focused on specific uses or if it's going to

622
00:31:55,720 --> 00:31:57,960
Speaker 16: be a more general kind of thing like the models

623
00:31:58,000 --> 00:31:59,840
Speaker 16: that underpin say chat GPT or.

624
00:32:00,880 --> 00:32:04,320
Speaker 4: To have just issued or developed a model called Tinker

625
00:32:04,400 --> 00:32:07,240
Speaker 4: and already the talked of as in the realm of

626
00:32:07,280 --> 00:32:10,760
Speaker 4: a fifty billion dollar market capitalization, it's on us to

627
00:32:10,840 --> 00:32:14,160
Speaker 4: remind ourselves why Mira Marati is taken so seriously by

628
00:32:14,200 --> 00:32:16,480
Speaker 4: this market for a hot second, she led open Ai,

629
00:32:16,600 --> 00:32:20,560
Speaker 4: but really she was the CTO, right, Yes, she was.

630
00:32:20,600 --> 00:32:24,320
Speaker 16: The CTO Opening Eye. She's worked add a number of

631
00:32:24,600 --> 00:32:26,600
Speaker 16: tech companies over the years, so she's been in the

632
00:32:26,680 --> 00:32:29,360
Speaker 16: industry for quite a long time. I think the challenge

633
00:32:29,680 --> 00:32:33,160
Speaker 16: that she's facing here with this company is can she

634
00:32:33,640 --> 00:32:36,720
Speaker 16: build large language models? Not just can she build them?

635
00:32:37,280 --> 00:32:40,400
Speaker 16: A handful of companies can build them. They require obviously

636
00:32:40,520 --> 00:32:44,360
Speaker 16: a lot of money, a lot of expertise, and a

637
00:32:44,440 --> 00:32:48,400
Speaker 16: lot of data, but also can she then convince companies

638
00:32:48,680 --> 00:32:50,959
Speaker 16: or some other form of customer to pay for them.

639
00:32:51,760 --> 00:32:56,360
Speaker 16: Tinker is an effort to help people fine tune models,

640
00:32:56,640 --> 00:32:59,880
Speaker 16: so it's not quite up to having It's not the

641
00:33:00,080 --> 00:33:02,520
Speaker 16: same as having a model and then having people use it.

642
00:33:03,200 --> 00:33:05,760
Speaker 16: But that was sort of their first step into business,

643
00:33:05,880 --> 00:33:08,000
Speaker 16: and they do have customers for that, so it'll be

644
00:33:08,040 --> 00:33:10,640
Speaker 16: interesting to see over the next year or so what

645
00:33:10,840 --> 00:33:12,040
Speaker 16: happens with their business.

646
00:33:13,240 --> 00:33:17,560
Speaker 3: We'd reported at the end of last year the Thinking

647
00:33:17,600 --> 00:33:20,280
Speaker 3: Machines was going out doing an early round at like

648
00:33:20,360 --> 00:33:22,960
Speaker 3: a fifty billion dollar valuation when it was basically a

649
00:33:23,040 --> 00:33:26,160
Speaker 3: handful of people, and then what followed was all kinds

650
00:33:26,200 --> 00:33:30,400
Speaker 3: of media reports about internally things not being well right,

651
00:33:30,520 --> 00:33:33,240
Speaker 3: people leaving. We never really got to the bottom of it.

652
00:33:33,360 --> 00:33:35,200
Speaker 3: I mean, this is a big piece of news for them,

653
00:33:35,280 --> 00:33:38,000
Speaker 3: but we have a sense of how it's going otherwise

654
00:33:38,080 --> 00:33:39,680
Speaker 3: beyond getting the backing of Nvidia.

655
00:33:40,840 --> 00:33:43,120
Speaker 16: Yeah, that is a bit of a question mark. We

656
00:33:43,480 --> 00:33:46,680
Speaker 16: know that the company has about one hundred employees and

657
00:33:47,400 --> 00:33:50,480
Speaker 16: it has, as you mentioned, it recently lost some employees

658
00:33:50,520 --> 00:33:54,000
Speaker 16: who went back to Opening I. Initially, when Mira Murradi

659
00:33:54,080 --> 00:33:57,080
Speaker 16: started this company, she started it with a quite a

660
00:33:57,160 --> 00:33:59,840
Speaker 16: number of people that came from Opening I, so that

661
00:34:00,000 --> 00:34:02,160
Speaker 16: it was certainly a signal if people were going back

662
00:34:02,240 --> 00:34:05,280
Speaker 16: to that company that they had come from. But I mean,

663
00:34:05,320 --> 00:34:07,840
Speaker 16: I think this is still a very new company, and

664
00:34:08,040 --> 00:34:11,880
Speaker 16: sometimes these things take time to get going, get off

665
00:34:11,920 --> 00:34:14,719
Speaker 16: the ground, and we'll see what happens throughout the rest

666
00:34:14,760 --> 00:34:15,160
Speaker 16: of this year.

667
00:34:16,280 --> 00:34:18,799
Speaker 2: Bloomberg's Retro mets top Reporting. Thank you very much.

668
00:34:19,080 --> 00:34:22,799
Speaker 3: Coming up on the show, HPE exceeds expectations on its

669
00:34:22,840 --> 00:34:26,080
Speaker 3: forecast for the current quarter. It's all about AI demand

670
00:34:26,400 --> 00:34:28,759
Speaker 3: and all about AI hardware demand. We can discuss what's

671
00:34:28,840 --> 00:34:32,520
Speaker 3: driving that outlet next with HPE CEO Antonio Neeri.

672
00:34:32,880 --> 00:34:33,960
Speaker 2: This is Blomberg Tech.

673
00:34:43,719 --> 00:34:47,080
Speaker 3: Shares of HPE modestly higher, up about six tens of

674
00:34:47,160 --> 00:34:49,400
Speaker 3: one percent. Been chopping around in the session, but the

675
00:34:49,440 --> 00:34:52,480
Speaker 3: company gave a strong sales and revenue projection for the

676
00:34:52,520 --> 00:34:53,160
Speaker 3: current quarter.

677
00:34:53,640 --> 00:34:54,320
Speaker 2: What's driving it?

678
00:34:54,440 --> 00:34:57,480
Speaker 3: Demand demand for AI hardware in particular, has discussed with

679
00:34:57,640 --> 00:35:01,320
Speaker 3: HPE CEO Antonio Nery, and let's start with that. You know,

680
00:35:01,800 --> 00:35:05,960
Speaker 3: clearly there is some staying power in that demand at

681
00:35:06,120 --> 00:35:08,520
Speaker 3: rack scale, you know, crystallize it for us.

682
00:35:08,600 --> 00:35:10,080
Speaker 2: What are you seeing in real time?

683
00:35:11,520 --> 00:35:14,279
Speaker 7: Yeah, good morning, Ed, and thanks for having me. We

684
00:35:14,440 --> 00:35:18,480
Speaker 7: saw a tremendous demand throughout the quarter. I think the

685
00:35:18,600 --> 00:35:21,400
Speaker 7: momentum in I continues in the build out of the

686
00:35:21,520 --> 00:35:24,280
Speaker 7: data center. But what I'm really pleased is the momentum

687
00:35:24,320 --> 00:35:27,680
Speaker 7: we see an enterprise. In fact, most of our revenue

688
00:35:27,719 --> 00:35:30,279
Speaker 7: in AI that we're recognize in Q one came from

689
00:35:30,320 --> 00:35:34,399
Speaker 7: the enterprise segment, which shows you two key elements. One

690
00:35:34,560 --> 00:35:38,680
Speaker 7: is the adoption of agentic AI into the company's workflow,

691
00:35:38,800 --> 00:35:41,239
Speaker 7: and second is the growing of influencing. A lot of

692
00:35:41,280 --> 00:35:45,560
Speaker 7: focuses always around the training and training these new models,

693
00:35:45,680 --> 00:35:48,840
Speaker 7: but for us, our focus being both the sovereign space

694
00:35:49,120 --> 00:35:53,760
Speaker 7: and the influencing and enterprise space. But in our portfolio,

695
00:35:53,840 --> 00:35:58,120
Speaker 7: we saw tremendous momentum networking. Our strategy where journeys has

696
00:35:58,200 --> 00:36:01,359
Speaker 7: paid off, and that's why we see double digits year

697
00:36:01,400 --> 00:36:03,040
Speaker 7: over your growth in the order intake.

698
00:36:03,760 --> 00:36:08,520
Speaker 3: You know post Juniper networking is is upside for you, right.

699
00:36:08,600 --> 00:36:10,840
Speaker 3: I think you have a lot of confidence that growth

700
00:36:10,880 --> 00:36:14,120
Speaker 3: will come in networking. For the audience that doesn't understand

701
00:36:14,160 --> 00:36:16,480
Speaker 3: what it is you do in that space. Why is

702
00:36:16,640 --> 00:36:20,160
Speaker 3: networking on the rise in parallel with just kind of

703
00:36:20,239 --> 00:36:22,280
Speaker 3: the broader AI hardware demand that you're seeing.

704
00:36:23,960 --> 00:36:27,000
Speaker 7: Well, I do believe ed and this was the coresis

705
00:36:27,040 --> 00:36:30,680
Speaker 7: of the Juniper acquisation that the next inflection point in

706
00:36:30,760 --> 00:36:35,239
Speaker 7: terms of disruption will come from the networking connectivity layer.

707
00:36:35,520 --> 00:36:38,120
Speaker 7: Think about when you build a data center, you have

708
00:36:38,280 --> 00:36:41,680
Speaker 7: to connect this data center to other data centers through

709
00:36:41,719 --> 00:36:44,839
Speaker 7: the Internet, and that's what we call data center interconnect.

710
00:36:45,000 --> 00:36:48,960
Speaker 7: Juniper has one of the most innovative products in the

711
00:36:49,080 --> 00:36:51,440
Speaker 7: route in space, by the way, this passport that we

712
00:36:51,520 --> 00:36:55,160
Speaker 7: grew mid twenty percent in the order intake. And then

713
00:36:55,239 --> 00:36:57,520
Speaker 7: once you are inside the data center, you need to

714
00:36:57,680 --> 00:37:00,640
Speaker 7: be able to connect large amount of GPU use and

715
00:37:00,800 --> 00:37:04,160
Speaker 7: CPUs together and that's why you need a data't set.

716
00:37:04,280 --> 00:37:06,880
Speaker 7: Data doesn't resewarching portfolio in addition to some of the

717
00:37:07,440 --> 00:37:10,840
Speaker 7: other core tenants of the technology and there you know,

718
00:37:11,080 --> 00:37:14,640
Speaker 7: the Juniper portfolio group mid forty percent in the order intake.

719
00:37:15,400 --> 00:37:18,560
Speaker 7: So I will say for us, that has been a

720
00:37:18,680 --> 00:37:22,640
Speaker 7: core tenant. That's why you know now they represent almost

721
00:37:22,920 --> 00:37:25,879
Speaker 7: thirty percent of the company revenues but more than half

722
00:37:25,960 --> 00:37:29,480
Speaker 7: of the profit. And for us, that's also an ability

723
00:37:29,520 --> 00:37:31,920
Speaker 7: to raise the outlook for the remainder of the year.

724
00:37:32,080 --> 00:37:35,640
Speaker 7: And also they're outlooking free cash flow because the structural

725
00:37:35,760 --> 00:37:39,239
Speaker 7: margins of that business and the working capital efficiency are very,

726
00:37:39,320 --> 00:37:40,960
Speaker 7: very different than the rest of the portfolio.

727
00:37:41,120 --> 00:37:43,800
Speaker 4: I mean you actually said, look, you're not done raising prices.

728
00:37:43,960 --> 00:37:47,160
Speaker 4: You talk about discipline and what is a very dynamic environment,

729
00:37:47,239 --> 00:37:48,360
Speaker 4: Antonio talk.

730
00:37:48,200 --> 00:37:51,080
Speaker 5: To us about well, how you're weathering.

731
00:37:50,680 --> 00:37:53,600
Speaker 4: The supply chain issue, in particularly the cost the memory

732
00:37:53,680 --> 00:37:54,399
Speaker 4: costs at the moment.

733
00:37:54,560 --> 00:37:55,400
Speaker 5: How do you navigate that?

734
00:37:56,880 --> 00:38:00,239
Speaker 7: Yeah, Carol, I mean, obviously the demand and supply has

735
00:38:00,320 --> 00:38:03,759
Speaker 7: a huge mismatch, and you actually need to go back

736
00:38:03,840 --> 00:38:06,680
Speaker 7: all the twenty two to twenty three timeframe to understand

737
00:38:06,719 --> 00:38:11,239
Speaker 7: how we got here. Accelerated by the hypercycle we see

738
00:38:11,520 --> 00:38:16,120
Speaker 7: in the AI space, but fundamentally in our guidance, the

739
00:38:16,200 --> 00:38:18,839
Speaker 7: new guidance, the new outlook that we raise for twenty

740
00:38:18,920 --> 00:38:23,480
Speaker 7: twenty six. We included our ability to see the supply

741
00:38:23,719 --> 00:38:26,040
Speaker 7: that we need to convert that into a revenue profit.

742
00:38:26,440 --> 00:38:29,000
Speaker 7: But the reality is that we do not have enough

743
00:38:29,080 --> 00:38:34,520
Speaker 7: supply against the order intake and the backlog, because otherwise

744
00:38:34,560 --> 00:38:37,560
Speaker 7: we'll have even a higher outlook. So what we're doing

745
00:38:37,680 --> 00:38:41,239
Speaker 7: we have taken three very unique steps. Number one is

746
00:38:41,520 --> 00:38:44,080
Speaker 7: securing as much supply as we can, but again we

747
00:38:44,120 --> 00:38:46,520
Speaker 7: don't have all the supply that we would like to have.

748
00:38:46,800 --> 00:38:50,360
Speaker 7: Number two is taking a very agile posture when it

749
00:38:50,440 --> 00:38:53,080
Speaker 7: comes down to pricing, and as I said yesterday, we

750
00:38:53,160 --> 00:38:57,360
Speaker 7: are not done raising pricing. I think that the cycle

751
00:38:57,400 --> 00:39:00,879
Speaker 7: will continue well into twenty twenty seven, although we will

752
00:39:00,960 --> 00:39:04,080
Speaker 7: hope that we will reach some sort of elevated price

753
00:39:04,200 --> 00:39:08,360
Speaker 7: in stability in twenty twenty six. And number three, you know,

754
00:39:08,520 --> 00:39:11,759
Speaker 7: my experience has thought me that ultimately you need to

755
00:39:11,840 --> 00:39:15,920
Speaker 7: have direct, transparent conversation both with customers and our partners.

756
00:39:16,040 --> 00:39:18,560
Speaker 7: Last week I was in Europe. I met more than

757
00:39:18,600 --> 00:39:22,800
Speaker 7: twenty customers the Mobile Congress in London, and they appreciate

758
00:39:22,880 --> 00:39:25,680
Speaker 7: that level of transparency, understanding the environment we're in.

759
00:39:26,040 --> 00:39:28,480
Speaker 4: I mean, you're actually forgoing supplying some equipment to mobile

760
00:39:28,600 --> 00:39:31,640
Speaker 4: service providers, So you're being choosy with who you work

761
00:39:31,719 --> 00:39:35,000
Speaker 4: with at this time of geopolitical angst and when we

762
00:39:35,200 --> 00:39:36,920
Speaker 4: question how much the Middle East is going to be

763
00:39:36,960 --> 00:39:39,560
Speaker 4: able to build out the data center capacity as once thought.

764
00:39:40,280 --> 00:39:43,600
Speaker 4: Has the Iran conflict impacted your view on the world

765
00:39:43,840 --> 00:39:46,839
Speaker 4: or indeed the supply of the world and chips.

766
00:39:48,280 --> 00:39:51,439
Speaker 7: Not on the supply side, Carol, And obviously our first

767
00:39:51,520 --> 00:39:56,400
Speaker 7: priority because we have businesses in that region, whether ue Qatar,

768
00:39:56,520 --> 00:39:59,920
Speaker 7: bar In, sauda Arabia, Israel, right is the well being

769
00:40:00,080 --> 00:40:03,600
Speaker 7: the safety of our employees. We have approximately one thousand

770
00:40:03,680 --> 00:40:06,080
Speaker 7: employees in that region and the good news all are

771
00:40:06,160 --> 00:40:09,560
Speaker 7: doing well at this point in time. But from a

772
00:40:09,600 --> 00:40:15,640
Speaker 7: supply perspective, no, But obviously as the conflict may gets elongated,

773
00:40:15,640 --> 00:40:18,520
Speaker 7: they may have other implications, particularly on the logistics side

774
00:40:18,560 --> 00:40:22,680
Speaker 7: of the equation. You know, look, air freight routes are

775
00:40:22,760 --> 00:40:25,239
Speaker 7: a little bit more complicated. We don't use boats of

776
00:40:25,360 --> 00:40:31,480
Speaker 7: ships for transporting our equipments. But over time we have

777
00:40:31,600 --> 00:40:33,920
Speaker 7: to assess if there is a revenue impact. What I

778
00:40:34,000 --> 00:40:36,680
Speaker 7: can tell you right now there is even more demand

779
00:40:36,960 --> 00:40:40,000
Speaker 7: then we saw particularly you know, three four months ago,

780
00:40:40,239 --> 00:40:42,480
Speaker 7: so we have to navigate that together. We have to

781
00:40:42,520 --> 00:40:43,239
Speaker 7: be smart about it.

782
00:40:44,320 --> 00:40:44,800
Speaker 2: Antonio.

783
00:40:44,960 --> 00:40:48,200
Speaker 3: You have never let me tear apart an HPE server,

784
00:40:48,480 --> 00:40:50,520
Speaker 3: and that's okay. But if I got my hands in

785
00:40:50,560 --> 00:40:53,800
Speaker 3: the compute trade, you'd have the high bandwidth memory around

786
00:40:53,800 --> 00:40:56,880
Speaker 3: the GPU wherever it comes from. You'd have the ddrs

787
00:40:56,960 --> 00:41:00,280
Speaker 3: around the CPU, and somewhere you'd have the SSDs.

788
00:41:00,000 --> 00:41:03,200
Speaker 2: And flash memory. Be very very specific.

789
00:41:03,480 --> 00:41:06,600
Speaker 3: Of those three, what's the biggest impact to you right

790
00:41:06,680 --> 00:41:11,279
Speaker 3: now in constraining your sales and your outlook, very simple.

791
00:41:11,120 --> 00:41:17,160
Speaker 7: Is DDR and nand or the SSDs. Hbm's less constrained,

792
00:41:17,320 --> 00:41:21,040
Speaker 7: but obviously a lot of the allocation on the capacity

793
00:41:21,120 --> 00:41:23,399
Speaker 7: has moved there. And as you know, there is also

794
00:41:23,400 --> 00:41:26,279
Speaker 7: a transition from the prior generation of hbms to the

795
00:41:26,360 --> 00:41:30,200
Speaker 7: new generation of HBM because you need more membody channels.

796
00:41:30,560 --> 00:41:34,600
Speaker 7: But it's really the DDR four and five which now

797
00:41:35,040 --> 00:41:37,919
Speaker 7: nobody almost use it, but DDR five and the nand

798
00:41:38,080 --> 00:41:39,360
Speaker 7: part which is the SSDs.

799
00:41:39,840 --> 00:41:40,760
Speaker 5: You said just earlier.

800
00:41:40,920 --> 00:41:43,520
Speaker 4: Look, one of your the three key steps you took

801
00:41:44,080 --> 00:41:46,000
Speaker 4: was to get as much supply as you could and

802
00:41:46,080 --> 00:41:48,000
Speaker 4: to ensure that that was locked in. You wish you

803
00:41:48,040 --> 00:41:50,000
Speaker 4: could have more Who is it that you need to

804
00:41:50,560 --> 00:41:52,719
Speaker 4: push more? Who is it do you need to have

805
00:41:52,840 --> 00:41:55,400
Speaker 4: more security for? How can you perhaps get hands on

806
00:41:55,480 --> 00:41:56,560
Speaker 4: further supply going forward.

807
00:41:57,960 --> 00:42:01,799
Speaker 7: Look, we have a long standard relationship with the three

808
00:42:01,960 --> 00:42:07,839
Speaker 7: core suppliers that provide DDR memory and hpms, and there

809
00:42:07,920 --> 00:42:11,520
Speaker 7: is a little bit a larger ecosystem for the land space,

810
00:42:11,719 --> 00:42:15,840
Speaker 7: but we're having weekly engagements with them. We are looking

811
00:42:16,000 --> 00:42:21,279
Speaker 7: to swap components driving different configurations, but the reality all

812
00:42:21,320 --> 00:42:23,080
Speaker 7: of them are significantly constrained.

813
00:42:23,160 --> 00:42:28,200
Speaker 3: Karl Antonio just twenty seconds, what's the dollar figure dollar terms,

814
00:42:28,280 --> 00:42:30,560
Speaker 3: and of what was left on the table, how much

815
00:42:30,640 --> 00:42:31,839
Speaker 3: better the outlook could have been.

816
00:42:33,719 --> 00:42:37,640
Speaker 7: Yeah, it's pretty sizable. I will say, you know, we

817
00:42:37,840 --> 00:42:42,560
Speaker 7: guided five pennies on the midpoint of the range and

818
00:42:42,760 --> 00:42:45,640
Speaker 7: then we raise our outlook for free cash flow by

819
00:42:46,080 --> 00:42:49,960
Speaker 7: almost two hundred million at the midpoint. But the backlog

820
00:42:50,120 --> 00:42:52,040
Speaker 7: is very very large at this point in time. If

821
00:42:52,080 --> 00:42:54,840
Speaker 7: you think about the AI system backlog is over five billion.

822
00:42:55,360 --> 00:43:00,239
Speaker 7: We raise again the networking for networks for AI goal

823
00:43:00,400 --> 00:43:02,319
Speaker 7: for the end of the year. So it's a very

824
00:43:02,400 --> 00:43:05,320
Speaker 7: sizeable backlog. So we hope we make progress as we

825
00:43:05,440 --> 00:43:09,400
Speaker 7: go along, but we categorize, we leveled it as a

826
00:43:09,520 --> 00:43:11,160
Speaker 7: proved guidance at this point in time.

827
00:43:11,280 --> 00:43:14,400
Speaker 4: Anthony Nari, CEO of HPE, thanks for the candid conversation.

828
00:43:14,760 --> 00:43:16,520
Speaker 5: We appreciate it. Now that does it with this addition

829
00:43:16,560 --> 00:43:17,040
Speaker 5: of Bloomberg

830
00:43:17,120 --> 00:43:21,000
Speaker 3: Tech ed, great interviews, great Bloomberg reporting, recap it on

831
00:43:21,080 --> 00:43:23,000
Speaker 3: the podcast new Where to find it on the terminal

832
00:43:23,040 --> 00:43:26,720
Speaker 3: and online on Apple, Spotify, and iHeart this is Bloomberg

833
00:43:26,800 --> 00:43:27,000
Speaker 3: Tech