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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 ever though in San Francisco.

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Speaker 2: This is Bloomberg Tech coming up. Wall Street is shedding

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Speaker 2: stocks it thinks are on the wrong side of the

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Speaker 2: AI revolution.

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Speaker 3: Plus Twilio founder Jeff Lawson has a new company, Nuclear Fusions,

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Speaker 3: startup Inertia. We'll discuss how the surging demand for electricity

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Speaker 3: is powering big.

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Speaker 2: Energy bets and shares of lift plunging off of the

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Speaker 2: company's latest earnings results. Will be joined by CEO David

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Speaker 2: Risher to unpack it all.

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Speaker 3: But first we turn our attentions to these public markets

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Speaker 3: that are actually being whip swored. On the day we

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Speaker 3: get the big jobs data that signals their strength, people

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Speaker 3: start to backtrack, maybe on when or where we could

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Speaker 3: see some sort of rate cut and it impacts treasuries,

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Speaker 3: but stops actually manage to having been in the red.

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Speaker 3: Now shake it off for up a tenth per percent

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Speaker 3: on then, as that one hundred managed climb back after

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Speaker 3: yesterday's lows. Bitcoin, though I'm afraid no respite from the

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Speaker 3: selling there were of by three percent sixty six thousand,

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Speaker 3: and what are.

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Speaker 4: You looking at underneath the hood.

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Speaker 2: I'm going to run through the earnings so that we're

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Speaker 2: going to get through throughout the hour. Lift is down

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Speaker 2: or on track to be down the most since August

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Speaker 2: of twenty twenty four. Its profit outlook has the street

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Speaker 2: a little bit worried because they don't have the answers

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Speaker 2: on why that outlook is weaker than consensus. T Mobile

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Speaker 2: actually missed on wireless subscribers in the court of gone,

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Speaker 2: was lower and is now higher three percent in the session,

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Speaker 2: and then Shopify be estimates. But there seems to be

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Speaker 2: this concern out there with the stock on track for

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Speaker 2: its biggest drop since April last year, that AI is

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Speaker 2: coming for it. That is a common theme we've heard

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Speaker 2: for a little while now.

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Speaker 5: Charac it is.

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Speaker 4: I'm looking at the Software index.

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Speaker 3: It's once again down by three point three percent, so

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Speaker 3: clearly having a torrid time as those rising fears just

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Speaker 3: about AI of pummeling shares of companies at risk been

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Speaker 3: caught on the wrong side of the new technology, from

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Speaker 3: small software makers to big wealth management firms. Just yesterday

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Speaker 3: at tax Strategy tool ed it rolled out by a

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Speaker 3: little known startup called Altruist.

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Speaker 6: She has a Charles.

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Speaker 3: Schwab, Raymond James LPO financialist. You'll see absolutely tumbling. Let's

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Speaker 3: get more on Blueboge Tech equity reporter Common Rhynicky. You've

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Speaker 3: been all over the implications and Anthropick's one thing. To

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Speaker 3: have these smaller LLM or AI offerings start to rule markets,

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Speaker 3: that's quite something.

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Speaker 7: Yeah, I think what it really shows here is that

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Speaker 7: kind of everyone's at risk right Investors have been so

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Speaker 7: quick to punish the shares of companies that might be

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Speaker 7: in the crosshairs here of any new innovation or disruption

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Speaker 7: from AI, and it really marks kind of a shift

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Speaker 7: or it's sort of two truths that are happening in

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Speaker 7: the market right now. One the sphere of an AI

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Speaker 7: bubble and sort of overspending and that the technology won't

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Speaker 7: live up to the hype, and then on the flip

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Speaker 7: side that the technology it's here and it's really good

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Speaker 7: and it's going to disrupt, you know, entire parts of

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Speaker 7: the market. So we're seeing these little pockets sell off

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Speaker 7: on these new things that are coming out, and investments

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Speaker 7: are really just trying to see what's next.

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Speaker 2: We were talking about your your story as a team

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Speaker 2: common just really one of the most read on the

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Speaker 2: terminal and on dot com. There are names in there

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Speaker 2: that are familiar, like some of the wealth managers for example,

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Speaker 2: and then there are names that really like even we

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Speaker 2: haven't heard of go through those impacted specifically.

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Speaker 7: Yeah, so some of the ones that were most impacted yesterday,

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Speaker 7: you know, Charles Schwab was one, Raymond James LPL Financial.

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Speaker 7: But then we've also seen you know, some stocks across

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Speaker 7: Europe also get hit. Not you know, super high profile

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Speaker 7: I think for some of us here in the US,

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Speaker 7: but you know, last week it was things you know,

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Speaker 7: into it saw a little bit of a sell off. Also,

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Speaker 7: tax you know, programming just really all over sort of

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Speaker 7: across the board. Insurres you know, had a little bit

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Speaker 7: of of a sell off as well. So it's a

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Speaker 7: very broad range of stocks that we're seeing here get hit,

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Speaker 7: and there is not there's not an easy way to

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Speaker 7: really think about what could be next.

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Speaker 4: Come in, Ryanikey, She's going to be writing what's next.

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Speaker 3: We so appreciate it for more on the software sell off,

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Speaker 3: but still the demand for hardware. We're joined by on

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Speaker 3: Care Crawford, EVP put Photio manager over an outjo and

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Speaker 3: you recently updated your twenty twenty three paper AI and

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Speaker 3: the declining cost to create.

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Speaker 4: Your thesis is now playing out in real time.

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Speaker 3: You've been writing, When AI can write software, the cost

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Speaker 3: to create the software plummets. I believe the market is

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Speaker 3: starting to question the terminal value of these businesses. I

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Speaker 3: will be more challenging. It will be more challenging for

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Speaker 3: companies who are not AI native. So how much longer

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Speaker 3: could this pressure build for?

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Speaker 6: You know?

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Speaker 8: I think again, when the terminal value is changing and

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Speaker 8: you don't really know what the end point is, it's

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Speaker 8: difficult to put a multiple on these stocks.

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Speaker 9: Now.

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Speaker 6: I would say that we're getting a.

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Speaker 8: Little bit of a phenomenon of the baby's getting thrown

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Speaker 8: out with breathwater in that all stocks are going down

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Speaker 8: regardless because there is such a fear factor right now,

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Speaker 8: and not all of the software stocks are created equal, right,

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Speaker 8: and there are going to be some beneficiaries, But one

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Speaker 8: thing is for sure, the entire industry needs to rethink.

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Speaker 8: And you know something that the Shopify CFO said this

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Speaker 8: morning that I thought was really interesting. He wrote, he said,

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Speaker 8: the rules of what is possible are being rewritten real time. Yeah,

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Speaker 8: And I think that's what you're seeing is when you

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Speaker 8: can create.

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Speaker 6: Bespoke software on the fly. And that's today.

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Speaker 8: What happens five years from now when these llms and

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Speaker 8: these you know, self coding mechanisms are actually even more

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Speaker 8: powerful by orders of magnitude. So anything digital, any digital asset,

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Speaker 8: you must call to question as to what is its future.

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Speaker 4: But this knee jerk of sell first, ask questions later?

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Speaker 3: What questions are you now asking to decide where you

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Speaker 3: can bossom pick which ones aren't going to be disrupted?

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Speaker 8: So I think it becomes again a very much a

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Speaker 8: stock pickers mark because there are some you have to

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Speaker 8: understand the architecture of how some of the software is

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Speaker 8: actually being built. I would say things like point Solutions

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Speaker 8: that are in the small to MidCap space very difficult

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Speaker 8: to own because that kind of end market that they're

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Speaker 8: addressing can easily get you served by, you know, companies

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Speaker 8: that are bigger or adjacent businesses, you know, so you

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Speaker 8: it's very difficult to own some of those. I think

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Speaker 8: companies that are larger cap, more platform like companies, parts

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Speaker 8: of those businesses.

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Speaker 6: Are at risk, not the entire thing.

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Speaker 8: We'll still have SaaS it will just exist at a

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Speaker 8: different margin structure, different growth rates than we are used to.

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Speaker 6: So how do you pick?

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Speaker 8: I think you have to understand the differences between all

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Speaker 8: the software. Security is not the same as enterprise software,

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Speaker 8: not the same as you know, mid and small cap

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Speaker 8: point solutions. So you have to be almost very very

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Speaker 8: deliberate in what you're looking at, and not just by

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Speaker 8: the group.

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Speaker 2: We've been zeroed in on this software story for so

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Speaker 2: many days in a row now and earnings of course,

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Speaker 2: but we should discuss the stronger than expected US jobs report,

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Speaker 2: payrolls in January rising by the most and more than

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Speaker 2: a year unemployment faoling unexpectedly. What does that signal for

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Speaker 2: the tech sector? What do our audience need to understand

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Speaker 2: about that?

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Speaker 8: So look, I think I think we're living in two

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Speaker 8: separate regimes right now, in that you know, the tech

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Speaker 8: sector is going through its own hiccups with understanding the

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Speaker 8: what AI will do to it. On the other hand,

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Speaker 8: you have the hardware sector inside of tech, which is

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Speaker 8: faring much better than software because they are the beneficiaries

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Speaker 8: of all of this AI and the infrastructure spending. So

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Speaker 8: how do would I read the unemployment report and attach

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Speaker 8: it to AI, I think today or over the next

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Speaker 8: few weeks, they're kind of not connected to one another,

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Speaker 8: and I won't read anything into it for the tech sector.

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Speaker 2: That would suggest that we are still zeroed in on

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Speaker 2: the capital expenditures numbers that will come from the Hyperscalers

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Speaker 2: and others, and that that will be the yardstick by

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Speaker 2: which we judge continued growth right for the bands of

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Speaker 2: this year. And how important a data set is that

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Speaker 2: going to be for you this year, Anka, I.

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Speaker 6: Think very much.

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Speaker 8: I think besides the Hyperscaler CAPEX numbers, I think we

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Speaker 8: have to look at demand. Token growth in the month

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Speaker 8: of January was twenty five percent growth month over month

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Speaker 8: from December. If you annualize that, that means token growth

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Speaker 8: for the year is going to be fourteenfold what we

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Speaker 8: saw last year.

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Speaker 6: Fourteen full token growth.

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Speaker 8: And that is just a measure of the amount of

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Speaker 8: intelligence that we are we are asking the system or

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Speaker 8: AI to do for us.

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Speaker 6: That is an incredible.

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Speaker 8: Amount of demand that is being put into the system,

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Speaker 8: and we will need an incredible amount of capex to

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Speaker 8: support it. And I will remind everyone we are in

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Speaker 8: the very early innings of adoption of artificial intelligence and

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Speaker 8: so that CAPEX number actually is necessary, and it doesn't

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Speaker 8: really scare me because as we as we move forward two, three,

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Speaker 8: four and five years, I do believe that our world

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Speaker 8: is changing in such a dramatic way that we are

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Speaker 8: going to need this kapex.

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Speaker 2: Uncle Crawlford, about a great to have you back on

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Speaker 2: the program. Thank you very much. Coming up, time to

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Speaker 2: launch SpaceX meet Xai. Yai may be key for the

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Speaker 2: company's next launch. Bloomberg Intelligence just initiated coverage and we

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Speaker 2: have it next. This is Bloomberg Tech.

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Speaker 3: SpaceX's acquisition of Xai has many wondering what the company's

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Speaker 3: next chapter will look like. The profitable rocket maker is

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Speaker 3: set to merge the AI startup, which is what he cause,

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Speaker 3: burning billions as it races to compete with other AI players.

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Speaker 3: All of this comes as SpaceX that you're looking at now,

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Speaker 3: is expected to IPO later this year. Today, UMEG Intelligence

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Speaker 3: has launched coverage as a combined entity and says the merger,

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Speaker 3: along with the planned IPO, could help fund investment in

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Speaker 3: larger launch vehicles and space based data centers. As new

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Speaker 3: reports suggests the IPO could value the company as much

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Speaker 3: as one point five trillion dollars for more want to

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Speaker 3: bring in a dream team. Roome meg Intelligence Roundtable. George

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Speaker 3: Ferguson's senior aerospace and defense analyst and manly seeing global

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Speaker 3: head of Tech Research, has been thinking about the Tesla implications. George,

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Speaker 3: we start with you on SpaceX. How much is it

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Speaker 3: a burden or a help to be now aligned with

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Speaker 3: XAI because it's certainly cash burn. Meanwhile, you think revenues

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Speaker 3: are what up to the tune of ten billion for

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Speaker 3: certain parts of SpaceX, so if not more.

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Speaker 10: Yeah, So we think launch is worth probably as revenue

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Speaker 10: generating probably at least ten billion, and we think that

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Speaker 10: this satellite constellation Starlink is probably eightish nine ish billion.

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Speaker 10: My colleague John Butler did the work on that, so

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Speaker 10: we're up actually probably closer to twenty billion. Look, I

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Speaker 10: think the combining with XAI is all about sort of

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Speaker 10: strategy and funding AI, I mean manned people of course

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Speaker 10: talk to it deeper, but I think that AI business

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Speaker 10: needs a lot more investment. I think Musk wants to

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Speaker 10: push it up in its competitiveness, and so I think

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Speaker 10: you put it inside this broader offering for SpaceX in

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Speaker 10: order to help move some money.

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Speaker 2: That way, Mandy, the combination you know, long term, it's

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Speaker 2: about compute in space right satellite space form factor data

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Speaker 2: center of XAI. In your research, continuing to look at

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Speaker 2: what we reported as being a potential tie up between Tesla,

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Speaker 2: XAI and SpaceX give me a thesis.

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Speaker 11: Well, so the way XAI has been looking to monetize

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Speaker 11: is through consumer subscriptions so far, and when I stack

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Speaker 11: them against let's say an open Ai, you know, they

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Speaker 11: are nowhere close to the scale that OpenAI is or

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Speaker 11: Gemini is in terms of consumer subscriptions. On the enterprise side, yes,

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Speaker 11: they have that big defense contract, but that's where you know,

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Speaker 11: the XAI revenue run rate is close to one billion,

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Speaker 11: and compare that to Anthropic and the others. There's not

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Speaker 11: growing at the same pace.

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Speaker 6: So because all these.

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Speaker 11: Large angrid models are converging and growing really fast, you

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Speaker 11: have to ask yourself, how are they going to fund

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Speaker 11: the next training run? And you need that sort of

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Speaker 11: funding to really keep up in terms of how these

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Speaker 11: models are developing. So I think the merger is really

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Speaker 11: an attempt to make sure that they don't lack the

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Speaker 11: funding because all these companies are tapping you know, the

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Speaker 11: private market, the debt market, and you know, as a

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Speaker 11: combined entity, they've already sort of come up with the

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Speaker 11: one point two five trillion.

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Speaker 6: Yes, they go public, probably.

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Speaker 11: One point five trillion could be achievable, but it's a

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Speaker 11: very high valuation multiple that they already have.

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Speaker 4: Mandy.

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Speaker 3: What's interesting in your research is you really compare and

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Speaker 3: contrast how many individual users are going to a standalone GROQUEI.

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Speaker 4: Yeah, not many, but there are.

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Speaker 3: Plenty of US Tesla users out there who could be

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Speaker 3: using it. How much should they need to lean in

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Speaker 3: to ensure that adoption is brought to bear in that respect?

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Speaker 5: Yeah, and you make a great point.

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Speaker 11: The digital assistant use case within a Tesla makes perfect sense.

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Speaker 11: I mean, why would they not deploy Rock inside a

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Speaker 11: Tesla as a digital assistant? And you already have you know,

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Speaker 11: ten million cars on the road where you could potentially

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Speaker 11: deploy that. So from that perspective, the feedback loop can

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Speaker 11: be very fast in terms of you know, them deploying

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Speaker 11: the product, the feedback in terms of how well it's

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Speaker 11: doing and constantly improving that. But you need that consumer

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Speaker 11: adoption because that's what all these lms you're competing against, Google,

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Speaker 11: Gemini and.

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Speaker 4: More guardrails as well, I might add.

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Speaker 11: Yeah, and that's where Grock so far has been kind

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Speaker 11: of I feel like they haven't really caught up in

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Speaker 11: terms of implementing the right guardrails, and people feel skeptical

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Speaker 11: about implementing them on the enterprise side, so there is

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Speaker 11: work to be done on that front.

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Speaker 2: This is a big moment where Bloomberg Intelligence is initiating coverage,

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Speaker 2: deep research of a big private entity that we think

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Speaker 2: will go public, right, George, the data in the deck

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Speaker 2: is so valuable. You're tracking the size of the Starlak consolation,

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Speaker 2: You're tracking launch cadence, and then our forecasted financials. Which

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Speaker 2: of those are going to be most important now that

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Speaker 2: SpaceX is hard pivoted to the Moon from Mars, George,

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Speaker 2: very quick.

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Speaker 10: So I guess where I see the money really being

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Speaker 10: generated from as the concl the satellite constellation around the Earth,

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Speaker 10: and that's going to bring broadband revenue, and it's going

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Speaker 10: to bring director sell revenue. The pivot to the Moon

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Speaker 10: and Mars, I think is a little more about sort

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Speaker 10: of marketing than it is about revenue generation. I'm all

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Speaker 10: about revenue and profit generation.

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Speaker 2: George ferguson Man deep seeing blue meg intelligence coverage of

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Speaker 2: SpaceX and XAI appreciate it a lot. There's a lot

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Speaker 2: going on in the world of Elon Inc. Jimmy Barr

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Speaker 2: Tony Wu, co founders of XAI, have announced they're leaving

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Speaker 2: the company after less than three years. By my count, Carroe,

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Speaker 2: that means that half of the about dozen initial founders,

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Speaker 2: which includes Elon Musk, have now gone.

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Speaker 3: And maybe that's to be expected when you see such

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Speaker 3: a difference in the architecture of the business. Now it's

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Speaker 3: subsumed into space X. What does that mean in terms

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Speaker 3: of the founding principles of this business? But is there

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Speaker 3: any read through Look, we're looking at their statements, their

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Speaker 3: resignation posts that they actually put on X. Of course,

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Speaker 3: what do you make of it, ed if someone who's

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Speaker 3: so deeply within the Elon Musk space, We.

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Speaker 2: Just don't know. When I read the merger documents, you know,

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Speaker 2: it was pretty clear and we reported this right that

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Speaker 2: XAI would operate as a standalone subsidiary because remember SpaceX

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Speaker 2: is subject to ITEAR rules the use of technology and

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Speaker 2: defense applications. Is that it is it about the reports

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Speaker 2: that Musk has been frustrated about the pace of ROX deployment.

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Speaker 2: But these are notable people who are leaders in field

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Speaker 2: and talents everything in this game.

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Speaker 3: And boy is a competition hot at the moment with Anthropic, Withnai,

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Speaker 3: with Xai, we continue to track at me while coming up.

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Speaker 3: Google another AI player that we keep golying on, but

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Speaker 3: it's also adding the way to shop within its AI search.

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Speaker 3: We'll discuss with the company's general manager of Ads and Commerce.

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Speaker 4: Stick with us as Bluembag Tech.

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Speaker 2: Google is adding shopping features to its AI search and

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Speaker 2: Gemini chatbot. The move comes as tech companies more broadly

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Speaker 2: look for ways beyond subscriptions to make money from the

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Speaker 2: AI tools. Let's discuss with Vigistron Novas and Google's vice

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Speaker 2: president and general manager of Ads and Commerce. There's always

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Speaker 2: been a relationship at least you know, for me personally

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Speaker 2: as well, between Google searching and then getting.

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Speaker 5: To a product.

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Speaker 2: But in AI mode, that's what we're talking about here.

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Speaker 2: AI mode there's just a more direct interaction to a

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Speaker 2: transaction right where you can go ahead and buy something

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Speaker 2: that's right.

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Speaker 5: How big are moments that for Google?

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Speaker 12: It is a massive moment for us. We see this

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Speaker 12: as not just a big moment for Google just sort

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Speaker 12: of stepping back in the last year, what we see

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Speaker 12: is shoppers have really changed their behaviors they are now

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Speaker 12: Typically shoppers had to pick between shopping fast or shopping smart,

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Speaker 12: and with AI, that trade off is kind of disappearing.

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Speaker 12: And therefore businesses now need a new playbook and a

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Speaker 12: lot of what they're doing now is all about how

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Speaker 12: to empower them and an AI mode. What we're seeing

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Speaker 12: is people's search behavior is completely evolving, from keyword search

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Speaker 12: to conversation or search, they upload pictures, There's so much

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Speaker 12: multimodal input that comes in, and that's just a moment

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Speaker 12: for us to rethink how we do the whole thing.

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Speaker 5: VIJA.

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Speaker 2: Anyone that watched the Super Bowl will know that the

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Speaker 2: inclusion of ads within a generative AI tool is let's

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Speaker 2: not say controversial, but that people are deciding if it's

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Speaker 2: what the consumer wants. Right, if you're interacting with an

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Speaker 2: AI do you want an AD to pop up? And

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Speaker 2: you've been testing different ad formats as part of this,

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Speaker 2: what do you think the consumer wants?

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Speaker 12: Yeah, obviously we spend a lot of time figuring out

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Speaker 12: what consumers want. We've had a twenty five year history

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Speaker 12: in satisfying human curiosity with search. In all of these experiences,

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Speaker 12: as have been a big part of that, and they

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Speaker 12: work because they're helpful and assistive to the person in

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Speaker 12: the moment. Now, with what's happening with AI mode, what

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Speaker 12: we see is the conversational modality is changing. It is

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Speaker 12: a moment for us to reimagine what ads are possible

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Speaker 12: and what will actually work. And there's a lot of

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Speaker 12: experimentation that we're doing. In fact, we have a couple

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Speaker 12: of announcements on that, but the foundations of what makes

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Speaker 12: this work continue from what we've learnt all these years,

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Speaker 12: and that's really based on a foundation of trust and safety.

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Speaker 3: Trust safety then tell us about how you become entrepreneurial

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Speaker 3: in your thinking, how you become creative in your thinking

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Speaker 3: that these don't feel unnerving to the purchaser.

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Speaker 4: They feel natural, organic and useful.

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Speaker 9: Let me actually give you an example.

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Speaker 12: So let's say you're in AI mode and you want

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Speaker 12: to purchase a lamp for your bedroom and you talk

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Speaker 12: to the you tell AI mode about your the de

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Speaker 12: call you like it's a modern decor, you want a

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Speaker 12: certain kind of lamp, this is a color, And you

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Speaker 12: get to a point where you know the product that

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Speaker 12: you want, you see the product that you want, the

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Speaker 12: feature that we just launched today, lets you purchase it

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Speaker 12: right there.

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Speaker 9: In that moment.

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Speaker 12: Now you're still very much in control of the purchase. However,

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Speaker 12: a lot of the grunt work of getting from what

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Speaker 12: I want to when I have it is removed in

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Speaker 12: this process.

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Speaker 9: So this feels a system.

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Speaker 12: This feels natural, and it's based on the same principles

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Speaker 12: that we've.

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Speaker 9: Done a lot of things before. Direct offers.

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Speaker 3: Who can I buy from when I found the perfect lamp?

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Speaker 3: Is it just at saan Wayfair? You've got partnerships there.

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Speaker 3: How is this expanding across the entire remit of partners

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Speaker 3: that you have.

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Speaker 12: Yeah, So direct office is an opportunity for retailers to

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Speaker 12: provide us a very specialized discount that our AI actually

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Speaker 12: matches with people during their shopping journeys and gives them

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Speaker 12: this very personalized discount, and it.

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Speaker 9: Allows retailers to just close the.

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Speaker 12: Sale in that moment because they motivate people to do this.

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Speaker 12: And the way it's going to scale is just we're

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Speaker 12: currently running.

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Speaker 9: A pilot with a lot of retailers.

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Speaker 12: There will be more that and come on board and

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Speaker 12: they just have to participate.

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Speaker 9: And this is a new ad format.

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Speaker 2: The technology needs to work for me. It's still a

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Speaker 2: technology story. So you want whatever appears in AI mode

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Speaker 2: to be relevant to the query. You know, that has

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Speaker 2: been a criticism of other generatsive AI tools. What's come

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Speaker 2: up in front of me has nothing to do with

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Speaker 2: what I'm talking about. How's Google solving for that?

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Speaker 9: There are many aspects to that.

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Speaker 12: I think it goes back to the foundation cod foundations

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Speaker 12: of search in general. We are in the constant quest

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Speaker 12: to make the results more relevant. One of the things

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Speaker 12: that can help in this space is also understanding the

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Speaker 12: context of the user better and AI mode is good

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Speaker 12: in that sense because we do have the opportunity to

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Speaker 12: go back and forth with the user.

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Speaker 9: So the questions we have as a follow on all

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Speaker 9: of these things help us learn a little bit.

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Speaker 12: More about the person, so we have the best shot

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Speaker 12: at producing results that are more relevant for them.

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Speaker 3: Very briefly, you talked about safety and privacy. It's a

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Speaker 3: criticism particularly of Elizabeth Warren, for example, worrying that you're

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Speaker 3: pushing people into making well purchases that they otherwise wouldn't

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Speaker 3: there privacy, how do you tackle that.

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Speaker 4: The way.

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Speaker 12: The fact that the foundations is people today have choice

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Speaker 12: to go purchase something or not. There are many many

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Speaker 12: opportunities that you have to click on that buy button

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Speaker 12: in many places, in many different products. Those same principles

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Speaker 12: of control and choice continue to exist in this domain

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Speaker 12: as well, and it's still on the person to choose

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Speaker 12: to click on that button and make that purchase.

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Speaker 3: VIDIA, it's been great having you Google's VideA Universe, and

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Speaker 3: we thank you. Coming up Twilio founder Jeff Lawson. It's

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Speaker 3: got a new company. It's a nuclear company. He joins

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Speaker 3: us to discuss the energy startup four hundred and fifty

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Speaker 3: million dollars Series A. That's next disciplining big tech. Welcome

443
00:23:00,920 --> 00:23:03,480
Speaker 3: back to Bloomberg Tech. Checking in on these markets which

444
00:23:03,520 --> 00:23:06,320
Speaker 3: have been whipswored by macro data. By of course, jobs

445
00:23:06,320 --> 00:23:08,040
Speaker 3: coming in stronger than expected. What does that mean for

446
00:23:08,080 --> 00:23:10,080
Speaker 3: an overall policy from the Federal Reserve in terms of

447
00:23:10,160 --> 00:23:13,320
Speaker 3: rate cuts coming maybe July rather than June. But I'm

448
00:23:13,359 --> 00:23:15,600
Speaker 3: looking at individual stock store on the move. Check out

449
00:23:15,640 --> 00:23:19,520
Speaker 3: the earnings story coming from Lyft, a surprise revenue miss

450
00:23:19,560 --> 00:23:20,840
Speaker 3: on their fiscal fourth quarter.

451
00:23:21,119 --> 00:23:21,359
Speaker 5: Look.

452
00:23:21,640 --> 00:23:24,320
Speaker 3: Bookings are good, people are wanting to ride with Lyft,

453
00:23:24,400 --> 00:23:27,760
Speaker 3: But what about that profitability therein What about the revenue drivers?

454
00:23:27,840 --> 00:23:29,240
Speaker 4: What about the expansion outside the US.

455
00:23:29,240 --> 00:23:31,159
Speaker 3: We're going to have that conversation with the CEO a

456
00:23:31,160 --> 00:23:33,240
Speaker 3: little bit later, as you can see a painful day

457
00:23:33,240 --> 00:23:35,280
Speaker 3: off by fifteen percent, move on to some of the

458
00:23:35,320 --> 00:23:36,760
Speaker 3: other just keep indexes.

459
00:23:36,760 --> 00:23:39,640
Speaker 4: Look, this is the pushing the pull in the market. Software.

460
00:23:40,000 --> 00:23:44,560
Speaker 3: We have more anxiety about the latest AI formats, LM developments,

461
00:23:44,800 --> 00:23:47,400
Speaker 3: latest ways in which business models are getting disrupted, whether

462
00:23:47,400 --> 00:23:50,920
Speaker 3: it's in your financial services, whether it's in your legal services,

463
00:23:51,040 --> 00:23:53,760
Speaker 3: but also whether more broadly it's about what software is

464
00:23:53,760 --> 00:23:54,960
Speaker 3: going to do for you in the longer term and

465
00:23:54,960 --> 00:23:57,080
Speaker 3: how much you can charge for it. Software under pressure

466
00:23:57,119 --> 00:24:00,199
Speaker 3: again down three point six percent. That rotation continues, but

467
00:24:00,200 --> 00:24:02,600
Speaker 3: still the Kapax is there. Still the hardware is there.

468
00:24:02,640 --> 00:24:04,280
Speaker 3: Still the high bandwidth memory.

469
00:24:03,920 --> 00:24:06,960
Speaker 4: Anxiety and lack of it is there. And Micron goes higher.

470
00:24:07,000 --> 00:24:10,200
Speaker 3: So we're seeing still those chip maker's ed in the green.

471
00:24:10,440 --> 00:24:11,960
Speaker 4: What are you looking at?

472
00:24:11,880 --> 00:24:13,440
Speaker 5: It's the private markets.

473
00:24:13,720 --> 00:24:17,800
Speaker 2: Surging demand for electricity is powering big investments and energy

474
00:24:18,040 --> 00:24:23,320
Speaker 2: among them. Fusion startup Inertia, co founded by Twilio founder

475
00:24:23,440 --> 00:24:26,800
Speaker 2: former chairman and CEO Jeff Lawson, which just closed a

476
00:24:26,800 --> 00:24:30,560
Speaker 2: four hundred and fifty million dollars Series A funding round.

477
00:24:31,040 --> 00:24:33,800
Speaker 2: Jeff Lawson, co founder and CEO of Inertia's with us.

478
00:24:34,080 --> 00:24:35,720
Speaker 2: It's good to have you back on Bloomberg Tech.

479
00:24:35,920 --> 00:24:36,760
Speaker 5: Thanks great to be there.

480
00:24:37,000 --> 00:24:40,920
Speaker 2: A completely different field. It's not just fusion. I think

481
00:24:40,960 --> 00:24:43,359
Speaker 2: that let's start by you introducing us to Inertia, because

482
00:24:44,800 --> 00:24:48,639
Speaker 2: a fusion powered plant is completely continent your case, and

483
00:24:48,720 --> 00:24:53,359
Speaker 2: you cracking extremely powerful lasers, a sentence I never thought

484
00:24:53,359 --> 00:24:55,480
Speaker 2: i'd say on this show. Take it from there.

485
00:24:55,680 --> 00:24:59,600
Speaker 13: Well, Inertia is the commercial fusion energy company. That's because

486
00:24:59,640 --> 00:25:01,720
Speaker 13: in order to commercialize the technology, you need to prove

487
00:25:01,760 --> 00:25:04,480
Speaker 13: the science. So we are resting on the scientific proof

488
00:25:04,520 --> 00:25:06,360
Speaker 13: that it was made several years ago. You probably read

489
00:25:06,359 --> 00:25:09,560
Speaker 13: about it at Laurrence Livermore National Lab, and now we

490
00:25:09,640 --> 00:25:11,280
Speaker 13: are here to take it out of the lab and

491
00:25:11,320 --> 00:25:12,840
Speaker 13: bring it to the grid. And we do that in

492
00:25:12,840 --> 00:25:15,880
Speaker 13: three ways. One, of course, start with proven science. Number two,

493
00:25:16,200 --> 00:25:19,320
Speaker 13: we're gonna go build the world's most powerful laser. In fact,

494
00:25:19,320 --> 00:25:21,680
Speaker 13: the laser we're gonna build is a million times more

495
00:25:21,680 --> 00:25:23,680
Speaker 13: powerful than the one they use over at Laurence livermore

496
00:25:23,720 --> 00:25:26,520
Speaker 13: it's twenty times more efficient and one tenth the size.

497
00:25:26,720 --> 00:25:29,800
Speaker 13: Then we're gonna go build the world's first fusion fuel

498
00:25:29,840 --> 00:25:33,399
Speaker 13: target manufacturing plant. And then lastly, we're gonna bring all

499
00:25:33,440 --> 00:25:35,880
Speaker 13: those things together into a grid scale one point five

500
00:25:35,960 --> 00:25:38,520
Speaker 13: gigawatt power plant that we hope to bring online in

501
00:25:38,560 --> 00:25:39,080
Speaker 13: the twenty three.

502
00:25:39,200 --> 00:25:39,880
Speaker 5: There's a lot there.

503
00:25:40,000 --> 00:25:42,560
Speaker 2: There's a lot of we are going to with lasers,

504
00:25:42,640 --> 00:25:44,960
Speaker 2: like we break the story about Substrate for example, which

505
00:25:45,000 --> 00:25:48,040
Speaker 2: is also using X rays in lithography that was born

506
00:25:48,080 --> 00:25:50,399
Speaker 2: out of the National Labs here in California. There's some

507
00:25:50,440 --> 00:25:53,520
Speaker 2: interesting tangents there. That's all to say, it sounds like

508
00:25:53,560 --> 00:25:55,840
Speaker 2: you probably need four hundred and fifty million dollars to

509
00:25:55,880 --> 00:25:58,280
Speaker 2: get started. If you're gonna pull all of this off.

510
00:25:58,320 --> 00:25:59,440
Speaker 2: What's the first priority.

511
00:25:59,680 --> 00:26:02,320
Speaker 13: Well, usion energy is not a chief endeavor, so yes,

512
00:26:02,600 --> 00:26:05,119
Speaker 13: it will be capital intensive. That's why we're so happy

513
00:26:05,320 --> 00:26:07,560
Speaker 13: to be starting off with such a great group of

514
00:26:07,600 --> 00:26:10,040
Speaker 13: investors for our Series A. And if you think about it,

515
00:26:10,040 --> 00:26:13,440
Speaker 13: commercialization really requires three things. First of all, you need

516
00:26:13,520 --> 00:26:16,080
Speaker 13: proven science, so we've got that. Second is you need

517
00:26:16,080 --> 00:26:18,600
Speaker 13: a great team. And so for a team to go commercialize,

518
00:26:18,640 --> 00:26:20,959
Speaker 13: you've got my co founder and US the lead designer

519
00:26:21,000 --> 00:26:24,840
Speaker 13: of the experiment at Laurence Livermore that achieved this huge milestone.

520
00:26:25,119 --> 00:26:27,919
Speaker 13: My other co founder might Done ran the program that

521
00:26:28,000 --> 00:26:30,679
Speaker 13: designed the power plant based on that experiment. And then

522
00:26:30,720 --> 00:26:33,800
Speaker 13: I'm bringing business experience to the domain as well. And

523
00:26:33,840 --> 00:26:35,560
Speaker 13: so you look at the team that we're building, which

524
00:26:35,600 --> 00:26:37,880
Speaker 13: is a team that isdigned to commercialize. And the third

525
00:26:37,920 --> 00:26:40,160
Speaker 13: thing you need is funding. And that's why this funding

526
00:26:40,160 --> 00:26:41,760
Speaker 13: announcement is such a big part of our story.

527
00:26:42,480 --> 00:26:45,400
Speaker 3: What's interesting is you hit while the iron is hot,

528
00:26:45,520 --> 00:26:48,520
Speaker 3: so to speak, in terms of a need, an energy need,

529
00:26:48,560 --> 00:26:49,800
Speaker 3: and it's coming from the very top.

530
00:26:49,840 --> 00:26:50,879
Speaker 4: It's coming from the administration.

531
00:26:51,040 --> 00:26:54,560
Speaker 3: Just listen to what a key administration official told us today.

532
00:26:55,480 --> 00:26:57,959
Speaker 14: Now we're facing this AI arms raised with China. We

533
00:26:58,040 --> 00:27:01,119
Speaker 14: need more power, we need energy. The way to have

534
00:27:01,240 --> 00:27:04,320
Speaker 14: energy addition is to stop stop getting rid of the

535
00:27:04,359 --> 00:27:07,120
Speaker 14: stuff that already works. And of course that includes our

536
00:27:07,440 --> 00:27:10,760
Speaker 14: fossil fuel baseload and the PGM market. Seventy percent of

537
00:27:10,800 --> 00:27:15,800
Speaker 14: the power was coming from hydrocarbons during those storms. I mean,

538
00:27:15,840 --> 00:27:18,040
Speaker 14: America and the world is dependent on it is going

539
00:27:18,119 --> 00:27:19,959
Speaker 14: to be in long future.

540
00:27:20,560 --> 00:27:23,919
Speaker 3: US and Teri Sectuary there Doug Bergham speaking Jeff, how

541
00:27:24,119 --> 00:27:28,280
Speaker 3: quickly can you provide this very much needed alternative energy source.

542
00:27:29,520 --> 00:27:32,000
Speaker 13: Well, I think investment in new energy sources is always

543
00:27:32,040 --> 00:27:34,040
Speaker 13: a long term play. No matter what kind of energy

544
00:27:34,080 --> 00:27:36,760
Speaker 13: you're talking about, it takes time to develop the technology.

545
00:27:37,000 --> 00:27:39,280
Speaker 13: Our goal is to have the first fusion pilot plant

546
00:27:39,320 --> 00:27:42,760
Speaker 13: online in the twenty thirties available to start producing grid

547
00:27:42,800 --> 00:27:46,560
Speaker 13: scale energy. And our solution is a grid scale answer

548
00:27:46,680 --> 00:27:50,080
Speaker 13: to our energy problems, which can provide enough power to

549
00:27:50,200 --> 00:27:53,639
Speaker 13: power a million homes or a medium to large sized

550
00:27:53,680 --> 00:27:55,240
Speaker 13: city at any one point in time.

551
00:27:55,280 --> 00:27:56,359
Speaker 5: And that's just the first plant.

552
00:27:56,520 --> 00:27:58,600
Speaker 13: After that we'll move on to building the second, the third,

553
00:27:58,640 --> 00:28:00,480
Speaker 13: the fourth, and keep stamping these things.

554
00:28:00,280 --> 00:28:04,200
Speaker 3: Out, stamping these fusion things out, which could go either direction.

555
00:28:04,520 --> 00:28:06,720
Speaker 4: I love talking about the big lasers. I also love

556
00:28:06,800 --> 00:28:11,080
Speaker 4: talking about big magnets. Jack. Why was this the technology

557
00:28:11,080 --> 00:28:13,119
Speaker 4: you bet on when it comes to nuclear fusion.

558
00:28:14,320 --> 00:28:16,359
Speaker 13: Well, the reason why we're betting on the laser based

559
00:28:16,359 --> 00:28:19,280
Speaker 13: fusion approach is because it has actually worked. The basic

560
00:28:19,320 --> 00:28:22,399
Speaker 13: science of our approach thanks to more than sixty years

561
00:28:22,440 --> 00:28:24,480
Speaker 13: of work in the national labs and more than thirty

562
00:28:24,560 --> 00:28:27,679
Speaker 13: billion dollars invested by the government to get to this

563
00:28:27,800 --> 00:28:31,439
Speaker 13: huge milestone where it actually produced energy, and so now

564
00:28:31,840 --> 00:28:34,080
Speaker 13: that is the time when you want to go commercialize.

565
00:28:34,080 --> 00:28:37,080
Speaker 13: And that experiment was first proven in December of twenty

566
00:28:37,119 --> 00:28:39,600
Speaker 13: twenty two, but since then has been now run many

567
00:28:39,680 --> 00:28:42,760
Speaker 13: times and in fact increase the yield or the amount

568
00:28:42,800 --> 00:28:45,360
Speaker 13: of energy it creates multiple fold. And so that's why

569
00:28:45,400 --> 00:28:47,160
Speaker 13: this is the time to take it out of the

570
00:28:47,240 --> 00:28:49,120
Speaker 13: lab and bring it to the grid because of the

571
00:28:49,160 --> 00:28:51,160
Speaker 13: proven science that has demonstrated.

572
00:28:51,320 --> 00:28:56,080
Speaker 2: Is the skepticism the pause is that we have been

573
00:28:56,080 --> 00:29:00,760
Speaker 2: talking about this technology for quite a long time. Projections

574
00:29:00,800 --> 00:29:03,840
Speaker 2: are always wrong. And when it gets commercialized, do you

575
00:29:03,840 --> 00:29:05,880
Speaker 2: have a kind of line of sight to that, will

576
00:29:05,880 --> 00:29:07,959
Speaker 2: you do it in California, for example, or is that

577
00:29:08,080 --> 00:29:10,680
Speaker 2: just not realistic in this state? You're looking elsewhere in

578
00:29:10,720 --> 00:29:14,080
Speaker 2: the United States. Give us the kind of like plans

579
00:29:14,120 --> 00:29:17,280
Speaker 2: operationally to make this real absolutely well.

580
00:29:17,320 --> 00:29:19,760
Speaker 13: We have a twelve to twenty four month period right

581
00:29:19,800 --> 00:29:22,080
Speaker 13: now where we are approving the design validation of the

582
00:29:22,080 --> 00:29:24,760
Speaker 13: things we're going to go manufacture. Then we've got a

583
00:29:24,840 --> 00:29:27,280
Speaker 13: multi year stage where we're going to go actually make

584
00:29:27,320 --> 00:29:31,280
Speaker 13: the prototypes of our laser units of our target manufacturing plant,

585
00:29:31,440 --> 00:29:33,320
Speaker 13: and after that that's when we break round on the

586
00:29:33,360 --> 00:29:35,600
Speaker 13: plant itself, where we put the plant we have not

587
00:29:35,680 --> 00:29:38,760
Speaker 13: yet decided. I think that's up for a determination over

588
00:29:38,760 --> 00:29:41,760
Speaker 13: the next several years. But the key thing for us

589
00:29:42,040 --> 00:29:44,240
Speaker 13: is that this is not about basic science. The thing

590
00:29:44,280 --> 00:29:47,320
Speaker 13: that introduces a lot of uncertainty into these types of

591
00:29:47,320 --> 00:29:50,160
Speaker 13: innovations is basic science. You never know if it's going

592
00:29:50,200 --> 00:29:52,920
Speaker 13: to work tomorrow or a decade or one hundred years

593
00:29:52,920 --> 00:29:53,760
Speaker 13: from now, or maybe never.

594
00:29:53,960 --> 00:29:55,040
Speaker 5: But when you're talking about.

595
00:29:54,840 --> 00:29:58,000
Speaker 13: Engineering, bringing a product to market, doing an industrial scale

596
00:29:58,080 --> 00:30:01,479
Speaker 13: up to go build bigger laser, there's more targets. You know,

597
00:30:01,560 --> 00:30:03,600
Speaker 13: those are the kinds of things that are predictable. Think

598
00:30:03,600 --> 00:30:07,320
Speaker 13: about you know, Apple. Every year they're bringing a brand new,

599
00:30:07,440 --> 00:30:10,880
Speaker 13: really hard to manufacture, incredibly innovative device to market in

600
00:30:10,960 --> 00:30:13,160
Speaker 13: terms of our new iPhones, and they figure out how

601
00:30:13,160 --> 00:30:14,680
Speaker 13: to take it to a factory and make a billion

602
00:30:14,680 --> 00:30:17,320
Speaker 13: of them. Well, that's basically what our approach is. It's

603
00:30:17,320 --> 00:30:19,760
Speaker 13: just we're making lasers and targets and bringing those together

604
00:30:19,800 --> 00:30:20,360
Speaker 13: into a plant.

605
00:30:20,560 --> 00:30:22,960
Speaker 2: Jeff, over a period of many years, you came on

606
00:30:23,000 --> 00:30:25,600
Speaker 2: this program as the leader of a public company, whether

607
00:30:25,640 --> 00:30:28,760
Speaker 2: the stock was up, down or sideways, and in difficult

608
00:30:28,800 --> 00:30:33,000
Speaker 2: moments right now, software, which was your former love, former domain,

609
00:30:33,400 --> 00:30:36,120
Speaker 2: is having a difficult moment because people are concerned that

610
00:30:36,200 --> 00:30:39,480
Speaker 2: AI will just render it obsolete. Which you just tell

611
00:30:39,520 --> 00:30:42,320
Speaker 2: me what you're making of that moment and you know

612
00:30:42,640 --> 00:30:44,080
Speaker 2: your former company, but the field.

613
00:30:44,440 --> 00:30:45,200
Speaker 5: Yeah, absolutely.

614
00:30:45,280 --> 00:30:47,840
Speaker 13: I mean I did make this call back then, which

615
00:30:47,960 --> 00:30:49,920
Speaker 13: was to say that I believe that software as a

616
00:30:49,920 --> 00:30:53,200
Speaker 13: service companies would struggle in this AI era. Not because

617
00:30:53,200 --> 00:30:55,160
Speaker 13: people are going to go ask Claude to go just

618
00:30:55,160 --> 00:30:57,120
Speaker 13: make them their own SaaS. I don't think that's the reason,

619
00:30:57,400 --> 00:30:59,800
Speaker 13: but because per seat pricing is going to be a

620
00:30:59,800 --> 00:31:03,360
Speaker 13: big challenge. In fact, services that are back end infrastructure

621
00:31:03,440 --> 00:31:06,640
Speaker 13: APIs like tulio is, I think those are the services

622
00:31:06,640 --> 00:31:09,400
Speaker 13: that win because they provide the services that agents are

623
00:31:09,440 --> 00:31:11,280
Speaker 13: going to be able to plug into and be able

624
00:31:11,320 --> 00:31:13,800
Speaker 13: to build all sorts of new and interesting use cases.

625
00:31:13,960 --> 00:31:15,560
Speaker 13: But I think the idea that you've got a workflow

626
00:31:15,560 --> 00:31:17,320
Speaker 13: that's based on a number of employees you have doing

627
00:31:17,320 --> 00:31:19,520
Speaker 13: that workflow, I think those companies can get disrupted.

628
00:31:19,520 --> 00:31:20,600
Speaker 5: And I think that's what you're seeing in the markets

629
00:31:20,680 --> 00:31:21,040
Speaker 5: right now.

630
00:31:21,240 --> 00:31:24,720
Speaker 3: And Jeff, is it an overreaction from your perspective because

631
00:31:24,800 --> 00:31:28,200
Speaker 3: many are just throwing everything out baby with bath water,

632
00:31:28,440 --> 00:31:31,320
Speaker 3: rather than being discerning about whether this is a platform engagement,

633
00:31:31,320 --> 00:31:33,320
Speaker 3: whether or not it's just a change in the way

634
00:31:33,320 --> 00:31:35,840
Speaker 3: of pricing the offering in the future.

635
00:31:35,640 --> 00:31:38,000
Speaker 4: Rather than having to rebuild from the ground up.

636
00:31:40,160 --> 00:31:42,440
Speaker 13: Well, I'm firmly in the camp of not throwing away

637
00:31:42,440 --> 00:31:45,320
Speaker 13: babies with bathwater, and so I think that over time

638
00:31:45,600 --> 00:31:48,800
Speaker 13: investors will discern which companies are well poised for success

639
00:31:48,800 --> 00:31:50,320
Speaker 13: in this era as well as which ones are going

640
00:31:50,360 --> 00:31:52,760
Speaker 13: to struggle because they have the innovator's dilemma based on

641
00:31:52,800 --> 00:31:53,560
Speaker 13: their pricing models.

642
00:31:54,200 --> 00:31:57,440
Speaker 2: Jeff Lawson, co founder, CEO of Inertia, former CEO and

643
00:31:57,640 --> 00:32:00,200
Speaker 2: co founder of Twitter, really appreciate all of that. Thank

644
00:32:00,200 --> 00:32:01,920
Speaker 2: you very much. Carry it more news.

645
00:32:02,120 --> 00:32:04,239
Speaker 3: Yeah, it's time now for talking tech. Up ed and

646
00:32:04,280 --> 00:32:07,959
Speaker 3: first up AI. Humanoid robotics start up Atronics raise five

647
00:32:08,000 --> 00:32:10,440
Speaker 3: hund twenty million dollars in a new funding round, valuing

648
00:32:10,480 --> 00:32:12,600
Speaker 3: the company more than five point five billion dollars now.

649
00:32:12,640 --> 00:32:15,600
Speaker 3: The fresh capital will support the commercialization of its Apollo

650
00:32:15,720 --> 00:32:18,600
Speaker 3: robot and help expand scale its systems.

651
00:32:19,040 --> 00:32:19,360
Speaker 9: Plus.

652
00:32:19,440 --> 00:32:22,720
Speaker 3: Chinese AI firm Jpoo says it is set to release

653
00:32:22,800 --> 00:32:25,680
Speaker 3: GLM five, an upgrade to its flagship model with a

654
00:32:25,720 --> 00:32:28,280
Speaker 3: new version is designed to handle more complex coding and

655
00:32:28,480 --> 00:32:32,200
Speaker 3: ingenetic tasks, putting it in direct competition with Anthropics clawed Opus.

656
00:32:32,600 --> 00:32:36,200
Speaker 3: She says GLM five will officially roll out on Thursday,

657
00:32:36,960 --> 00:32:40,200
Speaker 3: and China's top chip maker s miic or Smith is

658
00:32:40,240 --> 00:32:43,160
Speaker 3: wanting that the surge and AI chip spending.

659
00:32:42,880 --> 00:32:45,000
Speaker 4: Maybe pulling forward years.

660
00:32:44,680 --> 00:32:47,360
Speaker 3: Of future demand, increasing the risk that some data.

661
00:32:47,160 --> 00:32:49,840
Speaker 4: Center capacity can ultimately sit ig.

662
00:32:50,480 --> 00:32:53,520
Speaker 3: AI related infrastructure investment is projected to exceed three trillion

663
00:32:53,560 --> 00:32:56,520
Speaker 3: dollars in the next five years, according to Moody's Ratings.

664
00:32:57,960 --> 00:33:01,160
Speaker 2: Okay, coming up, we'll discuss lifts and with LIFT CEO

665
00:33:01,720 --> 00:33:02,440
Speaker 2: David Risher.

666
00:33:02,840 --> 00:33:16,440
Speaker 5: This is Bloomberg Tech. Okay.

667
00:33:16,440 --> 00:33:19,280
Speaker 2: Shares of lifted down around fifteen percent, on track for

668
00:33:19,320 --> 00:33:22,080
Speaker 2: the biggest drop since August of twenty twenty four. The

669
00:33:22,120 --> 00:33:25,240
Speaker 2: stock's under pressure, but in the company's earnings, the company

670
00:33:25,320 --> 00:33:28,160
Speaker 2: kind of set out on working on a global expansion

671
00:33:28,320 --> 00:33:31,640
Speaker 2: and new product offerings. Lift CEO David Risher is here

672
00:33:31,640 --> 00:33:33,880
Speaker 2: with us in San Francisco. I want to go to

673
00:33:33,920 --> 00:33:35,360
Speaker 2: the core business because you know, I'm going to get

674
00:33:35,360 --> 00:33:38,120
Speaker 2: to Roba Taxi shortly. But you know, like the things

675
00:33:38,120 --> 00:33:41,400
Speaker 2: that are good for you are black Chaufford rides, airports,

676
00:33:41,440 --> 00:33:44,280
Speaker 2: those are higher value and so like my interpretation of

677
00:33:44,560 --> 00:33:47,000
Speaker 2: reading all the analyst response at least is that they

678
00:33:47,040 --> 00:33:49,800
Speaker 2: looked at the kind of profit outlook and said, that's

679
00:33:49,840 --> 00:33:53,040
Speaker 2: a bit below what we expected. What were the factors

680
00:33:53,040 --> 00:33:56,320
Speaker 2: behind that? If those kind of high value segments are doing.

681
00:33:56,160 --> 00:33:58,760
Speaker 15: Great, I mean, it's so much a good question, right, Look,

682
00:33:58,800 --> 00:34:02,720
Speaker 15: we had a blow up order and so record bookings, accelerating,

683
00:34:03,320 --> 00:34:07,560
Speaker 15: record profits, never been higher, record cash flow, record customers. Okay,

684
00:34:07,600 --> 00:34:09,400
Speaker 15: so all that is really good, and what it shows

685
00:34:09,440 --> 00:34:12,200
Speaker 15: is the customer obsession is what drives profitable growth.

686
00:34:12,360 --> 00:34:14,200
Speaker 5: Now there's no question it.

687
00:34:14,120 --> 00:34:16,319
Speaker 15: Sounds like analysts are looking at things like margin, but

688
00:34:16,400 --> 00:34:18,160
Speaker 15: as you just pointed out, a lot of our biggest

689
00:34:18,160 --> 00:34:20,080
Speaker 15: growth is actually in the highest value mode, it's highest

690
00:34:20,120 --> 00:34:22,239
Speaker 15: margin modes. So we've got a lot of conviction that

691
00:34:22,280 --> 00:34:24,480
Speaker 15: we're in a good place and only getting better.

692
00:34:24,880 --> 00:34:26,719
Speaker 2: You and I spent some time together at the end

693
00:34:26,760 --> 00:34:28,640
Speaker 2: of last year and we went kind of a bit

694
00:34:28,719 --> 00:34:32,680
Speaker 2: deeper on the robotypes plan. Yeah, it's through partnerships and

695
00:34:32,920 --> 00:34:35,880
Speaker 2: it's leveraging what you believe is a strength in fleet management.

696
00:34:35,960 --> 00:34:36,319
Speaker 5: That's right.

697
00:34:36,600 --> 00:34:40,640
Speaker 2: Succinctly update us on everything that's due to happen, then please.

698
00:34:40,480 --> 00:34:41,560
Speaker 15: Oh my good I mean, this is gonna be a

699
00:34:41,560 --> 00:34:43,879
Speaker 15: big year for self driving cars, right, and let's start

700
00:34:43,920 --> 00:34:46,239
Speaker 15: with sort of the big picture. When self driving cars

701
00:34:46,280 --> 00:34:48,200
Speaker 15: come on to a network like cars, it tends to

702
00:34:48,200 --> 00:34:50,319
Speaker 15: expand the market. And you would expect that because it's

703
00:34:50,360 --> 00:34:52,880
Speaker 15: a cool new product. It's reliable. You know, you can

704
00:34:52,960 --> 00:34:54,959
Speaker 15: kind of space out, you can text and not worry

705
00:34:54,960 --> 00:34:57,279
Speaker 15: about a driver over hearing a phone call something like that.

706
00:34:57,400 --> 00:34:59,880
Speaker 15: So that's really good. Okay, So then what happens is

707
00:35:00,120 --> 00:35:01,719
Speaker 15: you've got to make sure that you're well positioned with

708
00:35:01,760 --> 00:35:03,480
Speaker 15: all the best players in the market. If you're on

709
00:35:04,080 --> 00:35:06,279
Speaker 15: our in our business, we are. We've got way more

710
00:35:06,320 --> 00:35:08,719
Speaker 15: partnership as you know. We've got a buy do partnership

711
00:35:08,719 --> 00:35:10,759
Speaker 15: that we've just announced is going to be live in

712
00:35:10,800 --> 00:35:13,240
Speaker 15: London later this year. We just worked out an agreement

713
00:35:13,280 --> 00:35:15,160
Speaker 15: with the city of Hamburg in Germany to be the

714
00:35:15,160 --> 00:35:17,680
Speaker 15: first robotaxi provider there. So that's wonderful. So you've got

715
00:35:17,680 --> 00:35:19,960
Speaker 15: the partnerships, you've got the cities lined up, and now

716
00:35:20,000 --> 00:35:21,799
Speaker 15: you've got to be able to manage this fleet. And

717
00:35:21,840 --> 00:35:23,680
Speaker 15: as you just said, this is not the sexy part,

718
00:35:23,719 --> 00:35:25,400
Speaker 15: but it's so important. You've got to be able to

719
00:35:25,400 --> 00:35:27,880
Speaker 15: make sure these cars are charged, clean and ready to go.

720
00:35:28,120 --> 00:35:30,080
Speaker 15: We have a flex Drive subsidiary that's been doing that

721
00:35:30,120 --> 00:35:32,080
Speaker 15: for years. We think we're the best in class, and

722
00:35:32,200 --> 00:35:35,080
Speaker 15: our goal is to be maybe five percent cheaper than

723
00:35:35,120 --> 00:35:37,840
Speaker 15: any other place with higher quality to put robo taxes

724
00:35:37,840 --> 00:35:38,400
Speaker 15: on the network.

725
00:35:39,400 --> 00:35:44,240
Speaker 3: Cheaper That doesn't immediately make me think profit driving David

726
00:35:44,280 --> 00:35:47,040
Speaker 3: in that way. And so like talk about your targets.

727
00:35:47,080 --> 00:35:49,200
Speaker 3: You've got this one billion EMIT del by twenty twenty

728
00:35:49,239 --> 00:35:51,279
Speaker 3: seven and Lumberg Intelligence is warring.

729
00:35:51,360 --> 00:35:53,360
Speaker 4: That looks pretty ambitious at this stage.

730
00:35:54,360 --> 00:35:56,800
Speaker 15: Well, so when I say cheaper, I mean the lowest

731
00:35:56,840 --> 00:35:58,879
Speaker 15: cost way right. And that's the thing is, I want

732
00:35:58,880 --> 00:36:01,560
Speaker 15: our cost position to be really good. To your point,

733
00:36:01,600 --> 00:36:03,360
Speaker 15: like our goals, which we set out a couple of

734
00:36:03,400 --> 00:36:06,040
Speaker 15: years ago, our top line growth of twenty of fifteen

735
00:36:06,080 --> 00:36:07,160
Speaker 15: percent mid teens.

736
00:36:07,000 --> 00:36:07,520
Speaker 5: Year on year.

737
00:36:08,000 --> 00:36:10,319
Speaker 15: We're very much on track for that, and bottom line

738
00:36:10,360 --> 00:36:13,879
Speaker 15: growth getting to a billion dollars of adjusted EBADOT which

739
00:36:13,880 --> 00:36:14,880
Speaker 15: will be four percent margin.

740
00:36:15,120 --> 00:36:16,120
Speaker 5: Very much on track for that.

741
00:36:16,360 --> 00:36:18,000
Speaker 15: But what it means is you've got to be really

742
00:36:18,040 --> 00:36:20,759
Speaker 15: smart about managing your costs. You've got really good about

743
00:36:20,800 --> 00:36:23,000
Speaker 15: kind of managing your quality, and we're doing really well

744
00:36:23,000 --> 00:36:23,359
Speaker 15: on both.

745
00:36:23,360 --> 00:36:24,480
Speaker 5: We've just made progress.

746
00:36:24,520 --> 00:36:27,120
Speaker 15: As I say, we've got our best profitable, highest profitable

747
00:36:27,160 --> 00:36:28,239
Speaker 15: quarter just to bask you.

748
00:36:28,280 --> 00:36:31,240
Speaker 3: For I think therefore, go back to the av world

749
00:36:31,320 --> 00:36:33,840
Speaker 3: because there is this ongoing anxiety that the competition is

750
00:36:33,880 --> 00:36:35,080
Speaker 3: just going to get so fierce?

751
00:36:36,160 --> 00:36:37,759
Speaker 4: Are you set up to win?

752
00:36:37,960 --> 00:36:40,600
Speaker 3: What are you saying to convince the investor base they

753
00:36:40,640 --> 00:36:42,160
Speaker 3: shouldn't be setting off your shareside this?

754
00:36:43,000 --> 00:36:45,640
Speaker 15: Yeah, I think that the way to think about avs

755
00:36:45,680 --> 00:36:48,320
Speaker 15: is they're going to be good for the whole industry,

756
00:36:48,400 --> 00:36:50,359
Speaker 15: the rideshare industry, because, as they say, it's a good

757
00:36:50,360 --> 00:36:52,919
Speaker 15: new product at a lower cost. And I think why

758
00:36:53,000 --> 00:36:56,120
Speaker 15: we are particularly well positioned is we have demand that

759
00:36:56,239 --> 00:36:59,080
Speaker 15: runs to the two three four million people a day level,

760
00:36:59,120 --> 00:37:00,840
Speaker 15: So we have lots of demand for this, and you

761
00:37:00,880 --> 00:37:01,479
Speaker 15: know what they want.

762
00:37:01,600 --> 00:37:03,560
Speaker 5: They want a fast, reliable pick up. It's going to

763
00:37:03,600 --> 00:37:04,440
Speaker 5: get them where they want to go.

764
00:37:04,800 --> 00:37:06,960
Speaker 15: Sometimes they'll want to be driven by a human, sometimes

765
00:37:07,000 --> 00:37:07,640
Speaker 15: by a robot.

766
00:37:07,760 --> 00:37:08,480
Speaker 5: Just kind of depends.

767
00:37:08,480 --> 00:37:10,399
Speaker 15: And so this idea of a hybrid network, I think

768
00:37:10,560 --> 00:37:12,919
Speaker 15: it's a real strength, particularly when you sort of marry

769
00:37:12,920 --> 00:37:13,719
Speaker 15: it with fleet is.

770
00:37:13,680 --> 00:37:15,319
Speaker 2: There a case study for you to reflect on in

771
00:37:15,320 --> 00:37:18,200
Speaker 2: real terms. Then like Atlanta, mame mobility. What are you

772
00:37:18,280 --> 00:37:20,799
Speaker 2: learning there, by the way, what's the status of it

773
00:37:20,840 --> 00:37:22,839
Speaker 2: in terms of like fully, driver lists, et cetera.

774
00:37:22,920 --> 00:37:23,439
Speaker 5: Yeah, for sure.

775
00:37:23,480 --> 00:37:26,360
Speaker 15: So we're in Atlanta, have we still have safety drivers

776
00:37:26,400 --> 00:37:28,280
Speaker 15: in the car just to make sure everything is going well?

777
00:37:28,320 --> 00:37:29,680
Speaker 15: And you know, because we want this to be an

778
00:37:29,680 --> 00:37:31,800
Speaker 15: incredibly high quality, incredibly safe experience.

779
00:37:32,000 --> 00:37:33,680
Speaker 5: What we're learning is people love it. I mean, this

780
00:37:33,719 --> 00:37:34,440
Speaker 5: is the interesting thing.

781
00:37:34,560 --> 00:37:37,000
Speaker 15: People before they when they hear about av's are often

782
00:37:37,000 --> 00:37:38,799
Speaker 15: a little skeptical. They sound like, I'm not sure I'm

783
00:37:38,800 --> 00:37:40,560
Speaker 15: going to be excited about taking a car that's driven

784
00:37:40,560 --> 00:37:43,480
Speaker 15: by a robot. You know, five minutes later they've kind

785
00:37:43,480 --> 00:37:44,080
Speaker 15: of fallen in love.

786
00:37:44,120 --> 00:37:47,160
Speaker 5: And that cialization is highlight. What's the mentric? Yeah, super high,

787
00:37:47,200 --> 00:37:47,640
Speaker 5: super high.

788
00:37:47,640 --> 00:37:50,239
Speaker 15: Basically, I mean as much supply as we can get

789
00:37:50,320 --> 00:37:51,840
Speaker 15: we can put on the road, because there's plenty of

790
00:37:51,920 --> 00:37:53,759
Speaker 15: demand for these things. I think if you want to

791
00:37:53,800 --> 00:37:56,399
Speaker 15: look at a case study for broadly speaking, maybe look

792
00:37:56,440 --> 00:38:00,160
Speaker 15: at the transition from DVDs to streaming. Right it's a

793
00:38:00,160 --> 00:38:02,360
Speaker 15: similar sort of technology shift, and look how big streaming

794
00:38:02,480 --> 00:38:04,040
Speaker 15: is today compared to DVDs in the past.

795
00:38:04,160 --> 00:38:06,640
Speaker 5: It's going to be that kind of wave, Stevin.

796
00:38:06,760 --> 00:38:09,440
Speaker 3: You know what else, I love a lot of New

797
00:38:09,520 --> 00:38:13,680
Speaker 3: Yorkers love the bikes, and boy has this snow made

798
00:38:13,719 --> 00:38:16,319
Speaker 3: things really difficult. Hate to get personal hit, but I mean,

799
00:38:16,360 --> 00:38:18,839
Speaker 3: look at these pictures. I am really struggling to get

800
00:38:18,840 --> 00:38:20,759
Speaker 3: my bike out. I'm really struggling to get it back in.

801
00:38:21,080 --> 00:38:22,880
Speaker 3: How much of a hit is this to have to

802
00:38:22,920 --> 00:38:26,160
Speaker 3: refund customers? How much are you thinking about what the

803
00:38:26,160 --> 00:38:28,360
Speaker 3: city and you can work together with to make this

804
00:38:28,520 --> 00:38:31,000
Speaker 3: a really costly business for you in these sorts of temperatures.

805
00:38:31,800 --> 00:38:33,600
Speaker 15: Yeah, I mean, look, this has been a big, big

806
00:38:33,680 --> 00:38:36,160
Speaker 15: learning for us. Obviously there's a huge snowstorm. I'm told

807
00:38:36,160 --> 00:38:38,560
Speaker 15: that we're about seventy five percent dugout right now, which

808
00:38:38,600 --> 00:38:40,759
Speaker 15: is amazing, but that takes a lot of work, a

809
00:38:40,760 --> 00:38:42,879
Speaker 15: lot of just physical labor to make sure the bike

810
00:38:42,920 --> 00:38:45,360
Speaker 15: stations are available. I think people have taken something like

811
00:38:45,400 --> 00:38:47,200
Speaker 15: half a million rides in the last couple of days,

812
00:38:47,200 --> 00:38:50,120
Speaker 15: which is pretty amazing considering the cold and the snow. Anyway,

813
00:38:50,320 --> 00:38:52,600
Speaker 15: working super closely with the city on it really making

814
00:38:52,600 --> 00:38:54,879
Speaker 15: sure that the availability of these bikes, which as you say,

815
00:38:55,000 --> 00:38:57,319
Speaker 15: we run behind the scenes as highs we can get it.

816
00:38:57,440 --> 00:38:59,799
Speaker 2: I asked Bloomberg Tech producer Justin Now to pop his

817
00:38:59,840 --> 00:39:01,960
Speaker 2: head out the window and check, because like here in

818
00:39:02,080 --> 00:39:06,120
Speaker 2: the bay, obviously we don't get confronted by snow. But

819
00:39:06,239 --> 00:39:07,799
Speaker 2: you know, I went back and read my history about

820
00:39:07,800 --> 00:39:10,800
Speaker 2: the kind of micro mobility part of the business. It

821
00:39:10,880 --> 00:39:13,560
Speaker 2: is a smaller part on revenue spasis, but clearly it's

822
00:39:13,600 --> 00:39:14,560
Speaker 2: still important to you.

823
00:39:14,840 --> 00:39:15,840
Speaker 5: It is. How important?

824
00:39:15,960 --> 00:39:18,960
Speaker 15: Yeah, I mean, look, as you say, financially it's relatively small,

825
00:39:19,000 --> 00:39:20,759
Speaker 15: but this is the future. Look, when you talk about

826
00:39:20,760 --> 00:39:23,200
Speaker 15: av's that's part of the future. When you talk about micromobility,

827
00:39:23,239 --> 00:39:25,319
Speaker 15: people Once people get on an e bike, it's I

828
00:39:25,360 --> 00:39:26,960
Speaker 15: say this sometimes it's kind of like meeting a person

829
00:39:27,000 --> 00:39:29,160
Speaker 15: who's just started pickleball, Like they have to tell you

830
00:39:29,200 --> 00:39:31,439
Speaker 15: about it, how amazing this thing is same with e bikes,

831
00:39:31,440 --> 00:39:31,839
Speaker 15: you know what I mean.

832
00:39:31,880 --> 00:39:33,960
Speaker 5: So it's like, so this is sometimes you can see

833
00:39:34,000 --> 00:39:34,480
Speaker 5: the future.

834
00:39:34,560 --> 00:39:36,440
Speaker 15: And if you look out three, five, ten years, you're

835
00:39:36,440 --> 00:39:37,920
Speaker 15: going to see more people on e bikes. You're going

836
00:39:37,960 --> 00:39:40,200
Speaker 15: to see more people in self driving cars, and you know,

837
00:39:40,239 --> 00:39:41,640
Speaker 15: and of course you're also going to see people in

838
00:39:41,760 --> 00:39:42,439
Speaker 15: human driven cars.

839
00:39:42,440 --> 00:39:43,320
Speaker 5: And that's what we're building.

840
00:39:43,800 --> 00:39:46,440
Speaker 4: Just a personal request. I really want less e bikes,

841
00:39:46,600 --> 00:39:48,160
Speaker 4: one of the bogs down one the ones. We will

842
00:39:48,160 --> 00:39:49,800
Speaker 4: fight for them. Here lift David Rischer.

843
00:39:50,280 --> 00:39:51,359
Speaker 9: So appreciate your time.

844
00:39:51,520 --> 00:39:53,120
Speaker 4: I got to think about my health, David. I've got

845
00:39:53,120 --> 00:39:53,839
Speaker 4: to think about my health.

846
00:39:53,840 --> 00:39:56,000
Speaker 15: Thank you, gotcha.

847
00:39:56,239 --> 00:39:59,719
Speaker 3: Well, coming up, we're going to head to a discussion

848
00:40:00,040 --> 00:40:03,560
Speaker 3: on Instagram and Adam Massari is set to testify today

849
00:40:03,880 --> 00:40:07,239
Speaker 3: in a jury trial examining whether social media companies built

850
00:40:07,280 --> 00:40:10,200
Speaker 3: their products to be addictive for kids. More on that next.

851
00:40:10,400 --> 00:40:28,799
Speaker 3: This is a Bloomberg Tech. Later today, Instagram head Adam

852
00:40:28,800 --> 00:40:31,359
Speaker 3: Maisari will become the first social media boss to take

853
00:40:31,360 --> 00:40:33,839
Speaker 3: the stand in a case alleging the products, like those

854
00:40:33,880 --> 00:40:37,640
Speaker 3: from Meta and YouTube are designed deliberately to addict users.

855
00:40:37,880 --> 00:40:40,720
Speaker 3: A's discussed this from Bloomberg social media reporter Alex Levine.

856
00:40:40,760 --> 00:40:44,799
Speaker 3: There are plenty of cases, plenty of claims, but it

857
00:40:44,880 --> 00:40:48,440
Speaker 3: really does start in La today with Anna Massari.

858
00:40:48,960 --> 00:40:49,319
Speaker 4: It does.

859
00:40:49,360 --> 00:40:51,480
Speaker 16: And I think the interesting thing is we've seen these

860
00:40:51,480 --> 00:40:53,840
Speaker 16: conversations play out over so many years. At this point,

861
00:40:54,120 --> 00:40:57,399
Speaker 16: Massi testified before Congress already, as did you know many

862
00:40:57,440 --> 00:41:00,920
Speaker 16: of the big tech CEOs, including some of them like Zuckerberg,

863
00:41:00,920 --> 00:41:02,000
Speaker 16: who we're going to be seeing.

864
00:41:01,800 --> 00:41:02,760
Speaker 9: Testify in this trial.

865
00:41:04,040 --> 00:41:06,359
Speaker 16: Most Area testified half a decade ago, and we're still

866
00:41:06,360 --> 00:41:07,799
Speaker 16: start of talking about the same things. I think the

867
00:41:07,880 --> 00:41:10,279
Speaker 16: key thing that is different now is that most of

868
00:41:10,320 --> 00:41:13,520
Speaker 16: the past legal challenges to social media companies have centered

869
00:41:13,560 --> 00:41:16,759
Speaker 16: on the content that users have posted, and for the

870
00:41:16,760 --> 00:41:18,880
Speaker 16: most part, tech companies have been able to wiggle their

871
00:41:18,880 --> 00:41:21,839
Speaker 16: way out of any accountability for that because of a

872
00:41:21,880 --> 00:41:24,440
Speaker 16: little known statue called Section two thirty, which basically just

873
00:41:24,520 --> 00:41:26,560
Speaker 16: lets them off the hook legally for things that people

874
00:41:26,600 --> 00:41:29,560
Speaker 16: post on the platforms. The key difference now is that

875
00:41:29,719 --> 00:41:32,239
Speaker 16: it's not about the content that users are posting that

876
00:41:32,280 --> 00:41:35,839
Speaker 16: they're arguing is harmful. What they're arguing is harmful is

877
00:41:35,920 --> 00:41:38,680
Speaker 16: actually the design. So they're making this a personal injury.

878
00:41:39,160 --> 00:41:41,960
Speaker 16: They're making personal injury claims here, saying that it is

879
00:41:41,960 --> 00:41:45,920
Speaker 16: the algorithm that prioritizes engagement, that it is the scrolling,

880
00:41:46,200 --> 00:41:48,920
Speaker 16: that that is what is that it is personal injury,

881
00:41:49,040 --> 00:41:53,560
Speaker 16: the personal injury being like addiction being body dysmorphia and

882
00:41:53,600 --> 00:41:55,839
Speaker 16: all these issues we've heard about, and that it's more

883
00:41:55,840 --> 00:41:57,360
Speaker 16: about that than it is about.

884
00:41:57,120 --> 00:41:58,040
Speaker 5: The actual content.

885
00:41:58,440 --> 00:42:00,319
Speaker 2: Alex, That's the bit that I once understand, and so

886
00:42:00,480 --> 00:42:03,719
Speaker 2: in this case, right just go a bit deeper. The

887
00:42:03,760 --> 00:42:07,399
Speaker 2: plaintiffs are arguing what has happened to them, and of course,

888
00:42:07,440 --> 00:42:09,600
Speaker 2: like when Miss Massi takes the stamp, what is it

889
00:42:09,640 --> 00:42:13,200
Speaker 2: that the companies themselves or the platforms are arguing in

890
00:42:13,280 --> 00:42:16,239
Speaker 2: response about the addictiveness or not of the design of

891
00:42:16,280 --> 00:42:16,920
Speaker 2: the platform.

892
00:42:17,480 --> 00:42:20,399
Speaker 16: So a lot of the claims that you know, we've

893
00:42:20,400 --> 00:42:23,400
Speaker 16: been hearing over the years about the sorts of mental

894
00:42:23,440 --> 00:42:26,440
Speaker 16: health harms to young users are going to be some

895
00:42:26,480 --> 00:42:28,160
Speaker 16: of the same things that we were hearing about in

896
00:42:28,160 --> 00:42:32,080
Speaker 16: this trial. One of the plaintiff's lawyers, just I believe

897
00:42:32,080 --> 00:42:35,120
Speaker 16: earlier this week, was describing these platforms as if they're

898
00:42:35,120 --> 00:42:36,920
Speaker 16: a digital casino. So we're going to hear a lot

899
00:42:36,920 --> 00:42:40,320
Speaker 16: of the same sort of addiction, you know, addiction claims

900
00:42:40,360 --> 00:42:42,480
Speaker 16: and sort of what that has led to from a

901
00:42:42,520 --> 00:42:45,040
Speaker 16: mental health perspective. I think what the company is really

902
00:42:45,080 --> 00:42:46,680
Speaker 16: going to be focusing on, and that we might hear

903
00:42:46,680 --> 00:42:50,919
Speaker 16: from most SARI today, is that they believe that they have,

904
00:42:51,120 --> 00:42:54,240
Speaker 16: you know, the sorts of programs and features and tools

905
00:42:54,239 --> 00:42:57,239
Speaker 16: in place within the company that are really showing that

906
00:42:57,320 --> 00:43:01,480
Speaker 16: they do care about protecting children. I think though that

907
00:43:01,120 --> 00:43:04,319
Speaker 16: that that it remains to be seen. It remains to

908
00:43:04,320 --> 00:43:06,360
Speaker 16: be seen sort of what else is in the testimony today?

909
00:43:06,719 --> 00:43:09,759
Speaker 3: It's also about legal precedents, right, because we're going to

910
00:43:09,840 --> 00:43:12,720
Speaker 3: have meta But then we're looking to TikTok and others.

911
00:43:13,320 --> 00:43:15,760
Speaker 16: Sure, and I think and so TikTok and Snap settled

912
00:43:15,800 --> 00:43:16,600
Speaker 16: their pieces of this.

913
00:43:16,719 --> 00:43:18,640
Speaker 5: They're not off the hook for for you know, other

914
00:43:19,120 --> 00:43:19,759
Speaker 5: for for like.

915
00:43:19,880 --> 00:43:22,360
Speaker 16: Other parts of the trials that are going to be

916
00:43:22,400 --> 00:43:24,520
Speaker 16: playing out through the rest of the first quarter or

917
00:43:24,680 --> 00:43:27,640
Speaker 16: second quarter. But I think that what we've seen is

918
00:43:27,680 --> 00:43:29,440
Speaker 16: that a lot of times these companies just sort of

919
00:43:29,480 --> 00:43:31,080
Speaker 16: try to throw money at the issue to make it

920
00:43:31,120 --> 00:43:33,879
Speaker 16: go away. And I think, and I think, here we're

921
00:43:34,080 --> 00:43:36,520
Speaker 16: we're at this may be the start of a much

922
00:43:36,560 --> 00:43:38,120
Speaker 16: longer process.

923
00:43:38,120 --> 00:43:40,520
Speaker 2: Bloomberg's Alex Lavigne, thank you very much.

924
00:43:40,560 --> 00:43:40,719
Speaker 5: Now.

925
00:43:40,719 --> 00:43:43,240
Speaker 2: That does it for this edition of Bloomberg Tech.

926
00:43:43,360 --> 00:43:45,359
Speaker 3: That forgets to check out the podcast, you can find

927
00:43:45,360 --> 00:43:47,800
Speaker 3: it on the terminal, on a line on Apple and Spotify.

928
00:43:48,520 --> 00:43:50,239
Speaker 9: This is Bloomberg Tech m