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

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

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Speaker 1: and Eva Loow in San Francisco.

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Speaker 2: This is Bloomberg Tech coming up, Meta Iron, Google's AI chips.

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Speaker 2: According to reports, how the market is questioning in Vidia's

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Speaker 2: long term dominance. Prashujinping brings the issue of sovereignty over

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Speaker 2: chiphub Taiwan back on the agenda with Donald Trump during

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Speaker 2: their phone call. And Apple eliminates sales roles in a

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Speaker 2: rare layoff to streamline the way it offers products to

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Speaker 2: businesses and governments.

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Speaker 3: We have the details.

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Speaker 2: The first, let's check in on these markets that are

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Speaker 2: trying to digest a wall of delayed data at the

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Speaker 2: same time as baking in maybe an eighty percent chance

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Speaker 2: of a FED rate cut. That's what the market price

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Speaker 2: is in but all eyes on tussle at the top

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Speaker 2: in AI dominance, looking at a five ten percent drop

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Speaker 2: on the Nasdaq one hundred on the upside, some key

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Speaker 2: players on the drownd side, we're seeing a key drag

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Speaker 2: from some of the key chip names.

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Speaker 3: In particular.

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Speaker 2: Let's delve into what's moving this particular index, which does

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Speaker 2: lag the SMP and the DOW. Today we're seeing in

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Speaker 2: video off by five percent. No wonder then, ASAK one

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Speaker 2: hundred is underwater when the world's most dominant player from

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Speaker 2: a market cap perspective is currently off by five percent.

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Speaker 2: Big moves for a four trillion dollar company, and in

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Speaker 2: large part it's because of Alphabet that actually is trimming

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Speaker 2: its games but still at a new record high. This

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Speaker 2: is we understand meta eyeing tpused. This is all about

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Speaker 2: the dominance of where we get the chips of the

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Speaker 2: future to train our models as well as use our models.

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Speaker 2: Meta is the key.

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Speaker 3: Line of questioning.

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Speaker 2: Here is meta eyeing tpused from Google to put in

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Speaker 2: its own data centers. They're the reports coming from the

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Speaker 2: information today, let's talk about the market reaction. Equity reporter

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Speaker 2: around Nostellica is here with us Ryan. It's monumental move

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Speaker 2: that Alphabet has had since mid October. It's added a

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Speaker 2: trillion in market capitalization. Were starting to become aware of

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Speaker 2: its prowess in AI and in chips.

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Speaker 4: Yeah, absolutely, thanks for having me so. Yeah, certainly sentiment

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Speaker 4: has really reversed on Alphabet, and I think people are

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Speaker 4: really appreciating how dominant. It is across every layer of

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Speaker 4: the AI stack. Of course, it recently released the latest

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Speaker 4: update to Gemini, which was seen as very strong across

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Speaker 4: all the major benchmarks that people used to evaluate AI models.

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Speaker 4: The chip business is getting a lot of attention lately,

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Speaker 4: between the report with Meta and the deal with Anthropic.

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Speaker 4: People really see this as a really significant potential competitor

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Speaker 4: to Nvidia, as you were discussing earlier, and beyond that,

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Speaker 4: it has a huge cloud business that is seeing accelerating growth.

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Speaker 4: It has so much data and users and distribution and talent.

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Speaker 4: It really has all of the pieces, and that has

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Speaker 4: really helped the stock surge, not just recently but over

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Speaker 4: the past several months. It is by far the biggest

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Speaker 4: performer or the best performer of the mag seven this year.

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Speaker 2: I think Counterpoint Research analyst and co founder Nail shar

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Speaker 2: and a story on Blueberg saying it's a sleeping giant

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Speaker 2: in the AI race and is fully awoken. But many

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Speaker 2: have been trading for the last few days ever since

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Speaker 2: Gemini three release.

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Speaker 3: Maybe going short open.

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Speaker 2: AI suppliers and long alphabet suppliers. Is that still bearing out.

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Speaker 2: I'm looking at Oracle down once again.

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Speaker 4: Ryan Oracle has really been under a lot of pressure lately.

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Speaker 4: I think it's on track for its biggest one month

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Speaker 4: dropped since two thousand and one, so certainly a real

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Speaker 4: reversal there. It does seem like people are moving towards

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Speaker 4: the Google and Alphabet suppliers, which is companies like Broadcom,

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Speaker 4: while at the same time the companies that are more

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Speaker 4: connected to open Ai. You mentioned Oracle, also AMD, Microsoft

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Speaker 4: to a certain extent, these companies have really been under

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Speaker 4: a lot of pressure lately.

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Speaker 2: It's worth reminding though that yes, Alphabet's been on a tear,

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Speaker 2: and actually its valuations have started to trade way higher

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Speaker 2: than we're used to I thinking about twenty six twenty

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Speaker 2: seven times future earnings, but still leap from just mysoft.

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Speaker 3: Ahead of it in terms of market cap is Apple.

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Speaker 2: I think it's got another seven percent to go to

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Speaker 2: hit that, but fifteen percent to go to in video.

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Speaker 2: We're not questioning longer term at this exact moment. In

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Speaker 2: Vidia's dominance in AI and semiconductors.

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Speaker 4: Well, NBA right now has I think ninety percent or

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Speaker 4: so share of the data center market. If there is

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Speaker 4: potential for Alphabet to start eating into that, I think

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Speaker 4: that would change a lot of calculations for people in

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Speaker 4: terms of how much can Alphabet scale this business. I

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Speaker 4: have talked to someone who thinks that this could potentially

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Speaker 4: be worth more than Alphabet's cloud business, potentially up to

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Speaker 4: nine hundred billion dollars. So that is a huge potential

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Speaker 4: market there. And if that means that Nvidia starts losing

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Speaker 4: market share, I think people will start reassessing how to

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Speaker 4: value that company and its growth and its valuation. Now,

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Speaker 4: I will just simply say that the AI market is

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Speaker 4: growing so rapidly that people do see room for a

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Speaker 4: lot of big players, and even Alphabet remains a major

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Speaker 4: customer to in Vidia just because there is so much

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Speaker 4: demand for compute right now, people don't want to be

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Speaker 4: behold into any single supplier. It does suggest that there's

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Speaker 4: a lot of room for growth to go around, even

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Speaker 4: if we start seeing some erosion and market share at Navidia.

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Speaker 2: And I'm pretty sure Jenson Wang will be responding to

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Speaker 2: any concerns about erosion and market share round a SELCA.

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Speaker 3: We so appreciate you joining.

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Speaker 2: Let's dig in to further analysis here with Stephanie Anagai

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Speaker 2: to the conversation which has global market strategist Japing Morgan

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Speaker 2: Asset Management and has four trillion dollars in assets under management,

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Speaker 2: four trillion, rather similar to the market capitalizations in some

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Speaker 2: of these companies, And I'm interested, Stephanie, does it matter

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Speaker 2: to you from a market sentiment perspective if there's scribbling

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Speaker 2: at the top of the US domiciled companies.

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Speaker 3: Is it a.

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Speaker 2: Worry that we'll see perhaps in Vidia being questioned in

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Speaker 2: terms of its dominance.

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Speaker 3: I think it's quite healthy.

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Speaker 5: We've seen the AI trade has delivered enormous returns for

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Speaker 5: markets over the last two years, and we're I think

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Speaker 5: all kind of experiencing the sire of relief with this exhale.

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Speaker 5: I guess in a way, we've moved from a rise

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Speaker 5: tied lifting all boats or more choppier waters, and investors

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Speaker 5: are being far more scrutinizing when it comes to how

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Speaker 5: much is being spent, the quality of those investments. We're

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Speaker 5: seeing as this AI trade continues to grow in its enormity,

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Speaker 5: the investment being made, and the moat in such big

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Speaker 5: questions and shifting. So I think it's quite healthy that

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Speaker 5: we're focusing on selectivity. I mean, this is what you

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Speaker 5: want to see to prevent a dot com bubble, and

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Speaker 5: we're looking at valuations today, we don't really see a

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Speaker 5: big risk of that happening amongst the big tech firms.

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Speaker 2: So even these worries about debt in particular, and you

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Speaker 2: are coming to us with a viewpoint that is cross asset.

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Speaker 2: In many ways, we have seen a real desire to

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Speaker 2: get into AI related debts. Some of the bond sales

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Speaker 2: that come from the likes of Alphabet and the likes

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Speaker 2: of Meta and an Oracle.

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Speaker 3: Have been scooped up.

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Speaker 2: But there's been this worry that in the longer term

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Speaker 2: it might start to maybe pull back on overall demand.

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Speaker 2: We might see some of these big hyper scales coming

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Speaker 2: to market so often that it drives up prices for others.

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Speaker 3: It's possible.

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Speaker 5: I mean, I think first just looking at the magnitude

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Speaker 5: of how much AI investment is needed or being spent

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Speaker 5: already by any way that you cut it, the amount

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Speaker 5: of spending right now is enormous. But just like Cardia,

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Speaker 5: isn't that expensive for a billionaire. When looking at these

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Speaker 5: capex relative to the sales from these companies, relative to

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Speaker 5: their current revenue growth, which has also grown significantly, it's

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Speaker 5: actually not that extreme. So we're seeing this increasing move to.

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Speaker 3: Tap into debt markets.

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Speaker 5: But for us, it's not so much these companies getting overextended,

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Speaker 5: but actually more so a reflection of a better sorry,

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Speaker 5: a better capital structure. You know, there's some investments like

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Speaker 5: these data centers that are going to be invested over

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Speaker 5: multiple years. It might make more sense to tap debt

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Speaker 5: markets for some of these deals or an off balancing structure.

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Speaker 2: So for example, we're going to be looking not only

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Speaker 2: at AI investment surging, but at credit default swaps of

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Speaker 2: Oracle surging. Has that just been acting as a bell

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Speaker 2: weather and the necessary bell weather to stop reflecting some

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Speaker 2: of the risks that maybe people been ignoring for the.

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Speaker 3: Past few months.

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Speaker 5: I think it's it's very apt, and not all of

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Speaker 5: these companies have the establishments, you know, they differ in

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Speaker 5: many different ways and also in their sources of revenue.

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Speaker 5: And I think it makes sense that you know, Oracle

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Speaker 5: is one of the more you know, risk your companies

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Speaker 5: that is tapping these bum markets and you're seeing that

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Speaker 5: being reflective in CDs spreads. But that also can't be

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Speaker 5: extrapolated to the entire shift right now towards debt markets

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Speaker 5: to help finance these data center bills, and I'll also add, look,

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Speaker 5: when it comes to cloud services, that business model is

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Speaker 5: one of the most cash generative business models.

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Speaker 3: In the world.

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Speaker 5: So at the end of the day, these bonds are

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Speaker 5: also being tied to services business operations that have tended

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Speaker 5: to do quite well for these companies.

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Speaker 2: It's interesting, of course, deciding where the margin a cruise.

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Speaker 2: We're going to have l HP after the bell, Many

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Speaker 2: feeding that margin is being eroded because of the cost

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Speaker 2: of memory. Meanwhile, we'll getting Micron next week with its

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Speaker 2: Sennings and Many anticipating they're strong because.

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Speaker 3: Of the memory demand there.

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Speaker 2: From your perspective, is there still room to run in

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Speaker 2: just the tech trade more broadly, or has that shift

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Speaker 2: into more value names and certainly with the context of

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Speaker 2: the FED change things longer term into the end of

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Speaker 2: the year.

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Speaker 5: We still think we're quite early in this AI wave,

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Speaker 5: but we've seen a chapter or two, and moving forward,

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Speaker 5: I think the focus is not going to only be

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Speaker 5: on compute needs and capacity needs, but also on AI

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Speaker 5: utilization and what companies are really critical for that, whether

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Speaker 5: it's in software, what companies are leading the way in

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Speaker 5: financials and entertainment, in adopting AI, and then also how

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Speaker 5: once we learn more about the end user demand for

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Speaker 5: AI and the pricing power of these AI services, that's

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Speaker 5: going to give us a lot of clarity around the

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Speaker 5: ROI around these AI investments.

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Speaker 3: So is that what we need?

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Speaker 2: Is it ultimately the revenues of companies outside of the

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Speaker 2: world of tech to vindicate that? What pushes us higher

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Speaker 2: in terms of real contextview? Is it December when we

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Speaker 2: get the FED decision? What is the catalyst do you

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Speaker 2: think for us to reassess where we are in valuation?

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Speaker 5: I'd say it's less of the kind of macro back

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Speaker 5: job early here and much more of the proof point

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Speaker 5: around the monetization of AI. I think the more that

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Speaker 5: you see businesses ramping up their IT budgets, the stickiness

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Speaker 5: that you see in those investment spending and then also

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Speaker 5: AI delivering and we've seen some proof cases of that

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Speaker 5: so far. Coding has been a huge factor of that.

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Speaker 5: But once you see more companies, particularly outside of tech

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Speaker 5: maybe tech adjacent, coming at their earnings calls and talking

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Speaker 5: about their AI generated savings, I think that's going to

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Speaker 5: be a really important next lever for the AI trade.

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Speaker 2: And then one about the Leva FERRATUALI the companies that

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Speaker 2: are adulting the clients that are cooling you on a

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Speaker 2: daily basis, are they saying do I double down more

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Speaker 2: in tech? Are they saying I need to double out

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Speaker 2: outside of the world of tech.

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Speaker 5: I think it's it's diversifying that tech exposure. You know,

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Speaker 5: after a long run in these AI names, you don't

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Speaker 5: want all your ex in one basket because it is.

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Speaker 3: Probably not.

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Speaker 5: At least you want to right size some of that

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Speaker 5: exposure bill to top of all of those games that

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Speaker 5: we've experienced in position for how this AI wave is

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Speaker 5: going to evolve, there will undoubtedly be losers and winners,

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Speaker 5: but we also don't want to be out of the market.

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Speaker 3: And that's another thing that we're trying.

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Speaker 5: To talk to clients about because even when you call

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Speaker 5: a bubble correctly, if you weren't in the market from

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Speaker 5: nineteen ninety five to nineteen ninety nine, you would have

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Speaker 5: missed out on over four hundred percent in total return

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Speaker 5: in the Nasdaq. You were right, but you've locked in

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Speaker 5: years of underperformance. So when it comes to us f

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Speaker 5: Wing Marcus today, we don't see that real risk of

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Speaker 5: a systemic bubble, but we do see a real opportunity

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Speaker 5: to just make sure that portfolios are built for resiliency

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Speaker 5: and they're also built to take advantage of how this

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Speaker 5: ai wave continues.

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Speaker 2: To evolve, well, hopefully keep having you on a zimbubble

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Speaker 2: or indeed the narrative does evolve. We so appreciate Stephanie

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Speaker 2: Aliaga JPM Wollngan asset management can cross tech for us.

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Speaker 2: Mean while coming out with China, Shijin paying revives talks

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Speaker 2: to the sovereignty of a Taiwan and a phone call

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Speaker 2: with President Trump.

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Speaker 3: More on that next, This is boom Bag Tech.

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Speaker 2: Chinese President Xijingping well has revived the topic of China's

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Speaker 2: sovereignty over Taiwan in a phone call with President Trump yesterday,

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Speaker 2: discussion that didn't come up during their face to face

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Speaker 2: meeting last month in Beijing. From Magxinia, tech editor Mike

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Speaker 2: Sheppard joined us for the latest and Mike remind us

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Speaker 2: from a tech perspective, Taiwan, we know its dominance and chips,

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Speaker 2: We understand its integral nature to the tech ecosystem. What

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Speaker 2: is happening between Xijiping and Trump on.

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Speaker 6: This Well, what was interesting yesterday, Carr is that we

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Speaker 6: get two very different versions of this phone call between

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Speaker 6: the leaders of the world's two largest economies. The first

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Speaker 6: version came from Beijing. The official state news agency SHINOA

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Speaker 6: put out its version of the conversation, presenting it really

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Speaker 6: as one centered on the question of Taiwan. And then

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Speaker 6: a few hours later we heard from President Donald Trump

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Speaker 6: himself on truth social posting that they had had a

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Speaker 6: great conversation about issues including soybeans and other matters, rare earths,

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Speaker 6: and other key topics that were deared to the US president,

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Speaker 6: but he made no mention of Taiwan there Now, while

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Speaker 6: he did not bring it up, several hours later, he

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Speaker 6: did call the new Prime Minister of Japan, Sanai Takaichi,

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Speaker 6: who had enraged Beijing with comments a few weeks ago.

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Speaker 6: You'll remember Caro saying that Japan would consider leaping to

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Speaker 6: Taiwan's defense in the event that China.

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Speaker 7: Were to try to take it.

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Speaker 6: Now, all of this is huge implications for the supply

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Speaker 6: chain of semiconductors, as you noted, especially Taiwan semiconductor. It's

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Speaker 6: one of the world's largest producers of AI chips, and

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Speaker 6: they are moving some of their production, as we know,

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Speaker 6: to the US they've pledged one hundred and sixty five

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Speaker 6: billion dollars in investment implants here in the United States,

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Speaker 6: but they would still retain a significant amount of their

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Speaker 6: capacity on the island. Therefore, any question of Taiwan really

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Speaker 6: does bring up tech issues for US.

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Speaker 2: And talking of tech issues, the administration once again trying

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Speaker 2: to signal its commitment seeing the ai infrastructure build out

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Speaker 2: akin to the Apollo Mission or to the Manhattan Project, Mike.

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Speaker 6: Now they are talking about a Manhattan Project like effort

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Speaker 6: in this Executive Order to call Genesis that President Donald

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Speaker 6: Trump signed yesterday, But it was really more a call

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Speaker 6: to action for various agencies to start working together more closely,

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Speaker 6: and that includes the Department of Energy and its National

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Speaker 6: Research laboratories. But when we talk about Manhattan Project, though,

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Speaker 6: we do need to remember that that effort took thirty

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Speaker 6: six billion dollars in real dollars today from back then

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Speaker 6: in the nineteen forties, as the United States was in

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Speaker 6: the race to develop a nuclear weapon ahead of the

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Speaker 6: Access Powers. This is a very different time. We are

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Speaker 6: not seeing new money being pledged towards this effort. It's

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Speaker 6: important to remember, Caro, though, that the US already has

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Speaker 6: put a significant investment in production of chips that would

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Speaker 6: be needed for artificial intelligence, and that is the Chips

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Speaker 6: and Science Act of twenty twenty two that put tens

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Speaker 6: of billions of dollars in loans and grants and other support,

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Speaker 6: including tax incentives, to support the development of a chip

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Speaker 6: industry domestically that would help artificial intelligence take hold and

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Speaker 6: gain ground and lead the world, as President Biden and

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Speaker 6: President Donald Trump now say they would like the US

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Speaker 6: to do.

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Speaker 7: Now, we are.

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Speaker 6: Seeing companies like open ai push for further investment to

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Speaker 6: support data centers, and that would include extending some of

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Speaker 6: those tax credits to data centers. So it'll be interesting

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Speaker 6: to see how this develops and whether more of those

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Speaker 6: tax credits will go to some of those AI projects.

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Speaker 6: Just Caro, as we are wrestling with those questions of

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Speaker 6: whether we are seeing too much money going into this space.

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Speaker 2: Max make Shepard all the context from Washington, We appreciate it.

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Speaker 3: Meanwhile, it's time for talking tech now.

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Speaker 2: First up, Ali Baba reported thirty four percent growth in

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Speaker 2: its cloud unit during the September quarter. Despite the gain,

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Speaker 2: though spending on consumer subsidies data centers that's eaten into

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Speaker 2: its profits the company's ADRs, as you can currently see,

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Speaker 2: just training.

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Speaker 3: Off by some two percent day.

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Speaker 2: Meanwhile, Anthropic it's got a new model, claud Opens four

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Speaker 2: point five that the company says it's better at coding

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Speaker 2: and office tasks such as financial analysis and creating presentations

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Speaker 2: or spreadsheets.

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Speaker 3: It's part of anthropics efforts.

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Speaker 2: To compete with open Ai with Google business customers. And Meanwhile,

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Speaker 2: open ais a new tool to generate personalized shopping guides.

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Speaker 2: The company trained a version of GBT five Mini.

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Speaker 3: Model to ask follow up questions.

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Speaker 2: Draw answers from reviews published on what the company considers

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Speaker 2: higher quality websites. Now, let's talk Apple, because, in a

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Speaker 2: rare mood for the company, the tech giant has eliminated

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Speaker 2: dozens of sales roles in an effort to streamline the

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Speaker 2: way it offers its products to businesses, schools, and governments.

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Speaker 2: For more on this breaking story, Mark German joins us,

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Speaker 2: it's not one hundreds, We're.

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Speaker 3: Talking tens of people.

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Speaker 2: But still it's notable because we don't often see layofs

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Speaker 2: of that one.

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Speaker 8: Yeah, to your point, it was several dozen people across

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Speaker 8: Apple sales division, and this sales division they partner with

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Speaker 8: carriers across the world to sell iPhones, but they also

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Speaker 8: partner with enterprises, large scale businesses, government organizations, schools, educational institutions,

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Speaker 8: major universities across the world to sell products like iPhones, iPads,

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Speaker 8: max and you name it. And over the course of

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Speaker 8: this month, there was a big streamlining rounds of layoffs, including,

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Speaker 8: like I said, several dozen people. There were account managers,

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Speaker 8: they are called account executives for specific government agencies, for

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Speaker 8: specific university systems, people who partner in pitch companies on

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Speaker 8: buying Apple products. There are these tiny Apple store like

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Speaker 8: fixtures called briefing centers at Apple offices in California and Texas,

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Speaker 8: and the people managing that, for the most part, we're

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Speaker 8: laid off as well. And so this is going to

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Speaker 8: change how Apple sells products to these different organizations. The

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Speaker 8: majority of products are bought through what's called the channel,

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Speaker 8: so third party retailers, and so those products are still

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Speaker 8: going to sell, but quite a bit of a shake

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Speaker 8: up here for Apple sells products and delivers these devices

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Speaker 8: to the major customers. And of course, as you said,

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Speaker 8: a rare layoff for Apple.

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Speaker 2: Apple did respond to your reporting and saying we're continuing

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Speaker 2: to hire, and those employees can.

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Speaker 3: Apply for new roles.

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Speaker 2: Mark, But what do you think this signifies more broadly

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Speaker 2: about how Apple is trying to streamline, trying to become

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Speaker 2: more efficient, trying to ensure that it doesn't seem like

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Speaker 2: it's a lagged in this age of AI.

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Speaker 8: Yeah, you know, I don't think this has much to

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Speaker 8: do with AI. I think this has to do with

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Speaker 8: cutting roles internally to lower costs because they realize most

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Speaker 8: of these sales are happening from the channel and there's

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Speaker 8: a lot of duplicate efforts internally with the channel, so

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Speaker 8: the third party retailers. So I think it's just one

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Speaker 8: of your classic layoffs to create more efficiency and cost cutting,

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Speaker 8: rather than having much to do with artificial intelligence. In

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Speaker 8: terms of layoffs related to AI at Apple, I guess

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Speaker 8: the only thing you've seen related to AI from Apple

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Speaker 8: that has to touch a layoff was the self Drenking

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Speaker 8: Car project job cuts of one thousand people in the

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Speaker 8: beginning of twenty twenty four, and that was actually to

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Speaker 8: do more AI rather than because of AI. So they

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Speaker 8: moved a lot of those folks over to the Generative

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Speaker 8: AI division. But I haven't seen any job cuts at

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Speaker 8: Apple to date because of AI. That doesn't mean they're

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Speaker 8: not going to happen, but so far they haven't.

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Speaker 3: Well that's far.

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Speaker 2: We started to still see some job cuts across technology

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Speaker 2: and Mark Gurman, you've been at the front of that reporting.

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Speaker 3: We really appreciate it.

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Speaker 2: Now coming up, Michael Barry stands by his in video

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Speaker 2: criticisms as after calling out the company for stop by

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Speaker 2: gap facts for compensation dilusion, but in video itself responded

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Speaker 2: to analysts.

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Speaker 3: More on that next. As a Bloomberg Tech.

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Speaker 2: As we've been reporting in video shares, they are under

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Speaker 2: pressure today, Competition fierce when it.

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Speaker 3: Comes from Alphabet and TPUs.

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Speaker 2: We understand reporting that Meta is eyeing potentially turning to

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Speaker 2: Google for its chips in its data centers in the future.

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Speaker 2: But there's also Michael Burry there isn't there standing by

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Speaker 2: his criticism of the company after a video pushed back

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Speaker 2: on his earlier analysis of stock based compensation of share

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Speaker 2: buybacks for more Bluemberg equity supporter com and Ryanikey reminds

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Speaker 2: us of what the Cassandra as he dubs himself, has

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Speaker 2: been saying. Michael Burry laid on issues about the circularity

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Speaker 2: of tech deals, worries about the interoperability of what big

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Speaker 2: tech are currently doing in the world of generator AI.

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Speaker 2: What did he take issue though, within video when it

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Speaker 2: comes to share buybacks and stock compensation.

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Speaker 9: Yeah, so, I think most basically his argument is that

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Speaker 9: the amount of stock based compensation is diluting you know,

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Speaker 9: owner's power, that if you hold the stock, it's being

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Speaker 9: diluted by the stock based compensation. And so it really

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Speaker 9: is just another thing and sort of a myriad of

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Speaker 9: things that he has called out within Vidia in recent weeks.

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Speaker 9: And yeah, we saw the company, you know, push back.

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Speaker 9: There was a memo that they sent to some Wall

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Speaker 9: Street analysis is according to a Baron's report that said,

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Speaker 9: you know, we think his math is wrong. Kind of

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Speaker 9: explained the situation a little bit better and also very

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Speaker 9: latantly stated, you know, we're not Enron. We're not you know,

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Speaker 9: there's no fraud here. But but Bury really said, you know,

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Speaker 9: I stand by my analysis that you know about the

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Speaker 9: stock based compensation, dilution share buybacks. And he also said,

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Speaker 9: you know, I'm not comparing Nvidia to Enron, I'm comparing

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Speaker 9: it too Cisco, which I thought was really interesting thinking

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Speaker 9: about Cisco, you know, in the dot com era, it

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Speaker 9: had this huge run up, but it was really associated

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Speaker 9: with the over build in fiber optic cable. So he's

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Speaker 9: you know, comparing that to what's happening now, I guess

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Speaker 9: with the video with data centers, and these are really

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Speaker 9: some of the biggest you know, concerns or pain points

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Speaker 9: that we're seeing in this debate over if AI is

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Speaker 9: a bubble, and you know, in video shares are down.

440
00:22:17,520 --> 00:22:19,280
Speaker 9: I think they were down as much as six percent today.

441
00:22:19,320 --> 00:22:21,640
Speaker 9: We're seeing you know, more than two hundred billion dollars

442
00:22:21,640 --> 00:22:24,960
Speaker 9: in market value just wiped off, and we're also watching

443
00:22:25,400 --> 00:22:28,520
Speaker 9: the sort of twenty percent level and videas nearing twenty

444
00:22:28,560 --> 00:22:32,080
Speaker 9: percent draw down from it's high at the end of October,

445
00:22:32,160 --> 00:22:33,680
Speaker 9: which was a significant level.

446
00:22:33,440 --> 00:22:36,800
Speaker 3: For the shares technical band market. Extraordinary. Thank you very much,

447
00:22:36,800 --> 00:22:37,600
Speaker 3: comment and Rhanick.

448
00:22:37,640 --> 00:22:40,040
Speaker 2: He always has some of the most read stories across

449
00:22:40,080 --> 00:22:41,200
Speaker 2: all of the technology moves.

450
00:22:41,240 --> 00:22:42,840
Speaker 3: You've got to keep them up to date with it.

451
00:22:42,880 --> 00:22:46,399
Speaker 2: Meanwhile, coming up Google's potential chip deal with Meta's raising

452
00:22:46,480 --> 00:22:49,240
Speaker 2: questions about in Vidia's dominance and the erase for AI leadership.

453
00:22:49,280 --> 00:22:52,240
Speaker 3: More on that take next. This is boom Bag Tech

454
00:23:03,040 --> 00:23:03,480
Speaker 3: Welcome back.

455
00:23:03,480 --> 00:23:05,960
Speaker 2: To Bloomberg Tech, let's take a check on these markets,

456
00:23:06,000 --> 00:23:08,760
Speaker 2: because we have seen some sell off continuing.

457
00:23:08,400 --> 00:23:09,600
Speaker 3: In the world of technology.

458
00:23:09,680 --> 00:23:13,400
Speaker 2: Unlike SMP, unlike the Dow, the Moon music remains resolutely

459
00:23:13,400 --> 00:23:13,760
Speaker 2: in the red.

460
00:23:13,760 --> 00:23:15,000
Speaker 3: We're off by four tenths of percent.

461
00:23:15,320 --> 00:23:18,320
Speaker 2: We're seeing some of the big tech names, namely in video.

462
00:23:18,400 --> 00:23:19,120
Speaker 3: On the downside.

463
00:23:19,160 --> 00:23:22,080
Speaker 2: We're still questioning valuations as we get that myriad of

464
00:23:22,200 --> 00:23:24,280
Speaker 2: data that comes late to the party when it comes

465
00:23:24,320 --> 00:23:27,320
Speaker 2: to certainly our own consumer sentiment seems to be on

466
00:23:27,359 --> 00:23:30,040
Speaker 2: the low side. But we're seeing retail sales maybe pointing

467
00:23:30,080 --> 00:23:32,159
Speaker 2: towards whether or not we've got some resiliency in the

468
00:23:32,280 --> 00:23:35,159
Speaker 2: overall macroeconomic picture. But in Vidia is more a story

469
00:23:35,280 --> 00:23:37,200
Speaker 2: of its resiliency versus competition.

470
00:23:37,280 --> 00:23:38,560
Speaker 3: We're down by four percent.

471
00:23:38,600 --> 00:23:42,280
Speaker 2: Once again, we're wondering if other chips will be created

472
00:23:42,400 --> 00:23:45,360
Speaker 2: by other players, like Alphabet for example. It's TPUs maybe

473
00:23:45,400 --> 00:23:47,680
Speaker 2: being eyed by Meta. That story we're going to delve into.

474
00:23:47,920 --> 00:23:51,120
Speaker 2: We're seeing both shares trade higher. Oracle and any name

475
00:23:51,200 --> 00:23:54,520
Speaker 2: really in the open Ai ecosystem has been a typical

476
00:23:54,600 --> 00:23:56,720
Speaker 2: short for the last week or so, as people question

477
00:23:56,840 --> 00:24:00,479
Speaker 2: open AI's dominance versus Gemini. Three for example, so Oracle

478
00:24:00,480 --> 00:24:03,800
Speaker 2: once again off by another two percent. Let's really dig

479
00:24:03,840 --> 00:24:05,840
Speaker 2: in them into the story of the day of alphabet

480
00:24:06,200 --> 00:24:08,320
Speaker 2: really giving m video a run for its money, certainly

481
00:24:08,320 --> 00:24:11,400
Speaker 2: a market capitalization front at least Mandy saying, you're here,

482
00:24:11,480 --> 00:24:13,320
Speaker 2: senior techanalyst a roomag intelligence.

483
00:24:13,640 --> 00:24:17,520
Speaker 3: You have for months, if not years, been reminding me and.

484
00:24:17,400 --> 00:24:20,399
Speaker 2: Our viewers of the power of the ecosystem of Google

485
00:24:20,520 --> 00:24:21,440
Speaker 2: and TPUs.

486
00:24:21,880 --> 00:24:23,440
Speaker 3: Why now are we only just getting it?

487
00:24:24,760 --> 00:24:25,000
Speaker 7: Wow?

488
00:24:25,080 --> 00:24:28,840
Speaker 10: Because Gemini three showed that you could use the TPUs

489
00:24:28,960 --> 00:24:32,160
Speaker 10: both for training the model and for inferencing. I mean,

490
00:24:32,240 --> 00:24:36,480
Speaker 10: so far the story was all these secondary providers could

491
00:24:36,560 --> 00:24:39,800
Speaker 10: be used for inferencing. The fact that TPUs were used

492
00:24:39,800 --> 00:24:44,120
Speaker 10: for training Gemini three and most likely for Entropics Frontier

493
00:24:44,160 --> 00:24:47,680
Speaker 10: model as well. So two of your three frontier models

494
00:24:47,960 --> 00:24:51,120
Speaker 10: are using TPUs, And I think there is an acknowledgement

495
00:24:51,200 --> 00:24:54,960
Speaker 10: now from the market that TPUs are comparable to in

496
00:24:55,119 --> 00:24:58,960
Speaker 10: video in terms of functionality, and they are a lot cheaper,

497
00:24:59,000 --> 00:25:01,879
Speaker 10: which is why I'm not surprised to see Meta do that.

498
00:25:02,080 --> 00:25:05,600
Speaker 10: I mean, they will raise CAPEX and you know, going

499
00:25:05,640 --> 00:25:08,960
Speaker 10: to the secondary provider who is a much cheaper option

500
00:25:09,840 --> 00:25:12,520
Speaker 10: makes sense. And I think a market will like it

501
00:25:12,560 --> 00:25:15,040
Speaker 10: when they raise the capex and say that we'll be

502
00:25:15,119 --> 00:25:17,360
Speaker 10: using Google as a secondary provider.

503
00:25:17,960 --> 00:25:21,119
Speaker 2: These are thus just reports as it stands, Mandy.

504
00:25:21,320 --> 00:25:22,800
Speaker 3: But what's interesting has.

505
00:25:22,640 --> 00:25:27,000
Speaker 2: Been looking at Jensen Wang's reaction when Alphabet or others

506
00:25:27,000 --> 00:25:29,960
Speaker 2: have made inroads into some of their key clients, Anthropic

507
00:25:30,040 --> 00:25:34,600
Speaker 2: for example, getting a load of gtpus from Google, and

508
00:25:34,640 --> 00:25:37,240
Speaker 2: then we see more of a deal done between in

509
00:25:37,400 --> 00:25:38,439
Speaker 2: Video and Anthropic.

510
00:25:38,560 --> 00:25:41,440
Speaker 3: We know that in Videos double down on open AI with.

511
00:25:41,400 --> 00:25:44,359
Speaker 2: One hundred billion dollars being offered in return for GPUs

512
00:25:44,400 --> 00:25:46,920
Speaker 2: being considered in their future training. So what do you

513
00:25:46,960 --> 00:25:49,000
Speaker 2: think the response mechanism could be of Nvidia.

514
00:25:50,720 --> 00:25:53,720
Speaker 10: I mean, right now, in Video's problem is no one

515
00:25:53,840 --> 00:25:56,920
Speaker 10: wants to pay the you know, the high costs they

516
00:25:56,960 --> 00:25:59,280
Speaker 10: have for their chips, and that gets reflected in in

517
00:25:59,359 --> 00:26:03,320
Speaker 10: Video's margin. Seventy five percent gross margin is something we

518
00:26:03,359 --> 00:26:06,679
Speaker 10: have never seen with a semiconductor company. So from that

519
00:26:06,880 --> 00:26:10,520
Speaker 10: perspective that you know, the providers who are doing inferencing

520
00:26:10,800 --> 00:26:14,359
Speaker 10: are offering their products below costs. Even an open AI

521
00:26:14,440 --> 00:26:19,040
Speaker 10: when it's deploying you know it's chatbot at scale, I

522
00:26:19,080 --> 00:26:22,399
Speaker 10: would argue, you know, their gross margins are negative because

523
00:26:22,440 --> 00:26:26,320
Speaker 10: they're offering their product at below their costs. In the

524
00:26:26,320 --> 00:26:29,399
Speaker 10: case of Google, they're deploying you know, generative AI at

525
00:26:29,480 --> 00:26:32,560
Speaker 10: scale on search and across their family of apps, and

526
00:26:32,600 --> 00:26:35,440
Speaker 10: they're able to do it without really hurting their margins

527
00:26:35,480 --> 00:26:39,160
Speaker 10: because their cost phase is much lower. They're running their

528
00:26:39,200 --> 00:26:42,639
Speaker 10: infrastructure a lot more efficiently, and so that is where

529
00:26:42,760 --> 00:26:45,879
Speaker 10: the problem lies is you can't just keep subsidizing the

530
00:26:45,960 --> 00:26:49,040
Speaker 10: inferencing because the cost of your chips is so high.

531
00:26:49,040 --> 00:26:51,960
Speaker 10: With Nvidia, and you've got to find a way to

532
00:26:51,960 --> 00:26:55,119
Speaker 10: bring down the cost. Everyone wants more inferencing. You just

533
00:26:55,200 --> 00:26:56,960
Speaker 10: have to bring down the cost so that you know

534
00:26:57,000 --> 00:26:58,560
Speaker 10: they can do it profitaboud.

535
00:26:59,320 --> 00:27:02,480
Speaker 2: Well, see how the response does indeed turn out, and

536
00:27:02,840 --> 00:27:05,439
Speaker 2: how they compare the software offerings as well as the hardware.

537
00:27:05,520 --> 00:27:08,560
Speaker 2: Man keep seeing a BlueBag intelligence always across the story.

538
00:27:08,560 --> 00:27:09,479
Speaker 3: We so appreciate it.

539
00:27:09,600 --> 00:27:12,440
Speaker 2: More insight into AI and indeed margins is set to

540
00:27:12,480 --> 00:27:16,640
Speaker 2: come after the Bell, Dell HP they report Bluebag's DNA.

541
00:27:16,520 --> 00:27:17,959
Speaker 3: Bass gives us the preview.

542
00:27:17,960 --> 00:27:21,200
Speaker 2: We're just hearing about the very healthy margins that Nvidia has,

543
00:27:21,440 --> 00:27:24,359
Speaker 2: and in many ways it's because the margins of Dell

544
00:27:24,440 --> 00:27:26,719
Speaker 2: and HP and server offerers are much thinner.

545
00:27:27,800 --> 00:27:28,879
Speaker 3: Sure, Yeah, Dell.

546
00:27:29,600 --> 00:27:31,680
Speaker 11: The most watched part of Dell's business for the last

547
00:27:31,680 --> 00:27:33,680
Speaker 11: couple of quarters has been its AI server.

548
00:27:33,520 --> 00:27:37,639
Speaker 3: Business, many most all running in media.

549
00:27:37,400 --> 00:27:41,280
Speaker 11: GPUs, and they have some a really marque list of customers.

550
00:27:41,320 --> 00:27:42,800
Speaker 3: There's corewe there's XCAI.

551
00:27:43,400 --> 00:27:45,480
Speaker 11: We reported the other week there they just got a

552
00:27:45,560 --> 00:27:48,439
Speaker 11: deal for the first Armenian AI data center.

553
00:27:48,760 --> 00:27:50,200
Speaker 3: The problem is, in order.

554
00:27:49,960 --> 00:27:52,840
Speaker 11: To win some of those deals and to execute on them,

555
00:27:53,119 --> 00:27:53,720
Speaker 11: Dell has.

556
00:27:53,600 --> 00:27:55,480
Speaker 3: Had to put up with some pretty.

557
00:27:55,200 --> 00:27:59,400
Speaker 11: Narrow margins over at eachp. The margin pressure is now

558
00:27:59,440 --> 00:28:03,920
Speaker 11: coming from memory, so HP, the memory chips that they

559
00:28:04,080 --> 00:28:07,800
Speaker 11: need to use for their personal computers are also rising

560
00:28:07,800 --> 00:28:10,320
Speaker 11: in price, and so there's a concern for the future

561
00:28:10,680 --> 00:28:12,719
Speaker 11: numbers from HP about how they're going to handle that

562
00:28:12,800 --> 00:28:15,040
Speaker 11: march an impact on the PC side.

563
00:28:14,760 --> 00:28:16,639
Speaker 2: I mean it as well for Micron, who's earning has

564
00:28:16,640 --> 00:28:20,119
Speaker 2: come the week after. But what's interesting more broadly is

565
00:28:20,160 --> 00:28:23,000
Speaker 2: have they from a stock perspective, from an investor perspective,

566
00:28:23,240 --> 00:28:26,560
Speaker 2: ridden the AI wave? How much have people been looking

567
00:28:26,640 --> 00:28:29,920
Speaker 2: to HP more broadly for AI to be the real

568
00:28:29,960 --> 00:28:30,800
Speaker 2: winning staff for it?

569
00:28:30,960 --> 00:28:34,760
Speaker 11: So there for them, it's more on the aipcs, so

570
00:28:34,920 --> 00:28:39,520
Speaker 11: an increasing percentage of their personal computers are these aipcs

571
00:28:39,600 --> 00:28:42,440
Speaker 11: which have a different special chip, not an in video one,

572
00:28:43,040 --> 00:28:47,760
Speaker 11: in order to run AI functions natively in the personal computer.

573
00:28:47,800 --> 00:28:51,920
Speaker 11: They're also really riding an upgrade wave with Windows ten

574
00:28:52,040 --> 00:28:54,880
Speaker 11: going out of support, people are needing to upgrade to

575
00:28:54,920 --> 00:28:55,880
Speaker 11: Windows eleven.

576
00:28:55,600 --> 00:28:59,320
Speaker 3: So that's been helping them. But having that comme.

577
00:28:59,160 --> 00:29:01,520
Speaker 11: At a time where they're going to have to potentially

578
00:29:01,840 --> 00:29:04,720
Speaker 11: incur higher cost for the memory going into those machines

579
00:29:05,040 --> 00:29:05,760
Speaker 11: is a concern.

580
00:29:06,800 --> 00:29:09,000
Speaker 2: Well, you're going to be busy after the earning spell

581
00:29:09,040 --> 00:29:11,760
Speaker 2: tonight in most then of us across all things l NHP,

582
00:29:11,880 --> 00:29:13,560
Speaker 2: we keep an eye out. Meanwhile, coming up, we go

583
00:29:13,600 --> 00:29:16,320
Speaker 2: to the private markets. Sequoia Capital partner Brian Halligan is

584
00:29:16,360 --> 00:29:18,120
Speaker 2: going to be with us and how you.

585
00:29:18,080 --> 00:29:22,560
Speaker 3: Model for a desirable founder and CEO that has changed

586
00:29:22,560 --> 00:29:32,080
Speaker 3: for venture investors. That's sex the su Blomberg Tech Look.

587
00:29:32,120 --> 00:29:35,040
Speaker 2: We've spent the show, perhaps the last week of shows,

588
00:29:35,640 --> 00:29:39,360
Speaker 2: with ourselves and investors questioning in videas valuation and the

589
00:29:39,400 --> 00:29:42,120
Speaker 2: prospect of a so called AI bubble in their public markets.

590
00:29:42,240 --> 00:29:45,240
Speaker 2: Our next guest says, it's actually a private market issue

591
00:29:45,320 --> 00:29:47,719
Speaker 2: if you think about AI bubbles. Brian Halligan is with US,

592
00:29:47,760 --> 00:29:49,560
Speaker 2: partner at Sequoia Capital, professor at.

593
00:29:49,520 --> 00:29:51,040
Speaker 3: MIT, co founder of HubSpot.

594
00:29:51,280 --> 00:29:54,000
Speaker 2: That just goes on, Brian, what are you seeing in

595
00:29:54,000 --> 00:29:58,040
Speaker 2: the private markets in certain isolated instances that maybe reflect

596
00:29:58,120 --> 00:29:59,920
Speaker 2: some anxiety that we're getting in the public markets.

597
00:30:00,480 --> 00:30:03,280
Speaker 12: Well, I'm old enough that I lived through the last bubble, Caroline,

598
00:30:03,560 --> 00:30:07,560
Speaker 12: and history doesn't repeat itself. But at rhymes, yeah, and

599
00:30:07,560 --> 00:30:11,320
Speaker 12: there's definitely some rhyming going on. Like man, the evaluations

600
00:30:11,360 --> 00:30:15,719
Speaker 12: are high, and they're high early. The thing that's different is,

601
00:30:16,880 --> 00:30:21,160
Speaker 12: my goodness, is their galactic level growth in these startups.

602
00:30:21,200 --> 00:30:27,480
Speaker 12: The demand is amazing and it sort of started at

603
00:30:27,520 --> 00:30:30,960
Speaker 12: the model level and then went to infrastructure. The app

604
00:30:31,080 --> 00:30:34,400
Speaker 12: level companies are absolutely ripping now and so it's an

605
00:30:34,440 --> 00:30:35,080
Speaker 12: interesting time.

606
00:30:35,160 --> 00:30:36,719
Speaker 7: It's different than ninety nine.

607
00:30:37,040 --> 00:30:40,280
Speaker 2: How are you therefore setting up as you're helping CEOs become.

608
00:30:40,040 --> 00:30:41,280
Speaker 3: From startup to scale up.

609
00:30:41,320 --> 00:30:44,320
Speaker 2: Mindset and eleven Labs, for example, which I heard time

610
00:30:44,360 --> 00:30:46,640
Speaker 2: and time again getting adopted, even Jensen Wang saying how

611
00:30:46,720 --> 00:30:49,240
Speaker 2: much he's loving that particular product. That's a CEO you're

612
00:30:49,280 --> 00:30:52,120
Speaker 2: helping navigate. What do you say to them in these moments?

613
00:30:53,760 --> 00:30:57,080
Speaker 12: So if I were a founder and I were warried

614
00:30:57,120 --> 00:30:58,920
Speaker 12: it were a bubble, I would do a couple of things.

615
00:30:59,600 --> 00:31:01,880
Speaker 12: First thing I would do is in my next round,

616
00:31:01,880 --> 00:31:03,480
Speaker 12: I would take a little bit of money off the table.

617
00:31:03,840 --> 00:31:04,000
Speaker 6: Oh.

618
00:31:04,520 --> 00:31:06,240
Speaker 12: The second thing I would do is that would raise

619
00:31:06,280 --> 00:31:08,440
Speaker 12: a lot more than a planned because if it is

620
00:31:08,440 --> 00:31:10,480
Speaker 12: a bubble and it dips and it eventually comes back,

621
00:31:10,520 --> 00:31:12,000
Speaker 12: you want enough to last through.

622
00:31:12,080 --> 00:31:13,400
Speaker 7: Those would be the two places.

623
00:31:13,200 --> 00:31:15,280
Speaker 3: Twenty twenty one mindset A little bit, A little bit.

624
00:31:15,360 --> 00:31:16,840
Speaker 12: Yeah, A lot of those companies twenty twenty on, a

625
00:31:16,840 --> 00:31:18,760
Speaker 12: lot of great companies just didn't kind of make it out.

626
00:31:18,800 --> 00:31:22,160
Speaker 12: They didn't raise enough, they didn't make it through. And

627
00:31:22,200 --> 00:31:24,360
Speaker 12: if you look at ninety nine or the bubble era,

628
00:31:25,080 --> 00:31:27,120
Speaker 12: you know, some good companies came out of there. Google

629
00:31:27,200 --> 00:31:29,280
Speaker 12: came out of there, Amazon came out of their sales,

630
00:31:29,280 --> 00:31:31,920
Speaker 12: short dot Com came out of there. And so even

631
00:31:32,000 --> 00:31:34,840
Speaker 12: if the valuations are really inflated, if you can find

632
00:31:34,840 --> 00:31:38,000
Speaker 12: some amazing founders, I mean, there's there's going to be

633
00:31:38,240 --> 00:31:40,640
Speaker 12: a lot more than three that come out of this one.

634
00:31:41,080 --> 00:31:43,960
Speaker 2: There's a lot more than three companies trying to get

635
00:31:43,960 --> 00:31:45,600
Speaker 2: it on each other's space as well. And this is

636
00:31:45,600 --> 00:31:47,560
Speaker 2: where kind of marketing comes in. And I want your

637
00:31:47,600 --> 00:31:50,920
Speaker 2: brain space as someone who's helped led HubSpot founded it.

638
00:31:50,960 --> 00:31:53,400
Speaker 2: But also you've got this great new book out. I'm

639
00:31:53,400 --> 00:31:55,840
Speaker 2: no deadhead, but I know many people are. You are

640
00:31:55,840 --> 00:31:58,080
Speaker 2: a quintessential dead head and loved all things about the

641
00:31:58,120 --> 00:31:59,760
Speaker 2: Grateful Dead. But you think we should look at the

642
00:31:59,760 --> 00:32:01,880
Speaker 2: Great Dead as a marketing model as well.

643
00:32:01,920 --> 00:32:03,200
Speaker 3: What is it about community?

644
00:32:03,280 --> 00:32:05,480
Speaker 2: What is it about I'm sure it's not about bootlegging

645
00:32:05,520 --> 00:32:07,680
Speaker 2: music that you think is the thing to repeat.

646
00:32:08,080 --> 00:32:11,080
Speaker 12: Absolutely, there's so much founders can learn from Jerry Garcia

647
00:32:11,080 --> 00:32:14,320
Speaker 12: in the Grave of Dead. First of all, Garcia was

648
00:32:14,480 --> 00:32:19,160
Speaker 12: like the ultimate and original Silicon Valley founder founded in

649
00:32:19,200 --> 00:32:22,320
Speaker 12: Palo Alto, built an amazing company. He did a lot

650
00:32:22,360 --> 00:32:25,400
Speaker 12: of interesting marketing things that all the founders I coach

651
00:32:25,440 --> 00:32:25,920
Speaker 12: are trying to do.

652
00:32:26,080 --> 00:32:26,680
Speaker 7: First thing he.

653
00:32:26,560 --> 00:32:29,760
Speaker 12: Did was he kind of created a whole category around

654
00:32:29,760 --> 00:32:32,000
Speaker 12: this thing called jam bands that lots of people following around.

655
00:32:32,080 --> 00:32:35,120
Speaker 12: Hard to do. Second thing he did is he didn't

656
00:32:35,160 --> 00:32:37,760
Speaker 12: use traditional ways to market his product, like radio stations

657
00:32:37,760 --> 00:32:39,960
Speaker 12: and albums. He'd let people come in with all their

658
00:32:39,960 --> 00:32:42,400
Speaker 12: equipment and record the concerts in trade tapes.

659
00:32:42,480 --> 00:32:42,800
Speaker 7: He went.

660
00:32:43,120 --> 00:32:45,800
Speaker 12: He was like the first viral marketer in Silicon Valley.

661
00:32:46,280 --> 00:32:48,400
Speaker 12: The third thing he did that I think is quite

662
00:32:48,440 --> 00:32:52,240
Speaker 12: remarkable is he didn't like that ticket Master in the

663
00:32:52,280 --> 00:32:55,840
Speaker 12: scalpers made all the money and inflated the prices for

664
00:32:55,920 --> 00:33:00,160
Speaker 12: his customers. So he disintimated those two layers. And so

665
00:33:00,160 --> 00:33:01,959
Speaker 12: we're going to start a ticketing company and we're going

666
00:33:02,000 --> 00:33:04,360
Speaker 12: to sell tickets directly to customers. We're going to cut

667
00:33:04,360 --> 00:33:06,760
Speaker 12: out Ticketmaster and all this scalfer. So he fought in

668
00:33:06,800 --> 00:33:09,240
Speaker 12: a very original way. He was He was a radically

669
00:33:09,520 --> 00:33:12,720
Speaker 12: first Principles founder. He rhymes a lot with Jensen Hwang,

670
00:33:12,840 --> 00:33:15,120
Speaker 12: rhymes a lot with Sam Almon, rams a lot with

671
00:33:15,200 --> 00:33:15,920
Speaker 12: Steve Jobs.

672
00:33:16,800 --> 00:33:18,800
Speaker 2: What's interesting, And I'm going to keep going with this

673
00:33:18,840 --> 00:33:23,480
Speaker 2: grateful dead analogy because I love it. Disbanded in nineteen

674
00:33:23,560 --> 00:33:26,920
Speaker 2: ninety five after the passing of Jerry Garcia, and there's

675
00:33:26,960 --> 00:33:30,600
Speaker 2: been different combinations since that, and co we see some

676
00:33:30,720 --> 00:33:34,440
Speaker 2: artistic differences. Dare I say there's a few artistic differences

677
00:33:34,640 --> 00:33:37,480
Speaker 2: at Sequoia at the moment and invention Will Broady, You've

678
00:33:37,480 --> 00:33:39,000
Speaker 2: been at Sequoia for a year, There's been a lot

679
00:33:39,000 --> 00:33:41,160
Speaker 2: of change at the top. How are you seeing the

680
00:33:41,280 --> 00:33:43,719
Speaker 2: venture community set up for this moment? What can they

681
00:33:43,800 --> 00:33:47,120
Speaker 2: learn from grateful dead and from entrepreneurialism at this moment.

682
00:33:47,160 --> 00:33:49,400
Speaker 12: I think Sequoia is particularly well set up at the moment.

683
00:33:49,880 --> 00:33:51,719
Speaker 12: The two new leaders were fantastic that have been there

684
00:33:51,720 --> 00:33:54,880
Speaker 12: a long time. They have amazing track records, and like

685
00:33:55,080 --> 00:33:59,040
Speaker 12: I think of the stack as like the hardware, the labs, infrastrate,

686
00:33:59,120 --> 00:34:02,560
Speaker 12: the apps is well positioned with amazing investments across all

687
00:34:02,600 --> 00:34:05,120
Speaker 12: of them, particularly at the app level, and particularly you're

688
00:34:05,120 --> 00:34:07,520
Speaker 12: in New York City. Last night I had dinner with

689
00:34:07,560 --> 00:34:10,920
Speaker 12: the CEO of profound terrific company that does not SEO

690
00:34:11,080 --> 00:34:15,440
Speaker 12: but like SEO for Chetchubt and Gemini in the founder

691
00:34:15,440 --> 00:34:20,719
Speaker 12: of Rogo, fantastic CEO. Rogo is like AI for investment bankers.

692
00:34:21,360 --> 00:34:23,200
Speaker 12: I think this is emble manic of what's going on.

693
00:34:23,360 --> 00:34:25,560
Speaker 12: The app layer is starting to pop and Sequoya is

694
00:34:25,600 --> 00:34:27,400
Speaker 12: in a lot of these things. Other great companies in

695
00:34:27,400 --> 00:34:31,239
Speaker 12: New York basis is selling to accountants. You've got Crosby

696
00:34:31,560 --> 00:34:34,560
Speaker 12: in Harvey's selling to lawyers. New York is actually having

697
00:34:34,600 --> 00:34:36,239
Speaker 12: a moment in AI, and it's kind of at that

698
00:34:36,280 --> 00:34:36,840
Speaker 12: app level.

699
00:34:37,520 --> 00:34:40,880
Speaker 2: The app level is where perhaps the productivity really starts

700
00:34:40,880 --> 00:34:42,880
Speaker 2: to rain in. That is what the proof point is

701
00:34:42,920 --> 00:34:45,040
Speaker 2: needed many would say for the market, when actually you

702
00:34:45,120 --> 00:34:48,120
Speaker 2: and I are not just using it for our own personal life.

703
00:34:48,280 --> 00:34:50,560
Speaker 2: But see productivity go up into the right and companies

704
00:34:50,600 --> 00:34:52,480
Speaker 2: start doubling down on the purchase.

705
00:34:52,160 --> 00:34:53,000
Speaker 3: Of these applications.

706
00:34:53,160 --> 00:34:55,319
Speaker 2: What does that show up when don't we stop even

707
00:34:55,400 --> 00:34:58,279
Speaker 2: talking about an AI bubble because we see the.

708
00:34:58,200 --> 00:35:04,000
Speaker 12: Productivity, Well, there's just giant demand and galactic growth. One

709
00:35:04,040 --> 00:35:06,919
Speaker 12: of the interesting things about all these founders is I'm like, well,

710
00:35:06,960 --> 00:35:09,759
Speaker 12: you're certainly going to grow with less people, right, and

711
00:35:09,800 --> 00:35:12,879
Speaker 12: they say, well, actually no, we're hiring, really hiring aggressively,

712
00:35:13,440 --> 00:35:16,000
Speaker 12: and so like this. Some people are like AI is

713
00:35:16,040 --> 00:35:19,480
Speaker 12: going to make humans unnecessary. They're like, now, we're going

714
00:35:19,560 --> 00:35:22,600
Speaker 12: to make users unstoppable. And that's sort of the mindset

715
00:35:22,640 --> 00:35:25,359
Speaker 12: across most of these AI startups. So they're pressing hard,

716
00:35:25,600 --> 00:35:29,279
Speaker 12: hiring hard, growing hard, and I think you'll start seeing

717
00:35:29,280 --> 00:35:31,840
Speaker 12: over the next couple of years big productivity advantages like

718
00:35:31,880 --> 00:35:35,520
Speaker 12: HubSpot uses AI across the enterprise and customers support in

719
00:35:35,719 --> 00:35:39,080
Speaker 12: R and D, massive productivity benefits across a couple of

720
00:35:39,080 --> 00:35:39,600
Speaker 12: big parts.

721
00:35:39,480 --> 00:35:40,040
Speaker 7: Of the enterprise.

722
00:35:40,200 --> 00:35:42,560
Speaker 2: I mean, we're using juice box, which helps with hiring

723
00:35:42,560 --> 00:35:43,680
Speaker 2: in the world of AI as well.

724
00:35:43,920 --> 00:35:45,560
Speaker 12: One of my favorite founders of companies on fire.

725
00:35:45,680 --> 00:35:48,960
Speaker 3: Yes, it's been wonderful having you here. You're on far too.

726
00:35:49,239 --> 00:35:52,000
Speaker 2: Enjoy the rest of your thanksgiving. Grian Halligan in the

727
00:35:52,000 --> 00:35:54,000
Speaker 2: house Aquoia Capital partner that.

728
00:35:53,760 --> 00:35:58,080
Speaker 3: We thank him. Meanwhile, coming up, robots housekeepers? Are they

729
00:35:58,120 --> 00:35:59,080
Speaker 3: close to a reality?

730
00:36:00,080 --> 00:36:03,279
Speaker 2: To the CEO behind memo the robot trained on and

731
00:36:03,400 --> 00:36:13,560
Speaker 2: for your housework, bring bag text. Let's return to our

732
00:36:13,640 --> 00:36:17,000
Speaker 2: key story of the day. Shares Alphabet another record high.

733
00:36:17,000 --> 00:36:18,640
Speaker 2: They're rising as the company is said to be in

734
00:36:18,680 --> 00:36:21,520
Speaker 2: talks potentially with Meta over a deal to provide AI

735
00:36:21,600 --> 00:36:24,719
Speaker 2: chips TPUs Google Zone in house chips to Meta for

736
00:36:24,719 --> 00:36:26,760
Speaker 2: the future. It's all according to a report by The Information.

737
00:36:27,360 --> 00:36:29,520
Speaker 2: Let's go more on this and other trends in AI.

738
00:36:29,640 --> 00:36:32,560
Speaker 2: Want not to be using generative haarheng models for this holiday,

739
00:36:32,840 --> 00:36:35,160
Speaker 2: Davey Alba, you're with us and just first to the

740
00:36:35,160 --> 00:36:37,840
Speaker 2: bread and buster of Alphabet. How much has caught you

741
00:36:37,920 --> 00:36:41,040
Speaker 2: off guard? People working at Alphabet off guard that finally

742
00:36:41,080 --> 00:36:43,680
Speaker 2: we get the understanding of the vertical model integration.

743
00:36:45,080 --> 00:36:47,280
Speaker 13: You know, I don't know that I was necessarily caught

744
00:36:47,320 --> 00:36:49,960
Speaker 13: off guard by that. I think that this has been

745
00:36:50,000 --> 00:36:52,600
Speaker 13: creeping up for a while, but it does seem like

746
00:36:52,640 --> 00:36:55,000
Speaker 13: the rest of the industry is catching up to this

747
00:36:55,120 --> 00:37:00,560
Speaker 13: idea that GPUs have enormous value and are really could

748
00:37:00,640 --> 00:37:04,160
Speaker 13: be a really valuable part of you know, people's AI

749
00:37:04,360 --> 00:37:07,520
Speaker 13: mixes that you know in video is not the only

750
00:37:08,120 --> 00:37:10,040
Speaker 13: game in town when it comes to chips.

751
00:37:10,520 --> 00:37:13,520
Speaker 2: Certainly, we've heard Da Davidson, you've heard Bernstein, You've had

752
00:37:13,520 --> 00:37:16,040
Speaker 2: a lot of analysts saying this could be a really

753
00:37:16,080 --> 00:37:18,719
Speaker 2: individual way of selling it. And interestingly, now maybe not

754
00:37:18,760 --> 00:37:21,040
Speaker 2: just for Google Cloud, but TPUs in and of themselves,

755
00:37:21,040 --> 00:37:24,319
Speaker 2: but aside from Alphabet, what they're doing in terms of

756
00:37:24,320 --> 00:37:27,719
Speaker 2: the chip stack, their models, how are people going to

757
00:37:27,760 --> 00:37:29,840
Speaker 2: be using them this holiday You've got a great story

758
00:37:29,840 --> 00:37:32,479
Speaker 2: out about the anxiety perhaps this is going to create

759
00:37:32,480 --> 00:37:33,080
Speaker 2: in the kitchen.

760
00:37:34,360 --> 00:37:38,800
Speaker 13: Yeah, we published a story this morning about how food

761
00:37:38,880 --> 00:37:45,480
Speaker 13: bloggers are warning consumers about AI recipe slop ahead of Thanksgiving.

762
00:37:46,680 --> 00:37:50,160
Speaker 13: We talked to twenty two food bloggers ahead of the

763
00:37:50,200 --> 00:37:53,800
Speaker 13: holiday season, and all of them report, you know, traffic

764
00:37:53,840 --> 00:37:59,840
Speaker 13: declines and AI Frankenstein recipes that remix their recipes and

765
00:38:00,120 --> 00:38:03,280
Speaker 13: pull in bits and pieces of other recipes to create

766
00:38:03,800 --> 00:38:07,280
Speaker 13: content that is not accurate. That where if you follow

767
00:38:07,360 --> 00:38:11,720
Speaker 13: the actual recipe steps that are generated by these AI models.

768
00:38:12,239 --> 00:38:16,880
Speaker 13: You could come out with literal slab, you know, inedible food.

769
00:38:17,160 --> 00:38:23,680
Speaker 13: And it's really confusing people these days sort of where

770
00:38:23,719 --> 00:38:29,280
Speaker 13: to find quality content on food recipes this this holiday season.

771
00:38:29,560 --> 00:38:32,400
Speaker 2: Maybe stick to the source for now at least. Davey

772
00:38:32,440 --> 00:38:36,080
Speaker 2: Alba punning all puns. I thank you. Meanwhile, let's talk

773
00:38:36,120 --> 00:38:38,360
Speaker 2: about what else you need helping you in your kitchen.

774
00:38:38,480 --> 00:38:41,480
Speaker 2: Maybe it's robots. Well, they've been busy dancing, they've been boxing,

775
00:38:41,520 --> 00:38:43,719
Speaker 2: they've been running marathons, so why are they not doing

776
00:38:43,719 --> 00:38:47,240
Speaker 2: more of your chores? This is memo from AI startup

777
00:38:47,280 --> 00:38:50,920
Speaker 2: Sunday Robotics. The company says it's robot is purpose built

778
00:38:50,920 --> 00:38:54,480
Speaker 2: for housework, trained on millions of episodes of everyday household routines.

779
00:38:54,680 --> 00:38:58,880
Speaker 2: Sunday's co founder and CEO, Tony Jao joins us, now you're.

780
00:39:00,280 --> 00:39:00,680
Speaker 3: Robot.

781
00:39:01,160 --> 00:39:04,600
Speaker 2: It's kind of humanoid like a little but not totally.

782
00:39:04,960 --> 00:39:06,920
Speaker 2: How does it differ from other robotics.

783
00:39:08,120 --> 00:39:10,600
Speaker 14: Yeah, I think we just think about safety as a

784
00:39:10,640 --> 00:39:14,120
Speaker 14: really high priority item, and we define it as being

785
00:39:14,200 --> 00:39:16,880
Speaker 14: like passively safe. And what it means is that you

786
00:39:16,920 --> 00:39:19,799
Speaker 14: can put the robot into any configuration and you can

787
00:39:19,840 --> 00:39:23,120
Speaker 14: cut power and the robot will still be stable. So

788
00:39:23,320 --> 00:39:25,960
Speaker 14: this is why we build this whole mobile base as

789
00:39:25,960 --> 00:39:26,760
Speaker 14: opposed to lex.

790
00:39:28,000 --> 00:39:31,880
Speaker 2: Where did you ultimately come to decide that this was

791
00:39:31,920 --> 00:39:34,759
Speaker 2: the way in which you should think about robotics, Maybe

792
00:39:34,760 --> 00:39:37,600
Speaker 2: not in a humanoid manner, maybe just with real safety. First,

793
00:39:37,640 --> 00:39:40,040
Speaker 2: you've got a stellar background, you're a Google Deep Mind,

794
00:39:40,040 --> 00:39:42,839
Speaker 2: Tesla Auto Power at Google X. You're also, of course

795
00:39:42,920 --> 00:39:45,280
Speaker 2: just coming out of stealth with a call thirty million

796
00:39:45,760 --> 00:39:46,520
Speaker 2: to put to work.

797
00:39:48,360 --> 00:39:48,600
Speaker 7: Yeah.

798
00:39:48,640 --> 00:39:51,680
Speaker 14: I think the the biggest way we think very differently

799
00:39:51,800 --> 00:39:54,440
Speaker 14: is actually on how to train these robots, not just

800
00:39:54,520 --> 00:39:55,439
Speaker 14: the design, but.

801
00:39:55,520 --> 00:39:57,640
Speaker 7: How it obtained its intelligence.

802
00:39:58,239 --> 00:40:01,600
Speaker 14: So normally people train their robot through tally operation, which

803
00:40:01,640 --> 00:40:04,320
Speaker 14: essentially means that you kind of lock into the robot and.

804
00:40:04,239 --> 00:40:05,480
Speaker 7: Control how it moves.

805
00:40:05,800 --> 00:40:08,160
Speaker 14: But the way we learn is actually very very different

806
00:40:08,560 --> 00:40:12,520
Speaker 14: that we learn from humans directly. That's essentially we design

807
00:40:12,560 --> 00:40:16,640
Speaker 14: these device a glove that captures how human do their chores,

808
00:40:17,040 --> 00:40:20,320
Speaker 14: and we're able to transfer those data directly into the robots,

809
00:40:20,480 --> 00:40:22,759
Speaker 14: and that's how the robot is able to learn from

810
00:40:22,800 --> 00:40:24,480
Speaker 14: like hundreds of humans simultaneously.

811
00:40:25,600 --> 00:40:29,440
Speaker 2: These robots don't come cheap, but interestingly, Tony you're not

812
00:40:29,480 --> 00:40:31,160
Speaker 2: looking to sell immediately, you're.

813
00:40:30,960 --> 00:40:32,320
Speaker 3: Looking to beta test.

814
00:40:32,520 --> 00:40:35,680
Speaker 2: Now, how are you finding the right people to bring

815
00:40:35,719 --> 00:40:37,160
Speaker 2: these robots into their home?

816
00:40:38,280 --> 00:40:40,760
Speaker 14: Yeah, so if you look at our website, we actually

817
00:40:40,800 --> 00:40:45,000
Speaker 14: have a huge signup doc for people who are interested,

818
00:40:45,440 --> 00:40:47,280
Speaker 14: and we're ready to get more than a few thousand

819
00:40:47,320 --> 00:40:48,360
Speaker 14: of these applications.

820
00:40:48,719 --> 00:40:49,960
Speaker 7: So what we're going to work on.

821
00:40:49,960 --> 00:40:53,760
Speaker 14: Next is to like very carefully sit through all these

822
00:40:54,120 --> 00:40:58,000
Speaker 14: applications and find people who what we call like founding families,

823
00:40:58,440 --> 00:41:00,799
Speaker 14: who'll be there to give us feedback, will be there

824
00:41:00,880 --> 00:41:03,319
Speaker 14: to kind of shape what a product will look like

825
00:41:03,400 --> 00:41:03,920
Speaker 14: in the future.

826
00:41:04,480 --> 00:41:07,320
Speaker 3: What do you think the hardest element for these robots.

827
00:41:06,920 --> 00:41:09,400
Speaker 7: Is the hardest element?

828
00:41:11,360 --> 00:41:14,560
Speaker 14: I think it will be how people will react to

829
00:41:14,600 --> 00:41:18,120
Speaker 14: this like big robot in their homes. And again, this

830
00:41:18,160 --> 00:41:20,920
Speaker 14: is the first time that anyone has put a mobile

831
00:41:20,920 --> 00:41:24,480
Speaker 14: manipulator like a robot with arms into real living homes,

832
00:41:24,640 --> 00:41:26,680
Speaker 14: and this is something that we're incredibly excited about. I

833
00:41:26,719 --> 00:41:29,640
Speaker 14: think will be people will be pleasantly surprised by how

834
00:41:29,760 --> 00:41:30,440
Speaker 14: useful it is.

835
00:41:31,120 --> 00:41:31,759
Speaker 3: Why do you.

836
00:41:31,680 --> 00:41:36,080
Speaker 2: Think so many tech companies do end up turning to humanoids,

837
00:41:36,440 --> 00:41:39,439
Speaker 2: to turning to the physical form of a human rather

838
00:41:39,520 --> 00:41:41,440
Speaker 2: than a more stable basis you have.

839
00:41:43,160 --> 00:41:48,160
Speaker 14: Yeah, I think if you're working on environments that with

840
00:41:48,239 --> 00:41:51,200
Speaker 14: a lot of stairs, or you're working on environments with

841
00:41:52,080 --> 00:41:56,319
Speaker 14: like you know, like hills, I think having lux will

842
00:41:56,360 --> 00:41:59,520
Speaker 14: be helpful in that case and for us in our

843
00:41:59,560 --> 00:42:02,920
Speaker 14: first we decide to go for a veal base just

844
00:42:02,960 --> 00:42:06,200
Speaker 14: for the simplicity, for lowering costs and to allow us

845
00:42:06,200 --> 00:42:06,800
Speaker 14: to move faster.

846
00:42:07,400 --> 00:42:08,640
Speaker 3: Talk to us about costs.

847
00:42:08,880 --> 00:42:12,160
Speaker 2: You have managed to raise seed funding from Sarah Gao

848
00:42:12,360 --> 00:42:15,360
Speaker 2: a conviction. You've now got money coming in led by Benchmark.

849
00:42:15,800 --> 00:42:18,040
Speaker 3: What is the key cost for you? Is it the talent?

850
00:42:18,360 --> 00:42:20,279
Speaker 3: Is it the hardware? What is it?

851
00:42:21,640 --> 00:42:21,879
Speaker 7: Yeah?

852
00:42:22,120 --> 00:42:25,480
Speaker 14: So our hardware is actually quite differentiated from a lot

853
00:42:25,480 --> 00:42:28,960
Speaker 14: of humanoids. Even at quantity zero when we prototype it

854
00:42:29,040 --> 00:42:31,399
Speaker 14: these days, it costs around twenty five K to make,

855
00:42:31,960 --> 00:42:35,880
Speaker 14: and at quantity around like five thousand, we're able to

856
00:42:35,880 --> 00:42:38,879
Speaker 14: get a cost to below ten K. So I think

857
00:42:38,920 --> 00:42:41,279
Speaker 14: we'll be ending up selling it around to five to

858
00:42:41,320 --> 00:42:44,920
Speaker 14: ten K in the final price. And this is we're thinking.

859
00:42:44,719 --> 00:42:47,120
Speaker 7: About robots, not like another car.

860
00:42:46,960 --> 00:42:50,920
Speaker 14: Like purchase, but more like a fancy smartphone or a laptop.

861
00:42:51,960 --> 00:42:55,640
Speaker 2: How does American ingenuity when it comes to robotics stack

862
00:42:55,760 --> 00:42:57,840
Speaker 2: up to that of Asia in China and how you

863
00:42:57,920 --> 00:43:01,200
Speaker 2: seeing your own supply chain develop Yeah.

864
00:43:00,960 --> 00:43:05,000
Speaker 14: This is a great question. I think American has incredible

865
00:43:05,320 --> 00:43:09,520
Speaker 14: mechanical engineers, software engineers, but we are lacking in terms

866
00:43:09,560 --> 00:43:14,040
Speaker 14: of some of the supply chain infrastructures. So I think

867
00:43:14,120 --> 00:43:18,759
Speaker 14: we're at the point that we need to leverage some

868
00:43:18,800 --> 00:43:22,920
Speaker 14: of the growing supply chains the humanoids in China and

869
00:43:22,920 --> 00:43:25,480
Speaker 14: we actually share a lot of components with them so

870
00:43:25,520 --> 00:43:28,399
Speaker 14: that we can have the economy of scale before us

871
00:43:28,400 --> 00:43:29,840
Speaker 14: shipping like millions of robots.

872
00:43:30,560 --> 00:43:34,640
Speaker 2: Any Ja, CEO of robotic startup Sunday, fascinating to have

873
00:43:34,680 --> 00:43:34,880
Speaker 2: you on.

874
00:43:35,040 --> 00:43:35,759
Speaker 3: Thank you very much.

875
00:43:35,800 --> 00:43:39,040
Speaker 2: Indeed, that does it for this edition at Blueberg Tech,

876
00:43:39,280 --> 00:43:40,960
Speaker 2: we do want to remind you of the market moves

877
00:43:41,000 --> 00:43:43,480
Speaker 2: today in video under significant pressure off of its lows,

878
00:43:43,480 --> 00:43:46,160
Speaker 2: still down by four percent. As we question is dominance

879
00:43:46,280 --> 00:43:49,440
Speaker 2: in the world of chips to train as.

880
00:43:49,280 --> 00:43:50,360
Speaker 3: Well as use your models.

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Speaker 2: That competition coming maybe from Alphabet TPUs, maybe Meta Eyeing

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Speaker 2: buying some for its data centers of the future as

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Speaker 2: Information is currently reporting Oracle once again off by one

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Speaker 2: point nine percent from New York.

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Speaker 3: This is Bloomberg Tech. Don't forget to check out the

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Speaker 3: podcast