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Speaker 1: We're from markhard where Innovation, Money and Power Collie in

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Speaker 1: Silicon Valley, NBN. This is Bloomberg Technology with Caroline Hyde

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Speaker 1: and Ed Ludlowly.

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Speaker 2: From San Francisco. This is Bloomberg Technology coming up full

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Speaker 2: market coverage, a stock stage, a Tuesday turnaround after six

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Speaker 2: point five trillion dollars was white from global markets Monday. Plus,

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Speaker 2: we sit down with the CTO of palenteer is Ai

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Speaker 2: demand boosts, the company's outlook, and the US rules. Google

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Speaker 2: illegally monopolized the search market through exclusive deals. It's a

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Speaker 2: bit of a sigh of relief on Tuesday. Call it

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Speaker 2: a dead cat bounce, call it a turnaround. This is

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Speaker 2: what financial markets look like. Notable gains on the Nasdaq

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Speaker 2: one hundred chip stocks recovering after it was technology that

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Speaker 2: led the selling in equity markets on Monday. A lot

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Speaker 2: of volatility around growth concerns and also economic data. And

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Speaker 2: then Bitcoin is interesting as well. You saw a fifty

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Speaker 2: six thousand US dollars per token. Later in the show

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Speaker 2: will go deeper on that. Let's move over to the

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Speaker 2: single names, because earnings are a big part of the

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Speaker 2: story in the moment. There are two in particular that

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Speaker 2: we're looking at. I really want to focus on Uber

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Speaker 2: up almost eight percent, strong growth, brooking, strong on the

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Speaker 2: bottom line. We're going to get to our reports on

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Speaker 2: that in just a moment, and then coming up the

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Speaker 2: CTO of Palenteer updated outlook for revenue, updated outlook for

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Speaker 2: profit AI demand from both commercial and government customers. That's

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Speaker 2: going to be a really interesting conversation in the next segment.

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Speaker 2: Stay tuned for it. We'll go back to markets. Joining

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Speaker 2: us for more is David Bunsen, chief investment Officer and

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Speaker 2: the Buntson Group, which oversees five point seven billion dollars

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Speaker 2: in assets under management. The story Monday was about some

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Speaker 2: of the megacap tech names, some of those that have

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Speaker 2: fueled the gains in the first half of the year.

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Speaker 2: Some of those names you were particularly concerned about, so

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Speaker 2: you sat on the sidelines away from them. How do

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Speaker 2: you react to the last twenty four hours?

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Speaker 3: Then, well, it's interesting that with that sector, it wasn't

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Speaker 3: really a Monday story per se. It had started July tenth.

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Speaker 3: The Nasdaq's drawdown had reached thirteen and a half percent yesterday,

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Speaker 3: so there was already quite a bit of slide going on,

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Speaker 3: and I think that does stem from the overvaluation. It's

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Speaker 3: pretty stunning when you see a name like Nvidia that

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Speaker 3: had been down twenty five to thirty percent and was

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Speaker 3: still trading at about fifty nine times earnings thirty times

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Speaker 3: sales after such a violent correction.

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Speaker 4: I think even today.

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Speaker 3: When you look at Apple still being negative with this

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Speaker 3: kind of bounce back rally today, it does feel to

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Speaker 3: me like a rotation of leadership is very well underway.

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Speaker 2: You raise a point that we reiterated in yesterday's show

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Speaker 2: that Friday marks four straight weekly declared on the Nasdaq

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Speaker 2: one hundred. Right, you were already in correction territory. I

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Speaker 2: guess a question for someone like you, David, is what

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Speaker 2: do you do now? You know, is the draw down

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Speaker 2: or so called technical correction enough that you start to

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Speaker 2: look at some of the names that for many they

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Speaker 2: are teenas or there is no alternative to holding them.

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Speaker 3: Well, there are probably investor categories that might want to

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Speaker 3: look at something there. They certainly shouldn't be doing it

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Speaker 3: because they believe it's reached a value. These are still

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Speaker 3: extremely expensive stocks for US as dividend growth investors. The tiny,

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Speaker 3: little paltry dividend that the NVIDIAs, Metas and Apples pay

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Speaker 3: is not enough to wet our beak, and so they

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Speaker 3: would have a long way to go to start returning

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Speaker 3: cash to shareholders for us.

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Speaker 4: To be attracted.

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Speaker 3: But even apart from our dividend growth orientation, the valuations

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Speaker 3: are still very high. I began professionally managing money in

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Speaker 3: the nineteen nineties, and so I will be forever scarred

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Speaker 3: by the reality of what happens when you overpay for

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Speaker 3: very good companies, very big and successful companies that have

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Speaker 3: a generational success, and yet their stock goes nowhere for ten, fifteen,

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Speaker 3: twenty years, and Cisco's case still.

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Speaker 4: Hasn't got back to nineteen ninety nine levels.

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Speaker 3: Those stories are real, and I believe very much they

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Speaker 3: will be the case with some of these names too.

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Speaker 4: I think investors have to be careful.

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Speaker 2: I find names like Cisco, maybe even Adele interesting as

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Speaker 2: sort of appendages to the infrastructure build out that's happening.

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Speaker 2: I wrote about it in Today's Tech Daily. Right if

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Speaker 2: you think about the growth concern, investmental capital expenditures into

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Speaker 2: data center seem to be plowing ahead regardless of short

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Speaker 2: term jitters, Is there an opportunity for you in that space, David,

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Speaker 2: or do you think it's just overdone already at this point.

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Speaker 3: Well, and that's a very interesting story because there you

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Speaker 3: can get cash flow. And so, for example, Blackstone has

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Speaker 3: become one of the largest investors institutionally in the data

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Speaker 3: center's story, and we're able to get a real yield

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Speaker 3: and become a landlord, and so to speak to data

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Speaker 3: center become a little bit less concerned with the underlying

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Speaker 3: business fundamentals as to how much Microsoft's going to buy

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Speaker 3: from Nvidia or what the monetization of AI is going

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Speaker 3: to be. A lot of those questions are not answered,

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Speaker 3: They're not going to get answered in time soon. But

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Speaker 3: data center becomes a story that from a brick and

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Speaker 3: mortar standpoint, you can make money in.

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Speaker 2: I find that so interesting. Never before did I think

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Speaker 2: I'd be writing about reates in my technology newsletter. And

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Speaker 2: I use their forecast or data to kind of look

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Speaker 2: at the market pipeline as a whole earnings. You know,

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Speaker 2: this morning we wake up pallentis strength uber strength. Last

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Speaker 2: week we learned from Microsoft in particular that if you

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Speaker 2: miss estimates, you're in trouble. But there's still a long

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Speaker 2: way to run in this earnings period. Have you taken

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Speaker 2: a sort of lesson on the whole from those that

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Speaker 2: have already reported.

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Speaker 4: Yeah, about seventy five percent of the way through.

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Speaker 3: It does appear that overall earnings growth is not going

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Speaker 3: to be eight percent is targeted on the quarter, but

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Speaker 3: probably closer to five percent.

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Speaker 4: Year over year, earning's.

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Speaker 3: Growth still looks like it going to be about twelve percent,

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Speaker 3: but some base effects are going to make that a lot.

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Speaker 4: Harder in the next quarters.

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Speaker 3: And so I think the story of Microsoft is a

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Speaker 3: kind of microcosm of the underlying reality that there's this

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Speaker 3: sort of feedback loop at play that Microsoft goes up

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Speaker 3: because it's buying a lot from Nvidia, and then Video

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Speaker 3: goes up because Microsoft's buying a lot from it, and

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Speaker 3: of course there's a lot of other customers. But this

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Speaker 3: idea that AI doesn't ever have to tell you how

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Speaker 3: it's going to make money for real other than an

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Speaker 3: infrastructure backbone story is problematic. And if those revenues are

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Speaker 3: going to be going down before they've converted to profits,

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Speaker 3: then you have an unwind that has to take place

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Speaker 3: of a feedback loop.

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Speaker 2: David for the technology set to them, what happens in

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Speaker 2: the second half of the day.

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Speaker 3: Ye, I would be somewhat unconstructive on the space on

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Speaker 3: a valuation basis, But we are hardly the types of

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Speaker 3: technology investors that you normally would be speaking to. Just

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Speaker 3: to the sense of our boring insistence on free cash flow,

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Speaker 3: you know, we own Broadcom in the portfolio. We don't

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Speaker 3: believe it got to the valuation overstretch that Nvidia has.

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Speaker 3: And then we own old tech names like IBM and

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Speaker 3: Cisco that have ongoing free cash flow from oldline businesses,

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Speaker 3: but obviously new growth opportunities. That's the way we would

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Speaker 3: prefer to play it, just simply because we think perfection

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Speaker 3: was priced in to the more contemporary technology names.

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Speaker 2: David, I do not for a second. Thank you are boring.

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Speaker 2: David Bunsen, CEO at the Punts and Groove, has been

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Speaker 2: great sorts you. Thank you very much. Let's get back

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Speaker 2: to the earning story. Speak about Uber reporting results before

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Speaker 2: the opening bell this morning. I want to bring in

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Speaker 2: Bloomberg's Nati Lung, who covers the gig economy platform for us,

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Speaker 2: and just go straight to gross bookings. It's basically everything

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Speaker 2: that runs through the Uber platform apart from the tips,

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Speaker 2: but within that there are loads of stories. What did

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Speaker 2: you learn about Uber, Natalie, Yes.

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Speaker 5: So from growth brookings it includes ride share and delivery

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Speaker 5: and user frequency and as well as driver activity on

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Speaker 5: the platform is all time high. And this result is

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Speaker 5: really showing how Uber's strategy of pushing into new areas

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Speaker 5: new use cases are playing out really well for them.

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Speaker 5: And for example, they launch affordable options such as shuttles

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Speaker 5: and lower cost shared rides. That's sort of contributing to

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Speaker 5: the counterstick close story that the CEO talked about on

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Speaker 5: their call and helping them sort of be whether they're

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Speaker 5: sort of consumer downturn bluebergs.

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Speaker 2: And that's all across Uber, and there's a lot more

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Speaker 2: to come in that space. Of course, Door Dash Uber

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Speaker 2: and then we get Lift and that will give us

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Speaker 2: a bigger picture of the strength in that market. Coming

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Speaker 2: up on this program, we're joined by Shyam Sanka, CTO

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Speaker 2: of Palenteer discussing the company's earnings released after the closing

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Speaker 2: bell Monday. One of the big movers to the upside

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Speaker 2: this morning. Commercial customers, military customers, government customers. That's the conversation.

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Speaker 2: This is Bloomberg Technology. Let's keep it going with Ernie's coverage.

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Speaker 2: Palenteer out with results, boosting its revenue guidance for the

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Speaker 2: year to arrange of two point seventy four billion to

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Speaker 2: two point seven five billion, ahead of estimates, with AI

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Speaker 2: demand also helping Palenteer boost its profit outlook. There's been

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Speaker 2: growth in business with government and in commercial customers. Aipalenteers

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Speaker 2: artificial intelligence platform and other products has quote transformed the

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Speaker 2: business in level more than a year. CTO Sean Sanka

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Speaker 2: joins us to discuss. Sean on the call this transformation

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Speaker 2: that doctor cart was talking about. I've been to a

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Speaker 2: few AIP cons, but as CTO, you're working on it.

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Speaker 2: I think just explain the basics of that. What's changed

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Speaker 2: with Impaleteer from a technology perspective and in the domain

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Speaker 2: of artificial intelligence.

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Speaker 6: Well, what you really see in the market is this

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Speaker 6: massive bottleneck between prototyping and production, and that happens to

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Speaker 6: be where AIP is most differentiated.

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Speaker 7: And that differentiation is built on a decade of deep.

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Speaker 6: Technical investments, investments like the ontology, the OSDK, the security

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Speaker 6: and business primitives that we built throughout the platforms like functions, actions, automations,

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Speaker 6: and this pipeline that we have that's really focused on

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Speaker 6: addressing that, and I think what's changed as you've been

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Speaker 6: to these AIP cons is that the market now understands

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Speaker 6: how severe that bottleneck is. I think your last guest

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Speaker 6: was just talking about that. Where it is so easy

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Speaker 6: to build a charismatic AI prototype. That's about the amount

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Speaker 6: of effort of building a PowerPoint slide, but it's also

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Speaker 6: that amount of utility. An Unlike traditional deterministic software, this

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Speaker 6: kind of powerful stochastic genie that is LM's, it requires

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Speaker 6: a lot more work to get to production, maybe ten

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Speaker 6: to one hundred times as much work, and that requires

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Speaker 6: the tool chain that we've assembled with AP.

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Speaker 2: I've had a few conversations with your CEO at his

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Speaker 2: cap about his frustration with the PowerPoint deck versus the

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Speaker 2: reality of shipping products. We'll put that to one side

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Speaker 2: for now. Show on there was a heavy emphasis on

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Speaker 2: working with the military in the written materials and on

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Speaker 2: the call. Could you just get the basics of what

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Speaker 2: you'll work with the military looks like present day.

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Speaker 6: Look In the commercial world, you call it a value

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Speaker 6: chain from the hand of your supplier to the hand

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Speaker 6: of your customer. In the military, it's about a kill

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Speaker 6: chain from censor to shooter. But really, at the most

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Speaker 6: abstract level, it's the same thing. We're trying to enable

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Speaker 6: our war fighters to have information dominance and decision advantage.

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Speaker 6: How can I see you know, to quote Sun Sou,

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Speaker 6: if you know your enemy and you know yourself, you're

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Speaker 6: going to win, and so you know, how can I

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Speaker 6: see everything there is to know about the threat? How

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Speaker 6: can I understand everything I have to combat and deter

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Speaker 6: any aggression from those threats? And when you look at

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Speaker 6: the geopolitical landscape right now, it could not be more dangerous.

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Speaker 6: Everything that's going on in Eastern Europe, the massive tensions

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Speaker 6: that exist in the Middle East, and the ongoing issues

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Speaker 6: that we had in de during aggression in the Pacific.

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Speaker 2: Beyond sort of the importance of data. Where does plan

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Speaker 2: to sit in the defense ecosystem or even the defense

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Speaker 2: supply chain. Are you sort of very closely aligned with

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Speaker 2: hardware makers the aerospace community, or is the model more

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Speaker 2: focused on you just going direct to different arms of

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Speaker 2: the defense base with government.

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Speaker 7: Yeah, we're touching all of it. One of the things

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Speaker 7: I'm most excited about.

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Speaker 6: I've been calling the First Breakfast as an antidote to

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Speaker 6: the nineteen ninety three last Supper that led to the

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Speaker 6: consolidation of our defense industrial base, where it went from

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Speaker 6: fifty one primes down to five, because we forget that

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Speaker 6: at the dawn of World War Two, we didn't have

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Speaker 6: a defense industrial base.

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Speaker 2: We had an.

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Speaker 6: American industrial base. Chrysler made missiles, General mills, this Serial

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Speaker 6: company made inertial guidance systems. And so we have this

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Speaker 6: moment right now in defense tech where one hundred billion

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Speaker 6: dollars or more of capital has been deployed, a bolust

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Speaker 6: of founders have shown up. There's a lot of creativity,

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Speaker 6: a lot of energy as a company that that's kind

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Speaker 6: of been pathfinding over twenty years. Not only have we

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Speaker 6: developed our software that gives these warfighters unique advantage like

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Speaker 6: the Maven contract a CDO just recently awarded for nearly

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Speaker 6: half a billion dollars, but really this software infrastructure that's

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Speaker 6: required to deliver modern American software to the battlefield in

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Speaker 6: air gapped environments at speed, at pace, well ahead of

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Speaker 6: the threats of twenty twenty seven. So we've been working

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Speaker 6: very closely with both traditional primes, integrating our software to

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Speaker 6: their hardware, helping them actually with production. If you think

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Speaker 6: about the fifty percent of our business that's commercial oriented.

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Speaker 6: How do we build jet engines and satellites faster, better, cheaper?

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Speaker 6: In addition to the new entrants who need to achieve

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Speaker 6: scale and time. Value of money is everything for them.

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Speaker 2: Whenever we have an executive on this program, I always

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Speaker 2: got to tell audience and say, you know, what is

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Speaker 2: that you want to know from? In this case Palenteer,

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Speaker 2: I would say most of the questions were about warp speed,

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Speaker 2: very basic ones. Why now with warp speed and the

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Speaker 2: obstacles to rolling it out making it sort of more

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Speaker 2: readily available, pervasive out their shop.

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Speaker 7: Yeah, that's great.

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Speaker 6: I'm so excited about warp speed is what I'm spending

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Speaker 6: all of my time on and really shaping the R

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Speaker 6: and D roadmap around warp speed is our modern American

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Speaker 6: operating system for manufacturing and the reason why now you know,

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Speaker 6: for the better part of twenty years we have helped

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Speaker 6: traditional manufacturers build planes, trains, automobiles, and ships. But most

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Speaker 6: of those folks are stuck in a legacy mode of

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Speaker 6: how they're operating, and you're able to help here or there.

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Speaker 6: But what's unique about the reindustrialization movement that's happening right

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Speaker 6: now in America is that these founders, they're alumni of Palenter,

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Speaker 6: of Tesla, of SpaceX, and they understand that the traditional

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Speaker 6: erp plm PLC software doesn't really work. That most of

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Speaker 6: these successful companies have to build their own software and

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Speaker 6: that is really an unaffordable journey.

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Speaker 7: So that there's this massive opportunity to take.

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Speaker 6: The power of AIP and the historical experiences we have

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Speaker 6: throughout the value chain of production to help our customers

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Speaker 6: bend their atoms better with bits.

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Speaker 4: SEUP.

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Speaker 2: I have your CTO. I kind of have some quick

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Speaker 2: fire questions that I've never been able to answer about

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Speaker 2: Palenteer in the first instance, AIP and warp speed over

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Speaker 2: the year. What's the kind of key foundational model LM

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Speaker 2: that you've been building on top of. I know you

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Speaker 2: partner as well as sort of think in house about

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Speaker 2: the model or foundation level, but that's a question that

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Speaker 2: comes up quite a lot. Who are you building on

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Speaker 2: top of and working with?

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Speaker 6: Well, I think all the value is really going to

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Speaker 6: create up the application layer, and what we've seen in

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Speaker 6: production is that you actually need a menagerie of models

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Speaker 6: and you you know, if you think about the most

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Speaker 6: expensive frontier model out there right now, it's a thousand

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Speaker 6: times more expensive than the cheapest open source model for

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Speaker 6: ten percent more ELO, we're ten percent more IQ as

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Speaker 6: a rough proxy, that's not a compelling price performance trade off.

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Speaker 6: You already see that the first versions of GPT four

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Speaker 6: have been sunset. You know, we need to think about

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Speaker 6: this problem as Okay, what is the infrastructure I need

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Speaker 6: to make I'm turning my software into something that is

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Speaker 6: now stochastic.

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Speaker 7: It's not deterministic anymore.

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Speaker 6: Pure scientists that we have trained, they are all used

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Speaker 6: to writing deterministic code.

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Speaker 2: You know.

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Speaker 6: This is this harkens more to how we think about

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Speaker 6: the transition from analog circuits to digital circuits. The sort

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Speaker 6: of error correction you need, the sort of infrastructure you

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Speaker 6: need to think about having so that you can have

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Speaker 6: the abstraction to.

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Speaker 7: Think about these things as being digital. That's where I.

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Speaker 6: Think the coluy is going to be go ahead, sorry,

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Speaker 6: And so we're very focused on helping our customers get

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Speaker 6: the right models for the right use cases. Auto evaluate

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Speaker 6: that auto automatically generate iterations on the prompts that get

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Speaker 6: them there. And part of our theory is that really

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Speaker 6: prompts our for developers, chat is a dead end. We're

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Speaker 6: guiding our customers through this journey here. They get there

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Speaker 6: pretty quickly to realize that we should be thinking of

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Speaker 6: l is a new type of runtime, the way I

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Speaker 6: might write a Python function.

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Speaker 7: Well, okay, I'm going to write an LBM function too.

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Speaker 2: Sean. For the commercial half of the business, is there

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Speaker 2: a specific Hyperscale cloud platform or partner that you work

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Speaker 2: with to support its growth.

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Speaker 7: We're working with all of them.

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Speaker 6: We have deep and very valuable relationships there, so we're

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Speaker 6: very happy with that.

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Speaker 2: This last year has been about AIP and growth. What's

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Speaker 2: the next twelve months like for Palenteer sham Well, I.

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Speaker 6: Think it's really about deepening the investments that we have

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Speaker 6: with AIP that are addressing this bottleneck between prototyping and production.

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Speaker 6: You know, if you look at David Kahn at Sequoia's

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Speaker 6: article on the six hundred billion dollar whole in revenue.

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Speaker 7: I think this is where the whole is.

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Speaker 6: This is the bottleneck in the market is the most

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Speaker 6: important problem to solve, and the folks who solve at first,

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Speaker 6: and I think we're in the poor position there, have

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Speaker 6: the opportunity to take the entire market.

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Speaker 2: Sham sangk Has, CTO of Paleteer. It's great to have

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Speaker 2: you back on Bloomberg Technology. Thank you so much. In

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Speaker 2: other tech news, Google lost in a historic anti trust

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Speaker 2: suit after the tech giant had paid twenty six billion

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Speaker 2: dollars to become the default search engine on smartphone web browsers.

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Speaker 2: I want to bring in bloombos Lea Nyland, who's been

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Speaker 2: reporting with the team on this case. Let's start with

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Speaker 2: the basics the decision and how it will impact Alphabet

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Speaker 2: or Google Alphabet, the parent of Google.

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Speaker 8: Yeah, so the decision that came out yesterday is actually

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Speaker 8: just the one on whether Google violated the law. So

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Speaker 8: the judge found that Google has an illegal monopoly over

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Speaker 8: two markets, the general search services market, which is what

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Speaker 8: consumers used to find things on the web, and search

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Speaker 8: text ads, which are the ones that are at the

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Speaker 8: top of the page that sort of try and luy

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Speaker 8: you to websites. And so this was just as I

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Speaker 8: said on the liability. Now we're going to go to

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Speaker 8: sort of the next phase of the case, which is

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Speaker 8: where we're going to talk about what to do about this,

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Speaker 8: And the Justice Department hasn't necessarily said what they want yet,

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Speaker 8: But there's sort of several options that people think, one

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Speaker 8: of which is a potential breakup. So they could ask

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Speaker 8: for the court to make Google sell off the Chrome

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Speaker 8: browser or the Android operating system, both of which were

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Speaker 8: key things in the trial. Or the judge could go

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Speaker 8: with maybe like a slightly lesser penalty or remedy, which

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Speaker 8: such as making Google share some of the data that

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Speaker 8: it collects that sort of underlies all of its search

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Speaker 8: results with other search engines like Being or search startups.

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Speaker 2: You kind of answered what happens next, but a lot

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Speaker 2: of questions I got on social media was does the

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Speaker 2: ruling have teeth? If you see what I mean? You know,

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Speaker 2: there are some opinion pieces out there about this being

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Speaker 2: kind of a hollow victory for regulators and the stocks

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Speaker 2: kind of flat. It's hard to gauge how serious this

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Speaker 2: is is guess what I'm saying about alphabet, But it

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Speaker 2: is important, right.

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Speaker 8: Yeah, It's probably not going to take any impact immediately.

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Speaker 8: Google has said it's going to appeal. Those sorts of

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Speaker 8: appeals generally take a year to eighteen months, so of

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Speaker 8: course this is going to delay any actual changes to

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Speaker 8: Google's search engine or its business. But what the decision

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Speaker 8: means is that long term, like Google is definitely going

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Speaker 8: to have to change its practices. It's not going to

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Speaker 8: remain the sort of dominant search engine for very long,

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Speaker 8: and it may end up having to hive office. I said,

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Speaker 8: certain parts of its business which have provided you know,

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Speaker 8: a nice cushion to the company.

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Speaker 2: The one thing that you did.

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Speaker 8: See sort of yesterday was there was a pretty sharp

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Speaker 8: stock seller for Apple because twenty billion dollars of that

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Speaker 8: twenty six billion dollars that you mentioned had been going

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Speaker 8: to Apple every year. It simply, you know, had this

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Speaker 8: contract with Google. It got twenty billion dollars pretty much

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Speaker 8: for not doing anything except letting them be the default

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Speaker 8: on the iPhone. And this ruling definitely puts that in

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Speaker 8: danger because the judge said, you can't have this exclusive

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Speaker 8: agreement like that when you're this dominant, You have to

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Speaker 8: let other people compete. So it might end up changing

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Speaker 8: some aspects of the iPhone. There are a lot of

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Speaker 8: questions about you know, right now, Apple is creating very

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Speaker 8: similar agreements with Google and open Ai over AI chatbats

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Speaker 8: and such that could be on the iPhone. Will it

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Speaker 8: have to make some changes to those agreements because of

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Speaker 8: this decision.

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Speaker 2: That's possible, and we reiterate lear that. Of course, Google

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Speaker 2: has said it plans to appeal the decision anyway. Bloomberg's

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Speaker 2: near Nyland. Thank you, It's official. Vice President Kamala Harris

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Speaker 2: tapped Minnesota Governor Tim Waltz as her running mate this

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Speaker 2: in an effort to build an electoral coalition of coastal

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Speaker 2: progressives and Midwest moderates to block Donald Trump from returning

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Speaker 2: to the White House. Let's get out to DC and

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Speaker 2: bloombost kdie lines. I have to be honest, I didn't

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Speaker 2: know as much about Tim Waltz before this morning, or

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Speaker 2: indeed the work done in the state of Minnesota. But

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Speaker 2: that's the logic we've just outlined for the appointment. What

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Speaker 2: else do we know?

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Speaker 9: Yeah, and it is worth noting that he probably doesn't

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Speaker 9: have the same name recognition as others we understood to

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Speaker 9: be on the shortlist for vice presidential contention. Nonetheless, he

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Speaker 9: is a very popular governor in his state of Minnesota,

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Speaker 9: so he's someone with executive experience. He also served twelve

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Speaker 9: years in the House of Representatives. Before that, he was

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Speaker 9: a high school teacher a coach. He served in the

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Speaker 9: US military and he's from rural Nebraska. So while he

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Speaker 9: doesn't actually represent a swing state, Minnesota has been safely

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Speaker 9: blue since nineteen seventy two, when it went for Richard Nixon,

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Speaker 9: the last time it went for a Republican. He does

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Speaker 9: have proximity and therefore perhaps accessibility to those rest belt

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Speaker 9: voters in Michigan, Wisconsin, and Pennsylvania, the states that make

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Speaker 9: up that critical blue wall. This is also someone that

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Speaker 9: we know has been favored by labor. The UAW President

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Speaker 9: Sean Fain has spoken highly of him. The AFLCIO just

438
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Speaker 9: endorsed him today. In the aftermath of Harris's announcement, he

439
00:22:58,840 --> 00:23:01,800
Speaker 9: was well known and liked among gen Z young people.

440
00:23:01,840 --> 00:23:04,080
Speaker 9: He of course has gone quite viral on social media

441
00:23:04,119 --> 00:23:07,080
Speaker 9: in recent weeks, being the first one to coin jd

442
00:23:07,200 --> 00:23:10,280
Speaker 9: Vance and Donald Trump the Republican ticket as being weird,

443
00:23:10,359 --> 00:23:13,120
Speaker 9: something the Harris campaign has seized on. And of course,

444
00:23:13,160 --> 00:23:17,119
Speaker 9: he also has enjoyed democratic control of the state legislator

445
00:23:17,720 --> 00:23:20,320
Speaker 9: legislature in Minnesota in his second term as governor, which

446
00:23:20,320 --> 00:23:23,960
Speaker 9: has allowed him to enact a more progressive agenda, codifying

447
00:23:24,200 --> 00:23:28,760
Speaker 9: abortion rights, paid family leave, lunches for young children in schools,

448
00:23:28,800 --> 00:23:30,840
Speaker 9: that kind of thing that has won him a lot

449
00:23:30,840 --> 00:23:32,399
Speaker 9: of praise with the left. I would just note that

450
00:23:32,440 --> 00:23:35,800
Speaker 9: as the exact reason why you're already receiving Republicans out

451
00:23:35,840 --> 00:23:39,200
Speaker 9: criticizing him today, the Trump campaign casting him as dangerously

452
00:23:39,280 --> 00:23:41,320
Speaker 9: liberal and a radical leftist.

453
00:23:41,680 --> 00:23:45,240
Speaker 2: Today, I would note that some of the reaction I

454
00:23:45,320 --> 00:23:49,239
Speaker 2: saw on social media was exactly the reaction to the

455
00:23:49,280 --> 00:23:52,840
Speaker 2: weird memes that circulated after he gave that interview, particularly

456
00:23:52,880 --> 00:23:54,880
Speaker 2: among the bench, capital and tech community, which we'll get

457
00:23:54,880 --> 00:23:57,040
Speaker 2: to in a second. This played out on social media

458
00:23:57,040 --> 00:24:00,199
Speaker 2: in part the official confirmation, But I believe that you know,

459
00:24:00,240 --> 00:24:03,199
Speaker 2: we're likely to hear from Vice President Harris and I

460
00:24:03,240 --> 00:24:06,040
Speaker 2: guess some more about her rationale. Could you just walk

461
00:24:06,040 --> 00:24:10,359
Speaker 2: our audience through, now that the tickets established, what happens next?

462
00:24:10,800 --> 00:24:12,920
Speaker 9: Yeah, well, of course Harris has confirmed this on her

463
00:24:13,440 --> 00:24:15,600
Speaker 9: campaign X account. We do expect there will be some

464
00:24:15,680 --> 00:24:18,200
Speaker 9: kind of video that rolls out also on social media

465
00:24:18,240 --> 00:24:21,000
Speaker 9: at some point today, and then she will be appearing

466
00:24:21,240 --> 00:24:25,359
Speaker 9: alongside Governor Walls at a rally in Philadelphia this evening

467
00:24:25,400 --> 00:24:27,680
Speaker 9: that is going to kick off a multi swing state

468
00:24:27,720 --> 00:24:29,680
Speaker 9: tour that the two will embark on over the next

469
00:24:29,720 --> 00:24:32,200
Speaker 9: five days. They're going to be, for example, in Detroit

470
00:24:32,200 --> 00:24:35,240
Speaker 9: tomorrow rallying with the United Auto Workers in part, and

471
00:24:35,280 --> 00:24:37,520
Speaker 9: so they'll be visiting a number of these battlegrounds straights.

472
00:24:37,560 --> 00:24:39,320
Speaker 9: Of course, we were told not to read into the

473
00:24:39,320 --> 00:24:41,560
Speaker 9: fact that their event today is in Philadelphia, at the

474
00:24:41,600 --> 00:24:45,159
Speaker 9: hometown of Pennsylvania Governor Josh Shapiro, who was not in

475
00:24:45,200 --> 00:24:48,040
Speaker 9: fact selected, So perhaps not reading into that was the

476
00:24:48,080 --> 00:24:50,000
Speaker 9: right thing to do. We of course have heard from

477
00:24:50,080 --> 00:24:52,639
Speaker 9: Governor Shapiro today saying that he intends to work to

478
00:24:52,680 --> 00:24:55,679
Speaker 9: get Harris support in Pennsylvania and defeat Donald Trump. But

479
00:24:55,680 --> 00:24:57,879
Speaker 9: it also plays into this notion, the idea that he

480
00:24:58,000 --> 00:25:00,560
Speaker 9: was not selected. You're seeing some Republicans, including the vice

481
00:25:00,600 --> 00:25:04,320
Speaker 9: presidential nominee on the Republican ticket, Jade Vans, casting the

482
00:25:04,359 --> 00:25:08,199
Speaker 9: decision not to choose Shapiro as anti Semitic, given that

483
00:25:08,200 --> 00:25:10,040
Speaker 9: he is a Jewish Man and had been more pro

484
00:25:10,160 --> 00:25:13,160
Speaker 9: Israel and had received a lot of backlash among progressives

485
00:25:13,160 --> 00:25:16,280
Speaker 9: for his handling of pro Palestinian protests earlier this year,

486
00:25:16,320 --> 00:25:18,800
Speaker 9: So that the idea that labor didn't like him as

487
00:25:18,880 --> 00:25:21,040
Speaker 9: much may have played a role in Harris's decision not

488
00:25:21,119 --> 00:25:23,160
Speaker 9: to choose Shapiro. So I'm not sure if we will

489
00:25:23,160 --> 00:25:25,920
Speaker 9: see him on stage alongside Harris and Walls tonight, but

490
00:25:25,960 --> 00:25:27,640
Speaker 9: that is the next time and the first time really

491
00:25:27,720 --> 00:25:30,199
Speaker 9: you will see this ticket together in person, now that

492
00:25:30,200 --> 00:25:32,080
Speaker 9: the announcement has been made, with of course, less than

493
00:25:32,119 --> 00:25:34,960
Speaker 9: two weeks to go until the Democratic National Convention.

494
00:25:34,640 --> 00:25:38,159
Speaker 2: In Chicago, Bloomberg's Kaylee Lynes, thank you very much. I

495
00:25:38,240 --> 00:25:40,600
Speaker 2: want to bring in Shruti Shah who's a partner at

496
00:25:40,640 --> 00:25:45,080
Speaker 2: Symphonic Capital and one of the signatories of VC's for Kamala,

497
00:25:45,280 --> 00:25:48,040
Speaker 2: a list of more than two hundred bench catalysts that

498
00:25:48,280 --> 00:25:51,400
Speaker 2: have publicly backed Kamala Harris and her White House bid,

499
00:25:51,400 --> 00:25:53,879
Speaker 2: and Shruti shar joins us from Durham in the swing

500
00:25:53,920 --> 00:25:58,719
Speaker 2: state of North Carolina. Let's just start with the ticket.

501
00:25:58,800 --> 00:26:03,359
Speaker 2: Your reaction to Vice President Harris selecting Governor Waltz's as

502
00:26:03,400 --> 00:26:04,159
Speaker 2: her running.

503
00:26:03,920 --> 00:26:08,680
Speaker 10: Mate, Yeah, well, thanks for having me, ed, and I

504
00:26:08,760 --> 00:26:12,439
Speaker 10: am excited that we finally have a vice presidential choice

505
00:26:12,520 --> 00:26:15,840
Speaker 10: on the ticket. I think Governor wals is an excellent

506
00:26:15,920 --> 00:26:20,280
Speaker 10: choice for Vice President Harris, and I'm excited to see

507
00:26:20,320 --> 00:26:21,480
Speaker 10: what the two of them are going to be able

508
00:26:21,560 --> 00:26:25,080
Speaker 10: to do together. I think as far as it as

509
00:26:25,119 --> 00:26:27,840
Speaker 10: far as it relates to tech. We're still learning more

510
00:26:27,880 --> 00:26:33,760
Speaker 10: about Vice President about Governor Waltz's policies with regard to tech,

511
00:26:33,840 --> 00:26:35,800
Speaker 10: but I think that he's done a lot in the

512
00:26:35,840 --> 00:26:40,200
Speaker 10: state of Minnesota. Just Minnesota has been declared a tech

513
00:26:40,240 --> 00:26:42,800
Speaker 10: hub by the Biden Harris administration. In addition to that,

514
00:26:44,200 --> 00:26:46,840
Speaker 10: he's also done a lot to expand rural broadband access.

515
00:26:47,680 --> 00:26:52,240
Speaker 2: Truty the week that followed the assassination attempts on former

516
00:26:52,280 --> 00:26:55,600
Speaker 2: President Trump and then the selection of J. D Vance

517
00:26:55,680 --> 00:26:58,199
Speaker 2: is his running mate, we had many bench catalysts on

518
00:26:58,240 --> 00:27:01,800
Speaker 2: this program, and though from let's call it Silicon Valley

519
00:27:01,800 --> 00:27:05,160
Speaker 2: more broadly, who came out in support. They basically said,

520
00:27:05,560 --> 00:27:09,960
Speaker 2: we see jd Vance as being a good voice for

521
00:27:10,200 --> 00:27:14,760
Speaker 2: entrepreneurs and somebody with experience in our industry. Are you

522
00:27:14,880 --> 00:27:18,240
Speaker 2: able to make the same arguments for the Harris Waltz

523
00:27:18,280 --> 00:27:20,920
Speaker 2: ticket at this stage? How do you think they will

524
00:27:20,960 --> 00:27:23,040
Speaker 2: approach your world directly?

525
00:27:24,560 --> 00:27:28,560
Speaker 10: Yeah, I think it's a great question, and I just

526
00:27:28,600 --> 00:27:30,400
Speaker 10: want to start by saying that I don't think many

527
00:27:30,400 --> 00:27:32,879
Speaker 10: of the mentor capitalists who came out in support of

528
00:27:32,880 --> 00:27:36,760
Speaker 10: Trump are representative of the entire industry as we know.

529
00:27:37,400 --> 00:27:41,240
Speaker 10: Kamala Harris is from Silicon Valley. She grew up in Oakland,

530
00:27:41,640 --> 00:27:44,640
Speaker 10: and she's been a longtime supporter of the tech industry,

531
00:27:45,280 --> 00:27:48,359
Speaker 10: and she shares a lot of values that many of

532
00:27:48,440 --> 00:27:50,520
Speaker 10: us in the tech industry have, particularly with regard to

533
00:27:50,520 --> 00:27:55,920
Speaker 10: immigration and ensuring that we are continuing to drive innovation

534
00:27:56,080 --> 00:27:58,879
Speaker 10: in this country with policies that are favorable to tech.

535
00:28:00,119 --> 00:28:01,119
Speaker 7: I really believe.

536
00:28:00,920 --> 00:28:06,800
Speaker 10: Strongly that we should be thinking more broadly about the

537
00:28:06,800 --> 00:28:11,000
Speaker 10: ways in which the tech industry has more diverse viewpoints

538
00:28:11,040 --> 00:28:14,600
Speaker 10: than the ones that I think some very vocal members

539
00:28:14,600 --> 00:28:16,240
Speaker 10: of the industry espouse.

540
00:28:17,200 --> 00:28:20,200
Speaker 2: True, So we're showing some of the leading names, I

541
00:28:20,240 --> 00:28:23,320
Speaker 2: suppose so are signatories to the VCS for Kamala. What

542
00:28:23,480 --> 00:28:26,239
Speaker 2: is the one biggest factor that unites you in your

543
00:28:26,280 --> 00:28:29,520
Speaker 2: support of Harris or I guess the opposite, The biggest

544
00:28:29,520 --> 00:28:32,879
Speaker 2: factor that makes you concerned about a Trump presidency.

545
00:28:34,520 --> 00:28:36,240
Speaker 10: I mean, I think one of the biggest things that

546
00:28:36,280 --> 00:28:39,600
Speaker 10: worries me about a Trump presidency is just Trump's past

547
00:28:39,680 --> 00:28:42,760
Speaker 10: history with immigration policy. Is if you look at the

548
00:28:42,840 --> 00:28:45,040
Speaker 10: number of unicorns in this country that have been started

549
00:28:45,080 --> 00:28:47,840
Speaker 10: by immigrants, I believe it's around half of all of

550
00:28:47,880 --> 00:28:49,840
Speaker 10: the unicorns in the United States have been started by

551
00:28:49,880 --> 00:28:53,680
Speaker 10: immigrant founders. About eighty percent of them have immigrants and

552
00:28:53,760 --> 00:28:58,000
Speaker 10: leadership roles. The H one B policy VISA policy is

553
00:28:58,080 --> 00:29:01,680
Speaker 10: really critical for the tech industry, and Trump does not

554
00:29:01,880 --> 00:29:05,120
Speaker 10: have a positive track record there. So I think having

555
00:29:05,360 --> 00:29:08,640
Speaker 10: a president who understands the importance of immigration in this

556
00:29:08,720 --> 00:29:10,120
Speaker 10: country is really critical.

557
00:29:11,480 --> 00:29:15,040
Speaker 2: Truty Sharp, partner at Symphonic Capital, thank you for joining

558
00:29:15,120 --> 00:29:18,160
Speaker 2: us here on Bloomberg Technology again one of the signatories

559
00:29:18,520 --> 00:29:20,640
Speaker 2: to VCS for Kumala. Now coming up on the show,

560
00:29:20,840 --> 00:29:22,800
Speaker 2: we're going to take a look at open Ai as

561
00:29:22,840 --> 00:29:26,520
Speaker 2: the company's co founder Greg Brockman and John Shulman take

562
00:29:26,960 --> 00:29:30,880
Speaker 2: a step back from their roles, marking a shift in

563
00:29:30,920 --> 00:29:34,120
Speaker 2: company management. An important story coming up next this is

564
00:29:34,160 --> 00:29:48,320
Speaker 2: Bloomberg Technology. Open Ai is undergoing a shift another one

565
00:29:48,440 --> 00:29:51,480
Speaker 2: as both co founders step back from leading the company.

566
00:29:51,520 --> 00:29:55,000
Speaker 2: Greg Brockman, who's president of open Ai, is going on

567
00:29:55,080 --> 00:29:58,040
Speaker 2: sabbatical until the end of the year, while John Shulman

568
00:29:58,480 --> 00:30:01,320
Speaker 2: is leaving the company to join rive Away, I start

569
00:30:01,400 --> 00:30:04,400
Speaker 2: up anthropic. I want to ran Bloomberg's Rachel Metz. This

570
00:30:04,600 --> 00:30:08,840
Speaker 2: was big news yesterday evening. I think the important starting

571
00:30:08,880 --> 00:30:12,040
Speaker 2: point is Greg Brockman is important. Rachel he's going on

572
00:30:12,080 --> 00:30:14,600
Speaker 2: a leave of absence, sort of define what we know

573
00:30:14,720 --> 00:30:15,240
Speaker 2: about that.

574
00:30:15,720 --> 00:30:18,640
Speaker 11: Yeah, so we know that he informed the company that

575
00:30:18,680 --> 00:30:20,960
Speaker 11: he's going to be taking off until the end of

576
00:30:21,000 --> 00:30:23,840
Speaker 11: the year. He's been at the company from the very beginning.

577
00:30:23,960 --> 00:30:26,080
Speaker 11: In fact, I've been told by people that the very

578
00:30:26,080 --> 00:30:30,080
Speaker 11: earliest days of open Ai were happening at his apartment.

579
00:30:30,680 --> 00:30:33,320
Speaker 11: So he's definitely been an integral piece of the Company's

580
00:30:33,360 --> 00:30:36,120
Speaker 11: one of the top people there, someone that Sam Maltman,

581
00:30:36,360 --> 00:30:39,840
Speaker 11: the CEO, often looks to. So it's going to be

582
00:30:39,920 --> 00:30:42,000
Speaker 11: interesting to see what happens while he's out.

583
00:30:42,200 --> 00:30:44,080
Speaker 2: We're showing a post that he did on X kind

584
00:30:44,080 --> 00:30:47,080
Speaker 2: of confirming the details of it, and he looks like

585
00:30:47,120 --> 00:30:50,200
Speaker 2: he needs to break Shulman is a name I'm less

586
00:30:50,200 --> 00:30:53,640
Speaker 2: familiar with. What was he doing at open ai, And

587
00:30:53,680 --> 00:30:55,640
Speaker 2: I guess how significant is it that he's jumping over

588
00:30:55,720 --> 00:30:58,640
Speaker 2: to Anthropic of all places that I would say that's

589
00:30:58,720 --> 00:30:59,560
Speaker 2: a pretty big deal.

590
00:31:00,240 --> 00:31:04,000
Speaker 11: A couple people make that kind of move recently from

591
00:31:04,080 --> 00:31:07,320
Speaker 11: open Ai, But yeah, John Shulman also someone who's been

592
00:31:07,320 --> 00:31:10,440
Speaker 11: there from the very beginning. With him gone and with

593
00:31:10,720 --> 00:31:13,560
Speaker 11: Greg on leave, that means there, to my account, is

594
00:31:13,640 --> 00:31:17,120
Speaker 11: like two people from the original twenty fifteen blog posts

595
00:31:17,160 --> 00:31:19,680
Speaker 11: saying welcome, this is opening Eye here all of our

596
00:31:19,720 --> 00:31:22,920
Speaker 11: founding members. We have Sam Altman, we have watched Arember.

597
00:31:23,880 --> 00:31:25,680
Speaker 11: I believe those are the two people that are remaining

598
00:31:25,720 --> 00:31:29,120
Speaker 11: there in addition to these guys. So it's going to

599
00:31:29,160 --> 00:31:31,160
Speaker 11: be interesting to see what happens going forward.

600
00:31:31,520 --> 00:31:35,600
Speaker 2: So, I mean the information reported in the first instance

601
00:31:35,600 --> 00:31:37,520
Speaker 2: that Greg Brockman was leaving, then I think we've got

602
00:31:37,520 --> 00:31:40,920
Speaker 2: some clarification that it's it's a leave of absence or

603
00:31:40,920 --> 00:31:46,000
Speaker 2: a sabbatical. But the point being in aggregate people track

604
00:31:46,120 --> 00:31:48,920
Speaker 2: open AI. There's been a lot of change. It seems

605
00:31:49,040 --> 00:31:52,400
Speaker 2: a very long time ago that Sam Altman was briefly ousted,

606
00:31:52,760 --> 00:31:56,680
Speaker 2: So so summarize it all looked for the audience. You know,

607
00:31:57,200 --> 00:32:00,560
Speaker 2: where do they stand now versus that fate for weekend

608
00:32:00,880 --> 00:32:02,160
Speaker 2: of November twenty three.

609
00:32:02,320 --> 00:32:06,080
Speaker 11: Yeah, it seems so long ago, but also not so.

610
00:32:06,600 --> 00:32:09,080
Speaker 11: A lot of leadership has changed and left, and a

611
00:32:09,120 --> 00:32:11,440
Speaker 11: lot of things have been reorganized there, especially in the

612
00:32:11,480 --> 00:32:15,560
Speaker 11: safety portion of the company. Sam obviously is back from

613
00:32:15,600 --> 00:32:21,720
Speaker 11: that time in November. Ilia Saskiverer, who helped organize the

614
00:32:22,000 --> 00:32:23,960
Speaker 11: coup to remove Sam in the first place, and then

615
00:32:24,040 --> 00:32:26,560
Speaker 11: recanted has left the company.

616
00:32:26,960 --> 00:32:28,160
Speaker 2: Jan Niki, who.

617
00:32:28,200 --> 00:32:32,840
Speaker 11: Had worked on super alignment at the company, is gone.

618
00:32:33,800 --> 00:32:36,840
Speaker 11: And we also have a bunch of other people in

619
00:32:36,920 --> 00:32:40,040
Speaker 11: leadership positions, such as John Shulman, that have left. And

620
00:32:40,080 --> 00:32:42,400
Speaker 11: now we also have as as we said, Greg Brockman

621
00:32:42,720 --> 00:32:45,280
Speaker 11: has is stepping stepping back.

622
00:32:45,360 --> 00:32:46,560
Speaker 2: I mean to be fair, he's.

623
00:32:46,440 --> 00:32:47,840
Speaker 11: Been there for a long time. He's done a lot

624
00:32:47,840 --> 00:32:49,480
Speaker 11: of work, so maybe he just wants a break.

625
00:32:49,600 --> 00:32:52,000
Speaker 2: For a lot of people that that's not so important important,

626
00:32:52,040 --> 00:32:54,040
Speaker 2: particularly if you're you're in the world of engineering. You

627
00:32:54,040 --> 00:32:56,440
Speaker 2: want to say, what's open AI shipping right now? What

628
00:32:56,560 --> 00:32:59,240
Speaker 2: is the latest thing for them on the product side?

629
00:32:59,640 --> 00:33:02,280
Speaker 11: Well, that's that'sten an excellent question. Actually, So they have

630
00:33:02,960 --> 00:33:06,040
Speaker 11: thank you the very you're welcome. They have all the

631
00:33:06,160 --> 00:33:10,080
Speaker 11: models that are in Chat, GPT and including GPT four

632
00:33:10,160 --> 00:33:12,840
Speaker 11: ROH which was released in the past couple months. But

633
00:33:12,960 --> 00:33:16,320
Speaker 11: the most recent thing is voice Mode, which is in alpha,

634
00:33:16,440 --> 00:33:19,200
Speaker 11: which means it's been released to a very limited number

635
00:33:19,200 --> 00:33:21,520
Speaker 11: of people. The company has not said how many people,

636
00:33:22,360 --> 00:33:24,840
Speaker 11: but it's not that many people from what I can tell,

637
00:33:24,960 --> 00:33:27,000
Speaker 11: and they're slowly going to be releasing it to more

638
00:33:27,040 --> 00:33:29,200
Speaker 11: people in the coming weeks and months through the fall.

639
00:33:29,480 --> 00:33:31,880
Speaker 2: I actually asked them if I could get my hands

640
00:33:31,920 --> 00:33:34,520
Speaker 2: on it, so to speak, let's see what happens. Bloomberg's

641
00:33:34,600 --> 00:33:38,120
Speaker 2: Rachel Metz thank you an important piece of reporting. That

642
00:33:38,160 --> 00:33:41,120
Speaker 2: does it for this edition of Bloomberg Technology. We're two

643
00:33:41,200 --> 00:33:44,160
Speaker 2: days into a week that has felt like an entire week.

644
00:33:44,480 --> 00:33:46,880
Speaker 2: Recap everything on the podcast. You know exactly where to

645
00:33:46,920 --> 00:33:52,200
Speaker 2: find it, all the Bloomberg platforms, including the Bloomberg Terminal, Apple, Spotify, iHeart,

646
00:33:52,560 --> 00:33:56,440
Speaker 2: and elsewhere. From San Francisco, just me this week. This

647
00:33:56,480 --> 00:34:00,440
Speaker 2: is Bloomberg Technology,