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

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

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Speaker 1: and Vla Low in San Francisco.

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Speaker 2: This is Bloomberg Tech coming up. Memory chip prices strike again,

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Speaker 2: this time taking a toll on Cisco, even as AI

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Speaker 2: demand surges will break it down.

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Speaker 3: Plus, Apple's revamp of its Siri voice assistant hits new

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Speaker 3: snags that could delay the release of some features.

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Speaker 2: An Altruist launched AI tax planning tool, and within ours

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Speaker 2: wealth management stocks tumbled. The CEO of Outist joins us

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Speaker 2: to discuss.

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Speaker 3: First we check in on these markets that in many

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Speaker 3: ways are still digesting AI disruption. We focus in on

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Speaker 3: Cisco that I know you'll go to. We're off by

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Speaker 3: one percent on the Nastak one hundred. That anxiety of costs,

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Speaker 3: of memory costs really building into the benchmark. But more broadly,

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Speaker 3: we're all eyes on costs for consumers. CPI print coming

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Speaker 3: later in the market. Just a little bit nervous ahead

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Speaker 3: of that. Bitcoin down another day of five percentage point

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Speaker 3: sixty seven thousand is where we trade for this particular

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Speaker 3: risk asset ed What.

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Speaker 2: Are you looking at there's a lot of pieces of

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Speaker 2: consecutive red on the screens we've been putting up. Cisco

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Speaker 2: no exception, down eleven percent, on track for its biggest

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Speaker 2: drop since May twenty twenty two, and prior to today's session

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Speaker 2: it was up eleven percent and had momentum. Outlook is

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Speaker 2: strong on sales for the current period. AI demands there,

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Speaker 2: but there's margin compression memory chip prices. Let's get more

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Speaker 2: with Bloomberg Intelligence senior hardware and network analysts Wou jin Ho.

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Speaker 2: It's actually probably a good time to remind us what

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Speaker 2: Cisco is and what Cisco does. But the memory issue

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Speaker 2: is factoring in to servers, and right now you are

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Speaker 2: leading with your REACT piece on the Bloomberg terminal with

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Speaker 2: the memory chip issue. What do we need to know.

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Speaker 4: So really quickly with Cisco, is is the leading global

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Speaker 4: provider of a networking equipment for corporate as well as

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Speaker 4: for data centers, and they've actually had a foray into

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Speaker 4: cloud data centers to help power some of the AI workloads. Now,

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Speaker 4: what happened last quarter? They got the AI orders, you know,

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Speaker 4: momentum and drove a lot of the upside on sales,

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Speaker 4: but the gross margin issue especially given from the dram,

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Speaker 4: pricing is a lot bigger than we had thought.

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Speaker 3: Chuck Robins, though, was trying to say that, like, if

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Speaker 3: anyone's going to navigate this, they're going to navigate it.

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Speaker 3: They're already going to their suppliers. They're already thinking about

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Speaker 3: increasing prices. How well can they handle it?

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Speaker 4: Yeah, So we did the math for the third quarter,

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Speaker 4: there's about two hundred basis point product derosion on gross margin.

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Speaker 4: But when we look at the fourth quarter, you know

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Speaker 4: they're able to stabilize that. Now, if we think about

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Speaker 4: twenty twenty seven, they actually have a fairly sizable software business,

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Speaker 4: and if software really starts wrapping up, we could start

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Speaker 4: seeing some gross mogstability in twenty twenty seven.

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Speaker 3: W Jino Blomberg Intelligence. There we have your p Cisco's

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Speaker 3: results outlook pinched by memory price spike. Look, all of

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Speaker 3: this is as Cisco's numbers. They highlight the heavy infrastructure

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Speaker 3: build out, but have investors really questioning what happens next

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Speaker 3: for the implications to software too, joining us now to

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Speaker 3: go across the whole gamut. As Tony one portfolio manager

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Speaker 3: tro Price Science and Technology Fund in his latest thoughts,

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Speaker 3: he says that the recent software sell off is more

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Speaker 3: of a valuation reset and that companies that successfully embed

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Speaker 3: AI into workflows could drive stronger productivity and margins over time.

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Speaker 3: And Tony, what's so great about having your voice on this?

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Speaker 3: Shows you time and time again have been like the

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Speaker 3: proof point the ROI is going to be productivity gains.

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Speaker 3: Suddenly we've got the productivity gains. Saw a shining a

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Speaker 3: light that we're actually worried it's going to really hit valuations.

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Speaker 3: How do you weigh where we are in terms of

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Speaker 3: the selloff and if it's overdone?

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Speaker 5: Yeah, well, I think it's the key question the market

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Speaker 5: is wrestling with bright now. And so when I think

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Speaker 5: about it, it's it's really just what is on the

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Speaker 5: right side of change, I think, and what is the

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Speaker 5: inevitability here? And I think what you're seeing with plod

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Speaker 5: cowork is that these agents are becoming more autonomous and

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Speaker 5: they can actually execute things on their own, do more

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Speaker 5: than just like you have to prompt something like chat

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Speaker 5: to be team, you know, for for your your own

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Speaker 5: personal chat bete. But so what it's it's exciting is

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Speaker 5: that these enterprise agents, I think, are going to be

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Speaker 5: more more of a force here and as we're starting

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Speaker 5: to see that really pay off. And when we think

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Speaker 5: about it, like I think the why the markets reacting

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Speaker 5: to the way it is because if you think about

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Speaker 5: it in the in the future, like, there are wide

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Speaker 5: sweeping implications for what modes mean, like in terms of

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Speaker 5: software as well as information services. So it's definitely a

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Speaker 5: disruptive potential force here.

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Speaker 3: Disruptive to some of those in your portfolio. I'm looking

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Speaker 3: you are exposed to salesforce, you are exposed to accenture,

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Speaker 3: which people have been worrying about the consultants side of

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Speaker 3: the equation. Even Shopify and delimit strong numbers got beaten

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Speaker 3: up yesterday. How are you being discerning at this moment

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Speaker 3: of where you actually buy into on a dip because

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Speaker 3: you do think that they'll manage to ride the software disruption.

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Speaker 5: Yeah, so I think that you know there it's more

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Speaker 5: important that you are the platform and not just a feature.

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Speaker 5: Because you're a feature than AI agents can encode software

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Speaker 5: so easily and coustiously that you essentially can be embedded

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Speaker 5: in a bigger platform. And so I think that increasingly

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Speaker 5: you want to make sure that you're the layer that

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Speaker 5: all these agents need to coalesce around, and I think

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Speaker 5: that there's going to be a lot of change here

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Speaker 5: in terms of and we honestly don't know. And so

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Speaker 5: I think the market is in a sell first and

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Speaker 5: ask questions later mode, and so it's important to kind

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Speaker 5: of stay on the right side and manage these these

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Speaker 5: changes in the market.

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Speaker 2: Tiny Nvidia CEO agents one has this line that software

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Speaker 2: where is now worth paying for? The example he would

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Speaker 2: give is Cursor. But I guess what we're trying to

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Speaker 2: do with you is define what software is that is

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Speaker 2: worth paying for. You just brought up Salesforce, right, so

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Speaker 2: in the most recent quarter they had like more than

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Speaker 2: a billion dollars of arr directly from AI and they

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Speaker 2: talk about all the almost nineteen thousand agent force deals

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Speaker 2: that they have, yet they're still subject to this same

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Speaker 2: AI is going to eat software's lunch chat that's going

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Speaker 2: on right now. Why do people not believe the traction

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Speaker 2: that they have as an example that it's worth paying for.

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Speaker 6: Yeah, So I think.

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Speaker 2: There's two things that I think about.

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Speaker 5: One is that there's just so much competition when you

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Speaker 5: think about all these AI neted startups and they're pricing

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Speaker 5: in a way that is really compelling and to drive

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Speaker 5: ad option essentially, So you think about like image generation, right,

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Speaker 5: like you pay twenty dollars at chat to bet you

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Speaker 5: can generate unloaded images, whereas on Adobe or Firefly you

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Speaker 5: have to pay per image generation. But you know, you

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Speaker 5: can think about you know, cloud cowords in a similar

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Speaker 5: way in terms of what they're pricing versus the value

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Speaker 5: that they're delivering is really compelling. And so I think,

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Speaker 5: what you just have a lot of price inflation, and

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Speaker 5: that's why I think a lot of these you know,

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Speaker 5: software companies are looking to drive adoption. You know, the

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Speaker 5: economics aren't as good as the seat based model, and

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Speaker 5: we're going from like a sea based model to outcomes

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Speaker 5: and agents, which is a very different, different version.

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Speaker 2: Just for reference to audience. And as that one hundred

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Speaker 2: near session low is now down one and a quarter percent.

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Speaker 2: This story started in Europe in August. I was there

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Speaker 2: in London. I remember the day all the European software

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Speaker 2: that was sold off. Earlier today we spoke to the

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Speaker 2: ceman CEO. Let's get his take.

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Speaker 7: We know that AI has the potential to disrupt some

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Speaker 7: of the software businesses. I mean, if you talk about software,

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Speaker 7: it runs on call centers and the like. I mean,

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Speaker 7: that's fully on the block. But if you go on

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Speaker 7: the other end on industrial software, simulation software, physics, space software,

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Speaker 7: let's we don't see that at all. We rather see

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Speaker 7: an enrichment of the software which allows more and more

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Speaker 7: customers to use it.

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Speaker 2: Another software CEO whose fate is very closely tied to

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Speaker 2: a relationship with Nvideo, and I think it's important to

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Speaker 2: point that out. But his argument is that actually specialized

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Speaker 2: software is getting better because of AI. You buy that.

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Speaker 5: I think so, And I think that most I think

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Speaker 5: a lot of the economics that we've made and the

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Speaker 5: specialization of the niches, so you know, domain specific knowledge

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Speaker 5: will always be very important, and so you know, I

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Speaker 5: think just general knowledge is probably going to be not

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Speaker 5: take the economics, and so what really matters is like

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Speaker 5: being able to put this into ROI now. At the

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Speaker 5: same time, it doesn't mean that if you have a

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Speaker 5: stronger model, that doesn't mean it doesn't create more competition

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Speaker 5: for existing comments. So I don't think the battle is

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Speaker 5: one or loss for these companies, and I think it's

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Speaker 5: just important that they stay you know, front footed and

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Speaker 5: incorporating the latest technology trends into their.

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Speaker 3: Platform and your science Technology fund has all the trends.

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Speaker 3: You're in hardware, you're in software, you're number one holding

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Speaker 3: is alphabet at the moment, then in video. But going

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Speaker 3: back full circle to Cisco who exposure there? What do

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Speaker 3: you think about the memory price implications and how you

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Speaker 3: can weather who are the winners and loses in that situation?

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Speaker 2: How long for?

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Speaker 5: Yeah, So I think that you know, memory is fascinating

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Speaker 5: industry because one is secular trends and AI is now

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Speaker 5: this new tam and then we've also gone through multiple

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Speaker 5: years of under investment because memory has been a downturn.

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Speaker 6: So I think one it.

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Speaker 5: Is that they're secular, but there's also cyclcal elements a bit.

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Speaker 5: So you know, in terms of our portfolio, we definitely

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Speaker 5: own a lot of the bottlenecks where the economic value

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Speaker 5: capture is in such as memory. At the same time,

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Speaker 5: I think that you know, Cisco networking and this is

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Speaker 5: also going to be part of in AI. I think

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Speaker 5: that the implications of the memory a shortage is actually

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Speaker 5: becoming more widespread and having implications that a.

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Speaker 2: Lot of times the market didn't think about.

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Speaker 5: So you know, it is fascinating, But I do think

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Speaker 5: Cisco continue to be a really impeign company that's driving

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Speaker 5: a lot on this AI adoption tourny one.

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Speaker 2: Duro price great heavy back on Bloomberg Tech. Really appreciate it.

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Speaker 2: Now coming up, Apple's new Sery runs into more snags

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Speaker 2: during testing. What's delaying the AI up grade? We have

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Speaker 2: the Bloomberg recording next, This is Bloomberg Tech. Apple's revamp

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Speaker 2: of its Siri voice assistant has hit new challenges that

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Speaker 2: could delay the release of some features, according to sources,

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Speaker 2: Bloombo's Mark German, as always, has the details. Let's get

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Speaker 2: into the details. What do we need to know? What

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Speaker 2: you what are you hearing from inside the company about

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Speaker 2: the latest snag with artificial intelligence powered Sery.

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Speaker 8: So Apple has been planning to launch the new Seri.

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Speaker 9: This is an AI revamp that initially introduced in June

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Speaker 9: twenty twenty four at its Developers conference, and I'm going

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Speaker 9: to tap into features like your personal data to fulfill

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Speaker 9: queries onstream content and precise control of apps. It was

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Speaker 9: supposed to launch next month in March. Is part of

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Speaker 9: an update called iOS twenty six point four. This had

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Speaker 9: been the plan for several months. It's actually been the

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Speaker 9: plan for well over a year at this point. Now

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Speaker 9: that is being delayed and staggered potentially right now. The

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Speaker 9: delay is moving many of the serie capabilities to iOS

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Speaker 9: twenty six dot five that's scheduled for some time in

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Speaker 9: May June, so about a couple month delay. But some

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Speaker 9: of the features, including the personal context features, those are

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Speaker 9: likely not to launch until September as part of iOS

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Speaker 9: twenty seven, so a bit of a delay there, you know. Altogether,

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Speaker 9: it's possible that these new features are not going to

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Speaker 9: launch until two years after they were initially introduced. The

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Speaker 9: setback for Apple here is not necessarily in bone sales.

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Speaker 9: People are still buying iPhones. You saw the eighty five

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Speaker 9: billion dollars worth of sales, which could have even been

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Speaker 9: more if not for some manufacturing challenges. But the challenge

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Speaker 9: is where is OpenAI whereas anthropic? Where are Google going

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Speaker 9: to be at the end of this year? Right, They're

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Speaker 9: likely to be well ahead where they are today. So

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Speaker 9: that's really what we have to pay attention to.

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Speaker 3: Mett's talk Mark about how therefore the Google introgation is going,

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Speaker 3: because in many ways it just fits into the overall

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Speaker 3: Apple intelligence stop start and how they're going to be

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Speaker 3: building out from within.

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Speaker 9: My belief is the only reason they're going to be

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Speaker 9: able to get this done this year, which I still

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Speaker 9: believe they're going to do despite these latest snags during testing,

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Speaker 9: is because their models are being improved by the Google

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Speaker 9: Gemini team as part of that collaboration between the research

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Speaker 9: labs at both Apple and Google. So that is going

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Speaker 9: to be a major boon for them. I do expect

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Speaker 9: this big serie chatbot revamp in iOS twenty seven coming

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Speaker 9: out with the iPhones in the fall to be pretty

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Speaker 9: nifty and pretty cool, a new way to control your

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Speaker 9: phone with AI. They just need to pull it off,

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Speaker 9: and I think they will. But the big question is

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Speaker 9: how impressive is it going to be in September compared

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Speaker 9: to whatever Open AI in Gemini has for their own

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Speaker 9: customers and bropias with Quad in six months from now.

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Speaker 3: Bloomberg's Mark German, we appreciate you on all things latest

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Speaker 3: with Apple. Meanwhile, let's turn our attention to social media now,

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Speaker 3: because Instagram boss Adam Massari testified in a landmark case

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Speaker 3: they're like in social media to digital casinos. Masari told

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Speaker 3: the jury he doesn't consider problematic use of platforms like

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Speaker 3: meta to be the same as quote critical addiction. Blom

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Speaker 3: Meg's legal reporter Madlyn Mackelberg is with us. You were

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Speaker 3: there in the hearing, NLA, and so the push on

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Speaker 3: Adam Massari anything different. We've heard on other occasions that

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Speaker 3: he's testified.

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Speaker 10: So he covered a lot of similar ground to things

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Speaker 10: that we've heard before in congressional testimony and other public

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Speaker 10: remarks about this issue of social media addiction. Obviously, what's

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Speaker 10: unique here is the context in which he was giving

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Speaker 10: this testimony, which is this landmark case that's testing whether

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Speaker 10: juries will respond to claims that these companies knowingly designed

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Speaker 10: their platforms to addict young users. But we heard him

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Speaker 10: cover a lot of different ground. As you noted, he

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Speaker 10: talked about this issue of clinical addiction versus problematic use,

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Speaker 10: which is something that we hear a lot from social

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Speaker 10: media companies, referring to people who use the platforms more

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Speaker 10: than maybe they feel good about. We also heard him

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Speaker 10: talk about some internal emails in the company when he

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Speaker 10: was asked about them by the lawyer about decisions they

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Speaker 10: were weighing about certain features on the platform related to

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Speaker 10: photo filters on Instagram.

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Speaker 2: Mad this is a process, right, both sides present arguments

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Speaker 2: of plaintiffs, what do we need to know about what

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Speaker 2: happens next, who else might testify, and what is under

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Speaker 2: consideration at the cort of the trial. That's right.

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Speaker 10: So we're in week one of what's expected to be

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Speaker 10: an eight week trial. Adam Asseri was the first executive

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Speaker 10: to be called to the stand, but he will certainly

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Speaker 10: not be the last. As you said, this is the

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Speaker 10: plaintiff's case. They kind of get the first chunk of

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Speaker 10: time at the trial, and then the defendants in this case,

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Speaker 10: Meta in Google, they're going to get a chance to respond.

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Speaker 10: We're also expecting to hear from Mark Zuckerberg of Meta,

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Speaker 10: We're expecting to hear from YouTube CEO Neil Mohan at

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Speaker 10: some point, and we're also we kind of got a

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Speaker 10: little bit of a preview that we're going to be

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Speaker 10: hearing from executives a little bit further down the food

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Speaker 10: chain who maybe we're weighing in on some of these decisions.

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Speaker 10: But the heart of this case is about this question

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Speaker 10: of addiction and whether or not these platforms knowingly designed

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Speaker 10: these tools to hook young users resulting in mental health struggles.

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Speaker 10: This case, of course, centers on one individual, a young

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Speaker 10: woman from California and her specific allegations, but it represents

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Speaker 10: thousands of other cases that have been filed. So there's

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Speaker 10: a lot of attention on the outcome here because it's

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Speaker 10: going to dictate how the rest of these cases are decided.

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Speaker 2: Bloomberg's Madeline Meckeberg, thank you very much. Now coming up,

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Speaker 2: Altruist introduces a new AI way to plan your taxes.

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Speaker 2: We speak with Jason wenk Voucheris's CEO. Next, this is

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Speaker 2: Bloomberg Tech.

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Speaker 11: AI.

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Speaker 3: It's added again and it's been a wonkey, worrying few

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Speaker 3: days for wealth managers and those who had shares in

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Speaker 3: it considering the technology's impact. It's all started Tuesday, a

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Speaker 3: startup called Altruist added a tax planning tool to its

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Speaker 3: AI model Hazel. Within hours, wealth management stocks had tumbled.

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Speaker 3: Charles schwab lpl Woymond James, as you see significantly, and

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Speaker 3: that's as investors really.

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Speaker 11: Feared AI disruption.

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Speaker 3: The CEO of the company that kicked all of this off, Altruists,

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Speaker 3: Jason Wank Jason, did this citate you?

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Speaker 11: I surprise?

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Speaker 12: Yeah. I think, to be incredibly honest, I don't think

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Speaker 12: anybody could have possibly thought that you know, somewhere between

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Speaker 12: you know, tens of billions and maybe one hundred billion

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Speaker 12: of market cap would be wiped out in a week.

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Speaker 12: AI is very powerful and certainly the market you know

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Speaker 12: reaction tells me that there's a lot of people voting

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Speaker 12: with their dollars in the potential disruption.

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Speaker 3: Jason, all of this is about enabling well wealth managers

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Speaker 3: individual wealth managers. From your part, just remind us what

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Speaker 3: altruist does and what this new development in terms of

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Speaker 3: building in a tax tool does to help them.

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Speaker 8: Yeah, absolutely so.

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Speaker 12: I think the nail on the head, like the most

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Speaker 12: important thing is that we're not trying to displace financial

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Speaker 12: advisors or wealth managers.

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Speaker 8: It's not AI taking those jobs away.

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Speaker 12: It's AI to help empower them to do better work

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Speaker 12: for their clients. And in many ways, when people think

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Speaker 12: about wealth management, you know, there's there's deservedly a lot

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Speaker 12: of critique. It could be things like why is it

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Speaker 12: so hard to get access? Like the minimums are really high,

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Speaker 12: Why are the costs very high? Why are the results

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Speaker 12: sometimes inconsistent? And a lot of that is, you know,

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Speaker 12: because of how much manual process and human labors involved.

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Speaker 8: You just have a lot of limiting factors.

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Speaker 12: AI very much is an incredible equalizer, you know, effectively

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Speaker 12: with a tool like Hazel what used to take you know,

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Speaker 12: teams that were you know, ten or more people working

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Speaker 12: hundreds of hours and costing many tens of thousands of dollars,

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Speaker 12: It can be done in like two to three minutes.

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Speaker 12: And you know, users, the advisor users in our case

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Speaker 12: are paying one hundred is dollars per month, right, so

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Speaker 12: much more affordable, much more accessible.

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Speaker 8: And tax is just the start.

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Speaker 12: We launched tax first for what it's worth, because it's

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Speaker 12: tax season in the US. We wanted that to be

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Speaker 12: our first agent, you know, to kind of get out

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Speaker 12: there in the hands wealth managers and advisors. But this

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Speaker 12: is a pretty good harbinger of the things that will come.

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Speaker 12: There'll be more agents doing more work that will continue

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Speaker 12: to make advice better, more affordable, and more accessible.

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Speaker 2: Jason, you will have noticed that some of the CEOs

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Speaker 2: of those wealth managers appeared on television in the days

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Speaker 2: that followed the selloff. His child swab listened to what

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Speaker 2: they had to say.

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Speaker 13: AI is a real accelerant. So it was puzzling to

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Speaker 13: see our stock sell off related AI because we're benefiting

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Speaker 13: it from it in multiple ways and bringing it to

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Speaker 13: our clients.

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Speaker 2: It's allowing us to reach new clients.

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Speaker 13: And I think that's why you won't see the population

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Speaker 13: of advisor's shrink, is that AI will be useful in

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Speaker 13: reaching new clients that we couldn't before.

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Speaker 2: Has mister West or any other leader in that field

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Speaker 2: phoned you since Tuesday and would you be open to

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Speaker 2: working with these companies literally jointly. Yes.

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Speaker 12: So no, I haven't heard directly from mister Worcester, but

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Speaker 12: we already actually work with any dependent financial advisor. One

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Speaker 12: of the reasons why we stood up Hazel as a

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Speaker 12: separate entity altruist of course as a direct competitor to Schwab, Fidelity,

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Speaker 12: LPL and others. But we wanted Hazel to be something

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Speaker 12: that any advisor could use to help any client, and

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Speaker 12: we didn't want them to be forced into using a

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Speaker 12: single custodian. So there are already thousands of advisors who

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Speaker 12: use Schwab, for example, as a custodian that are leveraging

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Speaker 12: Hazel and doing so to drive better outcomes for their

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Speaker 12: clients and a lot more operational efficiency for themselves. And

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Speaker 12: we have i'd say, in the last forty eight hours.

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Speaker 12: It's in the hundreds of very large, significant, you know,

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Speaker 12: wealth management companies around the world reaching out to see

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Speaker 12: if there's a way that we can partner. So definitely

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Speaker 12: looking forward to, you know, putting this tool in as

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Speaker 12: many hands of as many capable advisors in the US

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Speaker 12: as possible.

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Speaker 2: Let's talk about the tool and the technology. You know,

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Speaker 2: what are the data sets that the underlying model was

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Speaker 2: trained on. What is your coal competence that if there

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Speaker 2: is concern from the legacy industry of wealth management, what

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Speaker 2: is it they should be concerned about, And what is

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Speaker 2: it that makes this model powerful?

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Speaker 12: Well, so kind there's two parts that I'll try to

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Speaker 12: be succinct. But the first is I think what causes

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Speaker 12: concern is not so much just the AI. It's the

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Speaker 12: fact that we also have built a very modern infrastructure

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Speaker 12: for our wealth business. So the things you can do

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Speaker 12: on altruists you just can't do anywhere else in terms

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Speaker 12: of the speed of opening accounts and funding accounts and

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Speaker 12: managing of assets. If you take AI and you put

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Speaker 12: it on top of bad, antiquated infrastructure, you're not going

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Speaker 12: to get a ton of value. It's you know, kind

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Speaker 12: of akin to putting like a you know, self driving

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Speaker 12: on a horse and buggy or something like that, Like

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Speaker 12: you need to have like an entire vertically integrated ecosystem.

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Speaker 12: I think, if anything, that's maybe where some of the

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Speaker 12: market concern is is that what happens if something like

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Speaker 12: you know is digital and it's twenty or thirty percent better,

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Speaker 12: you add AI and multiple agents, it becomes two or

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Speaker 12: three hundred percent better. Now advisors could very much have

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Speaker 12: a shift in where they decide to custoy their assets.

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Speaker 12: That could be pretty monumentally difficult to overcome for some

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Speaker 12: of the big incumbents.

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Speaker 8: As far as what makes the model unique.

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Speaker 12: The way that we built Hazel's we rely, like most

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Speaker 12: AI companies, on having really clean data.

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Speaker 8: So data is a huge advantage.

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Speaker 12: I think, you know, mister Worcester was right in saying

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Speaker 12: that data is very critical. In the case of the custodian.

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Speaker 12: You have the ultimate system of record meeting, right. We

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Speaker 12: have all of this data on clients transactions. That makes

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Speaker 12: it a lot easier to give personalized analysis and feedback

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Speaker 12: back to the users.

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Speaker 2: Jason Mike Vouchers, Sorry, we're out of time. Grateful for

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Speaker 2: the conversation coming out. We look at way moo. We'll

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Speaker 2: be right back. This is blue bag attack.

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Speaker 3: Welcome back to Bloomberg Tech. A lot of action in

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Speaker 3: the markets today, let's just go through it. In terms

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Speaker 3: of the NASDA benchmark currently off by one point eight percent,

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Speaker 3: we're at session lows. This is as we worry about

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Speaker 3: earnings and how they're implicated by prices. Prices and Memory

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Speaker 3: Cisco in particular down significantly. I want to shine a

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00:23:14,720 --> 00:23:16,760
Speaker 3: night and off by eleven percent. So we've got that

438
00:23:16,840 --> 00:23:20,240
Speaker 3: anxiety about whether AI is really helping these businesses or

439
00:23:20,240 --> 00:23:22,680
Speaker 3: whether pricing pressure is hurting them. Remember the macro pictures

440
00:23:22,720 --> 00:23:25,080
Speaker 3: are about get CPI data, so pricing pressure for the

441
00:23:25,119 --> 00:23:27,399
Speaker 3: consumer too. As we build up to that, the market's

442
00:23:27,480 --> 00:23:30,240
Speaker 3: just a little bit jittery. Add Ian over in European

443
00:23:30,240 --> 00:23:32,679
Speaker 3: trading check it out by twenty percent. This is the

444
00:23:32,680 --> 00:23:36,000
Speaker 3: payments company Fintech, which is being hit by consumer.

445
00:23:35,640 --> 00:23:37,000
Speaker 11: Sentiment being on the downside.

446
00:23:37,000 --> 00:23:39,040
Speaker 3: Also a week of dollar hit them as well in

447
00:23:39,119 --> 00:23:42,480
Speaker 3: terms of payments, revenue and their earnings outline, just really

448
00:23:42,520 --> 00:23:44,600
Speaker 3: not living up to expectations. We move on and have

449
00:23:44,640 --> 00:23:47,119
Speaker 3: a think about what's happening when you've got exposure to

450
00:23:47,160 --> 00:23:50,760
Speaker 3: the private sector now many are saying, I'm talking BTIG

451
00:23:50,880 --> 00:23:53,320
Speaker 3: in particular, saying that soft bank is like the way

452
00:23:53,359 --> 00:23:56,200
Speaker 3: to play open Ai exposure as well, because they've already

453
00:23:56,200 --> 00:23:58,840
Speaker 3: put thirty six billion dollars in. They're looking to increase

454
00:23:58,880 --> 00:24:00,600
Speaker 3: that maybe up to the tune of thirds thirty billionaire.

455
00:24:00,600 --> 00:24:02,639
Speaker 3: They have an eleven percent stake in the business, and

456
00:24:02,680 --> 00:24:05,160
Speaker 3: actually they managed to post a profit, the fourth quarterly

457
00:24:05,160 --> 00:24:07,560
Speaker 3: profit in a row, not seen since twenty twenty one,

458
00:24:07,680 --> 00:24:10,159
Speaker 3: as their investment stake in open Ai goes up and

459
00:24:10,200 --> 00:24:13,480
Speaker 3: to the right, helping offset some other losses. But the

460
00:24:13,520 --> 00:24:16,160
Speaker 3: stock traded in the US if you're looking at depository

461
00:24:16,160 --> 00:24:17,560
Speaker 3: receipts currently up by thirty percent.

462
00:24:17,600 --> 00:24:19,000
Speaker 11: So we'll dig into that a little bit more.

463
00:24:19,080 --> 00:24:19,159
Speaker 9: End.

464
00:24:19,240 --> 00:24:21,240
Speaker 3: But it's interesting what's happening in terms of a soft

465
00:24:21,240 --> 00:24:21,800
Speaker 3: bank story.

466
00:24:22,960 --> 00:24:25,320
Speaker 2: It's a little bit like shuffling the deck and like

467
00:24:25,560 --> 00:24:27,760
Speaker 2: you know, you have to sell at certain holdings to

468
00:24:27,840 --> 00:24:31,600
Speaker 2: fund investment deeper into the names that you're most interested in.

469
00:24:31,760 --> 00:24:34,280
Speaker 2: Like it's not rocket science at the end of the day.

470
00:24:34,320 --> 00:24:36,920
Speaker 2: Carry one quick question to ask is like how wide

471
00:24:36,960 --> 00:24:40,040
Speaker 2: soft banks is exposed beyond one single name like open Ai,

472
00:24:40,640 --> 00:24:43,280
Speaker 2: Given how ferocious the lab battle is right now.

473
00:24:43,440 --> 00:24:46,080
Speaker 3: Yeah, opening either in on Remember they've been going into

474
00:24:46,320 --> 00:24:49,720
Speaker 3: chip design companies like I'm peer, they've been looking real

475
00:24:49,760 --> 00:24:51,800
Speaker 3: exposure to ARM but they've been coming back on their

476
00:24:51,800 --> 00:24:54,359
Speaker 3: T mobile exposure to be able to get more into

477
00:24:54,359 --> 00:24:58,320
Speaker 3: these privately held businesses. Also been buying private equity companies

478
00:24:58,400 --> 00:25:00,359
Speaker 3: right because they want the exposure to day to center.

479
00:25:01,280 --> 00:25:03,200
Speaker 2: Yeah, I'm glad the team got that up. I mean,

480
00:25:03,560 --> 00:25:07,000
Speaker 2: you know, far right column says everything. Let's get to

481
00:25:07,119 --> 00:25:09,120
Speaker 2: another story in the private markets that we've been trying

482
00:25:09,119 --> 00:25:12,800
Speaker 2: to update you on regularly. Anthropic is close to locking

483
00:25:12,840 --> 00:25:14,920
Speaker 2: in a deal to raise more than twenty billion dollars

484
00:25:14,960 --> 00:25:18,120
Speaker 2: in a funding round co led by investors including Pieter

485
00:25:18,240 --> 00:25:22,280
Speaker 2: Teal's founder's fund, The Shore and DRAGONEA that's, according to

486
00:25:22,280 --> 00:25:25,920
Speaker 2: Bloomberg's sources, one of the largest startup funding rounds ever.

487
00:25:26,200 --> 00:25:29,680
Speaker 2: And who's been across at Bloomberg's VC reporter Natasha mascarinus

488
00:25:29,880 --> 00:25:33,359
Speaker 2: important detail, Actually, Like some of those names are are interesting.

489
00:25:34,160 --> 00:25:36,159
Speaker 2: You know, dragone has come up a lot recently. Weimo

490
00:25:36,720 --> 00:25:38,760
Speaker 2: was something we reported a couple of weeks ago. What

491
00:25:38,800 --> 00:25:40,400
Speaker 2: do we need to know? Like how close is this

492
00:25:40,520 --> 00:25:42,320
Speaker 2: and who's leading? Yeah, listen.

493
00:25:42,359 --> 00:25:44,800
Speaker 14: On Monday, we were discussing where are the crossover funds

494
00:25:45,119 --> 00:25:48,000
Speaker 14: in this round? Now we have some examples with d Shah,

495
00:25:48,080 --> 00:25:52,400
Speaker 14: with DRAGONEIR. We also have a continuing list of traditional

496
00:25:52,480 --> 00:25:54,919
Speaker 14: venture capitalists in the round. What I'm really paying attention

497
00:25:55,000 --> 00:25:57,520
Speaker 14: to is the addition of Founder's Fund to the cap table.

498
00:25:57,600 --> 00:26:00,800
Speaker 14: That's an early open AI investor and founder just Want

499
00:26:00,840 --> 00:26:03,080
Speaker 14: is known for concentrated bets, So it is interesting to

500
00:26:03,119 --> 00:26:05,359
Speaker 14: see an early open AI investor go ahead and be

501
00:26:05,400 --> 00:26:07,960
Speaker 14: a major investor in this anthropic ground. And Founder's Want

502
00:26:08,000 --> 00:26:11,359
Speaker 14: is not alone. We're seeing Sequoia as well in the round,

503
00:26:11,920 --> 00:26:12,680
Speaker 14: and the list grows.

504
00:26:13,800 --> 00:26:14,480
Speaker 11: It's interesting.

505
00:26:14,840 --> 00:26:18,440
Speaker 3: I know another VEC that makes concentrated bets and doesn't

506
00:26:18,480 --> 00:26:22,159
Speaker 3: go in to other companies that seem to directly compete.

507
00:26:22,280 --> 00:26:25,200
Speaker 3: So how much of a taboo is it that vcs

508
00:26:25,680 --> 00:26:28,600
Speaker 3: back to horses, almost like having some sub option here.

509
00:26:29,880 --> 00:26:31,920
Speaker 14: Yeah, I mean it used to be a huge taboo

510
00:26:32,160 --> 00:26:35,399
Speaker 14: because of concerns about information leakage or how do you

511
00:26:35,440 --> 00:26:37,080
Speaker 14: really take that out of your head when you're in

512
00:26:37,119 --> 00:26:40,720
Speaker 14: a partner meeting and discussing you know two different roadmaps

513
00:26:40,720 --> 00:26:43,040
Speaker 14: that are you know, both in this case going towards

514
00:26:43,080 --> 00:26:46,320
Speaker 14: a version of AGI. What I would say and what

515
00:26:46,359 --> 00:26:50,520
Speaker 14: we are reporting is that certain investors get certain information rights.

516
00:26:50,520 --> 00:26:52,440
Speaker 14: In the case of open AI, sources are telling us

517
00:26:52,480 --> 00:26:55,560
Speaker 14: that if you have a certain amount of ownership in

518
00:26:55,600 --> 00:26:58,679
Speaker 14: the company, that comes with its own information. And so

519
00:26:58,760 --> 00:27:01,840
Speaker 14: what we're really wondering is who's taking big checks in

520
00:27:01,880 --> 00:27:03,679
Speaker 14: these rounds, because they are the ones that will have

521
00:27:03,720 --> 00:27:06,280
Speaker 14: to answer the questions of how are we keeping the

522
00:27:06,320 --> 00:27:07,840
Speaker 14: information separate?

523
00:27:08,160 --> 00:27:11,479
Speaker 2: Bloombergs and Sasha Mascarinas, thank you very much. Now, Waimo

524
00:27:12,040 --> 00:27:14,600
Speaker 2: is aiming to have a big year, adding new US

525
00:27:14,600 --> 00:27:18,840
Speaker 2: and international markets. The robotax firm also recently raised sixteen

526
00:27:18,880 --> 00:27:22,880
Speaker 2: billion dollars from parent Alphabet and other outside investors. Co

527
00:27:22,960 --> 00:27:26,040
Speaker 2: CEO Taqijra Maracana sat down with us and discuss the

528
00:27:26,040 --> 00:27:30,159
Speaker 2: company's plans, including whether it's considering an IPO. Listen to this.

529
00:27:31,000 --> 00:27:34,439
Speaker 15: We are just laser focused on execution, you know, building

530
00:27:34,480 --> 00:27:38,879
Speaker 15: weimo to be financially responsible, operationally excellent, and then make

531
00:27:38,920 --> 00:27:42,160
Speaker 15: sure we maintain the safety culture. Like that's what we're

532
00:27:42,200 --> 00:27:45,480
Speaker 15: really focused on. Having this vote of confidence, as you said,

533
00:27:45,520 --> 00:27:48,960
Speaker 15: not only from Alphabet, but from our three colleagues. From

534
00:27:48,960 --> 00:27:52,000
Speaker 15: this round and from all of the new investors who

535
00:27:52,080 --> 00:27:55,000
Speaker 15: decided to join our cap table and the existing ones

536
00:27:55,200 --> 00:27:57,439
Speaker 15: who doubled down on their belief that this is the

537
00:27:57,520 --> 00:28:02,240
Speaker 15: right opportunity to fund, and we just feel humbled, but

538
00:28:02,280 --> 00:28:05,280
Speaker 15: also there's a lot to do, so we're really focused

539
00:28:05,320 --> 00:28:07,840
Speaker 15: on making sure that we can scale, focusing on our

540
00:28:07,880 --> 00:28:12,000
Speaker 15: two first international launches, you know, London and Tokyo, and

541
00:28:12,200 --> 00:28:13,880
Speaker 15: scaling across the United States.

542
00:28:14,200 --> 00:28:16,840
Speaker 2: We do not yet have, although there has been progress

543
00:28:16,880 --> 00:28:21,560
Speaker 2: towards just this week, a federal level framework set of rules.

544
00:28:23,119 --> 00:28:26,520
Speaker 2: What do you think the direction of travel is with that?

545
00:28:26,760 --> 00:28:29,280
Speaker 2: And you know, clearly if you had to deal with

546
00:28:29,280 --> 00:28:31,120
Speaker 2: one set of rules and not a city by city,

547
00:28:31,200 --> 00:28:35,199
Speaker 2: let alone state by state basis or case, you'd be

548
00:28:35,280 --> 00:28:37,480
Speaker 2: making a bit more progress. I suppose.

549
00:28:37,920 --> 00:28:40,360
Speaker 15: Yeah, we think it's really important that there is a

550
00:28:40,400 --> 00:28:45,200
Speaker 15: federal av standard. We've been advocating for sort of a

551
00:28:45,240 --> 00:28:49,440
Speaker 15: safety case based approach because the technologies are different and

552
00:28:49,480 --> 00:28:51,680
Speaker 15: we think that the burdens should be on companies to

553
00:28:51,760 --> 00:28:54,880
Speaker 15: demonstrate why you believe your technology is safe enough. We

554
00:28:54,920 --> 00:28:58,200
Speaker 15: also think there should be transparency requirements. You know, people

555
00:28:58,200 --> 00:29:00,640
Speaker 15: should have to demonstrate how many trips.

556
00:29:00,240 --> 00:29:01,040
Speaker 16: Are you providing?

557
00:29:01,240 --> 00:29:02,920
Speaker 15: You know, I don't right now.

558
00:29:03,120 --> 00:29:04,400
Speaker 2: The balance isn't quite there.

559
00:29:04,440 --> 00:29:07,280
Speaker 15: I mean, some states require a lot of reporting, some

560
00:29:07,360 --> 00:29:10,520
Speaker 15: don't require as much reporting. I think the United States

561
00:29:10,560 --> 00:29:14,160
Speaker 15: has an opportunity with this technology to lead globally, and

562
00:29:14,160 --> 00:29:16,719
Speaker 15: I don't think you can lead globally if it's a

563
00:29:16,760 --> 00:29:20,920
Speaker 15: framework that's governed by multiple jurisdictions across the states. And

564
00:29:21,000 --> 00:29:23,600
Speaker 15: it's a way to slow down the adoption of this

565
00:29:23,720 --> 00:29:26,880
Speaker 15: technology not only in the US, but in other markets.

566
00:29:27,760 --> 00:29:31,400
Speaker 2: New York City, New York City, not necessarily New York State.

567
00:29:31,920 --> 00:29:34,880
Speaker 2: A lot of people want to know what's the roadblock

568
00:29:34,920 --> 00:29:39,520
Speaker 2: there upon the expression and what's the timeline. You know,

569
00:29:40,120 --> 00:29:43,960
Speaker 2: you work closely with the authorities, but that is a

570
00:29:43,960 --> 00:29:45,240
Speaker 2: big potential market.

571
00:29:45,640 --> 00:29:47,760
Speaker 15: Yeah, that's a market where you know, we're just going

572
00:29:47,840 --> 00:29:51,160
Speaker 15: to have to do the work and demonstrate our safety

573
00:29:51,200 --> 00:29:54,840
Speaker 15: outcomes and earn the trust and shizzle away at it

574
00:29:54,920 --> 00:29:55,360
Speaker 15: over time.

575
00:29:55,400 --> 00:29:57,160
Speaker 2: Do they have the rules for you to follow?

576
00:29:57,560 --> 00:30:01,240
Speaker 15: They do not have rules that allow the human operator

577
00:30:01,320 --> 00:30:03,520
Speaker 15: to be removed from vehicle entirely.

578
00:30:03,280 --> 00:30:05,760
Speaker 2: And until that changes, and until that changes.

579
00:30:05,880 --> 00:30:08,520
Speaker 15: But you know, there is an interest in doing this

580
00:30:08,640 --> 00:30:11,000
Speaker 15: in the state, even outside of the city, and that

581
00:30:11,040 --> 00:30:14,440
Speaker 15: gives us an opportunity to grow more fans, and fans

582
00:30:14,560 --> 00:30:18,000
Speaker 15: actually are calling for this in cities where our technology

583
00:30:18,040 --> 00:30:21,760
Speaker 15: can't be deployed. We are seeing organic campaigns spring up

584
00:30:22,080 --> 00:30:24,000
Speaker 15: saying I want WEIMO in my town.

585
00:30:24,000 --> 00:30:28,520
Speaker 2: San Francisco. The Bay Area is my home. London's where

586
00:30:28,520 --> 00:30:31,040
Speaker 2: I grew up. And I was studying the map of

587
00:30:31,080 --> 00:30:35,880
Speaker 2: the burros that you propose to launch in and you

588
00:30:35,920 --> 00:30:38,120
Speaker 2: correct me if my math is wrong, but just based

589
00:30:38,160 --> 00:30:41,120
Speaker 2: on those burroughs at launch, this seems to be the

590
00:30:41,160 --> 00:30:45,520
Speaker 2: biggest citywide deployment from the start that you guys will

591
00:30:45,560 --> 00:30:47,360
Speaker 2: have done. Is that correct?

592
00:30:49,360 --> 00:30:49,440
Speaker 17: It?

593
00:30:49,600 --> 00:30:50,880
Speaker 2: Possibly, it's correct.

594
00:30:50,880 --> 00:30:53,360
Speaker 15: I mean, we're in the phases of figuring out the

595
00:30:53,440 --> 00:30:57,120
Speaker 15: specifics around the launch and figuring out the actual framework

596
00:30:57,200 --> 00:30:59,200
Speaker 15: around the launch, and so I don't want to speak

597
00:30:59,240 --> 00:31:01,600
Speaker 15: to definitive word about what we're going to do. But

598
00:31:01,840 --> 00:31:05,880
Speaker 15: what you're speaking to is we're not gated by the technology, right,

599
00:31:06,000 --> 00:31:08,640
Speaker 15: and we have the appetite to scale, and we want

600
00:31:08,640 --> 00:31:11,080
Speaker 15: to partner and do so safely, and we want to

601
00:31:11,120 --> 00:31:13,680
Speaker 15: earn trust. So that's like there's a lot of levers

602
00:31:13,720 --> 00:31:16,240
Speaker 15: there that we have to figure out how to strike

603
00:31:16,280 --> 00:31:18,120
Speaker 15: the right balance and how to make sure that we're

604
00:31:18,160 --> 00:31:21,080
Speaker 15: introducing it to the community both to meet the demand

605
00:31:21,120 --> 00:31:21,840
Speaker 15: and a grow I.

606
00:31:21,840 --> 00:31:24,920
Speaker 2: Asked, because of those twenty cities to come, London is one, yes,

607
00:31:25,440 --> 00:31:28,440
Speaker 2: and London is now outside of the European Union, but

608
00:31:28,480 --> 00:31:30,800
Speaker 2: it's you know, it's kind of your europe launch. Yes,

609
00:31:31,280 --> 00:31:34,880
Speaker 2: what was that experience like? Within London's regulatory framework and

610
00:31:35,200 --> 00:31:37,520
Speaker 2: the UK's regulatory framework.

611
00:31:37,160 --> 00:31:41,640
Speaker 15: They've been extremely forward leaning and interested in seeing how

612
00:31:41,680 --> 00:31:44,560
Speaker 15: this technology could actually improve safety.

613
00:31:44,120 --> 00:31:44,960
Speaker 11: On their roadways.

614
00:31:45,240 --> 00:31:47,040
Speaker 15: And that's where I think, you know, we find a

615
00:31:47,080 --> 00:31:52,240
Speaker 15: sweet spot when people are less sort of complacent about

616
00:31:52,240 --> 00:31:52,960
Speaker 15: the status quo.

617
00:31:53,520 --> 00:31:57,840
Speaker 3: Weimo co CEO to Keedra Mawakana ed. I mean, we've

618
00:31:57,880 --> 00:31:59,880
Speaker 3: got to check out the full interview. It's on Bloomberg

619
00:32:00,720 --> 00:32:03,440
Speaker 3: or as a special edition of Bloomberg Tech Podcast. But

620
00:32:04,040 --> 00:32:07,440
Speaker 3: really your conversation is about this extrawnay expansion into London

621
00:32:07,600 --> 00:32:11,960
Speaker 3: and just what pace of growth, what signals you discerning

622
00:32:12,040 --> 00:32:13,320
Speaker 3: as to how big it can be there.

623
00:32:14,040 --> 00:32:15,960
Speaker 2: Now you and I have both called London home, right

624
00:32:16,000 --> 00:32:18,520
Speaker 2: and we've taken the bus and the tube and gone

625
00:32:18,560 --> 00:32:21,520
Speaker 2: on bikes, and so how do they get past what's

626
00:32:21,520 --> 00:32:24,720
Speaker 2: a really established transit network and right now there are

627
00:32:24,880 --> 00:32:29,080
Speaker 2: several dozen Waymo vehicles testing in London. They are doing

628
00:32:29,120 --> 00:32:31,960
Speaker 2: that in more than thirty of the sixty London boroughs,

629
00:32:32,280 --> 00:32:34,600
Speaker 2: you know, like sixty percent of the city's already covered.

630
00:32:34,720 --> 00:32:37,600
Speaker 2: That's way beyond what, for example, Tesla's doing in Austin

631
00:32:37,720 --> 00:32:40,440
Speaker 2: right even the Bay Area here in terms of their

632
00:32:40,480 --> 00:32:43,760
Speaker 2: scale on footprint. So there's a big expectation that if

633
00:32:44,000 --> 00:32:47,800
Speaker 2: this labor government is doing okay and passes the rules,

634
00:32:48,200 --> 00:32:50,400
Speaker 2: then they can do this for real this year, no

635
00:32:50,560 --> 00:32:53,720
Speaker 2: safety driver or supervisor and have a commercial service at

636
00:32:53,760 --> 00:32:56,960
Speaker 2: scale that they haven't done that quickly in the United States.

637
00:32:57,080 --> 00:33:00,520
Speaker 3: Look, this is about disruption of transport, and it looks

638
00:33:00,560 --> 00:33:02,840
Speaker 3: as though transport and logistics is in the either storm

639
00:33:02,880 --> 00:33:04,560
Speaker 3: in terms of disruption today as well. If you're looking

640
00:33:04,600 --> 00:33:06,320
Speaker 3: at the markets now, you and I have just been

641
00:33:06,320 --> 00:33:09,000
Speaker 3: pouring over some of these market moves extraordinary for c H.

642
00:33:09,080 --> 00:33:11,560
Speaker 3: Robinson down by twenty one percent, but you're actually seeing

643
00:33:11,560 --> 00:33:14,560
Speaker 3: the Dow Transports the index having its worst day since

644
00:33:14,600 --> 00:33:17,880
Speaker 3: April of twenty twenty five, currently down by five percent.

645
00:33:18,240 --> 00:33:21,800
Speaker 3: Now traders are telling Bloomberg Kuneneagel, for example, which is

646
00:33:21,840 --> 00:33:26,400
Speaker 3: another European logistics company, traders are citing an announcement coming

647
00:33:26,400 --> 00:33:27,800
Speaker 3: from Algorithm Now.

648
00:33:27,840 --> 00:33:29,640
Speaker 11: Algorithm is a.

649
00:33:29,360 --> 00:33:32,280
Speaker 3: US traded company and they've announced that as a leading

650
00:33:32,280 --> 00:33:35,840
Speaker 3: AI technology company. They've published a new white paper demonstrating

651
00:33:35,880 --> 00:33:40,160
Speaker 3: its semi cab platform reduces empty freight miles by more

652
00:33:40,200 --> 00:33:42,920
Speaker 3: than seventy percent across active customer networks. Is this the

653
00:33:43,000 --> 00:33:44,640
Speaker 3: next aishooter drop ed?

654
00:33:45,520 --> 00:33:47,520
Speaker 2: It was a severe sharp drop and we got to

655
00:33:47,600 --> 00:33:49,920
Speaker 2: keep looking into it. Coming up from the program Back

656
00:33:49,960 --> 00:33:53,280
Speaker 2: to Ai similarly secures one hundred million dollars in new

657
00:33:53,280 --> 00:33:59,480
Speaker 2: funding as it looks to scale predictive AI mimics human behavior.

658
00:34:00,000 --> 00:34:02,480
Speaker 2: We have more on that next. This is Bloomberg Tech.

659
00:34:14,160 --> 00:34:17,640
Speaker 2: AIS Startups similarly has raised one hundred million dollars in

660
00:34:17,680 --> 00:34:20,319
Speaker 2: a new funding round led by Index Ventures. Similarly as

661
00:34:20,320 --> 00:34:24,600
Speaker 2: an AI lab, the wantes to help companies predict human behavior,

662
00:34:24,880 --> 00:34:27,720
Speaker 2: in fact a chain of human behaviors for more. June

663
00:34:27,760 --> 00:34:30,440
Speaker 2: Park similarly CEO and one of the founding group is

664
00:34:30,480 --> 00:34:33,720
Speaker 2: with us in San Francisco. This is very interesting because

665
00:34:33,760 --> 00:34:37,759
Speaker 2: the field has tried to develop a model that is

666
00:34:38,040 --> 00:34:42,240
Speaker 2: a behavior model, and it has had mixed success some failure.

667
00:34:43,239 --> 00:34:46,799
Speaker 2: Let's start by talking about the data sets that the

668
00:34:46,800 --> 00:34:50,040
Speaker 2: model's trained on. How did you develop what you think

669
00:34:50,080 --> 00:34:53,120
Speaker 2: is a model that does give you an insight or

670
00:34:53,120 --> 00:34:56,560
Speaker 2: a prediction of how a human might behave? Absolutely, thank

671
00:34:56,600 --> 00:34:58,080
Speaker 2: you for having me. Glad to be here.

672
00:34:58,520 --> 00:35:00,920
Speaker 17: So similarly is a company that are spinning out of

673
00:35:00,960 --> 00:35:05,000
Speaker 17: Stanford where we led frontier research on creating agents and

674
00:35:05,080 --> 00:35:08,920
Speaker 17: simulations that leverages generate to BAI technology. So today we

675
00:35:09,080 --> 00:35:12,359
Speaker 17: partner with millions of real people and combine our data

676
00:35:12,400 --> 00:35:16,440
Speaker 17: collection strategy with our modeling strategy to create agents that

677
00:35:16,480 --> 00:35:20,120
Speaker 17: can actually behave in a much more generalizable context. So

678
00:35:20,280 --> 00:35:23,799
Speaker 17: data here we might leverage include interview data that's unstructured,

679
00:35:23,960 --> 00:35:25,799
Speaker 17: which is the kind of data in the past was

680
00:35:25,920 --> 00:35:26,600
Speaker 17: very hard to.

681
00:35:26,600 --> 00:35:29,879
Speaker 2: Leverage literally people telling you their life stories. That's exactly right.

682
00:35:30,239 --> 00:35:33,520
Speaker 17: So we actually create ai interviewer that can go talk

683
00:35:33,520 --> 00:35:36,240
Speaker 17: to people one on one, voice to voice and actually

684
00:35:36,239 --> 00:35:39,680
Speaker 17: get life stories and their preferences around different policies and

685
00:35:39,719 --> 00:35:41,160
Speaker 17: so forth with their consent.

686
00:35:42,440 --> 00:35:45,920
Speaker 3: And to what end I mean to build simulations that

687
00:35:45,960 --> 00:35:51,560
Speaker 3: are one hundred percent accurate to then populate with agents tune.

688
00:35:51,719 --> 00:35:53,120
Speaker 11: How do you see this work?

689
00:35:53,200 --> 00:35:58,680
Speaker 3: You've done proving more disruption to the market right now, right.

690
00:35:58,920 --> 00:36:02,480
Speaker 17: So today we work Fortune ten companies in retailer businesses,

691
00:36:02,760 --> 00:36:07,759
Speaker 17: to personal finance, to CpG companies and even to polling companies.

692
00:36:07,920 --> 00:36:11,680
Speaker 17: And today our customers use our technology to do concept testing. So,

693
00:36:11,719 --> 00:36:13,840
Speaker 17: for instance, CBS has been a close partner for the

694
00:36:13,880 --> 00:36:17,160
Speaker 17: past five months where CBS has now created hundreds of

695
00:36:17,200 --> 00:36:20,719
Speaker 17: thousands of similis or quote unquote agents that represent their

696
00:36:20,760 --> 00:36:24,360
Speaker 17: real customers, so they can actually do simulated focus groups

697
00:36:24,600 --> 00:36:27,040
Speaker 17: to understand better and there's in their pain points and

698
00:36:27,040 --> 00:36:30,520
Speaker 17: their use cases, but also to do story layout designs

699
00:36:30,600 --> 00:36:34,200
Speaker 17: and so forth. Galop, another polling company that's highly well

700
00:36:34,280 --> 00:36:37,120
Speaker 17: established and respected, is now working with similar to create

701
00:36:37,200 --> 00:36:41,399
Speaker 17: digital panel of their agents or their simulated human panels. Now,

702
00:36:41,440 --> 00:36:44,239
Speaker 17: one last example that's interesting is, of course many of

703
00:36:44,280 --> 00:36:47,759
Speaker 17: our customers are Fortune five hundred companies that regularly have

704
00:36:47,920 --> 00:36:51,400
Speaker 17: earnings call, so use case there might actually be creating

705
00:36:51,440 --> 00:36:54,120
Speaker 17: simulation of their earnings call. So this is an ass

706
00:36:54,239 --> 00:36:57,480
Speaker 17: that we get frequently. So in the recent earnings call

707
00:36:57,520 --> 00:37:00,319
Speaker 17: we simulated we actually can predict eight out of ten

708
00:37:00,440 --> 00:37:02,520
Speaker 17: questions that are actually asked.

709
00:37:02,200 --> 00:37:02,719
Speaker 2: In this court.

710
00:37:02,800 --> 00:37:07,600
Speaker 3: Sorry, June, you're saying the analysts almost don't need to

711
00:37:07,600 --> 00:37:10,880
Speaker 3: be asking that question. You can predict with eighty seventy

712
00:37:11,719 --> 00:37:14,760
Speaker 3: what analysts are going to ask CEO CFOs on earning course.

713
00:37:15,680 --> 00:37:16,600
Speaker 2: That's exactly right.

714
00:37:16,760 --> 00:37:19,880
Speaker 17: In fact, I actually before our conversation today, I had

715
00:37:19,880 --> 00:37:22,399
Speaker 17: a chance to simulate the questions that twelve you might ask.

716
00:37:22,719 --> 00:37:24,640
Speaker 17: And these are some other questions that actually have come

717
00:37:24,719 --> 00:37:25,600
Speaker 17: up in our assimilation.

718
00:37:26,040 --> 00:37:30,719
Speaker 2: Oh so you seem well prepared. Look, there's a degree

719
00:37:30,719 --> 00:37:35,200
Speaker 2: of skepticism about Similarly, to be fair, the founding members

720
00:37:35,200 --> 00:37:37,960
Speaker 2: four of you are are largely academics at Stamford. You

721
00:37:38,360 --> 00:37:40,839
Speaker 2: founded the company officially a year ago, but you've only

722
00:37:40,880 --> 00:37:43,799
Speaker 2: been training the model for seven months. You say you

723
00:37:43,840 --> 00:37:46,719
Speaker 2: have five months of commercial deals. How have you done

724
00:37:46,760 --> 00:37:49,000
Speaker 2: that so quickly? So similarly, it is a.

725
00:37:48,960 --> 00:37:53,279
Speaker 17: Real combination of amazing front your researchers that lasers the

726
00:37:53,320 --> 00:37:56,400
Speaker 17: ceiling of what the models are capable of today, but

727
00:37:56,560 --> 00:37:59,600
Speaker 17: also amazing product and engineers.

728
00:38:00,080 --> 00:38:01,600
Speaker 2: So here on the product.

729
00:38:01,280 --> 00:38:03,840
Speaker 17: Side, we have people like Laney Allen and Mikha Kapor

730
00:38:04,040 --> 00:38:07,160
Speaker 17: who have been product and engineering leader in the space

731
00:38:07,400 --> 00:38:11,760
Speaker 17: of companies such as Figma and Heavier, and that combined

732
00:38:11,760 --> 00:38:13,840
Speaker 17: with the rest of the engineering team and the research

733
00:38:13,920 --> 00:38:17,440
Speaker 17: talent our neighbors, us to create frontier technology that actually

734
00:38:17,480 --> 00:38:18,960
Speaker 17: gets routed directly.

735
00:38:18,680 --> 00:38:23,160
Speaker 3: To product June Park, who has preempted all of our

736
00:38:23,239 --> 00:38:26,880
Speaker 3: questions through his own products of Civilly. We so appreciate

737
00:38:26,920 --> 00:38:29,440
Speaker 3: your time, Thank you very much. Indeed, let's just take

738
00:38:29,480 --> 00:38:32,400
Speaker 3: a look elsewhere in AI model world, because Alphabet currently

739
00:38:32,440 --> 00:38:35,719
Speaker 3: spiking higher, up almost a percentage point. This is they've

740
00:38:35,800 --> 00:38:39,480
Speaker 3: updated a release to Gemini three Gemini three Deep Thinking

741
00:38:39,520 --> 00:38:42,279
Speaker 3: Reasoning Model. It's an update and they say that the

742
00:38:42,320 --> 00:38:46,080
Speaker 3: mode hasn't been updated to solve modern science research challenges

743
00:38:46,120 --> 00:38:46,640
Speaker 3: in particular.

744
00:38:46,840 --> 00:38:46,920
Speaker 6: Ed.

745
00:38:47,080 --> 00:38:49,480
Speaker 3: Look, Alphabet has been on a tear because of the

746
00:38:49,560 --> 00:38:52,399
Speaker 3: idea that it's generative. AI is managing to leap frog

747
00:38:52,480 --> 00:38:54,160
Speaker 3: or at least push ahead some of the rivals.

748
00:38:55,440 --> 00:38:57,080
Speaker 2: Yeah, it has, and look at the spike in the

749
00:38:57,120 --> 00:39:00,000
Speaker 2: session on those headlines are coming up Coinbase releases earning

750
00:39:00,120 --> 00:39:02,840
Speaker 2: after the closing bell, We're going to preview what to expect.

751
00:39:02,920 --> 00:39:20,280
Speaker 2: This is Blomberg Tech, Asian delivery firm GRAB missed analysts

752
00:39:20,360 --> 00:39:23,480
Speaker 2: expectations on its full year forecast to sign that weaker

753
00:39:23,600 --> 00:39:27,000
Speaker 2: consumer sentiment is weighing on the company. Earlier, we spoke

754
00:39:27,040 --> 00:39:29,560
Speaker 2: with GRAB CFO Peter Owe. Listen to this.

755
00:39:30,440 --> 00:39:33,120
Speaker 18: It was an amazing year for us. We achieved some

756
00:39:33,239 --> 00:39:36,400
Speaker 18: new mastones in the business. We have now fifty million

757
00:39:36,440 --> 00:39:40,680
Speaker 18: monthly transacting users using our app constantly on a monthly basis.

758
00:39:40,719 --> 00:39:43,840
Speaker 6: We also showed great profitability.

759
00:39:43,200 --> 00:39:46,120
Speaker 18: In the business to over five hundred million dollars in

760
00:39:46,120 --> 00:39:48,440
Speaker 18: the Justicey you be done, but also our first YEARNIT

761
00:39:48,560 --> 00:39:51,480
Speaker 18: profit in the business. As we look at twenty twenty six,

762
00:39:51,880 --> 00:39:54,520
Speaker 18: we're firing on aus cylinders and we are seeing good

763
00:39:54,600 --> 00:39:57,680
Speaker 18: growth across all the platforms, across all the different products

764
00:39:57,680 --> 00:40:00,680
Speaker 18: that we have today. We are confident going into twenty

765
00:40:00,719 --> 00:40:03,960
Speaker 18: twenty six in terms of delivering that new guide that

766
00:40:04,000 --> 00:40:06,279
Speaker 18: we gave up for twenty twenty six, but also we

767
00:40:06,320 --> 00:40:08,719
Speaker 18: also gave a three year guidance at the same time

768
00:40:08,880 --> 00:40:11,680
Speaker 18: of twenty percent revenue growth from twenty twenty five through

769
00:40:11,719 --> 00:40:12,520
Speaker 18: twenty twenty eight.

770
00:40:13,040 --> 00:40:14,640
Speaker 11: So really long term perspective here.

771
00:40:14,760 --> 00:40:17,399
Speaker 3: Just dialing in on the profitability side, because you're right

772
00:40:17,840 --> 00:40:20,400
Speaker 3: many loving the fact that you surprised you delivered this

773
00:40:20,440 --> 00:40:23,719
Speaker 3: full year profit. What now to really drive up that

774
00:40:23,760 --> 00:40:27,160
Speaker 3: return on equity? Our Bloomberg Intelligence analyst talking about maybe

775
00:40:27,239 --> 00:40:29,200
Speaker 3: it's the net profit.

776
00:40:28,920 --> 00:40:29,960
Speaker 11: To really be driven higher.

777
00:40:30,040 --> 00:40:32,120
Speaker 3: Needs even more leaning into the fintech side of the

778
00:40:32,120 --> 00:40:33,760
Speaker 3: business or some cost cuts.

779
00:40:33,800 --> 00:40:34,520
Speaker 11: Where do you pull.

780
00:40:36,120 --> 00:40:38,040
Speaker 18: Yeah, there's really three big pillars when you look at

781
00:40:38,080 --> 00:40:40,759
Speaker 18: the business today. One is continuing the growth momentum in

782
00:40:40,800 --> 00:40:43,880
Speaker 18: our rights business as well as our deliveries business. And

783
00:40:43,880 --> 00:40:47,799
Speaker 18: our deliveries also now we have other products and services up.

784
00:40:47,840 --> 00:40:50,040
Speaker 18: One example at grocery delivery continues to be one of

785
00:40:50,120 --> 00:40:53,440
Speaker 18: our fastest growing it's actually growing one point seven times faster.

786
00:40:53,640 --> 00:40:55,120
Speaker 6: Than our food business today.

787
00:40:55,400 --> 00:40:58,560
Speaker 18: We also have dining in which is another unique set

788
00:40:58,560 --> 00:41:00,759
Speaker 18: of product features that brings people the restaurants.

789
00:41:00,760 --> 00:41:03,279
Speaker 6: Also, at the same time, financial service is.

790
00:41:03,280 --> 00:41:06,680
Speaker 18: Also condused to be our fastest growing segment about business today.

791
00:41:06,840 --> 00:41:09,120
Speaker 18: The loan box size that we have today, we've clicked

792
00:41:09,160 --> 00:41:12,040
Speaker 18: over a billion dollars in loan portfolio and we expect

793
00:41:12,040 --> 00:41:14,480
Speaker 18: to double that is we finish the year twenty twenty six.

794
00:41:14,560 --> 00:41:17,400
Speaker 18: So we've got all the different products working together to

795
00:41:17,480 --> 00:41:20,080
Speaker 18: really drive the growth in the missus but also margin

796
00:41:20,200 --> 00:41:21,480
Speaker 18: expansion at the same.

797
00:41:21,320 --> 00:41:25,080
Speaker 3: Time, delivery to fintech grabs CFO Peter Ue there talking

798
00:41:25,120 --> 00:41:27,880
Speaker 3: of fintech. Coinbase was about to report earnings after the

799
00:41:27,880 --> 00:41:30,280
Speaker 3: bell and this comes in the context of a deepening

800
00:41:30,320 --> 00:41:33,000
Speaker 3: crypto route which has seen the value of bitcoin in particular,

801
00:41:33,080 --> 00:41:36,000
Speaker 3: plunge look more than forty five percent from its peak,

802
00:41:36,160 --> 00:41:39,240
Speaker 3: and with that, expectations of coinbases revenue are not looking

803
00:41:39,239 --> 00:41:41,920
Speaker 3: pretty Bloomberg Senior crypto reporter Olga Caraff.

804
00:41:42,000 --> 00:41:42,880
Speaker 11: Joins us now.

805
00:41:43,160 --> 00:41:45,480
Speaker 3: And it's all about volumes, right, and they're going to

806
00:41:45,520 --> 00:41:48,080
Speaker 3: pull back, so inherently revenues must have been hit.

807
00:41:49,280 --> 00:41:55,359
Speaker 19: Absolutely, So what happens when prices drop continuously? A lot

808
00:41:55,360 --> 00:41:59,080
Speaker 19: of the retail traders stay on the sidelines, and of

809
00:41:59,120 --> 00:42:03,239
Speaker 19: course crypto exchanges they make a huge portion of their

810
00:42:03,239 --> 00:42:06,640
Speaker 19: revenues from transaction fees, so they see a huge drop

811
00:42:06,680 --> 00:42:07,000
Speaker 19: on that.

812
00:42:08,680 --> 00:42:11,719
Speaker 2: What I understand or I get the volumes concerned, But

813
00:42:11,880 --> 00:42:16,160
Speaker 2: I've always understood that like coinbase others that it competes with,

814
00:42:16,440 --> 00:42:20,080
Speaker 2: they benefit from volatility, right because volatility you have to

815
00:42:20,080 --> 00:42:23,080
Speaker 2: trade in one direction or another. Why is that not

816
00:42:23,600 --> 00:42:25,600
Speaker 2: a good moment for them?

817
00:42:26,120 --> 00:42:30,680
Speaker 19: So you're absolutely right, when there is high volatility, they

818
00:42:30,719 --> 00:42:34,680
Speaker 19: do benefit. But typically what happens is that you have

819
00:42:34,920 --> 00:42:39,560
Speaker 19: say several days of very high volatility, such as sort

820
00:42:39,560 --> 00:42:44,920
Speaker 19: of in October when bitcoin dropped a lot, or in

821
00:42:45,160 --> 00:42:48,520
Speaker 19: early February when again it dropped a lot. So you

822
00:42:48,600 --> 00:42:50,920
Speaker 19: can have a lot of volatility, a lot of trading

823
00:42:51,000 --> 00:42:54,399
Speaker 19: during those days, but then you can have a sort

824
00:42:54,440 --> 00:42:58,600
Speaker 19: of slower activity, lower activity in the days kind of

825
00:42:58,640 --> 00:43:03,399
Speaker 19: in between those very high volatility days, and that can

826
00:43:03,440 --> 00:43:08,840
Speaker 19: impact exchanges like for instance, we've already seen Robinhood report.

827
00:43:08,560 --> 00:43:11,960
Speaker 16: That is a fourth quarter crypto revenue.

828
00:43:11,560 --> 00:43:15,240
Speaker 19: Was down thirty percent, So things are not looking good

829
00:43:15,360 --> 00:43:19,239
Speaker 19: for crypto trading providers.

830
00:43:19,480 --> 00:43:23,279
Speaker 3: Briefly, Coinbase has tried to diversify ahead of this. They're

831
00:43:23,320 --> 00:43:26,520
Speaker 3: looking whether or not it's predictions markets, whether it's equity

832
00:43:26,560 --> 00:43:27,200
Speaker 3: trading alga.

833
00:43:27,280 --> 00:43:27,839
Speaker 11: Is that enough?

834
00:43:29,840 --> 00:43:34,600
Speaker 19: So Coinbase has been trying to diversify for many years

835
00:43:34,640 --> 00:43:38,839
Speaker 19: and these are really good initiatives that could really help

836
00:43:39,320 --> 00:43:42,600
Speaker 19: in the years ahead. But right now, for instance, they

837
00:43:42,760 --> 00:43:45,320
Speaker 19: just launched prediction markets, so it's a very.

838
00:43:45,239 --> 00:43:47,120
Speaker 16: Very small revenue driver.

839
00:43:47,440 --> 00:43:52,440
Speaker 19: They do have stable coin revenue coming from their revenue

840
00:43:52,440 --> 00:43:57,120
Speaker 19: share with Circle that is helping to stabilize revenues quite

841
00:43:57,160 --> 00:43:59,319
Speaker 19: a bit, but a.

842
00:43:59,280 --> 00:44:01,839
Speaker 16: Lot of the the revenue portions are.

843
00:44:01,719 --> 00:44:05,400
Speaker 2: Still all Bloomberg's ald creed. Thank you very much. That

844
00:44:05,520 --> 00:44:07,959
Speaker 2: does it carry for this edition of Bloomberg Tech