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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 EVA though in San Francisco.

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Speaker 2: This is Bloomberg Tech coming up.

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Speaker 3: Tesla plans for twenty five billion dollars in additional spending

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Speaker 3: this year to support Elon Maask's AI ambitions.

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Speaker 4: And Intel shares jump out of the company pledges to

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Speaker 4: support Musk's advanced chip manufacturing project terror Fat.

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Speaker 3: And we'll bring on more tech earnings and tech news.

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Speaker 3: We'll hear from the CEOs of Lift, Service Now and

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Speaker 3: IBM and First.

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Speaker 5: For the last day this week together in New York, Ed.

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Speaker 4: We look at what these markets are up to, and

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Speaker 4: we're actually managing to cling on to some sort of

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Speaker 4: green in the Nasdaq one hundred less so for the

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Speaker 4: NASDAK more broadly, and that we are seeing pressure more

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Speaker 4: on the investor mindset with oil climbing up again five

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Speaker 4: times set high. We're above one hundred and two on rent.

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Speaker 4: The geopolitical context remains. It impacts certain earnings like with

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Speaker 4: Service Now for example, but we're all tracked on earnings today.

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Speaker 3: Yeah, it's been exhausting, frankly, being on Eastern time and

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Speaker 3: covering Tesla's earnings in particular, right, really simple, storing massive

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Speaker 3: commitment to spending a lot of money to actually get

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Speaker 3: going on the two big projects, rob Taxi and Optimus,

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Speaker 3: the humanoid robot. The headline capex twenty five billion dollars

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Speaker 3: for this year, but actually the shares it down significantly,

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Speaker 3: not just because of that, but because of the commentary

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Speaker 3: they will enter negative free cash flow for the balance

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Speaker 3: of this year. That's a lot of money stuff, but

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Speaker 3: there's some execution as well. Let's get to Bloomberg's Crage Trudel,

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Speaker 3: who leads our coverage of auto's around the world.

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Speaker 2: That's where we probably start.

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Speaker 3: Right in the quarter, things look really good, and then

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Speaker 3: Tesla told us Craig we are going to spend in

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Speaker 3: a multi year investment cycle, and things got a little

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Speaker 3: bit sour.

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Speaker 6: Yeah, I think, you know, part of what you know

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Speaker 6: made the numbers look so good was maybe there was

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Speaker 6: a moment where everyone wondered whether or not Tesla was

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Speaker 6: actually going to go forward with this sort of high

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Speaker 6: wire act of you know, spending the twenty billion dollars

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Speaker 6: plus in capex that was outlined just a few months ago,

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Speaker 6: you know, for them to actually only have you know,

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Speaker 6: roughly two and a half billion dollars of CAPEX in

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Speaker 6: the first quarter, that that sort of sent a signal

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Speaker 6: potentially that spending wasn't going to be quite at the

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Speaker 6: pace that was flagged. Instead, a Musk hops on the

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Speaker 6: call and says, oh, no, we're going ahead with that,

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Speaker 6: and then some and I think that does give some

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Speaker 6: people pause when you have a car business that, yes,

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Speaker 6: is looking a little bit better than it was a

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Speaker 6: year ago, but a year ago it was really in

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Speaker 6: a brutal place.

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Speaker 2: Hold that's graphic on the screen. Just let me say

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Speaker 2: something real quick.

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Speaker 3: Carry The reason at first thing looks so rosy is

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Speaker 3: that CAPEX in the quarter was basically not trending toward

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Speaker 3: the twenty billion for the full year.

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Speaker 2: Anyway, everyone was like, oh, they're underspending.

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Speaker 3: And then it got confusing because they were like, wrong,

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Speaker 3: going to spend a lot more to take it.

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Speaker 4: Away, And I Musk really tried to articulate Craig why

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Speaker 4: that spending is necessary. We're going to get to terra

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Speaker 4: fab and chips in a moment, but with you, I

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Speaker 4: want the underlying fundamentals of where he's spending on cybercab

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Speaker 4: what about the humanoid robot focus. I mean, this is

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Speaker 4: what investors care about, less about car sales, more about AI.

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Speaker 7: Yeah.

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Speaker 6: I think that's right, and I think there's been honestly

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Speaker 6: a lot of patients with being able to sort of

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Speaker 6: look past the idea that a lot of these projects

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Speaker 6: have not earned meaningful return and you know, may not

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Speaker 6: for the time being. But you know, we've gotten to

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Speaker 6: a place now where this is a company that you know,

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Speaker 6: is primarily a car business where a couple of years

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Speaker 6: now they have actually seen decline in their sales and

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Speaker 6: decline in their position in their most important segment. And

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Speaker 6: we don't have clear indications of when exactly, you know,

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Speaker 6: this robo taxi project is actually going to be meaningful.

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Speaker 6: We had, if anything, a bit of incremental indication that

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Speaker 6: maybe next year, but that's you know, a bit far off.

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Speaker 6: And then also some indications too that the to miss

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Speaker 6: robot may not be you know ready, I'm the timelineers

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Speaker 6: as previously flagged, so some negative indicators from that perspective.

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Speaker 4: As well, shock me create Trudell slower timeline Bloomberg's crety Dow.

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Speaker 4: We thank you so much on all things, cars, fundamentals,

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Speaker 4: robots more in Tesla, though.

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Speaker 5: When in La.

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Speaker 4: Mosk has planned to spend three billion dollars to build

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Speaker 4: a research facility in Texas part of that ambitious chip

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Speaker 4: manufacturing project dubbed Terrifat bringing Intel along for the ride.

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Speaker 4: Bloombgs I King in San Francisco could really articulate why

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Speaker 4: three billion?

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Speaker 5: Is this some sort of like pilot that.

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Speaker 4: They do to see whether they can actually make the chips?

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Speaker 8: Yeah? I mean this is a long established practice in

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Speaker 8: the semikinductor industry where you make what's called a pilot line,

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Speaker 8: and this is your way of showing, well, does my

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Speaker 8: manufacturing technology work and do my designs work? And you're

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Speaker 8: kind of doing it on the cheap three billion dollars,

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Speaker 8: you know. I know that's a lot to me and

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Speaker 8: maybe not much to you, But in the chip industry

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Speaker 8: it really isn't very much at all. It's about ten

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Speaker 8: percent of what it would cost to build, you know,

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Speaker 8: a leading edge, full sized fab.

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Speaker 3: Last night, the news about Elon Musk's ship ambitions came

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Speaker 3: in the Q and a portion of the earnings call

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Speaker 3: and I needed some help. I turned to you for

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Speaker 3: that help to understand the pilot line bit that you

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Speaker 3: just explained, but he.

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Speaker 2: Dropped a big piece of news.

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Speaker 3: He said that the initiative will use Intel's fourteen A process.

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Speaker 3: What is Intel's fourteen A process? Why did Intel shares

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Speaker 3: jump when that headline hit?

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Speaker 8: Yeah, this process that we're talking about, this is basically

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Speaker 8: the recipe that they used to create semiconductors, and they're

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Speaker 8: looking for outside users of that. Basically, they don't have

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Speaker 8: enough of their own work, their own demand for their

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Speaker 8: own designs to be able to fill the factory, so

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Speaker 8: they need outside customers. So this raised the possibility that

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Speaker 8: guess what, you know, Elon Inc. Is going to be

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Speaker 8: a customer of Intel in some way. We don't know

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Speaker 8: whether he's just going to license that and do it himself,

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Speaker 8: or whether he's going to actually help Intel fill its fabs.

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Speaker 8: And I hopefully we'll find out later today when Intel.

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Speaker 3: Bloomberg Zy and King again really jumped in and helped

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Speaker 3: with understanding that when the news broke in Tesla surnings

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Speaker 3: call last night, there's a by the way coming the

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Speaker 3: Philadelphia Semiconductor Index or SOCKS, that's the main gauge of

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Speaker 3: chip stocks in the United States and actually around the world.

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Speaker 3: But the US sister Shares is up for a sixteenth

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Speaker 3: straight session, sixteen straight days of games, which is a record,

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Speaker 3: and that's astonishing when you think about the direction of

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Speaker 3: travel in newsflow the war in Iran. We've talked about

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Speaker 3: how the war in Iran has impacted the chip supply chain.

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Speaker 2: Carrow, but it's not even slowing down. We're up almost

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Speaker 2: three percent in the session.

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Speaker 4: That is just a phenomenal piece of data analysis that

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Speaker 4: you bring time and time again from Bloomberg. But what's

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Speaker 4: more important is also just how we navigate this and

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Speaker 4: what it means for the likes of Tesla and Intellers.

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Speaker 4: They try to bring chips into just about everything. This

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Speaker 4: is about AI and the use cases. Pierre Ferugu of

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Speaker 4: New Street Research says Tesla has a strong advantage versus

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Speaker 4: peers to quote win in order on robot taxing humanoid robots.

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Speaker 4: He maintains a BIKE rating on the stock for the

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Speaker 4: six hundred dollars price target.

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Speaker 5: You join us.

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Speaker 4: Now, how important is terrifab to that vision you have

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Speaker 4: on winning the AI race for Tesla.

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Speaker 7: It's a good question, Caroline.

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Speaker 9: The tarafab is like an enhancer for Tesla and so

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Speaker 9: SpaceX it's really like a player that is I would

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Speaker 9: say five to ten years out and probably closer to

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Speaker 9: ten than five. And the idea is that now that

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Speaker 9: we know compute is everything, you can achieve miracles with compute,

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Speaker 9: the key is going to become how can you deploy

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Speaker 9: compute faster than your competitors and at a lower unique costs?

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Speaker 9: And what he LEARNMSK is doing today with Tesla and SpaceX,

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Speaker 9: it's real project.

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Speaker 7: Sitting between the.

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Speaker 9: Two, he gets started to be in a position to

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Speaker 9: manufacture in how so you know, vertico like integration is

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Speaker 9: actually a major factor of course savings in an industry

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Speaker 9: where everybody has very high gorss margins. And he's going

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Speaker 9: to control the space at which he can actually build

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Speaker 9: chips and as he's planning to throw them into space

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Speaker 9: and to deploy data centers in space, and he has

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Speaker 9: like a pretty large rocket to do.

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Speaker 2: Yeah, that's j.

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Speaker 3: And forgive me for interrupting you, but what you're saying

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Speaker 3: doesn't match what Elon Musk is saying, right because he

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Speaker 3: got asked this on the call, are you doing terror fab?

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Speaker 3: The initiative to manufacture chips at scale because of unit economics,

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Speaker 3: and his response was no. He consistently says, because I

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Speaker 3: don't believe that TSMC and Samsung can match the supply

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Speaker 3: that Elon Inc. Needs to fulfill those long term ambitions.

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Speaker 9: Yeah, I think it's okay to say that today. But

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Speaker 9: the unity going to of TSMC probably ter a FAB

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Speaker 9: is never going to beat them. But TSMC is now

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Speaker 9: charging on average sixty five and growing person ghost margins.

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Speaker 9: So if you have your own manufacturing capabilities in a house,

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Speaker 9: whether you work magic against TSMC on you need economics

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Speaker 9: or not, you are actually working magic on your own

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Speaker 9: unit economics.

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Speaker 4: Therefore the efficiency gain. Look, all of this needs to

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Speaker 4: eventually be put to work. We need to vindicate a

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Speaker 4: twenty five billion dollar CAPEX expenditure.

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Speaker 5: Yet is it vindicated?

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Speaker 4: How if we're already seeing Optimus perhaps behind schedule. If

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Speaker 4: we're already questioning, really how quickly regulators can allow cyber

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Speaker 4: cab and the autonomous vision on the road, Does it

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Speaker 4: match the spending in the here and now?

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Speaker 9: It all depends, Caroline, on the timeframe you set for

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Speaker 9: yourself to put into perspective. Twenty five billion dollars of

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Speaker 9: CAPEX if you include FAB, about half that money is

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Speaker 9: going to be spent. I think on compus to be

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Speaker 9: in the same league as the frontier players in.

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Speaker 7: AI, and you need AI for FSD, you need.

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Speaker 9: AI for Optimus, and on top of that you have

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Speaker 9: the partnership with Grog of course with XAI. Then the

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Speaker 9: second aspect is Teslas this year is set to generate

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Speaker 9: but fifteen billion dollars of firm operating cash throw so

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Speaker 9: you know, the negative fakesue of Tesla this year might

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Speaker 9: be ten billion dollars if they manage to spend fast

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Speaker 9: and of the twenty five billion, which might be a challenge.

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Speaker 9: And they have a balance a cash balance studio of

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Speaker 9: about forty five billion dollars, So we're talking thing that

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Speaker 9: at the scale of Tesla is actually very reasonable. Then,

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Speaker 9: in terms of how do you get a return on

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Speaker 9: that robot XI, there is a broad consensus that once

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Speaker 9: it is you know, really scaled out, it's a multi

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Speaker 9: trillion dollar opportunity. So I mean, if the cost to

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Speaker 9: get into the race and win it is twenty five

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Speaker 9: million dollars, answer, it's a good price. Yeah, And then

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Speaker 9: you have Optimus a million optimists a year. How much

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Speaker 9: value is there in that? Another thing is that it's

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Speaker 9: going to take many years before we get right. So

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Speaker 9: it's a long it's a long duration investment.

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Speaker 2: You know.

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Speaker 3: Twenty five billion dollars is the big number, and later

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Speaker 3: in the show will explain why it's the big number. Pierre,

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Speaker 3: did you wake up this morning in the camp of

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Speaker 3: which some of your colleagues are in that there is

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Speaker 3: further evidence now that eventually SpaceX will merge with Tesla.

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Speaker 7: I won't tell you.

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Speaker 9: I think it's going to become more and more challenging

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Speaker 9: not to do it. For sure, a terrafab project like

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Speaker 9: the pilot line at Tesla in the first large scale manufacturing,

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Speaker 9: the faber at SpaceX, the cross accounting and you know

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Speaker 9: pricing between the two is going to become difficult. Yesterday

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Speaker 9: learn explained how you know optimist Roberts will remain connected

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Speaker 9: to the cloud. If they remain if they need very

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Speaker 9: large cloud blow it might be sphase data and third eployments, right,

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Speaker 9: and so there would be a question on how much

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Speaker 9: to slapez for like space on on Starship. So there

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Speaker 9: are a lot of good reasons for the merger to happen. Now,

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Speaker 9: you know, does that mean it's going to happen. I

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Speaker 9: wouldn't bridge that gap yet.

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Speaker 4: Yeah, fair good of New Street research or is a

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Speaker 4: joy having you on the show. Thank you for the analysis.

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Speaker 4: Now we are also watching shares of other earnings that

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Speaker 4: become thick and.

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Speaker 5: Fast over in Europe.

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Speaker 4: Look how noc here our performs that more than five

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Speaker 4: percent again, and AI an infrastructure story in many ways

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Speaker 4: for Nokia that manages to see European stocks on the

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Speaker 4: higher side for its particular play.

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Speaker 5: But boy are we in the eye of the software

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Speaker 5: storm right now.

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Speaker 2: Nouchi I'm the.

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Speaker 4: M eight percent Service now down the most in its history,

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Speaker 4: and that's after we once again have to our CEOs

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Speaker 4: to prove a negative and they're finding it very hard

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Speaker 4: to do so. They had twenty two percent growth in

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Speaker 4: subscription and still the stock dives. We're going to be

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Speaker 4: hearing more from the CEO of service now later this hour.

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Speaker 4: Let's around with all of that. But ed what else

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Speaker 4: we got coming up?

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Speaker 3: Yeah, it's going to talk about k Hinnicks posting a

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Speaker 3: sharp jump in quorly profit on booming AI memory demand.

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Speaker 2: But how long is that going to last?

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Speaker 7: This is Bloomberg Tech.

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Speaker 4: Let's look at Korean trading now, sk Heinex flat, but

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Speaker 4: the South Korean semiconductive manufacturer actually reported.

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Speaker 5: A fivefold jump in quarterly profit.

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Speaker 4: But the results are instead leaving investors questioning actually the

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Speaker 4: longevity of the AI boom. Bloomberg Asia stocks reporter who's

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Speaker 4: here in New York with SCFPN Lee can articulate why

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Speaker 4: the stock was under pressure with such a phenomenal set

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Speaker 4: of profit.

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Speaker 10: Yeah, absolutely, it was an absolutely stunning quarter. But also

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Speaker 10: you have to note that the shares have rise in

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Speaker 10: ninety percent so far just here, and if you look

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Speaker 10: at the past here the share has rose more than

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Speaker 10: the rise of more than five four on. The position

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Speaker 10: is pretty crowded. So even even the strong earnings can

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Speaker 10: trigger sell on the news. But I think investors wants

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Speaker 10: more evidence to see that earning is more structural than cyclical,

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Speaker 10: and that's going to take more than a couple of months.

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Speaker 10: So if you look at the memory ship in the past,

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Speaker 10: there has been bloom and bus cycle and even though

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Speaker 10: people agree that this is the supercycle that's percent unprecedented,

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Speaker 10: this is still subject to the could be subject to

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Speaker 10: the downtren This is a trillion dollar question. When that

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Speaker 10: downturn could start.

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Speaker 3: We're talking about the lucrative market for high bandwidth memory.

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Speaker 3: I always think about it as the funnel. It allows

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Speaker 3: the GPU to take the data quickly and process it quickly.

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Speaker 3: So you got sk Heinex, You've got Samsung and then

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Speaker 3: Micron in the United States. Beyond the numbers, was there

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Speaker 3: evidence that sk is winning a little bit in that

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Speaker 3: market of HBM.

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Speaker 10: Yeah, Actually, esk Heinezys, that's just the biggest, the supplier

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Speaker 10: of the HPM. It has about sixty percent market share

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Speaker 10: last year. But the rivals Samsung and Micron are catching up,

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Speaker 10: especially Samsung. But what's interesting is that because a lot

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Speaker 10: of these memory names have been diversifying, reallocating their production

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Speaker 10: resources to HBM, we're seeing stortages in the traditional M

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Speaker 10: and NAN that's giving pricing power to dis memory makers

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Speaker 10: and that's why we're seeing the strong numbers this quarter

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Speaker 10: for s k heinezin.

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Speaker 2: Those of you.

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Speaker 3: Only great to have you on Blombo Tech. Thank you

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Speaker 3: very much. A lot of news out there today, Cara.

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Speaker 4: Now is a lot from Asia. In fact, let's go there.

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Speaker 4: It's time for talking tech. First up, soft Bank is

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Speaker 4: seeking a ten billion dollar loan secured by its shares

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Speaker 4: in OPENINGI. It'scording to sources, the Japanese conglomerate has been

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Speaker 4: piling on debt as Fano Massiosti's son seeks the position

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Speaker 4: himself really is a lynchpin in the global AI boom

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Speaker 4: because of insuring SoftBank's debt.

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Speaker 5: Well, that's jumped after that news came out.

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Speaker 4: Plus ten Cent has unveiled a major upgrade to its

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Speaker 4: foundational AI model, with major advances in areas from complex

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Speaker 4: musing in particular to coding.

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Speaker 5: It's the first big test for the.

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Speaker 4: Company since it recruited a top researcher from Openingi. The

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Speaker 4: model has been made available via a suite of ten

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Speaker 4: Cent products, and TSMC plans to hold off on adopting

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Speaker 4: ASML's most cutting edge machine for chip production until twenty

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Speaker 4: twenty nine because it looks to save money. That's a

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Speaker 4: major blow the Dutch maker of some inconductive manufacturing equipment.

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Speaker 4: Now TSMC is its largest customer, according to Bloomberg supply

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Speaker 4: chain data. And by the way, look, each of these

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Speaker 4: advanced lithography machines cost over four hundred million dollars a

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Speaker 4: piece if you're looking at the top end.

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Speaker 3: At the top end, a lesson we learn on the

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Speaker 3: show the other day. A lot of people are out

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Speaker 3: shopping right now and coming up. Lift is claiming its

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Speaker 3: territory in London, Europe's largest righte hailing market. David Risher,

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Speaker 3: he's the CEO of Lyft. He's been shopping and buying

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Speaker 3: up some interesting companies. We're going to discuss that next.

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Speaker 3: This is Bloomberg Tech.

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Speaker 4: Lift is doubling down on this international expansion with a

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Speaker 4: deal to acquire gets UK business. This marks Lift's third

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Speaker 4: acquisition in just a year in a move to really

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Speaker 4: catch up with its large arrival Uber. David Rischer, CEO

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Speaker 4: of Lyft, joins us to share more in the company's

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Speaker 4: growth strategy. The growth strategy is international growth day and

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Speaker 4: what was it about gets UK optionality here that you liked?

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Speaker 11: So it is the big guy when it comes to

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Speaker 11: London's black cabs, and London's black cabs are awesome. I suspect, Carolyn,

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Speaker 11: you've taken one or two in your life and you

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Speaker 11: know they're amazing, right, So it's the best way to

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Speaker 11: get around London. And that's the idea is we are

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Speaker 11: a customer obsess company. We wanted to make sure that

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Speaker 11: we could increase our footprint in London. This roughly doubles

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Speaker 11: the size of our rides in London, which is wonderful.

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Speaker 11: And I think some like seventy five to eighty percent

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Speaker 11: of London cabs who have an app are going to

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Speaker 11: have access to Lift customers over time. So it's a

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Speaker 11: great win win.

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Speaker 4: Now, David, you know, I think the best way to

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Speaker 4: get around any city is to bike, and I'm pleased

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Speaker 4: that you're also doing the bike optionality the suntime they're

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Speaker 4: bikes you have in London. Just what does this mean

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Speaker 4: in terms of capital intensiveness? What does this mean in

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Speaker 4: terms of investment you need to make in these new cities.

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Speaker 11: Yeah, so it's not a very big capital raise. I

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Speaker 11: mean a nice thing about the sort of let's say

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Speaker 11: traditional ride share business, the one that we're in is

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Speaker 11: we don't own a lot of assets, right, the London

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Speaker 11: cabbes own their own cars. So really what this does

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Speaker 11: is it allows caves taxi cab drivers who are as

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Speaker 11: you know, brilliant, to sort of make even more of

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Speaker 11: their time. Right, They're going to have more customers thanks

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Speaker 11: to the Lift app and our promotion of the get

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Speaker 11: by Lift app. So anyway, that'd be great. They also

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Speaker 11: have a big business to business audience. They actually do

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Speaker 11: work with a VBC, they do work with the economists,

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Speaker 11: they do work with the Royal Albert Hall. So they've

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Speaker 11: got a really nice stable base that we can build on.

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Speaker 11: And then as mobility changes over time, becomes more autonomous

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Speaker 11: and so forth, it just gives us a bigger footprint

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Speaker 11: in the UK.

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Speaker 3: I want to understand, David, how this works in practice.

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Speaker 3: In Western Europe, the taxi cab is part of the culture,

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Speaker 3: and in London, of all places, the black cab is

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Speaker 3: at the heart of what it is to be a

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Speaker 3: Londoner or a visitor to that city. So how does

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Speaker 3: the consumer respond to a big American tech company coming

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Speaker 3: in and bringing a brand like get into its fold?

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Speaker 11: I mean, I hope, well, right, what we hope to

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Speaker 11: do is bring the same customer obsession that we bring

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Speaker 11: here every single day in North America, you know, all

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Speaker 11: across the world. As you guys know, we acquired Free

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Speaker 11: Now last year. They also have a significant presence in London,

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Speaker 11: across all of Europe really and we've seen great growth

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Speaker 11: with that brand and with our writers there because they're

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Speaker 11: now seeing you know, an even better experience. So look,

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Speaker 11: that's our general strategy is you know, customer obsessions with

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Speaker 11: drives profitable growth. And I just love to your point

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Speaker 11: the fact that we're going to be with the London

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Speaker 11: cab system in such a deep way because it's frankly

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Speaker 11: going to teach us something about great service. London cabies

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Speaker 11: are amazing in that way, and hopefully it'll allow us

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Speaker 11: to bring even better technology and sort of invest more

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Speaker 11: in the whole marketing of the idea of taking a

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Speaker 11: taxi when you're in London.

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Speaker 3: David, you've joined us regularly on Bloomberg Tech, and you've

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Speaker 3: been generous with your time in explaining how you've come

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Speaker 3: into Lyft over now almost three years. I think right

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Speaker 3: and put your print on it. The first part of

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Speaker 3: the story was getting to be a cash flow positive,

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Speaker 3: but you are in this kind of m and a

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Speaker 3: strategy and so could you just explain how going shopping

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Speaker 3: and acquiring companies in different markets is going to impact

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Speaker 3: the final financial health of the business and free cash

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Speaker 3: flow in particular for sure.

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Speaker 11: So yeah, it was actually my three year anniversary just

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Speaker 11: last week, ed, so three years, congratulation and thank you,

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Speaker 11: loving every minute of it. Look, if you think of

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Speaker 11: the stages, you're right. At first we were losing money.

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Speaker 11: We were losing cash. Now we're making money. We're profitable

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Speaker 11: quarter after quarter after quarter. We're throwing off over a

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Speaker 11: billion dollars in cash. So what that allows us to

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Speaker 11: do now is to grow through acquisition, exactly to your point.

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Speaker 11: We can take that cash and put it to use.

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Speaker 11: How are we growing We're growing largely internationally. Why because

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Speaker 11: that improves our sort of footprint and over time improves

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Speaker 11: our economics because this is a scale business. And then

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Speaker 11: if you look at the next stage, of course, with

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Speaker 11: self driving cars, you know, again having a large footprint

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Speaker 11: and having a large scale, frankly, is going to matter

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Speaker 11: even more. So this is the sort of journey we're on.

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Speaker 11: It's a step by step journey. But I love where

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Speaker 11: we're going, and I love the fact that both our

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Speaker 11: financial results and our customer results are improving every day

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Speaker 11: as a result.

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Speaker 4: How does the profitability focus fit into the robotaxi and

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Speaker 4: autonomous focus. I mean, I can tell you Black Cavies

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Speaker 4: aren't going to love that in many ways.

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Speaker 2: David, Well, so I think you're talking about two things.

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Speaker 11: I think you're right anytime you've got a big shift,

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Speaker 11: you have a set of people who are doing something

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Speaker 11: today and maybe they're worth they won't be doing that tomorrow.

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Speaker 11: That's something we're very very focused on in LYFT. I

439
00:21:33,480 --> 00:21:36,040
Speaker 11: can give you a very specific example. We're now rolling

440
00:21:36,080 --> 00:21:39,440
Speaker 11: out autonomous cars in Nashville with our partner, Weimo over

441
00:21:39,480 --> 00:21:41,560
Speaker 11: the course of this year, and we just announced that

442
00:21:41,640 --> 00:21:44,720
Speaker 11: half of the employees in our very large new depot

443
00:21:44,760 --> 00:21:47,800
Speaker 11: in Waimo. That will excuse me, in Nashville are drivers.

444
00:21:47,880 --> 00:21:50,320
Speaker 11: Half the employees are drivers, right, So part of it

445
00:21:50,359 --> 00:21:52,639
Speaker 11: is we have to help that transition happen when it

446
00:21:52,640 --> 00:21:56,159
Speaker 11: comes to profitability. Look, self driving cars over time, frankly,

447
00:21:56,160 --> 00:21:58,000
Speaker 11: should improve the economics of bride share.

448
00:21:58,119 --> 00:21:58,920
Speaker 7: It should lead to.

449
00:21:59,440 --> 00:22:01,920
Speaker 11: Because you know, they don't have a lot of the cast,

450
00:22:01,920 --> 00:22:03,920
Speaker 11: they don't have the same insurance cast and so forth.

451
00:22:04,200 --> 00:22:05,840
Speaker 11: But that's going to take a long long time. I

452
00:22:05,840 --> 00:22:08,120
Speaker 11: think right now it's really kind of early days for that.

453
00:22:08,280 --> 00:22:09,679
Speaker 11: We're just trying to figure out a way to kind

454
00:22:09,720 --> 00:22:12,720
Speaker 11: of make the transition in a very very thoughtful way.

455
00:22:13,040 --> 00:22:16,280
Speaker 3: David Rischer, CEO of Lyft, back in San Francisco. I'm

456
00:22:16,280 --> 00:22:18,040
Speaker 3: here in New York. We're grateful to have you all

457
00:22:18,040 --> 00:22:19,720
Speaker 3: the same. They're coming up. We're going to get back

458
00:22:19,720 --> 00:22:23,119
Speaker 3: to today's big story, and that is Tesla. The stock

459
00:22:23,200 --> 00:22:25,800
Speaker 3: is down because of the massive commitment to spend money

460
00:22:26,080 --> 00:22:30,240
Speaker 3: on the big projects, AI, robotics and Robotaxi, all of

461
00:22:30,280 --> 00:22:32,240
Speaker 3: which we just discussed a little bit with Lift CEO.

462
00:22:32,560 --> 00:22:34,200
Speaker 3: So my last day in New York City, but there's

463
00:22:34,280 --> 00:22:36,439
Speaker 3: so much more to do. We'll be right back. It

464
00:22:36,520 --> 00:22:50,280
Speaker 3: is halftime. This is Bloomberg Tech. Welcome back to Bloomberg Tech.

465
00:22:50,320 --> 00:22:53,280
Speaker 3: Our top story is Tesla's earnings. It's a story about

466
00:22:53,400 --> 00:22:57,320
Speaker 3: a massive investment cycle, and it's time for the big number.

467
00:22:57,359 --> 00:23:00,760
Speaker 3: The big number is twenty five billion dollars. That is

468
00:23:00,840 --> 00:23:04,880
Speaker 3: Tesla's capex commitment for the full fiscal year twenty twenty six.

469
00:23:04,920 --> 00:23:06,879
Speaker 3: So you're asking why is it a big number and

470
00:23:06,920 --> 00:23:09,360
Speaker 3: why are the shares down? Well, the most that Tesla's

471
00:23:09,400 --> 00:23:12,280
Speaker 3: ever spent in a full year is about eleven billion dollars.

472
00:23:12,320 --> 00:23:14,520
Speaker 3: That was in twenty twenty four. But now we know

473
00:23:14,640 --> 00:23:18,040
Speaker 3: about the robot line, the Optimist line going up in Fremont,

474
00:23:18,200 --> 00:23:20,600
Speaker 3: the bigger one going up in Texas, how they're going

475
00:23:20,640 --> 00:23:23,480
Speaker 3: to expand cybercab production, they're going to need the funds,

476
00:23:23,520 --> 00:23:25,520
Speaker 3: and when it comes to chips, A lot of people's

477
00:23:25,520 --> 00:23:28,679
Speaker 3: reaction to this was that twenty five billion is a

478
00:23:28,680 --> 00:23:30,520
Speaker 3: big number, but it's a drop in the ocean for

479
00:23:30,560 --> 00:23:31,240
Speaker 3: the industry.

480
00:23:31,400 --> 00:23:31,840
Speaker 2: What's up?

481
00:23:32,600 --> 00:23:34,440
Speaker 5: Have a quick look at what's happening with Microsoft.

482
00:23:34,640 --> 00:23:36,359
Speaker 4: There are some news that they're going to be offering

483
00:23:36,400 --> 00:23:40,119
Speaker 4: voluntary retirement some seven percent of the workforce on the

484
00:23:40,280 --> 00:23:41,160
Speaker 4: US workforce.

485
00:23:41,200 --> 00:23:42,440
Speaker 5: As it currently stands.

486
00:23:42,119 --> 00:23:45,639
Speaker 4: It's voluntary buyouts to just a small percentage. The coustock

487
00:23:45,720 --> 00:23:47,560
Speaker 4: is currently under pressure by two and a half percent.

488
00:23:47,600 --> 00:23:49,679
Speaker 4: But once again, this is going to fit into an

489
00:23:49,720 --> 00:23:53,719
Speaker 4: AI narrative where people can do more with AI productivity tools,

490
00:23:53,960 --> 00:23:56,040
Speaker 4: and therefore, how does that affect the labor market as

491
00:23:56,080 --> 00:23:58,320
Speaker 4: of now, Microsoft saying they're going to be offering voluntary

492
00:23:58,359 --> 00:24:01,399
Speaker 4: retirement to seven percent United States workforce ed.

493
00:24:01,520 --> 00:24:03,280
Speaker 5: But let's go back to Tesla as well.

494
00:24:03,280 --> 00:24:05,320
Speaker 4: Within all of this, because that is the earnings we're

495
00:24:05,320 --> 00:24:08,280
Speaker 4: focusing on. Investors maybe betting less on EV sales and

496
00:24:08,320 --> 00:24:11,119
Speaker 4: on AI and robotics these days, but vehicles are still

497
00:24:11,160 --> 00:24:12,880
Speaker 4: for the moment, the company's bread and butter.

498
00:24:13,119 --> 00:24:14,200
Speaker 5: So side back into.

499
00:24:13,960 --> 00:24:17,200
Speaker 4: Testla's quarterly performance from that angle. Jessica Coltell's hit this

500
00:24:17,680 --> 00:24:22,720
Speaker 4: Edmund's head of insights, so actually better return to some

501
00:24:22,760 --> 00:24:25,000
Speaker 4: sort of growth, particularly I was interested to see in

502
00:24:25,040 --> 00:24:27,000
Speaker 4: Europe as well as in US and Asia.

503
00:24:28,680 --> 00:24:30,679
Speaker 12: Yeah, I mean that really is their core business. And

504
00:24:30,720 --> 00:24:32,919
Speaker 12: I mean I think what we're seeing as an EV market,

505
00:24:32,920 --> 00:24:35,800
Speaker 12: it's not very you know, it's not very linear in

506
00:24:35,840 --> 00:24:38,000
Speaker 12: terms of growth. We're going to see starts and stops

507
00:24:38,040 --> 00:24:39,399
Speaker 12: and all those type of things like we've seen here

508
00:24:39,400 --> 00:24:41,879
Speaker 12: in the US, especially in regards to the federal tax credit.

509
00:24:42,200 --> 00:24:44,479
Speaker 12: But this market is going to continue to grow, So

510
00:24:44,560 --> 00:24:45,960
Speaker 12: it's not as if it's hit the end of a

511
00:24:46,040 --> 00:24:47,679
Speaker 12: runway or even close to the end of the runway.

512
00:24:47,680 --> 00:24:50,600
Speaker 12: It's just beginning. But it's gonna look a little lumpy,

513
00:24:50,680 --> 00:24:53,200
Speaker 12: like to get to full electrification, which I think most

514
00:24:53,240 --> 00:24:55,920
Speaker 12: automakers are on the same page thinking that is really

515
00:24:55,920 --> 00:24:56,720
Speaker 12: the end goal here.

516
00:24:58,560 --> 00:25:01,439
Speaker 4: I think the end goal as well in the stops

517
00:25:01,440 --> 00:25:02,919
Speaker 4: and the starts. How much has that to do with

518
00:25:02,920 --> 00:25:07,480
Speaker 4: the will.

519
00:25:05,359 --> 00:25:08,400
Speaker 12: Well, right now, it's tricky. I mean, we're not necessarily

520
00:25:08,480 --> 00:25:12,840
Speaker 12: seeing a massive consumer just push to interest right now

521
00:25:12,840 --> 00:25:15,440
Speaker 12: because of gas prices. And it's interesting because in twenty

522
00:25:15,440 --> 00:25:17,680
Speaker 12: twenty two and Russia and via to Ukraine. We actually

523
00:25:17,720 --> 00:25:22,119
Speaker 12: saw a much stronger traffic push towards EV's online then

524
00:25:22,160 --> 00:25:23,720
Speaker 12: we're seeing right now. And I think a lot of

525
00:25:23,720 --> 00:25:26,399
Speaker 12: that has to do in general affordability. Because gas prices

526
00:25:26,400 --> 00:25:29,720
Speaker 12: are high, especially markets where Tesla does very well, like California,

527
00:25:30,280 --> 00:25:32,080
Speaker 12: It's not high enough to make people want to buy

528
00:25:32,600 --> 00:25:34,919
Speaker 12: forty fifty thousand dollars vehicle. So it's a bit of

529
00:25:34,960 --> 00:25:37,840
Speaker 12: a different paradigm right now than we've seen in past

530
00:25:37,920 --> 00:25:40,240
Speaker 12: gas spikes in twenty twenty two and even in two

531
00:25:40,240 --> 00:25:40,720
Speaker 12: thousand and.

532
00:25:40,840 --> 00:25:43,760
Speaker 3: So this is the difficulty in understanding, Jessica. This is

533
00:25:43,760 --> 00:25:46,679
Speaker 3: what the CFO Vi Bettanaya has said. Whilst the recent

534
00:25:46,720 --> 00:25:49,760
Speaker 3: increasing gas prices has had a positive impact on the

535
00:25:49,880 --> 00:25:53,240
Speaker 3: order rate, this improvement started before the uptrending gas prices.

536
00:25:53,240 --> 00:25:55,399
Speaker 3: And basically what he goes on to say is that

537
00:25:55,520 --> 00:25:59,040
Speaker 3: Tesla credit themselves with having a broader.

538
00:25:58,760 --> 00:26:00,800
Speaker 2: Offering and a more affordes offering.

539
00:26:01,440 --> 00:26:03,600
Speaker 3: Try and wag through the data and see if you

540
00:26:03,640 --> 00:26:05,720
Speaker 3: agree with Tesla's CFO.

541
00:26:06,840 --> 00:26:09,120
Speaker 12: Well, they have an affordable offering and as far as

542
00:26:09,119 --> 00:26:12,080
Speaker 12: EV's go, right, I mean, and they have I think vehicles,

543
00:26:12,119 --> 00:26:15,159
Speaker 12: particularly the model why that match a lot of consumer preferences.

544
00:26:15,200 --> 00:26:17,520
Speaker 12: It's not a random sports car or you know, large

545
00:26:17,520 --> 00:26:20,600
Speaker 12: sedan or anything like that that matches up. And Tesla

546
00:26:20,640 --> 00:26:23,320
Speaker 12: hasn't been pretty elastic in terms of pricing. They've drop prices,

547
00:26:23,359 --> 00:26:26,359
Speaker 12: they have raised prices. They do a lot to match demand,

548
00:26:26,400 --> 00:26:30,439
Speaker 12: so I wouldn't necessarily say that their you know, sales

549
00:26:30,480 --> 00:26:34,359
Speaker 12: were skyrocketing before it gas prices. But I think what

550
00:26:34,400 --> 00:26:37,560
Speaker 12: they do well is they're well known. They saw increases

551
00:26:37,640 --> 00:26:40,080
Speaker 12: share in California sales in the first quarter. It was

552
00:26:40,119 --> 00:26:42,080
Speaker 12: a smaller pie, but they got a bigger part of

553
00:26:42,080 --> 00:26:45,280
Speaker 12: it because I think people also know their brand too,

554
00:26:45,359 --> 00:26:47,439
Speaker 12: so I mean they're synonymous with evs, so people just

555
00:26:47,480 --> 00:26:49,280
Speaker 12: looking for an EV's they think of Tesla, so they

556
00:26:49,280 --> 00:26:50,280
Speaker 12: have an advantage there.

557
00:26:50,920 --> 00:26:52,720
Speaker 3: I was talking a minute ago about the big number,

558
00:26:52,800 --> 00:26:55,720
Speaker 3: which was capital expenditures guide for the year. The small

559
00:26:55,840 --> 00:27:00,800
Speaker 3: number for me was the FSD subscription take right, So

560
00:27:00,840 --> 00:27:03,480
Speaker 3: they ended last year with one point one million FST

561
00:27:03,640 --> 00:27:06,080
Speaker 3: subscribers and it's gone up a little bit to one

562
00:27:06,119 --> 00:27:09,000
Speaker 3: point two eight million. It's only available in the United

563
00:27:09,000 --> 00:27:11,040
Speaker 3: States and some limited parts of Europe. But if you

564
00:27:11,040 --> 00:27:13,359
Speaker 3: think about how many Teslas there are on the roads

565
00:27:13,400 --> 00:27:16,560
Speaker 3: around the world. That does to me seem like the

566
00:27:16,600 --> 00:27:17,280
Speaker 3: small number.

567
00:27:19,000 --> 00:27:20,840
Speaker 12: I mean, it really is, and I think a part

568
00:27:20,880 --> 00:27:24,359
Speaker 12: of it is the consumer acceptance and understanding of what

569
00:27:24,440 --> 00:27:27,240
Speaker 12: it is, the safety of it all. I think it's

570
00:27:27,240 --> 00:27:30,120
Speaker 12: something that's exciting that people like it. They're thinking, oh,

571
00:27:30,240 --> 00:27:31,560
Speaker 12: this card pubs can help me drive.

572
00:27:31,640 --> 00:27:32,400
Speaker 2: That's fantastic.

573
00:27:32,440 --> 00:27:35,239
Speaker 12: I can focus on other things, but it really still is.

574
00:27:35,840 --> 00:27:38,560
Speaker 12: You know, it's largely unknown to I did the majority

575
00:27:38,560 --> 00:27:41,399
Speaker 12: of consumers, and Tesla does have a lot of more

576
00:27:41,520 --> 00:27:44,159
Speaker 12: mainstream consumers. They sure they had some early adopters, but

577
00:27:44,200 --> 00:27:46,479
Speaker 12: now they're getting mainstream books that are maybe not as

578
00:27:46,520 --> 00:27:49,040
Speaker 12: TechEd forward as you know, as we all think, yeah.

579
00:27:48,840 --> 00:27:51,040
Speaker 5: Just how does Elol must tackle that?

580
00:27:51,080 --> 00:27:54,399
Speaker 4: Because look, his huge pay package is actually linked to

581
00:27:54,440 --> 00:27:56,359
Speaker 4: adoption of FSD, and in many ways you sort of

582
00:27:56,359 --> 00:27:59,880
Speaker 4: apologize for the small numbers and tells us about.

583
00:28:01,400 --> 00:28:01,640
Speaker 13: Yeah.

584
00:28:01,680 --> 00:28:03,520
Speaker 12: I mean, I think it's something that it's going to

585
00:28:03,520 --> 00:28:05,239
Speaker 12: take time, it's going to have to grow. I mean,

586
00:28:05,280 --> 00:28:07,439
Speaker 12: they have a lot of hands in different parts at

587
00:28:07,440 --> 00:28:09,520
Speaker 12: this point, so I'm not sure how he's going to

588
00:28:09,880 --> 00:28:12,000
Speaker 12: accomplish it all. But I think, you know, obviously education

589
00:28:12,080 --> 00:28:13,800
Speaker 12: is important thing. How this is going to fit the

590
00:28:13,880 --> 00:28:17,600
Speaker 12: regulation because there's any time there's any sort of crash,

591
00:28:17,640 --> 00:28:20,679
Speaker 12: I mean, the news is definitely more proportionate than what

592
00:28:20,760 --> 00:28:23,879
Speaker 12: you'd see any place else. And I just think that

593
00:28:23,920 --> 00:28:26,639
Speaker 12: you're going to have to work on the public acceptance

594
00:28:26,680 --> 00:28:29,080
Speaker 12: of these type of technologies. I mean, people are very weary.

595
00:28:29,119 --> 00:28:32,080
Speaker 12: We just heard about AI replacing jobs. It just all

596
00:28:32,160 --> 00:28:34,840
Speaker 12: becomes a bit of a not a scary black box.

597
00:28:34,880 --> 00:28:36,680
Speaker 12: There's something to that effect to a lot of consumers.

598
00:28:36,920 --> 00:28:38,640
Speaker 3: I guess the last headline that we can give our

599
00:28:38,680 --> 00:28:40,920
Speaker 3: audience is that must said that by the end of

600
00:28:40,920 --> 00:28:44,400
Speaker 3: this year in the us FSD unsupervised. In other words,

601
00:28:44,440 --> 00:28:46,960
Speaker 3: eyes off the road, hands off the road. We'll see

602
00:28:47,000 --> 00:28:49,920
Speaker 3: if that happens. Jessica cole Well, head of Inside at Evans,

603
00:28:49,960 --> 00:28:52,960
Speaker 3: thank you very much. Another top story, and we're turning

604
00:28:52,960 --> 00:28:56,360
Speaker 3: to another of Musk companies. SpaceX is playing a bigger

605
00:28:56,440 --> 00:29:01,240
Speaker 3: role in President Trump's Golden Dome project previously known. According

606
00:29:01,280 --> 00:29:04,520
Speaker 3: to sources, SpaceX is among a group of companies developing

607
00:29:04,560 --> 00:29:08,720
Speaker 3: the operating system underpinning the Missile Defense System. Let's get

608
00:29:08,720 --> 00:29:12,080
Speaker 3: the latest with Bloomberg's Mike Shepherd, who's in DC. Let's

609
00:29:12,080 --> 00:29:14,480
Speaker 3: start just with the details Bloombergs reporting because this is

610
00:29:14,480 --> 00:29:15,520
Speaker 3: an important.

611
00:29:15,120 --> 00:29:17,200
Speaker 2: Story it is.

612
00:29:17,240 --> 00:29:19,960
Speaker 14: It's important because Golden Dome is one of the President's

613
00:29:19,960 --> 00:29:24,040
Speaker 14: signature initiatives when it comes to protecting the US against

614
00:29:24,040 --> 00:29:25,520
Speaker 14: the ballistic missile attack.

615
00:29:25,720 --> 00:29:26,840
Speaker 2: It's worth one hundred and.

616
00:29:26,840 --> 00:29:29,440
Speaker 14: Eighty five billion dollars over the course of the program

617
00:29:29,520 --> 00:29:33,920
Speaker 14: so far, and SpaceX will be providing, together with other companies,

618
00:29:33,960 --> 00:29:36,520
Speaker 14: a critical component in of it. Think of it as

619
00:29:36,560 --> 00:29:39,320
Speaker 14: the glue that holds all these networks together. It will

620
00:29:39,360 --> 00:29:43,120
Speaker 14: be essentially the operating system that helps the govern military

621
00:29:43,200 --> 00:29:46,520
Speaker 14: operations once this thing gets up off the ground. And

622
00:29:46,600 --> 00:29:49,320
Speaker 14: it is an expansion of the role that SpaceX had

623
00:29:49,360 --> 00:29:52,160
Speaker 14: already been playing in the project. It had been building

624
00:29:52,440 --> 00:29:56,080
Speaker 14: satellites through its Starlink platform, and it had also been

625
00:29:56,520 --> 00:30:02,400
Speaker 14: developing a military communications network through it star Shield technology,

626
00:30:02,440 --> 00:30:07,320
Speaker 14: and that is a classified version of Starlink that offers encryption,

627
00:30:07,640 --> 00:30:09,080
Speaker 14: especially for military use.

628
00:30:09,120 --> 00:30:13,800
Speaker 4: At MIKE, Where are we with the vision and then

629
00:30:14,240 --> 00:30:18,360
Speaker 4: the action towards Golden Dome, and really what the other

630
00:30:18,480 --> 00:30:21,720
Speaker 4: puzzle pieces are that need to be put in place, Well.

631
00:30:21,680 --> 00:30:23,400
Speaker 14: Caara, if you want to think of it as a puzzle,

632
00:30:23,440 --> 00:30:26,880
Speaker 14: there's still a lot of pieces to the side looking

633
00:30:27,000 --> 00:30:30,680
Speaker 14: to be placed in the right spots. There is a

634
00:30:30,800 --> 00:30:35,200
Speaker 14: long way to go before this thing actually becomes in

635
00:30:35,240 --> 00:30:39,520
Speaker 14: place and an effective defense against the kinds of missile

636
00:30:39,560 --> 00:30:43,120
Speaker 14: threats that have been warned about for years from China,

637
00:30:43,440 --> 00:30:46,360
Speaker 14: from Russia, and now even from Iran. There are questions

638
00:30:46,400 --> 00:30:51,240
Speaker 14: about the extent to which US adversaries could try to

639
00:30:51,240 --> 00:30:53,840
Speaker 14: strike the homeland here. And this is the reason why

640
00:30:53,880 --> 00:30:57,200
Speaker 14: the US has moved in this direction, and it has

641
00:30:57,280 --> 00:31:00,280
Speaker 14: turned to one of its most reliable space contract director

642
00:31:00,320 --> 00:31:03,400
Speaker 14: of SpaceX, to really help get this up off the ground.

643
00:31:03,400 --> 00:31:06,360
Speaker 14: And SpaceX is working with a couple of other signature

644
00:31:06,480 --> 00:31:09,440
Speaker 14: names who have taken on increased roles working with the

645
00:31:09,440 --> 00:31:13,760
Speaker 14: government that includes indoorial industries and palunteer technologies as well

646
00:31:14,080 --> 00:31:19,680
Speaker 14: on developing this communications and this operating system that we've

647
00:31:19,720 --> 00:31:20,960
Speaker 14: been reporting on today.

648
00:31:21,040 --> 00:31:23,720
Speaker 3: You know that that last point is so crucial. SpaceX

649
00:31:23,800 --> 00:31:26,800
Speaker 3: has this legacy with the US government which is about

650
00:31:26,840 --> 00:31:30,440
Speaker 3: ten to fifteen years in duration, hundreds of millions billions

651
00:31:30,440 --> 00:31:33,240
Speaker 3: in contracts NASA and the Defense apparatus. But if you

652
00:31:33,240 --> 00:31:36,360
Speaker 3: look at those other names and orill Impulse, we're talking

653
00:31:36,600 --> 00:31:40,160
Speaker 3: much smaller contracts. Right in the context of this broader story.

654
00:31:41,520 --> 00:31:41,960
Speaker 2: That's right.

655
00:31:41,960 --> 00:31:44,440
Speaker 14: They are much smaller contracts, and yet they are part

656
00:31:44,480 --> 00:31:47,840
Speaker 14: of this larger ecosystem ed And when you turn the

657
00:31:47,880 --> 00:31:51,959
Speaker 14: focus back to SpaceX also we do see this company

658
00:31:52,000 --> 00:31:54,440
Speaker 14: on the move. They are getting ready for an IPO

659
00:31:54,640 --> 00:31:57,160
Speaker 14: later this year that could value the venture at almost

660
00:31:57,200 --> 00:31:59,880
Speaker 14: two trillion dollars, and they are also making moves in

661
00:32:00,080 --> 00:32:02,680
Speaker 14: other directions to shore up other parts of the product

662
00:32:02,760 --> 00:32:04,200
Speaker 14: line that could help in.

663
00:32:04,240 --> 00:32:05,000
Speaker 2: This venture too.

664
00:32:05,080 --> 00:32:07,600
Speaker 14: They have reached an agreement or at least to have

665
00:32:07,720 --> 00:32:10,440
Speaker 14: the rights to acquire Cursor AI for up to sixty

666
00:32:10,480 --> 00:32:13,240
Speaker 14: billion dollars, and the idea would be to use its

667
00:32:13,360 --> 00:32:18,440
Speaker 14: coding and debugging ability to help the XAI segment of

668
00:32:19,360 --> 00:32:23,400
Speaker 14: SpaceX in its development and advancing its products. And you

669
00:32:23,440 --> 00:32:27,920
Speaker 14: could see very easily integrating those artificial intelligence capabilities as

670
00:32:27,960 --> 00:32:30,760
Speaker 14: well into this kind of operating system we've just been

671
00:32:30,760 --> 00:32:31,360
Speaker 14: talking about.

672
00:32:31,640 --> 00:32:34,680
Speaker 5: Mergs. Mike Shepherd from Golden don't we appreciate it? Thank you?

673
00:32:35,440 --> 00:32:37,840
Speaker 5: Now coming up easier to use AI tools.

674
00:32:37,880 --> 00:32:41,440
Speaker 4: Well, they've sparked an increase in abusive imagery online. That's

675
00:32:41,480 --> 00:32:45,680
Speaker 4: according to the difficulties of investigators. More on that research next,

676
00:32:45,760 --> 00:32:58,440
Speaker 4: this to bring back tech child pornography, and it's always

677
00:32:58,480 --> 00:33:01,480
Speaker 4: been a major scooch of the but a surge of

678
00:33:01,520 --> 00:33:04,920
Speaker 4: abusive material created with AI tools, it's making it hard

679
00:33:04,960 --> 00:33:07,840
Speaker 4: for investigators to pass the real from the fake, and

680
00:33:07,880 --> 00:33:10,320
Speaker 4: that can be the difference in knowing which cases need

681
00:33:10,440 --> 00:33:14,040
Speaker 4: urgent attention Nimos. Kurt Wagner, one of the report reporters

682
00:33:14,040 --> 00:33:16,640
Speaker 4: on Today's Big Take, joins us, now is a difficult

683
00:33:16,640 --> 00:33:19,280
Speaker 4: read whether you're a parent or not, Kurt, But really

684
00:33:19,320 --> 00:33:22,360
Speaker 4: this is about prioritization. This is about a lack of

685
00:33:22,440 --> 00:33:25,720
Speaker 4: resource for many who are trying to understand which child

686
00:33:25,800 --> 00:33:28,120
Speaker 4: to go out there and help and which one is

687
00:33:28,160 --> 00:33:30,719
Speaker 4: actually an AI generated piece of content.

688
00:33:32,360 --> 00:33:34,040
Speaker 2: Caroline. We spent six months working on this.

689
00:33:34,160 --> 00:33:37,760
Speaker 15: We talked to dozens of law enforcement officials who are

690
00:33:37,800 --> 00:33:41,120
Speaker 15: at the frontline specifically of child's safety crimes, and what

691
00:33:41,160 --> 00:33:44,120
Speaker 15: they told us is that because of AI, the number

692
00:33:44,120 --> 00:33:46,240
Speaker 15: of reports and tips they have to sift through is

693
00:33:46,640 --> 00:33:49,800
Speaker 15: jumping exponentially. And when you are sifting through, as you

694
00:33:49,840 --> 00:33:52,720
Speaker 15: point out, and not sure whether the image or the

695
00:33:52,800 --> 00:33:56,080
Speaker 15: video you're looking at depicts a real child in physical

696
00:33:56,160 --> 00:33:59,560
Speaker 15: danger or something that has been totally fabricated by an

697
00:33:59,720 --> 00:34:03,040
Speaker 15: LA or some other generative AI tool. They spend a

698
00:34:03,040 --> 00:34:06,160
Speaker 15: lot of time basically tracking down leads that don't lead

699
00:34:06,200 --> 00:34:09,480
Speaker 15: to actually helping a child endanger. And the fear is

700
00:34:09,520 --> 00:34:12,719
Speaker 15: that the more this happens, the more there is a

701
00:34:12,840 --> 00:34:14,880
Speaker 15: risk that a real child who's being harmed is going

702
00:34:14,920 --> 00:34:17,920
Speaker 15: to get overlooked because everyone's busy focused on the AI

703
00:34:18,040 --> 00:34:18,960
Speaker 15: generated stuff.

704
00:34:19,680 --> 00:34:23,720
Speaker 3: There is a mechanism here, so when a technology company

705
00:34:23,760 --> 00:34:27,439
Speaker 3: or a social media company finds that content that we're

706
00:34:27,480 --> 00:34:28,600
Speaker 3: talking about.

707
00:34:28,840 --> 00:34:30,279
Speaker 2: They report it.

708
00:34:30,320 --> 00:34:33,200
Speaker 3: They are required to report it to the National Center

709
00:34:33,280 --> 00:34:37,759
Speaker 3: for Missing and Exploited Children NCMEC, and from that there

710
00:34:37,840 --> 00:34:41,560
Speaker 3: is other data available. What does the data tell that

711
00:34:41,760 --> 00:34:43,080
Speaker 3: organization curve.

712
00:34:44,320 --> 00:34:47,120
Speaker 15: So ideally, when a big tech or media company finds

713
00:34:47,160 --> 00:34:50,160
Speaker 15: this and reports it ed, they would include a lot

714
00:34:50,200 --> 00:34:53,200
Speaker 15: of detail about the image or video, right, maybe an

715
00:34:53,239 --> 00:34:56,920
Speaker 15: IP address, a location, you know, how it was created,

716
00:34:57,000 --> 00:34:59,399
Speaker 15: or what types of tools were used, and then Nick

717
00:34:59,400 --> 00:35:02,160
Speaker 15: make that organiation you mentioned, They receive all of these

718
00:35:02,200 --> 00:35:04,560
Speaker 15: from all around the country and then redistribute them out

719
00:35:04,560 --> 00:35:08,000
Speaker 15: to state experts state investigators.

720
00:35:08,360 --> 00:35:09,800
Speaker 2: The problem is that a lot.

721
00:35:09,600 --> 00:35:12,279
Speaker 15: Of the tips that are coming into Nickmick, especially those

722
00:35:12,320 --> 00:35:15,000
Speaker 15: that are AI related, do not have any of that

723
00:35:15,160 --> 00:35:17,640
Speaker 15: necessary data to actually go do anything right, there is

724
00:35:17,640 --> 00:35:20,759
Speaker 15: no IP address, there is maybe no location, and so

725
00:35:21,080 --> 00:35:23,360
Speaker 15: Nick Mick is being flooded with these tips and doesn't

726
00:35:23,360 --> 00:35:25,959
Speaker 15: have anything to do with them. And so we've seen

727
00:35:26,000 --> 00:35:28,520
Speaker 15: Congress actually make a big deal about this. There is

728
00:35:28,640 --> 00:35:32,480
Speaker 15: just an investigation by Senator Grassley of Iowa where he's

729
00:35:32,480 --> 00:35:36,440
Speaker 15: trying to challenge these companies to report more useful information

730
00:35:36,560 --> 00:35:38,960
Speaker 15: to Nickmick, because if they just get a video or

731
00:35:39,040 --> 00:35:41,400
Speaker 15: photo but nothing else with it, there's no way that

732
00:35:41,440 --> 00:35:43,520
Speaker 15: they can go and actually try to stop a crime.

733
00:35:44,120 --> 00:35:48,040
Speaker 4: Kurt, I think the numbers are important here and they're astounding.

734
00:35:48,160 --> 00:35:50,800
Speaker 4: Last year the Clearinghouse received one and a half million

735
00:35:50,920 --> 00:35:54,600
Speaker 4: reports or suspected see some with ties to AI tools.

736
00:35:54,920 --> 00:35:56,800
Speaker 4: But the previous year it had just been sixty seven

737
00:35:56,880 --> 00:35:59,040
Speaker 4: thousand reports, and in twenty twenty three it's four thousand,

738
00:35:59,080 --> 00:36:01,000
Speaker 4: seven hundred, which is all too many, but one and

739
00:36:01,000 --> 00:36:05,160
Speaker 4: a half million to just talk us through the sun

740
00:36:05,239 --> 00:36:07,320
Speaker 4: exponential growth that you're talking about and the ways in

741
00:36:07,360 --> 00:36:10,480
Speaker 4: which people are trying to tackle it, Yeah.

742
00:36:10,280 --> 00:36:12,960
Speaker 15: I mean less than five thousand two years ago, now

743
00:36:13,040 --> 00:36:16,480
Speaker 15: one point five million in just two years, and growing

744
00:36:16,840 --> 00:36:18,359
Speaker 15: and expected to continue to grow.

745
00:36:18,480 --> 00:36:20,279
Speaker 2: And when you think about why that is.

746
00:36:20,320 --> 00:36:22,880
Speaker 15: I mean, you just have to look around at the

747
00:36:22,880 --> 00:36:25,560
Speaker 15: ease of use of these AI tools that have come up. Right,

748
00:36:25,920 --> 00:36:28,239
Speaker 15: all of these companies are spending tens of billions of

749
00:36:28,280 --> 00:36:31,560
Speaker 15: dollars on data centers and products, trying to move as

750
00:36:31,560 --> 00:36:34,239
Speaker 15: fast as humanly possible to get these AI tools, the

751
00:36:34,320 --> 00:36:37,439
Speaker 15: generative AI tools, into the hands of everyday consumers. Of course,

752
00:36:37,480 --> 00:36:40,080
Speaker 15: the downside being that that also helps the bad guys.

753
00:36:40,280 --> 00:36:44,960
Speaker 15: And so while AI related reports are still a small

754
00:36:45,160 --> 00:36:48,560
Speaker 15: sliver of the total number of reports that people get,

755
00:36:48,880 --> 00:36:52,320
Speaker 15: you can see Caroline how it's growing exponentially and expected

756
00:36:52,360 --> 00:36:52,920
Speaker 15: to continue.

757
00:36:52,960 --> 00:36:56,400
Speaker 3: So Oka, you said at the beginning of our conversation,

758
00:36:56,520 --> 00:36:59,239
Speaker 3: you spent six months reporting this as part of the

759
00:36:59,280 --> 00:37:03,440
Speaker 3: reporting course, had responses from a range of technology and

760
00:37:03,480 --> 00:37:07,359
Speaker 3: actually in particular AI companies. We've been showing those responses

761
00:37:07,400 --> 00:37:11,040
Speaker 3: on air throughout the conversation. But how would you summarize

762
00:37:11,400 --> 00:37:14,880
Speaker 3: the industry's response, either directly to your reporting or to

763
00:37:14,920 --> 00:37:17,480
Speaker 3: the issue that you that you are talking about.

764
00:37:18,760 --> 00:37:22,439
Speaker 15: A lot of the big companies are telling us that, hey,

765
00:37:22,480 --> 00:37:25,799
Speaker 15: we have safeguards in place, right, we don't allow if

766
00:37:25,800 --> 00:37:29,400
Speaker 15: someone goes to chat GPT and tries to role play

767
00:37:30,239 --> 00:37:33,319
Speaker 15: with a child, which is something that now happens. They

768
00:37:33,360 --> 00:37:35,760
Speaker 15: say that they have alert setup that they can detect

769
00:37:35,760 --> 00:37:38,800
Speaker 15: that report it, get it taken down. I think the issue,

770
00:37:39,000 --> 00:37:43,120
Speaker 15: ed is when you think about open source models. What

771
00:37:43,160 --> 00:37:45,600
Speaker 15: we've heard is that some of the open source technology

772
00:37:46,040 --> 00:37:49,160
Speaker 15: gets used. People download it to their personal device and

773
00:37:49,200 --> 00:37:52,600
Speaker 15: then they train it to create more child abuse material.

774
00:37:53,280 --> 00:37:55,720
Speaker 15: And that is where the technology can get really dangerous

775
00:37:55,760 --> 00:37:59,799
Speaker 15: because people are able to tailor it towards a specific

776
00:38:00,440 --> 00:38:03,399
Speaker 15: thing that they want to create without any oversight from

777
00:38:03,440 --> 00:38:04,760
Speaker 15: the companies themselves.

778
00:38:04,760 --> 00:38:05,640
Speaker 7: And so when you're.

779
00:38:05,600 --> 00:38:08,280
Speaker 15: Using a mainstream product owned and operated by the company,

780
00:38:08,440 --> 00:38:11,200
Speaker 15: there tends to be more guardrails. But when people are

781
00:38:11,280 --> 00:38:12,920
Speaker 15: able to kind of take the stuff, put it on

782
00:38:12,960 --> 00:38:16,799
Speaker 15: the personal device, and manipulate it themselves, that's where you know,

783
00:38:16,840 --> 00:38:18,879
Speaker 15: these tools can become very powerful in a bad way.

784
00:38:19,320 --> 00:38:22,359
Speaker 2: Bloomberg Kirtwagner, thank you. Now.

785
00:38:22,360 --> 00:38:25,440
Speaker 3: Coming up, Warner Brothers shareholders approved the one hundred and

786
00:38:25,480 --> 00:38:29,680
Speaker 3: ten billion dollar sale to Paramount, but they don't sign

787
00:38:29,719 --> 00:38:33,080
Speaker 3: off on everything. We have the details from the investor meeting.

788
00:38:33,120 --> 00:38:41,920
Speaker 3: Next this is Bloomberg Tech. Texas Instruments is up almost

789
00:38:42,000 --> 00:38:44,520
Speaker 3: nineteen percent, on track for its best day since two

790
00:38:44,600 --> 00:38:47,960
Speaker 3: thousand at a record high, the biggest maker of analog chips,

791
00:38:48,280 --> 00:38:51,959
Speaker 3: massive demand from all the industrial machinery that goes into

792
00:38:52,000 --> 00:38:54,520
Speaker 3: building a data center. That's the story. That's one of

793
00:38:54,560 --> 00:38:55,600
Speaker 3: the best performers today.

794
00:38:55,719 --> 00:38:58,120
Speaker 4: I've got the reverse, the worst performer on the S

795
00:38:58,160 --> 00:39:00,600
Speaker 4: and P five hundred, in fact, the worst Perforeman's ever

796
00:39:00,880 --> 00:39:01,440
Speaker 4: for service.

797
00:39:01,520 --> 00:39:04,680
Speaker 5: Now after its earnings, we caught up with the CEO,

798
00:39:04,760 --> 00:39:06,120
Speaker 5: Bill McDermott.

799
00:39:05,600 --> 00:39:09,719
Speaker 4: As he tries to push back on the negativity around software.

800
00:39:09,920 --> 00:39:11,160
Speaker 5: Just take a listen to what he had to say.

801
00:39:12,600 --> 00:39:16,640
Speaker 4: Even though subscription growth is still twenty two percent, you

802
00:39:16,719 --> 00:39:17,719
Speaker 4: beat on revenue.

803
00:39:18,160 --> 00:39:19,879
Speaker 5: What more can you telemarket? Right now?

804
00:39:19,880 --> 00:39:23,520
Speaker 13: Bill, I think it's very important to deal with facts.

805
00:39:24,640 --> 00:39:28,560
Speaker 13: We're fifteen billion dollar enterprise software company, the fastest to

806
00:39:28,600 --> 00:39:32,040
Speaker 13: ever get they're growing at more than twenty percent. We

807
00:39:32,080 --> 00:39:35,399
Speaker 13: had a beaten raised quarter and we reiterated our full

808
00:39:35,480 --> 00:39:40,040
Speaker 13: year guidance. So that's the baseline for the conversation. And

809
00:39:40,280 --> 00:39:43,200
Speaker 13: we are a growth company, and I'll take the interview

810
00:39:43,239 --> 00:39:44,799
Speaker 13: anywhere you wanted to go.

811
00:39:45,600 --> 00:39:50,759
Speaker 4: Let's talk about geopolitical headwinds, Let's talk about acquisition integration expenses.

812
00:39:50,960 --> 00:39:53,640
Speaker 4: Maybe there's some of the areas that investors and the

813
00:39:53,680 --> 00:39:55,759
Speaker 4: analysts have said. Look, it made for a bit more

814
00:39:55,800 --> 00:39:58,600
Speaker 4: of a messy quarter than perhaps would have been easier

815
00:39:58,600 --> 00:40:00,400
Speaker 4: for you to tell the story on what is the

816
00:40:00,480 --> 00:40:02,840
Speaker 4: underlying growth rate of AI at the moment of adoption

817
00:40:02,960 --> 00:40:04,680
Speaker 4: of the service now products.

818
00:40:05,040 --> 00:40:07,920
Speaker 13: Yeah, what you have to realize is AI is the product,

819
00:40:08,040 --> 00:40:11,680
Speaker 13: it's the whole platform. So the platform is fully autonomous.

820
00:40:11,680 --> 00:40:15,279
Speaker 13: So when a customer buys our AI platform, it is

821
00:40:15,400 --> 00:40:19,560
Speaker 13: enabling every function of their company to be a native

822
00:40:19,640 --> 00:40:23,960
Speaker 13: AI company. So it's all AI now. And that's another thing.

823
00:40:24,120 --> 00:40:26,959
Speaker 13: We said we would do twenty and twenty twenty six

824
00:40:27,000 --> 00:40:32,760
Speaker 13: a billion of net new annual contract value on pure

825
00:40:32,840 --> 00:40:36,000
Speaker 13: AI additive to the platform. Yesterday we uped it to

826
00:40:36,040 --> 00:40:37,839
Speaker 13: a billion and a half and I think we'll run

827
00:40:37,880 --> 00:40:41,640
Speaker 13: through that. So if there's anything that the investors wanted,

828
00:40:41,760 --> 00:40:45,960
Speaker 13: they probably wanted a bigger beat. So we're beating every quarter,

829
00:40:46,040 --> 00:40:48,120
Speaker 13: maybe they wanted a bigger beat. And then the second

830
00:40:48,120 --> 00:40:50,839
Speaker 13: thing is, and it is true we acquired a couple

831
00:40:50,920 --> 00:40:52,080
Speaker 13: of companies.

832
00:40:52,040 --> 00:40:55,000
Speaker 3: That was Service Now CEO Bill McDermott. Sticking with software,

833
00:40:55,000 --> 00:40:57,080
Speaker 3: we're looking at IBM at one point in this session

834
00:40:57,480 --> 00:41:01,000
Speaker 3: having its biggest lowest level since a year ago eight

835
00:41:01,000 --> 00:41:04,680
Speaker 3: Pril twenty twenty five. Software AI, same story.

836
00:41:04,400 --> 00:41:07,440
Speaker 4: It is, and also similarly for IBM, it's a macro uncertainty.

837
00:41:07,480 --> 00:41:11,080
Speaker 4: That meant they didn't upgrade their revenue full year expected,

838
00:41:11,160 --> 00:41:11,839
Speaker 4: so they held it.

839
00:41:12,000 --> 00:41:14,080
Speaker 5: They held it, and that seemed cautious. Look.

840
00:41:14,239 --> 00:41:17,640
Speaker 4: I spoke with having Krishna just yesterday after the amid

841
00:41:17,640 --> 00:41:19,080
Speaker 4: the earnings, and he looked said.

842
00:41:18,960 --> 00:41:20,840
Speaker 5: I got better margins, better profit, better cash.

843
00:41:21,080 --> 00:41:24,680
Speaker 4: But he did see that there's a macroeconomy concern and

844
00:41:24,719 --> 00:41:26,560
Speaker 4: so he wasn't going to raise his guidance. But look,

845
00:41:26,600 --> 00:41:29,719
Speaker 4: he's talking about AI as well. It infiltrates all of it.

846
00:41:29,719 --> 00:41:32,680
Speaker 4: It's not just a model. They see tailwinds. The market

847
00:41:32,760 --> 00:41:35,240
Speaker 4: doesn't yet believe it. At the moment, there is angst

848
00:41:35,480 --> 00:41:38,719
Speaker 4: ed about these software businesses being disrupted by.

849
00:41:38,600 --> 00:41:41,280
Speaker 3: Them to say in the future, we will make more money.

850
00:41:41,440 --> 00:41:42,760
Speaker 3: This is the timeline, that's.

851
00:41:42,560 --> 00:41:44,480
Speaker 5: All, And for now they can't.

852
00:41:44,560 --> 00:41:47,080
Speaker 4: But for now we talk about what's happening with a

853
00:41:47,120 --> 00:41:50,920
Speaker 4: deal WBD as we know it, Warner Brothers, Discovery and

854
00:41:51,000 --> 00:41:54,680
Speaker 4: Paramount both down. Paramount down by five percent. But we

855
00:41:54,800 --> 00:41:56,640
Speaker 4: got that approval of the deal one hundred and ten

856
00:41:56,760 --> 00:41:59,600
Speaker 4: billion dollar acquisition of the company BI Paramount's guide outs,

857
00:42:00,080 --> 00:42:02,359
Speaker 4: let's turn our attention to what comes next. Bloemberg's media

858
00:42:02,360 --> 00:42:05,799
Speaker 4: reporter Hannah Miller. The investors okay with it? Now, we've

859
00:42:05,800 --> 00:42:07,680
Speaker 4: got to see if the regulators are yes.

860
00:42:07,840 --> 00:42:12,520
Speaker 16: That's the next step is to overcome any regulatory obstacles

861
00:42:12,560 --> 00:42:16,360
Speaker 16: that crop up, including anti trust reviews in both the

862
00:42:16,440 --> 00:42:17,480
Speaker 16: US and Europe.

863
00:42:17,760 --> 00:42:20,040
Speaker 3: Senator Elizabeth Warren has posted on x in the last

864
00:42:20,080 --> 00:42:22,600
Speaker 3: couple of minutes that the Paramount Warner Brothers deal is

865
00:42:22,600 --> 00:42:26,200
Speaker 3: an antitrust nightmare? Is that how the world feels about this, Hannah?

866
00:42:26,400 --> 00:42:29,200
Speaker 16: I think that's certainly how Hollywood feels about this. That

867
00:42:29,480 --> 00:42:33,719
Speaker 16: we've had a lot of celebrities, actors, writers, directors come

868
00:42:33,760 --> 00:42:36,879
Speaker 16: forward and actually sign an open letter protesting the deal,

869
00:42:37,040 --> 00:42:42,040
Speaker 16: arguing that this will affect jobs, it'll lower production, it'll

870
00:42:42,040 --> 00:42:45,759
Speaker 16: mean higher costs. They think that this consolidation, it would

871
00:42:45,800 --> 00:42:47,120
Speaker 16: have too much power in Hollywood.

872
00:42:47,160 --> 00:42:48,040
Speaker 5: I mean, it's getting personal.

873
00:42:48,080 --> 00:42:51,080
Speaker 4: If you go on TikTok, there's Jane Fonder and pretending

874
00:42:51,160 --> 00:42:54,319
Speaker 4: to be CBS news anchors that are being disrupted in

875
00:42:54,360 --> 00:42:57,840
Speaker 4: the future. Just what is the tactics that Hollywood now deploys.

876
00:42:57,880 --> 00:42:59,520
Speaker 4: What is it that we're likely to expect in terms

877
00:42:59,560 --> 00:43:00,080
Speaker 4: of the time.

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Speaker 16: I think we've already seen them really, you know, make

879
00:43:03,600 --> 00:43:06,000
Speaker 16: a big fuss about it in the press. This open

880
00:43:06,040 --> 00:43:08,600
Speaker 16: letter was a great move. We've seen a lot of

881
00:43:08,640 --> 00:43:12,600
Speaker 16: celebrities post on social media. Politicians have also come forward

882
00:43:12,760 --> 00:43:17,200
Speaker 16: and you know, expressed concern about the deal. So you

883
00:43:17,200 --> 00:43:19,560
Speaker 16: know there is going to be this sort of public

884
00:43:19,600 --> 00:43:20,880
Speaker 16: relations battle that'll be on the hurt.

885
00:43:20,960 --> 00:43:22,400
Speaker 2: At some point, we'll talk about the tech story.

886
00:43:22,440 --> 00:43:27,200
Speaker 3: Remember ViacomCBS Paramount plus HBO Max. That will be discussed

887
00:43:27,200 --> 00:43:29,879
Speaker 3: at some point. Bloomberg's Ala Miller, Thank you very much.

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00:43:30,120 --> 00:43:32,480
Speaker 3: That does it for this edition of Bloomberg Tech.

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Speaker 5: It has been such fun. Haven't you sat next to me?

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Speaker 2: It's been great. It's been a hell of a week.

891
00:43:37,080 --> 00:43:40,360
Speaker 3: I mean, newsflow wise, markets wise, earnings wise.

892
00:43:40,719 --> 00:43:42,319
Speaker 2: But I think we've we've been across it all.

893
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Speaker 4: You have, you're going to jet back to the West coast.

894
00:43:44,480 --> 00:43:45,880
Speaker 5: Don't forget to check out the podcast.

895
00:43:46,160 --> 00:43:47,800
Speaker 4: You can find on the terminal as well as online

896
00:43:47,800 --> 00:43:50,160
Speaker 4: on Apple, Spotify and iHeart from New York.

897
00:43:50,200 --> 00:43:51,879
Speaker 5: For this Thursday. This is Bloomberg Tech