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

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

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Speaker 1: and Eva Low in San Francisco.

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Speaker 2: This is Bloomberg Tech coming up in video. Plans to

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Speaker 2: invest one billion dollars over five years in a new

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Speaker 2: lab with Eli Lilly to speed up the use of

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Speaker 2: AI in the pharmaceutical industry.

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Speaker 3: Plus paramount sues Warner Brothers Discovery, and plans to nominate

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Speaker 3: directors to the board as it's increasingly hostile. Takeover bid

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Speaker 3: takes aim to upset Netflix.

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Speaker 2: An alphabet breaches four trillion dollars in market value. This

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Speaker 2: is CNBC reports Apple has picked Gemini to power Siri

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Speaker 2: this year, and.

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Speaker 3: Then those moves fall again, and really we've got anxiety

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Speaker 3: across the market's more broadly aired digging into the market

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Speaker 3: implications of what seems to be a dialing up.

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Speaker 4: In the administration's fight with a FED.

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Speaker 3: That is what the market tries to digest and why

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Speaker 3: we see a bit of a sell America theme at

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Speaker 3: the moment. Yes, they're small moves in terms of benchmarks

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Speaker 3: for stocks were down by a tenth of a percent

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Speaker 3: on anastat one hundred, but we're seeing bigger moves when

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Speaker 3: you're looking what's happening in the bomb market. In the dollar,

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Speaker 3: for example, we're seeing thirty year years once again, pushing

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Speaker 3: up two basis points, seeing the dollar on the downside.

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Speaker 3: All of this as we try and bake in the

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Speaker 3: macro implications. We look to CPI tomorrow, but you're looking

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Speaker 3: at what's underneath the hood right now.

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Speaker 2: Yeah, the big breaking news of the last hour is

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Speaker 2: that Apple has selected Google's Gemini model to power serri

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Speaker 2: this year. Reporting from CNBC citing an Apple's statement, and

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Speaker 2: you can see a big reaction in the stock the

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Speaker 2: moment the headlines hit, it has faded.

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Speaker 5: I'll take you back to.

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Speaker 2: November when Bloomberg's Mark German reported that a deal was

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Speaker 2: very close, that Apple would pay Google one billion dollars

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Speaker 2: per year and that this was just an interim until

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Speaker 2: Apple could get its own foundation models together, but or

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Speaker 2: continue to track that story. Another interesting trends action in

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Speaker 2: the market this morning Nvidia co investing one billion dollars

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Speaker 2: with Eli Lilly into a Silicon Valley based lab split

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Speaker 2: over five years. AI Pharmaceuticals for the details are interesting here,

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Speaker 2: Cara and we.

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Speaker 3: Want to get those details, and we can do exactly

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Speaker 3: that with Katie Greifeld, which is over at the JP

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Speaker 3: Morgan Healthcare Conference, indeed looking at Silicon Valley, getting ever

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Speaker 3: closer to the world of health.

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Speaker 4: Katie, what'd you make of this deal?

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Speaker 6: Yeah, this was a really interesting headline to wake up to,

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Speaker 6: especially when you remember that Eli Lilly partnered with Nvivia

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Speaker 6: back in October to develop an AI supercomputer. So the

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Speaker 6: details on this specific deals as you guys run through them.

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Speaker 6: One billion dollars OVERFI fears to build a facility in

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Speaker 6: Silicon Valley, basically a new lab. We don't have much

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Speaker 6: details when it comes to the terms beyond that, but

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Speaker 6: both companies have described this as a joint investment. And

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Speaker 6: I just actually spoke to the CFO of Eli Lilly,

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Speaker 6: Lucas Montarge, about what the ultimate vision is here when

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Speaker 6: it comes to AI and healthcare and this specific partnership,

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Speaker 6: and he said, this is really about drug discovery. It's

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Speaker 6: still early days, of course, they haven't built the facility yet,

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Speaker 6: but the hope is that this will help basically in

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Speaker 6: discovering drugs to really target and treat those really hard

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Speaker 6: to treat diseases. Think gene therapy for example. So again

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Speaker 6: early days, but some big ambitions.

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Speaker 2: Here Bloomber's Katie Greefelk just down the road at the

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Speaker 2: JP Morgan Healthcare conference all day long here in San Francisco,

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Speaker 2: less than two weeks into the new year, and in

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Speaker 2: Video is already dominating the news flow. I want to

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Speaker 2: get to Denny Fish, portfolio manager on the Global Technology

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Speaker 2: an innovation team at Janis Henderson and the Frank Reality visit.

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Speaker 2: In video is the top holding across a number of

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Speaker 2: funds that you manage. What do you make of that

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Speaker 2: relationship between in video and ELI.

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Speaker 7: Yeah, well it actually it makes a ton of sense

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Speaker 7: to me.

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Speaker 8: I mean, if we think about what's grabbed all the

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Speaker 8: attention over the last three years since the chat ChiPT moment,

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Speaker 8: it's effectively been the idea of the digital manifestation of AI.

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Speaker 8: But the reality is, if we think about the profoundness

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Speaker 8: of how this will impact the economy over time and

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Speaker 8: society more broadly, the physical manifestation of AI is as big,

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Speaker 8: if not a bigger opportunity. And if you think about

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Speaker 8: drug discovery and what that means for society, and then

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Speaker 8: just think about how far we've come with autonomous driving,

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Speaker 8: where we're going with robotics. They are all these different

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Speaker 8: verticals where it makes a lot of sense for Nvidia

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Speaker 8: to be leaning into. And frankly, in Vidia has got

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Speaker 8: some of the most the best talent in the world

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Speaker 8: and to see these partnerships with market leaders makes a

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Speaker 8: ton of sense.

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Speaker 7: And you know, I bring it back.

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Speaker 8: Sometimes you get this argument of circularity, but the reality is,

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Speaker 8: if you're Jensen and you have a firm belief at

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Speaker 8: just how big these market opportunities are, you should be

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Speaker 8: leaning and hard to this intment co investments into labs

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Speaker 8: like they're doing with Lily, or direct investments into companies

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Speaker 8: like they're potentially doing with Open AI.

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Speaker 2: We a let's one of miss driving. A lot of

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Speaker 2: people get it. I wrote about it extensively this weekend.

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Speaker 2: We're going to talk about it later in the program.

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Speaker 2: The go to market in the first instance is for

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Speaker 2: Mercedes that we use hardware and software. But with pharmaceuticals

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Speaker 2: drug discovery research, what is the go to market that

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Speaker 2: then video is going to pull off in the future.

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Speaker 7: Well to the so so far.

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Speaker 8: If we think about pharmaceuticals, where we've seen the early

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Speaker 8: benefits of AI have been in things like the clinical process,

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Speaker 8: you know, getting drugs that have already been discovered through

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Speaker 8: that process, internal efficiencies, marketing, things like that. The real,

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Speaker 8: you know, the big prize out there is drug discovery.

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Speaker 8: It's finding solutions to problems that we just couldn't get

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Speaker 8: there without AI or would have taken much much longer.

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Speaker 8: So the go to market is actually direct partnerships with

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Speaker 8: these companies to fast forward the discovery process.

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Speaker 4: Denny.

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Speaker 3: That speaks to the broadening out that we're starting to

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Speaker 3: see the market. We're also going to talk about that

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Speaker 3: the fact that we are seeing the S and P

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Speaker 3: five hundred actually doing a bit of a challenge in

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Speaker 3: terms of returns versus some of the mags seven names.

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Speaker 3: How much does it mean that we've already juiced the

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Speaker 3: valuation side of the equation for in video or is

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Speaker 3: AI infrastructure still going to be the key watchword for twenty.

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Speaker 8: Six Well, so far, all the data points would suggest

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Speaker 8: that the investments into AI infrastructure are going to continue

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Speaker 8: to be quite robust, and we would fully expect that,

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Speaker 8: and you know, and so far, you know, I just

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Speaker 8: looking back, you know, I mean Ed was talking about

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Speaker 8: alphabet earlier in the relationship with Apple. Just watching the

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Speaker 8: leap frogging effects you know with you know, open Ai

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Speaker 8: and Xai and Gemini, that's very healthy and as long

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Speaker 8: as we continue to see that. And then bringing back

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Speaker 8: to you know, in Vidia and VideA now is you

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Speaker 8: know they announced last week Ruben is fully in production

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Speaker 8: that is going to have materi aerial impacts as it

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Speaker 8: relates to inference. Productivity versus training and inference is the

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Speaker 8: next big handoff. And so if you're a believer that

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Speaker 8: we are now in this period where we're going to

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Speaker 8: start seeing increasing deployment of these applications, inference is going

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Speaker 8: to go through the roof and as a result, that's

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Speaker 8: going to continue to sustain the investment profile.

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Speaker 7: So we expected to be pretty healthy.

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Speaker 8: Now, I will say when you're talking about the broader

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Speaker 8: SMP five hundred, I mean one of the thesis that

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Speaker 8: we've had for the last three years as AI has

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Speaker 8: really started to take hold, is this is going to

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Speaker 8: be very impactful across all industries. And if you believe that,

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Speaker 8: and the companies that are able to get revenue lift

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Speaker 8: because of deployment of AI, but then at the same

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Speaker 8: time are able to drive cost efficiencies that should be

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Speaker 8: pretty good for multiples. So I'm not surprised to see

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Speaker 8: the market, you know, sort of anticipating of sorts, some

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Speaker 8: of the benefits we might get from AI.

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Speaker 3: The market's all allo got more discerning on who has

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Speaker 3: to pay for the AI of structure and what that

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Speaker 3: costs in terms of debt. Denny, I'm looking at again

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Speaker 3: yet another number, three trillion dollars, this time coming from

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Speaker 3: Moody saying how much the data center infrastructure build out

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Speaker 3: is going to cost up until twenty thirty. I mean,

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Speaker 3: McKinsey had already put seven trillion dollars on that number.

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Speaker 3: But is there a worry as to who has to

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Speaker 3: actually pay for it?

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Speaker 7: Denny, Well, of course there is.

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Speaker 8: You know, the good news is the majority of this

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Speaker 8: is being funded by the hyperscalers, and they generate the

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Speaker 8: free cash flows to actually be able to fulfill the

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Speaker 8: commitments that they've made. And they're all playing the twenty

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Speaker 8: year game. I mean, we've just seen that with you know,

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Speaker 8: the number of you know, power agreements they're being struck

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Speaker 8: between you know, Meta and Google, Microsoft and others, and

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Speaker 8: so we feel pretty good there now. Now clearly, you know,

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Speaker 8: the debt markets are going to have to remain healthy.

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Speaker 7: We're going to have to continue to see a.

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Speaker 8: Progress as it relates to you know, you know the

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Speaker 8: ambitions of these companies, but there's also something else too.

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Speaker 8: You know, when you look those numbers, there's also a

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Speaker 8: lot of double counting in it. You really have to

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Speaker 8: parse it back to get to the real meat of

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Speaker 8: what's necessary. And then you know, also there are natural

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Speaker 8: governors at just how fast this can get deployed. So

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Speaker 8: the deployment of the capital is going to be pretty measured.

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Speaker 8: And that's what makes this very different than say the

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Speaker 8: dot com era, where so much capital came into areas

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Speaker 8: like telecom infrastructure so fast and it was very easy

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Speaker 8: to get that stuff deployed.

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Speaker 7: This is hard.

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Speaker 8: You look at what's going on at Stargate and Abilene,

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Speaker 8: Texas and trying to put up ten gigawatts, the labor

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Speaker 8: that's needed, the power, just the expertise, and you know,

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Speaker 8: it's a heavy lift, and so that it'selfs a natural

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Speaker 8: governor on just how fast it can get deployed.

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Speaker 2: This is what we were trying to pass at CES

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Speaker 2: last week. The baseline assumption very quickly, Denny, is that

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Speaker 2: demand still outstrips in video and AMD's ability to supply.

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Speaker 2: How does that set us up for this year?

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Speaker 8: Well, that's good for infrastructure, you know, because you're in

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Speaker 8: a situation like that, and even a TSMC for example,

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Speaker 8: we're second topic.

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Speaker 7: Yeah, yeah, where you.

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Speaker 8: Know, you only have so much capacity and you can

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Speaker 8: only bring up incremental founderies so quickly, and so as

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Speaker 8: a result, that's really good from a pricing standpoint, particularly

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Speaker 8: when you're the only game in town, and for certain

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Speaker 8: aspects Nvidia is still the only game in town too.

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Speaker 3: Denny Fish Janis Henderson Investors. Great to have you on

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Speaker 3: all the games we are coming up. Paramount in ram

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Speaker 3: sub efforts to caught a plan merger between Warner Brothers

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Speaker 3: Discovery and Netflix.

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Speaker 4: The latest in the media merger saga. That's next. There's

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Speaker 4: a bloom Beg Tech Paramounts guidance.

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Speaker 3: Well, it says it plans to nominate directors to Warner

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Speaker 3: Brothers Discoveries board to vote against the approval of a

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Speaker 3: merger with Netflix, of course, and the company has filed

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Speaker 3: a suit to force Warner Brothers to disclose information about

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Speaker 3: the proposed Netflix Warner Brothers tie ups entertained a reporter

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Speaker 3: Pholix Jillette joins us on what is an ever more

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Speaker 3: hostile potential takeover?

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Speaker 4: So suing for more information? What do they want to

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Speaker 4: really demonstrate to invest us here?

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Speaker 9: I mean, at this point, you know, they're choosing to

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Speaker 9: raise the hostility versus raising their bit Yes, and I

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Speaker 9: think you know they've been campaigning shareholders join our tender offer.

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Speaker 9: You know, they've said for weeks now our offer was

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Speaker 9: better than the Netflix offer, accusing you know, the Warner

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Speaker 9: Brothers board of not being transparent during the proceedings. And

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Speaker 9: now they're making this move to essentially say, you know,

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Speaker 9: you have to provide the information to shareholders so they

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Speaker 9: can judge which is a better offer. And to do that,

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Speaker 9: you know, the big most contentious issue for months now

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Speaker 9: has been how do you value the cable assets that

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Speaker 9: Warner Brothers has planned to spin off? Prior to selling

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Speaker 9: the studios and the streaming business to Netflix.

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Speaker 2: The Netflix offered twenty seven dollars a share for the

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Speaker 2: studios and streaming spin off the cable networks paramount thirty

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Speaker 2: dollars a share all of it, but basically assigning a

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Speaker 2: value to the networks of zero dollars. This is a saga.

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Speaker 2: It will play out over time. It's a really difficult

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Speaker 2: question for you as editor, hear Felix, But like what

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Speaker 2: happens next? Is there a real chance that Paramount can

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Speaker 2: force a hostile takeover through this mechanism.

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Speaker 10: They're going to try?

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Speaker 7: You know?

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Speaker 9: I mean I think, like you know, the shareholders at

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Speaker 9: this point, a lot of them seem to be sitting

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Speaker 9: on the fence, you know, in terms of who they're

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Speaker 9: going to support. I think a lot of them are

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Speaker 9: hoping that Paramount would come in with a higher bid,

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Speaker 9: go over the thirty dollars to share. That hasn't happened yet,

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Speaker 9: you know, I think again, you know, David Ellison and

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Speaker 9: Paramount have been trying to advocate for this notion that

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Speaker 9: like the Netflix deal isn't even going to happen. They

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Speaker 9: have too many regulatory issues, you know, putting aside the

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Speaker 9: fact that if Paramount offer was except that they'd have

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Speaker 9: their own issues in Europe with the States. So yeah,

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Speaker 9: I mean, this continues to just get more and more hostile,

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Speaker 9: and this is you know, the next step is this

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Speaker 9: lawsuit and a proxy fight over the director's.

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Speaker 2: Bloomberg's Feenix to that, thank you very much. Another top

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Speaker 2: story deep seek founder liangwen Feng's hedge fund surge more

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Speaker 2: than fifty percent last year, making it the second best

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Speaker 2: performer among China quant funds.

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Speaker 5: The strong performance.

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Speaker 2: Helped boost the cash available for deep seek itself. Let's

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Speaker 2: get out to Bloomberg Tech executive editor Peter Elstrom. You know, two,

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Speaker 2: there's the Bloomberg story, right, which is about a quant

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Speaker 2: funds you know, amazing performance. But in the reporting it's

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Speaker 2: super clear the read through that people are making is

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Speaker 2: that that the returns on that the earnings from that

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Speaker 2: fund's performance can be taken by deep Seak's founder and

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Speaker 2: used to give deep seek a bigger budget for research

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Speaker 2: and deploying its models.

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Speaker 11: Right right, That's exactly true. Yeah, there, So there's a

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Speaker 11: finance story here, but there's also a tech story, and

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Speaker 11: the finance story is very impressive. The hedge fund high Fire,

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Speaker 11: that was really the place where deep seek was born,

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Speaker 11: had a great year. Returns were fifty seven percent on

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Speaker 11: average across their various funds. That is very good for

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Speaker 11: the founder early on one thing, as you mentioned, it's

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Speaker 11: good performance compared with the other quant funds out there,

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Speaker 11: but it also gives him a much bigger checkbook to

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Speaker 11: be able to go out there and make new investments

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Speaker 11: now that probably.

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Speaker 7: Would be in Deep Seek. Deep Seek has.

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Speaker 11: Gained a lot of momentum since a year ago when

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Speaker 11: they had their big breakthrough dropped the bombshell that they

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Speaker 11: had invented this LLM for a fraction of the cost

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Speaker 11: of Opening Eyes with very similar performance. So now you

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Speaker 11: could see the founder invest more money into Deep Seek,

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Speaker 11: perhaps chase after a little bit of the capital intensive

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Speaker 11: projects that we've seen in the US in particular, we

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Speaker 11: haven't seen those kind of investments in China so far,

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Speaker 11: or he could invest in other kinds of AI initiatives

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Speaker 11: that may not be directly the same as Deep Seek.

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Speaker 3: Because the whole claim about Deep Sek has it only

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Speaker 3: cost them six million dollars, And the theory in this

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Speaker 3: particular reporting is that maybe they're getting revenues of some

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Speaker 3: seven hundred million dollars That could go a long way

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Speaker 3: if you're thinking about the costs of doing the business here.

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Speaker 3: But talk to us about why they're able to outperform

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Speaker 3: so much. What is it they're betting on? How these

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Speaker 3: quant funds managing to be so superior.

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Speaker 11: Well, partly with Deep Seek early on. They were able

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Speaker 11: to do this because there were a lot of constraints

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Speaker 11: around the AI initiatives in particular, and we've written a

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Speaker 11: fair bit about High Flyer in the past. They have

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Speaker 11: these quantitative techniques to be able to outstrip their peers

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Speaker 11: within the China market. They were the number two fund

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Speaker 11: as you mentioned. Also, the China market has been on fire.

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Speaker 11: There have been a lot of investments in other kinds

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Speaker 11: of AI models. Deep Seak hasn't gone public yet, but

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Speaker 11: two of the other AI models within the country have

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Speaker 11: gone public, giving them a lot of momentum here. So

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Speaker 11: the market's been very strong. They've been able to take

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Speaker 11: advantage of that. But again, as you say, they now

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Speaker 11: have one hundred times as much money as they used

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Speaker 11: to start up Deep Seek in the early days. What

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Speaker 11: are they going to do with that money? How are

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Speaker 11: they going to invest it? They're not talking about this

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Speaker 11: publicly of course at this point, but we would expect

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Speaker 11: more things to come from High Flyer and Deep Seek

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Speaker 11: in the future.

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Speaker 3: I mean, just think many Max went public on Friday.

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Speaker 3: It was a ut one hundred percent. It's up fifteen

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Speaker 3: percent again today. Peter Elstrom on all things Asia. We

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Speaker 3: so appreciate it, thank you. Coming up a bit more

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Speaker 3: on Asia. Could Chinese evs actually dodge tariffs in the EU.

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Speaker 3: We're going to discuss the global electric vehicle wars the

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Speaker 3: Stephanie Valdez Street from Cox of Automotive that's next as.

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Speaker 4: A BlueBag tech.

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Speaker 3: The European Union is weighing minimum prices for evs exported

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Speaker 3: to the block from China that actually.

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Speaker 4: Would replace steep tariffs.

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Speaker 3: Is it a sign that we've got some easy trade

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Speaker 3: tensions even as the US presses Europe to.

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Speaker 4: Take it off line?

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Speaker 3: On Beijing here to discuss this is Stephanie Valdez Street,

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Speaker 3: director of Industry Insights for Cox Automotive. That was sort

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Speaker 3: of the latest news headline that we get that maybe

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Speaker 3: BYD will be able to sell into Europe and not

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Speaker 3: have to have huge tarifts that eats its margin. Are

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Speaker 3: we going to see them able to tackle the European

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Speaker 3: market and continue to scale.

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Speaker 12: Yeah, you know, we look at the electric vehicle market, right,

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Speaker 12: China definitely is dominating and you think about their strategy

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Speaker 12: is to export, Right, They've exported millions of vehicles and

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Speaker 12: they've had a lot of luck in increasing share and

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Speaker 12: so the UK, it's basically, you know, it's a compromise, right,

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Speaker 12: the EU, Europe needs to meet those climate goals, right,

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Speaker 12: they need to increase their EV adoption. They rely on

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Speaker 12: China for minerals, right, batteries. So I think it's a

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Speaker 12: good compromise, and I think China will continue to increase

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Speaker 12: their share in those markets, not only UK, but some

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Speaker 12: of the emerging markets as well.

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Speaker 3: Meanwhile, one of the biggest feedback I often get with

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Speaker 3: my social is about the lack of BYD or Chinese

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Speaker 3: offerings here in the United States.

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Speaker 4: Is that ever going to come.

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Speaker 3: To bat or they're just forever going to be shut

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Speaker 3: out because it's really bets on TESLA and also pushing

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Speaker 3: away against evs more broadly.

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Speaker 12: Yeah, you know, I think if you look at the US,

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Speaker 12: that's one thing I think both political parties can agree

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Speaker 12: on is to keep China out. And so I think

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Speaker 12: that's kind of where we're at now. Out in the future,

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Speaker 12: you know, maybe we could see something that similar happened

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Speaker 12: to the Japanese and the Koreans. They come, they build

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Speaker 12: a partner and they build manufacturing plants. But for now

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Speaker 12: the near term, we're not going to have those other

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Speaker 12: than the ones that we already have under Geele, which

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Speaker 12: is the Vulgo and pole Star. But definitely, I think

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Speaker 12: consumers are seeing evs when they travel abroad and getting

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Speaker 12: the experience of writing in a Chinese ozem and discovering

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Speaker 12: how good quality high tech, and so I think that

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Speaker 12: it would resonate with US consumers at the right.

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Speaker 5: Price, Stephanie.

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Speaker 2: Last week at CS Nvidia outlined a full stack hardware

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Speaker 2: software solution for autonomous driving, and it very quickly raised

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Speaker 2: questions about Tesla.

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Speaker 5: So I asked Jensen Wong about it. Listen to this.

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Speaker 13: I think the Tesla stack is the most advanced AV

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Speaker 13: stack in the world, and I think the Tesla AV

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Speaker 13: operations is the most advanced in the world, and I'm

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Speaker 13: I'm fairly certain that they were already using them to

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Speaker 13: end AI.

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Speaker 5: So here's the thing.

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Speaker 2: Mercedes will ship a vehicle this quarter capable of point

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Speaker 2: to point hands free using in video technology. Does Tesla

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Speaker 2: finally have something real in the market to worry about?

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Speaker 7: I think they do.

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Speaker 12: You think about some of the even Rivian's another player,

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Speaker 12: right that they at their aidea they announce their plan.

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Speaker 12: I think definitely there's a lot of competition, and I

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Speaker 12: still think, you know, we're making some tremendous progress, but

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Speaker 12: I think one of the challenges continues to be regulation safety.

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Speaker 12: But I think in a video announcement with that technology

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Speaker 12: to really identify those edge cases which will help with

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Speaker 12: regulations more credibility. So definitely Tesla has some challenges out

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Speaker 12: there for sure.

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Speaker 2: The reason I find it fascinating is that if you

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Speaker 2: are a Tesla owner, you know, you know all about

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Speaker 2: full self driving FSD. It's a part of the pitch

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Speaker 2: from Tesla, But the vast majority of the auto market,

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Speaker 2: at least in America, maybe they don't think about, Okay,

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Speaker 2: I'm going to buy a vehicle based on its ability

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Speaker 2: to do this. Do you think that changes from here

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Speaker 2: on in?

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Speaker 12: I think I think more from consumer, it's more about

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Speaker 12: the in car experience. So we keep hearing this software

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Speaker 12: defined vehicle, So I don't think they're going into like

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Speaker 12: I want this autonomous or full self driving. It's more

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Speaker 12: about what kind of consumer experience can I have with

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Speaker 12: technology in the vehicle, So in vehicle experience, So maybe

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Speaker 12: it's the ability to have some of that ADA as technology,

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Speaker 12: but I don't think that's the main driver, it's more

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Speaker 12: about what's that experience going to be and what other

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Speaker 12: options that vehicle provide for me.

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Speaker 3: In many ways, it's about marketing, getting people in the

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Speaker 3: cars to understand what it feels like, and then sort

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Speaker 3: of perpetuate that demand for wanting more and more and more.

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Speaker 3: From your perspective, is the consumer mindset there from a

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Speaker 3: safety perspective? Is the regulation there from that perspective as well?

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Speaker 7: Yeah?

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Speaker 12: I think so. I think that's the one thing if

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Speaker 12: you think about you know, I think when you think

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Speaker 12: about Robotechi's right, we're starting to see a lot of

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Speaker 12: that with wai Mo and Zook, some other companies, Tesla

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Speaker 12: getting consumers into those taxi atonos, So they're getting to

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Speaker 12: finally experience that. But I still think, you know, it's

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Speaker 12: been hard to get consumers to trans you know, kind

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Speaker 12: of consider electric vehicles to get into an autonomous vehicle

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Speaker 12: is in more so. So, I think there's still a

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Speaker 12: lot of work to be done in terms of credibility

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Speaker 12: and safety perceptions for autonomous vehicles from a consumer perspective.

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Speaker 2: Stephanie, look forward to twenty twenty six for US, What

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Speaker 2: is it you expect to have in United States? In

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Speaker 2: particular in electric vehicles and any growth that may or

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Speaker 2: may not come to your mind.

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Speaker 12: Yeah, you know, twenty twenty five it ended exactly where

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Speaker 12: we thought it would. Our report comes out tomorrow, but

441
00:21:38,840 --> 00:21:41,359
Speaker 12: we're going to be about two percent down your year

442
00:21:41,720 --> 00:21:45,000
Speaker 12: round up to one point three million vehicles. But going

443
00:21:45,040 --> 00:21:46,480
Speaker 12: into twenty twenty six, right.

444
00:21:46,400 --> 00:21:48,200
Speaker 7: We got rid of the characteristic.

445
00:21:48,359 --> 00:21:52,280
Speaker 12: We no longer have the IRA tax intetive, the corporate

446
00:21:52,320 --> 00:21:57,080
Speaker 12: average fuel economy standards have been less stringent, the Calvern waiver.

447
00:21:57,640 --> 00:22:00,560
Speaker 12: But given all that, we still have twenty plus new

448
00:22:00,600 --> 00:22:03,880
Speaker 12: EV's launching in twenty twenty six, so I think we're

449
00:22:03,880 --> 00:22:06,000
Speaker 12: still going to see momentum. It's going to be hard,

450
00:22:06,080 --> 00:22:07,440
Speaker 12: but I think consumers.

451
00:22:06,960 --> 00:22:08,080
Speaker 4: Are going to have more product.

452
00:22:08,760 --> 00:22:10,840
Speaker 12: But I think the one thing which we've talked about

453
00:22:10,840 --> 00:22:13,600
Speaker 12: before in the past is the affordability. If you look

454
00:22:13,640 --> 00:22:17,960
Speaker 12: at the current product lineupsues that are offered over sixty

455
00:22:17,960 --> 00:22:20,720
Speaker 12: percent for over sixty five thousand dollars.

456
00:22:20,960 --> 00:22:22,280
Speaker 5: So that's definitely about is Treat.

457
00:22:23,640 --> 00:22:25,679
Speaker 2: It's definitely about it is Treated, Director of Industry in

458
00:22:25,680 --> 00:22:28,200
Speaker 2: Size Cox Automotive, thank you so much for coming up.

459
00:22:28,280 --> 00:22:30,680
Speaker 2: We'll speak with LUTs Capital co founder Peter Herbert about

460
00:22:30,720 --> 00:22:35,200
Speaker 2: the firm's latest fund, its biggest fund. Silicon Valley meets DC.

461
00:22:35,640 --> 00:22:47,560
Speaker 2: This is Bloomberg Tech. Welcome back to Bloomberg Tech. There's

462
00:22:47,560 --> 00:22:49,119
Speaker 2: a story we're going to go deeper on later in

463
00:22:49,119 --> 00:22:52,840
Speaker 2: the program, the President saying that if credit card companies

464
00:22:52,880 --> 00:22:56,359
Speaker 2: don't respect an interest cap with ten percent in the year,

465
00:22:57,040 --> 00:22:58,920
Speaker 2: they will be facing uh.

466
00:23:00,359 --> 00:23:01,639
Speaker 5: Illegal activity claims.

467
00:23:01,680 --> 00:23:03,800
Speaker 2: Basically, these are two of the more tech focus names

468
00:23:03,840 --> 00:23:06,240
Speaker 2: Klana a firm, both down on the news. I would

469
00:23:06,240 --> 00:23:09,280
Speaker 2: note American Express is also down as an example significantly.

470
00:23:09,320 --> 00:23:10,960
Speaker 2: We're going to get to that later in the program.

471
00:23:11,160 --> 00:23:13,639
Speaker 2: Back to one of the top stories, we've had confirmation

472
00:23:14,080 --> 00:23:16,640
Speaker 2: from Google that it is entered into a multi year

473
00:23:16,680 --> 00:23:20,679
Speaker 2: agreement with Apple for Gemini to be the underpinnings of

474
00:23:20,760 --> 00:23:22,040
Speaker 2: the next gen AI SERI.

475
00:23:22,480 --> 00:23:23,400
Speaker 5: You see, when the.

476
00:23:23,359 --> 00:23:26,280
Speaker 2: News broke just before the program about ten thirty East

477
00:23:26,320 --> 00:23:28,440
Speaker 2: in time, a big jump in alphabet shares that sent

478
00:23:28,520 --> 00:23:30,240
Speaker 2: market cap through four trillion.

479
00:23:30,280 --> 00:23:31,000
Speaker 5: They then faded.

480
00:23:31,040 --> 00:23:33,879
Speaker 2: And I'd remind you as well that Bloomberg did report

481
00:23:33,880 --> 00:23:36,639
Speaker 2: in November that a deal was close for Apple to

482
00:23:36,680 --> 00:23:39,120
Speaker 2: pay Google about a billion dollars a year for use

483
00:23:39,359 --> 00:23:42,720
Speaker 2: of that very large one point two trillion parameter model,

484
00:23:43,080 --> 00:23:43,760
Speaker 2: Carol what else.

485
00:23:44,200 --> 00:23:46,840
Speaker 3: Well, let's dig in on Alphabet and it's recent moves,

486
00:23:46,840 --> 00:23:49,280
Speaker 3: but also where it stands versus the rest of the market.

487
00:23:49,280 --> 00:23:52,200
Speaker 3: Bluembog equity reporter comen Ryanikey has been really digging in

488
00:23:52,240 --> 00:23:54,280
Speaker 3: to the idea that maybe we're broadening out.

489
00:23:54,520 --> 00:23:57,320
Speaker 4: Maybe MAG seven isn't just a sure far bet. Alphabet

490
00:23:57,400 --> 00:23:58,840
Speaker 4: was a sure far bet last year.

491
00:23:59,040 --> 00:24:00,880
Speaker 3: Tell us about the discern that was starting to see

492
00:24:00,880 --> 00:24:02,480
Speaker 3: among these key megacaps.

493
00:24:02,760 --> 00:24:05,359
Speaker 14: Yeah, I mean what we saw last year is continuing

494
00:24:05,400 --> 00:24:07,160
Speaker 14: at least in the first few weeks of this year.

495
00:24:07,200 --> 00:24:10,679
Speaker 14: Where the winners and losers are really important. So Alphabet

496
00:24:10,720 --> 00:24:13,120
Speaker 14: was a winner last year. It was the biggest outperformer

497
00:24:13,160 --> 00:24:15,240
Speaker 14: and one of only two stocks in the MAG seven

498
00:24:15,240 --> 00:24:17,760
Speaker 14: group that outperforms the broader market, the other one being

499
00:24:17,760 --> 00:24:21,479
Speaker 14: in Nvidia. So it's really established itself as dominant in

500
00:24:21,520 --> 00:24:25,480
Speaker 14: the AI trend, and you know, maintaining that momentum going

501
00:24:25,480 --> 00:24:29,080
Speaker 14: forward is really important, especially considering that we're starting to

502
00:24:29,160 --> 00:24:31,520
Speaker 14: see some of these other names lag a little bit.

503
00:24:31,600 --> 00:24:33,960
Speaker 14: You know, they're just slowing down in terms of overall

504
00:24:34,000 --> 00:24:36,960
Speaker 14: growth and heading into earning season, some of those things

505
00:24:36,960 --> 00:24:39,920
Speaker 14: are expected to continue, right we're just we're hitting the

506
00:24:40,359 --> 00:24:43,320
Speaker 14: law of large numbers. Growth is expected to slow, and

507
00:24:43,400 --> 00:24:46,200
Speaker 14: so that might translate, you know, into stock games as well.

508
00:24:47,280 --> 00:24:50,199
Speaker 2: Been an interesting morning for Alphabet and Apple and the

509
00:24:50,240 --> 00:24:55,040
Speaker 2: news flow around the deal to improve Siri, and I

510
00:24:55,119 --> 00:24:59,080
Speaker 2: was reflecting on literally just the ticker, right, you know,

511
00:24:59,240 --> 00:25:02,600
Speaker 2: Alphabet saw and breaches four trillion dollars market cap then

512
00:25:02,720 --> 00:25:04,560
Speaker 2: kind of fade straight away. What did you see in

513
00:25:04,600 --> 00:25:08,120
Speaker 2: the markets this morning in terms of how people reacted.

514
00:25:08,480 --> 00:25:11,160
Speaker 14: Yeah, so we saw that big spike for both Google

515
00:25:11,240 --> 00:25:14,560
Speaker 14: and Alpha or sorry, Google and Apple shares. It did

516
00:25:14,600 --> 00:25:17,480
Speaker 14: push Google over four trillion in market cap. It's also

517
00:25:17,680 --> 00:25:21,640
Speaker 14: surpassed Apple recently, so it's the second largest company in

518
00:25:21,680 --> 00:25:23,560
Speaker 14: the s and P five hundred million in the market

519
00:25:23,800 --> 00:25:26,840
Speaker 14: other than in Nvidia. And it's interesting because you know,

520
00:25:26,880 --> 00:25:29,520
Speaker 14: we did see those those gains fade a little bit.

521
00:25:29,560 --> 00:25:32,000
Speaker 14: They're up now. I think both stocks are trading in

522
00:25:32,040 --> 00:25:34,800
Speaker 14: positive territory. But the overall market, I think we are

523
00:25:34,880 --> 00:25:37,879
Speaker 14: still seeing a lot of macro news weigh on this.

524
00:25:38,040 --> 00:25:40,080
Speaker 14: It's been a little bit all over the place today.

525
00:25:40,200 --> 00:25:43,679
Speaker 14: So big tech is obviously still super important. These stocks

526
00:25:43,680 --> 00:25:46,720
Speaker 14: are really important. They're so large they add a lot

527
00:25:46,760 --> 00:25:49,000
Speaker 14: and can weigh a lot on the broader index. But

528
00:25:49,200 --> 00:25:51,159
Speaker 14: we also are seeing that there is a broadening out.

529
00:25:51,200 --> 00:25:54,600
Speaker 14: People are looking at other sectors, and you know, it's

530
00:25:54,640 --> 00:25:57,240
Speaker 14: not just big tech that's always driving the gains and

531
00:25:57,320 --> 00:25:58,240
Speaker 14: losses in the market.

532
00:25:58,920 --> 00:26:01,360
Speaker 5: Bloombe's common. Ryan, Thank you very much.

533
00:26:01,400 --> 00:26:04,880
Speaker 2: Let's go from the public markets private markets, where interest

534
00:26:04,920 --> 00:26:08,360
Speaker 2: in frontier tech is incredibly high. Lux Capital is raised

535
00:26:08,400 --> 00:26:11,000
Speaker 2: one point five billion dollars for its ninth fund, the

536
00:26:11,119 --> 00:26:14,680
Speaker 2: largest in the venture firms more than twenty year history.

537
00:26:14,880 --> 00:26:17,240
Speaker 2: Peter Abair, partner and co founder of Lots Capitals with

538
00:26:17,320 --> 00:26:18,280
Speaker 2: us here in San Francisco.

539
00:26:18,520 --> 00:26:19,320
Speaker 5: Thank you for having me.

540
00:26:19,440 --> 00:26:22,480
Speaker 2: It's exciting news, right the ninth fund that the largest ever.

541
00:26:22,560 --> 00:26:25,240
Speaker 2: You turned away about a billion dollars of capital. What's

542
00:26:25,320 --> 00:26:28,240
Speaker 2: very interesting about it, though, is the consistency. The themes

543
00:26:28,240 --> 00:26:30,520
Speaker 2: are the same, the focus is the same. What do

544
00:26:30,560 --> 00:26:31,600
Speaker 2: you want to do with this fund?

545
00:26:32,000 --> 00:26:34,800
Speaker 15: So we will be doing more of the same, but

546
00:26:35,000 --> 00:26:37,680
Speaker 15: just accelerating that piece. And so we've been investing now

547
00:26:37,760 --> 00:26:42,400
Speaker 15: for more than twenty five years in physical, computational life sciences,

548
00:26:42,680 --> 00:26:44,960
Speaker 15: but really at the cutting edge and the frontier of

549
00:26:45,000 --> 00:26:48,120
Speaker 15: all these different scientific disciplines, and so as we say

550
00:26:48,280 --> 00:26:50,960
Speaker 15: science doesn't scale itself, and so that's where we're going

551
00:26:51,000 --> 00:26:52,520
Speaker 15: to be put in the thrust of our investment.

552
00:26:52,960 --> 00:26:55,879
Speaker 2: Peter, we enjoy having you on the program. The firm you,

553
00:26:56,480 --> 00:26:58,800
Speaker 2: Josh Dino when they cut when all of you come on.

554
00:26:59,320 --> 00:27:03,240
Speaker 2: But it seems very disciplined, very calm. Check sizes one

555
00:27:03,320 --> 00:27:07,639
Speaker 2: hundred thousand. This is exciting though. These are some of

556
00:27:07,680 --> 00:27:10,840
Speaker 2: the domains that are moving the fastest, and actually, would

557
00:27:10,840 --> 00:27:14,800
Speaker 2: you reflect that this administration has allowed you to do that?

558
00:27:15,480 --> 00:27:18,840
Speaker 2: The areas of defense, other areas adjacent to AI is

559
00:27:18,880 --> 00:27:21,920
Speaker 2: something that the Trump administration has moved quickly to support.

560
00:27:22,520 --> 00:27:26,840
Speaker 15: So certainly in areas like defense, kind of broadly reindustrialization

561
00:27:26,960 --> 00:27:29,280
Speaker 15: a lot of the things that we do. It's certainly

562
00:27:29,560 --> 00:27:33,159
Speaker 15: having a moment, and that's really reflected one from the

563
00:27:33,200 --> 00:27:36,400
Speaker 15: technological waves that are happening. One of the things that

564
00:27:36,880 --> 00:27:40,119
Speaker 15: is top of everyone's mind right now is physical AI.

565
00:27:41,119 --> 00:27:44,000
Speaker 15: Right now, there's also the JP Morgan healthcare event happening

566
00:27:44,040 --> 00:27:48,080
Speaker 15: in San Francisco. You're seeing huge breakthroughs in medical robotics

567
00:27:48,560 --> 00:27:52,280
Speaker 15: and in computational drug discovery. But absolutely, if you look

568
00:27:52,320 --> 00:27:56,360
Speaker 15: at the world, it's an uncertain place, and defense technology

569
00:27:56,520 --> 00:28:00,760
Speaker 15: has been really soaring as Large Nations state its rearm

570
00:28:00,840 --> 00:28:03,720
Speaker 15: and focus under Terrens Peter Andreill.

571
00:28:03,760 --> 00:28:05,360
Speaker 4: Of course, one of the key bets you made and.

572
00:28:05,320 --> 00:28:08,040
Speaker 3: We had Parmalucky on the show last week. From ces

573
00:28:08,920 --> 00:28:12,280
Speaker 3: of this fund, how much will be to double down

574
00:28:12,520 --> 00:28:15,160
Speaker 3: on prior bets or how much will be to take

575
00:28:15,200 --> 00:28:18,240
Speaker 3: out totally new bets on new companies coming to your inbox.

576
00:28:19,200 --> 00:28:21,480
Speaker 15: So the spirit of this fund is really thinking about

577
00:28:21,520 --> 00:28:25,000
Speaker 15: as a blank slate, starting with the portfolio of probably

578
00:28:25,040 --> 00:28:29,879
Speaker 15: forty to fifty core positions. Historically we've had companies that

579
00:28:29,960 --> 00:28:33,080
Speaker 15: have gone from one fund to another cross fund investing,

580
00:28:33,119 --> 00:28:36,040
Speaker 15: but it's generally not the focus and philosophy of each

581
00:28:36,119 --> 00:28:37,560
Speaker 15: new core fund that we.

582
00:28:37,600 --> 00:28:40,800
Speaker 3: Raise one hundred thousand dollars to one hundred million, that's

583
00:28:40,840 --> 00:28:44,400
Speaker 3: a huge breadth that you're giving yourselves. Where do you

584
00:28:44,440 --> 00:28:48,000
Speaker 3: think you will be excited to deploy? Have the foundational

585
00:28:48,040 --> 00:28:50,720
Speaker 3: models moved forward? Are we seeing more interesting areas of

586
00:28:50,800 --> 00:28:55,280
Speaker 3: more specific niche parts of the building the llms or

587
00:28:55,320 --> 00:28:57,800
Speaker 3: is it more about application? Where are you thinking about

588
00:28:57,920 --> 00:28:58,840
Speaker 3: getting excited for?

589
00:29:00,000 --> 00:29:02,360
Speaker 15: So our mental model and framework really is what we

590
00:29:02,440 --> 00:29:05,960
Speaker 15: describe as moving from two D two dimensional AI to

591
00:29:06,200 --> 00:29:10,360
Speaker 15: three D and that's really areas of robotics and automation

592
00:29:10,520 --> 00:29:14,360
Speaker 15: and biology, but it's seeing AI for the first time

593
00:29:14,480 --> 00:29:17,960
Speaker 15: coming out of the digital world into the physical And

594
00:29:18,120 --> 00:29:21,240
Speaker 15: just one example of that is really just the momentous

595
00:29:21,280 --> 00:29:26,920
Speaker 15: opportunity here. Approximately ninety percent of us GDP is not

596
00:29:27,080 --> 00:29:30,560
Speaker 15: digitally native. It is in the physical world. It's construction,

597
00:29:30,720 --> 00:29:34,160
Speaker 15: it's heavy industry, and that's thirty trillion dollars of total

598
00:29:34,320 --> 00:29:38,920
Speaker 15: us GDP. So the opportunity really exist for technology investors

599
00:29:39,640 --> 00:29:43,960
Speaker 15: focused on software, data other very specific areas that now

600
00:29:44,040 --> 00:29:49,040
Speaker 15: have the opportunity basically ten x total addressable market.

601
00:29:49,760 --> 00:29:52,080
Speaker 5: Peter, you're co founder of this firm.

602
00:29:52,240 --> 00:29:56,000
Speaker 2: Has the composition of the LP's changed from fund one

603
00:29:56,080 --> 00:29:58,880
Speaker 2: through to fund nine, and where the interests in you

604
00:29:59,000 --> 00:30:00,480
Speaker 2: is coming from?

605
00:30:00,760 --> 00:30:05,080
Speaker 15: Earliest a believer in LUX was a gentleman, Bill Conway,

606
00:30:05,080 --> 00:30:07,200
Speaker 15: one of the founders of the Carlisle Law, and so

607
00:30:07,240 --> 00:30:12,200
Speaker 15: he was LUX Venture is one. Today we manage seven

608
00:30:12,200 --> 00:30:16,680
Speaker 15: billion dollars on behalf of endowments and foundations and state pensions.

609
00:30:17,000 --> 00:30:21,760
Speaker 15: So obviously going from an initial individual albeit a quasi institution,

610
00:30:22,320 --> 00:30:25,240
Speaker 15: to now serving some of the world's most elite institutions.

611
00:30:25,480 --> 00:30:28,120
Speaker 2: I'm really interested in the core competencies you're looking for

612
00:30:28,240 --> 00:30:30,680
Speaker 2: later in the program. We have one X, the humanoid

613
00:30:30,760 --> 00:30:34,240
Speaker 2: robotics company on right, and they're out with their own

614
00:30:34,280 --> 00:30:37,600
Speaker 2: world model. But the problem right now is that in Nvidia,

615
00:30:37,720 --> 00:30:39,959
Speaker 2: as was made very plain last week, basically come out

616
00:30:40,000 --> 00:30:41,560
Speaker 2: and say we'll do it all for you on the

617
00:30:41,600 --> 00:30:45,760
Speaker 2: hardware and software side. If you're a humanoid robots company

618
00:30:46,000 --> 00:30:49,200
Speaker 2: or a different physical AI offering, what's left to look

619
00:30:49,280 --> 00:30:51,080
Speaker 2: for in that regard.

620
00:30:50,880 --> 00:30:51,800
Speaker 5: Believe in it or not.

621
00:30:52,680 --> 00:30:56,080
Speaker 15: There will not just be one company that rules all others,

622
00:30:56,080 --> 00:30:58,280
Speaker 15: and so there are a number of different opportunities. And

623
00:30:58,320 --> 00:31:01,719
Speaker 15: even with Nvidia, as prominas they are, there are other

624
00:31:01,840 --> 00:31:04,680
Speaker 15: up and comers on the silicon side, certainly on the

625
00:31:04,720 --> 00:31:08,760
Speaker 15: software side training there are a number of different companies

626
00:31:08,800 --> 00:31:13,760
Speaker 15: that are attacking again physical implementations of AI, from physical

627
00:31:13,760 --> 00:31:17,760
Speaker 15: intelligence that's here in San Francisco to Applied Intuition, which

628
00:31:17,800 --> 00:31:20,000
Speaker 15: is a lux portfolio company which is really creating an

629
00:31:20,040 --> 00:31:24,920
Speaker 15: operating system in mobility. So there is abundant opportunity.

630
00:31:24,680 --> 00:31:26,880
Speaker 3: And is that global Peter in terms of not just

631
00:31:27,040 --> 00:31:30,280
Speaker 3: the consumer base, but where these companies are getting birthed.

632
00:31:30,320 --> 00:31:31,200
Speaker 4: How broad do you go?

633
00:31:32,560 --> 00:31:36,160
Speaker 15: Well, one X not an American company. There are many

634
00:31:36,280 --> 00:31:41,120
Speaker 15: global competitors in this marketplace, even in AI. Right now,

635
00:31:41,120 --> 00:31:44,000
Speaker 15: we're an investor in a company called Sikana Ai based

636
00:31:44,000 --> 00:31:47,080
Speaker 15: in Japan. We've made probably about a dozen or so

637
00:31:47,200 --> 00:31:52,120
Speaker 15: investments across Europe in the last fund. So technology is global,

638
00:31:52,160 --> 00:31:56,720
Speaker 15: it's broadly distributed. San Francisco remains the epicenter, but you

639
00:31:56,760 --> 00:31:59,520
Speaker 15: will see it popping up coming out of a lot

640
00:31:59,560 --> 00:32:01,880
Speaker 15: of large research institutions across the globe.

641
00:32:02,560 --> 00:32:05,720
Speaker 3: Fascinating p DA Bere We thank you, partner co founder

642
00:32:05,800 --> 00:32:09,000
Speaker 3: lux Capital. On the latest fun announcement coming up, we

643
00:32:09,040 --> 00:32:11,640
Speaker 3: talk about that company one X, the maker of Neo.

644
00:32:12,120 --> 00:32:14,080
Speaker 3: It says this new AI model will give its home

645
00:32:14,240 --> 00:32:16,880
Speaker 3: robot the ability to learn new tasks from scratch.

646
00:32:17,240 --> 00:32:19,920
Speaker 4: I'll talk to the CEO. This has been their tech.

647
00:32:29,400 --> 00:32:31,680
Speaker 5: We've got news for you from the world of robotics.

648
00:32:31,800 --> 00:32:34,960
Speaker 2: One X Technologies, the maker of the humanoid robot Neo,

649
00:32:35,560 --> 00:32:37,959
Speaker 2: has an update for its one Ex World model, an

650
00:32:37,960 --> 00:32:41,600
Speaker 2: AI video model grounded in physics. According to the company,

651
00:32:41,600 --> 00:32:44,840
Speaker 2: the update will make Neo capable of executing on new

652
00:32:44,880 --> 00:32:48,160
Speaker 2: AI abilities with a simple text or voice prompt. That's

653
00:32:48,280 --> 00:32:51,720
Speaker 2: even if the robot has zero experience of doing the

654
00:32:51,800 --> 00:32:54,719
Speaker 2: task before joining us is one X Technology CEO and

655
00:32:54,800 --> 00:32:56,280
Speaker 2: CTO Bernt Bernick.

656
00:32:56,520 --> 00:32:57,760
Speaker 5: Welcome back to bloombog Tech.

657
00:32:58,120 --> 00:32:58,520
Speaker 10: Thank you.

658
00:32:58,720 --> 00:33:00,480
Speaker 7: I want to keep this really simple to start.

659
00:33:00,760 --> 00:33:03,600
Speaker 2: What is an example of a task that the update

660
00:33:03,640 --> 00:33:06,440
Speaker 2: to the model allows for NEO to do that it

661
00:33:06,480 --> 00:33:10,200
Speaker 2: couldn't have done for the first time without the update.

662
00:33:12,080 --> 00:33:14,720
Speaker 16: So I mean to me, it's all about like things

663
00:33:14,840 --> 00:33:20,760
Speaker 16: just being anything that you don't have in your data set,

664
00:33:20,880 --> 00:33:23,000
Speaker 16: but still being able to have a sensible approach.

665
00:33:23,440 --> 00:33:25,800
Speaker 10: So like the first time I really kind of.

666
00:33:25,760 --> 00:33:28,040
Speaker 16: Like saw this and it was a simple question, just

667
00:33:28,080 --> 00:33:30,560
Speaker 16: asking the robot, hey, can you can you pick the

668
00:33:31,200 --> 00:33:33,360
Speaker 16: post it note down from the board and read it?

669
00:33:33,400 --> 00:33:35,280
Speaker 16: But it might never have seen a post and it's like,

670
00:33:35,320 --> 00:33:37,719
Speaker 16: of course, we don't have any training data on us

671
00:33:37,800 --> 00:33:39,640
Speaker 16: using robots to like pick post it notes off a

672
00:33:39,680 --> 00:33:41,480
Speaker 16: board and like look at it and read what's on it.

673
00:33:42,000 --> 00:33:44,440
Speaker 10: But it can actually do it really well, and it's

674
00:33:44,480 --> 00:33:45,480
Speaker 10: not aji yet.

675
00:33:45,840 --> 00:33:49,720
Speaker 16: There are examples where it fails, But what it does

676
00:33:49,840 --> 00:33:53,800
Speaker 16: is the ability to have this very sensible approach to anything,

677
00:33:54,400 --> 00:33:56,960
Speaker 16: which is a cornerstone of learning, because now all you

678
00:33:57,040 --> 00:33:59,160
Speaker 16: need is the robots teaching themselves how to do all

679
00:33:59,200 --> 00:34:01,680
Speaker 16: these tasks by actually experimenting and doing this in the

680
00:34:01,800 --> 00:34:02,200
Speaker 16: real world.

681
00:34:03,120 --> 00:34:06,160
Speaker 5: This model, how good is it? You know?

682
00:34:06,240 --> 00:34:10,800
Speaker 2: Last week CS and Video extol the virtues of using

683
00:34:10,840 --> 00:34:14,560
Speaker 2: their open models, and inside Neo, the brain of Neo

684
00:34:15,160 --> 00:34:18,800
Speaker 2: is an Nvidia inferenced chip, right, so why not just

685
00:34:18,920 --> 00:34:20,800
Speaker 2: using Vidia's model too.

686
00:34:22,160 --> 00:34:24,120
Speaker 16: We have a very deep collaboration made in VideA and

687
00:34:24,160 --> 00:34:26,320
Speaker 16: we do use a lot of their technology in the stack.

688
00:34:28,480 --> 00:34:32,040
Speaker 16: I think to us also, it's very much about the

689
00:34:32,080 --> 00:34:35,600
Speaker 16: embodiment and the husband designed really over the last decade

690
00:34:35,640 --> 00:34:38,600
Speaker 16: to be as close to a human as possible. Because

691
00:34:38,640 --> 00:34:40,440
Speaker 16: if you take all of the knowledge that we have

692
00:34:40,480 --> 00:34:42,360
Speaker 16: in the world, like everything you can see on YouTube

693
00:34:42,440 --> 00:34:44,719
Speaker 16: or any kind of video content, and you train a

694
00:34:44,800 --> 00:34:47,279
Speaker 16: model on this and now you want to pick up

695
00:34:47,320 --> 00:34:51,360
Speaker 16: the cup or open the door, if your robot is

696
00:34:51,360 --> 00:34:54,680
Speaker 16: actually not similar enough to a human, this doesn't work

697
00:34:54,719 --> 00:34:56,880
Speaker 16: anymore because you wouldn't do the task in the same manner.

698
00:34:57,280 --> 00:34:59,399
Speaker 16: And this is quite unique to one because I think

699
00:34:59,520 --> 00:35:01,920
Speaker 16: there's there's a couple of things that really puts us apart,

700
00:35:02,200 --> 00:35:05,080
Speaker 16: and it's that we have robots that are so close

701
00:35:05,120 --> 00:35:07,719
Speaker 16: to humans in how they interact with the world, but

702
00:35:07,800 --> 00:35:10,600
Speaker 16: also that they're safe so they can actually try to

703
00:35:10,640 --> 00:35:13,279
Speaker 16: do something and if they fail, then the world is

704
00:35:13,280 --> 00:35:16,000
Speaker 16: still okay. You don't want your like I don't know,

705
00:35:16,040 --> 00:35:17,719
Speaker 16: you don't want your door to be scratched because there

706
00:35:17,760 --> 00:35:21,279
Speaker 16: was a robot trying to open it. And this puts

707
00:35:21,360 --> 00:35:23,719
Speaker 16: us in a category where our approach today it is

708
00:35:23,719 --> 00:35:26,120
Speaker 16: probably a bit different, like what the standard is now

709
00:35:26,160 --> 00:35:29,879
Speaker 16: and also why we're so we're very excited about these

710
00:35:29,880 --> 00:35:33,400
Speaker 16: world models where finally we see robotic scaling following the

711
00:35:33,400 --> 00:35:36,279
Speaker 16: same scaling laws as for example, video pre training from

712
00:35:36,320 --> 00:35:40,000
Speaker 16: the big video models, like so our video from our competitors,

713
00:35:40,040 --> 00:35:41,480
Speaker 16: and go in on.

714
00:35:41,440 --> 00:35:44,240
Speaker 3: The safety a little bit more because you're talking about

715
00:35:44,440 --> 00:35:47,600
Speaker 3: how it stops the door getting scratched, but for many

716
00:35:47,640 --> 00:35:51,040
Speaker 3: it's about well, when robots start learning to do things themselves,

717
00:35:51,320 --> 00:35:53,040
Speaker 3: therein lies the safety issue.

718
00:35:53,200 --> 00:35:54,600
Speaker 4: What god rails do you have in place?

719
00:35:56,280 --> 00:35:59,239
Speaker 16: So we have some exciting work on that that will

720
00:35:59,239 --> 00:35:59,879
Speaker 16: come out soon.

721
00:36:00,719 --> 00:36:02,000
Speaker 10: We are doing.

722
00:36:03,280 --> 00:36:05,640
Speaker 16: The proper things on the safety side with respect to

723
00:36:05,680 --> 00:36:09,319
Speaker 16: following the standards, helping develop some of the standards, but

724
00:36:09,360 --> 00:36:13,399
Speaker 16: also ensuring that we, for example, have externally audited work

725
00:36:13,440 --> 00:36:17,080
Speaker 16: of our safety work by an independent by independent third party,

726
00:36:17,080 --> 00:36:20,520
Speaker 16: of course, But to me, safety has many layers. You

727
00:36:20,600 --> 00:36:23,479
Speaker 16: have what we call like the passive intrinsic safety, where

728
00:36:23,600 --> 00:36:26,200
Speaker 16: humans are actually very safe in the sense that we have.

729
00:36:26,200 --> 00:36:28,640
Speaker 10: To actively work to hurt each other.

730
00:36:29,000 --> 00:36:31,840
Speaker 16: We don't hurt each other by accident usually, and this

731
00:36:31,920 --> 00:36:33,960
Speaker 16: is something we build into the machine the same kind

732
00:36:33,960 --> 00:36:37,640
Speaker 16: of just you have to be soft, compliant, lightweight, low energy,

733
00:36:37,880 --> 00:36:40,960
Speaker 16: so you can be safe in among people and in

734
00:36:41,000 --> 00:36:43,760
Speaker 16: the environment. And then of course you have the AI alignment,

735
00:36:43,960 --> 00:36:46,200
Speaker 16: which is equally important, and how do you ensure that

736
00:36:46,440 --> 00:36:49,719
Speaker 16: you can always take the safest leads to risky path

737
00:36:49,800 --> 00:36:52,239
Speaker 16: for whatever task you want to achieve. And this is

738
00:36:52,280 --> 00:36:55,759
Speaker 16: also something that's incredibly exciting about these world models that

739
00:36:56,120 --> 00:36:58,640
Speaker 16: they understand the world so well and also how the

740
00:36:58,640 --> 00:37:01,680
Speaker 16: physical world works, that you can not only ask for

741
00:37:01,800 --> 00:37:04,080
Speaker 16: how am I going to do this task, but how

742
00:37:04,120 --> 00:37:05,680
Speaker 16: am I going to do this task in a manner

743
00:37:05,760 --> 00:37:06,960
Speaker 16: that is as safe as possible?

744
00:37:07,120 --> 00:37:10,040
Speaker 10: What are the things that could possibly be wrong go wrong?

745
00:37:10,320 --> 00:37:11,760
Speaker 10: And then the model actively.

746
00:37:11,400 --> 00:37:13,200
Speaker 16: Reasons about like, hey, here are the things that I

747
00:37:13,239 --> 00:37:15,480
Speaker 16: can visualize going wrong here, So I'm going to take

748
00:37:15,480 --> 00:37:17,040
Speaker 16: this path here, which is the safest path.

749
00:37:17,600 --> 00:37:21,280
Speaker 2: The problem being sold for other than ultimately accelerating deployment

750
00:37:21,320 --> 00:37:23,640
Speaker 2: of NEO in the real world. Is the burden of

751
00:37:23,680 --> 00:37:27,279
Speaker 2: real world data gathering and a lot of focus right

752
00:37:27,280 --> 00:37:31,319
Speaker 2: now on simulation and synthetic data. Just talk a little

753
00:37:31,320 --> 00:37:33,760
Speaker 2: bit about that. Some of our audiences is our industry,

754
00:37:33,840 --> 00:37:35,920
Speaker 2: they're more technical and they'll want to know how you

755
00:37:35,960 --> 00:37:37,640
Speaker 2: did it with this particular model.

756
00:37:38,480 --> 00:37:41,719
Speaker 16: Yeah, so I think really, if you boil it down

757
00:37:41,800 --> 00:37:45,799
Speaker 16: to the simplest parts, it is if your embodiment, if

758
00:37:45,840 --> 00:37:48,799
Speaker 16: your robot is close enough to a human, then all

759
00:37:48,840 --> 00:37:52,560
Speaker 16: of these learnings that they have from video they actually

760
00:37:52,560 --> 00:37:56,160
Speaker 16: transfer pretty well. And once you can do that, and

761
00:37:56,200 --> 00:37:58,719
Speaker 16: your robot can now approach almost any task as long

762
00:37:58,719 --> 00:37:59,040
Speaker 16: as you.

763
00:37:58,960 --> 00:37:59,600
Speaker 10: Can ask for it.

764
00:38:01,800 --> 00:38:05,400
Speaker 16: Your intelligence doesn't scale with the amount of data you

765
00:38:05,400 --> 00:38:06,920
Speaker 16: can collect with humans anymore.

766
00:38:07,080 --> 00:38:09,080
Speaker 10: It actually scales with the number of robots.

767
00:38:08,760 --> 00:38:12,400
Speaker 16: You've deployed, because now you just need enough robots actually

768
00:38:12,480 --> 00:38:14,640
Speaker 16: trying to do all these things, and trying to do

769
00:38:14,680 --> 00:38:18,040
Speaker 16: these things are useful, right, so you also produce useful work.

770
00:38:18,080 --> 00:38:20,280
Speaker 16: But in the process, the robot learns and it quickly

771
00:38:20,280 --> 00:38:23,040
Speaker 16: gets better, and now you just want enough robots across

772
00:38:23,040 --> 00:38:26,480
Speaker 16: society and doing enough different tasks so that you get

773
00:38:26,480 --> 00:38:29,880
Speaker 16: a very large data coverage and then you're progressing very

774
00:38:29,680 --> 00:38:33,719
Speaker 16: well on your scaling laws towards general intelligence. But it's

775
00:38:33,719 --> 00:38:38,000
Speaker 16: now independent of actually having to use humans to gather

776
00:38:38,040 --> 00:38:38,680
Speaker 16: the data to a.

777
00:38:38,680 --> 00:38:42,080
Speaker 2: Tel operations sideration exactly, burn x CEO and c to

778
00:38:42,280 --> 00:38:44,120
Speaker 2: one X. Is great to have you back here on

779
00:38:44,200 --> 00:38:46,560
Speaker 2: boomby Tech. Thank you very much. Carry plenty of other

780
00:38:46,600 --> 00:38:47,200
Speaker 2: news headlines.

781
00:38:47,320 --> 00:38:49,560
Speaker 3: Yeah, and it's time now for talking tech and first start.

782
00:38:49,680 --> 00:38:52,800
Speaker 3: Mon Meta has appointed a formatop advisor to President Trump

783
00:38:52,840 --> 00:38:56,160
Speaker 3: to a newly created senior management role. Done Power McCormick

784
00:38:56,400 --> 00:38:59,600
Speaker 3: will guide the company's aim structure efforts, including securing future

785
00:38:59,600 --> 00:39:03,719
Speaker 3: funding from governments and investors for massive data center projects.

786
00:39:04,000 --> 00:39:06,879
Speaker 3: Plus Meta also says it has shut down almost five

787
00:39:06,960 --> 00:39:10,239
Speaker 3: hundred and fifty thousand accounts in Australia to comply with

788
00:39:10,320 --> 00:39:13,600
Speaker 3: the country's new social media ban for kids Now. The law,

789
00:39:13,640 --> 00:39:16,160
Speaker 3: which came into effect last year, mandates that services like

790
00:39:16,200 --> 00:39:19,040
Speaker 3: Facebook and Instagram block under sixteen year olds from having

791
00:39:19,040 --> 00:39:21,360
Speaker 3: accounts or face fines ab up to thirty three million

792
00:39:21,400 --> 00:39:25,280
Speaker 3: dollars and anthropic. While it's making a push into healthcare

793
00:39:25,360 --> 00:39:28,280
Speaker 3: with tools that allow patients and doctors to access medical

794
00:39:28,320 --> 00:39:29,800
Speaker 3: information on its AI chatbot.

795
00:39:29,960 --> 00:39:30,120
Speaker 5: Now.

796
00:39:30,120 --> 00:39:31,840
Speaker 4: The new offering is compliant with.

797
00:39:31,960 --> 00:39:35,720
Speaker 3: US medical privacy regulations, allowing providers and consumers to field

798
00:39:35,760 --> 00:39:36,880
Speaker 3: protected health.

799
00:39:36,680 --> 00:39:39,400
Speaker 5: Data ed okay, coming up.

800
00:39:39,440 --> 00:39:42,359
Speaker 2: Credit card issuers are on edge as President Trump calls

801
00:39:42,400 --> 00:39:45,440
Speaker 2: for a ten percent interest rate cap on credit cards

802
00:39:45,600 --> 00:39:46,200
Speaker 2: for one year.

803
00:39:46,239 --> 00:39:48,399
Speaker 5: We had the details. Next, this is Bloomberg Tech.

804
00:39:56,960 --> 00:39:59,759
Speaker 2: Allmark is partnering with alphabet to offer Ali and hands

805
00:39:59,760 --> 00:40:02,880
Speaker 2: show being on Google Gemini's platform part of the retailer's

806
00:40:02,960 --> 00:40:06,400
Speaker 2: race to apply the technology across its operations, from apparel

807
00:40:06,520 --> 00:40:09,960
Speaker 2: and consumables to entertainment and food products. Customers will be

808
00:40:10,000 --> 00:40:13,600
Speaker 2: able to purchase items on Gemini's browser or mobile app

809
00:40:13,719 --> 00:40:14,520
Speaker 2: in the coming months.

810
00:40:14,600 --> 00:40:16,640
Speaker 3: Carot, let's talk a bit more about the consumer and

811
00:40:17,120 --> 00:40:19,800
Speaker 3: tech and payments. For example, because President Trump is pushing

812
00:40:19,920 --> 00:40:22,160
Speaker 3: for a one year cap on credit card interest rates

813
00:40:22,239 --> 00:40:24,279
Speaker 3: at ten percent, it's a move that could end the

814
00:40:24,320 --> 00:40:27,120
Speaker 3: credit card industry and also ripple through major buy now,

815
00:40:27,160 --> 00:40:30,520
Speaker 3: pay later providers. Newberg's page Smith, who covers consumer finance,

816
00:40:30,800 --> 00:40:35,600
Speaker 3: is right here. Look, is this going to send shock

817
00:40:35,640 --> 00:40:39,280
Speaker 3: waves and people towards alternative forms of financing? If, for example,

818
00:40:39,320 --> 00:40:42,080
Speaker 3: these credit cards just can't serve the broad consumer that

819
00:40:42,120 --> 00:40:42,600
Speaker 3: they used to.

820
00:40:42,600 --> 00:40:45,200
Speaker 4: If ten percent is the cap, that's a really good question.

821
00:40:45,400 --> 00:40:48,919
Speaker 17: I think we are a bit early to exactly see

822
00:40:48,920 --> 00:40:52,560
Speaker 17: how consumers are responding to this, because it's still early days.

823
00:40:52,680 --> 00:40:55,840
Speaker 17: Just to be clear, President Trump's statement on Friday evening

824
00:40:55,840 --> 00:40:59,240
Speaker 17: and then again last night was really just saying that

825
00:40:59,080 --> 00:41:03,240
Speaker 17: that credit card companies, essentially issuers and big banks should

826
00:41:03,320 --> 00:41:06,480
Speaker 17: be doing this. The legal levers that he can actually

827
00:41:06,480 --> 00:41:10,760
Speaker 17: pull to implement such a proposal would are still very unclear.

828
00:41:10,920 --> 00:41:13,759
Speaker 17: We haven't seen any sort of formal proposals. So to

829
00:41:13,840 --> 00:41:17,000
Speaker 17: sort of say that there would be fintech, you know,

830
00:41:17,040 --> 00:41:20,719
Speaker 17: maybe fintech alternatives, sort of folks along the lines of

831
00:41:21,040 --> 00:41:24,600
Speaker 17: so Fi or a firm or Klarna actually stepping into

832
00:41:24,640 --> 00:41:28,319
Speaker 17: the void is a bit premature. But although shares have

833
00:41:28,440 --> 00:41:31,960
Speaker 17: sort of seemed to react and seem to be identifying

834
00:41:31,960 --> 00:41:33,680
Speaker 17: some potential opportunities along those.

835
00:41:33,560 --> 00:41:37,399
Speaker 2: Lines, Paige, the president's claim is that some credit card

836
00:41:37,520 --> 00:41:40,759
Speaker 2: providers are charging twenty eight up to thirty percent, as

837
00:41:40,760 --> 00:41:43,560
Speaker 2: he put it, but also that the consumer might not

838
00:41:43,640 --> 00:41:46,200
Speaker 2: even realize that they're being charge interest at a rate

839
00:41:46,239 --> 00:41:49,640
Speaker 2: of thirty percent. In our reporting and in evidence, do

840
00:41:49,719 --> 00:41:52,880
Speaker 2: we know that to be true among some credit card providers.

841
00:41:53,320 --> 00:41:55,120
Speaker 17: I mean, I'll speak for myself. I think it would

842
00:41:55,160 --> 00:41:57,440
Speaker 17: be fair to say that it was a good reminder

843
00:41:57,520 --> 00:41:59,640
Speaker 17: this morning and over the weekend to be checking interest

844
00:41:59,719 --> 00:42:02,799
Speaker 17: rates on credit cards, as every consumer should be. So

845
00:42:03,040 --> 00:42:06,000
Speaker 17: I think it is fair that it's not a widely

846
00:42:06,120 --> 00:42:10,640
Speaker 17: known fact of exactly how much it costs consumers to

847
00:42:10,800 --> 00:42:14,359
Speaker 17: be swiping, tapping using their credit cards across the board.

848
00:42:14,880 --> 00:42:16,719
Speaker 3: You do a great job, but reminding us that this

849
00:42:16,800 --> 00:42:18,880
Speaker 3: is still at these stages and enacting any sort of

850
00:42:18,920 --> 00:42:21,719
Speaker 3: proposed cap would take, I believe, in act to congress,

851
00:42:22,800 --> 00:42:25,640
Speaker 3: but push us forward to what some of the key

852
00:42:25,680 --> 00:42:29,719
Speaker 3: executives have said in response to this potential move.

853
00:42:29,960 --> 00:42:32,680
Speaker 17: Most definitely a quote that stood out to me just

854
00:42:32,719 --> 00:42:34,759
Speaker 17: before I came on air as I saw that the

855
00:42:35,160 --> 00:42:39,440
Speaker 17: Sofi CEO, Anthony Noto. I believe the quote was giddy

856
00:42:39,520 --> 00:42:43,280
Speaker 17: up in terms of the opportunities for Sofi. Sofi actually

857
00:42:43,520 --> 00:42:46,439
Speaker 17: does offer credit card to its consumers, but it's really

858
00:42:46,520 --> 00:42:48,839
Speaker 17: a firm that's kind of best known for student loan

859
00:42:48,880 --> 00:42:52,719
Speaker 17: refinancing and also personal loans for consumers. So that's kind

860
00:42:52,719 --> 00:42:57,360
Speaker 17: of a flavor of the tones some executives are striking,

861
00:42:57,400 --> 00:43:00,600
Speaker 17: while others are maybe kind of hanging back and seeing

862
00:43:00,640 --> 00:43:03,920
Speaker 17: substantively what impact this could have or what opportunities it

863
00:43:03,960 --> 00:43:05,320
Speaker 17: could present for their businesses.

864
00:43:06,320 --> 00:43:09,239
Speaker 2: Bloomberg's page fith thank you very much. That does it

865
00:43:09,280 --> 00:43:11,799
Speaker 2: for this edition of Bloomberg Tech carry.

866
00:43:11,800 --> 00:43:14,319
Speaker 3: So much already as we kick off this week, don't

867
00:43:14,360 --> 00:43:15,960
Speaker 3: forget to check out our podcasts. You can find out

868
00:43:16,000 --> 00:43:19,080
Speaker 3: on the Terminal, sells online on Apple, Spotify, and iHeart.

869
00:43:19,840 --> 00:43:22,520
Speaker 4: Back in San Francisco and New York. No longer in Vegas.

870
00:43:22,640 --> 00:43:23,600
Speaker 4: This is Bloomberg Tech