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Speaker 1: From Mahard where Innovation, money and power Collie in Silicon

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Speaker 1: Vallet NBN. This is Bloomberg Technology with Caroline Hyde and

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Speaker 1: Ed Ludlow.

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Speaker 2: I'm Ed Ludlow here in San Francisco.

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Speaker 3: Caroline hids off today. This is Bloomberg Technology. Coming up

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Speaker 3: on the program. Apple hits a three trillion dollar valuation.

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Speaker 3: Will we close above that historic market cat milestone remains

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Speaker 3: to be seen? The co founder of DeepMind now the

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Speaker 3: CEO of Inflection AI. He discusses the potential of his

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Speaker 3: company after raising a whopping one point three billion dollars

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Speaker 3: and more trouble for Adobe's purchase of Figma, with the

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Speaker 3: UK joining the EU and US in multiple probes of

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Speaker 3: that twenty billion dollar deal. Such a big week in

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Speaker 3: the world of ten technology.

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Speaker 2: But there is one.

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Speaker 3: Big story this Friday, and it is Apple, the first

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Speaker 3: company ever to hit a three trillion dollar market cap.

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Speaker 2: And this chart tells the story.

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Speaker 3: Look at the nine hundred billion dollars of marketcap we've

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Speaker 3: added so far in twenty twenty three.

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Speaker 2: What is the story here? We will get to it in.

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Speaker 3: Just a moment, but I remind you last time we

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Speaker 3: were near that level was at the beginning of twenty

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Speaker 3: twenty two.

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Speaker 2: It's taken us that long to get back.

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Speaker 3: There is one person you want to speak to on

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Speaker 3: a daylight today, and it is Bloomberg's Mark German.

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Speaker 2: Mark.

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Speaker 3: You cover this company as close as any journalist on

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Speaker 3: the planet. I want to start by asking you, what

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Speaker 3: kind of a moment is this? What kind of a

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Speaker 3: milestone in technology history is registering a three trillion dollar

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Speaker 3: market cap?

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Speaker 4: Ed?

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Speaker 5: Thank you so much for having me. Obviously, this is

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Speaker 5: a incredibly big milestone for Apple or any technology company really,

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Speaker 5: and for Apple to do this, to get back to

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Speaker 5: where it was in early twenty twenty too. You saw

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Speaker 5: what happened to the broader market over the last year

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Speaker 5: or so, and to really come back up that mountain

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Speaker 5: and get back here is a big moment for the company.

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Speaker 5: I think that you saw a slew of departures, not

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Speaker 5: only at the executive level, but maybe at the mid

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Speaker 5: level or lower level of the company. And one continued

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Speaker 5: theme on those departures was people felt like their RSUs

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Speaker 5: or the restricted stock units would not really pay out

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Speaker 5: as much money as maybe they can earn in another company. Right,

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Speaker 5: and the stock coming back up right, I think that

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Speaker 5: could be a key way that Apple will be able

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Speaker 5: to retain people moving forward as well. It adds new

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Speaker 5: excitement to the rank and file at the company. They

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Speaker 5: know what they're working towards. In terms of an ultimate

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Speaker 5: payout for the consumer doesn't really have an impact, but

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Speaker 5: I think internally at Apple it is a something they'll

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Speaker 5: never say, but I think it is a quite a

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Speaker 5: positive moment.

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Speaker 3: That deep reporting is such a good point as well.

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Speaker 3: It has not yet been brought up in our coveragejumping

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Speaker 3: big Television of the three trillion dollar milestone. The short

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Speaker 3: term story for the consumer has been about Vision Pro,

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Speaker 3: but you argue in Today's Tech Daily that actually, if

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Speaker 3: you think about the three trillion dollar market cap, it's

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Speaker 3: not got much to do with Vision Pro at all.

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Speaker 2: What's your point, Yeah, I think we would hit.

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Speaker 5: This three trillion dollar market cap whether or not Apple

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Speaker 5: announced the vision Pro head set in June at the

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Speaker 5: Developers Conference or not. I think the Vision Pro story,

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Speaker 5: you know, very long term for Apple could become an

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Speaker 5: Apple Watch or iPad sized opportunity, which is about twenty

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Speaker 5: five billion annually to the bottom line. And that's in

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Speaker 5: the real long term. In the short term, you're not

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Speaker 5: likely to see this generate more than two to four

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Speaker 5: billion a year annually for Apple, which is essentially very small.

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Speaker 5: It's one to two percent of their overall annual revenue.

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Speaker 5: It's really the ecosystem play, right. It's the idea that

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Speaker 5: the ecosystem walks you in and such, where you're going

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Speaker 5: to own an iPhone, an iPad, a Mac, and then

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Speaker 5: every few years or so, you're going to upgrade to

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Speaker 5: new models. On top of that, you're going to subscribe

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Speaker 5: to services, you're going to use Apple Care, You're going

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Speaker 5: to visit Apple retail stores. You're going to buy more

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Speaker 5: accessories like air pods, the Apple Watch, and maybe one

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Speaker 5: day the Vision Pro at a cheaper price as well.

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Speaker 5: And so that's stickiness, the idea where consumers are willing

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Speaker 5: to spend extra. They're really willing to shell out for

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Speaker 5: a new iPhone, the priciest iPhone, the most storage you

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Speaker 5: can get. That's what makes this company so lucrative and

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Speaker 5: so special to the shareholder. One really important aspect that

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Speaker 5: we also haven't touched upon about this three trillion dollar

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Speaker 5: measure is that we're heading right into iPhone season. Believe

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Speaker 5: it or not, We're only about two months away from

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Speaker 5: the iPhone fifteen going on sale. The iPhone fifteen is

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Speaker 5: going to be a pretty significant upgrade on both the

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Speaker 5: low end models and the high end models for the

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Speaker 5: first time in three years since the iPhone twelve lunch

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Speaker 5: in twenty twenty.

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Speaker 2: New design, big camera.

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Speaker 5: Improvements, changes to the display on the cheaper models, You're

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Speaker 5: going to see another big iPhone upgrade cycle, and so

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Speaker 5: I think consumers are excited to only be eight weeks

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Speaker 5: away from that, and shareholders see that excitement, and so

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Speaker 5: you're likely to see a big influx of purchases in

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Speaker 5: just a few months, which obviously is going to drive

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Speaker 5: attention to purchasing the stock too mark quickly.

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Speaker 3: The one phrase or word you haven't used is artificial intelligence.

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Speaker 3: Why are we not talking about Apple in the context

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Speaker 3: of artificial intelligence?

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Speaker 5: Because Apple has really set out this recent AI boom

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Speaker 5: right over the past year or so. You've seen Microsoft,

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Speaker 5: Google obviously open AI with chat GPT. You've seen Amazon

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Speaker 5: all throw around the buzzword and talking about their new

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Speaker 5: generative AI chatbots and such. Apple has really stood on

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Speaker 5: the sidelines here and I don't anticipate any significant new

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Speaker 5: AI related service from Apple to launch until the tail

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Speaker 5: end of twenty twenty four calendar year twenty twenty four

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Speaker 5: at the earliest, so there's not much there. I know

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Speaker 5: there's been some speculation for analysts and such that Apple

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Speaker 5: is nearing some sort of generative AI ecosystem. There's nothing

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Speaker 5: coming soon, and so I don't really think that could

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Speaker 5: be priced into the stock as of yet. And I

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Speaker 5: don't have necessarily think that Apple is planning very significant

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Speaker 5: AI initiatives other than a new AI based health coaching

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Speaker 5: service for next year. But obviously the mother of all

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Speaker 5: AI projects, as Jim Cookers said, is the company's self

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Speaker 5: driving car. You're unlikely to see that at least for

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Speaker 5: another four years or so.

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Speaker 3: Yeah, you and I have done a lot of digging

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Speaker 3: on the subject of an Apple self driving car. Will

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Speaker 3: stick with it. Bloombo's Mark Gum and everything Apple, Thank

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Speaker 3: you so much. Happy Friday. All right, that's top story

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Speaker 3: number one. Top story number two. We go to the UK,

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Speaker 3: where anti trust regulators launching an in depth investigation into

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Speaker 3: Adobe's twenty billion dollar purchase of Figma. Let's get the details.

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Speaker 3: Bloomberg's Caffine Gemmel live for us in London. Cafriine, what

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Speaker 3: do we know.

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Speaker 6: Hi, thanks very much for having me.

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Speaker 7: So what we know today is that the CMEs of

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Speaker 7: the Competition of Markets Authority has said that as the

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Speaker 7: Adobe FIGMADY will need an in depth investigation unless the

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Speaker 7: companies offer any remedies up to solve these anti trust

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Speaker 7: problems that they have found.

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Speaker 6: In the stage of this investigation. So, I mean, the

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Speaker 6: main concerns from the CME are that within the design

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Speaker 6: platform and the supply of screen tools as well. I know,

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Speaker 6: these are two areas where Adobe and Pegma really compete,

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Speaker 6: and the main concerns are that if Adobe bis stigma,

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Speaker 6: then that means that they're really going to take that

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Speaker 6: competition away and that could rise that could leave to

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Speaker 6: arise in prices for customers and could also stifle innovation.

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Speaker 3: We're just showing the shares of Adobe at one point

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Speaker 3: six percent near session highs. When you get a headline

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Speaker 3: like this, sometimes the stocks react negatively because the market's thinking, wow,

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Speaker 3: maybe this deal won't happen.

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Speaker 2: Is that the situation we're in now?

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Speaker 3: How serious is this development in the context of a

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Speaker 3: deal actually getting done?

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Speaker 6: We look, I mean, it's too early right now to

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Speaker 6: know whether it's serious or not. I mean these deals.

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Speaker 1: Will obviously always get some scrutiny. This particular deal is

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Speaker 1: also getting scrutiny as Ideal and the US and as

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Speaker 1: potentially pheasans go to meet in Europe. So I mean

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Speaker 1: we'll need to, you know, find out what the CME

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Speaker 1: finds and its potential fees to investigation, whether we know

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Speaker 1: it's going to be CDs or not.

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Speaker 6: These are just the stages that these sorts of deals

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Speaker 6: have to go through. And of course you know that

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Speaker 6: the CME that people rely on. Consumers in the UK

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Speaker 6: rely on the CME to do to make sure that

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Speaker 6: consumers are getting a good deal.

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Speaker 3: The Moss Catherine Gemel in London. How often is this happening?

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Speaker 3: The trifecta US, EU, UK regulators when we're talking about

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Speaker 3: a tech deal. Great reporting this Friday, we'll stick with it.

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Speaker 2: Now.

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Speaker 3: Coming up, Inflection AI CEO is going to join us

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Speaker 3: to discuss the company's one point three billion dollar fundraise

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Speaker 3: and it came from the likes of Reid Hoffman, Bill

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Speaker 3: Gates in Nvidia. Big conversation coming up this is Bloomberg Technology.

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Speaker 3: Inflection AI has just announced it's raised one point three

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Speaker 3: billion dollars in funding from investors like in Video and

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Speaker 3: Earlier this year, Inflection Ai launched its first product, a

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Speaker 3: chatbot called Pie, a personally modeled AI chat box. Inflection

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Speaker 3: Ai CEO and co founder The Stuffa Sullyman joins us

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Speaker 3: from London. Interestingly, Congratulations, what is frankly a monster a round?

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Speaker 3: Why did you need to raise that much money?

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Speaker 2: Thanks?

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Speaker 8: Ed, Yeah, great to be with you and appreciate the congrats.

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Speaker 8: I mean, you know, we're really building the cutting edge

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Speaker 8: of machine learning models today in order to create Pie,

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Speaker 8: our conversational AI, and that requires vast amounts of compute.

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Speaker 8: I mean, we're actually constructing the largest supercomputer in the

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Speaker 8: world today, built on in videos h one hundred chips,

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Speaker 8: and that's obviously very expensive, but as a startup, it

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Speaker 8: actually enables us to get the best of both worlds.

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Speaker 8: Have you know, the kind of resources that you might

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Speaker 8: otherwise expect in a big tech company, but also the speed,

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Speaker 8: agility and super high quality talent that you get when

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Speaker 8: you bring together the kinds of folks that have been

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Speaker 8: involved in building all the last generation of models at

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Speaker 8: deep Mind, Open Ai and at Google.

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Speaker 3: All the attention goes to Nvidia because they are participating

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Speaker 3: in this roundless stuffer that you are building the underlying

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Speaker 3: LM or foundation model using their H.

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Speaker 2: One hundred GPUs.

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Speaker 8: Yeah, I mean, we have an incredible partnership with them.

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Speaker 8: They've been fantastic to us. Not only they investing in

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Speaker 8: this round, but they made us their key partner to

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Speaker 8: get access to H one hundreds and we've built a

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Speaker 8: really powerful cluster with them. Just a couple of days ago,

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Speaker 8: we announced that we have the fastest performing AI cluster

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Speaker 8: in the world, and in a few months time we

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Speaker 8: will have increased the size of that cluster to be

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Speaker 8: the second largest supercomputer on the planet, which is just

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Speaker 8: an incredible opportunity. And so we're super grateful for their partnership,

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Speaker 8: but also the partnership with Microsoft. You know, we use Azure.

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Speaker 8: They have an incredible cluster there and their AI infrastructure

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Speaker 8: is also second.

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Speaker 2: To none the stuff.

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Speaker 3: When I bumped into you in the corridor of Bloomberg

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Speaker 3: Technology Summit last week, I reminded you of something you

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Speaker 3: said on stage about AI development being a meritocracy. You

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Speaker 3: mentioned that in passing, but I wondered if you could

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Speaker 3: elaborate on what you meant by that.

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Speaker 8: Well, it's a meritocracy in the sense that you know,

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Speaker 8: the cutting edge is also being open sourced and made

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Speaker 8: available to millions of developers around the world to adapt, innovate,

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Speaker 8: and experiment. And it's kind of an incredibly interesting time

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Speaker 8: because on the one hand, models are getting bigger and better,

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Speaker 8: and on the other they're also getting more efficient, smaller,

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Speaker 8: and cheaper to run. So both directions of progress are

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Speaker 8: really being turbocharged in this new wave of AI, and

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Speaker 8: that's incredibly meritocratic, and we're seeing huge innovation at both

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Speaker 8: ends of the.

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Speaker 3: Spectrum supercharged in a meritocratic way. But you guys, is

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Speaker 3: kind of have a big advantage in the access to

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Speaker 3: the H one hundreds and the compute that the GPUs provide.

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Speaker 3: Is there a way you can kind of quantify the

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Speaker 3: advantage for me? I think a lot of people will say, Okay,

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Speaker 3: we have Inflection AI and PI, how is it similar

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Speaker 3: and or different to open AI and GPT three point five.

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Speaker 3: What is the difference technologically and the goals as well?

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Speaker 8: Yeah, it's a fair question. So our most recent LLLM,

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Speaker 8: which is called Inflection one, is actually better on all

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Speaker 8: of the public benchmarks than googled Palm one. Deep minds

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Speaker 8: Chinchilla and open AI's three point five, which is a

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Speaker 8: great achievement for a small organization like ours. In terms

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Speaker 8: of the total amount of compute that we have, the

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Speaker 8: twenty two thousand, h one hundreds in aggregate represents three

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Speaker 8: times more computation than was required to train GPT four

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Speaker 8: according to the best guesses of the rumors online these days.

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Speaker 8: So it is a seismic amount of compute and it

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Speaker 8: gives us an enormous advantage because we're really concerned with

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Speaker 8: building the absolute cutting edge, like we really want to

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Speaker 8: build the absolute best experiences in the world, so that

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Speaker 8: when you get access to your personal AI PI, it's

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Speaker 8: going to be aligned with your interests and on your

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Speaker 8: team and increasingly be able to do useful things for you.

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Speaker 8: It will book, you know, appointments for you, it will

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Speaker 8: plan holidays, it will buy things for you and be

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Speaker 8: a scheduler. And everybody has been waiting for that moment

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Speaker 8: when the real personal digital assistant arrives, and we think

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Speaker 8: that PI, you know PI dot ai where where you

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Speaker 8: can access it online, is really going to be that assistant.

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Speaker 3: Mastafa, you use words like building and I think about

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Speaker 3: your past, your career, your time, at DeepMind Google. How

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Speaker 3: important is it right now that you are basically moving

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Speaker 3: from research to real world introduction of a tool. You know,

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Speaker 3: a lot of people are calling to hit the brakes

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Speaker 3: right on development. That seems to be the point of differentiation,

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Speaker 3: moving from development of future generations and models, or indeed

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Speaker 3: the research arm to having a product that businesses and

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Speaker 3: consumers can use.

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Speaker 8: I mean, AI is now coming of age. I mean

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Speaker 8: this is going to be the meta technology of our times.

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Speaker 8: It is going to be the technology that enables everything else,

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Speaker 8: just like electricity or steam powered the last huge industrial revolutions.

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Speaker 8: And so that's very exciting for us because it's a

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Speaker 8: very practical time. You know, we're doing applied research, We're

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Speaker 8: getting things to work in production and at scale with

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Speaker 8: millions of users. And that's really the fun of it

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Speaker 8: is making these things very very useful and seeing how

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Speaker 8: they save time, improve performance, increase quality, and increasingly enable

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Speaker 8: us to actually create completely new experiences that technology has

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Speaker 8: never enabled before. I kind of think of this as

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Speaker 8: a new sort of design material. It's a new clay

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Speaker 8: that we can use to mold and create emergent, entirely personalized,

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Speaker 8: and completely adaptive experiences. Which are going to feel very

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Speaker 8: unlike the kinds of static user interfaces that we've had

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Speaker 8: of the past. I mean, today, a website is a

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Speaker 8: website is a website. It's just static and two D

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Speaker 8: and it stays the same. Tomorrow, your conversational AI is

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Speaker 8: going to enable you to experience dynamic, real time generated

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Speaker 8: personalized user interface and it will feel really magical compared

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Speaker 8: to what we've had in the past and.

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Speaker 2: The stuff of what scares you in all of this.

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Speaker 8: I think what's amazing about this technology is that it

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Speaker 8: is the ultimate force amplifier. Right wherever there is a

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Speaker 8: desire to take actions in the world to generate new content,

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Speaker 8: you know, people are now going to have access to

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Speaker 8: a tool that makes that easier to plan and to create.

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Speaker 8: And of course, you know that's going to lower the

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Speaker 8: barrier to entry to create chaos, and some people, you know,

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Speaker 8: with bad intentions will choose to you know, amplify the

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Speaker 8: disruption unfortunately. But I'm pretty confident that the vast majority

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Speaker 8: of people are going to use these tools for incredible

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Speaker 8: good and that where there are downsides, like for example,

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Speaker 8: the spread of misinformation or the growth of counterfeit people

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Speaker 8: which imitate you know, real humans. I think we're going

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Speaker 8: to get control of that pretty quickly.

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Speaker 3: And the stuff that you're joining us from London, What

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Speaker 3: are you doing in my old stumping ground, my hometown.

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Speaker 8: Well, this week coming up, we have a hackathon with

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Speaker 8: our team. We relocate to a different city, you know,

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Speaker 8: every month or so we get together and we work

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Speaker 8: on a project. So you should expect some exciting new

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Speaker 8: product features to be coming out in the next few week.

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Speaker 8: It's just a great time to be building. So it's

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Speaker 8: great to be here in London in the summer too.

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Speaker 3: Inflection Ai CEO Ma Stuffer Salum and I feel like

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Speaker 3: I see you every week. But we're on a good trend.

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Speaker 3: Thank you so much. All right, time for talking tech.

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Speaker 3: First up, the Netherlands published new export controls that will

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Speaker 3: restrict more ASML chip making machines from being sent to China.

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Speaker 3: This new Dutch regulation will force ASML to apply for

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Speaker 3: shipping licenses to export DUV systems as early as September first,

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Speaker 3: and one of Apple's biggest supplies, Foxconn, will invest about

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Speaker 3: two hundred and forty six million dollars in two new

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Speaker 3: projects in Kwang Nin, Vietnam. The plants FECV and FMMV

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Speaker 3: FOXCN were awarded investment licenses and will begin operations in

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Speaker 3: twenty twenty four and twenty twenty five.

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Speaker 2: Plus.

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Speaker 3: Airbnb is dismissing data in a viral that suggested revenue

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Speaker 3: for property owners in some US cities was down nearly

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Speaker 3: fifty percent. Airbnb spokesperson Sam Rendell said the data is

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Speaker 3: not consistent with airbnbs and that the demand for short

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Speaker 3: term rentals is alive and well. Joining us for more

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Speaker 3: to talk about this, Bloomberg's Natalie Lung.

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Speaker 2: Natalie explain this one to me.

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Speaker 3: There was a viral tweet we're going to show it

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Speaker 3: in just a moment that said basically, people that have

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Speaker 3: Airbnb homes, they're not making any money.

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Speaker 5: Right.

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Speaker 4: So this viral tweet that came out around two days

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Speaker 4: ago that showed had data from all the rooms which

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Speaker 4: aggregates Airbnb and vrbo listings data showing in cities like Sevenville, Phoenix,

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Speaker 4: Austin are seeing revenue per listing drop nearly fifty percent,

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Speaker 4: And what's god people talking about this data is obviously

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Speaker 4: the fixed, but also people were wondering like the accuracy

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Speaker 4: and veracity of the data because there was an other

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Speaker 4: data source, air DNA, which is also widely cited by

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Speaker 4: the hotel and short ton reto industry.

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Speaker 5: Their data shows for.

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Speaker 4: The same markets, the decline is more muted, so around

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Speaker 4: in the single digits.

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Speaker 5: The biggest one was around nine percent.

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Speaker 2: Natalie very quickly Airbnb's response to.

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Speaker 4: This, Yes, they are saying this data is not consistent

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Speaker 4: with what they're seeing. They reported a record revenue in

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Speaker 4: verset Q and they're still seeing shorng demand for short tomentos.

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Speaker 3: All right, Bloomberg's Natalie Lung reporting there out of the

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Speaker 3: East Coast.

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Speaker 2: Thank you so much.

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Speaker 3: Welcome back to Bloomberg Technology and lovel here in San Francisco.

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Speaker 3: Let's turn to data intelligence platform Elation, recently named Data

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Speaker 3: Governance Partner of the Year. But get this by both

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Speaker 3: Snowflake and Data Bricks CEO Sachin Sanghani joins me here

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Speaker 3: in San Francisco for more. Find this really interesting because

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Speaker 3: Snowflake and Data Bricks, you could call them frenemies, you

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Speaker 3: could call them rivals. They both pick you. That's an

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Speaker 3: interesting triangle. Tells me about the relationship with both companies.

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Speaker 9: And thank you for having me. It's great to be here.

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Speaker 9: Both companies are quite interesting. They had their conferences this week,

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Speaker 9: both at the same time, both at the same time,

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Speaker 9: which may have been purposeful, may not. But both of

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Speaker 9: them had twelve thousand attendees at the conference. So while

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Speaker 9: they both operate in the same space, they also have

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Speaker 9: quite distinct audiences that use them and leverage them and

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Speaker 9: quite indicative of the growth in data. If you think

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Speaker 9: about ten years ago when Elation was founded, when data

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Speaker 9: Bricks was roughly founded, with Snowflake was roughly found, did

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Speaker 9: these are companies that had twenty five hundred people at

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Speaker 9: the biggest data conferences and now you have almost ten

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Speaker 9: times that amount. So both of them are indicative of

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Speaker 9: the massive growth and data and we're excited to be

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Speaker 9: a part of it and excited to rationalize all of

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Speaker 9: this data in the ecosystem.

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Speaker 3: I reported recently that data Bricks is SQL producted passed

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Speaker 3: one hundred million dollars in any lized revenue. That's an

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Speaker 3: area they're trying to grow. Kind of puts them in

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Speaker 3: an interesting territory with Snowflake. How do you work with

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Speaker 3: both companies? What is the interplay between Elation and their

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Speaker 3: both sort of that I mean everything from mL and

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Speaker 3: AI through to cloud based enterprise is interesting.

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Speaker 9: So we describe Elation as a data intelligence platform.

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Speaker 2: What is that?

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Speaker 9: What does that exactly mean? Well, if you live inside

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Speaker 9: of the standard enterprise, think about companies like Pfizer and

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Speaker 9: Cisco and Virsion Australia, Nasdaq, all of whom are Elation customers.

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Speaker 9: These are companies that have petabytes and petabytes of data

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Speaker 9: strewn across data bricks, Snowflake, Amazon, Microsoft, Oracle, all of

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Speaker 9: this legacy technology that it has existed since, you know,

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Speaker 9: the last twenty years. And so what Elation does is

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Speaker 9: it provides a map of your data so that any

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Speaker 9: person can find and understand and trust that data. And

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Speaker 9: as a result of that, we have over five hundred customers,

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Speaker 9: thirty five of Fortune one hundred use us. But the

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Speaker 9: broad idea is that if you exist inside of one

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Speaker 9: of these companies, you basically are existing. If you don't

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Speaker 9: have Elation, you know, as somebody maybe who's trying to

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Speaker 9: navigate the streets of Boston without a map, or you know,

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Speaker 9: try to navigate the web without Google, it's just impossible

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Speaker 9: to do, and we provide you with that capability.

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Speaker 3: The specific use case right now is generative AI and

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Speaker 3: indistinction from AI more broadly, because you have to use

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Speaker 3: your own data set to make a generative AI tool

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Speaker 3: relevant to whatever it is you do. Where does elation

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Speaker 3: come in in that process?

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Speaker 9: So you can't do trusted AI without having tru data

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Speaker 9: and your own data and on your own data or

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Speaker 9: even on third party data. So, if you think about

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Speaker 9: all of these models, they operate on a garbage in,

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Speaker 9: garbage out basis, and particularly in the realm of structured data,

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Speaker 9: if you have a really highly well trained model operating

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Speaker 9: off of very bad, poor data, what you're going to

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Speaker 9: have is multiplicatively wrong answers. You're going to get consistently

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Speaker 9: bad outputs. And so the ability to be able to

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Speaker 9: locate the right data sets, the ability to understand that

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Speaker 9: the data that you're feeding this model is appropriate and

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Speaker 9: well is a massive problem that every one of these

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Speaker 9: companies has to deal with, and frankly that Snowflake in

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Speaker 9: Data Bricks has to deal with in order to get

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Speaker 9: their customers to adopt at scale.

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Speaker 3: The simple concept of the company elation is to help

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Speaker 3: an organization it's users find manage trust the data that

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Speaker 3: they're looking at. There's a lot of interest in your company,

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Speaker 3: the transparency. You raised one hundred and twenty three million

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Speaker 3: dollars in November. I think that's right, and Data Bricks

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Speaker 3: is Drum did participate in that, so that's correct. You

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Speaker 3: are their most trusted partner, but they also are one

439
00:24:05,160 --> 00:24:09,680
Speaker 3: of your back is interesting. Valuation as well, one point

440
00:24:09,680 --> 00:24:12,320
Speaker 3: seven billion dollars. How do you grow your business from here?

441
00:24:13,480 --> 00:24:16,440
Speaker 9: We were, interestingly at that point in time in November,

442
00:24:16,480 --> 00:24:18,919
Speaker 9: thirty percent up up when the rest of the market

443
00:24:18,960 --> 00:24:19,960
Speaker 9: was seventy percent in.

444
00:24:20,000 --> 00:24:22,840
Speaker 2: Top line growth. Valuation got it.

445
00:24:22,880 --> 00:24:25,280
Speaker 9: So our prior around valuation was done in twenty twenty one,

446
00:24:25,400 --> 00:24:27,720
Speaker 9: which was a close to the top of the market,

447
00:24:27,800 --> 00:24:30,239
Speaker 9: not quite exactly the top of the market, but we

448
00:24:30,240 --> 00:24:31,800
Speaker 9: were still fifty percent up when the rest of the

449
00:24:31,800 --> 00:24:34,439
Speaker 9: market was seventy percent out, which is an indication to

450
00:24:34,480 --> 00:24:37,119
Speaker 9: both the strength of the market but also the strength

451
00:24:37,119 --> 00:24:39,760
Speaker 9: of the business in that last round. Snowflake was an

452
00:24:39,800 --> 00:24:43,640
Speaker 9: investor in our company. And why are these companies invested

453
00:24:43,680 --> 00:24:46,119
Speaker 9: Because fundamentally, you can have all of the data in

454
00:24:46,200 --> 00:24:49,000
Speaker 9: the world, but if you can't use it, if you

455
00:24:49,000 --> 00:24:52,640
Speaker 9: can't understand it, if people aren't enabled with it, then

456
00:24:52,680 --> 00:24:57,280
Speaker 9: it's really quite valueless. And so from our perspective and

457
00:24:57,320 --> 00:25:01,480
Speaker 9: I think from theirs, the real problem is enabled hundreds,

458
00:25:01,480 --> 00:25:04,280
Speaker 9: if not thousands of people within the enterprise to actually

459
00:25:04,400 --> 00:25:07,200
Speaker 9: leverage this stuff at scale, and the road has been

460
00:25:07,640 --> 00:25:10,719
Speaker 9: up into the right in general, but there is a

461
00:25:10,760 --> 00:25:13,919
Speaker 9: lot of variation in terms of the returns on these projects,

462
00:25:14,160 --> 00:25:18,000
Speaker 9: and so being able to return more reliably by basically

463
00:25:18,000 --> 00:25:20,879
Speaker 9: building skills within each of these companies, within many of

464
00:25:20,880 --> 00:25:23,320
Speaker 9: these companies, that's real trick, and that's what we try

465
00:25:23,320 --> 00:25:23,880
Speaker 9: to help.

466
00:25:23,680 --> 00:25:27,040
Speaker 3: With Elation CEO Sachi and Sanghani here with us in

467
00:25:27,080 --> 00:25:28,800
Speaker 3: San Francisco and Bloomberg Technology.

468
00:25:29,080 --> 00:25:31,600
Speaker 2: Thank you. Sum up and actually coming up, we'll talk.

469
00:25:31,400 --> 00:25:35,160
Speaker 3: To Data Breaks, investor Race Capital about AI some more,

470
00:25:35,240 --> 00:25:37,520
Speaker 3: also the m and A landscape and what the VC

471
00:25:37,680 --> 00:25:41,000
Speaker 3: world has to say about literally everything we've been talking

472
00:25:41,000 --> 00:25:43,560
Speaker 3: about on the show, ed if Young Race Capital coming

473
00:25:43,680 --> 00:25:46,159
Speaker 3: up next, Let's get a quick check on Uber shares

474
00:25:46,359 --> 00:25:49,199
Speaker 3: where they're trading up one point six percent in the session.

475
00:25:49,280 --> 00:25:52,359
Speaker 3: Piece of news out overnight Parents, if you're listening, Uber

476
00:25:52,760 --> 00:25:56,000
Speaker 3: is giving parents and caregivers the option to request a

477
00:25:56,160 --> 00:25:59,320
Speaker 3: ride on the app with a car seat as part

478
00:25:59,320 --> 00:26:01,960
Speaker 3: of a part and a shit with Nuna Baby which

479
00:26:02,000 --> 00:26:05,080
Speaker 3: is a baby gear brand, but it's kind of a PSA.

480
00:26:05,480 --> 00:26:06,240
Speaker 2: If you're out.

481
00:26:06,080 --> 00:26:10,320
Speaker 3: There and you're a new A user and you got kids, well,

482
00:26:10,359 --> 00:26:11,439
Speaker 3: they've been thinking about that.

483
00:26:11,920 --> 00:26:12,680
Speaker 2: This is Blomberg.

484
00:26:22,800 --> 00:26:26,359
Speaker 10: AI has been a technology and development for decades. A

485
00:26:26,400 --> 00:26:29,760
Speaker 10: lot of tech actually, I think originates from gaming as

486
00:26:29,800 --> 00:26:30,440
Speaker 10: a use case.

487
00:26:30,640 --> 00:26:33,239
Speaker 11: These large language models are very exciting and what they

488
00:26:33,280 --> 00:26:35,280
Speaker 11: can do is they can act as an interface where

489
00:26:35,280 --> 00:26:37,040
Speaker 11: you can finally talk to the computer in a way

490
00:26:37,080 --> 00:26:38,360
Speaker 11: that you couldn't perform.

491
00:26:38,160 --> 00:26:39,000
Speaker 2: With John AI.

492
00:26:39,440 --> 00:26:42,440
Speaker 10: There is a new surface area, I would say in

493
00:26:42,520 --> 00:26:46,520
Speaker 10: terms of both increasing productivity of workers, for example artists

494
00:26:46,560 --> 00:26:47,080
Speaker 10: and gaming.

495
00:26:47,200 --> 00:26:51,680
Speaker 12: AI will move into industries that were or are underserved

496
00:26:51,680 --> 00:26:52,280
Speaker 12: by SaaS.

497
00:26:52,400 --> 00:26:54,960
Speaker 11: AI is something where it can impact drug design, It

498
00:26:54,960 --> 00:26:57,400
Speaker 11: can impact how we think about healthcare, how we allocate

499
00:26:57,440 --> 00:26:58,800
Speaker 11: healthcare resources.

500
00:26:58,840 --> 00:27:02,000
Speaker 13: I think San Francisco inarticular with the AI boom, is

501
00:27:02,400 --> 00:27:04,000
Speaker 13: really about cerebral value.

502
00:27:04,160 --> 00:27:06,680
Speaker 12: You'll walk around and literally, like how they talked about

503
00:27:06,720 --> 00:27:09,520
Speaker 12: in the nineties, you'll see garage doors open and people

504
00:27:09,600 --> 00:27:12,640
Speaker 12: on their computers and you'll literally see on the telephone

505
00:27:12,920 --> 00:27:15,879
Speaker 12: polls flyers for AI happy hours, AI Hakathans.

506
00:27:15,960 --> 00:27:18,679
Speaker 13: The smartest people in the world are sitting in those cafes,

507
00:27:19,600 --> 00:27:23,320
Speaker 13: you're having discussions not just about starting their companies, but

508
00:27:23,359 --> 00:27:26,040
Speaker 13: also what is the cutting edge of what these AI

509
00:27:26,080 --> 00:27:26,840
Speaker 13: models can do.

510
00:27:29,840 --> 00:27:32,399
Speaker 3: That was just some of our VC guests weighing in

511
00:27:32,440 --> 00:27:35,520
Speaker 3: on AI and the rise of San Francisco is the

512
00:27:35,520 --> 00:27:39,720
Speaker 3: epicenter of what's happening in AI. Let's move away from

513
00:27:39,760 --> 00:27:42,479
Speaker 3: the Bay and stick with AI though, and check out

514
00:27:42,560 --> 00:27:45,120
Speaker 3: other headlines in the world a venture capital.

515
00:27:45,440 --> 00:27:46,840
Speaker 2: Just take a look at climate tech.

516
00:27:46,920 --> 00:27:50,040
Speaker 3: It's been an investing bright spot since twenty twenty one

517
00:27:50,400 --> 00:27:54,080
Speaker 3: reikian deals as other sectors stagnated, but the first half

518
00:27:54,119 --> 00:27:57,320
Speaker 3: of twenty twenty three saw a forty percent decrease in

519
00:27:57,359 --> 00:28:01,040
Speaker 3: climate venture funding that according to Climate Tech VC, growth

520
00:28:01,040 --> 00:28:05,240
Speaker 3: and late stage startups saw the biggest declines. Union fifty four,

521
00:28:05,320 --> 00:28:08,159
Speaker 3: an African startup backed by Tiger Global, is entering the

522
00:28:08,240 --> 00:28:11,240
Speaker 3: race to developed super apps as investors look to tap

523
00:28:11,280 --> 00:28:15,800
Speaker 3: the continents increasingly tech savvy market. Called Chitchat, it offers

524
00:28:15,800 --> 00:28:19,560
Speaker 3: secure messaging paired with dollar based virtual cards that it

525
00:28:19,600 --> 00:28:23,240
Speaker 3: developed with MasterCard. The new app could debut in September

526
00:28:23,560 --> 00:28:27,440
Speaker 3: and back to AI Intel leading a Series B financing

527
00:28:27,480 --> 00:28:31,480
Speaker 3: round in the German AI startup alef Alpha, whose luminous

528
00:28:31,560 --> 00:28:35,479
Speaker 3: language model competes with open ais Chat GPT. That's all

529
00:28:35,520 --> 00:28:39,560
Speaker 3: according to Handle's Blat, citing sources. Nvidia and SAP also

530
00:28:39,640 --> 00:28:44,360
Speaker 3: participating in the more than one hundred million euro financing round.

531
00:28:45,040 --> 00:28:47,200
Speaker 3: Right back here to the US as digging some more

532
00:28:47,280 --> 00:28:50,200
Speaker 3: into the bench capital landscape and AI and bring in

533
00:28:50,440 --> 00:28:52,760
Speaker 3: Edith Jung, partner at Race Capital.

534
00:28:53,200 --> 00:28:54,680
Speaker 2: That's a lot to take in, Edith.

535
00:28:54,800 --> 00:28:57,720
Speaker 3: But where's your head at right now when it comes

536
00:28:57,720 --> 00:28:58,640
Speaker 3: to investing in AI?

537
00:29:00,240 --> 00:29:03,760
Speaker 14: I think you know, AI literally is happening everywhere, all

538
00:29:03,800 --> 00:29:07,640
Speaker 14: at once. And this week or one of our portfolio companies,

539
00:29:07,680 --> 00:29:12,240
Speaker 14: Data Brick acquired Mosaic mL for one point three billions.

540
00:29:12,280 --> 00:29:16,920
Speaker 14: And obviously we're also really really into any infrastructure layer

541
00:29:17,320 --> 00:29:20,720
Speaker 14: that is supporting the AI ecosystem.

542
00:29:20,960 --> 00:29:22,200
Speaker 2: Let's go ahead.

543
00:29:22,520 --> 00:29:24,400
Speaker 3: No, I'm sorry to interrupt you if I think we

544
00:29:24,600 --> 00:29:27,440
Speaker 3: should stick with it and jump on this Data Bricks

545
00:29:27,560 --> 00:29:31,040
Speaker 3: acquiring Mosaic pretty significant acquisition.

546
00:29:31,720 --> 00:29:32,800
Speaker 2: What did you make of it?

547
00:29:32,880 --> 00:29:35,320
Speaker 3: The rationale behind it, and what it's going to do

548
00:29:35,440 --> 00:29:38,480
Speaker 3: to shake things up right now here in Silicon Valley

549
00:29:38,480 --> 00:29:39,360
Speaker 3: in San Francisco.

550
00:29:40,480 --> 00:29:43,320
Speaker 14: Yeah, it's really exciting time to be Like, if you

551
00:29:43,360 --> 00:29:47,880
Speaker 14: look at what Data Brick. Particularly in the last sixty days,

552
00:29:47,920 --> 00:29:54,240
Speaker 14: they acquire three different companies a Kia which is focused

553
00:29:54,320 --> 00:29:58,360
Speaker 14: on more on the data governance, and then also Rubercon

554
00:29:58,480 --> 00:30:02,200
Speaker 14: which is two weeks ago that focus on data storage.

555
00:30:02,440 --> 00:30:06,280
Speaker 14: So now having will say it MLO, it basically complete

556
00:30:06,320 --> 00:30:09,720
Speaker 14: the story building their own l M. I think like

557
00:30:09,760 --> 00:30:13,080
Speaker 14: the world the world is heading is in some sense,

558
00:30:14,160 --> 00:30:19,200
Speaker 14: I think enterprise AI is having an iPhone moment. Everybody

559
00:30:19,240 --> 00:30:22,320
Speaker 14: want a piece of AI, but not just because you

560
00:30:22,400 --> 00:30:24,719
Speaker 14: want to do AI doesn't mean that you will do

561
00:30:24,760 --> 00:30:27,440
Speaker 14: it well. And I think a lot of the enterprise

562
00:30:27,760 --> 00:30:30,720
Speaker 14: it's a little weary about feeding data to open the

563
00:30:30,760 --> 00:30:33,360
Speaker 14: AI is just like a black box, right. The world

564
00:30:33,600 --> 00:30:37,440
Speaker 14: wants open source, the world wants to keep the propriety data. Hence,

565
00:30:37,600 --> 00:30:40,760
Speaker 14: you know, data bricks comes in because it's really built

566
00:30:40,800 --> 00:30:44,480
Speaker 14: on a parte Spark, which is an open source framework,

567
00:30:44,800 --> 00:30:47,640
Speaker 14: and now you'll be able to basically build your own

568
00:30:47,840 --> 00:30:51,400
Speaker 14: enterprise l M on your own data. So that's really

569
00:30:51,440 --> 00:30:54,840
Speaker 14: what the Data break is pushing. So at the end

570
00:30:54,880 --> 00:30:56,960
Speaker 14: of the day, you know, we've been always been saying,

571
00:30:57,080 --> 00:31:00,760
Speaker 14: you know, data is really the key goal mine and

572
00:31:00,840 --> 00:31:02,720
Speaker 14: for us, the way that we look at in terms

573
00:31:02,720 --> 00:31:05,920
Speaker 14: of our investment is who actually have access to a

574
00:31:05,960 --> 00:31:10,040
Speaker 14: propriety data. And it's not just about training a general

575
00:31:10,240 --> 00:31:13,680
Speaker 14: personal AI because if you think about it, right, if

576
00:31:13,720 --> 00:31:17,840
Speaker 14: I asked a question to CHATGBT on, you know, who

577
00:31:17,920 --> 00:31:22,800
Speaker 14: is the best doctor My mom recently have a heart

578
00:31:22,840 --> 00:31:25,640
Speaker 14: problem and she would rather talk to a doctor that

579
00:31:25,760 --> 00:31:29,720
Speaker 14: have seen you know, thousands of similar patients with similar

580
00:31:29,800 --> 00:31:33,360
Speaker 14: dcs versus just you know, I'm not quite sure where

581
00:31:33,360 --> 00:31:37,120
Speaker 14: the data come from. So essentially that's what your data brains.

582
00:31:36,960 --> 00:31:40,360
Speaker 2: Is trying to do. So there's also the transactional part

583
00:31:40,360 --> 00:31:40,600
Speaker 2: of this.

584
00:31:40,720 --> 00:31:43,520
Speaker 3: We had Ali Godzi, the CEO Data Breaks, on the

585
00:31:43,520 --> 00:31:46,440
Speaker 3: show two weeks ago and he said, quote, when it

586
00:31:46,480 --> 00:31:49,680
Speaker 3: comes to AI right now, you have to pay up,

587
00:31:49,800 --> 00:31:53,120
Speaker 3: in other words, get your check book out valuations.

588
00:31:53,480 --> 00:31:55,040
Speaker 2: This is getting a little big.

589
00:31:55,080 --> 00:31:57,560
Speaker 3: You think about Inflection, we reported with the CEO and

590
00:31:57,600 --> 00:32:01,720
Speaker 3: the show today one point three billion around at four

591
00:32:01,760 --> 00:32:03,560
Speaker 3: billion proportionately.

592
00:32:04,120 --> 00:32:05,120
Speaker 2: What do you make of that?

593
00:32:05,960 --> 00:32:10,560
Speaker 14: Yeah, I really enjoyed your segment with the CEO. Inflection

594
00:32:11,200 --> 00:32:15,040
Speaker 14: one point three billion dollar rates is exactly the same

595
00:32:15,080 --> 00:32:19,600
Speaker 14: amount how much Ali have paid for Mosaic mL and

596
00:32:20,000 --> 00:32:23,760
Speaker 14: but as you mentioned earlier in the show, that Inflection

597
00:32:23,880 --> 00:32:27,680
Speaker 14: actually has access to the H one hundred and their

598
00:32:27,720 --> 00:32:31,760
Speaker 14: partnership with Nvidia, which to me is super fascinating and

599
00:32:31,840 --> 00:32:34,480
Speaker 14: in some sense, yes, there's a lot of hype, but

600
00:32:34,600 --> 00:32:37,640
Speaker 14: yet I think there is like so much room and

601
00:32:37,680 --> 00:32:40,640
Speaker 14: things going on that can be improved, not let alone.

602
00:32:40,880 --> 00:32:44,880
Speaker 14: Obviously everybody is saying that we're building on LLM, but

603
00:32:44,920 --> 00:32:47,240
Speaker 14: we also need to think about data privacy. We need

604
00:32:47,280 --> 00:32:50,480
Speaker 14: to think about how do we correct hallucination. We don't

605
00:32:50,480 --> 00:32:53,680
Speaker 14: want to just come up with some random recommendation for

606
00:32:53,760 --> 00:32:56,120
Speaker 14: which doctor to go to, because there's a lot more

607
00:32:56,160 --> 00:32:58,160
Speaker 14: tuning need to be done well.

608
00:32:58,320 --> 00:33:03,400
Speaker 3: Edith on the point of Inflection, H one hundreds are

609
00:33:03,520 --> 00:33:07,000
Speaker 3: everything in the AI story right now, the GPU compute power.

610
00:33:07,240 --> 00:33:09,760
Speaker 3: Are you phoning up Ali at Data Bricks and saying

611
00:33:10,280 --> 00:33:13,160
Speaker 3: what GPUs have you guys got access to you know

612
00:33:13,720 --> 00:33:15,760
Speaker 3: down the road? How much of a concern is that

613
00:33:15,800 --> 00:33:18,040
Speaker 3: for you as a bench capitalist.

614
00:33:19,480 --> 00:33:22,440
Speaker 14: I think you know Ali have previously explained before, which

615
00:33:22,480 --> 00:33:27,240
Speaker 14: is when you're training as much smaller subset of data set.

616
00:33:27,680 --> 00:33:31,640
Speaker 14: What Reinflection is really focusing on is literally building a

617
00:33:31,800 --> 00:33:36,760
Speaker 14: personal LM. Right so there could be an ad at

618
00:33:37,040 --> 00:33:38,880
Speaker 14: a GPT or either GPT.

619
00:33:39,520 --> 00:33:41,120
Speaker 2: But what data.

620
00:33:40,960 --> 00:33:44,280
Speaker 14: Bricks is focusing on is training a much more success

621
00:33:44,400 --> 00:33:48,640
Speaker 14: smaller data set of data, enterprise data. So in that

622
00:33:48,760 --> 00:33:51,360
Speaker 14: sense you don't really need sort of the the H

623
00:33:51,400 --> 00:33:56,360
Speaker 14: one hundred to train. But absolutely Nvidia is on fire,

624
00:33:56,720 --> 00:34:01,960
Speaker 14: being over a trillion dollars in market now and being

625
00:34:02,000 --> 00:34:05,680
Speaker 14: able to secure the chipset, especially the H one hundred

626
00:34:05,800 --> 00:34:09,359
Speaker 14: Order eight one hundred is super super important for not

627
00:34:09,400 --> 00:34:11,560
Speaker 14: all LOM focused companies.

628
00:34:13,520 --> 00:34:16,160
Speaker 3: Edith Young, Race Capital General partner, is so good to

629
00:34:16,160 --> 00:34:18,400
Speaker 3: catch up. Thank you for joining us out of New

630
00:34:18,480 --> 00:34:29,880
Speaker 3: York time now for what's going viral and it's the

631
00:34:29,960 --> 00:34:33,319
Speaker 3: number one trending on Google trends right now. In a

632
00:34:33,400 --> 00:34:36,520
Speaker 3: six to three ruling, this US Supreme Court has stricken

633
00:34:36,719 --> 00:34:41,320
Speaker 3: President Biden's plan to forgive student loans for some borrowers.

634
00:34:41,719 --> 00:34:44,560
Speaker 3: To get more context about this ruling and who is

635
00:34:44,600 --> 00:34:49,000
Speaker 3: the most impacted, Bloomberg's Ryan Tige beckwith in Washington, and

636
00:34:49,200 --> 00:34:51,520
Speaker 3: of course I will Street reporter Narlie Bassek out in

637
00:34:51,520 --> 00:34:55,000
Speaker 3: New York bear with me headlines crossing the Bloomberg terminal.

638
00:34:55,400 --> 00:34:58,080
Speaker 3: President Biden saying in a tweet, the High Court ruling

639
00:34:58,080 --> 00:35:01,680
Speaker 3: on student loans is quote unthinkable. The Supreme Court striking

640
00:35:01,719 --> 00:35:04,840
Speaker 3: down student debt relief is wrong. The fight is not

641
00:35:05,160 --> 00:35:08,840
Speaker 3: over on student loan relief. That is President Biden's response

642
00:35:09,239 --> 00:35:11,560
Speaker 3: ryan based on the court decision.

643
00:35:11,600 --> 00:35:13,000
Speaker 2: Where do we stand right now?

644
00:35:15,040 --> 00:35:18,319
Speaker 15: This doesn't leave President Biden with a lot of options.

645
00:35:18,400 --> 00:35:21,520
Speaker 15: There's a lot of smaller, sort of more targeted things

646
00:35:21,520 --> 00:35:24,320
Speaker 15: that he can do for giving student loans to certain

647
00:35:24,400 --> 00:35:28,120
Speaker 15: sort of groups like veterans or people who have become

648
00:35:28,120 --> 00:35:30,200
Speaker 15: disabled or something like that. But he's not going to

649
00:35:30,200 --> 00:35:32,880
Speaker 15: be able to do the kind of large scale program

650
00:35:33,000 --> 00:35:36,600
Speaker 15: here like he was proposing. They pretty much ruled that out.

651
00:35:37,600 --> 00:35:40,000
Speaker 15: So I think you're going to see a push for

652
00:35:40,080 --> 00:35:42,520
Speaker 15: these smaller things, and then you're going to see a

653
00:35:42,560 --> 00:35:45,360
Speaker 15: renewed push in twenty twenty four as a campaign issue

654
00:35:45,560 --> 00:35:47,919
Speaker 15: for Congress to take some kind of action on its own.

655
00:35:49,480 --> 00:35:51,840
Speaker 3: Shanali, we need to talk about the technology side of

656
00:35:51,880 --> 00:35:55,640
Speaker 3: this story, in particular fintech. The market reaction was in

657
00:35:55,719 --> 00:35:58,000
Speaker 3: SOFI volatility, and I think we had a hole at

658
00:35:58,040 --> 00:35:58,680
Speaker 3: one point.

659
00:35:59,200 --> 00:36:00,160
Speaker 2: What is happening that.

660
00:36:00,480 --> 00:36:03,080
Speaker 16: Yeah, remember there's not a lot of lenders that are

661
00:36:03,080 --> 00:36:05,600
Speaker 16: in the student loan environment here. In fact, the government

662
00:36:05,640 --> 00:36:07,879
Speaker 16: itself is one of the largest banks when you think

663
00:36:07,880 --> 00:36:10,080
Speaker 16: about it, because there's so much debt on the government's

664
00:36:10,080 --> 00:36:13,480
Speaker 16: own balance sheet. In terms of private lenders, it's SOFI,

665
00:36:13,600 --> 00:36:16,200
Speaker 16: which is really one of the biggest out there, and

666
00:36:16,280 --> 00:36:18,960
Speaker 16: SOFI has initially built so much of their business model

667
00:36:19,000 --> 00:36:22,560
Speaker 16: around it. Ed this end of the student loan pause

668
00:36:22,680 --> 00:36:25,560
Speaker 16: is poised to help SOFI, but remember, as students start

669
00:36:25,600 --> 00:36:28,000
Speaker 16: to look to refinance, interest rates are much much higher.

670
00:36:28,040 --> 00:36:30,919
Speaker 16: There are a lot of unknowns about what this moratorium

671
00:36:30,920 --> 00:36:33,120
Speaker 16: pose it mean, and what it means now that many

672
00:36:33,160 --> 00:36:36,600
Speaker 16: others forty million people may not anymore face this idea

673
00:36:36,719 --> 00:36:40,360
Speaker 16: of forgiveness. What does it also mean for SOFI at large?

674
00:36:40,360 --> 00:36:43,000
Speaker 16: You've had SOFI anyways over the last couple of years

675
00:36:43,280 --> 00:36:46,160
Speaker 16: really start to pivot as business model away from being

676
00:36:46,239 --> 00:36:50,200
Speaker 16: so reliant on student loans and looking to other areas instead.

677
00:36:50,480 --> 00:36:53,640
Speaker 16: So big question marks about the through loan business moving forward,

678
00:36:53,880 --> 00:36:56,440
Speaker 16: as well as what this still large part of Sofi's

679
00:36:56,440 --> 00:37:00,000
Speaker 16: business model means, but also how they continue to keep

680
00:37:00,080 --> 00:37:02,319
Speaker 16: diversifying away from it and who fills the student loan

681
00:37:02,360 --> 00:37:03,120
Speaker 16: gaps in between.

682
00:37:04,360 --> 00:37:07,360
Speaker 3: Ryan So much of the Bloomberg technology audience might be

683
00:37:07,520 --> 00:37:10,600
Speaker 3: technology sector employees founders who are trying to make their

684
00:37:10,600 --> 00:37:12,759
Speaker 3: world weigh in the world with student loans, and it

685
00:37:12,840 --> 00:37:16,759
Speaker 3: makes me ask you the politics of this. This was

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00:37:16,840 --> 00:37:19,760
Speaker 3: really important for Biden's presidency.

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00:37:21,440 --> 00:37:24,239
Speaker 15: Right, I mean, this is one of those things young

688
00:37:24,280 --> 00:37:27,439
Speaker 15: people helped put him in the White House, and this

689
00:37:27,520 --> 00:37:30,240
Speaker 15: is a top priority among a lot of young people.

690
00:37:30,840 --> 00:37:34,280
Speaker 15: So he's going to be motivated to continue to push

691
00:37:34,320 --> 00:37:37,399
Speaker 15: on this. His hands are tied unless he wins back

692
00:37:37,440 --> 00:37:40,280
Speaker 15: the House and the Senate. It's possible that he could

693
00:37:40,320 --> 00:37:43,319
Speaker 15: do something in a second term if he had both

694
00:37:43,360 --> 00:37:47,360
Speaker 15: of those through budget reconciliation, which doesn't subject to the filibuster.

695
00:37:47,760 --> 00:37:50,719
Speaker 15: So there's some potential there that this could be an

696
00:37:50,760 --> 00:37:53,480
Speaker 15: issue that he runs on and one that would motivate

697
00:37:53,520 --> 00:37:57,200
Speaker 15: young people, which I think benefits And politically, it's clearly

698
00:37:57,280 --> 00:38:01,839
Speaker 15: on demographic lines. Republicans of all come out against, you know,

699
00:38:01,880 --> 00:38:04,960
Speaker 15: in favor of this Supreme Court ruling who are running

700
00:38:04,960 --> 00:38:07,640
Speaker 15: for president, So it'll probably be a dividing line in

701
00:38:07,640 --> 00:38:08,760
Speaker 15: the twenty twenty four election.

702
00:38:09,440 --> 00:38:10,400
Speaker 2: Shnali target.

703
00:38:10,400 --> 00:38:13,239
Speaker 3: Also moving to the downside, the economic impact here on

704
00:38:13,280 --> 00:38:14,440
Speaker 3: the consumer is a big thing.

705
00:38:14,680 --> 00:38:15,319
Speaker 2: It's a huge thing.

706
00:38:15,400 --> 00:38:18,279
Speaker 16: Remember, student loans on average are between two hundred to

707
00:38:18,320 --> 00:38:21,520
Speaker 16: three one hundred dollars a month ed. That is concert tickets,

708
00:38:21,520 --> 00:38:25,080
Speaker 16: that is food, that is travel, that is retail. So

709
00:38:25,360 --> 00:38:27,640
Speaker 16: of course you are going to see an impact here.

710
00:38:27,719 --> 00:38:29,960
Speaker 16: Remember we've been in a moratorium, which is why we

711
00:38:30,040 --> 00:38:33,480
Speaker 16: have that huge unknown here in terms of what that

712
00:38:33,600 --> 00:38:36,400
Speaker 16: impact will be for so many borrowers to have to

713
00:38:36,440 --> 00:38:39,800
Speaker 16: not only start repaying again, but also have to contend

714
00:38:40,239 --> 00:38:42,920
Speaker 16: with the idea that much of this won't be forgiven.

715
00:38:44,160 --> 00:38:45,040
Speaker 2: Bloomberg's Ryan T.

716
00:38:45,160 --> 00:38:48,120
Speaker 3: Beckwith Shnali Basseck on the story, will continue to track.

717
00:38:48,600 --> 00:38:50,960
Speaker 2: That does it for this edition of Bloomberg Technology.

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Speaker 3: But catch me on a Twitter spaces in an hour's time,

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00:38:54,800 --> 00:38:59,080
Speaker 3: Apple three trillion mark germ, and this is Bloomberg Technology,