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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 Vla Lolow in San Francisco.

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Speaker 2: This is Bloomberg Tech coming up.

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Speaker 3: President Trump says more chip tariffs are coming soon. This

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Speaker 3: is the administration explores equipping chips with location tracking tech.

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Speaker 2: Will have the latest from Washington.

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Speaker 4: Plus, Booming AI demand is sending Palenties second quarter revenue

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Speaker 4: soaring above a billion dollars, Chez hit a new record.

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Speaker 3: And AMD also out of earnings after market close today

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Speaker 3: with the company under pressure to show benefits from the

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Speaker 3: AI race, and.

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Speaker 4: We take a check on the markets amid what is

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Speaker 4: yet more earnings to come from AMD and a digestion

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Speaker 4: of weaker services data that seems to be putting up

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Speaker 4: pressure in a macro picture is underwhelming. We're off by

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Speaker 4: five ten percent on nasback one hundred, but ed get

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Speaker 4: to the nitty gritty, what's moving in terms of points here?

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Speaker 3: Yeah, the earning story is palent here and we're up

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Speaker 3: eight percentage points forty eight percent growth on the top line,

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Speaker 3: which the company attributes to astonishing AI contribution. We're going

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Speaker 3: to get deeper into that in the program. Another top

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Speaker 3: story that we're tracking is what's happening with AI chips

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Speaker 3: in particular. So we're going to talk about how tariffs

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Speaker 3: are coming, specifically for semiconductors. But then there's the reporting

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Speaker 3: overnight that the US is thinking about putting location trackers

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Speaker 3: on AI chips that are exported to certain countries. In fact,

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Speaker 3: overnight we caught out with a key White House official

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Speaker 3: who talks about it.

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Speaker 2: Listen to this.

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Speaker 5: There is discussion around very important research and development that

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Speaker 5: needs to be done on potentially the types of software

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Speaker 5: or even physical changes you could make to the chips

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Speaker 5: themselves to do better location tracking. And that's something that

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Speaker 5: we're exploring and we explicitly included in the plant.

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Speaker 4: We can get to order this with Boomberg's Mike Shepherd,

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Speaker 4: who's joining us.

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Speaker 6: Now from Washington.

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Speaker 4: Look, I want to go into the broader picture first

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Speaker 4: and foremost, we are anticipating semiconductors to be in the

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Speaker 4: eye in the storm when it comes to localized tariffs.

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Speaker 4: How much is that impacting the future of companies wanting

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Speaker 4: to import.

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Speaker 7: Well, that's a great question, Carol, because the administration, while

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Speaker 7: it has spared chips imports from the country by country tariffs.

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Speaker 7: We are now expecting Donald Trump to sign an order

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Speaker 7: in the next week or so putting tariffs on semiconductors

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Speaker 7: as a category, no matter the point of origin, and

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Speaker 7: the countries will have to negotiate individual levels perhaps after that.

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Speaker 7: But they have made clear that just like with pharmaceuticals

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Speaker 7: and autos and copper and so many other categories of goods,

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Speaker 7: they want to see more of them produced here in

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Speaker 7: the US, and they see tariffs as a great means

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Speaker 7: of doing so. Now, what this means is that industries

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Speaker 7: such as automakers, even the chip plants themselves, will face

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Speaker 7: higher costs. And then when you look at the hyperscalers,

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Speaker 7: Open AI and the companies that we've seen report their

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Speaker 7: earnings this week and announced even further intentions of expanding

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Speaker 7: their investment in artificial intelligence, they run the risk CARAF

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Speaker 7: facing higher costs on those two from chips. Chips are

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Speaker 7: one of the biggest inputs that these hyperscalers face in

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Speaker 7: terms of cost, and adding to that, adding up to

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Speaker 7: a twenty five percent tariff would be tricky. Now we've

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Speaker 7: been anticipating this since April, when the Commerce Department opened

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Speaker 7: its investigation into whether tariffs were called for. It's under

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Speaker 7: a so called two thirty two process. It would be

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Speaker 7: on sounder legal ground than the reciprocal country by country tariffs.

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Speaker 7: We will have to see what the timing looks like

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Speaker 7: and whether or not they carve out any exemptions as well,

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Speaker 7: including for semiconductor manufacturing equipment, which is key to expanding

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Speaker 7: here in the US as well.

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Speaker 3: Mike location track, we have the United States willing to

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Speaker 3: export technology, the American tech stack around the world, but

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Speaker 3: it wants to track where the chips are going. Can

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Speaker 3: you explain that one to us?

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Speaker 7: Yes, Ed, And it really is central to the administration's

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Speaker 7: plan to try to expand the American tech stack, especially

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Speaker 7: advanced AI chips. They want to be able to sell

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Speaker 7: those more broadly and more widely, including even into the

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Speaker 7: Chinese market. We saw the promise to loosen restrictions on

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Speaker 7: those aged twenty chips.

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Speaker 2: But it's more than just China.

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Speaker 7: It's being able to sell those semiconductors across the Middle East.

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Speaker 2: We saw all the deals that.

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Speaker 7: Were reached with the United Arab Emirates and in Saudi Arabia,

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Speaker 7: But to be able to sell in those markets. The

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Speaker 7: US wants to also have the capability of tracking those

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Speaker 7: chips to ensure that they are not somehow diverted or

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Speaker 7: smuggled or resold or somehow end up in the wrong hands.

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Speaker 7: And that would include not only China, but Russia, in Iran,

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Speaker 7: every series that the US does not want to see

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Speaker 7: gain capability when it comes to artificial intelligence, which of course,

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Speaker 7: as we know, has a dual use capability.

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Speaker 4: Mike Sheffin always on the nose. We thank you from Washington. Meanwhile,

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Speaker 4: Taiwanese prosecutors, well, they've arrested six people suspected of stealing

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Speaker 4: trade secrets from TSMC. Now, the chipmaker reported former and

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Speaker 4: current staff to authorities on suspicion they illegally obtained core technology.

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Speaker 6: The prosecutors searching the homes of some staff.

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Speaker 4: Now authorities are now trying to find out if data

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Speaker 4: had been leaked to other parties.

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Speaker 3: Z okay Onto Earnings Palenteer reported a forty eight percent

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Speaker 3: increase in revenue for the second quarter to more than

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Speaker 3: a billion dollars. The company cited the quote astonishing impact

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Speaker 3: of AI on its business. Bloomberg Intelligence senior analyst Mandy

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Speaker 3: Singh joins US. Now, this is the story that everyone's

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Speaker 3: talking about this morning, Palenteer's continued momentum. At BI, you

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Speaker 3: went a little bit deeper on how that's playing out,

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Speaker 3: But did you just summarize where they were strong and

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Speaker 3: what's happening with Palenteer right now?

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Speaker 1: Yeah.

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Speaker 8: I mean when you look at the US commercial segment,

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Speaker 8: that is what everyone is excited about. It grew ninety

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Speaker 8: three percent for the full year. Their guide is eighty

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Speaker 8: five percent, and look for a small segment with over

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Speaker 8: a one point two billion dollar run rate, it's very

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Speaker 8: impressive growth. But I think the metric that we still

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Speaker 8: don't understand is how much is the AI contribution to

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Speaker 8: their top line growth. I mean, when you look at

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Speaker 8: a Microsoft Azure number, you know the AI contribution to

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Speaker 8: growth this seventeen percent when they grew thirty nine percent.

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Speaker 8: In the case of Palenteer, yes they grew forty eight percent,

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Speaker 8: but we don't know how much of that is driven

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Speaker 8: by AI because their business model is dependent on contract wins.

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Speaker 8: And yes, they gave us a number around the backlock

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Speaker 8: growth and the remaining deal value on the commercial side,

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Speaker 8: but we just don't know enough to quantify that AI contribute.

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Speaker 8: And for a company that's trading at such a rich

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Speaker 8: valuation multiple, you want to have a better sense of,

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Speaker 8: you know, what that flow through of AAR revenue is.

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Speaker 4: Okay, So perhaps more transparency on a breakdown of numbers, Mandy,

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Speaker 4: what about who they're taking market share from, who's losing

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Speaker 4: out as we see Palenteer make these wins.

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Speaker 8: Yeah, their core business is data ware housing, So you

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Speaker 8: have the legacy vendors like terror Data and Oracle, and

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Speaker 8: you also have cloud companies like Snowflake and Data Breaks.

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Speaker 8: And we know Data Breaks is also doing very well

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Speaker 8: in terms of growth rates. So now that Palenteer is

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Speaker 8: expanding on the commercial side, it'll be interesting to see

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Speaker 8: if they can sustain this kind of growth rates, because

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Speaker 8: you're right, it will come at the expense of someone

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Speaker 8: on the commercial side. Clearly, the hyperscalers are looking to

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Speaker 8: get a piece of that pie as well. Because Microsoft,

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Speaker 8: the way it's positioning Copilot, it's trying to solve some

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Speaker 8: of the use cases that Valenteer is looking to solve

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Speaker 8: by applying LLLM. So to my mind, the commercial side growth,

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Speaker 8: the sustainability of growth is going to determine whether it

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Speaker 8: can sustain this valuation, and so far I find the

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Speaker 8: net new arr metric from the Hyperscalers far exceeds what

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Speaker 8: Talenteer has had a given. Talenteers is a much smaller company.

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Speaker 4: Great context, Blue meg Intelligence, Senior analyst, man Leeve Seeing,

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Speaker 4: thank you so much. Let's dive into all of this,

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Speaker 4: particularly into the valuation front. Stephanie Aliaga is with US,

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Speaker 4: joining us from Global Market Strategy JP morgas Asset Management

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Speaker 4: market Insights team.

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Speaker 6: Stephanie, we look at.

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Speaker 4: Something like Palenteer five hundred percent in twelve months, the

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Speaker 4: most expensive stock on the s and P five hundred

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Speaker 4: When you're looking at where it prices versus future sales,

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Speaker 4: does it seem vindicated to you these sorts of valuations.

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Speaker 9: There's absolutely some AI enthusiasm at play here, and I

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Speaker 9: think there's some and I think in this earning season

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Speaker 9: we have heard from a variety of companies, not just

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Speaker 9: these megacap tech companies, that they're seeing early signs of

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Speaker 9: success US with AI. AI is helping them run more efficiently.

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Speaker 9: It's writing more and more of their code, something like

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Speaker 9: fifty percent of code, and it's also helping them hit

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Speaker 9: more shots on target, particularly in marketing. But beyond that,

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Speaker 9: I think when it comes to valuations, you need to

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Speaker 9: on one end, look at the stability in the core

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Speaker 9: business operations of some of these companies, these megacap tech

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Speaker 9: companies which remain highly profitable, and then also some of

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Speaker 9: the upside moving forward in the AI race. And when

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Speaker 9: it comes to the leading megacap tech companies, at least,

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Speaker 9: we actually think valuations aren't as overstretched because core earnings

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Speaker 9: are so robust and they are actively reinvesting a lot

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Speaker 9: of those profits that cash flow into the next paradigm shift,

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Speaker 9: and we're already seeing some early signs of success these

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Speaker 9: Faces and Shovels companies.

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Speaker 6: They're also digging for goals and seeing the fruits of that.

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Speaker 3: Stephanie, when you were lost with us in April, it

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Speaker 3: was just after the sort of deep seat chaos and

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Speaker 3: we discussed how the semiconductor industry in particular was in

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Speaker 3: the crosshairs of the administration. A lot has changed since then.

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Speaker 3: Actually the administration has been very supported at exporting American technology.

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Speaker 3: But just on the stories we touched on tariffs on

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Speaker 3: semiconductors and location tracking on chips, there's a risk premium.

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Speaker 9: There is there absolutely geopolitics remains a risk. Policy has

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Speaker 9: been a headwind for the sector in recent months and

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Speaker 9: particularly today, but we are seeing a shift from this

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Speaker 9: administration towards a greater appreciation around what safeguarding US dominance in.

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Speaker 6: AI really looks like.

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Speaker 9: Right, And I will add, outside of these risks around tariffs,

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Speaker 9: what is really promising and quite structural, is that you

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Speaker 9: have seen a decline in over ninety percent around the

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Speaker 9: cost of inference for AI, and that has to do

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Speaker 9: with four factors. Hard hardware, the chips themselves have been

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Speaker 9: getting significantly more efficient. Algorithms are getting more efficient, how

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Speaker 9: much data, how many numbers they need a crunch, we're

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Speaker 9: right sizing models. We now have mini models that we

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Speaker 9: can access, and we've seen this democratization in models, particularly

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Speaker 9: open source. So all of that has led to this

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Speaker 9: pretty remarkable decline in the costs of AI, and I

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Speaker 9: think we need to put that to one side when

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Speaker 9: thinking about these risks for tariffs.

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Speaker 3: We shall borrow the acronym going forward. What I've wanted

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Speaker 3: to ask you for a real long time, is there

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Speaker 3: an America ink trade going on here where you just say,

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Speaker 3: I bet on these companies that are most likely to

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Speaker 3: succeed if this American strategy pays off.

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Speaker 9: I think it's a really interesting point, particularly in the

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Speaker 9: context of this debate around US exceptionalism, because when I

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Speaker 9: look at the markets right now, I don't see so

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Speaker 9: much US exceptionalism, but exceptionalism from a few US companies,

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Speaker 9: and that is really what is driving the markets right now.

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Speaker 9: We're actually less concerned about valuations for let's say, the

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Speaker 9: Magnificent seven and.

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Speaker 6: A bit more concerned about the other four.

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Speaker 9: Ninety three because without that structural tailwind when it comes

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Speaker 9: to AI, it's a bit harder to see the cyclical

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Speaker 9: tailwinds for the rest of the market. But when it

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Speaker 9: comes to these leading tech companies, I think you do

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Speaker 9: have unprecedented fundamentals and the continued dominance, particularly in tech services.

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Speaker 9: Now there's plenty for investors to consider, and we still

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Speaker 9: think portfolios should be diversified, but at a time like this,

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Speaker 9: you maybe don't want to be diversifying away from that

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Speaker 9: epicenter of innovation.

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Speaker 6: Okay, so interesting that you got the.

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Speaker 4: Acronym hard tech is where everyone's looking at from a

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Speaker 4: spot up culture but also into these big Magnificent seven.

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Speaker 4: But then drip feed it down into where if you

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Speaker 4: are going to diversify the sector, is that are deploying

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Speaker 4: it right or indeed, is it more about the picks

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Speaker 4: and shovels a little bit more, it's about the energy,

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Speaker 4: It's about the infrastructure and that slide of the equation

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Speaker 4: that keeps you in the AI spin.

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Speaker 9: Absolutely, the AI value is continuing to expand we think

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Speaker 9: that infrastructure layer is huge, and that is a secular

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Speaker 9: story that is still in its early innings. The amount

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Speaker 9: of energy that we're going to need, the investment that

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Speaker 9: we're going to need in a variety of different sources,

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Speaker 9: and also everything that goes into those data centers themselves,

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Speaker 9: the cooling equipment, the server technology, and so forth. So

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Speaker 9: we still think there's a significant investment case there. But

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Speaker 9: what's also really interesting in particularly what we're seeing in

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Speaker 9: private markets this year, is this explosion of AI applications,

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Speaker 9: the kind of applications that businesses are going to need

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Speaker 9: because it's not enough to give their worker SHATBT.

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Speaker 6: They need domain.

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Speaker 9: Specific AI applications that are actually productivity enhancing and show

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Speaker 9: up in the bottom line. We're seeing more and more

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Speaker 9: of those come to market this year, mostly in private markets,

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Speaker 9: but I think that's a trend that is worth watching

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Speaker 9: and just getting going.

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Speaker 4: And then they move from private markets into public markets

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Speaker 4: and we see what happened with Figma and the bephoria

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Speaker 4: around that particular listing.

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Speaker 6: Are you seeing more of.

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Speaker 4: Your clients wanting to know about what IPOs to get

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Speaker 4: into or indeed how to make that move from private

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Speaker 4: markets into public markets.

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Speaker 9: I think there's a lot of interest there, as there

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Speaker 9: should be. I mean, there's a lot of anticipation for

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Speaker 9: a pickup an Ibo activity, and I think the fundamentals

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Speaker 9: and the promise and a lot of those AI startups

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Speaker 9: could be the thing that really gets enthusiasm and comfort going.

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Speaker 6: In the market again.

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Speaker 9: But I also don't think that we're going to go

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Speaker 9: back to that kind of post pandemic period. A lot

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Speaker 9: of these companies have different goals beyond just being profitable

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Speaker 9: AI companies. Right, many of the LM providers that are

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Speaker 9: in private markets, they're on the race to AGI, and

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Speaker 9: so there is also this appreciation that these AI companies

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Speaker 9: can get a lot of the capital that they need

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Speaker 9: in private markets, maybe they will more control over their companies.

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Speaker 9: So if you want to access that opportunity set, don't

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Speaker 9: just wait for them to go public. I think you

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Speaker 9: might want to look at your private market exposure and

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Speaker 9: then also maybe some of those tactical opportunities that do

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Speaker 9: arise this year.

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Speaker 3: Stephanie Aliago, JP Morgan. Great to have you back on

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Speaker 3: the show. Thank you very much. Now coming up, Joe Faff,

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Speaker 3: known for making early bets on comempanies like Alphabet and

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Speaker 3: Amazon while at tro Price, joins us discuss his new

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Speaker 3: role head of Growth for Eclipse, a VC firm.

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Speaker 2: That's next. This is Bloomberg Tech.

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Speaker 3: Investor Joepath's scale trow Price's US growth stock Fund from

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Speaker 3: fifty seven billion dollars to over one hundred and fifty

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Speaker 3: billion dollars in assets, and had twenty years at the

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Speaker 3: investment firm, making early bets on companies like Amazon, Alphabet,

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Speaker 3: and Meta.

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Speaker 2: He's now joining a Clipse, a.

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Speaker 3: VC firm focused on physical industries as the head of

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Speaker 3: growth and also as a partner.

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Speaker 2: Joe's here with us in San Francisco.

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Speaker 3: I've always known you to be somebody who is higher

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Speaker 3: up the cap table of big companies that then go public.

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Speaker 3: Rivian is the example that I know best, late growth

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Speaker 3: stage round and anchor investor in in what was huge

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Speaker 3: IPO in twenty twenty one. You've been lures to bench

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Speaker 3: capital in Silicon Valley.

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Speaker 10: Why good to be with you, Ed, I appreciate it.

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Speaker 10: I'd say, you know, you've known me for a long

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Speaker 10: time now, and I've been a builder of companies over

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Speaker 10: time and investing capital, and those companies that they've scaled,

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Speaker 10: I think what really attracted me eclips and opportunities. I

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Speaker 10: really think we're at a seminal moment right now for

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Speaker 10: physical industries. So if you look at the companies that

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Speaker 10: have been created now, both private and public, and you

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Speaker 10: look at the market caps, there's been significant interest in

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Speaker 10: those and there's capital formation around them.

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Speaker 6: The other parts of.

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Speaker 10: This, I would say, you know, all this is coming

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Speaker 10: together at once. Is US policy, which you talked about

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Speaker 10: earlier in the segment, I think is very supportive of

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Speaker 10: bringing industries back.

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Speaker 11: To the United States.

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Speaker 10: And the other thing is clearly an inordinate amount of

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Speaker 10: innovation around artificial general intelligence and ultimately artificial superintelligence, which

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Speaker 10: I think provides this unique intersection of hardware and software

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Speaker 10: to transform physical industries.

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Speaker 3: So the reindustrialization piece is something that comes up in

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Speaker 3: this program all the time. There's a sort of national priority,

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Speaker 3: national interest, even sort of national pride elements to it.

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Speaker 3: How are you going to play that in your strategy.

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Speaker 10: I think it's important you talk about policy and ultimately

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Speaker 10: national security and economic sovereignty, and it's very important, I think,

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Speaker 10: you know, looking at the Eclipse team and why I

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Speaker 10: joined them specifically, they've been doing this for ten years

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Speaker 10: and had a vision a decade ago, and it's an

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Speaker 10: incredibly competent, amazing team. During my process, the time I

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Speaker 10: spent with Lere and the rest of the partners, they're passionate,

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Speaker 10: they're hungry.

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Speaker 6: They're true builders.

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Speaker 10: They roll up their sleeves, they get their hands dirty,

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Speaker 10: and they've done a lot of you know, early stage

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Speaker 10: enterprises and companies. Where I come in. I've allocated capital

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Speaker 10: at scale. I've looked at companies both private and public

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Speaker 10: that have grown, and I've worked alongside invested in many

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Speaker 10: of the best companies in the world. So to do

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Speaker 10: that as we scale these businesses and connect the two

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Speaker 10: together with that team, their technical experience, in my financial experience,

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Speaker 10: I think is really like a one plus one equals

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Speaker 10: three situation. They remind me I'll date myself here, ed,

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Speaker 10: but it's a team of Jerry Maguire's not.

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Speaker 6: Dating at all. It's an iconic movie, Joe.

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Speaker 4: I'm interested though, in just how you go from managing.

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Speaker 6: Tens of billions to going.

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Speaker 4: To managing and writing relatively small checks. What sort of

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Speaker 4: size checking you're going to be giving do you think

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Speaker 4: when you see these smaller scale companies that are going

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Speaker 4: to be huge?

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Speaker 10: Yeah, well, I mean initially, right at the early stage

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Speaker 10: or venture side of this Caroline, you're going to see

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Speaker 10: smaller check sizes. But we're going to scal all those

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Speaker 10: over time, both for companies as they hit a scale

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Speaker 10: velocity and they need more capital to continue to build

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Speaker 10: out their strategies. And on top of that, there will

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Speaker 10: be companies, as we've seen, that need bigger check sizes

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Speaker 10: earlier and getting more traction. A good example that is investment.

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Speaker 10: I made it both Trow and we've made it Eclipses

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Speaker 10: Androl to give you an example.

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Speaker 4: So, and Andrew, I immediately think of just the focus from

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Speaker 4: the government and indeed for many an investor right now,

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Speaker 4: just what was happening in point seventy two, for example,

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Speaker 4: on getting in on defense tech in particular. But I'm

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Speaker 4: interested in other particular areas where you think the US

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Speaker 4: is leaning in more that you want to be contributing

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Speaker 4: to thinking. When you think of red word, I'm thinking

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Speaker 4: about the supply chain more. But when you're thinking then

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Speaker 4: about Tesla is more about robotics. Where are your sweet

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Speaker 4: spot's going to be?

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Speaker 6: Do you think?

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Speaker 10: Yeah, I've heard a lot of experience in electric vehicles,

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Speaker 10: material science, battery technology, but that's also extended and I

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Speaker 10: think it ties in with what Eclipse has done well,

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Speaker 10: supply chain solutions, semiconductors. You mentioned defense tech, autonomy, So

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Speaker 10: there's a lot of crossing a paths given my experience

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Speaker 10: that I've had as a private and public investor, now

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Speaker 10: as I join.

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Speaker 3: Eclipse, Joe, you visited Tesla in twenty eleven. You took

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Speaker 3: Rivian public basically, and then you help them after going public,

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Speaker 3: just very quickly, What were the lessons you took from

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Speaker 3: M two companies?

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Speaker 10: Yeah, I mean, I think this is one of the

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Speaker 10: seminal moment for me on physical industries was in twenty

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Speaker 10: eleven when I visited Tesla. It was not just a

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Speaker 10: differentiated product ed but at the end of the day,

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Speaker 10: a different way of building right. The first principle ways

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Speaker 10: of thinking, and we see that permeating a lot of businesses.

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Speaker 10: And as we've had successful companies like Tesla, SpaceX, mentioned Anderill, Redwood,

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Speaker 10: there's people leaving those firms and they're starting new firms

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Speaker 10: as well. So the ecosystem is growing and they're taking

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Speaker 10: those lessons they learned with them, things that were really

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Speaker 10: important to me, and I think I can help businesses

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Speaker 10: with clearly, product market fit is very important. You know,

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Speaker 10: right product, right time, solving the right customer problem set

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Speaker 10: or providing them something that they don't have, that's very important.

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Speaker 10: I've always talked about, you know, additional acts beyond X one.

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Speaker 10: You look at Tesla, They've done that, SpaceX has done that,

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Speaker 10: Amazon's done that. So a handful of those and then

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Speaker 10: and then really what I've we've focused on growth for

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Speaker 10: many years is driving durable growth and making sure companies

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Speaker 10: are sustainable longer term. So I'll be working with most

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Speaker 10: of them to scale and ultimately get them to be

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Speaker 10: durable public companies.

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Speaker 3: Joe Farf on his second act, the Klip's partner and

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Speaker 3: head of growth, thank you very much. Coming up, Aberdabi's

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Speaker 3: MGX gets ready to raise billions for its own AI ambitions.

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Speaker 2: We have more in that story. Next, this is Bloomberg Tech.

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Speaker 6: It's the time now for talking tech.

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Speaker 4: First up Abadabi based MNGX is said to be considering

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Speaker 4: plans to raise billions of dollars as it ramps up

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Speaker 4: investments in AI. And that's according to sources the firm.

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Speaker 4: It plans to raise money through a fund structure and

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Speaker 4: can raise as much as twenty five billion. Now this

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Speaker 4: is his executives raising money from investors in Aberdabi, but

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Speaker 4: also beyond Plus and videos assembly partner on High and

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Speaker 4: actually saw sales growth slow in July, a signal that

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Speaker 4: tariff induced uncertainty is affecting electronics demand. Still, the company

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Speaker 4: hopes to ride that wave of continued AI spending from

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Speaker 4: its partners, even expanding its AI server assembly capacity in

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Speaker 4: the United States and shares of German ship maker in Finion,

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Speaker 4: where they're rising today after the company said that tariff

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Speaker 4: impact it's fourth quarter fiscal results will be less than anticipated.

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Speaker 4: Convenience CFOs Ben Schneider joined Blomberg TV Justselier today.

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Speaker 11: I would say from today's perspective, the indirect tariff guestimate

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Speaker 11: for this fiscal quarter is probably less pronounced than we

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Speaker 11: thought last quarter. On the other hand, we are faced

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Speaker 11: with more and more negative currency headwinds from the weakening dollar,

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Speaker 11: which offsets to a certain extent.

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Speaker 3: Coming up, eleven Labs bets on AI Music, launching a

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Speaker 3: new service called eleven Music. We speak with CEO Matthew Sanazuski.

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Speaker 2: Next, this is Bloomberg Tech.

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Speaker 6: Welcome back to Bloomberg Tech.

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Speaker 4: In on those markets that just starling back from some

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Speaker 4: of the record highs have been out recently, we're seeing

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Speaker 4: then that one hundred and down sixt ten percent. There's

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Speaker 4: more of a creeping anxiety about the macro picture in

439
00:23:04,520 --> 00:23:07,440
Speaker 4: the United States that services data looking week, remember the

440
00:23:07,560 --> 00:23:10,239
Speaker 4: jobs data that President Trump took issue with the end

441
00:23:10,280 --> 00:23:12,920
Speaker 4: of last week also painted a less than rosy picture

442
00:23:12,920 --> 00:23:15,240
Speaker 4: about the economy. We take some money off the table

443
00:23:15,480 --> 00:23:18,360
Speaker 4: as we digest also earnings. We're expecting amd after the bell,

444
00:23:18,440 --> 00:23:20,919
Speaker 4: but let's look forward as also how we're seeing some

445
00:23:21,600 --> 00:23:24,960
Speaker 4: companies still at record highs. Palenteered doing the work today,

446
00:23:24,960 --> 00:23:28,080
Speaker 4: we're up almost seven percent, new record high after it's

447
00:23:28,080 --> 00:23:30,760
Speaker 4: already climbed five hundred percent in the last twelve months.

448
00:23:30,960 --> 00:23:33,439
Speaker 6: This is their earnings managed to impress a billion dollars

449
00:23:33,440 --> 00:23:34,400
Speaker 6: in terms of revenue.

450
00:23:34,480 --> 00:23:37,119
Speaker 4: We'll also see in Global Foundries, though not such a

451
00:23:37,160 --> 00:23:39,639
Speaker 4: pleasing picture that they actually beat in terms of their

452
00:23:39,640 --> 00:23:43,600
Speaker 4: fiscal quarter reported, but it's in anxiety around profitability looking

453
00:23:43,600 --> 00:23:45,879
Speaker 4: forward that midpoint of the range not met. When it

454
00:23:45,880 --> 00:23:47,680
Speaker 4: looks for their forecast, we're off by nine percent of

455
00:23:47,760 --> 00:23:48,880
Speaker 4: the contract ship make red.

456
00:23:50,000 --> 00:23:53,959
Speaker 3: Okay, take a look at this chart showing the sales

457
00:23:54,000 --> 00:23:57,600
Speaker 3: slump Tesla's posting in key European markets.

458
00:23:57,640 --> 00:23:59,000
Speaker 2: These numbers come as.

459
00:23:59,000 --> 00:24:02,639
Speaker 3: Chinese rival Bed picks up sales team in the region.

460
00:24:02,920 --> 00:24:05,399
Speaker 3: I want to get out to Bloomberg Stefan Nikola to

461
00:24:05,440 --> 00:24:08,520
Speaker 3: talk about this because it's an ongoing story. It's a

462
00:24:08,560 --> 00:24:12,720
Speaker 3: trend that we've seen Tesla in some European markets dropping

463
00:24:12,800 --> 00:24:15,919
Speaker 3: off and China's BYD making ground.

464
00:24:15,920 --> 00:24:18,640
Speaker 2: How would you sum the data we got most recently.

465
00:24:19,840 --> 00:24:23,359
Speaker 12: Yeah, I mean Tesla has been suffering in Europe for

466
00:24:23,400 --> 00:24:27,040
Speaker 12: the past few months and suffering in the key electric

467
00:24:27,160 --> 00:24:31,560
Speaker 12: vehicle markets Germany, the UK, France. Those are all markets

468
00:24:31,600 --> 00:24:35,480
Speaker 12: that are rising in terms of electric vehicle demand. So

469
00:24:35,560 --> 00:24:39,920
Speaker 12: you know that that's really bad for for Tesla. Of course, BYD,

470
00:24:40,160 --> 00:24:43,200
Speaker 12: by contrary, is sort of making a big push into

471
00:24:43,240 --> 00:24:47,320
Speaker 12: all of these markets selling electric cars but also hybrids

472
00:24:47,800 --> 00:24:50,320
Speaker 12: and yeah, and just in general, it's it's a really

473
00:24:50,359 --> 00:24:54,600
Speaker 12: bad picture for Tesla. Of course, the company has a

474
00:24:54,800 --> 00:24:58,160
Speaker 12: factory up and running in Germany and it needs demand.

475
00:24:58,480 --> 00:25:02,680
Speaker 4: Yeah, just try and weave the narrative for us, because

476
00:25:03,000 --> 00:25:05,159
Speaker 4: Tesla wanted the narrative to be. Look, we're moving to

477
00:25:05,240 --> 00:25:08,040
Speaker 4: a new production format, particularly with a model. Why that's

478
00:25:08,080 --> 00:25:10,679
Speaker 4: what's slowing down perhaps some of our end results, but

479
00:25:10,760 --> 00:25:13,720
Speaker 4: actually it seems to be political connotations. It seems to

480
00:25:13,720 --> 00:25:14,399
Speaker 4: be competition.

481
00:25:14,520 --> 00:25:16,520
Speaker 6: Is the issue? Is that really what we're seeing here?

482
00:25:18,160 --> 00:25:20,480
Speaker 12: Yeah, there are a lot of factors at play here.

483
00:25:21,200 --> 00:25:24,639
Speaker 12: The whole shifting to a new model and narratives is

484
00:25:24,720 --> 00:25:29,200
Speaker 12: way too simple to explain this drastic decline. Elon Musk's

485
00:25:29,560 --> 00:25:34,680
Speaker 12: political activities are certainly contributing to this sales slump, as

486
00:25:34,760 --> 00:25:40,439
Speaker 12: are better models from rivals, better prices from rivals, but also,

487
00:25:40,760 --> 00:25:45,399
Speaker 12: you know, just very shifting market dynamics in Europe, but

488
00:25:45,520 --> 00:25:49,520
Speaker 12: also more broadly, so you know, the overhaul of the

489
00:25:49,560 --> 00:25:53,520
Speaker 12: model why as you we certainly plays a role, but

490
00:25:53,640 --> 00:25:56,879
Speaker 12: it's definitely not the only reason for this really big slump.

491
00:25:57,440 --> 00:26:00,760
Speaker 4: Chefen Nicola, we thank you. For painting, but what is

492
00:26:01,080 --> 00:26:03,679
Speaker 4: an intricate picture? Meanwhile, we shift gears. We look at

493
00:26:03,720 --> 00:26:06,840
Speaker 4: AI voice startup eleven Laps its launching a new initiative

494
00:26:06,840 --> 00:26:11,120
Speaker 4: for businesses stepping into the world of AI music. Customers

495
00:26:11,160 --> 00:26:13,760
Speaker 4: can enter a prompt and generate a new tune within

496
00:26:13,840 --> 00:26:16,080
Speaker 4: a matter of minutes using eleven Music.

497
00:26:16,400 --> 00:26:17,439
Speaker 6: That's the name of the service.

498
00:26:17,760 --> 00:26:22,159
Speaker 4: Joining us now is Matty Sanychewski eleven Labs CEO. So

499
00:26:22,880 --> 00:26:26,280
Speaker 4: what is demand like, you're doing voices, but what was

500
00:26:26,320 --> 00:26:29,360
Speaker 4: the inbound? What scale you're seeing of companies wanting to

501
00:26:29,440 --> 00:26:30,399
Speaker 4: create their own music?

502
00:26:32,480 --> 00:26:35,480
Speaker 13: Carlin, thanks for having me here. So at eleven laps

503
00:26:35,520 --> 00:26:40,520
Speaker 13: we are doing AA audio research and product deployment. We

504
00:26:40,560 --> 00:26:42,200
Speaker 13: are on the mission to be the voice of technology,

505
00:26:42,359 --> 00:26:45,359
Speaker 13: build the most comprehensive AI audio platform in the world.

506
00:26:45,720 --> 00:26:49,200
Speaker 13: And now this includes eleven Music, which we are extremely

507
00:26:49,200 --> 00:26:51,840
Speaker 13: excited about because this is something that so many customers

508
00:26:51,880 --> 00:26:54,800
Speaker 13: came to our platform to create a voiceover, to create

509
00:26:54,800 --> 00:26:58,919
Speaker 13: a voices, to create narrations with our core models, but

510
00:26:59,040 --> 00:27:04,600
Speaker 13: frequently needed that additional background sounds, soundtrack in those productions,

511
00:27:04,720 --> 00:27:06,960
Speaker 13: which we will now be able to provide as part

512
00:27:06,960 --> 00:27:10,240
Speaker 13: of the service from creators all the way through the enterprises.

513
00:27:10,640 --> 00:27:13,680
Speaker 4: It's a fraught world where you're trying to navigate IP.

514
00:27:13,840 --> 00:27:16,680
Speaker 4: In particular, this is IP protective music because you've already

515
00:27:16,720 --> 00:27:19,560
Speaker 4: got the training of the models is based upon agreements

516
00:27:19,600 --> 00:27:19,920
Speaker 4: you have.

517
00:27:20,000 --> 00:27:21,560
Speaker 6: I'm thinking with Merlin Network.

518
00:27:21,640 --> 00:27:25,639
Speaker 4: That's for independent labels, it's a digital rights agencies.

519
00:27:25,680 --> 00:27:29,040
Speaker 6: Also got a similar deal with Cobalt Music Group. But

520
00:27:29,440 --> 00:27:31,679
Speaker 6: what about the bigger ones, what about UMG?

521
00:27:31,880 --> 00:27:35,440
Speaker 4: What about some of the other key music license holders?

522
00:27:35,760 --> 00:27:38,959
Speaker 4: Can you make deals with them?

523
00:27:39,080 --> 00:27:39,720
Speaker 11: It's exactly.

524
00:27:39,720 --> 00:27:42,320
Speaker 13: One of the very special things about the release of

525
00:27:42,320 --> 00:27:45,399
Speaker 13: the Model is that it's both extremely high quality, one

526
00:27:45,400 --> 00:27:48,119
Speaker 13: of the highest qualities in the market, while it's also

527
00:27:48,200 --> 00:27:52,399
Speaker 13: fully licensed and built in collaboration with the labels, with

528
00:27:52,480 --> 00:27:55,400
Speaker 13: the artists, with the publishers. Like you mentioned, we already

529
00:27:55,640 --> 00:27:57,960
Speaker 13: are very happy to be working with Merlin and Cobbalt

530
00:27:57,960 --> 00:28:02,400
Speaker 13: frequently referred to as the fourth both because they represent

531
00:28:02,480 --> 00:28:06,280
Speaker 13: respectively thirty thousand independent labels on the Maryland side and

532
00:28:06,320 --> 00:28:09,480
Speaker 13: over twenty five thousand songwriters on the Cobbalt side. So

533
00:28:09,680 --> 00:28:12,800
Speaker 13: really that gives us such a high ability to create

534
00:28:13,080 --> 00:28:15,960
Speaker 13: extremely high equality music. Of course, as we look into

535
00:28:15,960 --> 00:28:19,439
Speaker 13: the future, we would love to work with UMGWG Sony

536
00:28:20,040 --> 00:28:21,960
Speaker 13: and help we find a path in the future to

537
00:28:22,320 --> 00:28:25,600
Speaker 13: do so. As we think about our work across eleven Music,

538
00:28:25,640 --> 00:28:27,520
Speaker 13: that's one of the key elements to make sure that

539
00:28:27,760 --> 00:28:30,680
Speaker 13: the work we do is both licensed but also gives

540
00:28:30,680 --> 00:28:33,200
Speaker 13: you that broad commercial license to use it, which our

541
00:28:33,320 --> 00:28:36,760
Speaker 13: enterprises demand from gaming all the way to media entertainment

542
00:28:36,760 --> 00:28:37,840
Speaker 13: companies that we work with.

543
00:28:38,720 --> 00:28:42,000
Speaker 3: Maddie, are you talking with and negotiating with those major

544
00:28:42,040 --> 00:28:43,120
Speaker 3: labels you just named?

545
00:28:45,880 --> 00:28:48,800
Speaker 13: Currently the focus was on bringing the current model and

546
00:28:48,840 --> 00:28:51,840
Speaker 13: product to the market, So really we're deep with our

547
00:28:51,880 --> 00:28:57,240
Speaker 13: partners with Merlin, Cobalt, Source, Audio Lander and a number

548
00:28:57,280 --> 00:29:00,800
Speaker 13: of other partners that are in the mentioned in the

549
00:29:00,880 --> 00:29:04,680
Speaker 13: release to bring the current iteration. As we bring the

550
00:29:04,960 --> 00:29:07,720
Speaker 13: product to the market iterate, we will see whether we

551
00:29:07,760 --> 00:29:08,520
Speaker 13: can expand out further.

552
00:29:09,480 --> 00:29:11,640
Speaker 3: But it does a lot of interest in eleven Labs

553
00:29:11,640 --> 00:29:14,960
Speaker 3: in your business to what stage have you grown? Do

554
00:29:15,000 --> 00:29:17,960
Speaker 3: you have any financial metrics ar R or anything you

555
00:29:18,000 --> 00:29:20,040
Speaker 3: can tell us about how it's going for you over there.

556
00:29:22,440 --> 00:29:25,080
Speaker 13: We're doing really well as a company. A few months ago,

557
00:29:26,280 --> 00:29:28,160
Speaker 13: over half a year ago now, we crossed the one

558
00:29:28,200 --> 00:29:31,160
Speaker 13: hundred million ARR mark as a company and we've been

559
00:29:31,160 --> 00:29:34,200
Speaker 13: growing steadily since. Across the company, we have really those

560
00:29:34,200 --> 00:29:37,040
Speaker 13: two key offerings. The first one is that creative platform

561
00:29:37,240 --> 00:29:41,000
Speaker 13: which music will form those in those early and initial days,

562
00:29:41,480 --> 00:29:44,000
Speaker 13: and then the agendic platform where we help companies create

563
00:29:44,080 --> 00:29:47,200
Speaker 13: voice agent conversational agents. And over the last six months

564
00:29:47,200 --> 00:29:51,160
Speaker 13: we've just seen an explosion in companies building with both

565
00:29:51,200 --> 00:29:53,080
Speaker 13: parts of the use cases, both on the creative and

566
00:29:53,120 --> 00:29:56,920
Speaker 13: the agentic side. We're now partnering over a thousand different

567
00:29:56,920 --> 00:30:00,440
Speaker 13: companies in the market and over a five million creators

568
00:30:00,440 --> 00:30:02,640
Speaker 13: that come through the platform on a monthly basis.

569
00:30:02,680 --> 00:30:05,360
Speaker 4: I want to go back to those creators because can

570
00:30:05,400 --> 00:30:08,360
Speaker 4: you name any individual musicians, for example, that are willing

571
00:30:08,400 --> 00:30:10,280
Speaker 4: to go this way? I think of how Grimes is

572
00:30:10,320 --> 00:30:12,080
Speaker 4: really lent into this and she wants a fifty to

573
00:30:12,080 --> 00:30:14,640
Speaker 4: fifty split with her royalties going forward. But what are

574
00:30:14,640 --> 00:30:17,920
Speaker 4: the musicians you're seeing actually wanted to adopt this, Because,

575
00:30:17,920 --> 00:30:21,440
Speaker 4: as I say, it is fraud.

576
00:30:21,680 --> 00:30:27,000
Speaker 13: We are seeing a number of musicians in the space

577
00:30:27,080 --> 00:30:29,920
Speaker 13: that are keen to lean into the technology Iterate quicker

578
00:30:29,960 --> 00:30:32,280
Speaker 13: make it a little bit easier to create music. Currently,

579
00:30:33,120 --> 00:30:38,440
Speaker 13: as part of the eleven Music we are still crafting

580
00:30:38,480 --> 00:30:40,640
Speaker 13: the partnerships and will hopefully announce some of those in

581
00:30:40,680 --> 00:30:43,280
Speaker 13: the coming weeks. So I cannot name any on that

582
00:30:43,320 --> 00:30:45,920
Speaker 13: side just yet, but we've seen a wider set of

583
00:30:45,960 --> 00:30:49,600
Speaker 13: creators working with the tools to make it easier to

584
00:30:49,600 --> 00:30:52,840
Speaker 13: produce variations of the genres, make it easier to see

585
00:30:52,840 --> 00:30:57,120
Speaker 13: how the tracks can be produced before heading that production stuff.

586
00:30:58,760 --> 00:31:01,800
Speaker 3: Maddy, there's a lot of intro seen you and eleven

587
00:31:01,880 --> 00:31:04,520
Speaker 3: Labs kind of fits the profile of this like vertical

588
00:31:04,600 --> 00:31:09,600
Speaker 3: model where it's a potential aqua higher target right. I

589
00:31:09,640 --> 00:31:12,320
Speaker 3: just wonder what you make of what's happening right now,

590
00:31:12,560 --> 00:31:16,200
Speaker 3: the windsurf scenario and whether you might consider doing something

591
00:31:16,240 --> 00:31:19,080
Speaker 3: that joining a bigger entity to continue your work.

592
00:31:21,160 --> 00:31:24,239
Speaker 13: We are building generational company, are planning to go in

593
00:31:24,240 --> 00:31:27,400
Speaker 13: the pandemic and try to create and become the voice

594
00:31:27,440 --> 00:31:31,240
Speaker 13: of the technology, make computers speak, make information accessible across

595
00:31:31,320 --> 00:31:34,640
Speaker 13: voices like ross sounds across languages and now music. So

596
00:31:35,320 --> 00:31:37,600
Speaker 13: current default this to a create a company that can

597
00:31:37,840 --> 00:31:40,000
Speaker 13: that can create something special on its own, and we

598
00:31:40,040 --> 00:31:42,520
Speaker 13: think we can. So hopefully the opposite will be true

599
00:31:42,560 --> 00:31:45,000
Speaker 13: where we'll be able to acqui hire or acquire a

600
00:31:45,080 --> 00:31:47,800
Speaker 13: number of companies in the space on that path too.

601
00:31:48,000 --> 00:31:49,400
Speaker 13: To potential IPO in the future.

602
00:31:50,920 --> 00:31:54,160
Speaker 3: Matty Sanderschewski of eleven Labs, thank you very much. Now

603
00:31:54,160 --> 00:31:57,200
Speaker 3: coming up on the program, and thirteen partner Brent Murray

604
00:31:57,280 --> 00:32:00,200
Speaker 3: joins us talk about the firm's new investment in the

605
00:32:00,240 --> 00:32:02,520
Speaker 3: generative AI advertising space.

606
00:32:03,040 --> 00:32:05,240
Speaker 2: That's next. This is Winberg Tech.

607
00:32:17,560 --> 00:32:20,240
Speaker 3: Venture firm M thirteen is just led a ten million

608
00:32:20,280 --> 00:32:24,720
Speaker 3: dollar seed round in generative AI advertising platform Context. The

609
00:32:24,760 --> 00:32:27,560
Speaker 3: startups now the largest and most well funded gen AI

610
00:32:28,000 --> 00:32:31,720
Speaker 3: ADS platform to date, according to M thirteen. M thirteen

611
00:32:31,760 --> 00:32:34,720
Speaker 3: partner Brent Murray's here to tell us more. This space

612
00:32:34,800 --> 00:32:38,000
Speaker 3: is dynamic, right because it's highly analogous with Meta. You know,

613
00:32:38,040 --> 00:32:40,680
Speaker 3: a lot of the story around Meta is how their

614
00:32:40,760 --> 00:32:46,640
Speaker 3: working AI improves AD pricing, ad impact that core business.

615
00:32:47,040 --> 00:32:50,440
Speaker 3: You are looking at a very different scale and early stage.

616
00:32:50,640 --> 00:32:53,200
Speaker 3: I'd just love to get a presentation of the thesis

617
00:32:53,200 --> 00:32:56,000
Speaker 3: and why this is an interesting domain within AI.

618
00:32:57,520 --> 00:32:59,840
Speaker 14: Yeah, thank you Ed and Caroline as well for having me.

619
00:33:00,680 --> 00:33:03,440
Speaker 14: So we are excited to announce the coming out of

620
00:33:03,480 --> 00:33:07,320
Speaker 14: stealth of Context this morning. They're ten million dollar seed

621
00:33:07,400 --> 00:33:10,440
Speaker 14: round to really become the largest and most well funded

622
00:33:10,520 --> 00:33:14,120
Speaker 14: generative AD platform out there. And so to answer your

623
00:33:14,200 --> 00:33:17,400
Speaker 14: question our thesis on the space, Well, we see the

624
00:33:17,520 --> 00:33:20,600
Speaker 14: advertising world at a major inflection point, and I'm glad

625
00:33:20,640 --> 00:33:24,560
Speaker 14: you hit on meta. There's also industry incumbent Google that

626
00:33:24,720 --> 00:33:27,400
Speaker 14: have pioneered a lot of the previous waves.

627
00:33:28,560 --> 00:33:30,720
Speaker 2: We believe the first wave came sort of.

628
00:33:30,640 --> 00:33:34,200
Speaker 14: At the early two thousands when when Google pioneered digital

629
00:33:34,200 --> 00:33:37,760
Speaker 14: advertising as a space. Then shortly thereafter, a second wave

630
00:33:37,840 --> 00:33:40,560
Speaker 14: came in the twenty tens where social media platforms led

631
00:33:40,560 --> 00:33:44,600
Speaker 14: by Meta attracted new eyeballs, new audiences online, and ad

632
00:33:44,640 --> 00:33:46,600
Speaker 14: inventory exploded online.

633
00:33:47,000 --> 00:33:48,520
Speaker 2: And we see this as a third wave.

634
00:33:48,720 --> 00:33:52,160
Speaker 14: The one trillion dollar AD market, three quarters of which

635
00:33:52,240 --> 00:33:55,840
Speaker 14: is done through digital channels, is about to migrate towards

636
00:33:56,160 --> 00:33:59,560
Speaker 14: generative AI apps. You already see Open ai as the

637
00:33:59,600 --> 00:34:02,800
Speaker 14: fastest growing app of all time, closely getting to a

638
00:34:02,840 --> 00:34:06,239
Speaker 14: billion users. And it's not just them, they're just the

639
00:34:06,240 --> 00:34:08,760
Speaker 14: most well known. You have a lot of other generative

640
00:34:08,800 --> 00:34:12,480
Speaker 14: AI entertainment apps that are attracting audiences where context is

641
00:34:12,480 --> 00:34:13,640
Speaker 14: going to target them.

642
00:34:14,680 --> 00:34:18,160
Speaker 3: What is context's core competency? What is it that they're

643
00:34:18,200 --> 00:34:19,200
Speaker 3: actually doing good at.

644
00:34:20,320 --> 00:34:21,600
Speaker 2: Yeah, there are two things.

645
00:34:22,800 --> 00:34:26,719
Speaker 14: One is real time ad creation and the second is

646
00:34:27,040 --> 00:34:30,040
Speaker 14: hyper personalized contextual ads.

647
00:34:30,320 --> 00:34:31,640
Speaker 2: So I'll take those in pieces.

648
00:34:32,440 --> 00:34:35,439
Speaker 14: The first one, similar to what Mark Zuckerberg is hinted

649
00:34:35,480 --> 00:34:40,000
Speaker 14: at where Meta is going, is end to end advertising creative,

650
00:34:40,200 --> 00:34:44,440
Speaker 14: where AI and llms are powering much of not just

651
00:34:44,520 --> 00:34:47,640
Speaker 14: the targeting of audiences online, but the actual creation of

652
00:34:47,680 --> 00:34:51,439
Speaker 14: the advertisement itself, the asset knowing what copy to use,

653
00:34:51,520 --> 00:34:55,960
Speaker 14: what style, what color the image, the video. So what

654
00:34:56,120 --> 00:35:00,000
Speaker 14: context has done is enable an AD to be created

655
00:35:00,280 --> 00:35:03,920
Speaker 14: in real time using an LM. If you are in

656
00:35:04,000 --> 00:35:07,719
Speaker 14: a chat bot, then your ad will be served up

657
00:35:07,719 --> 00:35:09,960
Speaker 14: to you in real time, which means you don't have

658
00:35:10,040 --> 00:35:11,719
Speaker 14: to have a lot of wasted time on the ad

659
00:35:11,760 --> 00:35:15,399
Speaker 14: creative a lot of manual processes creating thousands of iterations.

660
00:35:15,600 --> 00:35:16,480
Speaker 2: So that's the first thing.

661
00:35:16,520 --> 00:35:21,480
Speaker 14: The second thing is contextual hyper personalized. So in the

662
00:35:21,520 --> 00:35:24,320
Speaker 14: old world, if you're on social media, I think a

663
00:35:24,360 --> 00:35:27,120
Speaker 14: great example is my family and I just had a baby.

664
00:35:27,680 --> 00:35:30,680
Speaker 14: Any of your viewers know that the months leading up

665
00:35:30,719 --> 00:35:32,879
Speaker 14: to a baby, the months following a baby, you are

666
00:35:32,920 --> 00:35:38,000
Speaker 14: targeted ad nauseum by the social media platforms because that

667
00:35:38,160 --> 00:35:41,719
Speaker 14: is when you're most likely to purchase baby goods. But

668
00:35:42,080 --> 00:35:44,640
Speaker 14: what happens after you purchase the crib, After you purchase

669
00:35:44,680 --> 00:35:47,120
Speaker 14: the high chair. You're not going to purchase them again,

670
00:35:47,280 --> 00:35:50,680
Speaker 14: But hundreds of dollars are wasted on you as an

671
00:35:50,719 --> 00:35:53,120
Speaker 14: audience because they're targeting you as a demographic and a

672
00:35:53,200 --> 00:35:56,560
Speaker 14: social behavior. Take that as what context is doing is

673
00:35:56,640 --> 00:36:00,640
Speaker 14: very different. They are targeting advertisements based on your real

674
00:36:00,719 --> 00:36:03,760
Speaker 14: time queries into the LM. So if you are chatting

675
00:36:03,800 --> 00:36:07,040
Speaker 14: through a chatbot telling it what you're interested in, an

676
00:36:07,080 --> 00:36:10,320
Speaker 14: advertisement will be created in real time based on exactly

677
00:36:10,360 --> 00:36:11,000
Speaker 14: what you're asking.

678
00:36:11,400 --> 00:36:13,840
Speaker 4: I kind of want to go to the regulatory or

679
00:36:14,280 --> 00:36:18,360
Speaker 4: unnerving part of this, because we're almost trusting these chatbots

680
00:36:18,400 --> 00:36:21,279
Speaker 4: more than say, the social media side of things. How

681
00:36:21,320 --> 00:36:24,800
Speaker 4: do I know the chatbot's not steering me during my questions?

682
00:36:24,920 --> 00:36:27,880
Speaker 4: How do I ensure that basically I'm not suddenly finding

683
00:36:27,880 --> 00:36:31,120
Speaker 4: the conversation being bent to certain direction because it wants

684
00:36:31,160 --> 00:36:32,040
Speaker 4: me a hooked in it.

685
00:36:32,120 --> 00:36:34,120
Speaker 6: So we're worried about addiction process again.

686
00:36:34,239 --> 00:36:36,719
Speaker 4: Or indeed forcing me to then bend my will to

687
00:36:36,800 --> 00:36:38,600
Speaker 4: end up buying something and perhaps I didn't need.

688
00:36:38,719 --> 00:36:39,920
Speaker 6: How are we going to regulate that?

689
00:36:41,040 --> 00:36:44,560
Speaker 14: Yeah, that's a great question, and I think the advertising industry,

690
00:36:44,800 --> 00:36:47,680
Speaker 14: because we're at this inflection point, we'll have to face

691
00:36:47,719 --> 00:36:51,000
Speaker 14: a lot of those questions right now, you see what

692
00:36:51,120 --> 00:36:54,319
Speaker 14: happened with social media platforms in the past. Consumers are

693
00:36:54,360 --> 00:36:58,560
Speaker 14: always hesitant to go on ad platforms like Meta or

694
00:36:58,560 --> 00:37:01,319
Speaker 14: Instagram and just be served at ad after ad after ad.

695
00:37:01,680 --> 00:37:04,759
Speaker 14: But then they become much better and influencers started talking

696
00:37:04,800 --> 00:37:06,919
Speaker 14: about things that you wanted to buy, and a lot

697
00:37:06,920 --> 00:37:10,480
Speaker 14: of advertisers and online merchants rely on some of these

698
00:37:10,520 --> 00:37:13,480
Speaker 14: ads to power their businesses to stay in business. I

699
00:37:13,520 --> 00:37:16,880
Speaker 14: think the advertisements in AI will have to follow the

700
00:37:16,880 --> 00:37:19,440
Speaker 14: same suit. You have to be able to trust that

701
00:37:19,600 --> 00:37:22,719
Speaker 14: the publisher or the chat bot isn't trying to steer

702
00:37:22,800 --> 00:37:24,520
Speaker 14: you in the in the right in the wrong direction.

703
00:37:25,040 --> 00:37:27,879
Speaker 14: That will lead to unnecessary churn. That's the last thing

704
00:37:27,960 --> 00:37:31,400
Speaker 14: that Context wants for their publishing customers. They want to

705
00:37:31,440 --> 00:37:35,440
Speaker 14: create seamless environments where you're having a regular chat.

706
00:37:35,920 --> 00:37:37,279
Speaker 2: The ad will be.

707
00:37:37,000 --> 00:37:40,759
Speaker 14: Promoted or shown in a way that you can distinguish

708
00:37:40,840 --> 00:37:44,360
Speaker 14: it from an advertisement versus non advertisement, that it's not

709
00:37:44,719 --> 00:37:47,000
Speaker 14: predatory or you know, going after you.

710
00:37:46,960 --> 00:37:47,680
Speaker 2: In a certain way.

711
00:37:48,160 --> 00:37:51,000
Speaker 14: But you'll you'll see a very very natural process where

712
00:37:51,000 --> 00:37:54,240
Speaker 14: the chatbots are feeding you up things that you already

713
00:37:54,320 --> 00:37:58,040
Speaker 14: want to see, already want to potentially purchase.

714
00:37:58,120 --> 00:38:00,560
Speaker 4: Well, Google already doing that. In the third part, chatbots,

715
00:38:00,560 --> 00:38:02,759
Speaker 4: It's fascinating. Brent Marrying, We thank you so much for

716
00:38:02,760 --> 00:38:06,680
Speaker 4: your time talking about context. His partner over at thirteen. Now,

717
00:38:06,840 --> 00:38:09,680
Speaker 4: let's talk about how women are holding well then eighteen

718
00:38:09,680 --> 00:38:12,640
Speaker 4: percent of all partner and above rolls in venture capital.

719
00:38:12,719 --> 00:38:15,480
Speaker 6: It's all according to data just released by All Rays Now.

720
00:38:15,520 --> 00:38:18,200
Speaker 4: It's a nonprofit that aims to increase gender parity in

721
00:38:18,239 --> 00:38:21,040
Speaker 4: tech and VC, and it's saying that this is ahead

722
00:38:21,080 --> 00:38:23,800
Speaker 4: of its projected schedule. We spoke with All Raised CEO

723
00:38:23,920 --> 00:38:26,120
Speaker 4: Page Buckner about where that change can be seen.

724
00:38:26,120 --> 00:38:30,680
Speaker 15: Most twenty percent of decision making roles at VC firms

725
00:38:30,680 --> 00:38:34,400
Speaker 15: with a billion plus and aom have women decision makers,

726
00:38:34,400 --> 00:38:37,560
Speaker 15: which is super exciting. And so when you think about

727
00:38:37,560 --> 00:38:40,120
Speaker 15: that stat what's important to us is that we're making

728
00:38:40,160 --> 00:38:42,360
Speaker 15: progress at scale. But we do know that at the

729
00:38:42,400 --> 00:38:45,520
Speaker 15: mega funds there are only three women holding managing partner roles,

730
00:38:45,719 --> 00:38:47,399
Speaker 15: and so there's a big opportunity for us to make

731
00:38:47,400 --> 00:38:48,080
Speaker 15: progress there.

732
00:38:48,200 --> 00:38:50,440
Speaker 4: And they're the funds with more than ten billion in

733
00:38:50,440 --> 00:38:54,280
Speaker 4: assets under management. Go back to the data therefore, page

734
00:38:54,360 --> 00:38:56,160
Speaker 4: of what this means?

735
00:38:56,640 --> 00:38:57,640
Speaker 6: Why is it?

736
00:38:57,760 --> 00:39:00,279
Speaker 4: What is helping those that are managing in excess one

737
00:39:00,360 --> 00:39:03,640
Speaker 4: bay and make those decisions have more decision makers who

738
00:39:03,680 --> 00:39:05,000
Speaker 4: are diverse in thought.

739
00:39:06,360 --> 00:39:06,600
Speaker 2: Yeah.

740
00:39:06,600 --> 00:39:08,920
Speaker 15: So I think the first thing to ground ourselves in

741
00:39:09,120 --> 00:39:10,839
Speaker 15: is that there's so much data and we talked about

742
00:39:10,880 --> 00:39:13,800
Speaker 15: this last year that makes the case for having diversity

743
00:39:13,800 --> 00:39:16,440
Speaker 15: and decision making roles at any kind of organization, and

744
00:39:16,480 --> 00:39:19,400
Speaker 15: this is especially important in venture capital when they are

745
00:39:19,440 --> 00:39:22,040
Speaker 15: making decisions about the kind of innovation that we're going

746
00:39:22,080 --> 00:39:24,200
Speaker 15: to fund in the private markets and then eventually in

747
00:39:24,239 --> 00:39:27,000
Speaker 15: the public markets as well. So it's important for us

748
00:39:27,000 --> 00:39:29,399
Speaker 15: to stay grounded in the results, and we talked about

749
00:39:29,440 --> 00:39:32,040
Speaker 15: this last year. But what we know is that diversity

750
00:39:32,080 --> 00:39:36,520
Speaker 15: is an advantage because diverse teams consistently outperform. They also

751
00:39:36,640 --> 00:39:39,520
Speaker 15: better reflect where the market is heading. And one important

752
00:39:39,560 --> 00:39:41,759
Speaker 15: data point that we've been thinking deeply about is the

753
00:39:41,760 --> 00:39:44,719
Speaker 15: one hundred trillion dollars in wealth that's projected to move

754
00:39:44,719 --> 00:39:47,160
Speaker 15: into the hands of women by twenty forty eight, and

755
00:39:47,200 --> 00:39:50,080
Speaker 15: that's just going to reshape everything in the economy. But

756
00:39:50,239 --> 00:39:52,839
Speaker 15: as we think about venture capital in particular, we've been

757
00:39:52,880 --> 00:39:54,920
Speaker 15: asking what does that look like for LPs, what does

758
00:39:54,960 --> 00:39:55,480
Speaker 15: that look.

759
00:39:55,360 --> 00:39:56,280
Speaker 6: Like for venture firms?

760
00:39:56,320 --> 00:39:58,640
Speaker 15: What does that look like for the kind of innovation

761
00:39:58,760 --> 00:39:59,520
Speaker 15: that these women are.

762
00:39:59,440 --> 00:40:00,360
Speaker 6: Going to be able to fund?

763
00:40:00,719 --> 00:40:04,960
Speaker 4: So is the thought process here more money lands in

764
00:40:05,200 --> 00:40:08,800
Speaker 4: female coffers, They therefore want to work with more diverse

765
00:40:08,960 --> 00:40:11,719
Speaker 4: LPs or allocate the LPs want to allocate to more

766
00:40:11,760 --> 00:40:15,000
Speaker 4: diverse vcs, and they want to be putting money to work.

767
00:40:15,719 --> 00:40:18,920
Speaker 4: How does that reflect in the portfolio companies that then

768
00:40:19,000 --> 00:40:22,160
Speaker 4: these diverse bench capitalists and decision makers are putting money into.

769
00:40:23,120 --> 00:40:23,319
Speaker 7: Yeah.

770
00:40:23,360 --> 00:40:25,720
Speaker 15: Absolutely so we know according to our friends at Kaufman

771
00:40:25,800 --> 00:40:28,760
Speaker 15: that women vcs are two times more likely to invest

772
00:40:28,760 --> 00:40:30,680
Speaker 15: in women's CEOs at the earliest stages.

773
00:40:31,160 --> 00:40:33,279
Speaker 6: And we know that there's opportunity there.

774
00:40:33,280 --> 00:40:36,400
Speaker 15: And often these markets have been overlooked because they've been

775
00:40:36,400 --> 00:40:39,080
Speaker 15: perceived as niche markets. But what's great is that when

776
00:40:39,120 --> 00:40:41,160
Speaker 15: you bring in more diversity on the VC side of

777
00:40:41,200 --> 00:40:43,120
Speaker 15: the equation or on the LP side of the equation,

778
00:40:43,200 --> 00:40:46,000
Speaker 15: by the way, that is felt across the capital stack,

779
00:40:46,080 --> 00:40:48,080
Speaker 15: and then the kinds of innovation that we can have

780
00:40:48,520 --> 00:40:51,080
Speaker 15: on the start up side of the equation shift, and

781
00:40:51,120 --> 00:40:54,920
Speaker 15: so what gets funded then also better reflects than part

782
00:40:54,960 --> 00:40:58,520
Speaker 15: of me the marketplace, which is of course commanded by women.

783
00:40:58,560 --> 00:41:02,160
Speaker 15: When we think about the person of decisions around consumer

784
00:41:02,280 --> 00:41:05,239
Speaker 15: and healthcare and education that are made by women every

785
00:41:05,320 --> 00:41:08,080
Speaker 15: day for household across this country. So it is really

786
00:41:08,120 --> 00:41:10,839
Speaker 15: reflecting not just where the market is today, but where

787
00:41:10,840 --> 00:41:13,160
Speaker 15: the market is going to be. And we're thinking about

788
00:41:13,160 --> 00:41:16,880
Speaker 15: the products that are therefore reflective of the consumer that

789
00:41:17,520 --> 00:41:19,520
Speaker 15: vcs really want to be building for because they're going

790
00:41:19,560 --> 00:41:21,000
Speaker 15: to be able to get outsize returns.

791
00:41:22,280 --> 00:41:25,640
Speaker 3: That was Paige Buckner, CEO of All Rays. Now coming

792
00:41:25,680 --> 00:41:29,520
Speaker 3: up investors, IAMD results after the closing bell, the latest

793
00:41:29,520 --> 00:41:34,520
Speaker 3: earnings expectations coming up next, and the analysis from Bloomberg Intelligence.

794
00:41:34,520 --> 00:41:36,719
Speaker 2: Stay with us. This is Bloomberg Tech.

795
00:41:43,239 --> 00:41:46,040
Speaker 3: AMD out with earnings after the bell, and investors are

796
00:41:46,040 --> 00:41:49,200
Speaker 3: putting increased pressure on the chip maker, expecting the company

797
00:41:49,239 --> 00:41:51,960
Speaker 3: to show benefits from the AI race. Here with more

798
00:41:52,160 --> 00:41:56,840
Speaker 3: Kungensavani of Bloomberg Intelligence, you put a number on this market.

799
00:41:56,840 --> 00:41:59,160
Speaker 3: In Vidia has ninety five percent of the market for

800
00:41:59,360 --> 00:42:03,200
Speaker 3: GPUs is AI accelerators. AMD has five percent. What do

801
00:42:03,239 --> 00:42:05,200
Speaker 3: they need to show us that they're making some ground?

802
00:42:05,600 --> 00:42:05,799
Speaker 2: Well?

803
00:42:05,800 --> 00:42:08,360
Speaker 16: This time around the non air things will be important,

804
00:42:08,360 --> 00:42:11,440
Speaker 16: PC gaining share, some maybe pair of pull in embedded,

805
00:42:11,520 --> 00:42:13,960
Speaker 16: coming recovery in three Q but as you said, the

806
00:42:14,000 --> 00:42:17,560
Speaker 16: second half AI ramp both in server CPU and GPU

807
00:42:17,640 --> 00:42:20,360
Speaker 16: will matter the most. They need to again show sustained

808
00:42:20,360 --> 00:42:23,840
Speaker 16: execution on the three fifty five ramp. There's a possibility

809
00:42:23,880 --> 00:42:26,480
Speaker 16: of the China series coming back and adding about five

810
00:42:26,560 --> 00:42:29,000
Speaker 16: hundred million to even more revenue in the second half,

811
00:42:29,239 --> 00:42:31,959
Speaker 16: and any visibility into the Helios which is the next

812
00:42:32,040 --> 00:42:34,920
Speaker 16: year server level REX solution, should be very positive.

813
00:42:35,680 --> 00:42:38,200
Speaker 6: Where have they been executing really well?

814
00:42:38,280 --> 00:42:40,319
Speaker 4: Because I feel like time and time again we're hearing

815
00:42:40,320 --> 00:42:43,120
Speaker 4: about the PC side of the equation, but the consumer

816
00:42:43,160 --> 00:42:44,200
Speaker 4: isn't too hot right now.

817
00:42:46,560 --> 00:42:49,120
Speaker 16: Definitely, the ND markets are struggling, but they have been

818
00:42:49,200 --> 00:42:52,600
Speaker 16: really gaining share on CPU, both on the PC and

819
00:42:52,640 --> 00:42:56,200
Speaker 16: the server, which has really been helping them outperform even

820
00:42:56,239 --> 00:42:59,520
Speaker 16: the unit weakness in the market. Secondly, we have heard,

821
00:42:59,640 --> 00:43:03,759
Speaker 16: you know news about them raising prices both on the PC,

822
00:43:03,960 --> 00:43:06,200
Speaker 16: the service if you, and the GPU side. That really

823
00:43:06,239 --> 00:43:09,120
Speaker 16: speaks to the strength of their adoption across the board.

824
00:43:09,800 --> 00:43:10,040
Speaker 1: Wow.

825
00:43:10,320 --> 00:43:12,400
Speaker 4: Planning to watch out for in terms of size and

826
00:43:12,400 --> 00:43:15,399
Speaker 4: market application countri In Savanni on Bloomberg Intelligence some great

827
00:43:15,440 --> 00:43:18,360
Speaker 4: notes coming out ahead of these earnings, and we'll analyze

828
00:43:18,400 --> 00:43:21,000
Speaker 4: them after the Bell as well. That does it though

829
00:43:21,000 --> 00:43:23,200
Speaker 4: for this edition of Bloomberg Tech, and so much to

830
00:43:23,239 --> 00:43:25,880
Speaker 4: be digesting in terms of the market. It's the macro pressure,

831
00:43:25,920 --> 00:43:28,000
Speaker 4: but we look at AMD after the Bell in terms

832
00:43:28,040 --> 00:43:29,080
Speaker 4: of the micro perspective.

833
00:43:29,520 --> 00:43:31,960
Speaker 3: Yeah, another day where chips are in focused because of policy.

834
00:43:32,000 --> 00:43:33,120
Speaker 2: But like AMD, best.

835
00:43:33,000 --> 00:43:35,759
Speaker 3: Performing chip stock so far this year, right, and they

836
00:43:35,800 --> 00:43:38,080
Speaker 3: have a lot of pressure on them. But it's an

837
00:43:38,120 --> 00:43:41,799
Speaker 3: interesting company to follow. Good analysis from Kunjan recap it.

838
00:43:41,800 --> 00:43:43,560
Speaker 3: As Cary said on the podcast, you know where to

839
00:43:43,560 --> 00:43:43,920
Speaker 3: find it.

840
00:43:43,920 --> 00:43:44,279
Speaker 2: It's on the.

841
00:43:44,280 --> 00:43:46,960
Speaker 3: Bloomberg terminal as well as online on all of those

842
00:43:47,000 --> 00:43:51,200
Speaker 3: great platforms Apple, Spotify, and on iHeart. From New York

843
00:43:51,239 --> 00:43:54,120
Speaker 3: City in San Francisco, this is Bloomberg Tech