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

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Speaker 2: From the heart of where innovation, money and power co

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Speaker 2: line in Silicon Valley n Beyond.

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Speaker 3: This is Bloomberg Technology with Caroline Hyde and Ed.

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Speaker 4: Ludlow, live from London and San Francisco. This is Bloomberg Technology,

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Speaker 4: coming up the first of the Magnificent seven.

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Speaker 3: Gear up to report results.

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Speaker 5: We've got you covered, plus whiz walks away from Google's

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Speaker 5: twenty three billion dollar offer.

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Speaker 4: Details ahead, and we bring you our exclusive interview with

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Speaker 4: Meta CEO Mark Zuckerberg.

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Speaker 5: Spotify up twelve percent in the session, subscribers paid, subscribers

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Speaker 5: up twelve percent year on year. There's a lot of

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Speaker 5: focus from the street on all of the cost saving

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Speaker 5: measures that they took and the bottom line, but let's

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Speaker 5: stick with the Spotify story. The company releasing those two

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Speaker 5: Q earnings this morning, bringing Bloombergs, Lucas Shaw, Lucas Leeds

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Speaker 5: screen Time, which is our coverage of the entertainment industry

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Speaker 5: and all things streaming, and Lucas we are so back

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Speaker 5: streaming is back taught me through Spotify.

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Speaker 6: Yeah, both Netflix and Spotify having very good earnings. Look,

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Speaker 6: Spotify is finally starting to look like a profitable business.

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Speaker 6: I think that's what investors are excited about. They had

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Speaker 6: both profit and margin. That was a head of expectations,

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Speaker 6: I believe, and perhaps most notably for them, they raised

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Speaker 6: prices and still beat on their premium subscriber edition. So

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Speaker 6: it feels like their ability to raise prices now and

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Speaker 6: going forward will be very strong, which is good news.

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Speaker 7: For everyone looking at it.

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Speaker 6: You know, they've done what Wall Street wanted, which is

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Speaker 6: cut costs and start living profits, and.

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Speaker 4: They have really focused in on the profitability over and above,

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Speaker 4: perhaps more broadly just subscriber growth in totality lucas there

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Speaker 4: was a little bit of weakness on the bigger, larger

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Speaker 4: number of those that aren't just paying premium.

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Speaker 8: Yeah.

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Speaker 6: I mean they've I've been surprised over the last couple

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Speaker 6: of years at their ability to continue to grow their

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Speaker 6: customer base, not just premium but the free. They've had

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Speaker 6: a lot of success in India and Southeast Asia, which

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Speaker 6: is I think part of how they've done it. But

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Speaker 6: they've looked, they've they've rained in the spending on podcasting.

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Speaker 6: They are investing a fair amount in audiobooks, and I

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Speaker 6: think there's some skepticism still as to how exactly that

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Speaker 6: will sort itself out.

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Speaker 7: But it's a company and it's a product that people

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Speaker 7: really like.

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Speaker 6: They're not canceling. Spotify has pricing power, and I think

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Speaker 6: that though there have been concerns about how that business

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Speaker 6: would look, it's starting to feel like a company that

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Speaker 6: investors can rely on.

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Speaker 5: Lucas, This might be a little out of left field,

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Speaker 5: but I'm still really focused on podcasts. I just find

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Speaker 5: the industry to be so vibrant. If you track the newsflow,

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Speaker 5: a lot of like individual names doing licensing deals for

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Speaker 5: themselves their own properties Spotify. Do they have anything to

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Speaker 5: say about that?

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Speaker 6: Well, Look, Spotify is now content to be a neutral

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Speaker 6: distributor of podcasts, right. They spent a bunch of money

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Speaker 6: buying podcast networks, funding originals, kind of securing exclusive rights

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Speaker 6: to shows because they use those to those podcasts and

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Speaker 6: those deals to bring people in and to get people

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Speaker 6: listening to.

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Speaker 7: Podcasts on Spotify. That is now happening.

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Speaker 6: Spotify feels less of a need to pay for people

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Speaker 6: and feels like it can, you know, just benefit from

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Speaker 6: from ad sales from shows that it doesn't control or

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Speaker 6: pay for it.

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Speaker 4: So sweetish Juggernaut, Lucas Shaw we so appreciate you bring us.

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Speaker 3: All things Spotify.

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Speaker 4: Meanwhile, sticking with results out of Europe and it's SAP.

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Speaker 4: See Christian Kind join Bloomberg to weigh in on his

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Speaker 4: company's results.

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Speaker 3: Just take a listen.

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Speaker 9: We are not only focusing on moving customers to the cloud.

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Speaker 9: We look into their business model transformation. So when you

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Speaker 9: talk about SEP, it's all about how can you correctually

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Speaker 9: quote your business. I mean, take take x on Mobile,

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Speaker 9: one of our largest the customers now in the cloud.

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Speaker 9: They are moving towards the new blanagies and our software

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Speaker 9: is key for that. Or when you talk about the

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Speaker 9: resiliency of supply chains, we infuse AI. We are connecting

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Speaker 9: your supply chain with this operations of thousands of your

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Speaker 9: suppliers and that's why SEP is so well in these days.

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Speaker 4: Let's bring in Blimberg Sonia in for more on this

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Speaker 4: and Sonia shares the record high.

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Speaker 3: Is it all about AI? Yes?

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Speaker 10: AI definitely plays a key role in SAP financial performance

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Speaker 10: and business development. We've seen some good growth on the

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Speaker 10: cloud revenue and also the backlock seems very healthy, so

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Speaker 10: we can expect a lot more of the growth to

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Speaker 10: come later this year.

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Speaker 3: And in the.

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Speaker 10: Coming months as the AI had a significant role in

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Speaker 10: attracting new customers as well as incentivizing the shift from

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Speaker 10: the on premise solution to the more lucrative cloud business.

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Speaker 5: On your end out of Europe. Thank you very much.

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Speaker 5: Another tech company that's reporting today, Tesla. Let's discuss what

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Speaker 5: to expect with Drin Hanley Warren, Capital CEO and head

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Speaker 5: of research and getting myself organized, putting my posts in

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Speaker 5: for the blog that will run later today. And I

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Speaker 5: think a big focus is going to be FSD and Hang,

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Speaker 5: You've got a particularly interesting.

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Speaker 11: View on that.

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Speaker 8: Why you watch the FSD so closely.

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Speaker 1: Because the car Selle data are very transparent, and it's

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Speaker 1: obvious that the car sales has hit a war, whether

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Speaker 1: whether in California, in China, in Europe. So in California

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Speaker 1: the data are very transparent. Cells down twenty four percent

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Speaker 1: year over year in Q two and California being twenty

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Speaker 1: five percent of.

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Speaker 3: The US ED sales.

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Speaker 1: In China it's down seven percent. That's on top of

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Speaker 1: very aggressive promotions.

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Speaker 7: In Europe, it's a different story.

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Speaker 8: There's a terriff.

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Speaker 1: Coming up now, the Chinese may car going to Europe

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Speaker 1: starting dismals will face twenty percent increase in terror. So

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Speaker 1: at this cross current, Tesla all of a sudden pivoted

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Speaker 1: its story from EB sales to FSD. Essentially, FSD is

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Speaker 1: a story of Okay, you buy a car, I give

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Speaker 1: you a driver. So that's supposed to be a differentiation

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Speaker 1: for Tesla car to you know, get more share in

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Speaker 1: the market. But it is argue, after speaking extensively to

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Speaker 1: the researchers in the field as well as the competition's

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Speaker 1: AI effort, that you know, it's a very immature product.

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Speaker 8: It takes time to get.

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Speaker 1: A higher quality data, probably a rework of the underlying

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Speaker 1: narrow AI system and waiting for more GPU cluster for

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Speaker 1: this to work.

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Speaker 4: Full self driving has actually been something both said and

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Speaker 4: I'm in with a lot lucky enough to be able

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Speaker 4: to do so. But why do you think the underlying technology,

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Speaker 4: the overall scale of investment the Tesla needs to make

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Speaker 4: it's so much larger than the market is factoring in

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Speaker 4: because this is always the long term bad that Tesla

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Speaker 4: is an AI play.

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Speaker 1: Yep, because a Tesla's AI vision is a little bit

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Speaker 1: different from everyone else. Everyone else in the industry relies

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Speaker 1: on sensors in addition to the underlying AI software. But

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Speaker 1: Tesla wants to differential yourself as a unique play in

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Speaker 1: autonomus AI therefore markets as the anti end vision only

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Speaker 1: AI play. But if you talk to people in the

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Speaker 1: in the area, they'll tell you that it's a very

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Speaker 1: difficult route because it needs a lot more expensive data

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Speaker 1: input and it requires a much more expensive AI system

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Speaker 1: to truly get you to L five. So now you

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Speaker 1: how there's a very big operation domain design and you

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Speaker 1: have limited hardware tour, but you want to achieve L five.

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Speaker 1: So that's a very very tall order.

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Speaker 5: Speaking of hardware, we were just showing some of the

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Speaker 5: things at the top of mind for retail investors. Right

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Speaker 5: they have a platform, say dot com, they can vote

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Speaker 5: or up vote their questions. What's missing from that is Robotaxi.

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Speaker 5: How critical is it that ehlon mask gives us a

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Speaker 5: date a new date for the Robotaxi unveil.

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Speaker 1: Well, Robotaxi, it's just the one manifestation of its vision

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Speaker 1: only e t e Ontonama's AI. But Robotaxi in fact

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Speaker 1: is easier to achieve than a be SE because there's

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Speaker 1: no one behind the wheel. So in terms of the

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Speaker 1: legal liability, it's much crystal clear. So if Robotaxi gets

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Speaker 1: into trouble, it's Tesla that's going to fully compensate and

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Speaker 1: financial legally responsible for the accident as opposed to FSD.

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Speaker 1: As long as it's not for the autonomous and the

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Speaker 1: Tesla doesn't claim one hundred percent legal responsibilities, you know

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Speaker 1: it's it's the insurance company needs to underwrite that risk.

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Speaker 1: That's why you see lots of waymows on the street

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Speaker 1: in selective cities. But there's no Tesla Robotaxi because it

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Speaker 1: requires a different sort of like hardware integrate, hardware software

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Speaker 1: and AI program integration.

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Speaker 3: Tesla doesn't have that.

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Speaker 4: We used to perhaps being patient on Elon's promises.

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Speaker 3: He's pushed back Robotaxi. He pushed back Optimus just in

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Speaker 3: the last day or so.

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Speaker 4: Does that matter in the longer term if we are

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Speaker 4: seeing a vision of AI. Look, this is also an

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Speaker 4: energy company that does relatively well.

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Speaker 1: Well. It's sure energy does extremely well, but energy is

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Speaker 1: not something you give it one hundred times surprise to

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Speaker 1: earning racial right, So I mean the stock is at

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Speaker 1: two hundred and fifty. The company is supposed to make

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Speaker 1: two dollars and fifty cents this year, so effectively you

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Speaker 1: are giving it one hundred and ten price to earning

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Speaker 1: racial Okay, so FSD story will get this to you

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Speaker 1: know where the company where Elon Musco want the stock

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Speaker 1: to go. So the mass is this, So FSD will

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Speaker 1: allow the penetration to go up to thirty percent of

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Speaker 1: all cars. So in the US, so USL is about

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Speaker 1: eighteen million cars, So that gives you six million cars

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Speaker 1: for Tesla because it's given you a car, give you

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Speaker 1: a driver, so people will buy it for forty five

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Speaker 1: thousand dollars. And then the company makes ten percent profit

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Speaker 1: margin and you give thirty times a price to earning

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Speaker 1: to that kind of a run rate to normalize the earning,

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Speaker 1: just like you give to Apple that gives you two

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Speaker 1: trillion valuations. That's the story they want to tell to

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Speaker 1: the investigation and pacify them.

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Speaker 7: Be true.

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Speaker 8: It might not be true.

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Speaker 1: It might be somewhere else get there first, and Tesla's not.

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Speaker 1: But if you give it twenty percent probability, that gives

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Speaker 1: you significantly less market evaluation than too Trilia.

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Speaker 4: Well said Johan Hanley Warren, Capital CEO and head of Research.

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Speaker 8: As we try to.

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Speaker 4: Vindicate that ninety three times future earnings.

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Speaker 3: That is the valuation of Tesla.

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Speaker 4: Look at what's happening with Alphabet, because it's not just

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Speaker 4: Tesla out after the bell, Google's parent company is as well.

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Speaker 3: We're up five tens percent.

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Speaker 4: Not only are you focusing on search, cloud and AI,

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Speaker 4: we're also thinking about the wiz rejection of Google's twenty

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Speaker 4: three billion dollar offer.

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Speaker 3: We we'll be talking about that later in the show

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Speaker 3: as well.

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Speaker 2: Ed.

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Speaker 5: All right, coming up, we bring in Bloomberg's Emily Chang

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Speaker 5: for more on that exclusive interview with Meta CEO and

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Speaker 5: founder Mark Zuckerberg.

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Speaker 8: As next, this is Bloomberg Technology.

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Speaker 4: Different Today, Meta unveiled the largest open source AI model ever,

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Speaker 4: Lama three point one. The company is also updating is

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Speaker 4: Meta AI service because.

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Speaker 3: Being built as a rival to chat Gipt. In an

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Speaker 3: exclusive interview with Blum.

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Speaker 4: Magsemi Chang, Meta CEO and founder Mark Zuckerberg opened up

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Speaker 4: about his strategy why believes it's superior to competitors like

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Speaker 4: open AI and Google.

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Speaker 3: Just take a listen.

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Speaker 12: So you're releasing Lama three point one in this family

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Speaker 12: of models big and small, including the biggest open source

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Speaker 12: model ever four hundred and five billion parier ors.

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Speaker 3: What is that jump unlock?

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Speaker 13: I actually think the main thing that people are going

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Speaker 13: to do, especially because it's open source, is to use

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Speaker 13: it as a teacher to train smaller models that they

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Speaker 13: use in different applications. If you just think about like

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Speaker 13: all the startups out there, or all the enterprises or

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Speaker 13: even governments that are trying to do different things, they

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Speaker 13: probably all need to at some level build custom model

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Speaker 13: for what they're doing. And it's really hard to do

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Speaker 13: that with closed systems out there, whether that's open AI

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Speaker 13: or Gemini, Google's thing or whatever. And this is like

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Speaker 13: gets to a pretty core part of our philosophy is

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Speaker 13: we don't believe there's.

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Speaker 8: Going to be like one AI to rule them all.

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Speaker 13: Our vision is that there's going to be millions or

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Speaker 13: just billions of different models out there, So.

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Speaker 3: Not one god, but many. Is that the way to

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Speaker 3: think about it?

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Speaker 13: Well, I don't think they're gonna be gods, but I

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Speaker 13: do think that to some degree, if you if you're

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Speaker 13: like an organization, you think you're going to create like

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Speaker 13: this one super intelligence that does have this feel to

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Speaker 13: me of like people trying to create a god. And

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Speaker 13: that's just I find that both the wrong way to

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Speaker 13: look at it, but just also very unappealing. You know,

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Speaker 13: there's almost two hundred million creators on our platforms. They

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Speaker 13: all are trying to build their community. People want to

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Speaker 13: interact with them. There aren't enough hours in the day. Like,

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Speaker 13: I want to make it that every single one of

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Speaker 13: them can easily train like an AI version of themselves

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Speaker 13: that they can like they can make it what they want.

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Speaker 13: So it's almost like a kind of artistic artifact that

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Speaker 13: they're putting out there for their community that allows their

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Speaker 13: community and interact with them, but also gives them control

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Speaker 13: over how that interaction happens.

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Speaker 12: Facebook, and you have been blamed for a lot of things.

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Speaker 12: Whether you agree or not, why should we trust you

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Speaker 12: with AI?

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Speaker 7: Well, it's a loaded question.

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Speaker 13: We have gotten blamed for a lot of things, and

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Speaker 13: I mean, look, I take our role in all this

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Speaker 13: stuff seriously, and I think we've tried to handle all

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Speaker 13: this as well as possible.

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Speaker 8: I'm not sure that it's all.

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Speaker 13: Been fair, but like I'd like to think that we're

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Speaker 13: an important and relevant company, so I think the scrutiny

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Speaker 13: is generally healthy. One of the defining things around open

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Speaker 13: source is that anyone can scrutinize the work, and because

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Speaker 13: of that, I think it just puts a lot of

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Speaker 13: pressure to make sure that the quality of the work

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Speaker 13: that you're doing gets better really quickly.

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Speaker 5: That was Bluebag's Emily Kang in her exclusive interview with

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Speaker 5: metas CEO and founder Mark Zuckerberg, and I'm delighted to

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Speaker 5: say that Emily joins me here on set in San Francisco.

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Speaker 5: It was so well timed the interview. It's critically important.

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Speaker 5: I think back to the last earnings call where zuckerbog

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Speaker 5: basically said we think MESI could be the world's leading

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Speaker 5: AI company, leading AI company, but he's coming from behind

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Speaker 5: a little bit. You did some catching up with him

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Speaker 5: in that conversation.

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Speaker 12: Absolutely, and you know, you know, the scale of Facebook, Instagram,

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Speaker 12: WhatsApp is incredible. So Meta Ai their chatbot is already

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Speaker 12: embedded across all of those products and Oculus as well,

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Speaker 12: and you can use it as.

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Speaker 3: A standalone chatbot.

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Speaker 12: But he says hundreds of millions of people are going

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Speaker 12: to be using this by the end of the year,

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Speaker 12: in fact before the end of the year, which he

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Speaker 12: believes what makes it the most used chatbot in the world.

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Speaker 12: So in just a few months. It's, you know, potentially

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Speaker 12: going to be eclipsing chat Ept. Now we talked a

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Speaker 12: little bit about the level of intelligence of these latest models,

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Speaker 12: and he admits he still believes there's a gap between

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Speaker 12: LAMA three point one and the most advanced models out there,

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Speaker 12: but they're already working on LAMA for and he says

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Speaker 12: they're closing that gap when that update comes out.

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Speaker 4: Emily, as you were mentioning in what he termed eluded question,

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Speaker 4: but look, he's used to criticism, but there's been a

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Speaker 4: little criticism about the fact that this is open source

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Speaker 4: and the quote unquote danger that that brings. How is

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Speaker 4: he responding to that element of things?

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Speaker 12: Look, I think broadly, and this is what he's been

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Speaker 12: saying now for many months. Open source is more safe,

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Speaker 12: is more secure. You've got all of these people around

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Speaker 12: the world building on it, using it. If they see

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Speaker 12: something wrong, Meta is going to get called out on it.

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Speaker 3: He said, and we'll fix it.

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Speaker 12: And the question is, though, what's in it for Meta?

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Speaker 12: It's not that there's nothing in this for them. If

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Speaker 12: the industry builds on this and it becomes an industry standard,

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Speaker 12: then Meta has more sway in how the industry evolves,

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Speaker 12: so he's saying, look, this is.

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Speaker 3: More safe, this is more secure.

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Speaker 12: But yes, this is also better for us. In fact,

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Speaker 12: he believes it's safer than the closed sources approaches of

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Speaker 12: open AI or Google because what's happening under the hood

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Speaker 12: there you can't necessarily see when something goes wrong. And

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Speaker 12: what's really interesting is, you know, without open source, he says,

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Speaker 12: I couldn't have built Facebook. This is a deep seated

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Speaker 12: part of his philosophy that he's had for twenty years.

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Speaker 12: It's definitely interesting. He understands why there's been pushback, why

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Speaker 12: some people see him as this sort of unlikely champion

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Speaker 12: of open source, given how Meta and Facebook and its

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Speaker 12: platforms have evolved. But he really doubled down that this

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Speaker 12: is the best way forward.

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Speaker 5: There is Mark Zuckerberg, the gold chain wearing new haircut,

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Speaker 5: MMA fighting man, and then there is Mark Zuckerberg at

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Speaker 5: the helm of a critically important company. You spent time

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Speaker 5: with him at his home in Tahoe. I did what

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Speaker 5: did you learn about either of those characters?

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Speaker 12: I learned how they relate to each other. Okay, and

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Speaker 12: you know, he just seems you know, I've met him

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Speaker 12: many times of the years I've interviewed him before, he

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Speaker 12: just seems more comfortable in his own skin than I've

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Speaker 12: than I've ever seen it. He also taught me how

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Speaker 12: to wakesurf. You know, Zuck has all these side quests.

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Speaker 12: Wakesurfing is one of them, and you know, we talked

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Speaker 12: about how he just has so much energy.

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Speaker 3: These things keep him focused.

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Speaker 12: Priscilla, his wife, was there as well.

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Speaker 3: She's like, I prefer when he's doing all these.

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Speaker 12: Other things because he just has so much energy to

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Speaker 12: burn off. But my main takeaway was that he's not

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Speaker 12: going anywhere. If you think about Jeff Bezos, you know

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Speaker 12: who left Amazon, Larry and Sergei who left Google. There

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Speaker 12: are all these major tech founders who have since stepped

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Speaker 12: back from their companies. Mark Zuckerberg isn't going anywhere. He said, Look,

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Speaker 12: I want to see this AI wave play out. You know,

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Speaker 12: it's ten or fifteen more years before the next platform

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Speaker 12: is going to emerge, and I'm going to be around

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Speaker 12: for that.

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Speaker 4: Emily Chang can't wait to see more of the interview.

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Speaker 3: We thank you so much.

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Speaker 4: Tune in to this exclusive interview with Mark Zuckerberg online

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Speaker 4: or on Bloomberg TV tonight it's six thirty pm Eastern time.

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Speaker 3: We've got some breaking news for you. Apple.

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Speaker 4: A foldable iPhone may be released in twenty twenty six.

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Speaker 4: This is according to the information. We're currently at session

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Speaker 4: highs on Apple stock on the back of that. Potentially

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Speaker 4: it could fold width wise like a calmshell, according to

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Speaker 4: people familiar, and it would make it similar look to

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Speaker 4: that Samsung Galaxy X flip z flip in fact, was

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Speaker 4: released all the way back in twenty twenty on Apple shares.

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Speaker 4: This is blue mere technology. Big news in cybersecurity. The

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Speaker 4: startup Whiz has rejected Google. The company has turned down

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Speaker 4: that Alphabet twenty three billion dollar takeover offer, sticking instead

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Speaker 4: to its plans for an IPO. According to a memo

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Speaker 4: that Bloomberg has seen, the move marks a blow for Google,

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Speaker 4: of course, which has tried to catch up to Microsoft

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Speaker 4: and Amazon on shoring up its security offerings for its

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Speaker 4: cloud services. Or please to bring in Dona woman for

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Speaker 4: more and Dana talk to us about why this was

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Speaker 4: so integral to compete with Microsoft, asure to take on

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Speaker 4: the other competitors in aws in the space of cloud.

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Speaker 14: So Wiz would have rounded out Alphabet's cybersecurity offerings. Company

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Speaker 14: had already acquired Mandy in a couple of years ago

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Speaker 14: for a little over five billion, and Wiz would have

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Speaker 14: just rounded out the portfolio with a real focus on

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Speaker 14: detecting cyber threats in various cloud platforms. It plugs into

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Speaker 14: a number of major existing platforms, including Microsoft, Azure, Amazon

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Speaker 14: Web Services. And these are Google's alphabets major competitors in

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Speaker 14: the cloud space, and it is an area where as

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Speaker 14: much as Google is one of the major known names,

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Speaker 14: it does still trail Microsoft and Amazon in that major

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Speaker 14: critical space.

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Speaker 5: You know, part of the even trusting part of our reporting,

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Speaker 5: according to sources, is that the leaders at Whiz are

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Speaker 5: also conscious about the regulatory environment antitrusts.

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Speaker 8: What do we understand there, Donna.

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Speaker 14: Smith indeed would have been a really risky deal for

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Speaker 14: Google had it.

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Speaker 3: Had had the agreement gone through.

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Speaker 14: Rather, had Wiz agreed to an acquisition Google, It's already

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Speaker 14: facing various regulatory challenges on everything from its search business

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Speaker 14: to its advertising tools, and probably even a much smaller

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Speaker 14: acquisition would have drawn some regulatory scrutiny. And we're talking

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Speaker 14: a twenty three billion dollar acquisition. This would have dwarfed

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Speaker 14: previous alphabet acquisitions, certainly even the Mandiant one that I

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Speaker 14: just mentioned, which was about five and a half billion,

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Speaker 14: and it would have been a huge risk indeed, which

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Speaker 14: according to our reporting, was one factor that the Whiz

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Speaker 14: team considered in choosing to IPO instead of being acquired,

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Speaker 14: that not only did it feel that it could build

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Speaker 14: more value as an independent company, but had some concerns

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Speaker 14: about whether it could even get past those regulatory hurdles

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Speaker 14: in the US and possibly elsewhere.

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Speaker 5: I think bugs Dona Wellman, thank you. Welcome back to

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Speaker 5: Bloomberg Technology. Ed Ludlow in San Francisco, and.

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Speaker 4: I'm Carine Hyde in London for the week.

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Speaker 5: The World of crypto and ETFs regulators approved the first

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Speaker 5: US ETFs investing directly in ether. That's according to filings

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Speaker 5: and statements from various asset managers.

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Speaker 8: Now trading is imminent.

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Speaker 5: So let's bring in Bloomberg's Katie Greifeld, friend of the show,

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Speaker 5: friend of mine, expert in all things cat meme related,

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Speaker 5: but also ETF's critically likes crypto. I think that why

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Speaker 5: this story is so interesting we've been talking about so much,

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Speaker 5: is that it's a good test for wider interest in

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Speaker 5: crypto markets.

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Speaker 3: Right you're exactly right. Demand beyond bitcoin.

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Speaker 15: Obviously, the Spot bitcoin ETFs HA started trading in January.

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Speaker 15: We're a wild success, really blue pass expectations. We have

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Speaker 15: seen eight spot ether ets speaking trading in the US

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Speaker 15: today and these are expected not to not draw as

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Speaker 15: much interest. But trading volume so far has been pretty impressive.

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Speaker 15: Over three hundred million in trading volume so far today.

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Speaker 3: But if you take a lot a look.

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Speaker 15: At Blackrocks Ether ETF in particular, the ticker there is ETHA.

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Speaker 15: Volume after the first hour was about fifty million dollars

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Speaker 15: maybe a we'll get to two hundred million by the

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Speaker 15: end of the day. Just for contexts, Blackrocks Spot Bitcoin

443
00:23:25,359 --> 00:23:28,119
Speaker 15: ETF did about a billion dollars on its first day

444
00:23:28,160 --> 00:23:31,320
Speaker 15: of trading, so impressive, but not quite Bitcoin levels.

445
00:23:32,680 --> 00:23:35,920
Speaker 4: It was never going to be Bitcoin levels, was it, Katie.

446
00:23:35,960 --> 00:23:37,960
Speaker 4: Can you just mark us up to what we all

447
00:23:38,000 --> 00:23:40,720
Speaker 4: anticipated and whether or not the flows are good enough

448
00:23:41,119 --> 00:23:42,640
Speaker 4: for what the market needed to see.

449
00:23:42,760 --> 00:23:43,440
Speaker 3: So if you take a.

450
00:23:43,359 --> 00:23:45,600
Speaker 15: Look at what the consensus is right now. There was

451
00:23:45,640 --> 00:23:49,160
Speaker 15: an interesting note from winter Mute trading. For example, typical

452
00:23:49,200 --> 00:23:53,680
Speaker 15: analyst projections translate into annualized inflows about four point eight

453
00:23:53,720 --> 00:23:57,040
Speaker 15: to six point four billion dollars for these Ether products.

454
00:23:57,040 --> 00:23:59,680
Speaker 15: That's in the first year, which again is not nothing

455
00:23:59,720 --> 00:24:02,840
Speaker 15: for a new ETF debut, especially in a new category.

456
00:24:02,880 --> 00:24:06,159
Speaker 15: That's pretty impressive. But it's just that the Bitcoin ETFs

457
00:24:06,200 --> 00:24:10,439
Speaker 15: received so much enthusiasm. Net inflows of seventeen billion dollars

458
00:24:10,520 --> 00:24:13,680
Speaker 15: even with the outflows that you've seen from GBTC. There's

459
00:24:13,720 --> 00:24:16,960
Speaker 15: some pretty difficult comps there. But again, it'll be interesting

460
00:24:17,040 --> 00:24:20,960
Speaker 15: to see how these develops, whether financial advisors in particular

461
00:24:21,000 --> 00:24:24,040
Speaker 15: will embrace these. There's a lot of question about how

462
00:24:24,080 --> 00:24:25,480
Speaker 15: do you market ether?

463
00:24:25,680 --> 00:24:28,680
Speaker 3: Bitcoin is digital gold? What's the sales pitch for.

464
00:24:28,720 --> 00:24:31,480
Speaker 15: Ether to a non crypto audience. Those are some of

465
00:24:31,480 --> 00:24:32,920
Speaker 15: the questions that need to be answered.

466
00:24:34,000 --> 00:24:38,200
Speaker 4: Oil Silver, We've heard it all, Katie Greifeld atf Queen.

467
00:24:38,240 --> 00:24:39,400
Speaker 3: We appreciate it.

468
00:24:39,400 --> 00:24:43,120
Speaker 5: It's time now for talking tech and first up, CrowdStrike

469
00:24:43,240 --> 00:24:47,920
Speaker 5: CEO George Kurtz is set to testify before Congress. A

470
00:24:48,040 --> 00:24:51,320
Speaker 5: US House committee is called on the Executive for questioning

471
00:24:51,600 --> 00:24:54,719
Speaker 5: after last week's Box software update that caused global computer

472
00:24:54,800 --> 00:24:59,200
Speaker 5: outstages worldwide. Additionally, the US Department of Transportation is open

473
00:24:59,280 --> 00:25:02,879
Speaker 5: in investigation and into Delta as it continues to see

474
00:25:02,920 --> 00:25:07,080
Speaker 5: major flight cancelations and delays from the CrowdStrike outage. Despite

475
00:25:07,240 --> 00:25:12,240
Speaker 5: rivals return to normality plus shares a CACAU falling in

476
00:25:12,320 --> 00:25:16,040
Speaker 5: South Korean as authorities have arrested its founder, Brian Kim

477
00:25:16,440 --> 00:25:20,320
Speaker 5: over alleged market manipulation. The billionaire internet entrepreneur was taken

478
00:25:20,320 --> 00:25:24,639
Speaker 5: into custody after hours of deliberation on fears of quote evidence,

479
00:25:24,720 --> 00:25:30,240
Speaker 5: destruction and flight Kim and CACAU spokespeople deny the allegations.

480
00:25:30,680 --> 00:25:33,080
Speaker 8: Okay, coming up here on Bloomberg Technology, We're gonna have

481
00:25:33,080 --> 00:25:33,520
Speaker 8: to take a.

482
00:25:33,520 --> 00:25:37,040
Speaker 5: Look at the world of AI and robotics, and that's

483
00:25:37,080 --> 00:25:40,639
Speaker 5: through the company. Mitra CEO Chris Walty joins us next

484
00:25:40,880 --> 00:25:43,520
Speaker 5: on the company's launched This is Bloomberg Technology.

485
00:25:59,040 --> 00:25:59,840
Speaker 3: Talking mention now.

486
00:26:00,160 --> 00:26:02,720
Speaker 4: Iconic Capital, an investment firm known for well managing the

487
00:26:02,760 --> 00:26:05,480
Speaker 4: fortunes of Silicon Valley's elite I, said it has raised

488
00:26:05,480 --> 00:26:08,119
Speaker 4: five point seventy five billion dollars for a new venture

489
00:26:08,160 --> 00:26:11,680
Speaker 4: focused fund, and yet another example of the continued concentration

490
00:26:11,760 --> 00:26:13,879
Speaker 4: of dollars in a handful of big VC firms.

491
00:26:14,000 --> 00:26:16,080
Speaker 3: This year of a seven billion dollars went.

492
00:26:15,960 --> 00:26:18,439
Speaker 4: To a group of funds at Andrews and Horowitz another

493
00:26:18,480 --> 00:26:21,800
Speaker 4: three billion dollars to Norwest Venture Partners ed.

494
00:26:21,840 --> 00:26:23,960
Speaker 8: What have you got now?

495
00:26:24,200 --> 00:26:29,040
Speaker 5: Mitra is a company focusing on industrial productivity with AI

496
00:26:29,359 --> 00:26:33,040
Speaker 5: but also three dimensional robotics and just today launched with

497
00:26:33,160 --> 00:26:36,639
Speaker 5: seventy eight million dollars in total financing through a Series B.

498
00:26:37,200 --> 00:26:40,199
Speaker 5: We're delighted to be joined by the CEO and co founder,

499
00:26:40,280 --> 00:26:43,600
Speaker 5: Chris Walty, who's also a former Tesla materials engineer who

500
00:26:43,640 --> 00:26:47,480
Speaker 5: helped build what was Tesla bott Now optimists, we'll get

501
00:26:47,520 --> 00:26:51,840
Speaker 5: to that this is interesting. It's not necessarily a crowded field,

502
00:26:52,080 --> 00:26:55,920
Speaker 5: but I'd say we've had several startups on the show

503
00:26:55,960 --> 00:26:59,240
Speaker 5: focusing in the field of robotics. What is specific about

504
00:26:59,280 --> 00:27:01,840
Speaker 5: your technology and your ambitions on how it's used in

505
00:27:01,840 --> 00:27:02,440
Speaker 5: the real world.

506
00:27:03,359 --> 00:27:05,600
Speaker 7: Sure, morning, thanks for having me on the show. Appreciate it.

507
00:27:06,680 --> 00:27:09,480
Speaker 11: Yeah, I mean the challenge is is that, you know,

508
00:27:09,640 --> 00:27:13,320
Speaker 11: ninety percent of warehouses today have no meaningful automation, so there's.

509
00:27:13,160 --> 00:27:14,440
Speaker 7: A product market fit gap.

510
00:27:14,680 --> 00:27:17,639
Speaker 11: At Tesla, we experienced some of the challenges with the

511
00:27:17,680 --> 00:27:21,479
Speaker 11: current state of automation. We endeavor to make something extremely simple,

512
00:27:21,840 --> 00:27:25,000
Speaker 11: user friendly, and the more simplistic we could make the

513
00:27:25,040 --> 00:27:29,320
Speaker 11: system the more cost effective and universal and flexible the

514
00:27:29,359 --> 00:27:30,680
Speaker 11: solution could be, and.

515
00:27:30,640 --> 00:27:32,679
Speaker 4: The more people want to use it. You've got fortune

516
00:27:32,720 --> 00:27:36,159
Speaker 4: one hundred customers already. Albertson's, for example, what is it

517
00:27:36,160 --> 00:27:38,080
Speaker 4: that the robotics are solving for them already?

518
00:27:38,880 --> 00:27:41,639
Speaker 7: What is it that robucks is not solving for them? Well,

519
00:27:42,119 --> 00:27:43,680
Speaker 7: if you look at the traditional state.

520
00:27:44,080 --> 00:27:46,439
Speaker 4: Is solving for them? What is the positive? What are

521
00:27:46,480 --> 00:27:48,159
Speaker 4: you finding that is already the application?

522
00:27:48,280 --> 00:27:48,600
Speaker 3: Chris?

523
00:27:49,040 --> 00:27:51,800
Speaker 7: Oh, sure, what is it we are solving?

524
00:27:52,080 --> 00:27:56,560
Speaker 11: Yeah, So we're focused on moving material at the palette level.

525
00:27:56,640 --> 00:27:58,280
Speaker 7: It's the bread and butter supply chain.

526
00:27:58,400 --> 00:28:00,600
Speaker 11: Almost everything you see around you has been in a

527
00:28:00,640 --> 00:28:03,880
Speaker 11: palette at one point, and we're doing that a much

528
00:28:03,920 --> 00:28:08,840
Speaker 11: more simple and an efficient way through the use of

529
00:28:09,040 --> 00:28:13,040
Speaker 11: just two products. It's really a steel ladder structure and

530
00:28:13,080 --> 00:28:15,879
Speaker 11: a bot that moves in full three dimensions, which allows

531
00:28:15,960 --> 00:28:19,720
Speaker 11: you to basically use software to define how your warehouse

532
00:28:19,760 --> 00:28:24,359
Speaker 11: operates versus traditionally using mechanisms like conveyors and elevators in

533
00:28:24,400 --> 00:28:24,800
Speaker 11: that sort.

534
00:28:25,520 --> 00:28:28,200
Speaker 5: Sony eight million dollars, I can't tell if that's quite

535
00:28:28,240 --> 00:28:31,919
Speaker 5: a lot of money or not enough money to launch

536
00:28:31,960 --> 00:28:34,520
Speaker 5: a product like this, scale it and put it into

537
00:28:34,560 --> 00:28:35,280
Speaker 5: the real world.

538
00:28:35,760 --> 00:28:39,640
Speaker 11: You tell me it sounds like a lot, of course,

539
00:28:40,200 --> 00:28:41,800
Speaker 11: but you know, you have to keep in mind that

540
00:28:41,960 --> 00:28:47,120
Speaker 11: there's seven different engineering disciplines, and you know it's incredibly

541
00:28:47,160 --> 00:28:51,960
Speaker 11: important to make the product reliable, scalable, et cetera. I mean,

542
00:28:52,120 --> 00:28:55,440
Speaker 11: you know, automotive companies spend quite a bit of money

543
00:28:55,440 --> 00:28:58,080
Speaker 11: on the manufacturing and the development to bring an electric

544
00:28:58,160 --> 00:29:02,600
Speaker 11: vehicle to market. Where essentially bringing a small electric vehicle

545
00:29:02,640 --> 00:29:05,920
Speaker 11: that not only moves three thousand pounds in the horizontal

546
00:29:05,920 --> 00:29:07,640
Speaker 11: plane but also moves it up and down.

547
00:29:08,320 --> 00:29:13,600
Speaker 4: Reliable, scalable, and with in a near term ideal of

548
00:29:13,640 --> 00:29:16,640
Speaker 4: being useful. Now, what's so interesting is your background having

549
00:29:16,640 --> 00:29:18,040
Speaker 4: been at Rivian but also at Tesla.

550
00:29:18,040 --> 00:29:20,160
Speaker 3: Look, we're about to get Tesla numbers, we're about to.

551
00:29:20,080 --> 00:29:24,000
Speaker 4: Fixate upon Optimus and once again a slight delay to

552
00:29:24,120 --> 00:29:27,640
Speaker 4: its practical use cases. What was it like at Optimists

553
00:29:27,680 --> 00:29:29,680
Speaker 4: and how did you decide that you had to do

554
00:29:29,760 --> 00:29:32,480
Speaker 4: something different because perhaps of the delays there.

555
00:29:33,680 --> 00:29:36,840
Speaker 11: Yeah, my background at Tesla was a very interesting one.

556
00:29:37,000 --> 00:29:40,480
Speaker 11: I had experienced both on the traditional warehouse side and

557
00:29:40,520 --> 00:29:42,360
Speaker 11: trying to get material flow up and running for it

558
00:29:42,560 --> 00:29:45,680
Speaker 11: to support Model three manufacturing. And then it was also

559
00:29:45,880 --> 00:29:49,040
Speaker 11: asked and tasked to lead the Optimist program. You know,

560
00:29:49,120 --> 00:29:54,360
Speaker 11: humanoid robotics absolutely fascinating. Had a blast working on those

561
00:29:54,400 --> 00:29:57,600
Speaker 11: problems and leading that team, But ultimately it's a bit

562
00:29:57,640 --> 00:30:01,400
Speaker 11: of a ninth inning problem and there's innings four, five, six,

563
00:30:01,440 --> 00:30:04,920
Speaker 11: and seven, et cetera that still need to be solved

564
00:30:05,120 --> 00:30:08,760
Speaker 11: and for the next ten to fifteen years of my career,

565
00:30:09,320 --> 00:30:11,120
Speaker 11: as well as the rest of the team that's joined

566
00:30:11,200 --> 00:30:14,360
Speaker 11: us on this mission. We're really solving a very critical

567
00:30:14,400 --> 00:30:18,560
Speaker 11: problem in supply chain with a very simple and direct

568
00:30:18,560 --> 00:30:19,160
Speaker 11: form factor.

569
00:30:20,120 --> 00:30:24,400
Speaker 5: I guess philosophically, but also from an engineering perspective, why

570
00:30:24,480 --> 00:30:28,320
Speaker 5: is the direction you're going better than the use case

571
00:30:28,360 --> 00:30:32,400
Speaker 5: of a humanoid robots such as Optimus in the warehouse

572
00:30:32,480 --> 00:30:33,920
Speaker 5: or manufacturing environment.

573
00:30:35,240 --> 00:30:37,400
Speaker 11: It's a great question. I don't know if one is

574
00:30:37,440 --> 00:30:39,480
Speaker 11: better or worse than the other. I would say they're

575
00:30:39,480 --> 00:30:42,800
Speaker 11: solving two very different problems, right. What's very unique about

576
00:30:42,880 --> 00:30:46,760
Speaker 11: humans is their ability to manipulate and reason at the edge.

577
00:30:47,240 --> 00:30:49,720
Speaker 7: And that's where a humanoid robot is very powerful.

578
00:30:50,640 --> 00:30:52,320
Speaker 11: But when you look at what's moved around in a

579
00:30:52,320 --> 00:30:55,040
Speaker 11: warehouse in a manufacturing facility, a lot of those that

580
00:30:55,120 --> 00:30:58,960
Speaker 11: material weighs hundreds, if not thousands of pounds, and that's

581
00:30:59,000 --> 00:31:01,480
Speaker 11: not something that a human really is designed to do.

582
00:31:02,080 --> 00:31:04,480
Speaker 11: So you can think of what we're building at MITRA

583
00:31:04,800 --> 00:31:09,320
Speaker 11: as something that's very complementary orthogonal to humanoid robotics or

584
00:31:09,400 --> 00:31:10,200
Speaker 11: humans in general.

585
00:31:11,760 --> 00:31:14,320
Speaker 4: Okay, so you're going to work in line, sort of

586
00:31:14,360 --> 00:31:17,720
Speaker 4: in a sophisticated manner to work alongside the future of

587
00:31:17,720 --> 00:31:18,480
Speaker 4: an optimus.

588
00:31:18,680 --> 00:31:20,160
Speaker 3: You've got strategic investment.

589
00:31:20,240 --> 00:31:23,040
Speaker 4: I'm assuming you've already raised what's seventy eight million dollars

590
00:31:23,080 --> 00:31:23,560
Speaker 4: in funding?

591
00:31:23,600 --> 00:31:25,920
Speaker 3: How expensive does this get? How many more rounds of

592
00:31:25,920 --> 00:31:26,680
Speaker 3: funding do you need?

593
00:31:26,760 --> 00:31:31,680
Speaker 11: Chris, Yeah, I mean our it's unclear at this point.

594
00:31:32,200 --> 00:31:35,920
Speaker 11: Our goal is to execute as quickly and as efficiently

595
00:31:35,960 --> 00:31:38,800
Speaker 11: as possible. You know, the next two years will develop

596
00:31:38,880 --> 00:31:42,040
Speaker 11: the system, invest in the team, and launch to a

597
00:31:42,040 --> 00:31:45,280
Speaker 11: few more customers, and in that time you know that

598
00:31:45,520 --> 00:31:50,040
Speaker 11: we'll be generating revenue and you know the funding environment

599
00:31:50,040 --> 00:31:51,920
Speaker 11: in two years. Who knows what that will be like.

600
00:31:52,000 --> 00:31:55,720
Speaker 11: But it's safe to say that funding can help accelerate

601
00:31:55,760 --> 00:32:00,280
Speaker 11: the mission. It's not necessarily always required for a company to.

602
00:32:00,320 --> 00:32:05,040
Speaker 4: Exist, particularly if you're aiming to be revenue generating, maybe

603
00:32:05,040 --> 00:32:09,040
Speaker 4: even profitability coming into your Lexicon micro CEO and co

604
00:32:09,080 --> 00:32:13,000
Speaker 4: founder Chris Welty. Great experience that you have, great pushing

605
00:32:13,040 --> 00:32:14,280
Speaker 4: us forward on what to expect.

606
00:32:14,280 --> 00:32:16,800
Speaker 3: How a robotic said, what have we got? Well?

607
00:32:16,840 --> 00:32:19,880
Speaker 5: From one Tesla alumni enterprise to another. A couple of

608
00:32:19,880 --> 00:32:22,680
Speaker 5: weeks ago, I took a trip on a boat, an

609
00:32:22,680 --> 00:32:26,280
Speaker 5: electric boat, and it's from startup ARC looking to launch

610
00:32:26,560 --> 00:32:27,640
Speaker 5: later this year.

611
00:32:27,680 --> 00:32:28,480
Speaker 8: Here's what it's like.

612
00:32:29,520 --> 00:32:32,200
Speaker 5: This is the Arc Sport, a fully electric boat from

613
00:32:32,200 --> 00:32:34,960
Speaker 5: Los Angeles based Ark, and it's built for water sports

614
00:32:35,080 --> 00:32:38,240
Speaker 5: on lakes and wide rivers, but it can go in saltwater.

615
00:32:38,400 --> 00:32:39,920
Speaker 5: So we got to test it out in the San

616
00:32:40,000 --> 00:32:43,320
Speaker 5: Francisco Bay. We set off from Sorcelito Harbor and with

617
00:32:43,400 --> 00:32:45,680
Speaker 5: Alcatraz and the Golden Gate Bridge coming into site, we

618
00:32:45,680 --> 00:32:47,720
Speaker 5: took it up to full speed and we led a rip.

619
00:32:49,360 --> 00:32:51,240
Speaker 8: Ark Sport can get up to forty.

620
00:32:50,960 --> 00:32:53,880
Speaker 5: Miles per hour, but it has a surprisingly intuitive drive

621
00:32:53,880 --> 00:32:57,080
Speaker 5: by wire system. The operating system also looks very similar

622
00:32:57,240 --> 00:33:01,840
Speaker 5: to Tesla's in esthetic and functionality. Using front and rear cameras,

623
00:33:01,920 --> 00:33:04,120
Speaker 5: you can see the front and check on the wakeboarder

624
00:33:04,120 --> 00:33:06,440
Speaker 5: who's in the back, or make sure no passengers have

625
00:33:06,520 --> 00:33:07,120
Speaker 5: fallen off.

626
00:33:07,360 --> 00:33:09,800
Speaker 8: Luckily, none of US did. The boat has five.

627
00:33:09,680 --> 00:33:11,840
Speaker 5: Hundred horse power from a two hundred and twenty six

628
00:33:11,960 --> 00:33:14,720
Speaker 5: killer what hour battery pack. That's almost double the battery

629
00:33:14,760 --> 00:33:17,680
Speaker 5: capacity powering Tesla's cyber truck. It's meant to give you

630
00:33:17,720 --> 00:33:20,120
Speaker 5: a full day on the water about forty six hours

631
00:33:20,120 --> 00:33:23,040
Speaker 5: of driving time. At two hundred and fifty eight thousand dollars,

632
00:33:23,240 --> 00:33:26,280
Speaker 5: arc Sports price is comparable to gas power boats from

633
00:33:26,360 --> 00:33:29,640
Speaker 5: names like Mastercraft, but the direct consumer sales model means

634
00:33:29,640 --> 00:33:31,920
Speaker 5: there's no dealer in the middle taking a cup. That

635
00:33:32,040 --> 00:33:34,280
Speaker 5: means art can spend a little bit more on the design,

636
00:33:34,360 --> 00:33:38,080
Speaker 5: the experience in the boat, upscale sound systems, and plush seating.

637
00:33:38,200 --> 00:33:40,360
Speaker 5: It's also got a really cool frust jet system for

638
00:33:40,440 --> 00:33:43,280
Speaker 5: docking up, and it can charge to eighty percent in

639
00:33:43,400 --> 00:33:45,760
Speaker 5: like forty five minutes. The boat's designed by a team

640
00:33:45,800 --> 00:33:49,040
Speaker 5: of former SpaceX and Tesla engineers, and it's shipping later

641
00:33:49,120 --> 00:33:51,800
Speaker 5: this year to go after a two billion dollar market

642
00:33:51,960 --> 00:33:54,040
Speaker 5: in twenty twenty four for wakeboats.

643
00:33:55,720 --> 00:33:56,320
Speaker 8: Two hundred and.

644
00:33:56,320 --> 00:33:58,920
Speaker 5: Fifty eight thousand dollars shipping later this year. Maybe Emily

645
00:33:59,000 --> 00:34:01,040
Speaker 5: Chang and Mark Zuckerberger got their eyes among.

646
00:34:00,880 --> 00:34:04,040
Speaker 3: Car Oh water sports.

647
00:34:04,120 --> 00:34:07,600
Speaker 4: Meanwhile, coming up Tech giants Apple and Micron. They're paying

648
00:34:07,640 --> 00:34:10,360
Speaker 4: it a visit to China as the US ramps up

649
00:34:10,440 --> 00:34:29,959
Speaker 4: chip cubs this blue bag technology.

650
00:34:27,280 --> 00:34:33,239
Speaker 12: I know, you've always been fascinated by China and you

651
00:34:33,320 --> 00:34:36,040
Speaker 12: learn to speak Mandarin, and what do you know about

652
00:34:36,080 --> 00:34:37,160
Speaker 12: where China is on.

653
00:34:37,200 --> 00:34:38,799
Speaker 3: AI and AGI?

654
00:34:41,880 --> 00:34:44,960
Speaker 13: I don't personally know a ton. There's this question, which

655
00:34:45,000 --> 00:34:48,320
Speaker 13: is how should the US approach kind of AI competition

656
00:34:48,400 --> 00:34:51,080
Speaker 13: with China. And there's one strain of thought which is like, okay, well,

657
00:34:51,080 --> 00:34:54,520
Speaker 13: we need to like lock it all down. And I

658
00:34:54,880 --> 00:34:57,360
Speaker 13: just happen to think that that's really wrong because the

659
00:34:57,480 --> 00:35:02,800
Speaker 13: US thrives on just kind of open and decentralized innovation.

660
00:35:02,840 --> 00:35:04,719
Speaker 13: I mean, that's the way our economy works. That's like

661
00:35:04,760 --> 00:35:08,439
Speaker 13: how we build awesome stuff. But I think the leading

662
00:35:08,480 --> 00:35:12,280
Speaker 13: companies should work with the US government and make sure

663
00:35:12,320 --> 00:35:15,319
Speaker 13: that our kind of national defense and things like that

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00:35:15,400 --> 00:35:19,360
Speaker 13: have sort of a perpetual first mover advantage on the

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00:35:19,400 --> 00:35:21,040
Speaker 13: leading technology in the world, so.

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00:35:20,960 --> 00:35:23,120
Speaker 3: We win the AI wars this way.

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Speaker 13: I think there there's the question of what can you

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00:35:25,280 --> 00:35:27,720
Speaker 13: hope to achieve If you're trying to say, okay, should

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00:35:27,719 --> 00:35:29,919
Speaker 13: the US try to be five or ten years ahead

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00:35:29,920 --> 00:35:32,319
Speaker 13: of China. I just don't know if that's if that's

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00:35:32,320 --> 00:35:35,680
Speaker 13: a reasonable goal. So I'm not sure if you can

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00:35:35,719 --> 00:35:38,520
Speaker 13: maintain that. But what I do think is a reasonable

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00:35:38,560 --> 00:35:44,160
Speaker 13: goal is maintaining a perpetual six months to eight month

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00:35:44,280 --> 00:35:47,400
Speaker 13: lead by making sure that the American company is in,

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00:35:47,440 --> 00:35:51,240
Speaker 13: the American folks working on this continue producing the best

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00:35:51,520 --> 00:35:54,760
Speaker 13: AI system.

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Speaker 5: That was Bloomberg's Milie Chang in more of very exclusive

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00:35:57,920 --> 00:36:00,799
Speaker 5: interview with Mark Zuckerberg, or that you can tune into

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00:36:00,800 --> 00:36:04,400
Speaker 5: the exclusive interview with Mark Zuckerberg online or on Bloomberg

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00:36:04,440 --> 00:36:08,400
Speaker 5: TV tonight at six thirty pm Eastern Time. Let's keep

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00:36:08,400 --> 00:36:12,280
Speaker 5: the conversation going on China's US Chip Cubs Titan. Leaders

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00:36:12,320 --> 00:36:15,560
Speaker 5: from tech giants Apple and Micron paid a visit to Beijing.

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Speaker 5: I want to bring in Bloombergs Mike Shepherd, who leads

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00:36:18,080 --> 00:36:22,480
Speaker 5: our coverage at the intersection of politics and technology, Apple's

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00:36:22,560 --> 00:36:26,680
Speaker 5: COO Jeff Williams, and Micron CEO and president Sanjai Morotra

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00:36:27,160 --> 00:36:31,120
Speaker 5: go to Beijing. It tells us how important, despite the

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00:36:31,120 --> 00:36:34,600
Speaker 5: political backdrop, the market is and the supply chain is.

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00:36:36,040 --> 00:36:39,399
Speaker 2: It really does and it was a great segue into

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00:36:39,480 --> 00:36:43,319
Speaker 2: our discussion. Now to hear from Mark Zuckerberg too, about

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00:36:43,360 --> 00:36:47,880
Speaker 2: the importance of China and this tension between the government

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00:36:47,880 --> 00:36:51,480
Speaker 2: here in Washington and with the industry around the world.

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00:36:52,040 --> 00:36:54,920
Speaker 2: Sees in the world's second largest economy, which is our

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00:36:54,960 --> 00:36:58,080
Speaker 2: opportunity both in terms of a large consumer market and

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00:36:58,120 --> 00:37:01,440
Speaker 2: also the importance of the productions apply chain lines that

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00:37:01,480 --> 00:37:05,680
Speaker 2: are embedded in China as well. So, as you hear

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00:37:05,719 --> 00:37:08,279
Speaker 2: from Zuckerberg, and as you see in the visit of

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00:37:08,320 --> 00:37:12,560
Speaker 2: these two key executives, China is super important for their

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00:37:12,600 --> 00:37:14,320
Speaker 2: continued growth and development.

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00:37:15,880 --> 00:37:19,439
Speaker 4: Basically, Mahotra and Williams, they're both part of this US

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00:37:19,560 --> 00:37:24,719
Speaker 4: China Business Council, right, And I'm kind of trying to

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00:37:24,760 --> 00:37:26,640
Speaker 4: understand how much you think that that sort of a

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00:37:26,719 --> 00:37:29,919
Speaker 4: council really achieves, how much they are able to get

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00:37:29,960 --> 00:37:35,560
Speaker 4: business done amid the tip for TAP from a geopolitical perspective, Well, it.

704
00:37:35,520 --> 00:37:38,560
Speaker 2: Does counter the tit for TAM. That's a great question, Carolyn,

705
00:37:38,600 --> 00:37:42,799
Speaker 2: because you do see all these concrete actions emanating from

706
00:37:42,880 --> 00:37:46,759
Speaker 2: the Biden administration in the form of export restrictions. These

707
00:37:46,800 --> 00:37:51,560
Speaker 2: are measures aimed at curbing China's access to advanced technology,

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00:37:51,880 --> 00:37:57,040
Speaker 2: especially semiconductors needed the power of technology like artificial intelligence

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00:37:57,080 --> 00:38:01,000
Speaker 2: that Mark Zuckerberg was just talking about. Now having this

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00:38:01,239 --> 00:38:05,120
Speaker 2: US China Business Council go and meet with very senior officials,

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00:38:05,120 --> 00:38:11,640
Speaker 2: including the Vice Premier Hi Lafeng and the Foreign Minister Wangi.

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00:38:11,760 --> 00:38:14,839
Speaker 2: These are important officials to meet and it carries some symbolism,

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00:38:15,120 --> 00:38:17,719
Speaker 2: and it allowed the Chinese to convey to the American

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00:38:17,760 --> 00:38:20,120
Speaker 2: business community at a very high level.

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00:38:19,920 --> 00:38:21,880
Speaker 3: That look, we still need you.

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00:38:21,960 --> 00:38:25,520
Speaker 2: We realize that politically there are some tensions, but we

717
00:38:25,640 --> 00:38:29,200
Speaker 2: do not want to see this decoupling that some in

718
00:38:29,480 --> 00:38:31,279
Speaker 2: Washington have been pushing for.

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Speaker 5: Just really quick, Mike, We can't get away from the

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00:38:34,480 --> 00:38:37,680
Speaker 5: US presidential election. But one strategy of the US is

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00:38:37,680 --> 00:38:40,000
Speaker 5: also to lean on its allies in respect to tech

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00:38:40,040 --> 00:38:43,040
Speaker 5: cubs in China. You know what's the chatter in that

723
00:38:43,080 --> 00:38:43,920
Speaker 5: respect in DC.

724
00:38:45,239 --> 00:38:48,719
Speaker 2: Well, that's a great question, because right now what we're

725
00:38:48,760 --> 00:38:52,680
Speaker 2: seeing is a little bit more reluctance in escalating those

726
00:38:52,760 --> 00:38:57,880
Speaker 2: restrictions on technology. Even further, we've seen pushback from the

727
00:38:57,960 --> 00:39:00,880
Speaker 2: Dutch and the Japanese against efforts by the US to

728
00:39:00,960 --> 00:39:06,359
Speaker 2: get the makers of chip making machinery to dial back

729
00:39:06,400 --> 00:39:11,760
Speaker 2: on servicing and the sending of spare parts to China

730
00:39:11,920 --> 00:39:16,040
Speaker 2: for some of those machines used in the production of

731
00:39:16,080 --> 00:39:17,920
Speaker 2: the fabrication of semiconductors.

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00:39:19,760 --> 00:39:23,319
Speaker 4: All eyes on the Dutch, the Japanese, China, US Mike

733
00:39:23,400 --> 00:39:24,879
Speaker 4: Sheppard is always across it all.

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Speaker 3: We so appreciate it. Thank you. And that does it

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00:39:27,840 --> 00:39:30,800
Speaker 3: for this very busy edition of Bloombog Technology.

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00:39:30,520 --> 00:39:34,360
Speaker 5: Ed very global London, New York. We're even in Austria

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00:39:34,400 --> 00:39:36,920
Speaker 5: at one point. Check out the pod. Big shout out

738
00:39:36,960 --> 00:39:40,000
Speaker 5: to the pod. Wherever you get your podcasts, Apple, Spotify

739
00:39:40,320 --> 00:39:42,640
Speaker 5: and on iHeart. A lot still to come this week

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00:39:42,680 --> 00:39:44,840
Speaker 5: in earnings. This is Bloomberg Technology