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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 Evvelow in San Francisco.

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Speaker 2: This is Bloomberg Tech coming up tech stocks They waiver,

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Speaker 2: I mean, the NASA one hundred's worst losing streak in

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Speaker 2: five months. Concerns about AI valuations dominate that as we

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Speaker 2: speak with Tom of Bravo about its acquisition of hr

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Speaker 2: software provided day.

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Speaker 3: Force in what is set to become one of the

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Speaker 3: investment firm's largest ever deals.

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Speaker 2: And Google introduces new consumer gadgets including smartphones, a watch,

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Speaker 2: and wireless earbuzz with AI front and center. But first

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Speaker 2: we turn our attention to these markets. We have been

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Speaker 2: read throughout most of the training day, but certain stocks

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Speaker 2: are doing their best to lift us into the green.

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Speaker 2: We're off by only a tenth of a percent, but

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Speaker 2: still that's three days of an ASA one hundred, the

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Speaker 2: longest losing streak. Sains back in April in video doing

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Speaker 2: its best to pullus higher.

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Speaker 3: So two in fact is Palenteer.

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Speaker 2: But check out how much Plenteer has been down over

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Speaker 2: the course of what had been a six day losing streak.

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Speaker 2: Eroding seven hundred billion dollars worth more than seventy three

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Speaker 2: billion dollars worth sorry of its market valuation, and we

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Speaker 2: have seen question marks over more broadly the evaluation among

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Speaker 2: so many of these AI winners.

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Speaker 3: But let's get a take on this.

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Speaker 2: What prompted some of the questioning of the valuations and

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Speaker 2: whether it will hold.

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Speaker 3: Denny Fish is with US portfolio.

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Speaker 2: Manager of course, of the Global Technology and Innovation team

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Speaker 2: of rich Annis Henderson Investors. It's a firm with one

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Speaker 2: and fifty seven billion dollars in assets and management. It

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Speaker 2: just launched its own artificial intelligence ETF yesterday. We're looking

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Speaker 2: at some of the names Denny in that ETF. Tough

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Speaker 2: timing amid the current pressure on some of the companies.

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Speaker 3: What do you make of this questioning?

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Speaker 4: Yeah, so, you know, importantly, the reason we launched this

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Speaker 4: fund is we believe this is the most profound technology

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Speaker 4: shift that we will see in our lifetimes. So whatever

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Speaker 4: happens over the next couple of months is going to happen.

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Speaker 4: But the direction of travel over the next twenty years

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Speaker 4: we think is very positive as it relates to AI.

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Speaker 4: And if we just you know, reflect back over the

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Speaker 4: last twenty five years and the cloud, the social and

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Speaker 4: the mobile boom. You know, that was you know, twenty

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Speaker 4: plus years of value creation. And you know, we think

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Speaker 4: AI has the potential to be even larger than that.

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Speaker 4: You know, and with that, you know, and and and

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Speaker 4: you know, given you know your question on valuations, it depends,

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Speaker 4: you know, I mean, they're clearly pockets of the market

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Speaker 4: where things are more speculative and very highly valued. So

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Speaker 4: you know, you you talked about Pollunteer for example, you know,

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Speaker 4: is a good example of a company that trades it

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Speaker 4: a very elevated multiple, great company, great fundamentals, extremely high valuation.

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Speaker 4: And then you know, you can come back to names

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Speaker 4: like Taiwan Semi Canductor, or you know, the trades at

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Speaker 4: a team's earnings multiple, or you know Nvidia that's still

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Speaker 4: trades that are reasonable multiple given its earnings growth rate

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Speaker 4: with really powerful secular trends, and you can get more

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Speaker 4: comfortable in areas like that, and so you know, our

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Speaker 4: whole goal also, you know, just to put a finer

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Speaker 4: point on this is you know, AI is going to

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Speaker 4: affect the entire economy. So the purpose of our fund

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Speaker 4: is it's going to be heavy with tech as you

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Speaker 4: can see in terms of the top holdings, but also

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Speaker 4: you'll find you know, names and financial services, consumer, healthcare,

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Speaker 4: industrial and other areas of the economy as well.

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Speaker 2: It's about where you think AI is going to enable

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Speaker 2: enhancelled benefits. So I'm interested by the fact that you

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Speaker 2: got eaten in their Blackstone with thinking real estate, with

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Speaker 2: thinking power. How much has that not been priced into

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Speaker 2: these sorts of companies yet, Denny.

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Speaker 4: Well, I think if we're looking you know, at the

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Speaker 4: next five to ten years, you can make a case

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Speaker 4: for you know, the companies that you know, we've populated

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Speaker 4: in the portfolio right out of the gate. And I mean,

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Speaker 4: you know, you you mentioned power. You know, it's probably

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Speaker 4: the single biggest constraint to deploying data centers right now

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Speaker 4: at the scale that we need and the density that

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Speaker 4: we need is getting you know, power available. And then

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Speaker 4: once you can get the power available, then you have

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Speaker 4: to cool the data centers. And there's an entire ecosystem

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Speaker 4: around that that specializes in, you know, in those dynamics,

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Speaker 4: and you know, clearly we want to participate in that,

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Speaker 4: you know, over the longer term.

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Speaker 2: What's really interesting is about the application of AI. So yes,

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Speaker 2: it's about the infrastructure that goes in and the power

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Speaker 2: that we talk about the real estate, but then it's

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Speaker 2: actually what it does for businesses. You talk about financials,

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Speaker 2: talk about healthcare. What do you make of this MIT

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Speaker 2: research report that everyone seems to be worrying about the

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Speaker 2: fact that ninety five percent of all pilots fail.

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Speaker 4: Well, we're in that phase right now. And you know,

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Speaker 4: you know, I was in the valley during the dawn

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Speaker 4: of the commercial Internet, and there were a lot of

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Speaker 4: failures at that point in time too. And you know,

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Speaker 4: during that period, you know, we had a boom, we

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Speaker 4: had a bust, and you know, and then it actually

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Speaker 4: created some of the most powerful companies on the planet

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Speaker 4: today and you know, laid the foundation for you know,

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Speaker 4: these these multi year trends. And so I think kind

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Speaker 4: of depends what you're doing. You know, there are clear

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Speaker 4: applications out there. Clearly people are getting you know, value

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Speaker 4: out of things like chat, GPT, deep research co pilots

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Speaker 4: being widely deployed, and then you have a lot of

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Speaker 4: experimental stuff that's going on in companies and it's the

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Speaker 4: nature of the beast that you know, you're generally gonna

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Speaker 4: you know, fail a little bit out of the gate,

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Speaker 4: and you learn from that the technology gets better, people

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Speaker 4: get sharper in terms of deploying the technology and figuring

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Speaker 4: out how to use it, how to change their behaviors.

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Speaker 4: And then you look back and you know, three or

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Speaker 4: five years and you're like, wow, I kind of you know,

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Speaker 4: misforced for the trees, Denny, what.

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Speaker 3: Was interesting you referenced the dot com era.

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Speaker 2: Just recently Intel was back at a profit in terms

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Speaker 2: of its price versus future profit.

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Speaker 3: It's valuation was akin to the dot com era. It

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Speaker 3: was so high.

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Speaker 2: What do you make of soft putting in money? I

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Speaker 2: know that's in the latest fund and also the government.

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Speaker 4: Yeah, so you know, Intel is an interesting one on

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Speaker 4: the valuation. The reason the multiple so high is because

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Speaker 4: earnings have absolutely collapsed. And so you know that that

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Speaker 4: pe equation, when the ego is a lot lower, the

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Speaker 4: pe goes up a lot. So not necessarily because the

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Speaker 4: market's been enthusiastic about Intel, but rather you know the

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Speaker 4: financial performance of the company. And then you know, I

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Speaker 4: just think, you know, you know, SoftBank confessing in Intel.

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Speaker 4: It's it's a little bit curious, you know, potentially or

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Speaker 4: or particularly given that you know they have their ownership

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Speaker 4: in arm uh. You know, they're participating in Stargate.

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Speaker 3: UH.

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Speaker 4: They bought amp here. So it's it almost feels like

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Speaker 4: a little bit of a hedge just because we're going

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Speaker 4: to be undersupplied with semiconductor content for years. It feels

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Speaker 4: like and you know, that and power are very significant constraints.

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Speaker 4: And I think if you just kind of look at

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Speaker 4: the behaviors we've seen so far and some of the

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Speaker 4: assets of soft banks selling down kind of feels like

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Speaker 4: they're selling down some of their traditional holdings that they've

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Speaker 4: had and things like you know, T Mobile for example,

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Speaker 4: and they're redeploying that into AI infrastructure and then the

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Speaker 4: US government. You know, I just don't know, because I mean,

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Speaker 4: there's a reason a Taiwan semi conductor has done as

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Speaker 4: well as they've done. They've just they've had leadership now

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Speaker 4: for a decade. Their competencies are just you know, much

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Speaker 4: more pronounced than Intel. Intel has been in a hole

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Speaker 4: for a long time. You know, Pat Gelsinger gave it

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Speaker 4: a shot for you know, almost five years. We have

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Speaker 4: a lot of respect for lip Bhutan. We invested with

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Speaker 4: him at Cadence Design for years. Exceptional leader but you know,

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Speaker 4: Intel's not going to get fixed overnight, and there's nothing

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Speaker 4: the government can do to change the disparity between Intel's

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Speaker 4: process technology in Taiwan Semiconductor.

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Speaker 2: Danny Fish of Janis Henderson Investors, fascinating to have you

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Speaker 2: on and thanks for talking us through the new ETF.

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Speaker 2: What name isn't in that ATF is Apple Many Field.

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Speaker 2: Perhaps it's lagging you on AI. Some other news coming

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Speaker 2: to us from Apple as Apple TV plus price is

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Speaker 2: going to be on the rise thirty percent to thirteen

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Speaker 2: dollars a month starting August the twenty first. Apple is

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Speaker 2: saying it's annual price, more broadly for streaming services is unchanged.

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Speaker 3: It's commenting on.

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Speaker 2: The streaming price hike in an email statement, we're off

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Speaker 2: by four tens percent.

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Speaker 3: Coming up, we're going to be talking.

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Speaker 2: To Toma Bravo managing partner there hold in Spain is

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Speaker 2: the private equity giant agrees to.

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Speaker 3: Buy HR software firm day Force. This is Blomberg Tech,

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Speaker 3: Toma Bravo.

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Speaker 2: It's just announced that it's agreed to purchase HR software

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Speaker 2: day Force, paying seventy dollars a share in cash. Following

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Speaker 2: day Force a twel point three billion dollars. Leased to

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Speaker 2: say that blueberg Brief and kid Danny Berga joins us

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Speaker 2: for a key conversation on the.

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Speaker 5: Mattap Carolin, thank you so much, and yes, let's underscore

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Speaker 5: how large this deal is. At twelve point three billion dollars,

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Speaker 5: it is the largest deal ever for Toma Bravo and

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Speaker 5: certainly one of the biggest this year for this market.

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Speaker 5: I am pleased to say that joining Caroline and Us

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Speaker 5: now is Toma Bravo managing partner hold in Space Holden.

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Speaker 3: Thank you so much, and.

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Speaker 5: Your biggest deal ever at a time when your rivals

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Speaker 5: have kind of been languishing trying to even get deals done,

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Speaker 5: and when they do it's kind of in the single

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Speaker 5: digit billions. So why the size of deal and why

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Speaker 5: this one now?

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Speaker 6: Yeah, well, thank you so much, first of all, Danny

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Speaker 6: for having me. It's a very exciting day, a very

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Speaker 6: exciting transaction for Toma Bravo. And look, we don't think

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Speaker 6: so much about, you know, the size of the deal.

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Speaker 6: We think a lot more about the fundamentals and what

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Speaker 6: we can do with the company. This company day for

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Speaker 6: us is an incredibly special company. We have followed it.

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Speaker 6: I met the founder who's still the CEO of the

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Speaker 6: company before he even merged his company into Sarridian in

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Speaker 6: twenty twelve. And so we've tracked this company for a

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Speaker 6: very long time. It's a very unique company in a

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Speaker 6: very important space. It's got product leadership in a big market,

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Speaker 6: and so we've followed this This deal has been in

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Speaker 6: the works for a long time with our firm, and

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Speaker 6: it has all the characteristics and attributes that we look

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Speaker 6: for in to Mobravo in terms of the quality of

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Speaker 6: the revenue, in terms of the product leadership that it has,

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Speaker 6: in terms of its AI roadmap, great management team, and

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Speaker 6: so we're just really excited to be able to have

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Speaker 6: this opportunity.

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Speaker 5: Weld, And you've been following this company for a while,

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Speaker 5: but a lot of folks have had their eyes on

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Speaker 5: human capital management companies. Just earlier this year, Paychecks for example,

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Speaker 5: acquired pay Core. A lot of public companies getting bought

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Speaker 5: up in human capital management. What are you seeing and

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Speaker 5: what are your peer seeing that public markets are not well?

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Speaker 6: You know, that's an interesting question. You know, we've so

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Speaker 6: day Force really fits our profile very well because it's

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Speaker 6: because of its revenue quality characteristics. So you know, just

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Speaker 6: to go back for the history for a minute, because

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Speaker 6: it's important to realize how unique this company is. When

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Speaker 6: David also founded this company, he codd an original product

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Speaker 6: and his thesis was, you know, core HR systems and

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Speaker 6: then payroll systems and workforce management systems were all separate systems,

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Speaker 6: separate databases, separate architecture. And David came with a very

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Speaker 6: innovative thought is why shouldn't all that exist in one database,

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Speaker 6: in one system. It provides a lot more customer value

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Speaker 6: one basically end the end unified platform and the employee

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Speaker 6: life cycle. So he came in and built a product

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Speaker 6: that does all of that, everything from recruiting performance management

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Speaker 6: to if I'm an hourly worker, I clock in and

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Speaker 6: I know in real time how much money I've earned,

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Speaker 6: and if I need to get paid, I get paid immediately.

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Speaker 6: They do the tax withholding for you. So it's a

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Speaker 6: really unique that value proposition that he built the company

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Speaker 6: on is still unique in this big growing market today.

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Speaker 6: And so look, we and this company has been probably

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Speaker 6: in the seventh or eighth inning of a services to

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Speaker 6: software transformation, and so what we're what we're really looking

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Speaker 6: to do is just accelerate what they're already doing as

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Speaker 6: a public company in a private context. And just to

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Speaker 6: answer maybe the last part of your question, this company

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Speaker 6: is having a historic year, right So, a couple weeks

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Speaker 6: ago they announced their earnings. They forty percent year over

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Speaker 6: year bookings growth for the first half of the year.

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Speaker 6: Recurring revenue is reaccelerating, which is kind of unusual in

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Speaker 6: enterprise software right now, and the market kind of yawned,

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Speaker 6: and we think there's a big disconnect and a big

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Speaker 6: opportunity to basically continue on the path that the company's on,

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Speaker 6: but accelerating it in a private context.

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Speaker 2: To be fat, anst hadn't yawned, there's only one cell

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Speaker 2: rating and they'd liked the earnings in many ways because,

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Speaker 2: as you mentioned, David, who set up this business thirty

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Speaker 2: years ago, has been able to take on the new iteration,

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Speaker 2: this generative AI. And I'm really interested, therefore, holden as

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Speaker 2: to how much generative AI can add value at this moment.

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Speaker 2: Is that already baked in, because we're all questioning valuations

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Speaker 2: of generative AI related companies right now.

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Speaker 6: Yeah, So, generative AI is a huge part of our

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Speaker 6: investment thesis here, as it is for most of our

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Speaker 6: software companies. But we think, I mean, I'm just taking

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Speaker 6: a step back in this human capital management space. Every

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Speaker 6: company in the world right now is thinking about how

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Speaker 6: to maybe reorganize themselves, how to infuse AI into their organization,

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Speaker 6: maybe how to change their organizational structure and hierarchies to

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Speaker 6: account for AI. What kind of skills are needed and

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Speaker 6: how much do they need to pay for those skills.

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Speaker 6: These are all people problems, and so we think this

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Speaker 6: space of general human capital management should be is a

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Speaker 6: great opportunity for AI. For those companies that have data,

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Speaker 6: they know how to protect the data, that have customer relationships,

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Speaker 6: and so we think it's a huge opportunity. They have

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Speaker 6: a really ambitious AI roadmap. They're already selling some AI

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Speaker 6: skews today, but there's a really ambitious roadmap for next

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Speaker 6: year to roll this, to roll out a bunch of

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Speaker 6: AI agents, and we think we can just help them

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Speaker 6: accelerate that path to basically an agentic AI human capital

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Speaker 6: management software company.

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Speaker 5: Well, then, just on that point, we would love to

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Speaker 5: get your views on an MIT related study that seems

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Speaker 5: to have gotten this market maybe a little bit worked up.

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Speaker 5: The study that ninety five percent of companies who took

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Speaker 5: on AI generative AI pilots, ninety five percent of them

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Speaker 5: sold zero return on investment. Given your work with these

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Speaker 5: types of companies and incorporating genitive AI ninety five percent,

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Speaker 5: does that number seem right to you?

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Speaker 6: Look, well, I would say we we focus. That's really interesting,

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Speaker 6: by the way, because I do think there has been

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Speaker 6: an issue with people really trying to measure ROI on

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Speaker 6: their projects. Everybody's using it, everybody is using it, everybody

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Speaker 6: knows it's the next big thing. But is ROI really

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Speaker 6: being you know, measured in it? And are people giving

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Speaker 6: customers things that they may not they may not want,

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Speaker 6: And so we've taken a very measured approach. We think

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Speaker 6: there's a real benefit to these SaaS companies that have

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Speaker 6: incumbency and they have data advantages. But I think at

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Speaker 6: that enterprise level, I think, look, obviously there's been a

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Speaker 6: lot of uh, the easy ones have been the call

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Speaker 6: center and product development. Those have been the two biggest

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Speaker 6: areas for AI. I'd say cases that have generated really

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Speaker 6: positive ROI. But I think there's also a lot of

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Speaker 6: sprawl too. There's a lot of tools being sold that

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Speaker 6: maybe aren't being totally utilized. You see that in some

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Speaker 6: of these earnings announcements. So I think it's just important

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Speaker 6: to be careful to ensure that there's connectivity between the

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Speaker 6: customer's roadmap and your roadmap. So we think it's really important,

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Speaker 6: but we are taking a little bit more measured approach

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Speaker 6: because if you sell something a customer that they're not

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Speaker 6: using or don't need, that creates problems down the road.

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Speaker 2: It's been great having you both join, in particular Danny Berger,

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Speaker 2: thank you for helping us bring the conversation, Tom a

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Speaker 2: brother managing partner Holden Spain fascinating, particularly on that MIT

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Speaker 2: report coming up. The CEO of Data Breaks joins us

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Speaker 2: to discuss the company's latest funding round and whapping valuation

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Speaker 2: of over one hundred.

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Speaker 3: Billion is a breen bag tech Data Bricks. It's raising

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Speaker 3: new funding and evaluation.

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Speaker 2: That tops one hundred billion dollars the series K investment

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Speaker 2: marks get this more than sixty percent increase in the

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Speaker 2: data analytics software providers value because it's.

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Speaker 3: A last raised money in December. Data Bricks CEO Ali

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Speaker 3: Godsi joins us.

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Speaker 2: Now, extraordinary growth, but extraordinary growth in apples of ai

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Speaker 2: that you're currently building and helping with businesses translate their data.

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Speaker 3: How do you accelerate that?

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Speaker 7: Yeah, that's why we did this investment round. We were

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Speaker 7: looking at the product portfolio and there's two areas we

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Speaker 7: really want to invest in, and that's how we did

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Speaker 7: the fundraise.

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Speaker 8: One islake Base.

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Speaker 7: So agents and AI is also going to revolutionize databases.

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Speaker 7: I know we're thinking about how it's affecting society, but

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Speaker 7: the database market is one hundred and five billion dollars

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Speaker 7: TAM and what.

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Speaker 8: We saw in the data is that eighty percent of.

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Speaker 7: The databases are now being created not by humans but

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Speaker 7: by AI agents. So that's why we want to invest

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Speaker 7: it in our database product called lake Base. And the

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Speaker 7: other side is a product we have called Agent Bricks,

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Speaker 7: where we really investing it in how do we actually

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Speaker 7: help automate tasks with agents at enterprises with high quality

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Speaker 7: and high fidelity without hallucinations. So that's why we're doing this.

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Speaker 7: We're investing it in those two areas that we think

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Speaker 7: have huge potential.

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Speaker 2: I can you kind of translate for audience, how different

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Speaker 2: the technology is needed to cater for an AIA rather

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Speaker 2: than a human.

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Speaker 7: Yeah, so the agents move much much much faster. You know,

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Speaker 7: we think we're slower, we take our time. These things

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Speaker 7: just go all the time, right, there's no stopping and

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Speaker 7: they can do it in perils.

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Speaker 8: So there's many, many of them doing it.

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Speaker 7: So what you need to make sure, For instance, if

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Speaker 7: you're providing infrastructure for them, like a database, you have

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Speaker 7: to make sure that the cost of that is low

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Speaker 7: because they're going to spawn up and try many many things.

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Speaker 7: It's almost like you can imagine that you have, you know,

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Speaker 7: one hundred interns unleashed, running off trying lots of different things.

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Speaker 8: So you'd like to lower the cost of it.

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Speaker 7: You want to make it super fast because if they're

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Speaker 7: trying to use a database and the database.

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Speaker 8: Is slow, now you've slowed down all of the interns.

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Speaker 8: So you don't want to do that.

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Speaker 7: So those are some of the things you have to

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Speaker 7: do differently, move faster, lower the cost, and let them

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Speaker 7: experiment more because they're going to try lots of stuff

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Speaker 7: and then they're going to pick the best result. That's

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Speaker 7: not how humans operate because we're more expensive. We take

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Speaker 7: our time, and then we're sort of you know, measure twice,

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Speaker 7: cut once kind of approach, which is not the case

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Speaker 7: with the agents.

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Speaker 2: Let's talk about expensive humans as you've been adding I

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Speaker 2: think about fifty percent by the end of this year, right,

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Speaker 2: adding three thousand to your headcount. How expensive are those

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Speaker 2: humans at the moment? Ali, given the talent wars we.

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Speaker 7: See, Yeah, I mean it's it's I thought already that

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Speaker 7: things were very expensive a few years ago, and we

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Speaker 7: pay top of the top of the market.

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Speaker 8: We compete with say Google, Meta and so on.

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Speaker 7: But I would say the last twelve months or six

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Speaker 7: months even have been insane. I mean, it just keeps

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Speaker 7: going higher and higher.

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Speaker 2: Two hundred million, Hi, Ali, Like, are you having to

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Speaker 2: counteract that?

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Speaker 9: Yeah?

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Speaker 7: I mean, look, we're seeing insane offers out there. If

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Speaker 7: the talent is really that good, we will pay up. However,

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Speaker 7: it is also the other side of it, which is

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Speaker 7: they're going to work together with their coworkers, and you

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Speaker 7: want to have equity and fairness. If you have two

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Speaker 7: people that have the same background, that they're equally good

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Speaker 7: just because someone started six months ago, you don't want

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Speaker 7: to suddenly have them make one tent of the person

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Speaker 7: that's sitting right next to them. So if you don't

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Speaker 7: keep that equity in mind, then people get unhappy, they.

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Speaker 8: Talk, and then they will move around.

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Speaker 7: And that's why we' seeing so much char learn in

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Speaker 7: the AI market right now. People are switching jobs, you know,

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Speaker 7: from this company and then back again and so on.

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Speaker 7: So you want to also be a little bit thoughtful

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Speaker 7: about it. But yeah, you have to pay up otherwise

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Speaker 7: that's the price to pay right now, What.

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Speaker 2: About paying up for M and A because I know

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Speaker 2: that you've raised funds potentially to do that.

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Speaker 3: What sort of.

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Speaker 7: Companies, Yeah, I mean, we're looking at across the board,

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Speaker 7: right like this database technology I talked about right late, Base,

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Speaker 7: which is separated computer storage for postcrafts. That's that's came

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Speaker 7: through an acquisition that we did for you know, billion dollars.

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Speaker 7: So we were continuing to be very very interested in these,

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Speaker 7: you know, in Burder's talent. AI is very interesting because

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Speaker 7: you know, on the one hand, we hear about these

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Speaker 7: companies that have crazy evaluations, right one hundred billion plus.

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Speaker 7: On the other hand, Yeah, but on the other hand,

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Speaker 7: there's a lot of startups that started three years ago,

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Speaker 7: two years ago, and you know, if the if the

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Speaker 7: revenue is not there and the business isn't working out,

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Speaker 7: but they have amazing talent and they have great, great

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Speaker 7: ideas maybe they didn't just have channel distribution, that those

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Speaker 7: are great and you can actually get them for you know,

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Speaker 7: I guess in the big scheme of things that you

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Speaker 7: could say cheap.

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Speaker 2: Let's just talk about these so called crazy valuations AALI,

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Speaker 2: because I know your phone has been running off the hook,

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Speaker 2: and then particularly the last few weeks, people wanting to

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Speaker 2: allocate more towards you.

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Speaker 3: Do you think that would happen right here?

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Speaker 2: Right now we suddenly see a slight rectification in valuations

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Speaker 2: in the public markets.

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Speaker 3: Are things cooling down? Are you worried by.

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Speaker 2: MIT reports reporting of a lack of impact of AI

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Speaker 2: pilots for example.

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Speaker 8: No, I mean, I've been pretty vocal about this as well.

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Speaker 7: You know, I'm very bullish on agents being able to

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Speaker 7: do all kinds of tasks. But we're looking at very

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Speaker 7: early innings and some people are already declaring success as

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Speaker 7: if we were done.

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Speaker 8: We're not. We're at the very beginning.

438
00:21:42,359 --> 00:21:44,480
Speaker 7: When it comes to agents, for instance, one of the

439
00:21:44,520 --> 00:21:47,879
Speaker 7: biggest challenges is that you know, when you unleash these agents,

440
00:21:47,880 --> 00:21:49,920
Speaker 7: you know, the hundreds in terns that go off, they

441
00:21:49,960 --> 00:21:52,520
Speaker 7: make errors, and you know, right out.

442
00:21:52,440 --> 00:21:54,119
Speaker 8: Of the box you might have thirty percent errors.

443
00:21:54,119 --> 00:21:56,159
Speaker 7: Would you hire a person that makes thirty percent of

444
00:21:56,240 --> 00:21:58,760
Speaker 7: a time completely crazy mistakes?

445
00:21:59,000 --> 00:22:01,159
Speaker 8: No, So we have to get the quality up.

446
00:22:01,160 --> 00:22:03,320
Speaker 7: That's what Agent Bricks is focused on, how to we

447
00:22:03,359 --> 00:22:06,119
Speaker 7: iterate and improve the quality. So I do think we

448
00:22:06,200 --> 00:22:09,160
Speaker 7: have to pace the investments and the expectations on the ROI.

449
00:22:09,640 --> 00:22:12,480
Speaker 7: What's timeline if we're thinking that, you know, these agents

450
00:22:12,520 --> 00:22:15,200
Speaker 7: are just going to work everywhere right now, and that's

451
00:22:15,200 --> 00:22:18,040
Speaker 7: not the case. It's going to take us years to

452
00:22:18,080 --> 00:22:20,760
Speaker 7: be able to see the impact of AI across the

453
00:22:20,800 --> 00:22:24,080
Speaker 7: board in organizations. You know, maybe even five years. It's

454
00:22:24,119 --> 00:22:26,360
Speaker 7: still going to be very impactful. It's still something that's

455
00:22:26,560 --> 00:22:28,600
Speaker 7: investing in. But you have to get that timeline right.

456
00:22:29,160 --> 00:22:31,879
Speaker 2: Always talking straight to us, Sally god Sie, we so

457
00:22:31,960 --> 00:22:32,520
Speaker 2: appreciate it.

458
00:22:32,640 --> 00:22:33,840
Speaker 3: Day to Bricks CEO.

459
00:22:33,960 --> 00:22:37,280
Speaker 2: Congrats on the series K Coming up, Google bets on

460
00:22:37,320 --> 00:22:39,960
Speaker 2: AI for its new devices. Will have the details what

461
00:22:40,080 --> 00:22:55,240
Speaker 2: the company has just launched. This is bloombag Tech. Welcome

462
00:22:55,280 --> 00:22:57,680
Speaker 2: back to bloombag Tech. We take a look at these

463
00:22:57,720 --> 00:23:00,200
Speaker 2: markets because it's been a volatile day of trade.

464
00:23:00,240 --> 00:23:02,600
Speaker 3: More broadly, we could be on track for three days.

465
00:23:02,480 --> 00:23:05,520
Speaker 2: Of losses on the Nastak one hundred worst stretch of

466
00:23:05,560 --> 00:23:08,399
Speaker 2: losses going back to March of this year. We're currently

467
00:23:08,400 --> 00:23:10,760
Speaker 2: off by three tens a percent Meta, dragging us a

468
00:23:10,760 --> 00:23:13,320
Speaker 2: little bit lower Netflix one points perspective on the downside.

469
00:23:13,400 --> 00:23:15,520
Speaker 2: On the higher side is like some nvidio. Let's look

470
00:23:15,520 --> 00:23:17,760
Speaker 2: at a rebound for Pan Andeer as well, because it

471
00:23:17,760 --> 00:23:20,960
Speaker 2: had had six straight days of losses running into today.

472
00:23:21,160 --> 00:23:23,480
Speaker 2: We get a little bit of a reprieve, but only

473
00:23:23,520 --> 00:23:26,040
Speaker 2: a tenth of a percent. More than seventy billion dollars

474
00:23:26,040 --> 00:23:28,960
Speaker 2: of market cap wiped off of this retail fan favorite.

475
00:23:29,000 --> 00:23:31,920
Speaker 2: As people question that valuation, you know, looking at profit

476
00:23:32,280 --> 00:23:35,440
Speaker 2: to price, price to profit in the future, well it's

477
00:23:35,600 --> 00:23:38,920
Speaker 2: training almost two hundred times. Looking at Intel on the downside,

478
00:23:38,920 --> 00:23:40,720
Speaker 2: off by a quarter of percent one and a quarter.

479
00:23:40,800 --> 00:23:43,040
Speaker 2: That's as we question what dilution or discount the US

480
00:23:43,080 --> 00:23:45,440
Speaker 2: government might get after the euphoria, the soft bank and

481
00:23:45,480 --> 00:23:46,639
Speaker 2: the US are indeed.

482
00:23:46,359 --> 00:23:47,080
Speaker 3: Putting in money.

483
00:23:47,200 --> 00:23:49,040
Speaker 2: And we finished on Google having a rather nice day,

484
00:23:49,119 --> 00:23:51,320
Speaker 2: up by four tens percent, a come off of its highs.

485
00:23:51,560 --> 00:23:54,080
Speaker 2: But all of this is following Google's introduction of new

486
00:23:54,080 --> 00:23:58,280
Speaker 2: consuming gadgets including phones, earbuds, smart watch, all with the

487
00:23:58,280 --> 00:24:00,600
Speaker 2: company's latest Gemini AI.

488
00:24:00,640 --> 00:24:01,720
Speaker 3: Really at the heart of it all.

489
00:24:01,960 --> 00:24:04,680
Speaker 2: We spoke with Rick Ostolo, his Google's SVP for Platforms

490
00:24:04,680 --> 00:24:06,560
Speaker 2: and Devices, about the company's launch event.

491
00:24:07,840 --> 00:24:11,840
Speaker 9: Gemini is a huge part of Google strategy. We've made

492
00:24:11,880 --> 00:24:14,959
Speaker 9: so much progress with it. We've got the best models around,

493
00:24:15,200 --> 00:24:17,760
Speaker 9: we have the best AI assistant. We are just so

494
00:24:17,840 --> 00:24:22,120
Speaker 9: excited about what we've talked about today. And I personally

495
00:24:22,160 --> 00:24:25,399
Speaker 9: love using the vo video model. You saw a video

496
00:24:25,480 --> 00:24:28,040
Speaker 9: of my dog talking in the show, which was really

497
00:24:28,080 --> 00:24:28,720
Speaker 9: fun to put together.

498
00:24:28,880 --> 00:24:30,040
Speaker 3: Ke poodle across something.

499
00:24:30,200 --> 00:24:33,479
Speaker 9: Yeah, he was his Portuguese water dog who somehow speaks English.

500
00:24:33,520 --> 00:24:37,439
Speaker 9: But it was really really fun to put that together.

501
00:24:37,640 --> 00:24:39,720
Speaker 9: And this is kind of the power of AI. Now

502
00:24:39,760 --> 00:24:42,399
Speaker 9: you can do things for fun, you can be a

503
00:24:42,400 --> 00:24:45,000
Speaker 9: lot more productive with it, and Gemini is the thing

504
00:24:45,040 --> 00:24:46,000
Speaker 9: that powers it all for us.

505
00:24:46,080 --> 00:24:47,879
Speaker 3: I mean, Steph Curry is doing things with sports.

506
00:24:47,920 --> 00:24:51,000
Speaker 2: You had Alex Cooper there caller Daddy doing things with camera.

507
00:24:51,359 --> 00:24:55,199
Speaker 2: I'm interested though, how this sets you apart. What are

508
00:24:55,240 --> 00:24:57,679
Speaker 2: you offering do you think versus the competition? How am

509
00:24:57,680 --> 00:24:58,720
Speaker 2: I going to be able to use it in a

510
00:24:58,720 --> 00:25:01,280
Speaker 2: wholly different way than currently I could with a.

511
00:25:01,240 --> 00:25:02,080
Speaker 3: Competitive out of that?

512
00:25:02,240 --> 00:25:04,159
Speaker 9: Yeah, well, I mean I think AI in general just

513
00:25:04,200 --> 00:25:07,719
Speaker 9: transforms all of these products on the phone. It's going

514
00:25:07,760 --> 00:25:10,000
Speaker 9: to make it so much easier to interoperate with your phone.

515
00:25:10,040 --> 00:25:11,760
Speaker 9: You just talk to the AI and it'll do things

516
00:25:11,760 --> 00:25:14,679
Speaker 9: for you. You can show your camera to the AI and

517
00:25:14,720 --> 00:25:16,719
Speaker 9: it'll give you hints about what to do, like if

518
00:25:16,720 --> 00:25:18,440
Speaker 9: you're trying to do a project at home, like fix

519
00:25:18,480 --> 00:25:21,720
Speaker 9: some plumbing or fix some shelves, whatever it is, AI

520
00:25:21,800 --> 00:25:26,280
Speaker 9: can help. And we're really excited about our partnership with

521
00:25:26,320 --> 00:25:31,359
Speaker 9: Stephen Curry to apply this AI technology to personal coaching.

522
00:25:31,800 --> 00:25:33,920
Speaker 9: And you know, he's I can't think of a better

523
00:25:33,960 --> 00:25:36,520
Speaker 9: person to work with than him. He has sleep coaches,

524
00:25:36,680 --> 00:25:40,639
Speaker 9: nutrition coaches, fitness coaches, and he wanted to work with

525
00:25:40,720 --> 00:25:45,159
Speaker 9: us to bring this kind of capability for personal coaching

526
00:25:45,200 --> 00:25:48,040
Speaker 9: to everyone. And so that's what that partnership is all about.

527
00:25:48,080 --> 00:25:50,120
Speaker 2: We're really excited to work with stuff and in many ways,

528
00:25:50,160 --> 00:25:52,680
Speaker 2: what you were helping oversee the purchase of fitbit back

529
00:25:52,680 --> 00:25:54,240
Speaker 2: in the day, it's now in the main factor of

530
00:25:54,240 --> 00:25:55,240
Speaker 2: a watch that you've got on.

531
00:25:56,720 --> 00:25:59,000
Speaker 3: What form factors are we yet to see?

532
00:25:59,119 --> 00:26:02,120
Speaker 2: We've got you've got the pods in your ears, you've.

533
00:26:01,920 --> 00:26:04,280
Speaker 3: Got the watch, you've got the phones right.

534
00:26:04,240 --> 00:26:07,880
Speaker 9: And I think there might be a few coming next Well,

535
00:26:07,880 --> 00:26:11,280
Speaker 9: we're bringing Gemini to a bunch of surfaces you use

536
00:26:11,320 --> 00:26:15,280
Speaker 9: every day, but probably be used very differently, like automotive.

537
00:26:15,560 --> 00:26:18,639
Speaker 9: There'll be Gemini in your car, on your television, in

538
00:26:18,680 --> 00:26:21,399
Speaker 9: your smart speakers and smart displays. But I think a

539
00:26:21,480 --> 00:26:24,200
Speaker 9: really exciting thing that'll come in the future is Gemini

540
00:26:24,280 --> 00:26:27,760
Speaker 9: and classes. So we're working on smart classes that'll run

541
00:26:27,840 --> 00:26:29,439
Speaker 9: Gemini as the main way you interact.

542
00:26:29,480 --> 00:26:31,080
Speaker 3: And for a year that you think that that'll be

543
00:26:31,080 --> 00:26:32,439
Speaker 3: coming out, is it?

544
00:26:32,440 --> 00:26:36,080
Speaker 9: It'll be sometime next year. It will be pretty exciting

545
00:26:36,080 --> 00:26:37,000
Speaker 9: to see.

546
00:26:37,440 --> 00:26:40,480
Speaker 2: Costallone Google's SVP of Platforms and Devices. Let's talk a

547
00:26:40,520 --> 00:26:43,200
Speaker 2: little bit more about all of this. Carolina Milanesi, she's

548
00:26:43,240 --> 00:26:45,320
Speaker 2: the president and principal Analystic Creative Strategies.

549
00:26:45,320 --> 00:26:48,520
Speaker 3: We were in Brooklyn together yesterday. Were I love reading.

550
00:26:48,240 --> 00:26:50,520
Speaker 2: Your report on this, and you said, look, Google avoided

551
00:26:50,560 --> 00:26:53,080
Speaker 2: any risky moves, so we didn't get the glasses in

552
00:26:53,080 --> 00:26:56,040
Speaker 2: the here and now. But the prowess you see is

553
00:26:56,200 --> 00:26:59,440
Speaker 2: that Gemini is just full force within these devices.

554
00:26:59,520 --> 00:27:01,160
Speaker 3: Absolutely, it's all about AI.

555
00:27:01,320 --> 00:27:04,800
Speaker 10: It's about really getting consumers to understand the power of

556
00:27:04,840 --> 00:27:07,960
Speaker 10: that AI can bring and as Rick was saying, it's

557
00:27:08,040 --> 00:27:10,960
Speaker 10: different things to different people. So what was amazing for

558
00:27:11,080 --> 00:27:14,280
Speaker 10: me yesterday will see the breath of people that they

559
00:27:14,320 --> 00:27:17,080
Speaker 10: were able to reach with all the guests that they had,

560
00:27:17,240 --> 00:27:20,359
Speaker 10: both from an age demographic but also from what is

561
00:27:20,440 --> 00:27:23,000
Speaker 10: the hook that you want? It can be editing, it

562
00:27:23,040 --> 00:27:26,240
Speaker 10: can be taking the best shot, it can be getting fitter.

563
00:27:26,640 --> 00:27:29,400
Speaker 2: And it can be photography. Andre de Wagner they had

564
00:27:29,440 --> 00:27:31,760
Speaker 2: over there. They had of course sports when it came

565
00:27:31,800 --> 00:27:34,240
Speaker 2: to Steph Curry or indeed Landon Norris was there. We

566
00:27:34,280 --> 00:27:37,399
Speaker 2: also thought about the way in which community is going

567
00:27:37,480 --> 00:27:40,000
Speaker 2: to use it for camera and for editing.

568
00:27:40,600 --> 00:27:41,760
Speaker 3: Is that really that important?

569
00:27:41,880 --> 00:27:44,560
Speaker 2: When do you think the camera is really going to

570
00:27:44,560 --> 00:27:45,680
Speaker 2: be the standout feature of so.

571
00:27:45,680 --> 00:27:49,359
Speaker 10: Many camera is already the sendout feature for so many consumers.

572
00:27:49,480 --> 00:27:52,159
Speaker 2: They take pixels had a better camera than anyone for ages.

573
00:27:52,240 --> 00:27:54,080
Speaker 3: That's what they kept on reminding us. But people are

574
00:27:54,080 --> 00:27:55,840
Speaker 3: still buying apples as dead it is.

575
00:27:55,880 --> 00:27:59,400
Speaker 10: But it's about what you can do from an editing perspective. Now,

576
00:27:59,440 --> 00:28:01,879
Speaker 10: it's not just about taking the best shot and taking

577
00:28:01,880 --> 00:28:05,359
Speaker 10: the friction away that there is today in getting to

578
00:28:05,400 --> 00:28:08,359
Speaker 10: their editing. So being able to tell an AI agent

579
00:28:08,560 --> 00:28:12,359
Speaker 10: Gemini or something else. How you want the picture to

580
00:28:12,400 --> 00:28:13,920
Speaker 10: look like, and what.

581
00:28:13,800 --> 00:28:17,680
Speaker 2: It really compares and contrast with is Apple's lacking right

582
00:28:17,840 --> 00:28:20,040
Speaker 2: the fact that we're going to be so easily integrating

583
00:28:20,080 --> 00:28:24,200
Speaker 2: Gemini AI from a spoken word perspective and it's Siri

584
00:28:24,400 --> 00:28:25,960
Speaker 2: is just so behind the curve. Do you think they're

585
00:28:25,960 --> 00:28:27,600
Speaker 2: going to eat any market share that?

586
00:28:28,320 --> 00:28:31,520
Speaker 10: I think it's always hard from a high end perspective

587
00:28:31,800 --> 00:28:35,880
Speaker 10: to see churm from Apple to anywhere else because user

588
00:28:35,960 --> 00:28:39,480
Speaker 10: consumer have more device than just their phone, and so

589
00:28:39,600 --> 00:28:42,400
Speaker 10: you're talking about a difficult choice to make.

590
00:28:42,840 --> 00:28:43,760
Speaker 3: We also still have.

591
00:28:43,760 --> 00:28:47,960
Speaker 10: A very beginning of DEI shift. Consumers are not walking

592
00:28:48,000 --> 00:28:50,880
Speaker 10: into a store asking for an AI phone. We talked

593
00:28:50,920 --> 00:28:54,440
Speaker 10: about is for Apple in June, but I do think

594
00:28:54,480 --> 00:28:56,640
Speaker 10: that time is sticking for Apple and we need to

595
00:28:56,680 --> 00:28:59,000
Speaker 10: see something sooner rather than later.

596
00:28:59,320 --> 00:29:03,480
Speaker 2: Instead, this almost helps Google show off the Android ecosystem.

597
00:29:03,840 --> 00:29:06,040
Speaker 2: You might not get a Google Pixel, but you might

598
00:29:06,040 --> 00:29:08,880
Speaker 2: get a Samsung and probably through this demonstration you can

599
00:29:08,920 --> 00:29:10,880
Speaker 2: see how well integrated that it's going to be as well.

600
00:29:11,000 --> 00:29:13,080
Speaker 2: How do they navigate this sort of frenemie moment?

601
00:29:13,560 --> 00:29:16,200
Speaker 10: Yeah, I think it's definitely first about the best of

602
00:29:16,320 --> 00:29:20,120
Speaker 10: Google on Pixel, and then you started to see Samsung

603
00:29:20,160 --> 00:29:25,320
Speaker 10: getting closer to their ecosystem and putting aside their aspirations

604
00:29:25,320 --> 00:29:29,360
Speaker 10: with Bisbee and really building on the power of Gemini.

605
00:29:29,520 --> 00:29:32,680
Speaker 10: So Motorola has been doing the same thing. So it's

606
00:29:32,760 --> 00:29:36,880
Speaker 10: definitely about the ecosystem, but also feeling that in my view,

607
00:29:37,040 --> 00:29:41,240
Speaker 10: Pixel is the answer to Apple within the Android world.

608
00:29:41,480 --> 00:29:44,160
Speaker 2: And they got a foldable phone, which in any way

609
00:29:44,520 --> 00:29:47,520
Speaker 2: goes head to head with the DOUGHI indorsed Samsung, but

610
00:29:47,560 --> 00:29:48,240
Speaker 2: Apple has.

611
00:29:48,120 --> 00:29:49,280
Speaker 3: No foldable right now.

612
00:29:49,680 --> 00:29:52,280
Speaker 2: I thought what was really interesting was when they put

613
00:29:52,280 --> 00:29:54,320
Speaker 2: them out on the market. Next week, we get the phones,

614
00:29:54,360 --> 00:29:56,360
Speaker 2: but watch us. A little bit later, I asked Rick

615
00:29:56,400 --> 00:29:58,520
Speaker 2: about whether it was a supply chain issue. He said, no,

616
00:29:59,240 --> 00:30:01,440
Speaker 2: are there any supply chain issues for these companies right now?

617
00:30:01,480 --> 00:30:04,080
Speaker 2: It guess it's chip correct designed and house what made

618
00:30:04,120 --> 00:30:06,200
Speaker 2: my TSMC? How does this whole come together?

619
00:30:06,640 --> 00:30:08,240
Speaker 10: I think, to be honest with you, as a marketing

620
00:30:08,280 --> 00:30:11,520
Speaker 10: strategy is more of a supply issue. Is about getting

621
00:30:11,720 --> 00:30:15,080
Speaker 10: before Apple and after Apple. Right we know that the

622
00:30:15,120 --> 00:30:19,520
Speaker 10: expectation is beginning of September usually for Apple, and so

623
00:30:19,920 --> 00:30:23,960
Speaker 10: getting in with the mass market product before the iPhone

624
00:30:24,000 --> 00:30:27,520
Speaker 10: is out, and then with something that is more appealing

625
00:30:27,600 --> 00:30:32,360
Speaker 10: to steal a niche market but very powerful users with

626
00:30:32,520 --> 00:30:35,720
Speaker 10: the foldable towards holiday season.

627
00:30:36,120 --> 00:30:39,200
Speaker 2: I think what really resonated did in your note was

628
00:30:39,240 --> 00:30:42,200
Speaker 2: the confidence that Google had at this event. I mean

629
00:30:42,240 --> 00:30:44,360
Speaker 2: it was just celebrity filled. It felt like you were

630
00:30:44,360 --> 00:30:47,160
Speaker 2: at a TV event. That was the whole idea and

631
00:30:47,240 --> 00:30:49,480
Speaker 2: narrative of it. Have they got the right to be

632
00:30:49,560 --> 00:30:50,560
Speaker 2: this confident right now?

633
00:30:50,600 --> 00:30:51,040
Speaker 3: Do you think?

634
00:30:51,280 --> 00:30:55,000
Speaker 10: I think is empowering for them instead of always focusing

635
00:30:55,040 --> 00:30:58,440
Speaker 10: on chasing Apple, to actually feel confidence in the product

636
00:30:58,440 --> 00:31:03,200
Speaker 10: that they have, which are iterative from last year but solid.

637
00:31:03,640 --> 00:31:07,520
Speaker 10: And I think that finding the confidence in their branding

638
00:31:07,920 --> 00:31:13,480
Speaker 10: is very important and broadening the addressable market that they're

639
00:31:13,520 --> 00:31:16,960
Speaker 10: aiming for is not just tech buyers anymore, is mass

640
00:31:16,960 --> 00:31:17,880
Speaker 10: market consumers.

641
00:31:18,400 --> 00:31:21,400
Speaker 2: Subway takes call a Daddy, had a lot for the

642
00:31:21,440 --> 00:31:24,640
Speaker 2: gen Z and the younger millennials. Currently, a Melnac loved

643
00:31:24,640 --> 00:31:27,680
Speaker 2: having a hair president and principal analyst at Creative Strategies

644
00:31:28,040 --> 00:31:31,400
Speaker 2: coming up, Runway CEO Christoval Venezuela is joining us to

645
00:31:31,440 --> 00:31:33,280
Speaker 2: talk about the company's blue tools for video.

646
00:31:33,040 --> 00:31:35,920
Speaker 3: And games creation. Says Blomberg Tech.

647
00:31:48,600 --> 00:31:59,880
Speaker 2: M AI startup Runway, which shook up Hollywood with a

648
00:32:00,360 --> 00:32:04,040
Speaker 2: video generation tools centering the world of gaming. Announcing today

649
00:32:04,400 --> 00:32:06,960
Speaker 2: is Runway Game World's beta, which.

650
00:32:06,760 --> 00:32:08,840
Speaker 3: Takes a step towards real time game creation.

651
00:32:08,960 --> 00:32:12,280
Speaker 2: Runway CEO and co founder Crystal Weal Venezuela joins us

652
00:32:12,320 --> 00:32:18,240
Speaker 2: now and because, well, how changeable or what a complete

653
00:32:18,480 --> 00:32:21,880
Speaker 2: shift is this for the gaming industry, because consumers are

654
00:32:21,880 --> 00:32:23,000
Speaker 2: going to be able to use it, but what does

655
00:32:23,000 --> 00:32:24,480
Speaker 2: it mean for the actual studios too?

656
00:32:24,840 --> 00:32:25,800
Speaker 8: Yeah, it's massive.

657
00:32:25,880 --> 00:32:28,000
Speaker 11: It's a big change in the same way that AI

658
00:32:28,120 --> 00:32:30,280
Speaker 11: was a big change for Hollywood as well. And now

659
00:32:30,280 --> 00:32:32,680
Speaker 11: it's important to understanding that organize. There are two parts

660
00:32:32,720 --> 00:32:35,920
Speaker 11: of how AI is affecting games. On the one end,

661
00:32:36,520 --> 00:32:38,840
Speaker 11: you have the real time rendering component of it that

662
00:32:38,880 --> 00:32:41,640
Speaker 11: we're working towards, basically means you're going to be able

663
00:32:41,680 --> 00:32:44,440
Speaker 11: to create pixels in a real time manner. And the

664
00:32:44,480 --> 00:32:47,400
Speaker 11: other one is the mechanics. Right, so how do games

665
00:32:48,200 --> 00:32:50,800
Speaker 11: manifest itself? What are the interactions that users would have

666
00:32:50,920 --> 00:32:54,720
Speaker 11: with them? From consumers to preach everyone and so game

667
00:32:54,760 --> 00:32:57,160
Speaker 11: World for us, it's kind of fland exploration and our

668
00:32:57,200 --> 00:33:00,440
Speaker 11: first product and the mechanics of non linear stories with

669
00:33:00,520 --> 00:33:03,640
Speaker 11: AI creating these worlds in real time and having players

670
00:33:03,720 --> 00:33:04,520
Speaker 11: experiment with them.

671
00:33:04,640 --> 00:33:06,360
Speaker 2: I mean, I think about the first time that we

672
00:33:06,480 --> 00:33:08,640
Speaker 2: first started bringing you on the show and hearing from

673
00:33:08,680 --> 00:33:11,920
Speaker 2: how much you were well disrupting Hollywood.

674
00:33:12,160 --> 00:33:14,120
Speaker 3: But Hollywood has now come to embrace.

675
00:33:13,760 --> 00:33:16,600
Speaker 2: You, using Amazon, using it in the latest works that

676
00:33:16,600 --> 00:33:18,040
Speaker 2: they're doing, and creating Christa.

677
00:33:18,040 --> 00:33:18,200
Speaker 7: Well.

678
00:33:18,320 --> 00:33:21,080
Speaker 2: How has the gaming studio reaction been. Has it been

679
00:33:21,080 --> 00:33:22,680
Speaker 2: bristling or has it been accepting.

680
00:33:23,560 --> 00:33:27,120
Speaker 11: It's somehow very similar to what I think Hollywood went

681
00:33:27,160 --> 00:33:29,280
Speaker 11: through a year and a half ago. I think it's

682
00:33:29,520 --> 00:33:32,600
Speaker 11: totally new. It takes time, but now I think most

683
00:33:32,600 --> 00:33:34,920
Speaker 11: of them have started to embrace it and understand it.

684
00:33:34,680 --> 00:33:38,280
Speaker 11: It's a radical technology or changes how you do your

685
00:33:38,320 --> 00:33:40,720
Speaker 11: work and need to embrace it. You need to understand it.

686
00:33:40,920 --> 00:33:42,959
Speaker 11: There's things that work really well, there's some things that

687
00:33:42,960 --> 00:33:45,400
Speaker 11: don't really work really well. But really it's about like

688
00:33:45,720 --> 00:33:48,280
Speaker 11: using it more. And so I would say that games

689
00:33:48,520 --> 00:33:51,280
Speaker 11: and the gaming world is where I would say Hollywood

690
00:33:51,360 --> 00:33:55,280
Speaker 11: was a year ago, and now as models progress get better,

691
00:33:55,400 --> 00:33:58,760
Speaker 11: become more usable, you start seeing most of those companies

692
00:33:58,760 --> 00:33:59,800
Speaker 11: now starting to adopt it.

693
00:34:00,240 --> 00:34:02,600
Speaker 2: I mean you can see the impact because it's being

694
00:34:02,720 --> 00:34:07,480
Speaker 2: used in features by big tech companies and big filmmakers Christovelt,

695
00:34:07,480 --> 00:34:10,000
Speaker 2: But how are you measuring impact for those that are

696
00:34:10,000 --> 00:34:12,040
Speaker 2: about to take it on? We're all talking about this

697
00:34:12,200 --> 00:34:14,640
Speaker 2: MIT study about whether or not ninety five percent of

698
00:34:15,200 --> 00:34:18,360
Speaker 2: these pilots for general to AI are failing. How do

699
00:34:18,440 --> 00:34:20,759
Speaker 2: you prove out that your technology as value?

700
00:34:21,680 --> 00:34:25,160
Speaker 11: Yeah, well you have to make sure it helps users somehow.

701
00:34:25,320 --> 00:34:28,320
Speaker 11: And look there's a challenge. And I think the challenge

702
00:34:28,400 --> 00:34:31,319
Speaker 11: that I think media and Vollywood recognize overtirement is these

703
00:34:31,360 --> 00:34:33,799
Speaker 11: are not tools that will make moves for you. And

704
00:34:33,800 --> 00:34:35,680
Speaker 11: I think there was a big assumption another time where

705
00:34:35,680 --> 00:34:38,560
Speaker 11: like many people thought, you just came into runway tight

706
00:34:38,719 --> 00:34:41,319
Speaker 11: movie and you get a movie out. Unfortunately, that's not

707
00:34:41,920 --> 00:34:44,480
Speaker 11: what the tchnology is doing. It's much more about you

708
00:34:44,520 --> 00:34:46,600
Speaker 11: being in control and you friending the right time. But

709
00:34:46,719 --> 00:34:49,879
Speaker 11: also you need to rewire your brain a little bit.

710
00:34:49,920 --> 00:34:52,840
Speaker 11: You need to retrain the mental models of how this

711
00:34:53,000 --> 00:34:55,520
Speaker 11: STAMLA you can help you. And that takes time. And

712
00:34:55,560 --> 00:34:57,839
Speaker 11: so how we manage to do that change with our

713
00:34:57,880 --> 00:35:01,720
Speaker 11: companies and customers and userss We help them and sometimes

714
00:35:01,719 --> 00:35:04,760
Speaker 11: we go and work alongside them. We have technical artists

715
00:35:04,800 --> 00:35:06,880
Speaker 11: that you can think about them as like for deploy

716
00:35:06,920 --> 00:35:11,120
Speaker 11: engineers that sit together with our companies and users and

717
00:35:11,400 --> 00:35:14,759
Speaker 11: enterprises and help them understand how run we can fit

718
00:35:14,800 --> 00:35:18,360
Speaker 11: in particular parts of our workload. You're a left For example,

719
00:35:18,360 --> 00:35:20,840
Speaker 11: it's our latest video generation model that allows you to

720
00:35:21,120 --> 00:35:23,600
Speaker 11: edit video in ways that you just couldn't do before.

721
00:35:24,080 --> 00:35:26,600
Speaker 11: And it works completely different from anything you've seen before,

722
00:35:26,760 --> 00:35:29,160
Speaker 11: and so it's not going to work based on what

723
00:35:29,239 --> 00:35:31,440
Speaker 11: you know you have to. There's an adjustment period, and

724
00:35:31,480 --> 00:35:34,320
Speaker 11: that adjustment period is critical for me. It's very similar

725
00:35:34,320 --> 00:35:36,840
Speaker 11: to perhaps how the cloud was a big change for

726
00:35:36,880 --> 00:35:40,000
Speaker 11: companies like it requires you to redo and reorganize some

727
00:35:40,040 --> 00:35:41,759
Speaker 11: of your teams to prepurpolous.

728
00:35:42,160 --> 00:35:44,000
Speaker 3: I'm talking about reorganizing teams.

729
00:35:44,440 --> 00:35:46,759
Speaker 2: I mean Mark Zuckerberg's been busy at that, and we

730
00:35:46,840 --> 00:35:50,719
Speaker 2: understand that previously hid perhaps eyed up Runway wanting to

731
00:35:50,760 --> 00:35:52,440
Speaker 2: be able to buy that to bring in for the

732
00:35:52,480 --> 00:35:55,760
Speaker 2: AI prowess. You not to back that's off the table,

733
00:35:56,200 --> 00:35:59,359
Speaker 2: But how many offers are you getting full runway? And

734
00:35:59,400 --> 00:36:01,919
Speaker 2: how you putting your employees that you might not jump

735
00:36:01,960 --> 00:36:03,960
Speaker 2: ship again.

736
00:36:04,400 --> 00:36:06,800
Speaker 11: I've been working on this for like almost seven eight years,

737
00:36:06,800 --> 00:36:09,479
Speaker 11: and I think we're now hitting an interesting inflection point.

738
00:36:09,520 --> 00:36:12,080
Speaker 11: It's getting too exciting for us to think about not

739
00:36:12,160 --> 00:36:15,000
Speaker 11: being independent like we want to remain independent. We have

740
00:36:15,040 --> 00:36:16,960
Speaker 11: the resources need, We have the best team in the

741
00:36:16,960 --> 00:36:19,400
Speaker 11: world doing that, some of the best research in the world.

742
00:36:20,440 --> 00:36:24,120
Speaker 11: Now is an inflection point in both quality, adoption penetration.

743
00:36:24,600 --> 00:36:27,080
Speaker 11: It's a really exciting time to build Runway, and I

744
00:36:27,160 --> 00:36:28,560
Speaker 11: think there's still a long way to go for.

745
00:36:28,600 --> 00:36:30,320
Speaker 3: Us first gaming product.

746
00:36:30,560 --> 00:36:32,279
Speaker 2: We thank you for talking us to game wels and

747
00:36:32,400 --> 00:36:34,399
Speaker 2: so much more that you've been doing with the LF

748
00:36:34,520 --> 00:36:37,960
Speaker 2: model and plenty more. Crystaval Venezuela, CEO and co founder Runway,

749
00:36:38,200 --> 00:36:41,280
Speaker 2: always great to catch up now. Sticking with AI deep Seek,

750
00:36:41,560 --> 00:36:43,759
Speaker 2: it just unveils and update to an older model that

751
00:36:43,840 --> 00:36:47,319
Speaker 2: it says surpasses a seminal are one on key benchmarks now.

752
00:36:47,360 --> 00:36:49,719
Speaker 2: Deep Seat said in a we chat post that the

753
00:36:49,719 --> 00:36:52,760
Speaker 2: new version answers queries much faster and marks the startup's

754
00:36:52,760 --> 00:36:57,799
Speaker 2: first step towards creating an AI agent and close to

755
00:36:57,840 --> 00:37:00,839
Speaker 2: the home in the US. Open AI CFO Sarah Fryar

756
00:37:01,000 --> 00:37:03,480
Speaker 2: has just told Bloomberg that the company could take a

757
00:37:03,520 --> 00:37:07,560
Speaker 2: page out of Amazon's playbook, following an approach inspired by Amazon,

758
00:37:07,600 --> 00:37:10,680
Speaker 2: which found success renting out at spare Cloud computing capacity.

759
00:37:10,880 --> 00:37:13,879
Speaker 2: Open ai could in the future sell access to data

760
00:37:13,920 --> 00:37:18,680
Speaker 2: centers and other physical infrastructure needed for aims, showing KAfari

761
00:37:19,280 --> 00:37:22,239
Speaker 2: was there for that conversation bringing out teasing out those

762
00:37:22,280 --> 00:37:24,880
Speaker 2: pieces of information because so far really does have to

763
00:37:24,880 --> 00:37:27,520
Speaker 2: think about the profitability of this business in the longer term.

764
00:37:28,239 --> 00:37:29,520
Speaker 3: That's right if you think about it.

765
00:37:29,560 --> 00:37:31,600
Speaker 12: So you know, I was also recently at a media

766
00:37:31,600 --> 00:37:33,560
Speaker 12: dinner with Sam Maltman when he said we planned to

767
00:37:33,600 --> 00:37:36,840
Speaker 12: spend trillions on infrastructure in the near future and that

768
00:37:36,880 --> 00:37:40,040
Speaker 12: economists might call that crazy, but they are marching on.

769
00:37:40,360 --> 00:37:42,880
Speaker 12: So as open ai starts to put this immense and

770
00:37:43,000 --> 00:37:46,040
Speaker 12: precedence in amount of capital rate toward data center expansion,

771
00:37:46,239 --> 00:37:48,560
Speaker 12: they're building expertise in that. And I think the long

772
00:37:48,640 --> 00:37:50,600
Speaker 12: term play here as well, how could we in the

773
00:37:50,600 --> 00:37:52,560
Speaker 12: future potentially market and capitalize on.

774
00:37:52,480 --> 00:37:54,799
Speaker 2: That expertise and be able to drive up revenues to

775
00:37:54,840 --> 00:37:57,160
Speaker 2: be getting better financing for the future of their own

776
00:37:57,160 --> 00:38:00,480
Speaker 2: infrastructure spend. At the moment, they've been funding that spend

777
00:38:00,719 --> 00:38:04,280
Speaker 2: by raising money and venture any nuance there, she really

778
00:38:04,320 --> 00:38:05,920
Speaker 2: did talk about how they've been able to sort of

779
00:38:06,000 --> 00:38:07,359
Speaker 2: raise more than they anticipated.

780
00:38:08,160 --> 00:38:11,000
Speaker 12: Yes, you know, they were planning on ten billion, and

781
00:38:11,200 --> 00:38:14,520
Speaker 12: one piece of THEIRNCH funding around. She said that they

782
00:38:14,520 --> 00:38:19,120
Speaker 12: have actually gotten to eleven billion because of access investor interest,

783
00:38:19,520 --> 00:38:23,160
Speaker 12: and more broadly, they're actually looking beyond just you know,

784
00:38:23,280 --> 00:38:26,520
Speaker 12: equity financing at this point. They're also, as Friar told us,

785
00:38:27,200 --> 00:38:30,959
Speaker 12: interested in debt and debt financing and they have said

786
00:38:30,960 --> 00:38:33,440
Speaker 12: that banks and private equity firms have approached them about this.

787
00:38:33,880 --> 00:38:36,040
Speaker 2: So interesting as we see how Metro has been financing

788
00:38:36,080 --> 00:38:38,879
Speaker 2: some of its data center needs, getting into the world

789
00:38:38,920 --> 00:38:42,319
Speaker 2: of private credit with Pimco and blue Out, what more

790
00:38:42,360 --> 00:38:44,839
Speaker 2: about the growth of the business when we're just coming

791
00:38:44,840 --> 00:38:47,560
Speaker 2: off the back of GPT five and some of the

792
00:38:47,680 --> 00:38:50,440
Speaker 2: throws around that. And they had to make some difficult choices,

793
00:38:50,480 --> 00:38:53,720
Speaker 2: particularly when it came to well, how nice the chatbot

794
00:38:53,800 --> 00:38:55,840
Speaker 2: is the people and how they bring back other models

795
00:38:55,880 --> 00:38:56,399
Speaker 2: and they're like.

796
00:38:57,000 --> 00:38:57,440
Speaker 3: That's right.

797
00:38:57,480 --> 00:38:59,760
Speaker 12: So it was definitely a bumpy rollout, I think bumpy

798
00:38:59,800 --> 00:39:02,640
Speaker 12: or they expected right. Also at that dinner, Alman you know, said,

799
00:39:02,800 --> 00:39:04,480
Speaker 12: we know, we messed up on.

800
00:39:06,000 --> 00:39:07,280
Speaker 3: The emotional attachment.

801
00:39:07,320 --> 00:39:09,799
Speaker 12: I think they did not realize that people fully had

802
00:39:10,239 --> 00:39:12,400
Speaker 12: to the older version of the chatbot, and they were

803
00:39:12,440 --> 00:39:14,600
Speaker 12: all sort of a lot of users were angry that

804
00:39:14,680 --> 00:39:19,880
Speaker 12: Opening initially deprecated the older model. However, I think overall

805
00:39:19,960 --> 00:39:22,719
Speaker 12: the bigger pictures that chat GBT is still you know,

806
00:39:23,239 --> 00:39:26,240
Speaker 12: the market leader right on consumer usage, just on chatbot

807
00:39:26,360 --> 00:39:30,279
Speaker 12: usage overall, and they still have this voracious demand as

808
00:39:30,320 --> 00:39:33,160
Speaker 12: Fryer put it, for the use of their products that

809
00:39:33,200 --> 00:39:34,839
Speaker 12: requires intensive compute and.

810
00:39:34,760 --> 00:39:37,280
Speaker 2: As racious demand to fund it as we were talking about.

811
00:39:37,480 --> 00:39:40,000
Speaker 2: And actually eventually retail want to be owning a piece

812
00:39:40,000 --> 00:39:43,680
Speaker 2: of this company more people democratization so spoken about eventually

813
00:39:43,680 --> 00:39:44,480
Speaker 2: they need to IPO.

814
00:39:44,600 --> 00:39:47,120
Speaker 3: But as a CFO, how is she thinking about that?

815
00:39:47,239 --> 00:39:51,239
Speaker 12: Sofra, I mean for Sarah fry Or, her job is

816
00:39:51,320 --> 00:39:54,200
Speaker 12: both to raise these funds right for open A, but

817
00:39:54,239 --> 00:39:57,920
Speaker 12: also be able to allocate that compute for these customers.

818
00:39:58,200 --> 00:40:00,759
Speaker 12: Oftentimes they're having to decide in advance which customers are

819
00:40:00,800 --> 00:40:04,120
Speaker 12: actually going to get the compute power they need to

820
00:40:04,200 --> 00:40:05,840
Speaker 12: run all the chat shop t products.

821
00:40:06,120 --> 00:40:08,480
Speaker 3: So it's it's a mix of fundraising.

822
00:40:07,960 --> 00:40:11,560
Speaker 12: Of keeping customers happy, keeping consumers happy, and figuring out

823
00:40:11,560 --> 00:40:13,880
Speaker 12: all the right the financing and money for it.

824
00:40:14,239 --> 00:40:16,600
Speaker 2: And then leave us to pulls showing KAfari talking us

825
00:40:16,640 --> 00:40:19,400
Speaker 2: through what was a fantastic conversation that she had with

826
00:40:19,600 --> 00:40:23,200
Speaker 2: Sarah Phire, CFO of open Ai. Now coming up SpaceX

827
00:40:23,360 --> 00:40:26,440
Speaker 2: other very highly valued private company tapping into luxury travel

828
00:40:26,480 --> 00:40:28,160
Speaker 2: as it expands its starlink services.

829
00:40:28,239 --> 00:40:30,040
Speaker 3: Were on that next as the bluebgg tech.

830
00:40:40,480 --> 00:40:44,040
Speaker 2: SpaceX has steadily built up it's starlink in flight Wi

831
00:40:44,040 --> 00:40:47,200
Speaker 2: Fi services for carriers like Air France, United, Virgin Atlantics

832
00:40:47,239 --> 00:40:49,839
Speaker 2: signing up. Now the company what's allying a Middle East

833
00:40:49,880 --> 00:40:52,359
Speaker 2: expansion as it holds discussions with the likes of Devi

834
00:40:52,520 --> 00:40:56,759
Speaker 2: based Emirates, Bloombergs Space reporter Sana Pashanka joins us now

835
00:40:56,800 --> 00:40:58,840
Speaker 2: with the details. How big a coup could this be

836
00:40:59,000 --> 00:41:00,560
Speaker 2: to get in with the Middle East and flies.

837
00:41:01,800 --> 00:41:05,520
Speaker 13: Yeah, it would be a really big moment for starlink.

838
00:41:05,960 --> 00:41:09,279
Speaker 13: As you mentioned, these are luxury airlines and a lot

839
00:41:09,320 --> 00:41:14,840
Speaker 13: of them are stopping points for really long haul international flights,

840
00:41:14,920 --> 00:41:18,520
Speaker 13: So it could be, you know, bring in a lot of.

841
00:41:18,520 --> 00:41:20,360
Speaker 3: Revenue for star languages.

842
00:41:20,400 --> 00:41:23,600
Speaker 13: SpaceX's most profitable unit.

843
00:41:23,960 --> 00:41:25,439
Speaker 3: Who has it been booting out the way?

844
00:41:25,680 --> 00:41:28,359
Speaker 2: Which have been the previous Wi Fi offerers, which I

845
00:41:28,400 --> 00:41:31,919
Speaker 2: say many would say they've been frustrated with in the past.

846
00:41:31,960 --> 00:41:35,319
Speaker 2: His WiFi doesn't always feel that great on board.

847
00:41:35,680 --> 00:41:41,360
Speaker 13: Yeah, so the previous The legacy operators are via sat SCS,

848
00:41:41,360 --> 00:41:46,000
Speaker 13: Intel SAD which was actually SCS acquired Intel SAD and

849
00:41:46,040 --> 00:41:51,640
Speaker 13: also Hughes, and those legacy operators have historically relied on

850
00:41:51,920 --> 00:41:56,000
Speaker 13: geostationary satellites which are much further away from Earth to

851
00:41:56,960 --> 00:41:59,360
Speaker 13: provide that Internet connectivity to airplanes.

852
00:42:00,239 --> 00:42:02,560
Speaker 3: What does the negotiation period look like.

853
00:42:02,920 --> 00:42:08,040
Speaker 2: How does Elon or indeed just SpaceX more broadly and

854
00:42:08,080 --> 00:42:10,160
Speaker 2: going over there win over these carriers.

855
00:42:10,160 --> 00:42:11,480
Speaker 3: What is it that they have to prove out.

856
00:42:12,600 --> 00:42:16,920
Speaker 13: So SpaceX It's been shown by third parties that Starlink

857
00:42:16,960 --> 00:42:20,719
Speaker 13: has delivered the fastest Wi Fi speeds across industry and

858
00:42:20,800 --> 00:42:24,439
Speaker 13: compared to those legacy operators, and that is in part

859
00:42:24,480 --> 00:42:27,480
Speaker 13: because their satellites are much closer to Earth, so you know,

860
00:42:27,600 --> 00:42:31,360
Speaker 13: the Internet has less distance to travel to getting to

861
00:42:31,440 --> 00:42:36,040
Speaker 13: those planes. But Starlink has a couple of negotiation tactics

862
00:42:36,040 --> 00:42:40,720
Speaker 13: that they use. They don't really allow airlines to announce

863
00:42:40,760 --> 00:42:43,440
Speaker 13: that they're using Starlink unless they'll equip on their whole fleet.

864
00:42:43,840 --> 00:42:47,359
Speaker 13: They also have put in this request that airlines offer

865
00:42:47,360 --> 00:42:49,840
Speaker 13: it to free for everyone on board, which you know

866
00:42:50,200 --> 00:42:53,759
Speaker 13: some airlines have, but some airlines have pushed back on

867
00:42:53,840 --> 00:42:57,520
Speaker 13: because they just want to offer it to their loyalty

868
00:42:57,880 --> 00:43:02,040
Speaker 13: customers and their loyalty program. So you know, they have

869
00:43:02,120 --> 00:43:04,400
Speaker 13: a couple of requests that they put in, but it

870
00:43:04,440 --> 00:43:08,319
Speaker 13: seems like the negotiations are unique for every airline and

871
00:43:08,360 --> 00:43:09,200
Speaker 13: every operator.

872
00:43:09,800 --> 00:43:13,440
Speaker 2: Sana Pashanka bringing us the latest on that part of SpaceX.

873
00:43:13,480 --> 00:43:14,279
Speaker 3: We appreciate it.

874
00:43:14,600 --> 00:43:16,800
Speaker 2: Let's get back to the public markets right now, because

875
00:43:16,840 --> 00:43:18,800
Speaker 2: we're back under pressure then, as back one hundred is,

876
00:43:18,840 --> 00:43:20,520
Speaker 2: you'll see down now for three straight days, two and

877
00:43:20,560 --> 00:43:23,520
Speaker 2: a half percent lower. We've been questioning above the board

878
00:43:23,520 --> 00:43:25,839
Speaker 2: the frothy valuations. One of them we've really been keeping

879
00:43:25,880 --> 00:43:29,359
Speaker 2: an eye out is on Palanteer. Now, its valuation, it's

880
00:43:29,400 --> 00:43:34,120
Speaker 2: multiple is extraordinary. I mean trades two hundred times future profits.

881
00:43:34,360 --> 00:43:36,600
Speaker 2: We're down fourteen percent in the last five days. In fact,

882
00:43:36,640 --> 00:43:40,360
Speaker 2: it's been low for seven straight trading days, the longest

883
00:43:40,800 --> 00:43:44,160
Speaker 2: sell off we've seen since March twenty twenty three. We'll

884
00:43:44,160 --> 00:43:46,600
Speaker 2: see how Alex Karp is responding to some of those shorts,

885
00:43:46,640 --> 00:43:49,399
Speaker 2: finally winning out some money, made about a billion one

886
00:43:49,400 --> 00:43:51,880
Speaker 2: point six billion in the period of loss.

887
00:43:51,960 --> 00:43:53,759
Speaker 3: Now that does it for this edition of Bloomberg Tech.

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00:43:54,080 --> 00:43:56,719
Speaker 2: Don't forget to check out the podcast, It's The Bloomberg

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00:43:56,719 --> 00:43:56,959
Speaker 2: Tech