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

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Speaker 1: from the heart of Silicon Valley with ed La though

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Speaker 1: in San Francisco.

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Speaker 2: This is Bloomberg Tech coming up.

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Speaker 3: SpaceX Shares feeling gravitational pool as must company launches its

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Speaker 3: debut bond sale plus tech stock sell off worldwide as

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Speaker 3: Wall Street gets.

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Speaker 2: An AI wake up call? Is the AI.

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Speaker 3: Boom sustainable and we go big on private markets? Mellow

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Speaker 3: Ventures raises its biggest ever haul with three billion.

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Speaker 2: Dollars to BACAI startups.

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Speaker 3: Partner Vinkie Ganisan joins us on set in San Francisco.

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Speaker 3: The headline on the Bloomberg is that Wall Street gets

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Speaker 3: an AI wake up call, but there has been a

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Speaker 3: global sell off in technology shares. It started Monday in

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Speaker 3: the US, but in career overnight is where it was

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Speaker 3: felt most. The cost be the best performing index in

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Speaker 3: the world so far this year, dropping ten percent from

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Speaker 3: a record high.

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Speaker 2: It is Samsung.

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Speaker 3: It is sk Heinix, which led questions about the sustainability

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Speaker 3: of the AI trade. Let's go over to the US

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Speaker 3: session where actually we're off session lows, but there is

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Speaker 3: still severe selling. The socks is down seven percent, then

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Speaker 3: as that one hundred is down two point six percent.

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Speaker 3: All of this about sustainability, durability of what's happening in

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Speaker 3: AI our top story SpaceX shares.

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Speaker 2: The stock actually briefly.

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Speaker 3: Dip below one hundred and fifty dollars, which was its

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Speaker 3: trading debut price from June twelve. We're getting new details

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Speaker 3: on the company's first ever bond offering, a five part

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Speaker 3: investment grade deal expected to raise at least twenty billion

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Speaker 3: dollars and rank among the biggest debt sales of the year.

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Speaker 3: The sale would primarily refinance debt while also giving investors

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Speaker 3: a fresh look that how Elon Musk companies are funding

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Speaker 3: growth across AI, satellites and space. Joining us now the

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Speaker 3: team Bloomberg Senior Markets at Call of Bay, Lipshow and

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Speaker 3: Emily Graffeo on the corporate credit side, and let's start

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Speaker 3: with you. What do we need to know about this

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Speaker 3: that sale, the structure of it, the terms.

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Speaker 4: Well, look, we need to know that this deal is

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Speaker 4: probably going to be one of the biggest investment grade

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Speaker 4: bond seals of the year, and it already got thirty

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Speaker 4: billion dollars of demand even before the deal was announced.

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Speaker 4: It's pricing later today. Bankers are taking orders from investors

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Speaker 4: right now. We do expect the deal to go pretty well.

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Speaker 4: So even though the stock is down today, ed this

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Speaker 4: bond sale expected to be successful, and it's getting an

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Speaker 4: investment grade rating, despite the fact that SpaceX is saying

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Speaker 4: it's going to be blowing through cash here. Really what

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Speaker 4: the ratings analysts are focusing on is the fact that

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Speaker 4: they have recurring revenue from their starlink business, they have

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Speaker 4: a dominant launch provider central to the US space program,

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Speaker 4: and of course they have access to enough liquidity to

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Speaker 4: keep funding that AI expansion. So it's a unique investment

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Speaker 4: grade rating here, but one that at least for right now,

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Speaker 4: investors are putting their confidence.

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Speaker 3: Behind Bailey and yesterday's program. Credit analyst Robert Schiffman said,

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Speaker 3: equity investors trade on hope looking at the stock now

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Speaker 3: at almost three percent in the session, but briefly in

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Speaker 3: negative territory. We're all talking about it hitting its trading

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Speaker 3: debut price. What's going on in this post IPO trade, and.

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Speaker 5: We're just still seeing supply and demand trying to be matched.

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Speaker 5: Obviously at the euphoria of listing day. Retail traders continuing

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Speaker 5: to plow through the stock last week as we saw

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Speaker 5: it hit a bit of an airpok, and we see

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Speaker 5: the volatility today, we still have to remember that we're

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Speaker 5: only looking at about five percent of the float available

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Speaker 5: for trading, so we're still well ahead of the lockup

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Speaker 5: whin insiders and long term investors and long term employees

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Speaker 5: are able to be selling the stock. So the big

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Speaker 5: question now going forward is what is the catalyst to

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Speaker 5: keep buying. As you mentioned, we saw some volatility on

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Speaker 5: an inter day basis, that's kind of par for the course.

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Speaker 5: And if we look back a few months to Sarah bros.

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Speaker 5: A really strong debut, a lot of chop, and then

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Speaker 5: kind of settling out in the few months ahead of

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Speaker 5: their first earnings report as a public company. So when

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Speaker 5: you're just looking at this again, it's kind of typical

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Speaker 5: playbook that we've seen with a lot of these high profile,

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Speaker 5: highly anticipated IPOs.

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Speaker 3: I would say that maybe one catalyst is they did

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Speaker 3: this demo, the Starfull mission, where they had this reusable

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Speaker 3: capsule in space. The idea is it was a demo

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Speaker 3: for in space manufacturing. Maybe not, We'll get into that

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Speaker 3: later on. And what I want to understand with this

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Speaker 3: is the significance of the investment grade rating that SpaceX got,

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Speaker 3: because the difference between SpaceX and some of its peers

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Speaker 3: in that domain is this is a company that's going

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Speaker 3: to be burning cash and with negative free cash.

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Speaker 2: Flow for a really long time. How does that work? Yeah,

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Speaker 2: that's exactly right.

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Speaker 4: They've really been treated differently here with the investment grade rating.

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Speaker 4: They're not like these other companies that are coming to

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Speaker 4: the investment grade bond market typically expect like a utility company.

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Speaker 4: So this has been, at least according to our reporting

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Speaker 4: what ratings analysts have said, a difficult credit to rate.

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Speaker 4: But again it goes back to that recurring revenue that

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Speaker 4: they have from other parts of their business. And this

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Speaker 4: idea here that this is investors taking a leap of

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Speaker 4: faith edge just like the equity investors did. The credit

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Speaker 4: sale is also kind of requiring that leap of faith,

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Speaker 4: people putting their trust behind Elon Musk. That narrative spreading

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Speaker 4: from the IPO into this inaugural bond sale as well.

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Speaker 3: Bailey, We're still talking about the mechanics of capital markets

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Speaker 3: as opposed to what SpaceX actually does. But this is

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Speaker 3: like an immediate post IPO period. You just explain that.

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Speaker 3: I think we should therefore talk a bit about the

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Speaker 3: green shoe. So they actually raised more money all told

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Speaker 3: than we initially reported, right, And the reason I bring

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Speaker 3: that up is because when they launched the bond sale,

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Speaker 3: they said that as of June nineteenth, SpaceX had more

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Speaker 3: than one hundred billion dollars of cash.

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Speaker 2: Explain that bit.

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Speaker 5: Yeah, so with the green shoe, that enables bankers to

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Speaker 5: sell more shares about fifteen percent relative to the total float.

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Speaker 2: So all told, bringing in north.

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Speaker 5: Of eighty five billion dollars does help bolster the company's

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Speaker 5: balance sheet.

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Speaker 2: But we really were in ed.

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Speaker 5: We were breaking some news around this, expecting the company

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Speaker 5: to tap the debt mark, expecting the company to refinance

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Speaker 5: some of its loans that had already had on its

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Speaker 5: balance sheet. So this was also well foretold when you're

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Speaker 5: talking to some of the investors as they were meeting

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Speaker 5: with the company in that road show. The big question

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Speaker 5: going forward, we do have the inclusion in the Nasdaq

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Speaker 5: one hundred early next month. We also will be expecting

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Speaker 5: analyst initiation reports on the other side of the fourth

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Speaker 5: of July holiday here in the US, so that'll be

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Speaker 5: something where we can start to see the likes of

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Speaker 5: Morgan Stanley and Goldmen Sachs, the two banks that under

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Speaker 5: wrote the IPO, what their analysts are saying. We're going

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Speaker 5: to see some pretty high numbers as it relates to

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Speaker 5: what the total addressable market of space could be. But

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Speaker 5: broadly speaking, we still are in this area where we're

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Speaker 5: waiting for fundamental news. And the thing that's going to

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Speaker 5: be interesting is SpaceX. As we saw with the partnership

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Speaker 5: with Reflection, AI going to lean on its ability to

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Speaker 5: maybe be a little bit of a hyperscaler or supply

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Speaker 5: some of that compute, or is it going to be

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Speaker 5: a narrative that really does shift to space starshipping the

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Speaker 5: other things that the company wants to do as it

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Speaker 5: relates to getting data centers orbiting the Earth.

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Speaker 3: I suspect those banks will be bullish. Bloomberg's Bailey lip

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Speaker 3: Schultz and Emily Graffee thank you both very much. Tech

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Speaker 3: stoks are under pressure after Korean chipmakers sell off spark

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Speaker 3: fears of AI sustainability. Despite SpaceX's breakout IPO giving the

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Speaker 3: trade momentum in recent days, investors worry that the I

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Speaker 3: boom may not be as durable as previously thought Martin Norton,

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Speaker 3: chief investment strategist An Mpaw of black on Bloomberg Tech.

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Speaker 3: I don't want to put words in your mouth, but

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Speaker 3: SpaceX is off to a good start and we got volatility,

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Speaker 3: and I think all of those things we'd expected.

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Speaker 6: Yes, I think that's absolutely the case. So of course

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Speaker 6: there was some trepedition heading into the SpaceX IPO. We

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Speaker 6: know those things can be volatile, but early days was

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Speaker 6: a good start. We have some volatility today. I think

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Speaker 6: we should anticipate continued volatility. There are those index inclusions,

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Speaker 6: there's the lackups expiring, so there are some things that

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Speaker 6: we need to keep an eye on as it pertains

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Speaker 6: to volatility there. I think volatility for the broader AI

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Speaker 6: trade too. We know there's been a lot of helium,

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Speaker 6: particularly on the chip side of things, and that creates

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Speaker 6: its own type of price bubble. I don't think it

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Speaker 6: necessarily suggests that AI isn't durable, but it certainly creates

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Speaker 6: volatility in markets.

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Speaker 3: The headline was Wall Streep getting a wake up call

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Speaker 3: in the AI trade. Is it specifically the AI parts

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Speaker 3: of the market that are vulnerable or is there just

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Speaker 3: sort of some inaggregate concern about equity markets from evaluation

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Speaker 3: perspective or from where we're trading right now.

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Speaker 6: I think the risk is coming from a few different places.

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Speaker 6: I do think it's predominantly around AI and the things

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Speaker 6: that have led us out of the concern around the

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Speaker 6: war so since April first, that has been the chip area,

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Speaker 6: memory and semi. So I do think concerns around what

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Speaker 6: we're going to see in earnings Micron reporting, whether that

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Speaker 6: gross margin expansion is still possible, I think that is

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Speaker 6: a question mark on investment minds. I also think there

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Speaker 6: is some I guess marginal concern around what rates mean

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Speaker 6: for equities. I don't look at the broad US equity

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Speaker 6: market though, is all that stretched. And I also think

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Speaker 6: there is some durability to earning. So I'm not sure

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Speaker 6: it's a predominant issue, but it's certainly something that's kind

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Speaker 6: of picking away at the sides of things.

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Speaker 3: I just if we could go back to what happened

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Speaker 3: in career overnight, I think it's just a very interesting

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Speaker 3: case study. So the cost fee, which I think been

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Speaker 3: the best performing index or benchmark so far this year,

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Speaker 3: basically entered a correction from a record high the night prior,

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Speaker 3: but it was Samsung and Skhinex that kind of led

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Speaker 3: those to clients. Is a strategist looking across asset and

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Speaker 3: looking at US equity markets. What's your interpretation of what

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Speaker 3: happened there?

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Speaker 6: Well, I guess my perspective is that to your point,

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Speaker 6: this is the best performing market that we've seen year

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Speaker 6: to date. There is an enormous amount of hardware AI

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Speaker 6: exposure in that market. There's been enormous amounts enthusiasm over

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Speaker 6: the supply constraints and just kind of the you know,

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Speaker 6: this is a different type of cycle than we typically

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Speaker 6: see in that part of the market, and I think

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Speaker 6: investors have just been pulled in and caught up in

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Speaker 6: the enthusiasm. And I think there's room and rationale to

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Speaker 6: expect continued moments of doubt like this when you have

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Speaker 6: prices rising triple digits.

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Speaker 3: Our top story is probably the SpaceX bond sale of

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Speaker 3: five part offering. We expect a price today. We talked

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Speaker 3: a lot in the last twenty four hours on the

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Speaker 3: show about how there is a difference between the psychology

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Speaker 3: and also methodology of an equity investor and the credit investor,

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Speaker 3: But is there something that links those two. What we

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Speaker 3: learn about SpaceX is business through its ability to tap

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Speaker 3: debt markets that might weigh on the equity as a consequence.

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Speaker 6: I do think there's a connection in this particular instance. Now,

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Speaker 6: I absolutely agree with that view that credit investors are

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Speaker 6: different than stock investors. There's a little bit more side awareness,

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Speaker 6: a little bit more protection on the fixed income side

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Speaker 6: than on the equity side. And yet when we're looking

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Speaker 6: at something like SpaceX with so much of its value

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Speaker 6: future loaded, I guess I would say it does seem,

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Speaker 6: given the enthusiasm that we're seeing around that issuance, that

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Speaker 6: bond investors are taking a bit of a queue from

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Speaker 6: equity investors and embedding a bit of hope in their

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Speaker 6: expectations for how SpaceX might perform on the debt side.

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Speaker 6: And of course, I think a broader point is just

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Speaker 6: how much money this is going to take to realize

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Speaker 6: some of the Elon Musk ambitions when it comes to SpaceX.

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Speaker 3: Empowers Martin Norton a market's perspective, but you always roll

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Speaker 3: with the biggest technology stories of the day, thank you

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Speaker 3: very much. Indeed, now coming up, we're going to speak

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Speaker 3: with seman CEO Roland Bush on bringing AI from a

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Speaker 3: digital world into the physical conversation, really looking forward to

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Speaker 3: that's next. This is Bloomberg Tech. AI is moving beyond

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Speaker 3: chatbots and onto the factory floor, helping manufacturers boost productivity,

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Speaker 3: with industrial AI becoming a bigger part of the conversation.

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Speaker 3: Joining us now is Semens CEO Roland Bush, who recently

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Speaker 3: met with European Commissioned President Ursula Vonderleon and other tech

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Speaker 3: leaders to discuss AI's role in Europe's industrial future. But

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Speaker 3: I would note as well, Roland, you were the first

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Speaker 3: conversation I had this year. We started twenty twenty six

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Speaker 3: with Jensen Wang talking about the transition into the physical world.

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Speaker 3: Just very simply to start. Why Semens is bedding so

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Speaker 3: much on industrial AI.

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Speaker 7: Actually big aus Semens is really geared for this moment

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Speaker 7: to bring AI into the real world.

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Speaker 8: We call industrial I. Why you need a couple of things.

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Speaker 7: Number one is you need to technology stack, and the

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Speaker 7: stack includes hardware and software.

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Speaker 8: Both is super relevant.

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Speaker 7: Number two is domain no how we know how to

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Speaker 7: build things we are on the shop floor.

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Speaker 8: The next one is data.

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Speaker 7: We have a lot of data, our own data, data

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Speaker 7: shared with our customers and partners, and we have the

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Speaker 7: trust of our customers because when the AI hits the

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Speaker 7: real world, hallucination is not an option, so you need

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Speaker 7: hardcore results.

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Speaker 8: They should work.

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Speaker 7: So we have all this and we can bring it

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Speaker 7: now to the real world and lustban released. We have

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Speaker 7: great partners like in media, like some others who are

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Speaker 7: with us in making this transformation for our customers.

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Speaker 3: I asked you at CES in January, give me a

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Speaker 3: case study, an example. There's some evidence of AI in

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Speaker 3: a factory anywhere in the world. Fast forward to June.

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Speaker 3: Is it real now? Is there to please? You can

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Speaker 3: point to please, it's really.

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Speaker 7: Now, and we can talk about the design face, the

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Speaker 7: manufacturing face, and the operation phase when you're asset sign

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Speaker 7: the field. But you talked about an example on the

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Speaker 7: shop floor. Actually, we now launched our King Engineering Agent.

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Speaker 8: What is it.

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Speaker 7: It is an agent which programs an industrial PC for you.

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Speaker 7: So if you have a cutting chop on the shop

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Speaker 7: floor and the cutting is not precise anymore, now an

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Speaker 7: engineer has to go there reprogram your PLC and so on.

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Speaker 7: Iing Engineering agent does it for you. You say, I

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Speaker 7: mean my cutting machine is not not working precisely. I

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Speaker 7: Engineering Agent analyzes the problem, breaks the problem down looks

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Speaker 7: for all the data. The machines the job which has

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Speaker 7: to be done, analyzes it, develops a code, runs it,

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Speaker 7: compiles it. If it doesn't work, it compiles it over again,

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Speaker 7: fixes it until you can say now go and you

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Speaker 7: push a bottom and then PLC is running with the

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Speaker 7: same precition as before. So we are brought king about

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Speaker 7: fifty percent high productivity while the quality of your programming

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Speaker 7: is increasing by eighty percent.

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Speaker 8: So this is real fact.

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Speaker 7: And now we're introduced using another function where electrical design

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Speaker 7: comes also into this into the place. Well, there's a real,

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Speaker 7: a real and this this what's not available in the market.

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Speaker 7: So far we broughder to the market and ready to

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Speaker 7: expand to other use cases.

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Speaker 3: To either by geography or bi sector. Is there a

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Speaker 3: market where there's just more speed here? You know, we

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Speaker 3: reflected on your meeting with European Commission. Notoriously red tape

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Speaker 3: can get in the way. What are you seeing in

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Speaker 3: different markets around the world.

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Speaker 7: I mean the hardest market as we can imagine currently

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Speaker 7: is AI factories and data centers.

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Speaker 8: And here we are really in the design pase together.

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Speaker 8: It's in DDIA.

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Speaker 7: We create a blueprint, a white paper for how an

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Speaker 7: a high factor in the future looks like and guess

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Speaker 7: what it's based on a digital twin. And this digital

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Speaker 7: twin is optimized with AI obviously, But if you talk

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Speaker 7: about other markets, for example, the fast growing markets which

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Speaker 7: really need productivity and speed, semiconductors, a lot of investment

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Speaker 7: going there, a lot of VAMS built, pharmaceutical there's a

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Speaker 7: lot of investment also going to the United States, and

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Speaker 7: aerospace and defense.

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Speaker 8: We are ramping up our.

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Speaker 7: Capabilities to produce stuff. So there are a lot of sectors.

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Speaker 7: And then last part of lease, I mean the car

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Speaker 7: industry is also under pressure. They need to evolve faster,

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Speaker 7: having faster cycles, being more flexible in their production. So

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Speaker 7: I can go on and on, and the topic is

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Speaker 7: always the same. You will be faster in your design

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Speaker 7: process using ANIE, coming from simulation which verifies to simulation

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Speaker 7: which creates new designs, and all the way in the factory,

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Speaker 7: way down to the point that you were deploying more

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Speaker 7: and more robots, and guess what robots are also AI

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Speaker 7: based automation devices.

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Speaker 3: Siemens has an almost one hundred and eighty year history

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Speaker 3: as an industrial company, an engineering company. You know the

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Speaker 3: sense I've had from you Roland in the times we've

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Speaker 3: spoken over the last twelve months and more is you

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Speaker 3: are positioning Semens as a technology company. How will your investments,

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Speaker 3: your attitude towards M and A reflect that. And when

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Speaker 3: you think about the market of today, the volatility we

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Speaker 3: see in the technology sector, do you think that Semens

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Speaker 3: is being treated and valued as a technology company.

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Speaker 7: Well, the short answer to the last point not yet

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Speaker 7: fun We are working working on it.

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Speaker 8: But the ons the point.

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Speaker 7: Over the last some fifteen years or so, we invested

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Speaker 7: thirty billion dollars in building.

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Speaker 8: Up our software suite.

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Speaker 7: It's the most comprehensive digital twin can be built with

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Speaker 7: Semen's technology, and it's a physics based digital twin with

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Speaker 7: a four year realistic representation using in media technologies for example.

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Speaker 8: So and that's unique.

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Speaker 7: All in all, the nine point four billion in twenty

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Speaker 7: twenty five, nine point five four billion euros in digital

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Speaker 7: revenue including software, we are going to double it.

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Speaker 8: Until twenty thirty.

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Speaker 7: So and this is a fast growing, high margin business

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Speaker 7: and it's now even super charged with AI. We rewrite

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Speaker 7: our software so it can be used by engineers and agents,

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Speaker 7: so that means you can see higher growth rates because

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Speaker 7: you can democratize it.

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Speaker 8: The accelerating simulation.

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Speaker 7: And again we are supercharging our operation software on the

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Speaker 7: shop floor. We say AI technology, so yes, and we

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Speaker 7: have that advantage again to combine the really needed world

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Speaker 7: you need both hardware is more important than ever. I

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Speaker 7: think about the data centers built, think about how robotics

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Speaker 7: school is through the market to the shop floor. So

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Speaker 7: therefore this combination is unique and we keep on going investing.

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Speaker 7: It's a transformation. You mentioned it almost surday the years

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Speaker 7: then the fastest transformation or history, and we are on

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Speaker 7: the right track. But it requires again it's super fast adoption,

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Speaker 7: but also of our customers, which we are supporting.

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Speaker 3: Fresh from one of Europe's biggest keynotes, Fresh from meeting

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Speaker 3: with Europe's most important leaders, Roland birsch Semen CEO, back

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Speaker 3: on Bloomberg Tech, thank you very much. Indeed, now coming up,

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Speaker 3: Qualcom is in advanced talks to acquire Modula. More on

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Speaker 3: this multi billion dollar buying spree and AI software the conversations.

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Speaker 2: Next, this is Bloomberg Tech.

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Speaker 9: It's time now for talking tech. I'm youhirah Anand first up,

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Speaker 9: the ongoing debate over AI replacing human workers just got

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Speaker 9: a reality check. Oracle's latest annual filing disclosed that it

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Speaker 9: slashed twenty one thousand jobs over the past year, with

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Speaker 9: AI deployment across operations driving some of those cuts.

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Speaker 2: This is Oracle is.

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Speaker 9: Under financial pressure due to an expensive AI data center

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Speaker 9: build out, plus soft Thanks Masayoshi's son is choosing Earth

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Speaker 9: over Space. Son publicly dismissed Elon Musk's grand vision for

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Speaker 9: orbital data centers, calling the math behind them a losing vet. Instead,

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Speaker 9: he says software will focus on building formidable data center

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Speaker 9: capacity here on Earth, and ten Cents is scaling back

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Speaker 9: its global gaming empire. The Chinese tech Titan is in

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Speaker 9: negotiations to unwind its massive post pandemic buying spree by

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Speaker 9: offloading minority stakes in multiple international gaming studios like Japan's Marvelous,

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Speaker 9: all to free up capital for in another. Then the

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Speaker 9: global AI race ed.

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Speaker 2: Thank you very much to hiring now.

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Speaker 3: Qualcom is making a multi billion dollar play to season

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Speaker 3: technology and talent at the cutting edge of AI, sources

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Speaker 3: tell Bloomberg. The companies in advanced talks to acquire software

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Speaker 3: company Modula for about four billion dollars. Bombos wrangle broke

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Speaker 3: the story with the team and joins us. Let's start

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Speaker 3: with the basics. What do we know about the deal?

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Speaker 10: Yeah, so they're in advanced talks to acquire a Modular

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Speaker 10: for about four billion dollars. It looks like that deal

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Speaker 10: could be announced as soon as the coming weeks. I

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Speaker 10: think this is part very much of this wider AI

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Speaker 10: race four race in AI for inference technology. Basically, that's

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Speaker 10: the process where you go from training models to actually

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Speaker 10: putting them into production. And we've seen i think probably

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Speaker 10: bookended by both the Grock video licensing deal that announts

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Speaker 10: just before Christmas. We all remember that on Christmas Eve,

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Speaker 10: and maybe also the Sam Bernova deal with Intel and

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Speaker 10: Vista Equity Partners, the private equity firm. This is just

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Speaker 10: really an arms race right now to see how quickly

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Speaker 10: we can start to some of these inference costs for enterprise.

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Speaker 10: What do we know about Modular in terms of like

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Speaker 10: who's already on the cat table where they've been valued

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Speaker 10: in their own private market activity. Yeah, I mean there's

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Speaker 10: quite a few famous VC firms in there, really only

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Speaker 10: Google Ventures, General Catalysts. General Catalyst is in there for

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Speaker 10: quite a significant chunk of the money. They were valued

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Speaker 10: at just over a billion dollars last September, I believe,

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Speaker 10: so this would kind of be a fairly quick turnaround

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Speaker 10: for those guys involved there. And I think this kind

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Speaker 10: of also gets at something else, which is we've been

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Speaker 10: hearing the callcom has been looking at companies like ten

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Speaker 10: Turrent again another player that sort of sits in and

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Speaker 10: around this influence AI sphere. Modular itself though, I mean,

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Speaker 10: really is that software layer. It's putting those hardware chips

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Speaker 10: and making them accessible to cross all different models. So

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Speaker 10: that's on you know, Intel and Video and then in

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Speaker 10: this case, soon to be Qualcom.

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Speaker 2: Should this saw come to plan.

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Speaker 3: Bloomberg Deals is right and good, Thank you very much.

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Speaker 3: Indeena coming up, Venki Gannism from Menlo Ventures joins us

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Speaker 3: talking about the firm's largest fundraise in its history and

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Speaker 3: how backing AI startups is forcing, frankly, a shift in strategy.

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Speaker 2: A really big and.

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Speaker 3: Extended conversation coming up alongside Bloomberg's Natasha Mascarenis. It's halftime

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Speaker 3: here in San Francisco. This is what markets look like.

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Speaker 3: We're off session lows, but technology is selling off. This

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Speaker 3: is Bloomberg Tech.

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Speaker 2: Welcome back.

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Speaker 3: To Bloomberg Tech Tuesday, June twenty third, there's a lot

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Speaker 3: going on our top stories, the AI selloff and SpaceX's

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Speaker 3: bumpy ride. Tech Equities reported Carmen Rhinikey standing by in

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Speaker 3: New York with all of it common ed.

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Speaker 11: Yeah, so we're seeing a lot of red on the

441
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Speaker 11: screen here today. The nasdac's down almost three percent. The

442
00:23:20,480 --> 00:23:23,520
Speaker 11: biggest laggards here are memory names, so we know this

443
00:23:23,680 --> 00:23:26,359
Speaker 11: was kicked off in South Korea when a local media

444
00:23:26,440 --> 00:23:28,679
Speaker 11: reports so that s k Heinex may be sort of

445
00:23:28,680 --> 00:23:31,000
Speaker 11: pulling back on its AI chips, maybe shifting to d

446
00:23:31,200 --> 00:23:33,679
Speaker 11: ram a little bit. That sent the market's tumbling, and

447
00:23:33,720 --> 00:23:38,080
Speaker 11: it's sort of it sort of gave some pause to

448
00:23:38,119 --> 00:23:39,760
Speaker 11: the AI trade that you've seen. So we can see

449
00:23:39,800 --> 00:23:44,040
Speaker 11: the biggest laggards here sand Discorp, Micron Technology, and Lamb.

450
00:23:44,080 --> 00:23:46,280
Speaker 11: So these are all memory names that are dragging that

451
00:23:46,440 --> 00:23:49,800
Speaker 11: market lower. Let's also take a look at SpaceX today,

452
00:23:50,000 --> 00:23:52,440
Speaker 11: so Sharre is actually seeing a little bit of dip buying.

453
00:23:52,520 --> 00:23:56,159
Speaker 11: Perhaps yesterday we saw a pretty concentrated sell off the

454
00:23:56,240 --> 00:23:58,840
Speaker 11: third day of declines that wiped more than four hundred

455
00:23:58,880 --> 00:24:01,480
Speaker 11: billion dollars in market value from the stock. That was

456
00:24:01,520 --> 00:24:05,840
Speaker 11: the second largest one day erasure of market cap in history,

457
00:24:05,840 --> 00:24:08,800
Speaker 11: following only in Nvidia, and brought the three data client

458
00:24:08,840 --> 00:24:11,360
Speaker 11: of more than six hundred billion dollars in values. So

459
00:24:11,760 --> 00:24:13,919
Speaker 11: seeing more green on the screen here Today, shares up

460
00:24:13,960 --> 00:24:16,280
Speaker 11: about two percent just off session highs.

461
00:24:16,520 --> 00:24:17,960
Speaker 2: Few key levels will be watching.

462
00:24:18,080 --> 00:24:20,920
Speaker 11: One is that one hundred and fifty dollars line that

463
00:24:21,080 --> 00:24:24,120
Speaker 11: was where the stock opened at its trading debut. And

464
00:24:24,320 --> 00:24:25,920
Speaker 11: the other one that we'll be looking at is a

465
00:24:25,960 --> 00:24:28,760
Speaker 11: two trillion dollar valuation. We're just sort of flirting with

466
00:24:28,800 --> 00:24:29,879
Speaker 11: that level right now.

467
00:24:30,160 --> 00:24:33,080
Speaker 3: Back to you ed the most common rhyanikey on what's

468
00:24:33,080 --> 00:24:34,520
Speaker 3: going on in tech in public markets.

469
00:24:34,600 --> 00:24:36,000
Speaker 2: Let's get to private markets.

470
00:24:36,320 --> 00:24:39,159
Speaker 3: Some of the big venture winners from the SPACEXIPO are

471
00:24:39,240 --> 00:24:42,680
Speaker 3: cashing in by raising new funds that includes Valor Equity

472
00:24:42,720 --> 00:24:46,320
Speaker 3: Partners founded by longtime MOSC ali Antonio Gressias. According to

473
00:24:46,400 --> 00:24:49,440
Speaker 3: sources values, Fund seven will have about two point five

474
00:24:49,440 --> 00:24:54,359
Speaker 3: billion dollars to invest in startups and more SpaceX shares bloom.

475
00:24:54,359 --> 00:24:58,000
Speaker 3: Most common Arroyo joins us with the details. Let's start

476
00:24:58,000 --> 00:25:00,520
Speaker 3: with the SpaceX part I find that so interesting. You know,

477
00:25:00,600 --> 00:25:03,560
Speaker 3: one of the big winners from the biggest Iphei in history,

478
00:25:03,600 --> 00:25:06,159
Speaker 3: we went over that. But part of the strategy of

479
00:25:06,200 --> 00:25:09,280
Speaker 3: new funds is to put them back into Elon Musk company.

480
00:25:10,160 --> 00:25:13,359
Speaker 12: So there Valor is now out there racing two point

481
00:25:13,359 --> 00:25:16,440
Speaker 12: five billion for this fund. But the quirk with it

482
00:25:16,480 --> 00:25:18,760
Speaker 12: is that the fund has already allocated a portion of

483
00:25:18,800 --> 00:25:21,679
Speaker 12: the funds in two SpaceX shares, which is kind of

484
00:25:21,680 --> 00:25:26,479
Speaker 12: interesting because SpaceX is already trading. But the Valor has

485
00:25:26,520 --> 00:25:29,000
Speaker 12: been fund raising for the past few months. It's not

486
00:25:29,480 --> 00:25:32,640
Speaker 12: happening like it's been happening since like late last year.

487
00:25:34,320 --> 00:25:37,360
Speaker 3: Where does two point five billions sit in the scope

488
00:25:37,400 --> 00:25:42,560
Speaker 3: of Valor's broader assets that it manages, Like this is

489
00:25:42,600 --> 00:25:46,240
Speaker 3: a big firm, right and particularly remind us the return

490
00:25:46,359 --> 00:25:48,760
Speaker 3: or the payoff of the value of their stake in

491
00:25:48,840 --> 00:25:49,960
Speaker 3: SpaceX post flow.

492
00:25:50,520 --> 00:25:53,240
Speaker 12: Right. Yeah, Valor is like said to have a massive

493
00:25:53,240 --> 00:25:56,160
Speaker 12: one fall of like the four percent steak and SpaceX,

494
00:25:56,880 --> 00:25:59,760
Speaker 12: But there are mainly an operational firm right late like

495
00:25:59,760 --> 00:26:02,240
Speaker 12: two like lend resources to the company to the investing,

496
00:26:03,359 --> 00:26:08,600
Speaker 12: so they have like very targeted investments. So they've they've

497
00:26:08,640 --> 00:26:10,879
Speaker 12: been in all of a lot of like the Elon

498
00:26:10,960 --> 00:26:13,720
Speaker 12: Musk's centures, but they also have a lot of consumer

499
00:26:14,000 --> 00:26:17,560
Speaker 12: companies as well that they're invested in. But I think

500
00:26:17,640 --> 00:26:20,560
Speaker 12: it like it kind of shows how much demand there

501
00:26:20,600 --> 00:26:23,359
Speaker 12: is right now for kind of like being part of

502
00:26:23,359 --> 00:26:23,920
Speaker 12: the winners.

503
00:26:25,280 --> 00:26:28,479
Speaker 3: Bloombo's Carmen Roya, thank you very much. Just take a

504
00:26:28,480 --> 00:26:32,119
Speaker 3: look at today's big number. Fifty billion dollars. That's how

505
00:26:32,160 --> 00:26:35,480
Speaker 3: much Apple Dabi's MGX has raised from regional and global

506
00:26:35,480 --> 00:26:39,960
Speaker 3: investors to accelerate spending on AI infrastructure and technology. That's

507
00:26:39,960 --> 00:26:42,320
Speaker 3: according to sources, who also say the firm has already

508
00:26:42,320 --> 00:26:45,880
Speaker 3: deployed capital from the new fund, which closed in recent weeks,

509
00:26:46,080 --> 00:26:49,639
Speaker 3: and that it ranks among the biggest ever dedicated AI

510
00:26:49,800 --> 00:26:54,600
Speaker 3: investment vehicles. More on venure capital and private Market's a

511
00:26:54,640 --> 00:26:58,040
Speaker 3: little closer to home, Menlo Ventures has raised three billion

512
00:26:58,119 --> 00:27:01,199
Speaker 3: dollars for new AI investments, the biggest fund raise in

513
00:27:01,240 --> 00:27:03,879
Speaker 3: the firm's fifty year history. The move reflects just how

514
00:27:03,960 --> 00:27:08,360
Speaker 3: dramatically venture capital is changing as investors' race to back

515
00:27:08,400 --> 00:27:12,240
Speaker 3: the next generation of AI winners. Bloomberg's Bench Capital reporter

516
00:27:12,320 --> 00:27:15,520
Speaker 3: Nasha Natasha Mascarinus broke this story, and she joins us

517
00:27:15,520 --> 00:27:19,119
Speaker 3: alongside Melaventure's partner. Thank you Againissan, and here we are

518
00:27:19,160 --> 00:27:21,680
Speaker 3: in San Francisco. It started with a really basic question,

519
00:27:22,520 --> 00:27:24,800
Speaker 3: thank you, why why raise the three billion?

520
00:27:25,359 --> 00:27:27,840
Speaker 13: First of all, Ed Natasha, thank you so much for

521
00:27:27,880 --> 00:27:31,480
Speaker 13: having us here. It's a privilege to enjoy the special

522
00:27:31,520 --> 00:27:35,000
Speaker 13: moment with you all. Fifty year history. I think this

523
00:27:35,119 --> 00:27:38,359
Speaker 13: moment to me really reminds me of the Renaissance. Like

524
00:27:38,440 --> 00:27:40,320
Speaker 13: I think when I look at the history of mankind

525
00:27:40,640 --> 00:27:43,040
Speaker 13: and look at what happened in the Renaissance of the fourteen hundreds,

526
00:27:43,320 --> 00:27:45,960
Speaker 13: AI is that moment where we actually have a new

527
00:27:45,960 --> 00:27:50,040
Speaker 13: technology coming in, we have this incredible density of talent.

528
00:27:50,880 --> 00:27:53,199
Speaker 13: It was in Florence and the Renaissance, it's in San

529
00:27:53,240 --> 00:27:56,960
Speaker 13: Francisco with AI, and you have these patrons and the

530
00:27:57,040 --> 00:28:03,000
Speaker 13: medicis funding these incredible whether that's Da Vinci Donatello and

531
00:28:03,240 --> 00:28:06,240
Speaker 13: the equal of that would be Dario and Dennis. And

532
00:28:06,280 --> 00:28:07,960
Speaker 13: so I think like that moment is here.

533
00:28:08,200 --> 00:28:10,720
Speaker 2: This is a new thesis to this show and going.

534
00:28:10,760 --> 00:28:14,359
Speaker 13: And to me, the our fifty year history meant that

535
00:28:14,400 --> 00:28:16,879
Speaker 13: we have had this history of finding these moments, and

536
00:28:16,920 --> 00:28:18,800
Speaker 13: for Menlo, it's a special moment to be able to

537
00:28:18,800 --> 00:28:23,040
Speaker 13: have that capital to fund these increbl entrepreneurs. There is

538
00:28:23,080 --> 00:28:26,440
Speaker 13: an amazing eighteen year old woman out there who's thinking

539
00:28:26,440 --> 00:28:29,280
Speaker 13: about starting a company and want her to know that

540
00:28:29,359 --> 00:28:31,040
Speaker 13: we are ready with our capital defund.

541
00:28:30,760 --> 00:28:33,440
Speaker 14: The speaking of history, I mean, this is not the

542
00:28:33,480 --> 00:28:36,640
Speaker 14: first time we've seen Menlo Ventures raise record funds.

543
00:28:36,680 --> 00:28:37,920
Speaker 2: I know in the past.

544
00:28:38,200 --> 00:28:40,600
Speaker 14: In two thousand and five, Menlo raised one point two

545
00:28:40,680 --> 00:28:43,360
Speaker 14: billion dollar fund, you know, eventually to cut it back

546
00:28:43,400 --> 00:28:46,520
Speaker 14: down to four hundred million to get back to the roots.

547
00:28:46,880 --> 00:28:48,240
Speaker 2: So what's different this time?

548
00:28:48,480 --> 00:28:52,360
Speaker 14: You know, what defends the choice to grow the fund size?

549
00:28:52,520 --> 00:28:54,600
Speaker 14: Really also talk to me a little bit about strategy

550
00:28:55,200 --> 00:28:57,960
Speaker 14: and sort of how you plan to invest such a

551
00:28:57,960 --> 00:28:58,840
Speaker 14: big amount of capital.

552
00:28:59,160 --> 00:29:00,000
Speaker 2: Great questions today.

553
00:29:00,480 --> 00:29:02,440
Speaker 13: I mean to me, it's all about making sure we're

554
00:29:02,480 --> 00:29:05,560
Speaker 13: the right capital for the right moment. So I believe

555
00:29:05,600 --> 00:29:08,680
Speaker 13: that we have a Goldilock strategy. We are big enough

556
00:29:08,760 --> 00:29:10,800
Speaker 13: to be able to fund all the companies that matter,

557
00:29:11,240 --> 00:29:13,440
Speaker 13: yet small enough to drive venture rates of return.

558
00:29:13,800 --> 00:29:15,120
Speaker 2: And that's really been our focus.

559
00:29:15,200 --> 00:29:18,560
Speaker 13: We want to be driven by scarcity, so our strategy

560
00:29:18,600 --> 00:29:21,880
Speaker 13: fits that, and we want to find the companies that

561
00:29:21,960 --> 00:29:24,400
Speaker 13: really madden and then concentrate on them.

562
00:29:24,480 --> 00:29:27,360
Speaker 3: Is this what you call avoiding the middle? The venture

563
00:29:27,360 --> 00:29:28,440
Speaker 3: capitals will middle.

564
00:29:28,280 --> 00:29:30,760
Speaker 13: Exactly to us, We want to be in the barbell.

565
00:29:30,920 --> 00:29:33,160
Speaker 13: We want to be in the earliest stage where we

566
00:29:33,160 --> 00:29:36,920
Speaker 13: can find amazing entrepreneurs pre product market fit, but get

567
00:29:37,000 --> 00:29:40,800
Speaker 13: high ownership, and then we want to back the clear

568
00:29:40,840 --> 00:29:44,160
Speaker 13: winners like we have done with an anthropic, a lovable

569
00:29:44,320 --> 00:29:48,080
Speaker 13: and open evidence companies that are scaling and making a difference.

570
00:29:48,360 --> 00:29:51,960
Speaker 14: How much does experimentation show up in check writing these days?

571
00:29:52,000 --> 00:29:54,880
Speaker 14: I know ed and I've spoken a lot about coconut seed.

572
00:29:55,000 --> 00:29:56,640
Speaker 2: Yeah, what's the point of a seed round?

573
00:29:56,720 --> 00:29:59,920
Speaker 14: Essentially exactly exactly, Like, how do you think about that?

574
00:30:00,080 --> 00:30:02,120
Speaker 13: Yeah, actually, it's funny you mentioned it. We actually came

575
00:30:02,200 --> 00:30:04,760
Speaker 13: up with the new STREETE siege strategy where we wanted

576
00:30:04,760 --> 00:30:06,840
Speaker 13: to be nimble, so we set aside a small group

577
00:30:06,880 --> 00:30:10,680
Speaker 13: of dedicated partners, people like Tim Talley, the CEOs Flank

578
00:30:11,120 --> 00:30:14,200
Speaker 13: joff Redfern, the chief product officer of a Classian Matt

579
00:30:14,240 --> 00:30:18,040
Speaker 13: Greening ditdas very dedicated group of people and had a

580
00:30:18,040 --> 00:30:21,360
Speaker 13: different focus. We could actually make an investment with a

581
00:30:21,440 --> 00:30:23,960
Speaker 13: smaller group of people without going to investment committee. So

582
00:30:24,000 --> 00:30:26,040
Speaker 13: I think experimentation has been a very core part of

583
00:30:26,040 --> 00:30:29,240
Speaker 13: our fifty year history. We've always wanted to make sure

584
00:30:29,280 --> 00:30:32,040
Speaker 13: that we are changing with the times, right, the most

585
00:30:32,080 --> 00:30:34,000
Speaker 13: important things we've got to change with the times. We

586
00:30:34,200 --> 00:30:37,280
Speaker 13: also experimented by funding neo labs, and so that's also

587
00:30:37,320 --> 00:30:38,360
Speaker 13: been an area of focus for.

588
00:30:38,360 --> 00:30:43,840
Speaker 3: US funding neolabs, giving founders access to computes, moving away

589
00:30:43,880 --> 00:30:47,960
Speaker 3: from certain areas biology for example, or not moving away

590
00:30:48,280 --> 00:30:51,200
Speaker 3: pairing back to focus unpack all of that.

591
00:30:51,600 --> 00:30:54,040
Speaker 13: Yeah, I think when I think about it, you probably

592
00:30:54,120 --> 00:30:57,600
Speaker 13: moved away more from like fintech and SaaS and more.

593
00:30:57,920 --> 00:30:59,400
Speaker 2: AI centric areas.

594
00:30:59,520 --> 00:31:02,440
Speaker 13: Right to me, biology could be one of those areas

595
00:31:02,440 --> 00:31:05,400
Speaker 13: in which AI makes a huge impact. I mean, look,

596
00:31:05,720 --> 00:31:08,560
Speaker 13: the most important things we can do are to help

597
00:31:09,320 --> 00:31:12,520
Speaker 13: lengthen human lifetime and improve the quality of life. I

598
00:31:12,600 --> 00:31:14,400
Speaker 13: think AI is going to make a big impact in

599
00:31:14,480 --> 00:31:16,480
Speaker 13: drug discovery and it's going to have one of the

600
00:31:16,520 --> 00:31:18,000
Speaker 13: most positive impacts on healthcare.

601
00:31:18,760 --> 00:31:20,560
Speaker 14: You know, you said something to me during our interview,

602
00:31:20,600 --> 00:31:22,720
Speaker 14: but this didn't make the story, but you said, you know,

603
00:31:22,760 --> 00:31:24,720
Speaker 14: in order to win and venture to be Contrarriyan, you

604
00:31:24,720 --> 00:31:27,600
Speaker 14: have to be right and you have to be alone

605
00:31:27,760 --> 00:31:30,000
Speaker 14: where an AI is menlo right and alone.

606
00:31:30,120 --> 00:31:32,840
Speaker 13: Right now, I'll tell you we were right and alone

607
00:31:32,880 --> 00:31:35,280
Speaker 13: in twenty twenty three when we led that round in

608
00:31:35,320 --> 00:31:39,160
Speaker 13: anthropic kudos to my partner Matt, who saw this opportunity.

609
00:31:39,640 --> 00:31:42,400
Speaker 13: Matt Tim They saw that this was going to be

610
00:31:42,440 --> 00:31:45,240
Speaker 13: a special company. If you remember that time everybody talked

611
00:31:45,240 --> 00:31:48,080
Speaker 13: about open Ai and Microsoft, they said the game was over.

612
00:31:48,160 --> 00:31:49,360
Speaker 13: Open Ai as a great winner.

613
00:31:49,480 --> 00:31:50,880
Speaker 2: Look, open Air is a great company.

614
00:31:51,160 --> 00:31:53,800
Speaker 13: But we had the conviction to put five hundred million

615
00:31:54,240 --> 00:31:56,520
Speaker 13: in an eighteen billion dollar round, and I think a

616
00:31:56,560 --> 00:31:58,480
Speaker 13: lot of people didn't want to. And that's what I

617
00:31:58,520 --> 00:32:01,120
Speaker 13: mean is you've got to be contrary in right. It's

618
00:32:01,160 --> 00:32:03,240
Speaker 13: not the first time we did that at Siri. We

619
00:32:03,320 --> 00:32:05,320
Speaker 13: did that with Uber, we did that with Vocal.

620
00:32:06,200 --> 00:32:07,680
Speaker 2: The state of play is really different.

621
00:32:07,680 --> 00:32:10,600
Speaker 3: Now we're going to talk a bit more about the field. Yeah,

622
00:32:10,840 --> 00:32:13,160
Speaker 3: you know, after we take a quick break. Something you

623
00:32:13,200 --> 00:32:15,480
Speaker 3: said about the eighteen year old, maybe it's not an

624
00:32:15,520 --> 00:32:19,000
Speaker 3: eighteen year old. But what we're seeing is rounds where

625
00:32:19,800 --> 00:32:23,080
Speaker 3: there are maybe a few founders, alumni of existing frontier

626
00:32:23,120 --> 00:32:26,600
Speaker 3: labs or academics. They're raising hundreds of millions of dollars

627
00:32:26,840 --> 00:32:30,680
Speaker 3: from the get go, and the venture firm seems to

628
00:32:30,720 --> 00:32:33,920
Speaker 3: be making a bet on those people their expertise and

629
00:32:33,960 --> 00:32:35,560
Speaker 3: their credentials in the field.

630
00:32:36,080 --> 00:32:37,440
Speaker 2: Is that where you expect this to go.

631
00:32:38,120 --> 00:32:40,960
Speaker 13: Yeah, I mean, to me, it's more about making sure

632
00:32:41,080 --> 00:32:43,960
Speaker 13: it's not about eighteen or eighty. It's about finding the

633
00:32:44,040 --> 00:32:46,960
Speaker 13: right person. I think what is different about this time

634
00:32:47,160 --> 00:32:49,520
Speaker 13: is that a lot of the right people are researchers,

635
00:32:49,640 --> 00:32:52,160
Speaker 13: which is different from what traditionally has been the focus.

636
00:32:52,640 --> 00:32:54,640
Speaker 13: And so the thing we spent a lot of time

637
00:32:54,640 --> 00:32:56,920
Speaker 13: at Menlo is thinking about how do we identify these people,

638
00:32:57,400 --> 00:32:59,640
Speaker 13: what are the characteristics that make this We've used a

639
00:32:59,640 --> 00:33:02,800
Speaker 13: lot of and AI internally, so I say we are

640
00:33:02,800 --> 00:33:04,600
Speaker 13: not all in on AI. We also all in on

641
00:33:04,680 --> 00:33:07,360
Speaker 13: AI internally to how we build the firm, and we

642
00:33:07,440 --> 00:33:09,520
Speaker 13: use that to identify the people and then when you

643
00:33:09,600 --> 00:33:11,520
Speaker 13: find them, the act with conviction.

644
00:33:13,000 --> 00:33:16,959
Speaker 14: I talked to the CEO of Axiom for my story today,

645
00:33:17,000 --> 00:33:18,160
Speaker 14: who's building.

646
00:33:17,840 --> 00:33:20,680
Speaker 13: A twenty one year old math genius?

647
00:33:20,800 --> 00:33:21,040
Speaker 4: Yeah?

648
00:33:21,080 --> 00:33:21,360
Speaker 8: I mean?

649
00:33:21,560 --> 00:33:23,920
Speaker 14: And something she said that was interesting was that, you know,

650
00:33:23,960 --> 00:33:27,720
Speaker 14: having technical debates with your vcs is actually a hugely

651
00:33:28,160 --> 00:33:29,960
Speaker 14: valuable ad in this moment. A lot of people invest

652
00:33:29,960 --> 00:33:31,720
Speaker 14: in AI, but a lot of people can't have those debates,

653
00:33:31,720 --> 00:33:33,520
Speaker 14: like talk to me about how you are, how you

654
00:33:33,520 --> 00:33:35,479
Speaker 14: are you personally are staying up to date on AI,

655
00:33:35,840 --> 00:33:37,960
Speaker 14: and how you're getting competitive intelligence right now? Where are

656
00:33:38,000 --> 00:33:40,760
Speaker 14: you finding? You know, these founders between eighteen and eighty.

657
00:33:41,160 --> 00:33:43,560
Speaker 13: Yeah, I mean to me, first thing is to be

658
00:33:43,640 --> 00:33:44,800
Speaker 13: able to do that, you've got to have the right

659
00:33:44,800 --> 00:33:46,840
Speaker 13: technical people in the building. And so we spend the

660
00:33:46,880 --> 00:33:49,600
Speaker 13: last four years making sure that we built the right

661
00:33:49,640 --> 00:33:52,080
Speaker 13: technical team. We talked a little bit about Tim our

662
00:33:52,360 --> 00:33:57,440
Speaker 13: co founder of splunk DD Matt Craning, jaff Redfern, and

663
00:33:57,480 --> 00:33:57,760
Speaker 13: then we.

664
00:33:57,800 --> 00:33:59,240
Speaker 2: Got to use the technology ourselves.

665
00:33:59,240 --> 00:34:00,720
Speaker 13: One of the things we did, and I think you

666
00:34:00,760 --> 00:34:03,520
Speaker 13: mentioned this in your story, we put microphones in a

667
00:34:03,560 --> 00:34:05,280
Speaker 13: lot of this, or the use of a new product

668
00:34:05,280 --> 00:34:08,400
Speaker 13: called Whisper, another founder that you're familiar with. And so

669
00:34:08,440 --> 00:34:10,640
Speaker 13: the idea is we got to use the technology. We

670
00:34:10,760 --> 00:34:13,440
Speaker 13: got to eat, drink our own champagne, as I like

671
00:34:13,520 --> 00:34:17,000
Speaker 13: to say, and then make sure that we are there

672
00:34:17,040 --> 00:34:18,919
Speaker 13: ready to meet the founders where they are.

673
00:34:19,239 --> 00:34:22,520
Speaker 3: Okay, Bloombergs and Sash Mascarinus and Meno Ventures is Vinky again,

674
00:34:22,560 --> 00:34:24,120
Speaker 3: I get to stay with us. We're going to take

675
00:34:24,160 --> 00:34:26,000
Speaker 3: a quick break and then we're going to get back

676
00:34:26,280 --> 00:34:30,879
Speaker 3: to the competition in the field of frontier labs valuations

677
00:34:31,239 --> 00:34:33,960
Speaker 3: and muscling in in a world where the mutual funds,

678
00:34:34,080 --> 00:34:36,480
Speaker 3: the strategics in everyone wants.

679
00:34:36,320 --> 00:34:38,160
Speaker 2: A piece of that pie. Stay with us. This is

680
00:34:38,200 --> 00:34:38,879
Speaker 2: Bloomberg Tech.

681
00:34:48,760 --> 00:34:52,480
Speaker 3: Meno Ventures caused its early investment in Anthropic a bet

682
00:34:52,560 --> 00:34:54,280
Speaker 3: the firm moment at the time.

683
00:34:55,080 --> 00:34:56,719
Speaker 2: All the way back.

684
00:34:56,880 --> 00:35:00,080
Speaker 3: In twenty twenty three, twenty four, Anthropic was viewed a

685
00:35:00,320 --> 00:35:03,600
Speaker 3: distant challenger to open AI. Today that wager stands as

686
00:35:03,600 --> 00:35:06,719
Speaker 3: one of the most consequential investments of the AI era.

687
00:35:06,840 --> 00:35:08,840
Speaker 2: But the field and world is different.

688
00:35:08,880 --> 00:35:11,640
Speaker 3: Bloomberg's VC report and attachment Maskrain is still with us

689
00:35:11,800 --> 00:35:15,040
Speaker 3: and there's been reporting on that along with Menlo Ventures partner.

690
00:35:15,560 --> 00:35:17,920
Speaker 2: Thank you Ganison. I say the field is different.

691
00:35:19,360 --> 00:35:21,719
Speaker 3: When you did that Anthropic round and in the last

692
00:35:21,719 --> 00:35:25,520
Speaker 3: block we talked about how consequential that was, everything happened

693
00:35:25,560 --> 00:35:32,080
Speaker 3: very quickly thereafter, many frontier labs raised money regularly, and

694
00:35:32,200 --> 00:35:36,560
Speaker 3: those joining the cap table were different. They weren't venture firms,

695
00:35:36,640 --> 00:35:43,080
Speaker 3: they were mutual funds, legacy Wall Street institutions, hyperscalers, other strategics.

696
00:35:43,880 --> 00:35:48,000
Speaker 3: How does that change impact affirm like Menlo.

697
00:35:48,640 --> 00:35:50,640
Speaker 13: It changes because it brings in a whole set of

698
00:35:50,719 --> 00:35:53,560
Speaker 13: new players. But I think they bring good perspective from

699
00:35:53,640 --> 00:35:56,719
Speaker 13: up We look at this as an opportunity to collaborate

700
00:35:57,000 --> 00:35:59,560
Speaker 13: and find people who again, look, when you have a

701
00:35:59,560 --> 00:36:03,120
Speaker 13: gold run, everybody rushes in. And I think it takes

702
00:36:03,160 --> 00:36:05,960
Speaker 13: a lot of capital to build these companies, and these

703
00:36:06,000 --> 00:36:09,080
Speaker 13: companies are these sorts as special. You spoke to Karina

704
00:36:09,120 --> 00:36:11,520
Speaker 13: of Actual Math. She has raised now o one hundred

705
00:36:11,520 --> 00:36:14,080
Speaker 13: and fifty million, And I think the way to make

706
00:36:14,560 --> 00:36:16,320
Speaker 13: you know, they say it takes a relay.

707
00:36:16,160 --> 00:36:16,759
Speaker 2: To raise a child.

708
00:36:17,600 --> 00:36:19,920
Speaker 13: It almost takes a country to raise one of these

709
00:36:19,960 --> 00:36:22,799
Speaker 13: AI companies. That's the amount of capital. And I think

710
00:36:22,880 --> 00:36:25,280
Speaker 13: we welcome the chance to collaborate some of these folks

711
00:36:25,280 --> 00:36:26,360
Speaker 13: and they've been great partners.

712
00:36:27,040 --> 00:36:30,479
Speaker 14: Bring me back into that moment of deciding to bet

713
00:36:30,520 --> 00:36:33,760
Speaker 14: the firm really on anthropic. I mean, my reporting says

714
00:36:33,800 --> 00:36:37,160
Speaker 14: that Menlo has put around one billion into the company,

715
00:36:37,200 --> 00:36:40,440
Speaker 14: a stake that is worth nearly fourteen billion. That's according

716
00:36:40,440 --> 00:36:43,439
Speaker 14: to sources familiar with the matter. I mean, that kind

717
00:36:43,480 --> 00:36:46,879
Speaker 14: of return is exciting. It's also the kind of thing

718
00:36:46,960 --> 00:36:49,080
Speaker 14: that I'm guessing you guys are looking to replicate. So

719
00:36:49,680 --> 00:36:51,160
Speaker 14: you know, what's the takeaway there and what are you

720
00:36:51,200 --> 00:36:52,719
Speaker 14: looking you know, how are you changing the way that

721
00:36:52,800 --> 00:36:53,719
Speaker 14: you write checks?

722
00:36:54,480 --> 00:36:57,880
Speaker 13: I think the formula is simple, right, And this has

723
00:36:57,880 --> 00:37:00,560
Speaker 13: been the same formula that's worked for a last of Tears,

724
00:37:00,600 --> 00:37:03,800
Speaker 13: which is go deep into an area, build a thesis,

725
00:37:04,480 --> 00:37:08,760
Speaker 13: find the best company that you can commit to that founder,

726
00:37:09,239 --> 00:37:12,000
Speaker 13: and then partner with them to build with conviction. And

727
00:37:12,120 --> 00:37:14,640
Speaker 13: every one of those things happened with Anthropic in the

728
00:37:14,680 --> 00:37:17,560
Speaker 13: sense that we look at the entire AI landscape, we

729
00:37:17,719 --> 00:37:20,480
Speaker 13: zeroed in on this very very special founding team of

730
00:37:20,560 --> 00:37:24,080
Speaker 13: Daniella and Dario and the six other founders, and then

731
00:37:24,080 --> 00:37:26,520
Speaker 13: my partner Matt said, we got to lean with conviction.

732
00:37:26,840 --> 00:37:30,280
Speaker 13: We all rallied around it, and we raised the biggest

733
00:37:30,600 --> 00:37:33,279
Speaker 13: capital we ever have, and then we have committed to

734
00:37:33,280 --> 00:37:35,680
Speaker 13: build the company with them. And to me, that's the

735
00:37:35,680 --> 00:37:38,440
Speaker 13: same formula we did with Uber, we did with Roku,

736
00:37:38,480 --> 00:37:40,680
Speaker 13: we did a chime, and I think the same formula

737
00:37:40,760 --> 00:37:44,520
Speaker 13: will do in the future. Really selective. We will find

738
00:37:44,719 --> 00:37:47,880
Speaker 13: we have a Goldilocks strategy to be the right size,

739
00:37:48,160 --> 00:37:50,719
Speaker 13: and then we find the right company, we will lead

740
00:37:50,760 --> 00:37:52,879
Speaker 13: with conviction and concentrate on them.

741
00:37:53,880 --> 00:37:56,239
Speaker 14: What do you make of the king making, you know,

742
00:37:56,440 --> 00:37:59,200
Speaker 14: in a queen making strategy that's across the Logan Valley,

743
00:37:59,239 --> 00:38:02,000
Speaker 14: the idea of sort of anointing a winner early on

744
00:38:02,160 --> 00:38:04,120
Speaker 14: by putting a lot of capital into them. That is

745
00:38:04,160 --> 00:38:07,839
Speaker 14: quite different than the anthropic bet that you guys made.

746
00:38:07,920 --> 00:38:11,080
Speaker 14: It was definitely you know, pre revenue and pre household.

747
00:38:11,160 --> 00:38:13,600
Speaker 2: Name well he did cool Daria, the Da Vinci of

748
00:38:13,680 --> 00:38:17,520
Speaker 2: this age. So I don't sorry, no, but yeah, I'm curious.

749
00:38:17,560 --> 00:38:20,080
Speaker 14: I guess the idea of you know, trying to find

750
00:38:20,080 --> 00:38:22,960
Speaker 14: that company not when it's you know, a multi billion valuation,

751
00:38:23,200 --> 00:38:25,400
Speaker 14: but when it's at a fifty million valuation and helping

752
00:38:25,440 --> 00:38:27,720
Speaker 14: it hit that that that those usual numbers.

753
00:38:27,760 --> 00:38:29,360
Speaker 13: So first of all, I love the fact that you

754
00:38:29,400 --> 00:38:30,280
Speaker 13: said queen making.

755
00:38:30,920 --> 00:38:32,000
Speaker 2: I'm a family.

756
00:38:32,000 --> 00:38:34,239
Speaker 13: We have three daughters and my wife is definitely the

757
00:38:34,280 --> 00:38:37,360
Speaker 13: queen of a house. So I like that. And Andiel

758
00:38:37,640 --> 00:38:40,040
Speaker 13: to us, you're right. The way I think we think

759
00:38:40,080 --> 00:38:42,800
Speaker 13: about queen making is to really find those companies early,

760
00:38:42,960 --> 00:38:45,000
Speaker 13: and we have found plenty of those. So while we

761
00:38:45,120 --> 00:38:48,080
Speaker 13: love Anthropic, it's an amazing company. I literally talked to

762
00:38:48,120 --> 00:38:51,360
Speaker 13: my investors about the ten Ai companies not named the

763
00:38:51,360 --> 00:38:54,799
Speaker 13: Anthropic be excited about a company like axiomth where we

764
00:38:54,880 --> 00:38:58,680
Speaker 13: found Karina invested in her very early on open evidence,

765
00:38:58,719 --> 00:39:02,080
Speaker 13: open evidence, which I know you have a connection with

766
00:39:03,480 --> 00:39:06,360
Speaker 13: are lovable. These are all companies they have found early.

767
00:39:06,440 --> 00:39:10,759
Speaker 13: That's really been Melo's focus. Find them early, partner with them,

768
00:39:11,239 --> 00:39:14,040
Speaker 13: size up with conviction, and then build.

769
00:39:15,239 --> 00:39:17,799
Speaker 3: I don't want to put you in a position where

770
00:39:17,800 --> 00:39:20,880
Speaker 3: you can't speak freely. And I get that you know anthropics,

771
00:39:20,880 --> 00:39:24,040
Speaker 3: file confidentially, et cetera. But it seems like there are

772
00:39:24,040 --> 00:39:26,080
Speaker 3: two stories that are both true. At the same time,

773
00:39:26,560 --> 00:39:30,120
Speaker 3: companies are staying private for longer, and yet we are

774
00:39:30,239 --> 00:39:35,400
Speaker 3: on the cusp of a very big I PO window.

775
00:39:35,480 --> 00:39:39,319
Speaker 3: How do you interpret that moment? And also like the

776
00:39:39,360 --> 00:39:43,120
Speaker 3: thing is that it's not just that private companies are

777
00:39:43,120 --> 00:39:46,440
Speaker 3: saying private longer, that the private market seem very willing

778
00:39:46,520 --> 00:39:50,040
Speaker 3: to support that duration and the level of funding that

779
00:39:50,080 --> 00:39:50,520
Speaker 3: they need.

780
00:39:51,920 --> 00:39:54,359
Speaker 13: It is very much true that the private markets are

781
00:39:54,400 --> 00:39:56,440
Speaker 13: probably the biggest I've seen in my lifetime. Right, I've

782
00:39:56,440 --> 00:39:58,640
Speaker 13: been doing mention capital now for my thirtieth year, and

783
00:39:59,200 --> 00:40:03,399
Speaker 13: they're deeper, and this allows companies to stay private, though

784
00:40:03,440 --> 00:40:05,719
Speaker 13: I think eventually my feeling is a lot of these

785
00:40:05,760 --> 00:40:07,799
Speaker 13: companies should go public for a couple of reasons. One,

786
00:40:07,920 --> 00:40:10,120
Speaker 13: we do want the public to participate. I think it

787
00:40:10,160 --> 00:40:13,120
Speaker 13: is very important that in this Ai revolution you see

788
00:40:13,120 --> 00:40:16,440
Speaker 13: it as democratic, yes, and I think it's good for

789
00:40:16,560 --> 00:40:19,320
Speaker 13: society to be able to participate as a wider group.

790
00:40:19,719 --> 00:40:22,600
Speaker 13: I also think that going public creates discipline for all

791
00:40:22,640 --> 00:40:26,239
Speaker 13: these private companies. You know, ultimately, you know, sunlight is

792
00:40:26,280 --> 00:40:30,080
Speaker 13: the best disinfectant, and there's no better sunlight than going public.

793
00:40:30,719 --> 00:40:34,400
Speaker 3: Melo Ventures partner Frankie Gnison and Bloomberg's Natasha Mascarinis, who

794
00:40:34,400 --> 00:40:37,839
Speaker 3: has detailed the firm's history and its latest fundraised three

795
00:40:37,880 --> 00:40:41,040
Speaker 3: billion in funds to back aiur startups on the Bloomberg

796
00:40:41,040 --> 00:40:43,880
Speaker 3: This Morning, Thank you very much. Like coming up, Meta

797
00:40:44,000 --> 00:40:47,400
Speaker 3: is joining the wearables game with its own smart glasses,

798
00:40:47,480 --> 00:40:51,799
Speaker 3: launching three models that are cheaper and fashion forward. My

799
00:40:51,880 --> 00:40:55,759
Speaker 3: producers tell me, hmmm, we'll discuss This is Bloomberg Tech.

800
00:41:05,160 --> 00:41:08,480
Speaker 3: Metha is launching its own smart glasses under its own

801
00:41:08,520 --> 00:41:12,680
Speaker 3: brand for the first time after popularizing wearables through RayBan

802
00:41:12,880 --> 00:41:16,520
Speaker 3: and Oakley partnerships. The tech giants glasses are cheaper and

803
00:41:16,640 --> 00:41:20,480
Speaker 3: even they keep writing this the team fashion forward, with

804
00:41:20,640 --> 00:41:24,760
Speaker 3: one model made in collaboration with Kylie Jenner. Bloomber's consumer

805
00:41:24,800 --> 00:41:28,120
Speaker 3: tech editor Mark German has the details. That's the distinction, right,

806
00:41:28,200 --> 00:41:31,440
Speaker 3: This is Meta All the way through still manufactured by

807
00:41:31,440 --> 00:41:32,360
Speaker 3: a sul Luck Sodoka.

808
00:41:32,840 --> 00:41:33,719
Speaker 2: But for you in.

809
00:41:33,680 --> 00:41:36,520
Speaker 3: Your reporting, like what is this is a moment for

810
00:41:36,560 --> 00:41:39,880
Speaker 3: the wearables category and the smart glasses category, you.

811
00:41:39,880 --> 00:41:40,640
Speaker 2: Know, it's interesting.

812
00:41:40,760 --> 00:41:42,960
Speaker 15: At the end of twenty twenty seven, Apple's going to

813
00:41:43,000 --> 00:41:46,520
Speaker 15: introduce N fifty, that's what it's called within Apple, but

814
00:41:46,600 --> 00:41:49,200
Speaker 15: that is the Apple Smart glasses. And of course, the

815
00:41:49,200 --> 00:41:52,680
Speaker 15: way Apple is vertically integrated, they are going to be designing,

816
00:41:53,200 --> 00:41:56,160
Speaker 15: in branding the glasses, you know, under the Apple name,

817
00:41:56,280 --> 00:42:00,480
Speaker 15: using their own design team. Meta has been licensing the designs,

818
00:42:00,480 --> 00:42:04,160
Speaker 15: are working with Esler Lexotica and basically taking ray Bands

819
00:42:04,239 --> 00:42:07,080
Speaker 15: Oakley's and they're planning other brands like Prada and making

820
00:42:07,120 --> 00:42:09,600
Speaker 15: those smart And for the first time, what Meta's doing

821
00:42:09,719 --> 00:42:12,200
Speaker 15: is they're launching their own Meta branded glasses.

822
00:42:12,440 --> 00:42:12,600
Speaker 2: Right.

823
00:42:12,640 --> 00:42:14,879
Speaker 15: These are not ray Bands, these are not Oakley's. These

824
00:42:14,920 --> 00:42:18,560
Speaker 15: are Meta design glasses by the Meta Industrial and Harvard

825
00:42:18,600 --> 00:42:21,840
Speaker 15: design team within the social networking giant, And so that

826
00:42:21,960 --> 00:42:22,200
Speaker 15: is a.

827
00:42:22,120 --> 00:42:22,959
Speaker 2: Big change for them.

828
00:42:23,040 --> 00:42:26,440
Speaker 15: And by going internal, going in house, that allows them

829
00:42:26,480 --> 00:42:29,640
Speaker 15: to come out with a low end and entry level

830
00:42:29,680 --> 00:42:32,120
Speaker 15: tier for their smart glasses. If you remember, at the

831
00:42:32,200 --> 00:42:35,040
Speaker 15: end of last year, Meta raised the prices of their

832
00:42:35,040 --> 00:42:38,360
Speaker 15: Smart classes with the second generation model, and so the

833
00:42:38,480 --> 00:42:40,840
Speaker 15: entry level of the ray bands, the smart ray bans

834
00:42:40,920 --> 00:42:44,560
Speaker 15: costs about three hundred and eighty dollars. These ones that

835
00:42:44,600 --> 00:42:46,600
Speaker 15: I'm wearing, these are the new Meta branded ones.

836
00:42:46,840 --> 00:42:48,399
Speaker 2: Those are wearing those I.

837
00:42:48,400 --> 00:42:51,120
Speaker 15: Am wearing them. Yes, normally wouldn't be wearing glasses on here,

838
00:42:51,200 --> 00:42:53,640
Speaker 15: but I had to bring these out. I just got

839
00:42:53,640 --> 00:42:55,799
Speaker 15: this prayer last night from Meta. So these are three

840
00:42:55,880 --> 00:42:59,320
Speaker 15: hundred dollars. So that comes in at eight hundred dollars less.

841
00:42:59,360 --> 00:43:02,080
Speaker 15: So that the big play here bringing something that is

842
00:43:02,120 --> 00:43:04,520
Speaker 15: a bit more affordable to market by using their in

843
00:43:04,560 --> 00:43:05,600
Speaker 15: house brand and design.

844
00:43:06,440 --> 00:43:09,239
Speaker 3: Okay, so we have definitively answered the question whether or

845
00:43:09,280 --> 00:43:12,200
Speaker 3: not these are fashion forward. They're fashion forward. I love

846
00:43:12,239 --> 00:43:15,239
Speaker 3: greatam and modeling them. Now you look great. Let's get

847
00:43:15,280 --> 00:43:17,960
Speaker 3: into the technology part. We only have thirty seconds, but

848
00:43:18,000 --> 00:43:20,759
Speaker 3: the capability of these relative to what Meta already had

849
00:43:20,760 --> 00:43:22,720
Speaker 3: out there. O, they're identical, they're the same.

850
00:43:23,040 --> 00:43:25,719
Speaker 15: But the cool anecdote I can give you is that

851
00:43:25,880 --> 00:43:29,319
Speaker 15: Meta seems to be seriously considering launching versions of its

852
00:43:29,320 --> 00:43:34,960
Speaker 15: classes without cameras. Now that creates a few interesting wrinkles here.

853
00:43:34,960 --> 00:43:37,359
Speaker 15: One that'll allow them to create new designs that are

854
00:43:37,360 --> 00:43:40,680
Speaker 15: maybe slimmer and lighter, with more battery life because fewer

855
00:43:40,719 --> 00:43:44,640
Speaker 15: components needing to be dedicated to the camera system. Also

856
00:43:44,719 --> 00:43:49,280
Speaker 15: more privacy conscious and no cameras audio only experience also

857
00:43:49,320 --> 00:43:51,439
Speaker 15: means cheaper, right.

858
00:43:51,520 --> 00:43:54,759
Speaker 3: Bloomberg's Mark Gunman, who leads our consumer technology team on

859
00:43:54,800 --> 00:43:57,520
Speaker 3: the metaglasses, thank you so much. That does it for

860
00:43:57,600 --> 00:44:00,319
Speaker 3: this edition of Bloomberg Tech really packed show lot going

861
00:44:00,320 --> 00:44:02,480
Speaker 3: on in public markets and in private markets in the

862
00:44:02,520 --> 00:44:05,440
Speaker 3: world of technology. Recap all of that on the podcast.

863
00:44:05,520 --> 00:44:07,480
Speaker 3: You can find it on the Bloomberg terminal as well

864
00:44:07,520 --> 00:44:10,759
Speaker 3: as online on Apple, Spotify, and iHeart.

865
00:44:10,920 --> 00:44:12,480
Speaker 2: Have a great day, it's a Bloomberg Tech