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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 Hyde in New York

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Speaker 1: and ever though in sent Francisco.

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Speaker 2: This is Bloomberg Tech coming up in videos, Next target

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Speaker 2: the PC and u AI chip sends and video and

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Speaker 2: Friends soaring while rival slide after Jensen Hung's Computech's.

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Speaker 3: Keynote, Plus All Eyes on SpaceX will dive into how

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Speaker 3: the upcoming Mega Ibo is already reshaping.

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Speaker 4: Wall Street and New York.

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Speaker 2: Tech Week kicks off today, will be joined by Tech

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Speaker 2: NYC CEO Judy Samuels on what to expect.

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Speaker 3: First, we check in on how the market is trying

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Speaker 3: to digest once again concerns about the Middle East conflict,

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Speaker 3: concerns that sees far is getting further away to have

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Speaker 3: any sort of long term perspective with the US and

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Speaker 3: Iranian talk seeming to break down at this current moment.

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Speaker 3: Look we're looking at we're two tens of percent higher

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Speaker 3: on the last that one hundred. Big tech is trying

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Speaker 3: to shake off the geopolitical risk ed. More broadly, take

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Speaker 3: a look.

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Speaker 2: At it, yep, Nvidia taking on the PC market for decades.

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Speaker 2: You find in Nvidia GPUs in the PC all about

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Speaker 2: gaming now.

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Speaker 4: It is the CPU.

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Speaker 2: It's based on ARM architecture, ARMS up sixteen percent. It

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Speaker 2: takes on the traditional market of Intel and AMD. They

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Speaker 2: are down significantly in Vidia outline the specs here they.

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Speaker 5: Are introducing RTX Spark. Everything we've learned over thirty three

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Speaker 5: years distilled into one show Blackwell RTXGPU with six thousand,

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Speaker 5: one hundred and forty four couter course, one petaflop of

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Speaker 5: AI performance, a custom twenty core Grace CPU built in

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Speaker 5: partnership with Media Tech, fused by m vlage one hundred

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Speaker 5: and twenty eight gigabytes of unified.

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Speaker 2: Member Bloomberg, Zy and King with us leads our courage

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Speaker 2: of semiconductors that was classically in video typically Jensen and

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Speaker 2: its presentation. What do we need to know about RTX

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Speaker 2: Spark super chip.

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Speaker 6: Yeah, I mean this has been go rumored for a

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Speaker 6: long time that in video would directly get into this

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Speaker 6: market with a CPU, and now we've finally seen it.

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Speaker 6: And the way he's packaged it is he's saying, look,

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Speaker 6: if AI is coming to the laptop, if we're going

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Speaker 6: to start talking to our laptops, if we're going to

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Speaker 6: start expecting them to think, we'll need a different kind

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Speaker 6: of chip. And here's what I prepared earlier.

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Speaker 4: And why is.

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Speaker 3: It so significant that it's better than XAVY six? Why

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Speaker 3: is this such a competitive threat as the market seems

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Speaker 3: to think, to the likes of Intel, to the likes

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Speaker 3: even of Qualcom.

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Speaker 6: Well, we asked them repeatedly, give us a comparison, how

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Speaker 6: is it better tell us exactly what it does that

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Speaker 6: the others can't. And what they said was wait and see,

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Speaker 6: will show you when these devices come out. But the

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Speaker 6: market is taking this as Look, there's been lots of

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Speaker 6: attempts to get our architecture into the PC market. None

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Speaker 6: of them have gone particularly well, apart from arguably Apples.

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Speaker 6: But guess what this is in Vidia. This is a

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Speaker 6: company with all the resources it needs, with an established

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Speaker 6: brand in PCs, and with a you know, a real target,

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Speaker 6: and a technology that can differentiate itself with. So that's

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Speaker 6: why we're seeing Intel and AMD trading off.

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Speaker 1: In Vidia is.

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Speaker 2: Holding its gains at four percent. I find that a

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Speaker 2: really interesting reaction. If you remember in and I know

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Speaker 2: it as a while ago, didn't you and I spend

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Speaker 2: a whole month testing aipcs. They're out there, they exists.

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Speaker 2: In that case, it was quite commonside. You know, what

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Speaker 2: do you make of that? You know, Jensen's kind of

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Speaker 2: introducing this as a brand new category, but I thought

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Speaker 2: it it is really there.

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Speaker 6: No, I mean it isn't. ARM in the PC is

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Speaker 6: not there. In VideA itself tried it more than a

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Speaker 6: decade ago. It didn't work out. So this isn't a

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Speaker 6: revolutionary idea at a fundamental level. But again, this is

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Speaker 6: in Vidia, this is the biggest company in the world, and.

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Speaker 3: This is media tech, and this is ARM and all

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Speaker 3: the other companies are getting a base bit in the

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Speaker 3: glory as well. Ian King, is so great to have

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Speaker 3: you on the show to start us off. Look and

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Speaker 3: video clearly remains at the very center of the AI trade,

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Speaker 3: but as the technology matures, investors have been diversifying their bets.

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Speaker 3: It's all of the future opportunities. We say, Matt witness

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Speaker 3: hither with us all Spring Global Investments portfolio manager. I

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Speaker 3: mean we had seen a bit of a well plateauing

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Speaker 3: of in videos rise of late, but now it manages

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Speaker 3: to rekick start. How much do you still need to

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Speaker 3: be in the very infrastructure the bowels of the AI trade.

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Speaker 7: Well, it's a great important question right now, and you

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Speaker 7: talked a little bit about some of the uncertainty in

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Speaker 7: the market. I think it's really forcing investors to remain

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Speaker 7: in some of those safe haven names. Ours cap take

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Speaker 7: tech makes sense, strong visibility with cash flows and their positioning,

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Speaker 7: But we think diversification is going to be important going forward.

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Speaker 7: Concentration risk is real, so we're taking a holistic approach

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Speaker 7: to tech in the AI ecosystem. As those CAPEX dollars

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Speaker 7: started to spread across the AI value chain, so big

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Speaker 7: cap tech names like the Mac seven are going to

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Speaker 7: play an important part with the evolution of AI, but

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Speaker 7: it's it's important to start allocating some of those investment

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Speaker 7: dollars to other areas of the market.

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Speaker 2: Matt, how seriously are you taking this nvideo entry into

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Speaker 2: the PC market with a CPU.

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Speaker 4: Well, it's important.

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Speaker 7: It's it's gonna that edge kind of compute as as

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Speaker 7: that that insatiable demand for more performance kind of extends

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Speaker 7: to those end products.

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Speaker 8: They're going to have to be more capable.

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Speaker 7: And so as we move to inference and AGENTIC AI

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Speaker 7: and physical AI, we're going to see different parts of

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Speaker 7: the market with different requirements and eventually new winners. So

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Speaker 7: I think it's important to have opportunities in different solutions

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Speaker 7: sets to handle that. And I think in Vidia coming

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Speaker 7: into the market there's going to play an important role.

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Speaker 3: We've seen entire adjacent industries take off that are at

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Speaker 3: the heart of the bottlenecks. We think of the energy

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Speaker 3: trade that has picked up, even nuclear companies. We've seen

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Speaker 3: the focus on new types of when it comes to

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Speaker 3: photonics map. But where are the opportunities that haven't already

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Speaker 3: been ridden in this wave?

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Speaker 7: Yeah, and you know, we're focusing a lot of our

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Speaker 7: time on the application layer right now. Look, we kind

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Speaker 7: of look and view as that the AI value chain

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Speaker 7: is having kind of five important layers. You've got energy

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Speaker 7: and power like you talked about you talked about the

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Speaker 7: infrastructure with Chips, computing infrastructure. You've got the hyperscalers, You've

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Speaker 7: got those AI models, and then on top of that

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Speaker 7: is the application layers. Those are those those adopters and

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Speaker 7: the abilities for industries where they're going to be able

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Speaker 7: to drive automation, decision support and kind of productivity gains.

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Speaker 7: They're going to need software to do a lot of

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Speaker 7: that for them, and that's where we've been spending a

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Speaker 7: lot of our time and focus from an investment perspective.

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Speaker 3: Oh, I don't want to front run and where ED

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Speaker 3: is going to take this conversation just after this map.

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Speaker 3: But software has been getting a bid today, So are

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Speaker 3: you a believer that there are going to be ultimate

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Speaker 3: winners out of us going to get more detailed on

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Speaker 3: the application there for us?

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Speaker 4: Well? I think so.

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Speaker 7: I mean, I think companies are starting to focus on

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Speaker 7: how important it is to have that AI is going

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Speaker 7: to be an important driver of what they're going to

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Speaker 7: be able to do, and they're going to need software

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Speaker 7: to come along with them along that journey. And so

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Speaker 7: those companies that have robust access to that data, that

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Speaker 7: have the operational scale and are fully embedded within their

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Speaker 7: customer suites, I think are going to be important to

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Speaker 7: have that ecosystem help get them to where they're going

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Speaker 7: to be going when it comes to productivity, enhancement or

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Speaker 7: whatever they're going to use AI for.

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Speaker 2: It's a very difficult morning to make sense of what's

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Speaker 2: going on in the world. I've got some d ram

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Speaker 2: and nan flash data we kind of breather in May

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Speaker 2: pricing slowed down flat on land. There is still an

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Speaker 2: ongoing situation in the Middle East. IDC has this forecast

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Speaker 2: for the PC market that the PC market overall is

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Speaker 2: going to decline eleven percent in calendar twenty six. When

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Speaker 2: you have a big moment like computechs and Jensen Wong,

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Speaker 2: probably the most important person in the world of technology

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Speaker 2: is on stage and the market reacts like it does,

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Speaker 2: do you actually learn anything useful from that map?

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Speaker 7: Well, I think it just gets back to the diversification

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Speaker 7: points that we want to make sure that our clients

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Speaker 7: are paying attention to. You need to have a holistic

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Speaker 7: approach to whatever you're doing from an investment perspective, whether

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Speaker 7: it be across all ten sectors. But I also think

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Speaker 7: it's important just within the AI ecosystem itself to make

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Speaker 7: sure you're spreading your bets across to all those layers,

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Speaker 7: to make sure when there are big moves, when there

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Speaker 7: are those bottlenecks that are going to force pricing up

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Speaker 7: and really force the agenda, I think you need to

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Speaker 7: make sure that you have exposure to all five layers

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Speaker 7: to make sure that you don't lose out on any

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Speaker 7: opportunities that are out there.

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Speaker 3: And Matt, we become very myopic sat in the United

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Speaker 3: States with the biggest winners being US names. But this

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Speaker 3: is a global supply chain and with some huge wins

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Speaker 3: in South Korea, over in Japan and Europe. Where are

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Speaker 3: you thinking geographically, Well.

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Speaker 7: I think it starts here, primarily here in the US,

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Speaker 7: but I think there are some names outside of the

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Speaker 7: US ASML I think is a great example of that.

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Speaker 7: That's going to play an important part of the build

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Speaker 7: out and their ability to help provide with these these

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Speaker 7: big customers need to drive those product sets in the

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Speaker 7: right direction.

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Speaker 4: Matt, just really really quick.

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Speaker 2: You don't stay up at night worried about memory bottlenecks

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Speaker 2: or you do lose a lot of sleep because of that.

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Speaker 4: I do, I absolutely do.

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Speaker 7: I think it's you know, these cycles are probably the

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Speaker 7: duration of these cycles are going to continue to evolve

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Speaker 7: and move out further down the timeline, and I think

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Speaker 7: a lot of that has to do with the fact

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Speaker 7: that these clean rooms are are are are There's only

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Speaker 7: a few of them out there, and they're they're.

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Speaker 4: Taking up time.

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Speaker 7: These green fields are going to take two to three

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Speaker 7: years before they come online. So I think the memory

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Speaker 7: names are going to continue to to to drive pricing

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Speaker 7: in the direction that they needed to do for the

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Speaker 7: stocks to work.

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Speaker 2: Matt Weimer of all Spring. Great to have you back

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Speaker 2: on the show, Thank you very much. Coming up, we're

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Speaker 2: going to discuss how the SpaceX I p O is

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Speaker 2: already changing the world of finance.

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Speaker 4: Details.

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Speaker 2: Next, the other big story out of copy techs and

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Speaker 2: in the markets is software. Look at the gains for

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Speaker 2: example on service now in Adobe very simply put Gensen

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Speaker 2: one arguing that the biggest users of software will be

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Speaker 2: AI agents.

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Speaker 4: Later in the show, will dig in. This is Bloomberg Tech.

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Speaker 3: Even before it begins trading. SpaceX is reshaping Wall Street.

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Speaker 3: Elon Musk is pitching investors on a company that combines

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Speaker 3: rockets and AI with a claimed market opportunity twenty eight

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Speaker 3: and a half trillion dollars. That is today's big number.

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Speaker 3: That scale is challenging long held assumptions about how companies

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Speaker 3: are valued, who ultimately drives stock market demand. Bloomberg's tech

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Speaker 3: equity reporter Isabeli is one of the team on today's

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Speaker 3: big take all about how already SpaceX is just so large,

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Speaker 3: so consequential that benchmark's having to tear up the rule

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Speaker 3: books as to how they let them start trading.

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Speaker 9: I couldn't have put it better tearing up the rule

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Speaker 9: book or read buysing if you want to be more diplomat.

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Speaker 9: So the companies behind the most familiar names you can

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Speaker 9: think of, like Nasak one hundred and foot Sea Russell,

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Speaker 9: they've already changed the rule book from Nasak the seasoning

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Speaker 9: period or the call it the waiting period when the

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Speaker 9: company debuts they go public, they have to sit there

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Speaker 9: for a while before three months before they'll consider you

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Speaker 9: to be part of the Nasak one hundred, they've narrowed

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Speaker 9: that down to fifteen days. For the foot Seat it's

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Speaker 9: five days. The SMPS and consultation from twelve months to

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Speaker 9: six months. So of course you have proponents saying, yes,

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Speaker 9: this is what we want because it will reflect the market.

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Speaker 9: But you have critics saying, but wait, when companies go public,

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Speaker 9: they're usually volatile, and don't you want to wait and

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Speaker 9: see first, you know, have a normal price discovery. So

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Speaker 9: of course, just like with anything, there's critics and there

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Speaker 9: are also supporters.

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Speaker 3: And the critics a worried perhaps about protections for retail

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Speaker 3: investors or new entrants. Meanwhile, SpaceX is all about the

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Speaker 3: retail investor. I mean a thirty percent allocation towards them.

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Speaker 3: How are we seeing this managing to go into sometimes

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Speaker 3: Elo Musk's criticism himself of passive investments exactly.

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Speaker 9: So in the tracking fund estimated to buy nearly twenty

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Speaker 9: billion dollars worth of SpaceX. That's massive. I don't think

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Speaker 9: we've seen that before. And going with retail traders. I

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Speaker 9: went on Reddit, which is the most reliable source on Earth.

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Speaker 9: That's obviously a joke. I'm smiling if people are just

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Speaker 9: listening and some people were like, why will we even

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Speaker 9: buy this? It will be in our brokerage, in the

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Speaker 9: index funds in just a few months. So there is

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Speaker 9: that thinking of like, how will this reshape passive investing?

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Speaker 9: Will traditional money managers have to reallocate because they don't

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Speaker 9: want to be too overweight on one thing? I mean,

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Speaker 9: where is SpaceX going to be categorized under even industrials

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Speaker 9: tech or communication? So lots of unknown but definitely consequential.

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Speaker 4: As well. Tough decisions need to be made.

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Speaker 2: We spent a lot of last week talking about Tesla

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Speaker 2: and the idea that SpaceX merges with Tesla in the future.

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Speaker 4: You just showed a quote from Scott Sogny.

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Speaker 2: One point he makes is that actually Tesla and Bitcoin

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Speaker 2: is examples. You know, and I thought about the bitcoin holdings

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Speaker 2: at SpaceX last week.

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Speaker 4: They might actually get hit by this.

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Speaker 9: Why because a play on Tesla and a play on

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Speaker 9: SpaceX is really just a belief on Elon. And if

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Speaker 9: you're invested in Tesla and SpaceX, are you too over

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Speaker 9: concentrated on an Elon?

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Speaker 10: Now?

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Speaker 9: I know that these are two separate companies, and within

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Speaker 9: those two companies are multiple companies, it feels like, and

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Speaker 9: they have different valuations, different technicals, but it's all really

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Speaker 9: Elon at the end of the day. So that's what

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Speaker 9: one analyst was telling me. Also, like, if you're into Tesla,

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Speaker 9: would you sell that because you're going to be in

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Speaker 9: SpaceX and SpaceX of course it's also in the index.

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Speaker 9: So really lots of over concentrated risk. And it's not

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Speaker 9: just Tesla. I mean, soon we'll see OpenAI, we'll see Anthropic,

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Speaker 9: which the show has covered a lot. What's that going

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Speaker 9: to mean for our for your brokerage as an ordinary American?

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Speaker 9: I mean, you're going to have such a huge concentration

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Speaker 9: of chech and do you want that for your portfolio?

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Speaker 2: Blimberg ysabarly tough stuff, Thank you very much. Now, when

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Speaker 2: the Apple Watch went on sale in twenty fifteen, it's

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Speaker 2: on not just the other high tech wearables but also

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Speaker 2: the wider mechanical watch market, and as you can see,

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Speaker 2: that meant hit to sales of traditional in that space. Now,

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Speaker 2: Apple's aiming to use that playbook in the eyewear market.

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Speaker 2: Bloombost Mark German leads our coverage of Apple and that's

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Speaker 2: the subject of this weekend's power On.

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Speaker 4: We've talked about.

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Speaker 2: This before, you know why how Apple and the ewear market.

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Speaker 2: But that historic playbook, you broke it down. What do

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Speaker 2: we need to know?

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Speaker 8: Yeah, it's interesting when the Apple Watch launch, you see

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Speaker 8: what you saw. Within a few years the entire mid

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Speaker 8: tier mechanical watch industry get completely upended.

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Speaker 9: Right.

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Speaker 8: There was Movado, there was Fossil, there was Swatch, and

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Speaker 8: those companies are still around, but their revenues are considerably

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Speaker 8: lower than they were.

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Speaker 4: A decade ago.

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Speaker 8: The high end of the watch market, companies like Pateech,

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Speaker 8: rolex ap all the hot ones right now and the

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Speaker 8: Covid luxury boom they did fine and what you're going

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Speaker 8: to see is the same thing play out in glasses

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Speaker 8: I believe where Apple and therefore other smart glasses makers

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Speaker 8: are going to completely disrupt that mid tier market brand.

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Speaker 8: It's like ray Ban, Warby Parker, etc. Now you've seen

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Speaker 8: ray Ban, Warby Parker Pivot too Smart. So really the

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Speaker 8: non smart glasses I think are going to.

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Speaker 4: Get wiped out.

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Speaker 8: But the very high end Maison Bonet Cardier, et cetera.

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Speaker 8: Those guys I think are going.

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Speaker 4: To be okay.

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Speaker 3: I mean, we really do see how RayBan has positioned

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Speaker 3: himself like Zotoca more broadly alongside Meta. But where do

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Speaker 3: you think has also managed to start to get in

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Speaker 3: ahead of the curve from a digital tech perspective, not

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Speaker 3: having to just depend on we'd tie ups like we

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Speaker 3: see with APN Swatch for example, which does prove pretty successful.

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Speaker 11: Yeah.

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Speaker 8: No, I mean Meta has done a great job. Obviously,

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Speaker 8: they've pioneered the space there. They're going to be launching

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Speaker 8: new smart glasses. I guess it's June first already, so

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Speaker 8: later this month. They have partnerships with Exertal Exotica. I

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Speaker 8: would expect them to eventually roll out new designs that

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Speaker 8: are in house form factors. So certainly it's going to

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Speaker 8: be a pretty booming space, and Apple coming into the

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Speaker 8: space with an iPhone exclusive pair of smart glasses is

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Speaker 8: going to drive interest for the rest the industry, and

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Speaker 8: I think Meta has a strong chance for the android

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Speaker 8: side of the world.

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Speaker 2: Very very quick, Mark, is there anything that we learn

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Speaker 2: from from the vision pro story about how they approach glasses?

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Speaker 8: You know, I think that there's really it's really two

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Speaker 8: different stories, you know, the vision Pro obviously thirty five dollars,

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Speaker 8: very heavy, very tech tour de force on the glasses

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Speaker 8: something quite lighter and a lot cheaper. So I think

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Speaker 8: they're two distinct categories.

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Speaker 3: Really is again one of the most read things over

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Speaker 3: the course of the weekend, Bloomberg's Mark Gum and thank

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Speaker 3: you very much, all things power on. Next coming up

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Speaker 3: from video generation to robotics, Luma Ai CEO and Jane's

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Speaker 3: going to be joining us on the next frontier in

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Speaker 3: artificial intelligence. This is blombag Tech.

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Speaker 2: Another Nvidio message at computext. The next AI race is

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Speaker 2: in the physical world. That brings us to Lumerai, which

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Speaker 2: is launching an open research lab focused on solving generalization

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Speaker 2: in robotics. Joining us now CEO and it Jane. We've

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Speaker 2: talked quite a bit on this program about the challenge.

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Speaker 2: The challenge is that in the physical world, models need

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Speaker 2: to be multimodal. But I think let's start by defining

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Speaker 2: the problem generalization. That might be a newer term. What

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Speaker 2: are you what are you getting at there?

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Speaker 12: Oh?

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Speaker 4: Thanks for having me.

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Speaker 12: First of all, currently, pretty much all robots are trained

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Speaker 12: by showing a few examples of specific tasks, So the

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Speaker 12: entire field basically trains one task at a time. If

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Speaker 12: you compare that to the world of language models, where

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Speaker 12: you know, you train a large model and then it

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Speaker 12: is able to handle a wide variety of tasks unseen

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Speaker 12: problems as well. So that is an example of generalization.

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Speaker 12: And in robotics we are we have this critical gap where,

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Speaker 12: like you know, we are just stuck in the value

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Speaker 12: of specific tasks in order for robotics to be impactful

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Speaker 12: in the world and for us to be able to

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Speaker 12: just talk to them and ask them like, hey, okay,

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Speaker 12: do this, then like you know, when you're done with that,

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Speaker 12: go go take care of that thing.

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Speaker 2: Maybe a new scenario being presented for the first time.

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Speaker 12: For the first time, So generalization is this problem of

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Speaker 12: how do we allow robots to solve generally any.

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Speaker 2: Task, even in a world where you have access to

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Speaker 2: a lot of synthetic or virtual data. Having a grounding

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Speaker 2: in physics the real physics of the world is a

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Speaker 2: principle challenge. So why is it that having an open

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Speaker 2: science phys quai that have is a solution?

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Speaker 4: It sounds somewhat abstract with.

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Speaker 12: Respect, right, So I mean there's two aspects to this.

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Speaker 12: One that it is open and the second is the

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Speaker 12: world that we are specifically doing.

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Speaker 4: Just open being open source, you mean open.

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Speaker 12: Being open science, open source, both of those aspects. So,

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Speaker 12: first of all, what the solution actually looks like from

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Speaker 12: our perspective. Currently, the best solution is brute forcing this approach,

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Speaker 12: which is gather data on every single task you can

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Speaker 12: imagining combination of task everything humans do from picking up

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Speaker 12: cups to you know, digging like you know, minds all

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Speaker 12: of these things and gather data one piece at a time.

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Speaker 12: That's a practically impossible solution. On the other hand, the

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Speaker 12: teams at LOOMA, the work we have been doing for

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Speaker 12: the past four years is in building out these general

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Speaker 12: systems out of multimodeal data Internet scale multimodel data and

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Speaker 12: extracting signals from that. That allows control, that allows simulation

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Speaker 12: of reality, and that allows physical control. So this lab's

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Speaker 12: job is to leverage that skill into physical AI. And

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Speaker 12: the second aspect of that this is open and I

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Speaker 12: think we would not be doing it any other way.

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Speaker 12: And I think this is really really significant. So if

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Speaker 12: you think about what physical AI would mean for the world,

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Speaker 12: it will be everywhere. It will be in our houses,

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Speaker 12: will be these systems. Robots will be manufacturing everything we

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Speaker 12: depend upon, everything we eat. There will be in our hospitals,

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Speaker 12: they will be in scientific labs, they will be on

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Speaker 12: our streets. It's completely it's completely untenable that one or

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Speaker 12: two people control this entire stack. So we want to

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Speaker 12: live in a world and we want to affect a

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Speaker 12: world where a small group of people can, like you know,

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Speaker 12: take these technologies and build them into productive systems. And

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Speaker 12: that's why it is an open linesation.

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Speaker 3: This seems like more of a philosophical push of yours,

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Speaker 3: the idea that we shouldn't have so much control. I mean,

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Speaker 3: it's almost akin to the encyclical by the Pope. But

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Speaker 3: Meta at one point was pushing open source and then

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Speaker 3: it moved away from that just the sheer amount of

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Speaker 3: money that needs to be made.

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Speaker 4: How are you going to fund this?

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Speaker 3: How do you commit to open science when everyone's so

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Speaker 3: worried about China and geopolitical tensions?

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Speaker 12: Right? I think, first of all, it has to be philosophical,

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Speaker 12: because it's not just a tool. This is going to

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Speaker 12: be technology. I mean, AI already is immensely impactful in

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Speaker 12: our world, beyond what anybody imagined even two years ago.

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Speaker 12: Physical AI will be deployed even faster because of the

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Speaker 12: economic impact it's going to have. So a physical philosophical

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Speaker 12: stance is absolutely necessary. But you're absolutely right funding these systems.

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Speaker 12: So what we believe actually is that this level of

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Speaker 12: control over means of production is actually not a tenable

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Speaker 12: economic situation anyway. You know, nations would not be happy

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Speaker 12: with one or two companies outside their borders controlling their

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Speaker 12: means of production. So we believe, actually this is not

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Speaker 12: just a philosophical stance, this is an economically sound stance

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Speaker 12: and building an ecosystem where like you know, chip partners,

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Speaker 12: the model brain providers like Quma, and deployment partners work

441
00:21:35,160 --> 00:21:38,159
Speaker 12: together to build these out into systems of productive work

442
00:21:38,359 --> 00:21:42,800
Speaker 12: is the right economic path and currently intelligence especially lllms,

443
00:21:42,800 --> 00:21:44,400
Speaker 12: are going on the wrong path here.

444
00:21:44,440 --> 00:21:48,600
Speaker 3: I mean we've got thirty seconds, you've raised nine hundred million. Seriously,

445
00:21:48,640 --> 00:21:50,840
Speaker 3: we're just seeing the output that Luma creates. But why

446
00:21:50,840 --> 00:21:52,919
Speaker 3: are you the right person when you've got world Labs

447
00:21:52,920 --> 00:21:54,160
Speaker 3: for example with faith Ailee.

448
00:21:56,200 --> 00:21:59,520
Speaker 12: I think the just like lllms were not or language

449
00:21:59,520 --> 00:22:02,760
Speaker 12: models were not solved by linguists, we believe to solve

450
00:22:02,760 --> 00:22:06,359
Speaker 12: physical AI, you need the systems of large scale, multimodel

451
00:22:06,400 --> 00:22:08,879
Speaker 12: data infrastructure. This is what Luma does. This is our

452
00:22:08,920 --> 00:22:11,480
Speaker 12: bread and butter and we have produced some of the

453
00:22:11,520 --> 00:22:14,240
Speaker 12: best models in this space on three D, on images,

454
00:22:14,240 --> 00:22:18,480
Speaker 12: on video, taking raw Internet data. This skill is I

455
00:22:18,480 --> 00:22:21,280
Speaker 12: think what is essential to solving this problem and that's

456
00:22:21,320 --> 00:22:23,000
Speaker 12: why we think we are one of the best suited

457
00:22:23,160 --> 00:22:24,399
Speaker 12: companies in the world to solve it.

458
00:22:24,880 --> 00:22:27,000
Speaker 3: Appreciate you coming on and bring us the news. I'm

459
00:22:27,000 --> 00:22:30,200
Speaker 3: at Jane of Luma AI. Have a great day. Meanwhile,

460
00:22:30,200 --> 00:22:32,719
Speaker 3: coming up, we're going to dive into the impact on

461
00:22:32,840 --> 00:22:34,880
Speaker 3: software and look at the stocks.

462
00:22:34,640 --> 00:22:35,320
Speaker 4: I'm currently seeing.

463
00:22:35,359 --> 00:22:39,160
Speaker 3: After Jensen Wang remarks over at computext, he's talking about

464
00:22:39,440 --> 00:22:41,960
Speaker 3: how software lives on in the age of generative AI,

465
00:22:42,359 --> 00:22:43,800
Speaker 3: and you're just going to be using more of it

466
00:22:43,880 --> 00:22:46,920
Speaker 3: on the Agentic from New York for San Francisco.

467
00:22:46,960 --> 00:22:47,560
Speaker 4: This is a BlueBag.

468
00:22:47,600 --> 00:23:02,760
Speaker 11: Tech Videos CEO Jensen Huang call agentic AI useful AI

469
00:23:02,840 --> 00:23:04,840
Speaker 11: and says it's going to be the next main driver

470
00:23:05,160 --> 00:23:08,960
Speaker 11: of AI adoption and AI investment. He calls it the

471
00:23:09,160 --> 00:23:13,879
Speaker 11: Agentic age. Hwang was in full salesman mode here at

472
00:23:13,920 --> 00:23:17,199
Speaker 11: Copy Text in Taipei. In Nvidia, of course, is a

473
00:23:17,240 --> 00:23:20,480
Speaker 11: dominant player in AI data centers, and Hoong says its

474
00:23:20,560 --> 00:23:26,280
Speaker 11: latest AI supercomputing platform, Vera Rubin, is now in full production,

475
00:23:26,840 --> 00:23:31,680
Speaker 11: Nvidia's most ambitious endeavor to date, he says. Now in

476
00:23:31,760 --> 00:23:35,680
Speaker 11: Nvidia is also launching a reboot into home computers. He

477
00:23:35,760 --> 00:23:39,920
Speaker 11: wants to quote reinvent the PC with its new RTX

478
00:23:39,920 --> 00:23:43,560
Speaker 11: Spark super chip built with Taiwan's media tech that will

479
00:23:43,600 --> 00:23:47,000
Speaker 11: be offered in high end laptops and desktops from the

480
00:23:47,160 --> 00:23:50,920
Speaker 11: likes of Dell and Lenovo this fall. It's in Vidia

481
00:23:51,080 --> 00:23:54,639
Speaker 11: stab at loosening the stranglehold of Intel and move the

482
00:23:55,040 --> 00:24:00,240
Speaker 11: humble home computer into the Agentic AI age. And you

483
00:24:00,240 --> 00:24:03,960
Speaker 11: know all that talk of late about AI eliminating jobs well,

484
00:24:04,080 --> 00:24:08,520
Speaker 11: Jensen Huang calls that notion complete nonsense. He says, a

485
00:24:08,640 --> 00:24:12,960
Speaker 11: gentic AI is going to be a generator of GDP

486
00:24:13,400 --> 00:24:17,920
Speaker 11: and that creates jobs. Stephen Engel Bloomberg News at copytechs

487
00:24:17,960 --> 00:24:20,399
Speaker 11: in Taipei were.

488
00:24:20,280 --> 00:24:23,040
Speaker 3: Welcome you back to bloombag tech and look, Stephen said

489
00:24:23,080 --> 00:24:25,280
Speaker 3: it all in video once again being bullish, and the

490
00:24:25,280 --> 00:24:27,480
Speaker 3: market is bullish in response for more than four percent

491
00:24:27,560 --> 00:24:29,840
Speaker 3: on the name. That's the biggest movement in a couple

492
00:24:29,840 --> 00:24:31,640
Speaker 3: of weeks. We're seeing it back at five point three

493
00:24:31,680 --> 00:24:32,439
Speaker 3: trillion dollars.

494
00:24:32,600 --> 00:24:33,000
Speaker 4: A lot of.

495
00:24:33,040 --> 00:24:36,840
Speaker 3: Analysis out there back of America seeing it is continually strengthening.

496
00:24:36,880 --> 00:24:39,520
Speaker 3: In video's systems mode. We've got City saying as a

497
00:24:39,600 --> 00:24:42,400
Speaker 3: number of positives. But it does have an effect.

498
00:24:42,119 --> 00:24:43,000
Speaker 4: On the rest of the market.

499
00:24:43,920 --> 00:24:46,359
Speaker 2: Yeah, and there is a massive rally in software names,

500
00:24:46,359 --> 00:24:49,040
Speaker 2: as Jensen Wong says, it's an incredible time to be

501
00:24:49,080 --> 00:24:50,760
Speaker 2: a software company. We're going to dig into it in

502
00:24:50,800 --> 00:24:54,400
Speaker 2: detail soon, but basically he's arguing the billions of AI

503
00:24:54,520 --> 00:24:57,080
Speaker 2: agents will be the biggest users of software, and so

504
00:24:57,119 --> 00:24:59,479
Speaker 2: the biggest customers of those companies that you see rallying

505
00:24:59,640 --> 00:25:00,280
Speaker 2: on your screen.

506
00:25:00,320 --> 00:25:01,360
Speaker 4: Let's get more on the.

507
00:25:01,320 --> 00:25:04,800
Speaker 2: Impact of a videos announcement Bloomberg Intelligence highlighting the demand

508
00:25:04,840 --> 00:25:08,600
Speaker 2: in the AIPC category is growing. Bloomberg Intelligence is Mandy,

509
00:25:08,600 --> 00:25:11,439
Speaker 2: you've seeing joins us for more. So interesting because actually,

510
00:25:11,440 --> 00:25:14,399
Speaker 2: like in aggregate, the PC markets under pressure, right, Mandy,

511
00:25:14,520 --> 00:25:18,080
Speaker 2: because of the memory pricing issue, because of the generations

512
00:25:18,080 --> 00:25:19,680
Speaker 2: to generations.

513
00:25:19,000 --> 00:25:20,480
Speaker 4: Of hardware out there.

514
00:25:20,800 --> 00:25:23,760
Speaker 2: Just bring the B thesis on how you respond to

515
00:25:23,840 --> 00:25:25,480
Speaker 2: the Spark super chip and what you think it will

516
00:25:25,520 --> 00:25:27,280
Speaker 2: do for the AIPC category.

517
00:25:28,000 --> 00:25:30,359
Speaker 13: Look, I mean what we have seen with the AI

518
00:25:30,600 --> 00:25:34,520
Speaker 13: server category. You know, server used to be a low

519
00:25:34,720 --> 00:25:37,439
Speaker 13: to mid single digit growth market and look at what

520
00:25:37,480 --> 00:25:39,719
Speaker 13: they have done, you know on the server side and

521
00:25:39,800 --> 00:25:42,879
Speaker 13: the data center side with this AI server category. And

522
00:25:43,680 --> 00:25:46,640
Speaker 13: Vidia is much more of a household name now than

523
00:25:46,680 --> 00:25:49,960
Speaker 13: it was probably a few years back, so it makes

524
00:25:50,000 --> 00:25:52,680
Speaker 13: sense for them to try out, you know, whether they

525
00:25:52,680 --> 00:25:56,399
Speaker 13: can have some traction on the PC side. And the

526
00:25:56,520 --> 00:26:00,720
Speaker 13: specs are you know, way different than the current proper PCs.

527
00:26:00,800 --> 00:26:05,600
Speaker 13: I mean one hundred and thirty two gigabytes of DRAM. Look,

528
00:26:05,880 --> 00:26:09,239
Speaker 13: these are the type of things that they were the

529
00:26:09,280 --> 00:26:12,080
Speaker 13: first ones to build. On the server side, it really

530
00:26:12,119 --> 00:26:15,679
Speaker 13: took off. And with AGAI one thing we know is

531
00:26:16,200 --> 00:26:19,720
Speaker 13: it requires a lot more compute across the board. So

532
00:26:20,440 --> 00:26:23,439
Speaker 13: I think it's an interesting area that they're trying to

533
00:26:23,480 --> 00:26:26,040
Speaker 13: get into. But I think the big picture here is

534
00:26:26,359 --> 00:26:30,920
Speaker 13: they want to address physical AI client PCs and really

535
00:26:31,440 --> 00:26:35,800
Speaker 13: create an ecosystem where they can work across the board, work.

536
00:26:35,560 --> 00:26:37,840
Speaker 3: Across the board, and people then question about the role

537
00:26:37,880 --> 00:26:40,440
Speaker 3: of their own work. Is interesting that well, once again,

538
00:26:40,480 --> 00:26:43,600
Speaker 3: just in one, I'm really trying to outline his view

539
00:26:43,720 --> 00:26:44,879
Speaker 3: that it went would use jobs.

540
00:26:44,920 --> 00:26:45,560
Speaker 1: Just take a listen.

541
00:26:46,400 --> 00:26:52,240
Speaker 14: People talk about AI reducing jobs, complete nonsense. It's causing

542
00:26:52,359 --> 00:26:55,320
Speaker 14: more software engineers to be hired because the output is

543
00:26:55,359 --> 00:26:58,440
Speaker 14: so incredible. People want to hire more soft engineers. This

544
00:26:58,560 --> 00:27:01,200
Speaker 14: is going to show up in our economy somehow soon.

545
00:27:01,920 --> 00:27:04,959
Speaker 14: And so the first thing is useful AI has arrived.

546
00:27:05,640 --> 00:27:09,320
Speaker 3: Mandy, do you align yourself that, yes, more engineers will

547
00:27:09,320 --> 00:27:11,159
Speaker 3: be there, so more software as you So this is

548
00:27:11,280 --> 00:27:13,240
Speaker 3: positive for the software names. So have they just been

549
00:27:13,280 --> 00:27:16,160
Speaker 3: so beaten up ahead of this that people are willing

550
00:27:16,200 --> 00:27:18,400
Speaker 3: to dig their toe? And if Jensen says too, I.

551
00:27:18,320 --> 00:27:22,359
Speaker 13: Mean what we saw this earning season with data dogs, Snowflake,

552
00:27:22,520 --> 00:27:25,720
Speaker 13: Mango dB is you know, it's hard to think of

553
00:27:25,840 --> 00:27:30,480
Speaker 13: a thesis where software gets completely disintermediated and everything is

554
00:27:30,480 --> 00:27:34,360
Speaker 13: built from scratch in terms of agent TAKEAI coming up

555
00:27:34,440 --> 00:27:36,800
Speaker 13: and really doing things. You know, in terms of the

556
00:27:36,840 --> 00:27:40,920
Speaker 13: plumbing work and everything. No, this will be built on

557
00:27:41,040 --> 00:27:44,840
Speaker 13: top of existing software. Yes, the UI will change, it

558
00:27:44,880 --> 00:27:48,600
Speaker 13: will be a lot more conversational natural language, but you know,

559
00:27:48,640 --> 00:27:51,240
Speaker 13: in terms of system of record and you know, things

560
00:27:51,240 --> 00:27:54,719
Speaker 13: that are that have been built over the years, no

561
00:27:54,760 --> 00:27:57,879
Speaker 13: one is talking about that anymore. And that's where you know,

562
00:27:57,920 --> 00:28:00,680
Speaker 13: some of these companies have shown that in their numbers.

563
00:28:00,680 --> 00:28:04,080
Speaker 13: With ARR growth accelerating, and if Jensen is saying you

564
00:28:04,119 --> 00:28:07,159
Speaker 13: would require a lot more software, folks, then probably you

565
00:28:07,160 --> 00:28:10,360
Speaker 13: know it is the right sort of thesis at least

566
00:28:10,440 --> 00:28:13,640
Speaker 13: for now that it will be positive and not as

567
00:28:13,760 --> 00:28:16,600
Speaker 13: disruptive that everyone thought, you know, a few months back.

568
00:28:16,680 --> 00:28:18,120
Speaker 13: When it comes to the software.

569
00:28:17,800 --> 00:28:21,200
Speaker 2: Stocks, Mandy Vinvidio's pitch, if you just boil it down,

570
00:28:21,320 --> 00:28:23,920
Speaker 2: is that if you're a experienced software engineer, you become

571
00:28:23,920 --> 00:28:26,880
Speaker 2: a manager of a team of AI agents. And then

572
00:28:26,960 --> 00:28:30,440
Speaker 2: last night, his further pitch is that those AI agents,

573
00:28:30,480 --> 00:28:33,640
Speaker 2: the billions and billions of them, are users of software.

574
00:28:33,880 --> 00:28:38,280
Speaker 2: The agents become the customers of the software company the

575
00:28:38,360 --> 00:28:40,480
Speaker 2: bi take on that, your take on that.

576
00:28:40,720 --> 00:28:43,240
Speaker 13: I mean there will be a multiplier effect. So when

577
00:28:43,280 --> 00:28:46,840
Speaker 13: you think about, you know, how any technical knowledge worker

578
00:28:47,320 --> 00:28:50,560
Speaker 13: would do their jobs going forward, they will be using

579
00:28:50,600 --> 00:28:53,080
Speaker 13: a lot more of these agents and there will be

580
00:28:53,160 --> 00:28:55,120
Speaker 13: you know, one is two ten or one is two

581
00:28:55,200 --> 00:28:59,000
Speaker 13: hundred in terms of the multiplier effect. So from that perspective,

582
00:28:59,120 --> 00:29:02,520
Speaker 13: you could see a lot more consumption of the software.

583
00:29:02,880 --> 00:29:05,840
Speaker 13: Now what that means in terms of end to end automation,

584
00:29:06,400 --> 00:29:08,800
Speaker 13: that's the part that you know, we still need to

585
00:29:08,840 --> 00:29:11,720
Speaker 13: figure out how it's going to impact the overall employment

586
00:29:11,760 --> 00:29:15,880
Speaker 13: picture because look, the college graduates that are passing out,

587
00:29:16,120 --> 00:29:18,480
Speaker 13: they were trained in a certain way. They moved up,

588
00:29:18,760 --> 00:29:20,360
Speaker 13: you know, in a certain way in terms of their

589
00:29:20,400 --> 00:29:24,360
Speaker 13: skill set. That I think notion in terms of how

590
00:29:24,400 --> 00:29:27,720
Speaker 13: the evolution took place has changed and it will change more.

591
00:29:27,760 --> 00:29:32,880
Speaker 3: With agenty GAI BlueBag Intelligence is Mande saying fascinating, thanks

592
00:29:32,920 --> 00:29:35,880
Speaker 3: for joining us. No Kwang's comments on the job market

593
00:29:36,000 --> 00:29:39,920
Speaker 3: that actually echoed by Nvidia's partner at Media Tech, the

594
00:29:40,000 --> 00:29:42,840
Speaker 3: company which is helping build that new ut X spark

595
00:29:42,920 --> 00:29:45,959
Speaker 3: super chip. Well, it expects it's AI data centership revenue

596
00:29:46,000 --> 00:29:49,240
Speaker 3: to multiply next year, impacting its own headcount. Now Vitz,

597
00:29:49,240 --> 00:29:51,560
Speaker 3: who is the company's corporate senior vice president, spoke to

598
00:29:51,560 --> 00:29:53,960
Speaker 3: Bloomag Stephen Engel over at comfortext.

599
00:29:54,080 --> 00:29:58,360
Speaker 10: So, as Jensen mentioned, AI is not going to kill jobs,

600
00:29:58,400 --> 00:30:00,880
Speaker 10: it's actually going to create jobs. And I think one

601
00:30:00,920 --> 00:30:03,800
Speaker 10: of the things we're seeing is our business is taking

602
00:30:03,840 --> 00:30:08,479
Speaker 10: off on a number of fronts and a number of

603
00:30:08,520 --> 00:30:13,640
Speaker 10: new businesses like automotive, IoT, data center, and wearables. Our

604
00:30:13,680 --> 00:30:18,680
Speaker 10: existing businesses are also increasing in future momentum because of

605
00:30:19,280 --> 00:30:23,120
Speaker 10: the introduction of more AI capabilities to change the user experience.

606
00:30:23,560 --> 00:30:27,120
Speaker 10: So actually we're in a situation where we're not reducing

607
00:30:27,160 --> 00:30:29,680
Speaker 10: the number of engineers, but actually expanding and hiring the

608
00:30:29,760 --> 00:30:33,120
Speaker 10: number of engineers, and we're chasing a lot more opportunity.

609
00:30:33,320 --> 00:30:35,640
Speaker 10: So I see this voting very well for us.

610
00:30:35,800 --> 00:30:37,920
Speaker 11: Do you agree with what Jensen said as well? Today

611
00:30:37,960 --> 00:30:41,720
Speaker 11: he says AI and what they're doing is a GDP generator.

612
00:30:41,960 --> 00:30:43,880
Speaker 11: I think the latest numbers that did come out of

613
00:30:44,120 --> 00:30:47,719
Speaker 11: as the forecast for Taiwan is almost ten percent growth

614
00:30:47,720 --> 00:30:50,200
Speaker 11: this year and GDP on top of seven.

615
00:30:50,000 --> 00:30:53,640
Speaker 10: Percent last year, and now we're going to see potentially

616
00:30:53,680 --> 00:30:58,080
Speaker 10: one tillion dollars in value probably this year or next year,

617
00:30:58,760 --> 00:31:02,000
Speaker 10: and I would say, with annoying the exact data, probably

618
00:31:02,080 --> 00:31:08,720
Speaker 10: Taiwan is experiencing its heyday and unprecedented growth here in technology,

619
00:31:08,760 --> 00:31:11,600
Speaker 10: and it's probably the center of the high tech world

620
00:31:11,640 --> 00:31:14,680
Speaker 10: here now because of the AI revolutions. Most of the

621
00:31:14,720 --> 00:31:19,480
Speaker 10: customers who are working the gate today are tier one hyperscalers,

622
00:31:19,560 --> 00:31:22,320
Speaker 10: and so I feel very good about the fundamentals of

623
00:31:22,360 --> 00:31:25,120
Speaker 10: the business we're pursuing, and I think we're pretty said

624
00:31:25,160 --> 00:31:27,160
Speaker 10: at least through the twenty thirty horizon.

625
00:31:29,000 --> 00:31:32,080
Speaker 2: Those media techs Vince who's thinking with Bloomberg Stephen Angel

626
00:31:32,160 --> 00:31:35,280
Speaker 2: at Computechs coming up. New York Tech Week kicks off today.

627
00:31:35,640 --> 00:31:39,680
Speaker 2: Tech NYCCO Julie Samuels joins us to discuss what happens

628
00:31:39,720 --> 00:31:40,520
Speaker 2: and what's to expect.

629
00:31:40,640 --> 00:31:41,840
Speaker 4: Cary, I'm actually.

630
00:31:41,600 --> 00:31:44,560
Speaker 3: Looking at a New York based company that is running

631
00:31:44,600 --> 00:31:47,840
Speaker 3: pretty hard today, IBM up almost ten percent. But the

632
00:31:47,880 --> 00:31:50,680
Speaker 3: weird thing, ed is it's all based on a nearly

633
00:31:50,800 --> 00:31:54,760
Speaker 3: six month old video it seems O'donald Trump, President Trump

634
00:31:54,800 --> 00:31:58,400
Speaker 3: phraising ibmco that sent the stock surging, and over the

635
00:31:58,400 --> 00:32:01,840
Speaker 3: weekend we saw many accounts, dozens of the one x

636
00:32:01,880 --> 00:32:05,800
Speaker 3: recirculating that video and Plleymarket Money actually wracked up some

637
00:32:05,960 --> 00:32:09,920
Speaker 3: hundred thousand views on it. Why now, Why the win now?

638
00:32:09,960 --> 00:32:12,360
Speaker 3: It seems to be the speculative mania some are talking

639
00:32:12,400 --> 00:32:21,280
Speaker 3: of in our analysis of Blue Beg Tech New York

640
00:32:21,440 --> 00:32:26,120
Speaker 3: Technique officially kicks off today with a record fifteen hundred events,

641
00:32:26,120 --> 00:32:30,000
Speaker 3: gathering thousands of founders, of venture capitalists, innovators across the

642
00:32:30,040 --> 00:32:32,440
Speaker 3: city joining us now to discuss the city is really

643
00:32:32,520 --> 00:32:35,360
Speaker 3: rising status as a global tech hub. Julie Samuel's president

644
00:32:35,360 --> 00:32:38,560
Speaker 3: and CEO of Tech NYC. So, Judy, is it a

645
00:32:38,640 --> 00:32:41,080
Speaker 3: rising status? What data can we look at to feel

646
00:32:41,120 --> 00:32:44,360
Speaker 3: that AI am all the tailwinds are benefiting this city.

647
00:32:44,600 --> 00:32:47,520
Speaker 15: Well, first of all, it is absolutely rising. It's booming.

648
00:32:47,520 --> 00:32:49,720
Speaker 15: It's booming of course in a lot of tech hubs

649
00:32:49,800 --> 00:32:52,440
Speaker 15: right now, but New York is absolutely feeling the benefit

650
00:32:52,560 --> 00:32:56,440
Speaker 15: We've got. We're hiring at twice the rate of San Francisco,

651
00:32:56,560 --> 00:32:59,160
Speaker 15: four times the rate of Boston in our tech sector

652
00:32:59,200 --> 00:33:03,560
Speaker 15: here last count we had over nine thousand startups. We're

653
00:33:03,640 --> 00:33:05,280
Speaker 15: raising venture year over year.

654
00:33:05,320 --> 00:33:06,120
Speaker 4: It's just I think.

655
00:33:05,960 --> 00:33:10,360
Speaker 15: It's doubled, like forty fifty percent. And that's, of course

656
00:33:10,360 --> 00:33:12,960
Speaker 15: against the backdrop of this entire AI boom. That is

657
00:33:13,040 --> 00:33:15,600
Speaker 15: just putting so much oxygen into the tech sector.

658
00:33:15,720 --> 00:33:18,360
Speaker 3: And it's not why New gets that oxygen is because

659
00:33:19,160 --> 00:33:21,800
Speaker 3: if it is financial industries, they're going to be disrupted.

660
00:33:21,840 --> 00:33:23,320
Speaker 3: What do you want to be without industry is if

661
00:33:23,360 --> 00:33:24,520
Speaker 3: it is going to be health how you want to

662
00:33:24,560 --> 00:33:25,320
Speaker 3: be the industry is?

663
00:33:25,720 --> 00:33:27,840
Speaker 15: I think that's a huge piece. There's a couple things happening.

664
00:33:27,920 --> 00:33:30,120
Speaker 15: Number one, of course, is that once the companies are

665
00:33:30,120 --> 00:33:33,240
Speaker 15: building within sectors, they have to be here. Like you

666
00:33:33,400 --> 00:33:35,959
Speaker 15: just said, you need the expertise, you need the smart capital.

667
00:33:36,680 --> 00:33:39,800
Speaker 15: You know, you need not just disrupting those industries. But

668
00:33:40,160 --> 00:33:43,320
Speaker 15: those industries are the clients, they're the customers. They're also

669
00:33:43,320 --> 00:33:45,000
Speaker 15: going to be your mentors. You know, that's how you

670
00:33:45,000 --> 00:33:47,800
Speaker 15: build network. But what we also see at the higher level,

671
00:33:47,920 --> 00:33:49,480
Speaker 15: and we're at this point that we saw in the

672
00:33:49,520 --> 00:33:53,200
Speaker 15: early twenty early two thousands to early twenty tens, when

673
00:33:53,200 --> 00:33:55,480
Speaker 15: the big tech companies and the big platforms build their

674
00:33:55,520 --> 00:33:58,960
Speaker 15: technology on the West Coast, they often come here when

675
00:33:58,960 --> 00:34:01,440
Speaker 15: it's time to figure out how do you monetize it

676
00:34:01,480 --> 00:34:03,240
Speaker 15: and who's going to buy it? How are they going

677
00:34:03,280 --> 00:34:05,720
Speaker 15: to use it? Like those are New York questions to

678
00:34:05,760 --> 00:34:08,600
Speaker 15: get answered, so I feel very great about where New

679
00:34:08,680 --> 00:34:11,360
Speaker 15: York sits right now in the ecosystem.

680
00:34:11,880 --> 00:34:13,360
Speaker 2: Jud I was going to say that, you know, a

681
00:34:13,480 --> 00:34:15,920
Speaker 2: year ago, the headline probably was that, you know, if

682
00:34:15,920 --> 00:34:19,399
Speaker 2: Silicon Valley and SF and the AI era where tech

683
00:34:19,400 --> 00:34:21,800
Speaker 2: companies are born, they go to New York to mature.

684
00:34:22,239 --> 00:34:24,800
Speaker 2: What in the last sort of twelve months of data

685
00:34:24,880 --> 00:34:27,640
Speaker 2: would either reinforce that for you or do you think

686
00:34:27,840 --> 00:34:29,919
Speaker 2: it isn't quite that story yet?

687
00:34:30,080 --> 00:34:31,680
Speaker 15: No, I think that's very true. I think you've got

688
00:34:31,680 --> 00:34:34,600
Speaker 15: two things happening in parallel that underscore that trend. First

689
00:34:34,640 --> 00:34:37,880
Speaker 15: of all, the largest frontier labs are hiring and growing

690
00:34:37,960 --> 00:34:42,240
Speaker 15: in New York, like crazy open AI, anthropic, huge new,

691
00:34:42,680 --> 00:34:45,400
Speaker 15: huge new real estate deals. They're hiring here.

692
00:34:46,239 --> 00:34:46,839
Speaker 4: So you see that.

693
00:34:46,960 --> 00:34:50,080
Speaker 15: And then you also just see the startups just again,

694
00:34:50,200 --> 00:34:53,040
Speaker 15: they're coming here as well. But it's a slightly different

695
00:34:53,080 --> 00:34:56,040
Speaker 15: flavor of startups that you get in San Francisco. And

696
00:34:56,080 --> 00:34:57,960
Speaker 15: I think that's okay. You know, I think that's good

697
00:34:57,960 --> 00:35:00,279
Speaker 15: for the country. In San Francisco you get a lot

698
00:35:00,280 --> 00:35:04,399
Speaker 15: of really really hard tech, and here in New York, again,

699
00:35:04,440 --> 00:35:07,759
Speaker 15: it's slightly more integrated into existing sectors. I feel very

700
00:35:07,800 --> 00:35:10,960
Speaker 15: bullish about that. I think that's long term, incredibly healthy

701
00:35:10,960 --> 00:35:13,920
Speaker 15: place for our city and our state's economy to be.

702
00:35:14,360 --> 00:35:18,040
Speaker 15: We actually see a lot of open source AI happening

703
00:35:18,080 --> 00:35:20,160
Speaker 15: here in New York, which is interesting. We're seeing a

704
00:35:20,160 --> 00:35:25,000
Speaker 15: lot of infrastructure happening here, also interesting. You know, people

705
00:35:25,040 --> 00:35:27,719
Speaker 15: are really excited right now, and people want to be

706
00:35:27,880 --> 00:35:30,520
Speaker 15: in New York City. I guess that is the underlying.

707
00:35:30,120 --> 00:35:34,000
Speaker 2: Well, Julie, let's finish on the experience of the highly

708
00:35:34,040 --> 00:35:38,839
Speaker 2: paid tech employee, software engineer, otherwise, who has options right.

709
00:35:38,880 --> 00:35:41,640
Speaker 2: Part of the story in SF has been the city

710
00:35:41,680 --> 00:35:44,320
Speaker 2: being a place where people want to spend their money

711
00:35:44,880 --> 00:35:48,320
Speaker 2: and live right alongside locality to their employer.

712
00:35:48,680 --> 00:35:50,920
Speaker 4: What's the New York City pitch equivalent?

713
00:35:51,280 --> 00:35:54,080
Speaker 15: Well, New York City does urbanism like no other city

714
00:35:54,120 --> 00:35:56,120
Speaker 15: in the world. Surely in the United States, but I

715
00:35:56,160 --> 00:35:58,320
Speaker 15: would argue the entire world. And I think there's so

716
00:35:58,400 --> 00:36:00,960
Speaker 15: much about the tech sector culture, so many people building

717
00:36:01,040 --> 00:36:03,279
Speaker 15: in tech want to be here. They want to be

718
00:36:03,360 --> 00:36:06,680
Speaker 15: or there's functioning public transit where the density of this

719
00:36:06,800 --> 00:36:11,680
Speaker 15: city lends itself to the kind of creativity and excitement

720
00:36:11,960 --> 00:36:14,560
Speaker 15: and diversity that you really can only get in a

721
00:36:14,600 --> 00:36:16,560
Speaker 15: city as dense as New York and you see that

722
00:36:16,680 --> 00:36:19,120
Speaker 15: like this is as Caroline said, this is the first

723
00:36:19,200 --> 00:36:21,640
Speaker 15: day of tech Week. Our tech week is much much

724
00:36:21,680 --> 00:36:24,560
Speaker 15: bigger than San Francisco's Tech Week. And that's because here

725
00:36:24,600 --> 00:36:26,239
Speaker 15: you can bounce every week.

726
00:36:26,480 --> 00:36:28,880
Speaker 4: Tech Week, I think is the point.

727
00:36:29,000 --> 00:36:31,319
Speaker 15: Yaps perhaps, But you know, I would one quick little

728
00:36:31,360 --> 00:36:33,160
Speaker 15: anecdote that might be a little too cute I have,

729
00:36:33,320 --> 00:36:35,600
Speaker 15: but I think it's very true. When you're in San Francisco,

730
00:36:36,160 --> 00:36:40,279
Speaker 15: you meet people who work in tech, tech people who

731
00:36:40,360 --> 00:36:42,719
Speaker 15: live in San Francisco. When you're in New York, you

732
00:36:42,760 --> 00:36:45,000
Speaker 15: meet New Yorkers who work in tech, and that just

733
00:36:45,040 --> 00:36:47,160
Speaker 15: it's a different it's a different dynamic, and I think

734
00:36:47,200 --> 00:36:49,440
Speaker 15: for our country you need both. I feel really good

735
00:36:49,480 --> 00:36:52,160
Speaker 15: about that. I think New York just is so it

736
00:36:52,200 --> 00:36:54,799
Speaker 15: feels so optimistic right now and there's just so much

737
00:36:54,880 --> 00:36:58,600
Speaker 15: building going on in the tech sector that I feel

738
00:36:58,600 --> 00:37:00,640
Speaker 15: like it's it's great. It's a great time to be

739
00:37:00,640 --> 00:37:01,480
Speaker 15: building in tech.

740
00:37:01,280 --> 00:37:01,799
Speaker 13: In New York.

741
00:37:02,560 --> 00:37:05,640
Speaker 2: Julie Samuel's President CEO of techmyc greats. Have you back

742
00:37:05,680 --> 00:37:07,720
Speaker 2: on the process. I really appreciate it. Thank you, Carol.

743
00:37:08,200 --> 00:37:09,440
Speaker 2: A lot more news out there today.

744
00:37:09,920 --> 00:37:11,920
Speaker 3: It is easy versus West clas you've got to love it,

745
00:37:11,960 --> 00:37:15,000
Speaker 3: but this time it's talking tech and first Up Wise

746
00:37:15,080 --> 00:37:18,000
Speaker 3: shares actually tumbling over in London today, the fintech giant

747
00:37:18,040 --> 00:37:21,440
Speaker 3: confirmed in his answering queries and Belgian prosecutors investigating an

748
00:37:21,440 --> 00:37:24,360
Speaker 3: alleged five hundred and eighteen million dollar money laundering network

749
00:37:24,600 --> 00:37:27,760
Speaker 3: linked to forward and drug trafficking. Why says the requests

750
00:37:27,800 --> 00:37:32,000
Speaker 3: are a routine part of highly regulated operations. Plus midge

751
00:37:32,000 --> 00:37:35,959
Speaker 3: ones quarterly losses. They're shrinking Espajian's regulatory warnings finally cool

752
00:37:36,080 --> 00:37:39,000
Speaker 3: China's brutal e commerce price war, but the food delivery

753
00:37:39,000 --> 00:37:41,640
Speaker 3: platform posted a narrower than expected nine hundred and sixty

754
00:37:41,680 --> 00:37:44,440
Speaker 3: one million dollar operating loss, but that the company remains

755
00:37:44,480 --> 00:37:46,399
Speaker 3: locked in that costly battle with Ali Baba and JD

756
00:37:46,480 --> 00:37:49,840
Speaker 3: dot Com and Uber. Well it's driving deeper into the

757
00:37:49,840 --> 00:37:52,120
Speaker 3: Middle East. The company is paying one hundred million dollars

758
00:37:52,160 --> 00:37:55,359
Speaker 3: in cash for saking Kareem, a regional souper app from

759
00:37:55,360 --> 00:37:58,120
Speaker 3: the Gulf that offers food and grocery delivery and payments.

760
00:37:58,280 --> 00:38:01,760
Speaker 3: That the deal accelerates ubers rapidly common expansion bulits across Europe,

761
00:38:01,760 --> 00:38:03,040
Speaker 3: Middle East and Africa.

762
00:38:03,320 --> 00:38:05,640
Speaker 2: Ed Okay coming out We're going to get back to

763
00:38:05,680 --> 00:38:08,640
Speaker 2: the SpaceX IPO and what to expect that's next.

764
00:38:08,680 --> 00:38:09,600
Speaker 4: The cis Blombog Tech.

765
00:38:18,360 --> 00:38:21,400
Speaker 3: Let's get back to the SpaceX IPO and get down

766
00:38:21,440 --> 00:38:24,480
Speaker 3: into the nittigritty of the numbers. New bloom Mug intelligence

767
00:38:24,520 --> 00:38:27,719
Speaker 3: analysis reveals SpaceX is some of the parts valuation could

768
00:38:27,800 --> 00:38:30,200
Speaker 3: hit nearly two trillion dollars. This is the company marches

769
00:38:30,239 --> 00:38:33,120
Speaker 3: towards its historic Wall Street debut, joining us now to

770
00:38:33,120 --> 00:38:37,200
Speaker 3: break down the framework. Blooemug Intelligences is George Ferguson. George's

771
00:38:37,280 --> 00:38:39,759
Speaker 3: interesting that Ed has been part of that reporting team

772
00:38:39,840 --> 00:38:42,120
Speaker 3: showing that well, maybe they're offering at one point eight

773
00:38:42,120 --> 00:38:44,799
Speaker 3: trillion dollar valuation, role perhaps two trillion, but you think

774
00:38:44,840 --> 00:38:46,920
Speaker 3: really it should add up to two trillion.

775
00:38:48,080 --> 00:38:50,320
Speaker 16: Oh well, so I think you know, what we wrote

776
00:38:50,480 --> 00:38:54,080
Speaker 16: was that we could see the market comparables that could.

777
00:38:53,840 --> 00:38:57,400
Speaker 17: Get it close to two trillion. And so today, you know, today,

778
00:38:57,400 --> 00:39:00,000
Speaker 17: I think we were even seeing some of the space

779
00:39:00,080 --> 00:39:05,800
Speaker 17: stocks come down as SpaceX lowered their I don't know their.

780
00:39:05,640 --> 00:39:08,600
Speaker 16: Targets, their guidance, whatever you want to call that, late

781
00:39:08,680 --> 00:39:11,319
Speaker 16: last week. So I mean we see comparables that could

782
00:39:11,360 --> 00:39:13,560
Speaker 16: get it there. I didn't say that was necessarily the

783
00:39:13,640 --> 00:39:17,360
Speaker 16: right price. They seem pretty rich, but there's comparables, a

784
00:39:17,360 --> 00:39:20,400
Speaker 16: lot of it built around the space launch business, which

785
00:39:20,920 --> 00:39:23,400
Speaker 16: we saw a sort of eighty to ninety times revenue.

786
00:39:24,080 --> 00:39:26,800
Speaker 2: You know, I was really surprised by the launch figure

787
00:39:26,840 --> 00:39:29,800
Speaker 2: because Elon Musk himself said repeatedly a number of times

788
00:39:29,840 --> 00:39:33,279
Speaker 2: in the last decade the launch business tops out right,

789
00:39:33,320 --> 00:39:35,200
Speaker 2: there is a limit to how much money you can

790
00:39:35,239 --> 00:39:38,360
Speaker 2: make putting payload into orbit. So why the TAM number

791
00:39:38,760 --> 00:39:42,000
Speaker 2: is literally everything? But that just give me the math

792
00:39:42,040 --> 00:39:43,240
Speaker 2: on how you arrived at the number.

793
00:39:44,320 --> 00:39:47,680
Speaker 16: Yes, I mean again, we looked really up market comparables,

794
00:39:47,719 --> 00:39:52,600
Speaker 16: and so from a space launch perspective, rocket labs in

795
00:39:52,640 --> 00:39:55,920
Speaker 16: the marketplace, and it's about an eighty to ninety times

796
00:39:55,960 --> 00:40:00,719
Speaker 16: revenue number on Rocket Labs valuation. And so we think

797
00:40:00,760 --> 00:40:04,840
Speaker 16: that SpaceX, if you plussed up the revenue inside the

798
00:40:04,920 --> 00:40:08,640
Speaker 16: launch business for the launch they're doing internally because they

799
00:40:08,640 --> 00:40:11,360
Speaker 16: don't book that in the revenue line, it's about eleven

800
00:40:11,400 --> 00:40:14,000
Speaker 16: billion dollars of revenue. And we plug sort of an

801
00:40:14,000 --> 00:40:16,880
Speaker 16: eighty to ninety multiple on that and got about a

802
00:40:16,960 --> 00:40:19,320
Speaker 16: trillion dollars in value out.

803
00:40:19,160 --> 00:40:22,880
Speaker 17: Of launch potentially. And then you know the other businesses

804
00:40:22,920 --> 00:40:23,680
Speaker 17: like Starlink.

805
00:40:24,080 --> 00:40:27,400
Speaker 16: I have colleagues here at Bloomberg Intelligence Intelligence that are

806
00:40:27,480 --> 00:40:30,520
Speaker 16: much smarter at that and did that valuation. But Starlink

807
00:40:30,640 --> 00:40:34,200
Speaker 16: kind of came in thirty to forty times revenue based

808
00:40:34,239 --> 00:40:35,520
Speaker 16: on some of their comparables.

809
00:40:35,719 --> 00:40:37,960
Speaker 17: It's not a lot of the value. It's another sort

810
00:40:37,960 --> 00:40:41,520
Speaker 17: of six hundred billion. And then the AI business came

811
00:40:41,560 --> 00:40:44,640
Speaker 17: in around four hundred billion, and that's man Deep Singh,

812
00:40:44,640 --> 00:40:46,560
Speaker 17: and that's a there's a lot of voodoo. I think

813
00:40:46,600 --> 00:40:49,560
Speaker 17: inside valuing AI companies. There's a lot of loss in there.

814
00:40:49,600 --> 00:40:51,120
Speaker 17: I can't totally figure out how.

815
00:40:50,960 --> 00:40:52,480
Speaker 18: He does that, do George.

816
00:40:52,640 --> 00:40:55,480
Speaker 3: And what's interesting is who's holding say Rocket Labs, and

817
00:40:55,520 --> 00:40:58,080
Speaker 3: how memified they've become, how much their valuations are based

818
00:40:58,080 --> 00:41:00,319
Speaker 3: on retail. The latest is out of our store that

819
00:41:00,600 --> 00:41:02,720
Speaker 3: SpaceX is going to reserve five percent of the shares

820
00:41:03,080 --> 00:41:05,840
Speaker 3: for certain employees and friends and they're not going to

821
00:41:05,840 --> 00:41:07,080
Speaker 3: perhaps be held to a lockup.

822
00:41:07,080 --> 00:41:11,120
Speaker 16: How much of an issue is that, sorry, I cut

823
00:41:11,200 --> 00:41:12,799
Speaker 16: the very tail at the lock up.

824
00:41:12,840 --> 00:41:13,880
Speaker 17: How much of an issue.

825
00:41:13,640 --> 00:41:15,640
Speaker 3: Is well the fact that they're not going to be

826
00:41:15,920 --> 00:41:18,840
Speaker 3: held or bound to a lock up in the same way.

827
00:41:19,000 --> 00:41:22,080
Speaker 17: Briefly, I mean, look, I think there's in any IPO,

828
00:41:22,120 --> 00:41:24,960
Speaker 17: there's always this challenge that as you come out of

829
00:41:25,000 --> 00:41:27,680
Speaker 17: the hype of the i PO, you'll see prices sort

830
00:41:27,719 --> 00:41:31,239
Speaker 17: of fall on it. And yeah, you know, I think

831
00:41:31,440 --> 00:41:34,520
Speaker 17: though there's going to be a lot of hype around SpaceX,

832
00:41:34,560 --> 00:41:36,640
Speaker 17: it doesn't mean I think the shares will hold the

833
00:41:36,760 --> 00:41:40,200
Speaker 17: value that they're coming out of the IPO at. But

834
00:41:40,320 --> 00:41:44,000
Speaker 17: I also think again it's a big enough player. It's

835
00:41:44,160 --> 00:41:49,680
Speaker 17: and it's in AI launch and in connectivity that I

836
00:41:49,719 --> 00:41:53,840
Speaker 17: think you should see maybe maybe some of that hangover

837
00:41:53,960 --> 00:41:56,640
Speaker 17: afterwards being less of an issue, but for sure I

838
00:41:56,640 --> 00:41:58,480
Speaker 17: think you'll you'll run into some challenges.

839
00:41:59,400 --> 00:42:01,680
Speaker 2: They beg Inteins, George Fergs and part of the team

840
00:42:02,000 --> 00:42:02,920
Speaker 2: crunching the numbers.

841
00:42:03,160 --> 00:42:03,919
Speaker 4: Thank you very much.

842
00:42:03,960 --> 00:42:07,560
Speaker 2: Amazon wants your next Prime order to include a banana.

843
00:42:07,920 --> 00:42:11,000
Speaker 2: The company is betting that same day delivery and Whole

844
00:42:11,040 --> 00:42:15,320
Speaker 2: Foods can finally unlock online grocery shopping at scale. Bloomost

845
00:42:15,320 --> 00:42:18,359
Speaker 2: Matt Day joins us with the deep dive. There is

846
00:42:18,600 --> 00:42:22,400
Speaker 2: no visitor more frequent to my front door than Amazon Prime,

847
00:42:23,040 --> 00:42:25,760
Speaker 2: But to this point, a banana is yet to arrive.

848
00:42:26,480 --> 00:42:28,120
Speaker 4: Take us inside your story. What do you get in

849
00:42:28,120 --> 00:42:28,399
Speaker 4: that here.

850
00:42:29,520 --> 00:42:32,120
Speaker 18: So Amazon, I think, maybe uniquely for them and their

851
00:42:32,120 --> 00:42:34,040
Speaker 18: long history, is trying to get groceries to your doorstep.

852
00:42:34,080 --> 00:42:35,399
Speaker 4: They've just started asking, right.

853
00:42:35,440 --> 00:42:37,600
Speaker 18: So if you ed go online today and you place

854
00:42:37,680 --> 00:42:39,719
Speaker 18: the same day order, it's very likely you're going to

855
00:42:39,719 --> 00:42:41,799
Speaker 18: see a little pop up show up near the end, like, hey,

856
00:42:41,800 --> 00:42:43,560
Speaker 18: do you want to add some perishables to this? About

857
00:42:43,560 --> 00:42:46,000
Speaker 18: some lettus have about some blueberries, that kind of thing.

858
00:42:46,040 --> 00:42:48,800
Speaker 18: And so it's that sort of in your face prompting

859
00:42:48,840 --> 00:42:50,680
Speaker 18: combined with some listenatures to come the back end that

860
00:42:50,760 --> 00:42:53,000
Speaker 18: makes Amazon think they can play a bigger part and

861
00:42:53,040 --> 00:42:54,239
Speaker 18: grocery talk us.

862
00:42:54,160 --> 00:42:56,560
Speaker 3: About you've been walking around the store. I mean, your

863
00:42:56,560 --> 00:42:59,200
Speaker 3: whole story is wonderful. It starts with Jason Bruschell. I

864
00:42:59,200 --> 00:43:02,040
Speaker 3: think that's how I'm now emerging from a big walk

865
00:43:02,080 --> 00:43:05,120
Speaker 3: in refrigerator. But how impactful is it that they have

866
00:43:05,200 --> 00:43:07,359
Speaker 3: the logistics down. It has to be the one day

867
00:43:07,360 --> 00:43:08,560
Speaker 3: turnaround that makes it work.

868
00:43:09,480 --> 00:43:11,400
Speaker 18: So it's really gotten them closer to customers in a

869
00:43:11,440 --> 00:43:13,360
Speaker 18: way that they couldn't before. I mean when they started

870
00:43:13,360 --> 00:43:15,080
Speaker 18: Fresh a couple of decades ago, they were doing it

871
00:43:15,120 --> 00:43:17,520
Speaker 18: from the suburbs. You know, a lot of produce was spoiling,

872
00:43:17,560 --> 00:43:20,080
Speaker 18: and stuff wasn't getting the density they needed to make

873
00:43:20,440 --> 00:43:22,600
Speaker 18: the math work. You know, now suddenly, with Amazon coming

874
00:43:22,600 --> 00:43:25,200
Speaker 18: out of the pandemic, with warehouses real close to a

875
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Speaker 18: lot of metros, there's more ability to kind of sprinkle

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Speaker 18: these these sort of mini chillers in and make a

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Speaker 18: small grocery distribution center perhaps near you.

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Speaker 3: And this is where the Amazon Whole Foods thesis bakes

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Speaker 3: in Bloomberg's Matt Day. It's a great story. Go read

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Speaker 3: it on the terminal our online. But meanwhile, and I'll

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Speaker 3: say for this edition of Bloomberg Tech Ed, yeah.

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Speaker 4: Don't forget to check out the podcast.

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Speaker 2: You can find it on the Bloomberg Terminal and outline,

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Speaker 2: on Apples, Spotify, and iHeart It is the very early

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Speaker 2: start of what I'm sure is going to be a

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Speaker 2: pretty crazy week in the world of technology. A lot

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Speaker 2: to recap from New York City and from San Francisco.

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Speaker 4: This is Bloomberg Tech