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Speaker 1: Bloomberg Audio Studios, Podcasts, radio news. Bloomberg Tech is live

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

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Speaker 1: and ever Low in sentences.

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Speaker 2: Go this is Bloomberg Tech coming up.

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Speaker 3: Bloomberg News pulls back the curtains on Apple's revamped Siri

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Speaker 3: design ahead of its WWDC debut.

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Speaker 4: Plus Snowflake jumps the most It's twenty twenty after the

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Speaker 4: software maker gave a stronger outlook and sign a six

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Speaker 4: billion dollar deal with Amazon.

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Speaker 5: We'll hear from the CEO.

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Speaker 3: And Meta introduces paid chatbot subscriptions to help offset its

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Speaker 3: AI infrastructure costs.

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Speaker 4: First we look at infrastructure and how actually an infrastructure

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Speaker 4: deal with Amazon is helping summon the narrative around Snowflake.

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Speaker 4: Extraordinary move, the biggest jump in the stock since twenty twenty.

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Speaker 4: We're looking at thirty four percent gain after a thirty

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Speaker 4: four percent in product revenue for this company. It was

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Speaker 4: a beat, it was a raise, and there's real acceleration

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Speaker 4: in people using their AI coding tool. In particular, twenty

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Speaker 4: two billion dollars added in a market cap so much

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Speaker 4: to digest today.

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Speaker 2: Ed a lot of news headlines this morning.

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Speaker 3: One coming from the information that Microsoft next week is

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Speaker 3: going to introduce a coding model, a model focused on coding,

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Speaker 3: a marketplace we've covered so much of late the stock

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Speaker 3: off session highs that we saw gains three and a

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Speaker 3: half almost four percent, So the market taking it seriously.

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Speaker 2: If we hear more, will give you more.

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Speaker 4: Well, and now we can give you more on what

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Speaker 4: to expect with Apple's Siri overhaul, because it's going to

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Speaker 4: take center stage in the company's next major software update,

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Speaker 4: and Bloomberg News has details on what to expect ahead

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Speaker 4: of the WWDC debut.

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Speaker 5: Now, these Bloomberg.

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Speaker 4: Created illustrations, they offer a look at the revamped Sery interface,

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Speaker 4: including a new chatbot style app that the images that

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Speaker 4: you're currently looking at they're based on information viewed by

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Speaker 4: Bloomberg and people with knowledge of the company's plans.

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Speaker 5: Well those plans.

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Speaker 4: It's Bloomberg Consumer Tech and Apple Managing editor Mark German.

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Speaker 5: So the look the feel is really building.

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Speaker 4: On some of the updates we've had in the past

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Speaker 4: and improving them.

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Speaker 6: Well, I'll just say this, this is an incredibly exciting

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Speaker 6: moment for Apple.

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Speaker 7: I think consumers should be pumped.

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Speaker 6: What Apple is doing here is they've seen everything that

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Speaker 6: Open Ai and Google and Anthropic have done. They believe

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Speaker 6: that AI has a place at the center of its products,

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Speaker 6: and they're finally going to implement that.

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Speaker 7: So this is a really big deal for consumers.

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Speaker 6: People have been clamoring for a version of Siri that

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Speaker 6: works properly for the better part of fifteen years, and

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Speaker 6: my strong belief as we are finally going to get

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Speaker 6: this fall So I see this as a major development

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Speaker 6: and accomplishment for Apple in its quest to try to

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Speaker 6: bring AI to the masses, taking a slightly different tack

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Speaker 6: than their rivals in the space.

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Speaker 3: There's two parts to this, as you report it. There's

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Speaker 3: the standalone Siri app, let's say it's a kin to

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Speaker 3: a chat GPT app. And then there's how you interact

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Speaker 3: with Siri on the screen of your phone. You know, however,

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Speaker 3: you open up the phone, we're going to go through

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Speaker 3: some of the images that you included in the story,

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Speaker 3: But could you just explain those two parts that we're detailing,

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Speaker 3: and as we cycle through the images, we're showing them on.

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Speaker 2: The screen right now.

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Speaker 3: Again, these are Bloomberg generated images based on our reporting,

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Speaker 3: both discussions with sources and documentation that we viewed but

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Speaker 3: those two new features.

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Speaker 6: Please, So to launch Sirie today, you say, obviously the

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Speaker 6: SII wake word or Hey Siri if you have an older.

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Speaker 7: Device, or you can hold down the power button.

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Speaker 6: That continues, and there's a new animation that pops out.

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Speaker 7: Of the dynamic island.

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Speaker 6: Obviously they added that with the fourteen proback in twenty

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Speaker 6: twenty two, so this is with modern iPhone hardware in mind.

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Speaker 6: The second thing you can do was swipe down from

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Speaker 6: the top center of the iPhone. So how you open

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Speaker 6: notifications today will be how you open a new serie

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Speaker 6: interface called search or Ask, and that essentially takes you

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Speaker 6: into a type to serie interface basically a system wide

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Speaker 6: AI agent. You can tell it to get things done

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Speaker 6: on your behalf. You can do searches on your device

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Speaker 6: as well as the open Web. So there's a perplexity

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Speaker 6: competitor from Apple, built by Apple, developed by Apple, designed

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Speaker 6: by Apple in there as well. And then there's the

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Speaker 6: serie app. You know, chatbots have taken the world by storm.

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Speaker 6: Chat GPT has nearly a billion users. People are using Gemini,

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Speaker 6: people are using claud. This is clearly something that people want.

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Speaker 6: So this is a product an app in line with

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Speaker 6: what you're getting from Google Gemini.

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Speaker 7: Obviously, as we reported last year, the.

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Speaker 6: Underlying models for a lot of these new technologies in

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Speaker 6: Siri are powered by Gemini and running on Google's cloud infrastructure.

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Speaker 4: How worried should like a chat che should open aib

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Speaker 4: right now? You reported that maybe they even considering legal action.

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Speaker 4: Is this going to be a real standalone competitor.

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Speaker 6: Well, we've seen this time and time again where Apple

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Speaker 6: releases a standalone app of its own that's built into

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Speaker 6: the operating system. I think chat GPT has the strongest brand.

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Speaker 6: Serie doesn't have a very strong brand. I still think

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Speaker 6: they should, you know, rebrand the whole effort. That's neither

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Speaker 6: here nor there, at least for now, but definitely having

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Speaker 6: a chatbop built into iOS, mac os, iPad os north

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Speaker 6: of two billion devices that is threatening. I would say

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Speaker 6: to Gemini to chat GPT and to Claude, especially if

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Speaker 6: over time Apple is able to make it really competitive

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Speaker 6: to what you're seeing to chat GPT.

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Speaker 7: So we'll have to see how this plays out.

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Speaker 6: Over time, but at least, you know, in the short term,

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Speaker 6: a lot of people are going to be introduced to

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Speaker 6: the concept of a chat or a conversational AI interface

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Speaker 6: who haven't used it before, despite the popularity of church.

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Speaker 3: Gipt, Bluebo's Mark Gumman, Thank you very much. Let's get

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Speaker 3: to another story. Shares a Salesforce up about a percentage point,

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Speaker 3: kind of a muted move, and you're gonna understand why.

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Speaker 3: In a few minutes time, the company gave a revenue

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Speaker 3: outlook for the current period that just fell short of

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Speaker 3: an this estimates Bloemberg's Brady Ford covering Salesforce with us.

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Speaker 3: Now we're kind of waiting for a different story in

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Speaker 3: a minute's time, which might give us some context on

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Speaker 3: that move. But what was the story of Salesforce? Forget

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Speaker 3: the outlook the numbers. What were they saying?

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Speaker 8: Application companies are trying to reinvent themselves as AI companies,

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Speaker 8: and so Salesforce came out and said, we have an

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Speaker 8: AI product that's kind of ramping and revenue.

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Speaker 7: We have all these.

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Speaker 8: Positive traction points, but our core products, for you know,

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Speaker 8: sales and service are slowing down, and so it's how

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Speaker 8: quickly can they reinvent themselves At this point, not very

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Speaker 8: I mean their app a percentage point today, but if

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Speaker 8: that chart zooms out, it has not been a rosy

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Speaker 8: picture for Salesforce, and the last year or two it's down.

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Speaker 5: Thirty percent year to date.

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Speaker 4: Brody, I loved your content on LinkedIn, just showing that,

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Speaker 4: like basically all earnings caols are now turning into podcasts,

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Speaker 4: so they're.

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Speaker 5: Trying to reframe and rebrand the way in which they

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Speaker 5: present these results.

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Speaker 4: But like many off is saying, this is still a

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Speaker 4: record quarter, So how do we get more confident that.

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Speaker 5: There is going to be that.

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Speaker 4: Second half inflection on organic growth?

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Speaker 8: Yeah, it's just that reacceleration story, right, I mean, their

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Speaker 8: biggest products keep slowing down, They've done acquisitions, they've added

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Speaker 8: new products, and so there's kind of a lot of

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Speaker 8: rosy things to look at. But until that kind of

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Speaker 8: core sales, cloud service, cloud that really built their house,

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Speaker 8: until that speeds back up due to AI, they're going

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Speaker 8: to stay in that penalty box.

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Speaker 3: Should we just very quickly talk about the actual story today?

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Speaker 3: Teams throw it up on the screen. Snowflake is going

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Speaker 3: absolutely parabolic.

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Speaker 9: Yep.

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Speaker 8: Why, it's a tale of two cities, right, I Mean

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Speaker 8: snow makes data infrastructure software, So a lot of companies

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Speaker 8: have their most important data sitting in Snowflake and they

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Speaker 8: have to use it if they want to do all

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Speaker 8: these cool AI features on top of it. And we're

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Speaker 8: seeing in the numbers that demand for Snowflake's products is

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Speaker 8: kind of going gangbusters. So they had a great day.

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Speaker 4: They did, and Brodiefd, you're gonna stick with us to

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Speaker 4: talk about a little bit more Snowflake so you can

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Speaker 4: see that move means it's added twenty two billion dollars

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Speaker 4: to its market capitalization. I mean after beating and expectations.

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Speaker 4: They've also locked in that massive six billion dollar infrastructure

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Speaker 4: deal with Amazon. We could talk about that with Brody

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Speaker 4: in a minute, but first of all, just take from

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Speaker 4: the Snowflake CEO himself, Shieto Ramaswami.

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Speaker 5: We spoke to Melia.

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Speaker 7: First of all, they're.

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Speaker 9: A longtime partner. They are the biggest cloud service provider

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Speaker 9: that be run on top of and on on top

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Speaker 9: of Azura as well as GCP. The really important thing

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Speaker 9: with Amazon is how we go to our customers.

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Speaker 2: Together.

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Speaker 9: Bothrians are extraordinary value that we deliver for our customers,

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Speaker 9: and with deals like this we get massive economies of

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Speaker 9: scale that let us pass on some of these savings

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Speaker 9: back to our customers. We ounced a huge change in

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Speaker 9: how we price AI that makes AI a lot less

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Speaker 9: expensive for our customers. It's aided by deals like this

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Speaker 9: because of this ability to bulk purchase confidently, which, as

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Speaker 9: I said, in turn, we give to our customers create

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Speaker 9: amazing products on top.

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Speaker 2: This deal makes us much more effective.

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Speaker 9: Together, Amazon is interested in solving customer problems, and having

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Speaker 9: a data platform is a key part of solving customer problems,

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Speaker 9: us being able to go to market together. We work

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Speaker 9: at every level of the hierarchy. Matt and I are garment,

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Speaker 9: the CEO and I are in constant touch, but so

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Speaker 9: on our teams. It's that ability to collaborate at a

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Speaker 9: deep level to solve complex problems, for example, like ed

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Speaker 9: data migration that makes us pretty unique.

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Speaker 2: It is truly a better gather story.

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Speaker 3: What timing Caroline's conversation with the CEO of snowfake and

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Speaker 3: audio issues there that's tech happens.

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Speaker 2: Is this stock up thirty five.

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Speaker 3: Percent because of a compute deal with AWS or is

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Speaker 3: it up for a different reason, Like it's really difficult

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Speaker 3: in this moment to see the impact of that relationship

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Speaker 3: in how the market's cheering the name today.

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Speaker 8: It seems that most of the rally is due to

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Speaker 8: their own products and the fact that they have a

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Speaker 8: pretty strong outlook. But the Amazon deal is interesting because

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Speaker 8: a big part of the software story right now is

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Speaker 8: when you're spending all of this money on LMS on

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Speaker 8: AI features, that does hurt your margins, and so anything

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Speaker 8: that companies are able to do to get economies of

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Speaker 8: scale and drive costs down, which appears to be what's happening,

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Speaker 8: that's also good news.

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Speaker 5: I mean, ed will know this more than anyone.

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Speaker 4: The graviton offering that comes from AWS and the idea.

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Speaker 4: In your story, you make clear that maybe they're pushing

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Speaker 4: towards that because of.

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Speaker 5: These efficiency gains.

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Speaker 4: And we're going to hear from Shreidar a little bit

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Speaker 4: later in the show about that brody, but well, broady,

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Speaker 4: how are we seeing the adoption of coding tools Cortex

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Speaker 4: in particular seven thousand more than subscriptions?

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Speaker 5: Is that a lot? When you're thinking about the competitors

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Speaker 5: out there.

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Speaker 8: It's interesting because a lot of application platforms, they've had

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Speaker 8: their own tools like a coding assistant or other productivity

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Speaker 8: tools right and there in the platform. In a lot

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Speaker 8: of cases, we hadn't seen great uptake for it. And

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Speaker 8: so Snowflakes saying, Hey, actually the coding tool that we're

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Speaker 8: putting on here is being used. It is driving revenue.

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Speaker 8: That's kind of new. We haven't seen that from a

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Speaker 8: ton of other companies. And so I think that's a

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Speaker 8: pretty significant positive point that's being reacted to here.

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Speaker 5: And you just think about where Shreidar comes from.

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Speaker 4: Like his whole business AI business was bought by Snowflake

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Speaker 4: neither before, So no wonder he's managing to integrate at

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Speaker 4: Bloomberg's brody Ford.

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Speaker 5: Great Nawk, thanks for joining now.

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Speaker 4: Coming up, Navy is going to be joining us making

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Speaker 4: a major push towards electric marine travel, deploying one hundred

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Speaker 4: electric vessels are across the Maldives. How nice because speaking

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Speaker 4: with the CEO next, that's Bloomberg Tech.

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Speaker 3: US based maritime technology company Navier is deploying one hundred

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Speaker 3: electric vessels across the Maldives to build an inter island

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Speaker 3: transportation network linking airports, resorts, local communities. Roll up marks

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Speaker 3: a major milestone for electrified marine mobility. Joining us now

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Speaker 3: as Navy CEO some pretty badasharia back on Bloomberg Tech,

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Speaker 3: and with respect, last time you're on the program. Nava

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Speaker 3: was in a very different place just getting started. Fast forward,

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Speaker 3: you're going to put one hundred of your vessels in

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Speaker 3: a really interesting market, and you're really ramping up commercially

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Speaker 3: with one hundred million dollar deal.

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Speaker 2: Let's start with Moldives. Piece.

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Speaker 3: When we lost FOT, you weren't looking at this kind

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Speaker 3: of luxury into island market.

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Speaker 2: Now you are.

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Speaker 10: Why Actually we always saw the potential for you know,

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Speaker 10: transportation with the N.

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Speaker 11: Thirty for islands.

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Speaker 10: But what is interesting here is that Maldives is this

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Speaker 10: place where there's a natural need for the technology, and

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Speaker 10: that aligns very well with our vision. You know, it

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Speaker 10: has a vision for twenty thirty net zero and there's

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Speaker 10: over a thousand islands today, there is over you know,

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Speaker 10: two thousand, eight hundred gas guzzling boats. The fit is

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Speaker 10: so on spot, and you know, we are very grateful

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Speaker 10: for the partners we found who have like the you

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Speaker 10: know JIH who have the similar vision of like developing

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Speaker 10: the country's infrastructure.

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Speaker 4: Let's talk about the N thirty Pioneer edition. What does

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Speaker 4: it show in terms of performance? What does it do

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Speaker 4: that's unlike anything that's on the market.

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Speaker 10: Yeah, absolutely, you know, at a high level, our goal

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Speaker 10: at NAVIA is to build a standardized foundational layer I

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Speaker 10: would say the best possible platform on the water with

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Speaker 10: you know, dual use case, whether that's transportation or defense.

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Speaker 11: And we are very focused on.

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Speaker 10: Making this reliable, long range and applicable for different kind

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Speaker 10: of sea states. So this is not just the deployment

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Speaker 10: of the boat, but this is also the deployment of

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Speaker 10: a network.

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Speaker 11: So usually when you go somewhere.

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Speaker 10: You know you have one of boats that takes you

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Speaker 10: to an island, but in this case, it's more like

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Speaker 10: what you see in the land.

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Speaker 11: What do you see in the air? Right?

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Speaker 10: You have United Airlines, you have Black Lane, you have

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Speaker 10: four seasons on the land right, But when it comes

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Speaker 10: to the water, there is no standardization.

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Speaker 11: And what is different is that you know.

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Speaker 10: You are in this beautiful resource right, which are so

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Speaker 10: into sustainability, and then you get on the water and there's.

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Speaker 11: A huge disconnect.

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Speaker 10: And that's where we really come in. You know, that

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Speaker 10: experience part of.

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Speaker 3: It some pretty How much pressure are you under to

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Speaker 3: deliver for JIH and the Moldives project, like a hundred

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Speaker 3: of these are they built? How quickly do you build them?

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Speaker 3: Where do they get built? How do they get to

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Speaker 3: the Moltives give us a sense of how real this is.

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Speaker 10: Yes, absolutely, so, just to give a background, you know,

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Speaker 10: jih is led by Mama Dali Jihana. He's one of

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Speaker 10: the most influential business leaders of Maldives who have literally

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Speaker 10: known as the man who built Maldives, driving force behind

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Speaker 10: most of the prestigious hotels and resorts like the World

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Speaker 10: War four seasons, and they have also a presence in

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Speaker 10: the GCC. So I'm really working very closely with them

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Speaker 10: the first year, this very year, we're starting with five vessels.

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Speaker 10: The first one is going to get there you know,

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Speaker 10: end of summer, and then we are going to test

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Speaker 10: out this fleet and at the same time we will

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Speaker 10: be working very closely with you know jih to plan

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Speaker 10: out the infrastructure, the routes and then the you know,

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Speaker 10: deployment is phased over the next three years. So there

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Speaker 10: is a bit of like the infrastructure planning, route planning

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Speaker 10: and then the software lure of it.

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Speaker 11: Right, So we want to make this.

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Speaker 10: A very seamless experience where Malis really become almost like

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Speaker 10: the you know, it becomes a playbook of how to

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Speaker 10: replicate this in you know, many other places.

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Speaker 4: Well, how do you replicate that into a defense narrative

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Speaker 4: not just luxury briefly.

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Speaker 10: Right, because you know, if you go back to it,

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Speaker 10: the company is really focused on what we called building

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Speaker 10: the generalized marine vessel platform and the that is the

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Speaker 10: standardized core.

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Speaker 11: Right.

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Speaker 10: If you forget the if you strip it to the

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Speaker 10: physics of it. The role of a vessel is to

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Speaker 10: carry per unit payload pernit mile reliably efficiently and go

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Speaker 10: the longest distance at speed. What do you put on

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Speaker 10: top of that, Like people often get caught into this, Oh,

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Speaker 10: is it is it a luxury boat?

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Speaker 11: It looks too pretty. No, forget it, it's just your physics,

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Speaker 11: you know, it's just.

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Speaker 10: The physics of the vessel, right, And our goal is

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Speaker 10: to get as many vessels out there as possible for

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Speaker 10: us to win as a generational maritime company. What you

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Speaker 10: have seen today in asymmetric warfare, Right, we have to

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Speaker 10: move away from exotic vessel building to standardize you know,

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Speaker 10: scalabild systems, and that you can only do when you're

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Speaker 10: going to dual use and commercial use cases forces you

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Speaker 10: to like ruthlessly cut down cost.

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Speaker 11: So that's a big part of it. When you have

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Speaker 11: dual use platforms.

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Speaker 10: You streamline everything the building, maintenance, supply chain and so on.

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Speaker 4: So some realty fascinating, some reti battetaria. Thank you for

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Speaker 4: joining us and Ivia and discussing the mold. These move meanwhile,

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Speaker 4: from electric vessels to electric vehicles. Weimo set to deploy

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Speaker 4: new autonomous vehicles purpose built forever taxi use without human supervision.

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Speaker 5: Now topped the OHI.

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Speaker 4: The cars will be made available to select riders in

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Speaker 4: San Francisco, Los Angeles and Phoenix. You were one of

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Speaker 4: those select individuals. Talk us through the ride you took

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Speaker 4: and am I saying it right?

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Speaker 3: Yeah, o hi ohi as in the city in SoCal

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Speaker 3: but also like oh hi. But for Weimo, like this

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Speaker 3: is very serious, right, this is the next phase of

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Speaker 3: them scaling. So the Bloomberg Tech audience has probably seen

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Speaker 3: one of the white Weimo Jaguar Eye paces. This is

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Speaker 3: a vehicle that Weimo developed with Zeka, an arm of

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Speaker 3: China's Gli, and you know it's a completely different design,

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Speaker 3: more like a shuttle. But the point being that they

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Speaker 3: final assemble these in Mesa, Arizona, and at scale of

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Speaker 3: like you're talking tens of thousands per annum.

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Speaker 2: So in the first instance. Yeah, free rides.

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Speaker 3: Get user feedback, but if Weimo's going to break into

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Speaker 3: the mainstream, this is what they see as being their

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Speaker 3: mass volume transporter.

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Speaker 5: Fascinating and particularly the China Angler as well.

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Speaker 2: Ed particularly the China Angle.

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Speaker 3: But I think that's the story here that because it's

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Speaker 3: final assembly in Mesa and that the skateboard arrives without

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Speaker 3: any of the self driving.

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Speaker 2: Tech, they get going here, you are there.

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Speaker 3: I am a lot of fun read the Bloomberg story

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Speaker 3: will have more on the socials later now coming up,

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Speaker 3: Andthropics explosive growth has turned into a highly sought after employer.

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Speaker 2: We've got more in that next. This is boombog Tech.

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Speaker 4: Landing a job at Anthropic is so fiercely competitive that

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Speaker 4: applicants are spending get this, six hundred dollars on.

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Speaker 5: Average on private interview coaching.

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Speaker 4: Candidates say the startups intense culture screen feels that's like

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Speaker 4: an interview and more like therapy on the company faces

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Speaker 4: some incredible pressure to survive economically while keeping its values.

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Speaker 4: People like really cammoring to be part of that story. Bloomo,

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Speaker 4: Joe Constance and place to say it is with us

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Speaker 4: you do this fascinating deep dive what it's like to

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Speaker 4: go through interview rounds and anthropic What is it like?

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Speaker 12: Well, I mean, I mean, for for the first things,

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Speaker 12: it's competitive. There are so many applicants. Now, there's so

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Speaker 12: many people who are you know, just would be thrilled

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Speaker 12: to join. Even the most seasoned engineers, the most high

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Speaker 12: level executives, recruiters tell me, are willing to take the

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Speaker 12: call from the recruiter and the interview process.

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Speaker 5: While a lot of it is.

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Speaker 12: Pretty standard, the culture interview, from what I hear from

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Speaker 12: candidates and recruiters is a little unusual and more of

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Speaker 12: you know, a lot of companies, the culture fit interview

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Speaker 12: is kind of a vibe check, just to make sure

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Speaker 12: you know you're not.

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Speaker 5: Odd.

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Speaker 12: But then this interview is a little bit more.

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Speaker 4: They want you to be old in many ways, I

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Speaker 4: mean differently, you'll push back against thoughts at all.

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Speaker 12: Different They have a very defined sense of their own

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Speaker 12: culture and so they are looking for particular people to

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Speaker 12: fit that environment.

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Speaker 2: Hey, Joe, a lot of people I know work at them.

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Speaker 3: FROPIK are pretty odd and if you're watching the show today,

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Speaker 3: you know where to find me. So if you're a candidate,

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Speaker 3: because I imagine Actually a lot of the blom Beg

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Speaker 3: Tech audience are aspirational. They want to go and work

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Speaker 3: at anthropic What does forty six hundred dollars actually get you? Like,

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Speaker 3: what are you paying for? And is it working?

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Speaker 8: Sure?

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Speaker 12: You know, part of this story was interesting to discover

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Speaker 12: a little bit more about this, this cottage industry of

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Speaker 12: interview prep companies, these career coaches that are really selling

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Speaker 12: you know, their services to help people prepare for these

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Speaker 12: what feels like and you know, oftentimes very high stakes

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Speaker 12: sorts of you know, rounds and rounds of these interviews

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Speaker 12: and skills assessments, and so in some cases it's just

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Speaker 12: some resources to prep candidates on you know, what types

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Speaker 12: of questions they can expect. For other coaches they're offering

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Speaker 12: mock interviews.

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Speaker 4: And what time can you expect because it is different,

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Speaker 4: you are going to be sort of pushed more than

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Speaker 4: you might be elsewhere because they're really so based on

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Speaker 4: the mission right briefly.

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Speaker 12: Right, So, I mean that's that's the thing that I've

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Speaker 12: heard from candidates that sometimes they're not quite expecting as

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Speaker 12: much intro respection, you know, these types of questions that

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Speaker 12: are really pushing folks to reflect on, you know, past experiences,

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Speaker 12: decisions they've made, have they felt about those decisions, which

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Speaker 12: is a bit unusual for people who are used to

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Speaker 12: just talking about, you know, this project they did at

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Speaker 12: work and how it went and my great.

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Speaker 5: Is failing being how how I'm too orientated?

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Speaker 3: Yes, exactly, the most constants with pretty much the most

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Speaker 3: read story on Bloomberg today, Thanks very much. Coming up,

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Speaker 3: Meta is selling consumer subscriptions to its METAAI chatbot for

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Speaker 3: the first time. Got that story next halfway through the program.

443
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Speaker 3: Heck of a lot more to come. This is Bloomberg Tech.

444
00:23:00,200 --> 00:23:02,760
Speaker 4: Welcome back to Bloomberg Tech. Let us take a look

445
00:23:02,760 --> 00:23:06,440
Speaker 4: at today's big number, twenty two billion dollars and counting

446
00:23:06,760 --> 00:23:09,520
Speaker 4: is how much the market cap for Snowflake has grown

447
00:23:09,640 --> 00:23:12,320
Speaker 4: from just one day's market gain. The stock we know

448
00:23:12,520 --> 00:23:15,119
Speaker 4: is surging today. So after the software maker gain a

449
00:23:15,160 --> 00:23:18,040
Speaker 4: stronger than expected annual outlook, we in fact managed to

450
00:23:18,040 --> 00:23:20,800
Speaker 4: speak with a Snowflake CEO, should A Ramaswami on how

451
00:23:20,840 --> 00:23:23,560
Speaker 4: AI is really helping to boost their bottom line.

452
00:23:23,760 --> 00:23:27,440
Speaker 9: Well, we had a landmark quarter caroline strongest sequential dollar

453
00:23:27,440 --> 00:23:31,040
Speaker 9: growth in company's history. Product revenue op to one point

454
00:23:31,119 --> 00:23:34,120
Speaker 9: three three four billion dollars of thirty four percent net

455
00:23:34,160 --> 00:23:36,720
Speaker 9: revenue retention rate, a key metric we watch up to one

456
00:23:36,760 --> 00:23:39,520
Speaker 9: hundred and twenty six percent. But I think the bigger

457
00:23:39,560 --> 00:23:42,760
Speaker 9: news really was this is the quarter where we clearly

458
00:23:42,800 --> 00:23:47,520
Speaker 9: showed that AI is compounding snowflakes advantage in data. We

459
00:23:47,600 --> 00:23:50,399
Speaker 9: did this ole fashion way by creating amazing products like

460
00:23:50,440 --> 00:23:53,800
Speaker 9: Snowflake Intelligence, which is our work agent, which doubled it

461
00:23:53,880 --> 00:23:57,560
Speaker 9: that option with respect to our accounts, and our coding agent,

462
00:23:57,720 --> 00:24:01,239
Speaker 9: which excoed our coco which is used by more than

463
00:24:01,320 --> 00:24:03,520
Speaker 9: seven thousand our counts. And this is what gives us

464
00:24:03,600 --> 00:24:06,280
Speaker 9: confidence in the business. That's why we raise that your

465
00:24:06,520 --> 00:24:10,119
Speaker 9: items from twenty seven to thirty one percent started performance.

466
00:24:10,200 --> 00:24:12,240
Speaker 9: But I think is much more of what does this

467
00:24:12,359 --> 00:24:14,679
Speaker 9: mean for our future that you're very happy with.

468
00:24:16,359 --> 00:24:18,720
Speaker 3: Another big story in the world of tech is Meta.

469
00:24:18,760 --> 00:24:20,760
Speaker 3: This is a two day chart. Yesterday the stock up

470
00:24:20,800 --> 00:24:24,479
Speaker 3: almost four percent. We're basically flat today. Meta is selling

471
00:24:24,560 --> 00:24:27,880
Speaker 3: consumer subscriptions to its Meta AI chatbot for the first time.

472
00:24:27,960 --> 00:24:31,480
Speaker 3: Two tiers basic tier seven dollars n nine cents a

473
00:24:31,560 --> 00:24:33,680
Speaker 3: month that's cool, and then one that's like kind of

474
00:24:33,720 --> 00:24:37,040
Speaker 3: higher tier Meta one plus like I guess those of

475
00:24:37,080 --> 00:24:39,920
Speaker 3: you that are more in it, we write a Bloomberg

476
00:24:39,960 --> 00:24:43,640
Speaker 3: News that this is about offsetting the AI infrastructure costs,

477
00:24:43,920 --> 00:24:45,920
Speaker 3: but other people have slightly different take. I want to

478
00:24:45,920 --> 00:24:49,760
Speaker 3: get to sweader Conjuria Wolf Research managing director joins us. Now,

479
00:24:50,200 --> 00:24:53,240
Speaker 3: because you put research out right when the when the

480
00:24:53,280 --> 00:24:57,640
Speaker 3: news comes, and you're basically saying our thesis, which we'd

481
00:24:57,640 --> 00:25:01,840
Speaker 3: already outlined, is playing out you multiple new revenue streams.

482
00:25:02,720 --> 00:25:06,320
Speaker 3: This is a potential bigger total addressable market for you,

483
00:25:06,760 --> 00:25:10,080
Speaker 3: rather than an action to offset that high capex or

484
00:25:10,160 --> 00:25:11,320
Speaker 3: high infrastructure spending.

485
00:25:13,119 --> 00:25:16,560
Speaker 13: Yeah, that's right, and thanks for having me ed so

486
00:25:16,800 --> 00:25:19,000
Speaker 13: at a high level. One of the deeper dives that

487
00:25:19,040 --> 00:25:22,240
Speaker 13: we did prior to the news coming out was part

488
00:25:22,240 --> 00:25:25,280
Speaker 13: of the reason why Meta is even underperforming Google and

489
00:25:25,440 --> 00:25:29,639
Speaker 13: Amazon is that Meta is spending like a hyperscaler without

490
00:25:29,680 --> 00:25:33,399
Speaker 13: any clear line of sight into demand that Google and

491
00:25:33,480 --> 00:25:36,560
Speaker 13: Amazon have in their hyperscale business. And so where is

492
00:25:36,640 --> 00:25:40,960
Speaker 13: Meta spending all this money to really justify this type

493
00:25:40,960 --> 00:25:44,080
Speaker 13: of spend and cap x and when will we see

494
00:25:44,080 --> 00:25:46,520
Speaker 13: that revenue? So that's the fundamental question, and so in

495
00:25:46,560 --> 00:25:48,879
Speaker 13: that when we dug deeper, it could be subscription or

496
00:25:48,920 --> 00:25:51,239
Speaker 13: it could be a gentic commerce or it could be

497
00:25:52,520 --> 00:25:55,040
Speaker 13: business AI. And now we're starting to see with this

498
00:25:55,119 --> 00:25:58,720
Speaker 13: product release of subscription across consumer businesses and Meta AI

499
00:25:59,160 --> 00:26:00,600
Speaker 13: that this could be the beginning of.

500
00:26:00,600 --> 00:26:02,879
Speaker 5: It about how this is going to scale.

501
00:26:02,920 --> 00:26:05,240
Speaker 4: First quarter, it was about one point three billion dollars

502
00:26:05,240 --> 00:26:07,000
Speaker 4: that was in the non advertising revenue.

503
00:26:07,040 --> 00:26:09,639
Speaker 5: So tiny, how much could that rise?

504
00:26:10,880 --> 00:26:14,439
Speaker 13: Yeah, so in the non advertising revenue right now, a

505
00:26:14,480 --> 00:26:16,920
Speaker 13: lot of it could be business AI that they are

506
00:26:16,960 --> 00:26:22,159
Speaker 13: actually monetizing in WhatsApp through their businesses in WhatsApp. Now

507
00:26:22,280 --> 00:26:26,200
Speaker 13: an add on is going to be subscription. So a

508
00:26:26,359 --> 00:26:29,960
Speaker 13: clearest comp that we have today is Snapchat, and Snapchat

509
00:26:30,040 --> 00:26:34,760
Speaker 13: arguably has surprisingly done a great job in converting its

510
00:26:35,160 --> 00:26:38,240
Speaker 13: da user, daily active users, and monthly active users to

511
00:26:38,320 --> 00:26:41,760
Speaker 13: subscriber base. If Meta can do something similar to that,

512
00:26:41,800 --> 00:26:43,960
Speaker 13: and I'm not saying it's going to be exactly the same,

513
00:26:44,280 --> 00:26:47,600
Speaker 13: but say low to mid single legit percentage of their

514
00:26:47,680 --> 00:26:52,560
Speaker 13: daus actually convert to subscription, well, that in itself implies

515
00:26:52,680 --> 00:26:56,000
Speaker 13: about a one to three percent percentage point uplift to

516
00:26:56,040 --> 00:26:59,719
Speaker 13: their revenue. In other words, approximately anywhere from five to

517
00:26:59,720 --> 00:27:04,480
Speaker 13: fIF teen billion dollars of incremental subscription revenue from consumers

518
00:27:04,480 --> 00:27:06,080
Speaker 13: in the next three to five years.

519
00:27:06,600 --> 00:27:07,520
Speaker 2: Look at it differently.

520
00:27:07,680 --> 00:27:12,520
Speaker 3: In a world where you're paying monthly for Chat, GPT, Clawed, Gemini, etc.

521
00:27:13,520 --> 00:27:16,520
Speaker 3: Is Meta AI worth paying eight dollars or twenty dollars

522
00:27:16,560 --> 00:27:17,360
Speaker 3: a month four.

523
00:27:18,920 --> 00:27:21,640
Speaker 13: That is going to be a key question for them.

524
00:27:21,680 --> 00:27:24,560
Speaker 13: So there is a consumer subscription piece, there is this

525
00:27:25,000 --> 00:27:29,560
Speaker 13: Meta AI type subscription piece which competes directly with Gemini

526
00:27:29,560 --> 00:27:31,879
Speaker 13: to your point, and then there is a business like

527
00:27:31,960 --> 00:27:35,160
Speaker 13: if you are a creator, then you can subscribe as well.

528
00:27:35,280 --> 00:27:39,119
Speaker 13: I see great value in the consumer subscription and the

529
00:27:39,160 --> 00:27:41,840
Speaker 13: creator subscription because it allows them to create more content.

530
00:27:42,160 --> 00:27:44,360
Speaker 13: But in the question that you are asking, where if

531
00:27:44,400 --> 00:27:47,119
Speaker 13: I have a Gemini subscription or a GPT, do I

532
00:27:47,160 --> 00:27:49,840
Speaker 13: need Meta AI, jury is still out on that you

533
00:27:49,840 --> 00:27:52,040
Speaker 13: would need it. If you're running out of capacity and

534
00:27:52,080 --> 00:27:54,320
Speaker 13: you're paying twenty bucks, you don't want to pay additional

535
00:27:54,400 --> 00:27:57,280
Speaker 13: higher tier to a one hundred or two hundred dollars tier,

536
00:27:57,320 --> 00:27:59,879
Speaker 13: and you want just a little bit more of access capacity,

537
00:28:00,040 --> 00:28:03,000
Speaker 13: maybe you bade bucks do Meta Maybe in that scenario

538
00:28:03,119 --> 00:28:06,960
Speaker 13: does make sense. Or if it is highly personalized social

539
00:28:07,080 --> 00:28:10,800
Speaker 13: sort of a use case that they give us which

540
00:28:10,840 --> 00:28:14,760
Speaker 13: cannot be created by Claude because they don't have that information,

541
00:28:15,400 --> 00:28:18,200
Speaker 13: or perhaps the GPD perhaps there is a use case

542
00:28:18,480 --> 00:28:22,160
Speaker 13: in those instances. Yes, but I'm not fully sure. Jury

543
00:28:22,160 --> 00:28:25,880
Speaker 13: is still out on that. On the meta AA subscription scaling.

544
00:28:25,760 --> 00:28:29,080
Speaker 4: Schredo Cajuria, great research, great analysis from Wolf Research.

545
00:28:29,160 --> 00:28:30,560
Speaker 5: We thank you, Mick.

546
00:28:30,680 --> 00:28:33,920
Speaker 4: NASA it selected Blue Origin, far Fly Aerospace and other

547
00:28:33,960 --> 00:28:36,200
Speaker 4: private space firms to help build out its long term

548
00:28:36,280 --> 00:28:40,120
Speaker 4: lunar ambitions. The agency says monthly missions could begin in

549
00:28:40,120 --> 00:28:43,160
Speaker 4: twenty twenty seven, laying the groundwork for astronauts striveging live

550
00:28:43,400 --> 00:28:46,200
Speaker 4: work on the lunar surface. We spoke with the NASA Administrator,

551
00:28:46,280 --> 00:28:48,840
Speaker 4: Jaredisingman about the timeline for a moon.

552
00:28:48,720 --> 00:28:52,480
Speaker 14: Base starting in twenty twenty seven. You should see see

553
00:28:52,480 --> 00:28:56,760
Speaker 14: a near monthly cadence of robotic landers on the Moon,

554
00:28:57,200 --> 00:28:59,920
Speaker 14: several rovers. In fact, we initially we provided in a

555
00:29:00,080 --> 00:29:04,640
Speaker 14: ward for the first two, you know, crude and autonomous

556
00:29:05,040 --> 00:29:07,920
Speaker 14: capable rovers for the lunar surface. So when our astronauts

557
00:29:08,000 --> 00:29:10,880
Speaker 14: arrive on Artemis four and twenty twenty eight, they're going

558
00:29:10,920 --> 00:29:13,200
Speaker 14: to already have some infrastructure of the moon base waiting

559
00:29:13,200 --> 00:29:13,479
Speaker 14: for them.

560
00:29:13,520 --> 00:29:15,840
Speaker 7: They're already going to have a rover waiting for them.

561
00:29:16,240 --> 00:29:20,560
Speaker 4: And then in that timeframe, it's not just intermittent anymore.

562
00:29:20,600 --> 00:29:23,080
Speaker 4: It's not just those monthly visits. But when do you

563
00:29:23,120 --> 00:29:26,600
Speaker 4: think people be working? Humans might even be living in

564
00:29:26,640 --> 00:29:28,040
Speaker 4: some capacity on the moon.

565
00:29:27,880 --> 00:29:32,520
Speaker 14: There, So we are approaching the moon base in phases.

566
00:29:32,720 --> 00:29:35,160
Speaker 14: So Phase one is a lot of littles. We are

567
00:29:35,200 --> 00:29:37,720
Speaker 14: dusting off the playbook that worked very well for NASA

568
00:29:37,720 --> 00:29:40,640
Speaker 14: in the nineteen sixties. We're getting back to an iterative approach.

569
00:29:40,680 --> 00:29:43,640
Speaker 14: So you know, there was the Mercury program before there

570
00:29:43,680 --> 00:29:46,560
Speaker 14: was Geminy, There was Geminy before Apollo, and an awful

571
00:29:46,600 --> 00:29:49,240
Speaker 14: lot of Apollo missions before we went right to the

572
00:29:49,280 --> 00:29:51,360
Speaker 14: moon landing on Apollo eleven. We are doing the same

573
00:29:51,400 --> 00:29:54,200
Speaker 14: thing now. So Phase one we're calling it a science

574
00:29:54,240 --> 00:29:57,280
Speaker 14: of survival. We're not going to lock in what the

575
00:29:57,600 --> 00:30:02,560
Speaker 14: mobility strategy should be, for logistics for astronauts, the power strategy,

576
00:30:02,640 --> 00:30:05,800
Speaker 14: the surface comms, the orbital coms. Why would we try

577
00:30:05,840 --> 00:30:08,280
Speaker 14: and nail and get all of that perfect today when

578
00:30:08,280 --> 00:30:09,640
Speaker 14: we haven't been to the Moon in more than a

579
00:30:09,640 --> 00:30:12,120
Speaker 14: half century. So Phase one will be a lot of

580
00:30:12,200 --> 00:30:15,960
Speaker 14: landings again, that near monthly cadence to learn and inform

581
00:30:16,080 --> 00:30:18,840
Speaker 14: Phase two, where perhaps now you're putting a lot more

582
00:30:18,880 --> 00:30:21,480
Speaker 14: tonnage on the lunar surface. You have a lot more

583
00:30:21,960 --> 00:30:25,160
Speaker 14: direction as to the type of hardware and capabilities you

584
00:30:25,240 --> 00:30:27,080
Speaker 14: want to lock in on, so you don't need to

585
00:30:27,120 --> 00:30:29,640
Speaker 14: have maybe monthly landings when we get into phase two,

586
00:30:29,920 --> 00:30:32,280
Speaker 14: but you have a lot more direction as to what

587
00:30:32,320 --> 00:30:34,760
Speaker 14: should work for our intended objectives, which is to build

588
00:30:34,800 --> 00:30:37,800
Speaker 14: out that habital environment. And then phase two we're going

589
00:30:37,880 --> 00:30:40,880
Speaker 14: to learn now having astronauts go from let's call it

590
00:30:41,440 --> 00:30:44,400
Speaker 14: a period of maybe even days on the lunar surface

591
00:30:44,440 --> 00:30:47,720
Speaker 14: in phase one, to potentially weeks in phase two, to

592
00:30:47,760 --> 00:30:49,680
Speaker 14: where you might get by the time we move into

593
00:30:49,680 --> 00:30:53,240
Speaker 14: Phase three, a similar astronaut rotation like you see on

594
00:30:53,280 --> 00:30:56,280
Speaker 14: the International Space Station, where we could have crews potentially

595
00:30:56,320 --> 00:30:58,840
Speaker 14: being on the lunar surface for months on end.

596
00:31:00,000 --> 00:31:03,640
Speaker 3: You don't have that marked on your calendar, administrator, when

597
00:31:03,680 --> 00:31:06,680
Speaker 3: phase three might have a base that has humans actually

598
00:31:06,760 --> 00:31:07,920
Speaker 3: living and working inside it.

599
00:31:09,040 --> 00:31:10,800
Speaker 7: Oh, we absolutely have time frames.

600
00:31:10,840 --> 00:31:13,240
Speaker 14: I mean we are looking at basically twenty twenty seven

601
00:31:13,320 --> 00:31:16,320
Speaker 14: through twenty twenty nine for phase one. You have twenty

602
00:31:16,360 --> 00:31:19,520
Speaker 14: twenty nine out into the early twenty thirties for Phase two.

603
00:31:19,760 --> 00:31:22,400
Speaker 14: But again, this is all going to be informed on

604
00:31:22,480 --> 00:31:25,480
Speaker 14: what we learn during those first landings in phase one.

605
00:31:27,200 --> 00:31:29,280
Speaker 2: That was NATA administrator Jared Isicman.

606
00:31:29,360 --> 00:31:30,880
Speaker 3: All right, coming up on the show, we're going to

607
00:31:30,880 --> 00:31:35,000
Speaker 3: be joined by Eric Vistria Benchmark for his outlook the

608
00:31:35,040 --> 00:31:38,600
Speaker 3: physical AI space. Yes, we're going to talk about hardware.

609
00:31:38,800 --> 00:31:48,680
Speaker 3: This has Belen bog Tech, French AI startup Mistro AI

610
00:31:48,800 --> 00:31:52,880
Speaker 3: is expanding into advanced manufacturing, striking deals with new customers

611
00:31:52,920 --> 00:31:56,320
Speaker 3: Airbus and BMW as it looks to so called physical

612
00:31:56,360 --> 00:31:59,360
Speaker 3: AI to fuel growth. CEO and co founder of the men,

613
00:31:59,400 --> 00:32:02,400
Speaker 3: she spoke with blue Ink Tech Europe's Tom McKenzie.

614
00:32:03,960 --> 00:32:05,280
Speaker 2: For US, it's a massive market.

615
00:32:05,880 --> 00:32:09,040
Speaker 15: We see in particular the europe is our in core

616
00:32:09,120 --> 00:32:14,000
Speaker 15: market and one of the Europeans the strength of Europe

617
00:32:14,080 --> 00:32:18,280
Speaker 15: is to is in its high end manufacturing. So it's

618
00:32:19,480 --> 00:32:23,000
Speaker 15: the manufacturing world. Is the thirty trillion market. If you

619
00:32:23,040 --> 00:32:26,400
Speaker 15: think of a police that I can bring to it,

620
00:32:26,560 --> 00:32:28,400
Speaker 15: if you only look at ten percent of that, you're

621
00:32:28,440 --> 00:32:30,920
Speaker 15: looking at the three trillion market. And that's happening in

622
00:32:30,920 --> 00:32:31,960
Speaker 15: the next five years.

623
00:32:32,720 --> 00:32:35,680
Speaker 4: Man Over in Asia, Mini Max is annualized revenue more

624
00:32:35,680 --> 00:32:38,240
Speaker 4: than double these past two months to at least three

625
00:32:38,280 --> 00:32:41,040
Speaker 4: hundred million dollars. As the Chinese AI startup prepares to

626
00:32:41,120 --> 00:32:43,920
Speaker 4: roll out its next flagship model, Mean Max co founder

627
00:32:43,920 --> 00:32:47,160
Speaker 4: and President's Yaean joined Blomberg Steven Engel on the sidelines

628
00:32:47,160 --> 00:32:49,280
Speaker 4: of thebs Asian Investment Conference over in Hong Kong.

629
00:32:50,920 --> 00:32:54,680
Speaker 16: Agent and the models are really important for manimalization, but

630
00:32:54,880 --> 00:32:58,000
Speaker 16: definitely the foundation model is a key. You will see

631
00:32:58,080 --> 00:33:03,480
Speaker 16: a better performance. Models with specialization and differentiation will drive

632
00:33:03,680 --> 00:33:07,920
Speaker 16: the token consumption, also drives the enterprise and the consumers

633
00:33:08,080 --> 00:33:12,680
Speaker 16: retention and the consumption. The model performers product commantization will

634
00:33:12,720 --> 00:33:16,400
Speaker 16: become the frive wear. So you will see we did

635
00:33:16,480 --> 00:33:19,200
Speaker 16: the end to end optimization with the whole group. There

636
00:33:19,240 --> 00:33:22,720
Speaker 16: are lots of innovation, technical innovation inside, so you will

637
00:33:22,760 --> 00:33:26,480
Speaker 16: see we can provide probably similar performers models with probably

638
00:33:26,560 --> 00:33:29,280
Speaker 16: lower price even sometimes higher margins.

639
00:33:29,600 --> 00:33:32,320
Speaker 17: Yeah, so how do you change your revenue mix where

640
00:33:32,720 --> 00:33:35,959
Speaker 17: most of your revenue is coming from your consumer facing

641
00:33:36,080 --> 00:33:40,680
Speaker 17: products like the chatboxing and also high level which just

642
00:33:40,800 --> 00:33:44,240
Speaker 17: last year's that's like the text to video generation. But

643
00:33:44,920 --> 00:33:47,800
Speaker 17: next year, as you go into your model three, right

644
00:33:47,880 --> 00:33:51,400
Speaker 17: from two point seven to three, how is your revenue

645
00:33:51,640 --> 00:33:52,560
Speaker 17: mixed going to change?

646
00:33:52,760 --> 00:33:52,920
Speaker 2: Yeah?

647
00:33:53,160 --> 00:33:55,640
Speaker 16: So the number you mentioned is last year's number, but

648
00:33:55,840 --> 00:33:59,200
Speaker 16: right now it's almost like fifty enterprise and fifty consumers,

649
00:33:59,240 --> 00:34:03,600
Speaker 16: so the enterprise increase a lot. And also, yes, the

650
00:34:03,720 --> 00:34:06,160
Speaker 16: model is a key, so we think the model is

651
00:34:06,240 --> 00:34:08,600
Speaker 16: our product, no matter it's a B to B R

652
00:34:08,920 --> 00:34:12,600
Speaker 16: digital C. It's all the Chinnel's for the commercialization. So

653
00:34:12,760 --> 00:34:16,040
Speaker 16: we've spent most of our resources and the spendings of

654
00:34:16,200 --> 00:34:18,960
Speaker 16: the model layer. Yes, we are going to release I'm

655
00:34:19,040 --> 00:34:22,040
Speaker 16: three very very soon in a few days, which probably

656
00:34:22,280 --> 00:34:26,240
Speaker 16: I think, which is the first open source native multi model.

657
00:34:27,440 --> 00:34:29,319
Speaker 3: That was Min and Max co founder and president Yea

658
00:34:29,480 --> 00:34:32,359
Speaker 3: Ian along with our own Stephen Engel. We're gonna stick

659
00:34:32,400 --> 00:34:34,880
Speaker 3: with AI and we're going to discuss the outlook for

660
00:34:35,000 --> 00:34:39,000
Speaker 3: physical AI with Eric Visher, a partner at Benchmark Today's

661
00:34:39,080 --> 00:34:39,840
Speaker 3: VC Spotlight.

662
00:34:40,320 --> 00:34:41,560
Speaker 2: Been really looking forward to this one.

663
00:34:41,760 --> 00:34:41,920
Speaker 14: Eric.

664
00:34:42,719 --> 00:34:45,960
Speaker 3: Ten years ago, you're very welcome to be here. Ten

665
00:34:46,080 --> 00:34:48,759
Speaker 3: years ago you led a series A in a little

666
00:34:48,840 --> 00:34:54,560
Speaker 3: company called Cerebras. Fast forward ten years what an IPO,

667
00:34:55,480 --> 00:34:58,360
Speaker 3: but it's indicative of where we're at right now in

668
00:34:58,440 --> 00:34:59,920
Speaker 3: this demand for fast inference.

669
00:35:01,480 --> 00:35:03,280
Speaker 2: I just want to start with that case study.

670
00:35:03,680 --> 00:35:06,239
Speaker 3: You know, the timing of this IPO and where it

671
00:35:06,320 --> 00:35:09,480
Speaker 3: fits in what's actually happening in physical AI right now.

672
00:35:10,920 --> 00:35:13,800
Speaker 18: Well, I think that it's very very clear that the

673
00:35:15,000 --> 00:35:20,040
Speaker 18: demand for inference and AI is operal charts, and I

674
00:35:20,080 --> 00:35:22,680
Speaker 18: don't think that's going to stop anytime soon. I think

675
00:35:22,719 --> 00:35:26,120
Speaker 18: it was Alex Scattered at at Whale Rock who kind

676
00:35:26,120 --> 00:35:29,239
Speaker 18: of recently said, if you think of the population of

677
00:35:29,280 --> 00:35:32,120
Speaker 18: the world, there's like one percent of the world.

678
00:35:32,280 --> 00:35:34,680
Speaker 2: Is maybe AI power users today.

679
00:35:35,160 --> 00:35:37,359
Speaker 18: So if one percent of the world is AI power

680
00:35:37,480 --> 00:35:42,160
Speaker 18: users today and we're completely compute constrained, for as far

681
00:35:42,239 --> 00:35:45,120
Speaker 18: as I can see, what happens when three percent of

682
00:35:45,160 --> 00:35:46,719
Speaker 18: the world, or four percent of the world, or five

683
00:35:46,719 --> 00:35:51,440
Speaker 18: percent of the world becomes AI super users or power users.

684
00:35:51,800 --> 00:35:54,160
Speaker 18: And so I think that we are going to be

685
00:35:54,520 --> 00:35:58,560
Speaker 18: in this compute constrained world for quite some time. And

686
00:35:59,800 --> 00:36:02,359
Speaker 18: I think that that's going to lead to a lot

687
00:36:02,400 --> 00:36:05,319
Speaker 18: of success in all of the hardware layers. And it's

688
00:36:05,360 --> 00:36:08,239
Speaker 18: also going to lead to the bottleneck moving around. You know,

689
00:36:08,640 --> 00:36:11,040
Speaker 18: some days, some months it's going to be memory, some

690
00:36:11,719 --> 00:36:13,480
Speaker 18: months it's going to be data center and power. Some

691
00:36:13,560 --> 00:36:16,360
Speaker 18: once it's going to be chips. And I think that

692
00:36:16,480 --> 00:36:17,680
Speaker 18: bottleneck is going to keep.

693
00:36:17,520 --> 00:36:20,399
Speaker 4: Moving around some days it's all of them combined, Derek,

694
00:36:20,520 --> 00:36:24,040
Speaker 4: So totally at this exact moment, where are the startups

695
00:36:24,120 --> 00:36:26,239
Speaker 4: you're most interested in, or the ones you already sit

696
00:36:26,280 --> 00:36:28,400
Speaker 4: on boards of starting.

697
00:36:28,080 --> 00:36:29,080
Speaker 5: To innovate at the edges?

698
00:36:29,120 --> 00:36:31,080
Speaker 4: When are we going to really see like movement of

699
00:36:31,120 --> 00:36:33,919
Speaker 4: photonics or a different type of compute being used.

700
00:36:34,840 --> 00:36:36,279
Speaker 11: Well, I think, I think you know.

701
00:36:36,400 --> 00:36:38,840
Speaker 18: One of the benefits of being an early stage ventry

702
00:36:38,880 --> 00:36:41,520
Speaker 18: capitalists is we have a very long time horizon, so

703
00:36:41,680 --> 00:36:44,439
Speaker 18: we're not trying to figure out what's going to happen

704
00:36:44,640 --> 00:36:47,200
Speaker 18: in eighteen months or twelve months or twenty four months.

705
00:36:47,520 --> 00:36:51,239
Speaker 18: We're really looking and trying to have some idea of

706
00:36:51,360 --> 00:36:54,520
Speaker 18: what might happen in five years or seven years or

707
00:36:54,600 --> 00:36:58,040
Speaker 18: ten years. And as we look out, we are really excited.

708
00:36:58,280 --> 00:37:01,840
Speaker 18: For example, we invested in star Cloud, which is a

709
00:37:01,920 --> 00:37:05,680
Speaker 18: space data center. We invested in Sunday Robotics, which is

710
00:37:05,760 --> 00:37:08,880
Speaker 18: a domestic robot And when you kind of work at

711
00:37:08,920 --> 00:37:12,200
Speaker 18: companies like that, they're very much on the frontier. It's

712
00:37:12,280 --> 00:37:14,880
Speaker 18: going to take a bunch of time. There are very

713
00:37:14,960 --> 00:37:19,759
Speaker 18: capital intensive projects, but they're amazing teams that are doing

714
00:37:20,320 --> 00:37:24,800
Speaker 18: really cool development, pushing the edge, and it's going to

715
00:37:24,840 --> 00:37:27,960
Speaker 18: require a lot of flexibility on their parts as the

716
00:37:28,040 --> 00:37:31,439
Speaker 18: market evolves. There's obviously a lot of unknowns, but there's

717
00:37:31,520 --> 00:37:33,879
Speaker 18: also tremendous possibilities.

718
00:37:35,560 --> 00:37:37,359
Speaker 3: I just want to go back to Cerebris for a minute,

719
00:37:37,480 --> 00:37:40,280
Speaker 3: and to some this is now ancient history is academic.

720
00:37:40,440 --> 00:37:44,120
Speaker 3: But two days before the IPO I broke a story

721
00:37:44,400 --> 00:37:47,600
Speaker 3: that arm and soft Bank had basically gone to Andrew

722
00:37:47,640 --> 00:37:52,040
Speaker 3: Feldman and said we'd buy you for a large number.

723
00:37:52,400 --> 00:37:55,000
Speaker 3: And Andrew very quickly shut it down, and the rest

724
00:37:55,120 --> 00:37:57,279
Speaker 3: is history, of course, because they went public. But it's

725
00:37:57,320 --> 00:37:59,680
Speaker 3: somebody that joined the board in twenty sixteen and has

726
00:37:59,680 --> 00:38:02,280
Speaker 3: had three if the five major investments go to IPO.

727
00:38:03,600 --> 00:38:06,279
Speaker 3: What would you have made of that outcome instead of

728
00:38:06,320 --> 00:38:06,920
Speaker 3: going public.

729
00:38:07,800 --> 00:38:11,880
Speaker 18: Well, obviously you can't comment on that specific reporting, but

730
00:38:12,600 --> 00:38:14,040
Speaker 18: we're a public company now.

731
00:38:14,320 --> 00:38:15,640
Speaker 11: I think that has opened up.

732
00:38:15,560 --> 00:38:17,800
Speaker 18: A lot of possibilities in terms of what we can do.

733
00:38:18,360 --> 00:38:20,800
Speaker 18: We've raised a ton of capital to finance the business

734
00:38:21,440 --> 00:38:25,080
Speaker 18: and allow us to take advantage of the tremendous demand.

735
00:38:25,360 --> 00:38:27,840
Speaker 18: And I think we're really at the beginning. You know,

736
00:38:27,920 --> 00:38:32,080
Speaker 18: if you feel like there is an end in sight,

737
00:38:32,239 --> 00:38:36,359
Speaker 18: you might take a different, different tact. But as far

738
00:38:36,400 --> 00:38:39,880
Speaker 18: as we can see the demand is tremendous.

739
00:38:40,360 --> 00:38:41,960
Speaker 4: You say, you're just backed a company and it's about

740
00:38:42,000 --> 00:38:44,200
Speaker 4: orbital space centers, and that immediately makes us all think

741
00:38:44,239 --> 00:38:46,640
Speaker 4: of SpaceX and how they're going to be sucking a

742
00:38:46,719 --> 00:38:48,320
Speaker 4: lot of the oxygen out of the room when it

743
00:38:48,360 --> 00:38:51,640
Speaker 4: comes to a public offering. How are your companies currently

744
00:38:51,719 --> 00:38:53,839
Speaker 4: feeling the need for cash? How are you thinking about

745
00:38:53,920 --> 00:38:56,960
Speaker 4: permanently fundraising or looking for exits right now?

746
00:38:57,960 --> 00:38:59,280
Speaker 11: Well, you know, it's really interesting.

747
00:38:59,440 --> 00:39:02,640
Speaker 18: Right now, the venture landscape is very much have and

748
00:39:02,760 --> 00:39:06,080
Speaker 18: have not, which is, if you are oriented around AI

749
00:39:06,400 --> 00:39:09,279
Speaker 18: and you're growing really quickly or have something that's very

750
00:39:09,360 --> 00:39:13,920
Speaker 18: much on the frontier, there's almost there's almost like limitless

751
00:39:14,880 --> 00:39:18,000
Speaker 18: cash available and funding available. And if you're not, even

752
00:39:18,040 --> 00:39:20,319
Speaker 18: if it's a good business that would have people would

753
00:39:20,320 --> 00:39:23,359
Speaker 18: have fallen on all over themselves. For you know, five

754
00:39:23,480 --> 00:39:26,480
Speaker 18: or six years ago, there's almost no funding available. So

755
00:39:26,719 --> 00:39:30,319
Speaker 18: it's a very it's a very bimodal setup right now,

756
00:39:30,440 --> 00:39:33,719
Speaker 18: which is which is challenging certainly, But each of these

757
00:39:34,239 --> 00:39:37,800
Speaker 18: financing eras kind of they come and go, and you know,

758
00:39:37,920 --> 00:39:39,400
Speaker 18: one of the really important things for any of these

759
00:39:39,440 --> 00:39:42,759
Speaker 18: companies or entrepreneurs is just to keep on, keep on

760
00:39:42,880 --> 00:39:46,800
Speaker 18: grinding and finding the way that there's building a company

761
00:39:47,440 --> 00:39:51,200
Speaker 18: that is impactful as a roller coaster. And it takes

762
00:39:51,239 --> 00:39:54,120
Speaker 18: a long time, and even if you take the Cerever's journey,

763
00:39:54,400 --> 00:39:56,359
Speaker 18: there's been lots and lots of ups and downs over

764
00:39:56,440 --> 00:40:01,120
Speaker 18: the time. Obviously, taking an AI sending conductor company public

765
00:40:01,239 --> 00:40:04,000
Speaker 18: in maybe twenty twenty six is about as good timing

766
00:40:04,040 --> 00:40:07,640
Speaker 18: as you can get. But you know, part of that

767
00:40:08,000 --> 00:40:12,719
Speaker 18: is the timing. Part of that's just luck. And then

768
00:40:13,000 --> 00:40:16,560
Speaker 18: obviously it's all built on top of a decade of

769
00:40:16,920 --> 00:40:18,160
Speaker 18: the team grinding.

770
00:40:17,800 --> 00:40:20,960
Speaker 4: At okasor another interesting one that we're looking at in

771
00:40:21,000 --> 00:40:23,319
Speaker 4: your portfolio. We have to talk about that another time. Eric,

772
00:40:23,360 --> 00:40:27,120
Speaker 4: it's great to have you on. Eric Vishrha of Benchmark.

773
00:40:27,560 --> 00:40:30,680
Speaker 4: Now coming up TikTok, but it's moving away from music,

774
00:40:30,960 --> 00:40:33,000
Speaker 4: scaling back tis with major music labels.

775
00:40:33,000 --> 00:40:34,799
Speaker 5: We'll dig into that next. This is Bloomberg Tech.

776
00:40:41,440 --> 00:40:45,320
Speaker 3: AI could provoke a fifteen percent displacement of knowledge workers.

777
00:40:45,360 --> 00:40:48,799
Speaker 3: That's according to Muddy Waters Capital CEO Carson Block, who

778
00:40:48,880 --> 00:40:52,279
Speaker 3: joined Bloomberg's has Linda Amen in an exclusive interview to

779
00:40:52,360 --> 00:40:53,800
Speaker 3: discuss AI demand listen to this.

780
00:40:55,880 --> 00:40:59,440
Speaker 19: Our house view is that we're going to see fifteen

781
00:40:59,719 --> 00:41:03,880
Speaker 19: percent displacement of knowledge workers. You know, we think it

782
00:41:03,960 --> 00:41:07,279
Speaker 19: could be as soon as three years, is it four?

783
00:41:07,520 --> 00:41:08,040
Speaker 2: Is it five?

784
00:41:08,760 --> 00:41:12,160
Speaker 19: At some point and it's in the single digit number

785
00:41:12,200 --> 00:41:15,719
Speaker 19: of years. This will this will be a factor or

786
00:41:15,800 --> 00:41:18,640
Speaker 19: this this will occur in our view, and yes, there

787
00:41:18,680 --> 00:41:21,680
Speaker 19: will be jobs that are created by AI, but we're

788
00:41:21,760 --> 00:41:26,680
Speaker 19: talking about net losses because the technology is increasing in

789
00:41:26,800 --> 00:41:30,960
Speaker 19: capability faster than we humans are able to adapt to it.

790
00:41:31,520 --> 00:41:33,760
Speaker 5: Let's talk about the displacement of music labels.

791
00:41:34,200 --> 00:41:36,600
Speaker 4: Maybe over at TikTok, because music has been called to

792
00:41:36,680 --> 00:41:40,640
Speaker 4: TikTok's identity since its days. Is musically helping artists like

793
00:41:40,719 --> 00:41:44,480
Speaker 4: Little nas X or Olivia Rodrigu just global stardom and

794
00:41:44,560 --> 00:41:47,160
Speaker 4: waits Now TikTok there is skinning back ties with major

795
00:41:47,200 --> 00:41:49,720
Speaker 4: music labels and focusing more directly on those artists.

796
00:41:49,920 --> 00:41:51,640
Speaker 5: It's according to sources, that's.

797
00:41:51,480 --> 00:41:55,080
Speaker 4: All discovered by Blue Megs Alex Levine along with Ashley Carmen.

798
00:41:55,960 --> 00:41:59,479
Speaker 5: What is happening with a company that identify with music?

799
00:41:59,520 --> 00:42:02,040
Speaker 4: I mean it's it's in the icon, it's in the branding.

800
00:42:02,239 --> 00:42:03,760
Speaker 4: How are they moving away from labels?

801
00:42:04,200 --> 00:42:04,880
Speaker 11: So exactly?

802
00:42:05,239 --> 00:42:07,600
Speaker 20: Music has really been part of its DNA since the

803
00:42:07,719 --> 00:42:10,160
Speaker 20: very beginning. It is the thing that made helped make

804
00:42:10,200 --> 00:42:13,040
Speaker 20: TikTok this global cultural phenomenon and got more than half

805
00:42:13,040 --> 00:42:17,680
Speaker 20: of America using it. Though TikTok continues importantly to work

806
00:42:17,719 --> 00:42:20,400
Speaker 20: with major music labels, including some of the world's biggest,

807
00:42:21,520 --> 00:42:26,160
Speaker 20: it is deprioritizing those relationships in part by building out projects,

808
00:42:26,200 --> 00:42:30,360
Speaker 20: prioritizing internal efforts to actually have products and services that

809
00:42:30,440 --> 00:42:34,360
Speaker 20: compete directly with the labels and that allow the company

810
00:42:34,480 --> 00:42:37,640
Speaker 20: to have sort of more direct relations with the artists

811
00:42:37,760 --> 00:42:39,200
Speaker 20: rather than through the representatives.

812
00:42:40,160 --> 00:42:43,880
Speaker 3: Alex, TikTok changed music. Now labels worry it's leaving them behind.

813
00:42:44,040 --> 00:42:47,000
Speaker 3: But present day, how does music work on TikTok. So

814
00:42:47,040 --> 00:42:49,960
Speaker 3: it's Friday night, I'm kicking back on the couch. I

815
00:42:50,080 --> 00:42:53,680
Speaker 3: go to YouTube on the TV and play concerts, music videos.

816
00:42:54,000 --> 00:42:57,239
Speaker 3: I don't think present day like, you know what yep,

817
00:42:57,400 --> 00:43:00,800
Speaker 3: music music video like, just explain what we're talking about mechanically.

818
00:43:01,280 --> 00:43:03,440
Speaker 20: So mechanically, when you open your app, you've got your

819
00:43:03,480 --> 00:43:06,839
Speaker 20: four you feed. Every video that you see is going

820
00:43:06,880 --> 00:43:09,279
Speaker 20: to have some sort of audio behind it, whether that's

821
00:43:09,320 --> 00:43:13,440
Speaker 20: people speaking or whether that's music, and oftentimes those sounds

822
00:43:13,600 --> 00:43:18,200
Speaker 20: are our songs that have gone viral, and sometimes it's

823
00:43:18,239 --> 00:43:21,719
Speaker 20: new songs from emerging artists. Sometimes it's made you know,

824
00:43:22,040 --> 00:43:24,719
Speaker 20: it's it's global hits from artists like Paul McCartney, like

825
00:43:24,760 --> 00:43:28,160
Speaker 20: Bruno Mars, and sometimes it's simply just you know, sort

826
00:43:28,200 --> 00:43:31,759
Speaker 20: of repetitive meme type noises that you can find through

827
00:43:32,040 --> 00:43:34,839
Speaker 20: through various other means on the app. But I think

828
00:43:34,880 --> 00:43:37,480
Speaker 20: that there's always sort of been this question, especially more recently,

829
00:43:37,600 --> 00:43:40,799
Speaker 20: about whether blowing up on TikTok or going viral can

830
00:43:40,840 --> 00:43:44,520
Speaker 20: actually mint a legitimate star and and have have them

831
00:43:44,600 --> 00:43:46,279
Speaker 20: develop really an enduring career from that.

832
00:43:47,320 --> 00:43:50,240
Speaker 3: Bloomberg's Alex Savine with what's going on in music on TikTok,

833
00:43:50,320 --> 00:43:51,200
Speaker 3: Thank you very much.

834
00:43:51,400 --> 00:43:54,120
Speaker 5: Character that does it for this edition of Bloomberg Tech.

835
00:43:54,200 --> 00:43:55,200
Speaker 5: What an edition has been?

836
00:43:56,480 --> 00:43:58,560
Speaker 3: Yeah, a lot of market moves, a lot of great interviews,

837
00:43:58,600 --> 00:44:00,720
Speaker 3: a lot of top stories, recap up on the podcast.

838
00:44:00,800 --> 00:44:02,880
Speaker 3: You know exactly where to find it all the Bloomberg

839
00:44:02,920 --> 00:44:05,239
Speaker 3: platforms and online, Apple, Spotify.

840
00:44:05,840 --> 00:44:07,759
Speaker 2: In iHeart, this is Bloomberg Tech