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Speaker 1: From Mahard of Where Innovation, Money and Power.

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Speaker 2: Collie in Silicon Vallet NBN. This is Bloomberg Technology with

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Speaker 2: Caroline Hyde and Ed Love Love.

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Speaker 3: I'm Caroline Hyde at Bloomberg's world headquarters in New York.

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Speaker 4: And I'm Ed Lovedlow in San Francisco. This is Bloomberg

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Speaker 4: Technology coming up.

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Speaker 3: Tesla asked its investors to approve Musk's fifty six billion

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Speaker 3: dollar pay package. Again, we discussed the vote on moving

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Speaker 3: to Texas and its criticism of the Delaware court that

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Speaker 3: voided Musk's twenty eighteen award.

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Speaker 4: Chip making equipment orders dive at ASML as Europe's most

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Speaker 4: valuable company faces a pullback from its Taiwanese and South

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Speaker 4: Korean customers.

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Speaker 5: Are chip stockpiles back.

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Speaker 3: And meanwhile we bring you a key conversation with the

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Speaker 3: CEO of debt laden Autos as the French IT company

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Speaker 3: seeks to save itself. That and so much more coming

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Speaker 3: up throughout this hour. But let's get your cook check

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Speaker 3: on these markets. And I want to shine a light

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Speaker 3: and what's happening ultimately an the equity market, which pulls

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Speaker 3: back a little bit from where it had been. We're

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Speaker 3: seeing a little bit of caution, of course, as we

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Speaker 3: digest a more hawkish federal reserve, as we think about

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Speaker 3: where JA power is really steering us in terms of

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Speaker 3: number of cuts ubs, thinking it'll be two for this

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Speaker 3: year starting in maybe September.

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Speaker 6: Nevertheless, we're seeing.

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Speaker 3: Actually bob markets continuing to outperform, and not as much

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Speaker 3: as they were earlier. Red currently seeing a twenty year

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Speaker 3: yield just down by about two three basis points to

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Speaker 3: remember big auction coming ahead of that all important Beige

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Speaker 3: Book as well, and moment we're moving on and have

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Speaker 3: a look at what's happening in the world of crypto,

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Speaker 3: because Bitcoin has actually been well on the downside, even

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Speaker 3: as the US dollar is for once falling now after

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Speaker 3: six this is the first time in six days we've

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Speaker 3: actually seen the US dollar give up some of its

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Speaker 3: strength as we maybe just digest what has been, of

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Speaker 3: course an apparent slightly more hawkish shift, where we're looking

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Speaker 3: at a bitcoin currently off by two and a half

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Speaker 3: percent riskasset's still cautious out there are but what are

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Speaker 3: you watching?

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Speaker 4: On the micro Tesla continues to dominate headlines and market moves.

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Speaker 4: We're down for a four straight session, that's the worst

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Speaker 4: run of declines since the beginning of this year, and

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Speaker 4: the stock is trading at its lowest level in almost

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Speaker 4: a year. And I don't think this is to do

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Speaker 4: with the two red headlines on the Bloomberg tumil. I

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Speaker 4: think the proxy revealing that investors will vote on that

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Speaker 4: original twenty eighteen pay package rejected by a court, and

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Speaker 4: the idea of incorporating in Texas. I don't think that's

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Speaker 4: the downward pressure. There's indigestion from the events of the

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Speaker 4: last four days, executive departures, layoffs, and Elon's comment about

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Speaker 4: going all in on ROBOTAXI, and I think a lot

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Speaker 4: of people are trying to understand what's happening, what is

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Speaker 4: the big picture and when does it happen. But the

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Speaker 4: stock's under a lot of pressure, while the talk from

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Speaker 4: Elon Musk on his own platform X is fighting talk.

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Speaker 5: And I think we've got some pretty good guests to

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Speaker 5: go through that with.

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Speaker 3: Yeah, let's talk about the fighting talk, let's talk about

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Speaker 3: the reaction throughout. Let's start the Bloomberg's Crow Todell, And

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Speaker 3: what's so interesting is ultimately coming out fighting to Delaware,

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Speaker 3: to the court, to the judge that said initially, this

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Speaker 3: twenty eighteen pay package is far too much and new

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Speaker 3: board are not doing enough to push back. But I'm

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Speaker 3: interested ased why the argument goes that it would cost

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Speaker 3: billions more to renegotiate a pay package rather than just

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Speaker 3: try and vote through the current one.

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Speaker 7: Yeah, I do think it's interesting that the board sort

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Speaker 7: of you know, portrayed this as a nuisance, really this

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Speaker 7: idea that they would have to take on additional expense

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Speaker 7: that you know, the company accounted for having to set

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Speaker 7: up these options and set aside this this compensation for

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Speaker 7: musk and essentially would have to sort of, you know,

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Speaker 7: do it over again. It does also strike me just

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Speaker 7: from the language that was used in the filing that

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Speaker 7: it was sort of musky in and that Robin Denholm,

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Speaker 7: the chair of the company, you know, referred to this

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Speaker 7: idea of essentially that the court had taken away shareholder

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Speaker 7: or voice. It was kind of a freedom of speech

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Speaker 7: esque defense of this pay package. But of course, you know,

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Speaker 7: share you know, corporate governance critics are not going to

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Speaker 7: look on this very family, this idea of you know,

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Speaker 7: this this award being slapped down and the board just

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Speaker 7: going back to the world with the same plan.

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Speaker 4: Craig, you've been reading the proxy for the last four

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Speaker 4: hours and thirty minutes.

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Speaker 5: I know you have been at your desk doing that.

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Speaker 4: There's some granular detail in it that Tesla explains a

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Speaker 4: randy compensation program and the idea of reincorporating in Texas

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Speaker 4: in parallel, and my read on it is that they

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Speaker 4: basically don't want to be accused of using the Texas

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Speaker 4: incorporation as leverage in this ongoing pay issue.

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Speaker 7: Yeah, they want to kind of pair these two basically

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Speaker 7: get the shareholders backing again. You know, I guess make

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Speaker 7: the case really that shareholders, if they didn't already know

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Speaker 7: what went into this award, the Delaware court just you know,

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Speaker 7: brought to light a lot of these details, made clear

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Speaker 7: what was going on here and really sort of put

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Speaker 7: it back in the court of the shareholders and say

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Speaker 7: are you okay with this or aren't you? And of

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Speaker 7: course you know, we have you know, heard rumblings reported

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Speaker 7: on this idea that retail shareholders were really sort of

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Speaker 7: an uproar, you know, based on just at least the

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Speaker 7: volume of complaints about the way this was handled. Also

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Speaker 7: Tesla saying in the proxy that four of its ten

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Speaker 7: biggest institutional investors wrote to the company or you know,

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Speaker 7: somehow communicated that they were in support of this award.

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Speaker 7: So there is some reason to think that perhaps this

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Speaker 7: stands a chance of being approved, just as it was back.

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Speaker 4: In twenty eighteen, it was a load of dents texts

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Speaker 4: to go through testas down a percentage point. That way

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Speaker 4: of sessions in moos created ow great work, Thank you

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Speaker 4: very much. Just keep a conversation going with Pierre Faragu,

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Speaker 4: who's head of Tech Infrastructure research at New Street Research.

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Speaker 4: Just give me one sentence, what is the story of

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Speaker 4: Tesla right now? Just one sentence.

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Speaker 8: I'm not very good at doing one sentence, you know that.

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Speaker 9: So the one sentence would be Testa is a car

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Speaker 9: manufacturer first and for the seable future, they need to

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Speaker 9: continue to be successful at manufacturing cars, and that's a

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Speaker 9: platform for them to do additional amazing things.

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Speaker 8: You have a donomous driving.

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Speaker 9: They are the most advanced player in the world on

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Speaker 9: supervised autonomous driving, so your car driving by itself under

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Speaker 9: your supervision. They're making exceptional progress. There is a very

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Speaker 9: long term, like over the horizon goal host of being

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Speaker 9: able to do Robert. It's a much more still challenging

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Speaker 9: thing to get to very uncertain so and there's also

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Speaker 9: the opportunity to develop human and humanoid robots on the

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Speaker 9: same technology platform as FIG And these things will take

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Speaker 9: us whichever way you look at it.

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Speaker 8: And we know Elon Musk is very impatient, wants to

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Speaker 8: accelerate that momentum, but it's going to take us.

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Speaker 9: And in the meantime, we also need Tesla to see

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Speaker 9: that Tesla is still going to be a very successful

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Speaker 9: g manufacturers being able over time to stabilize pricing, to

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Speaker 9: continue to gain share with the product they have in

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Speaker 9: the market today, and then in the next two three

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Speaker 9: years to be able to ramp a large volume, lower

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Speaker 9: price platform to continue to grow sustainably, and all that

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Speaker 9: should deliver revenue growth and margin expansion.

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Speaker 8: And that's really like the near term and the longer

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Speaker 8: term is yes. But and robotaxes.

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Speaker 4: It was a loaded question, but you did give me

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Speaker 4: one sentence just without full stops of punctuation.

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Speaker 5: That's okay.

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Speaker 4: The reason I ask it is I'm trying to understand

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Speaker 4: in the moment the downward pressure on the stock, and

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Speaker 4: I don't think it's got much to do with the

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Speaker 4: proxy So of all the headlines of the last four days,

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Speaker 4: which was of most interest to ups.

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Speaker 9: Of the last few days, the most important headlines have heard, well,

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Speaker 9: you know, the most important thing of the last few

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Speaker 9: days was actually to digest swear the headlines of last week.

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Speaker 8: And so we've heard, like, we've read a rota's report

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Speaker 8: mode to.

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Speaker 9: Being canceled and things like that, and we've had like

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Speaker 9: you know, ins and indications from Milan Musk and from

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Speaker 9: this last chief designer that the modity is probably not cancered.

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Speaker 9: And so the most important breakthrough earlier this week was

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Speaker 9: the understanding that probably what's happening to that Tesla is

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Speaker 9: that Tesla is going to prioritize designing developing the robot

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Speaker 9: taxi in Austin for now, because anyway, the model too

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Speaker 9: will need to wait for Mexico to ramp in millions

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Speaker 9: of units, and Mexico is still two three years away,

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Speaker 9: and so in the meantime, producing only a quarter of

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Speaker 9: a million Model twos in Austin doesn't really help.

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Speaker 8: It's it's like a very very little help.

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Speaker 9: And so my educated guess is that Elan understood that

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Speaker 9: and thought, okay, let's go all in on robot taxi.

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Speaker 9: Let's get a platform out, let's have a small flet

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Speaker 9: on which we can try out tests, improve the technology,

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Speaker 9: and then let's get back to the model too a

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Speaker 9: bit later. So hopefully next week when the company reports,

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Speaker 9: we get a bit more lightly on that. So that's

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Speaker 9: really the big thing of the last forty eight.

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Speaker 3: I like the fact that you give us your reasoning,

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Speaker 3: you interpretation, pr give us your reasoning on whether the

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Speaker 3: stock should be down by a third so far this year.

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Speaker 9: Yes, so if you look at the potential of the

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Speaker 9: company in the long run, that's a nonsense.

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Speaker 8: Tesla makes electric cards.

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Speaker 9: Electric cars remain I think the future of cards and

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Speaker 9: nobody on us can make electric cars as good as

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Speaker 9: Tesla and as cost efficiently. And if anything, this gap

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Speaker 9: is widening. I just took delivery of my new medal three,

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Speaker 9: the high Lander. Like the podcast Testa is making it

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Speaker 9: doing grades and very cost efficient.

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Speaker 8: Cards is still mind blowing.

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Speaker 9: So I think it's still a very powerful company in

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Speaker 9: the stock is vastly undervalued if you look at it

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Speaker 9: in the long run. In terms of short term developments,

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Speaker 9: I have a lot of sympathy for where the stock

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Speaker 9: is going has been going in the last three months

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Speaker 9: because Tesla has been hitting a wall in terms of growth,

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Speaker 9: has been forced to regius pricing lots, you know, like

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Speaker 9: finding themselves in a situation where they were raising increasing

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Speaker 9: productions faster than demon could could increase. So they have

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Speaker 9: to reset the bar and it's not going to happen

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Speaker 9: over a few weeks. We still have a lot of

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Speaker 9: inventories ri seeing are still low. Demand is still not

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Speaker 9: enough to comfortably absorb the production today, so we will

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Speaker 9: need some time, you know, to get the start back

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Speaker 9: on track, and of course the stock correcting to that

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Speaker 9: is the way the market works, and yes, I have

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Speaker 9: sympathy for that. TESTA is at a low point, is

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Speaker 9: between two gross trajectories, continues to work on you know,

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Speaker 9: long terim moonshots like Theroid and a robotaxi, and in

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Speaker 9: the meantime, like the next couple of years are going

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Speaker 9: to be years of recovery.

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Speaker 8: So ahead of that, seeing the stock quickening makes sense

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Speaker 8: to me, even.

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Speaker 9: If I would disagree with that blatantly with evaluation implied,

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Speaker 9: if she will get it over like a three to

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Speaker 9: five year horizon, yeah.

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Speaker 3: Giving us short term and the long term, we thank you,

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Speaker 3: Head of Tech infrastructures, research of a new street research.

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Speaker 6: Some of the longest sentences in the house, but we

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Speaker 6: love it.

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Speaker 3: Meanwhile, coming up tech earnings, they're upon us will dive

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Speaker 3: deeper into what to expect and the current state of

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Speaker 3: tech market's more broadly aired.

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Speaker 6: What are you looking at ahead of that conversation?

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Speaker 4: Philadelphia Semiconductor Index or SOCKS twenty seven names in the red,

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Speaker 4: three in the green.

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Speaker 5: Biggest drop on ASML.

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Speaker 4: The story chip contract manufacturers are not ordering chip making equipment.

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Speaker 5: What does that tell us? We'll explain next. This is Bloomberg.

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Speaker 3: Technology ASML chip equipment maker, having its worst day since

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Speaker 3: June twenty twenty two. Now put it into context, this

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Speaker 3: is a company that's up about twenty five percent so

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Speaker 3: far this year. Nevertheless, we see a pullback because it's

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Speaker 3: earnings really disappoint on the quarter that we've just seen

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Speaker 3: in terms of revenue. But also pointing to this quarter

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Speaker 3: we're already in saying it's going to be low where

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Speaker 3: the market had anticipated. Why, Well, we're seeing TSMC Samsung

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Speaker 3: pull back on perhaps some of the orders that we

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Speaker 3: thought for some of their overall chip equipment.

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Speaker 6: Now, this still shows a chip sector that's.

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Speaker 3: In recovery when you're thinking of our own purchases of

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Speaker 3: electronic goods, and they're like, but what of the AI

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Speaker 3: revolution ed? This is what's so interesting is the fact

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Speaker 3: that actually their number one market, it's China. Basically half

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Speaker 3: of their revenue is coming from China at the moment,

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Speaker 3: even though we see those sanctions, those curbs on such

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Speaker 3: very high EUV chip equipment going to China, they can't

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Speaker 3: get their hands on that, but actually they're still relatively

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Speaker 3: strong orders for the lower elements of where that chip

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Speaker 3: equipment has been going.

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Speaker 4: But notably, look their own revenues that China historically is

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Speaker 4: big market. The story of the anxiety is about not China, TSMC, Taiwan,

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Speaker 4: Samsung in South Korea, all this CAPEX commitment, but they're

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Speaker 4: not fronting up in the short term. The drop was

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Speaker 4: called on quarter sixty one percent for EUV lithography. Basically,

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Speaker 4: they ain't buying machines that make chips. Why let's dig

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Speaker 4: deeper into these markets tech earning seasons also around the

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Speaker 4: corner epek Oscar Diskay, a senior market analyst over at

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Speaker 4: Swiss quote. You know, there's the headlines governments in Europe

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Speaker 4: and the United States right in big checks, saying come

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Speaker 4: and build chip factories.

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Speaker 5: It's going to be great.

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Speaker 4: And Samsung and TSMC gave us better capex outlooks for

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Speaker 4: this year, and then we've got ASML and things aren't

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Speaker 4: going so rosy.

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Speaker 5: What was your read on that?

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Speaker 10: Well, Actually, ASML Earning is really disappointed this morning in

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Speaker 10: Europe and kind of raised a couple of ibros regarding

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Speaker 10: the sustainability of the demand growth from chip makers and

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Speaker 10: the sustainability.

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Speaker 2: Of the AI.

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Speaker 10: Really now, it's important to note that ASML said that

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Speaker 10: the second half of this year is going to see

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Speaker 10: a rebound and they actually kept their full year outlook unchanged,

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Speaker 10: but obviously because now we are used to have fantastic

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Speaker 10: results from chip makers while the ASML's disappointment. Disappointment has

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Speaker 10: been a sour taste in investors well mouth this morning.

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Speaker 10: And I'm actually we actually think that at some point

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Speaker 10: reasonably the growth and growth expectations for chip makers will

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Speaker 10: be leveling out, and it's just to see if this

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Speaker 10: is the quarter that we will see these expectations slow down,

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Speaker 10: and there is a possibility that we do see them

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Speaker 10: slow down, and that's going to be able to trigger

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Speaker 10: actually a sizeable downside correction and profit taking. But more

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Speaker 10: broadly regarding AI, well, ASMA also said that they expect

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Speaker 10: AI to continue to contribute to their revenues, and we

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Speaker 10: do also expect AI really to continue, maybe not in

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Speaker 10: the same way because we expect some changes within the

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Speaker 10: AI segments. So what I mean by that is we

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Speaker 10: saw so far the first phase, what we call the

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Speaker 10: first phase of the AI really benefit to companies, to

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Speaker 10: chip makers, data centers, cloud businesses, all these companies that

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Speaker 10: do provide AI tools other companies. But we are now

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Speaker 10: thinking that there will be second phase in this AI

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Speaker 10: really which is expected to benefit two companies that actually

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Speaker 10: invest in massively in AI technologies and tools and which

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Speaker 10: should shortly start well seeing the return on their AI investments.

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Speaker 6: So I think that even though we see.

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Speaker 10: Some disappointment in terms of chip maker earnings Squart, while

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Speaker 10: the AI really is not ready to end, you.

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Speaker 3: Pack brun that out to the rest of earning season

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Speaker 3: for us, because well, there's a lot of optimism that

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Speaker 3: AI is already going to be helping with costs for example.

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Speaker 10: Well, actually, the AI has been the major talking point

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Speaker 10: and the expectations are quite high.

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Speaker 5: Mind you.

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Speaker 10: I mean looking at the magnificent seven socks expectations around

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Speaker 10: learning's growth of around forty percent for the first quarter.

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Speaker 10: That's huge number. That's still down from fifty five percent

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Speaker 10: printed a quarter earlier. But if we single out stocks

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Speaker 10: like Tesla and Apple who have been in trouble in

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Speaker 10: the first quarter, will the expectation of earnings growth goes.

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Speaker 6: All the way up to eighty percent.

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Speaker 10: So now we're talking about a handful of AI sucks,

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Speaker 10: namely Nvidia, Microsoft, but also Google, and we're sold by

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Speaker 10: Facebook and Amazon. So I think that there is a

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Speaker 10: huge optimism out there. But what we will be really

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Speaker 10: interested in is who within this AI segment is going

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Speaker 10: to be profiting from the AI developments, whether it's going

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Speaker 10: to be the provider of AI tools or are we

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Speaker 10: going to shift towards well the user of the AI tools.

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Speaker 6: IMPAC great to have your voice in the show. Thank you.

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Speaker 3: Ipek Oscar Deshka, Senior market analyst, Swiss quote giving us

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Speaker 3: the context. JP Morgan, Chase c Jamie Dinan. He makes

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Speaker 3: no secret of his firm than it is all in

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Speaker 3: on artificial intelligence. Now the head of the world's biggest bank,

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Speaker 3: when he's laying out his vision for the future money

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Speaker 3: in this AI world.

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Speaker 6: He spoke with Bloomberg Original's.

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Speaker 3: Host Emily Chang about that and so much more in

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Speaker 3: the premiere episode of The Circuits second season, particul Listen.

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Speaker 2: The best needs be prepared for any business.

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Speaker 11: I think when you think about really think about things

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Speaker 11: that can go terribly wrong, can you survive them? You know,

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Speaker 11: it could be technology, it could be government regulations, it

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Speaker 11: could be it could be the literally the weather. If

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Speaker 11: you're a restaurant, you know that might close you down.

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Speaker 11: If you have if you lose this week's business, you're

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Speaker 11: out of business. Get enough cash. So you should think

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Speaker 11: all that through.

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Speaker 12: Bill Gates once said banking is necessary, banks are not

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Speaker 12: to what extent could AI or fintech replace traditional banks.

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Speaker 11: So I think, first of all, I remember him saying

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Speaker 11: banks of dinosaurs. I spoke to about it in nineteen

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Speaker 11: ninety seven, and obviously he was dead roll. We probably

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Speaker 11: agree to that, but he's not wrong. The technology changes everything.

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Speaker 11: And if anyone is complacent or arrogant or think that

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Speaker 11: because you have a big position to day, you get

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Speaker 11: a big position tomorrow, that's a mistake. But the end

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Speaker 11: of this fine what is banking. Someone's going to have

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Speaker 11: to hold the money. Someone's got to move the money,

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Speaker 11: someone's got to raise the money, someone's got.

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Speaker 2: To do research, you know, around money.

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Speaker 11: Those services will still be around, and you know, hopefully

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Speaker 11: we're doing it and using a lot have to do

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Speaker 11: a better job at it. But I've always thought it's

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Speaker 11: very possible that some tech thing, you know, distant intermediates

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Speaker 11: a piece of that. And I've been writing about you know,

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Speaker 11: big tech going to our business. We've got fintech. We

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Speaker 11: also have big tech, and they will embed payment systems

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Speaker 11: in there. Some we're going to white label banks kind

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Speaker 11: of what Apple did.

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Speaker 13: Uh.

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Speaker 2: You know, they have the right to do that. I'm

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Speaker 2: not against that.

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Speaker 11: I would be against unfair use of their position to

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Speaker 11: dominus in a business.

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Speaker 12: Well, Apple is going deeper into financial services.

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Speaker 6: Do you worry about the bank of Apple.

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Speaker 11: Well, we'll get to compete, so they have a tough competitor.

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Speaker 11: But you know they hold money, move money. Yeah, they're

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Speaker 11: a form of a competitor. You know, we also partner

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Speaker 11: with them, but I'm very used to partnering and competing

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Speaker 11: with lots of people.

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Speaker 12: Existential threat.

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Speaker 2: I don't think it's an existential threat. But I think

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Speaker 2: if we were complacent about it.

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Speaker 4: Yes, that was JP Morgan, Chase CEO, Jamie Diamond, and

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Speaker 4: right here on set, this is being back in the

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Speaker 4: regional so it's Emily Chang. Jamie Diamond is kind of

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Speaker 4: one of the most upset about people like high flying

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Speaker 4: executive in the person yep, the circuit series too.

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Speaker 5: That's kind of what's cool.

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Speaker 4: About it is you get people in a chair but

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Speaker 4: being themselves. How did you do it this time around?

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Speaker 14: Oh boy?

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Speaker 12: And this was my first time meeting him. You know,

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Speaker 12: it was all about London, right in London, and he

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Speaker 12: was just there for a tech conference really quick. It

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Speaker 12: was all about just making the most of every single minute,

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Speaker 12: the handshake, the walk to the interview room, the chit

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Speaker 12: chat that you know before we actually got quote unquote

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Speaker 12: on camera for you know, the formal sit down interview,

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Speaker 12: and you know, we talked about how many days he's

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Speaker 12: on the road a year, one hundred and twenty days.

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Speaker 6: We talked about childhood, family. I asked, you know, how

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Speaker 6: do you do it? He was like, what do you mean,

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Speaker 6: how do you juggle it all?

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Speaker 12: And I think he kind of appreciated that question because

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Speaker 12: it's you know, you normally ask that to a woman,

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Speaker 12: and you don't ask that to someone like date Jamie Dimond,

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Speaker 12: and he was like, look, I don't I don't socialize,

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Speaker 12: I don't go on red carpets. It's it's work and

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Speaker 12: family and that's what it is, and family actually comes first.

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Speaker 12: And so that's kind of where we started, and we

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Speaker 12: tried to open up from there.

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Speaker 3: You then, of course peel away not only about his

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Speaker 3: family and keutles for continuing to ask that of both

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Speaker 3: men and women, but ultimately about how AI is changing

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Speaker 3: his business. You're going to be thinking about AI for

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Speaker 3: the rest of the season as well.

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Speaker 6: I'm pretty sure AI is a.

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Speaker 12: Huge part of the season, as it is a huge

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Speaker 12: part of your show. I mean, it's a huge part

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Speaker 12: of our world. We can't avoid it. One of the

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Speaker 12: interesting questions I had for him is, you know, Chat JPM,

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Speaker 12: will something like that exist in the future where we

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Speaker 12: could ask, you know, I've got thirty years, what should

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Speaker 12: I do with my money? And he said, essentially yes,

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Speaker 12: it's already doing that. We've got AI embedded and everything.

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Speaker 12: It's gonna learn more and more about you. Will I

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Speaker 12: be able to say to Chat JPM. I want to

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Speaker 12: get rich quick, and he was like, I hope it

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Speaker 12: tells you that you're crazy, because that's just.

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Speaker 6: Not possible.

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Speaker 5: Really quickly. What can we expect this season?

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Speaker 8: Oh?

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Speaker 12: Well, I hope you love it. We're traveling. We're taking

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Speaker 12: you to Latin America, going on Netflix sets. That's sort

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Speaker 12: of the next leg of their global expansion. After South Korea,

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Speaker 12: we go to see Mary Bara and Troy. We spend

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Speaker 12: a lot of time with her and really get to

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Speaker 12: know her behind the scenes. Palmer Lucky, the founder of Oculus,

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Speaker 12: and of course Anderild now making drones and satellites, AI

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Speaker 12: and warfare.

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Speaker 6: I hope you love it, sure.

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Speaker 5: Well, Bloomberg Original's host Emily Change.

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Speaker 4: You can catch the full first episode tonight on Bloomberg

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Speaker 4: six pm Eastern.

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Speaker 6: Welcome back to Bloombog Technology. I'm Caroline Hyde in New.

442
00:22:28,680 --> 00:22:31,240
Speaker 5: York and I'm as our Vote in San Francisco.

443
00:22:31,320 --> 00:22:32,960
Speaker 4: A very quick check in on the markets and when

444
00:22:32,960 --> 00:22:36,440
Speaker 4: it comes to publicly traded technology names. I'm taking a

445
00:22:36,480 --> 00:22:41,879
Speaker 4: look at Tesla sevent tens four percent. It's paired its decline,

446
00:22:42,040 --> 00:22:44,000
Speaker 4: but we're still down for a fourth straight session. We

447
00:22:44,080 --> 00:22:46,200
Speaker 4: talked about the reasons why earlier in the show with

448
00:22:46,280 --> 00:22:49,800
Speaker 4: Pierre Ferugu it's a sock that's below five hundred billion

449
00:22:49,800 --> 00:22:53,080
Speaker 4: dollars now in market cap, and you know, I'm looking

450
00:22:53,119 --> 00:22:56,359
Speaker 4: for stories. I'm looking for clear downward direction in the market.

451
00:22:56,520 --> 00:23:00,159
Speaker 4: Tesla was one name that contributes to that. Bitcoins so

452
00:23:00,240 --> 00:23:02,919
Speaker 4: really interesting. Actually during the course of this show, Carrow,

453
00:23:03,160 --> 00:23:05,480
Speaker 4: you highlighted it earlier. But we're now down nearer to

454
00:23:05,560 --> 00:23:09,440
Speaker 4: sixty thousand US dollars per token on bitcoin, a drop

455
00:23:09,520 --> 00:23:12,320
Speaker 4: of four percent in the session of an asset that

456
00:23:12,400 --> 00:23:15,280
Speaker 4: trades twenty four to seven on the Bloomberg terminal. There's

457
00:23:15,280 --> 00:23:17,320
Speaker 4: a lot of writing about the real world use case

458
00:23:17,960 --> 00:23:22,280
Speaker 4: ATMs in Latin America becoming more mainstay, but also the

459
00:23:22,520 --> 00:23:26,359
Speaker 4: industry bracing for the harving loads of people saying they're ready.

460
00:23:26,400 --> 00:23:28,320
Speaker 4: But if you look at some of the technology related

461
00:23:28,359 --> 00:23:31,520
Speaker 4: stocks linked to crypto, those are also seeing some downward pressure.

462
00:23:32,000 --> 00:23:33,919
Speaker 5: And we other pieces of news from that industry too.

463
00:23:34,400 --> 00:23:36,439
Speaker 3: We do, and let's dive in because Gray Scale Investment

464
00:23:36,520 --> 00:23:39,280
Speaker 3: CEO Michael the Sonosheine. He says that the market for

465
00:23:39,480 --> 00:23:43,440
Speaker 3: US spot bitcoin ETFs, still relatively new, hasn't seen.

466
00:23:43,359 --> 00:23:45,520
Speaker 6: Wide scale institutional adoption at least.

467
00:23:45,840 --> 00:23:48,520
Speaker 3: In an exclusive conversation with blouobg TV, the CEO also

468
00:23:48,600 --> 00:23:51,359
Speaker 3: spoke about the SEC's approval of bitcoin ETFs and the

469
00:23:51,440 --> 00:23:53,520
Speaker 3: long road to regulatory approval.

470
00:23:53,520 --> 00:23:54,280
Speaker 6: Is what we have to say.

471
00:23:55,280 --> 00:23:57,040
Speaker 15: Even though they've been in market now for three and

472
00:23:57,080 --> 00:23:59,840
Speaker 15: a half months, we still do not have listed option

473
00:24:00,119 --> 00:24:03,800
Speaker 15: unspot bitcoin et apps. This is a really important feature

474
00:24:03,880 --> 00:24:07,240
Speaker 15: that investors want and they deserve. This can help investors

475
00:24:07,280 --> 00:24:11,600
Speaker 15: to manage their positions, manage risk. Ultimately, I do believe

476
00:24:11,680 --> 00:24:14,640
Speaker 15: that the SEC should be approving these so that they're

477
00:24:14,680 --> 00:24:17,320
Speaker 15: once again not in a position to be treating spot

478
00:24:17,359 --> 00:24:21,080
Speaker 15: bitcoin et apps disparately from you know, bitcoin futures based UTPs.

479
00:24:23,200 --> 00:24:26,280
Speaker 4: Let's stick with regulation. Moving on to stable coins now.

480
00:24:26,320 --> 00:24:28,480
Speaker 4: I'm to push in the House and the Senate to

481
00:24:28,600 --> 00:24:32,480
Speaker 4: pass such legislation as soon as next month. Senators Loomis

482
00:24:32,560 --> 00:24:36,760
Speaker 4: and Gillibrand are proposing a measure that would ban algorithmic

483
00:24:36,920 --> 00:24:39,600
Speaker 4: stable coins and say that bill would protect consumers in

484
00:24:39,760 --> 00:24:43,760
Speaker 4: US dollar while enabling innovation in payments. Scout to DC

485
00:24:43,800 --> 00:24:46,159
Speaker 4: with Bloombers Kaylee Lyons. I saw a headline on the

486
00:24:46,240 --> 00:24:50,120
Speaker 4: terminal this morning that says the legislation has momentum from

487
00:24:50,160 --> 00:24:51,440
Speaker 4: one of those senators, does it.

488
00:24:53,160 --> 00:24:53,360
Speaker 2: Yeah.

489
00:24:53,680 --> 00:24:57,119
Speaker 16: This is an effort by Gillibrand and Lummis, who have

490
00:24:57,359 --> 00:24:59,920
Speaker 16: had cryptoinitiatives in the past. They've introduced in the past

491
00:25:00,160 --> 00:25:03,480
Speaker 16: much wider legislation to kind of set regulatory standards for

492
00:25:03,600 --> 00:25:06,080
Speaker 16: the industry as a whole. This is a much narrow effort,

493
00:25:06,119 --> 00:25:08,679
Speaker 16: and perhaps the narrowness of it is what could actually

494
00:25:08,760 --> 00:25:10,720
Speaker 16: get this across the finish line in the way that

495
00:25:10,800 --> 00:25:13,600
Speaker 16: other efforts have not. Essentially, what we're talking about here

496
00:25:13,720 --> 00:25:15,639
Speaker 16: is a framework for stable coins, as you mentioned, that

497
00:25:15,640 --> 00:25:19,040
Speaker 16: would ban algorithmic stable coins. Perhaps the example of an

498
00:25:19,040 --> 00:25:21,680
Speaker 16: algorithmic stable coin that looms largest in our head is

499
00:25:21,840 --> 00:25:24,600
Speaker 16: terry USD, which of course was supposed to be pegged

500
00:25:24,640 --> 00:25:26,320
Speaker 16: one for once and the dollar, but instead of being

501
00:25:26,359 --> 00:25:29,639
Speaker 16: backed by reserved to use smart contracts to try to

502
00:25:29,760 --> 00:25:32,280
Speaker 16: keep that peg, which worked until it did not and

503
00:25:32,320 --> 00:25:35,480
Speaker 16: then collapsed in fabulous fashion with forty billion dollars being

504
00:25:35,520 --> 00:25:37,520
Speaker 16: wiped out. So they want to avoid that in the future.

505
00:25:37,600 --> 00:25:40,320
Speaker 16: What this regulation instead would do is not allow those

506
00:25:40,600 --> 00:25:44,800
Speaker 16: and require one to one reserve requirements for stable coin issuers,

507
00:25:44,840 --> 00:25:47,400
Speaker 16: so making sure there actually is a dollar for every

508
00:25:47,960 --> 00:25:50,520
Speaker 16: one dollar stable coin that is out there, would set

509
00:25:50,560 --> 00:25:53,119
Speaker 16: some other guidelines as well as to the authorities that

510
00:25:53,200 --> 00:25:55,880
Speaker 16: federal and state regulators would have. And interestingly, it also

511
00:25:55,920 --> 00:25:59,520
Speaker 16: would establish the FDIC as being able to recover lost

512
00:25:59,520 --> 00:26:03,639
Speaker 16: assets these stable coins collapse. Obviously, again, that's probably a

513
00:26:03,720 --> 00:26:06,360
Speaker 16: product of what we have seen in the past. Will

514
00:26:06,440 --> 00:26:09,080
Speaker 16: this initiative actually move forward is kind of a question

515
00:26:09,200 --> 00:26:11,399
Speaker 16: that will be left to the Democratic Senator from Ohio

516
00:26:11,480 --> 00:26:13,560
Speaker 16: and chair of the Banking Committee, Shared Brown. He is

517
00:26:13,720 --> 00:26:16,639
Speaker 16: largely a cryptoskeptic, but in an interview this week he

518
00:26:16,760 --> 00:26:18,919
Speaker 16: did suggest that he could see the stable coin measure

519
00:26:19,160 --> 00:26:21,720
Speaker 16: attached to other measures that have gone through his committee,

520
00:26:21,720 --> 00:26:25,159
Speaker 16: including safe banking for marijuana companies in the clawbacks of

521
00:26:25,200 --> 00:26:28,280
Speaker 16: compensation for executives of collapse banks. So we'll see if

522
00:26:28,320 --> 00:26:30,440
Speaker 16: this initiative does move forward in the Senate. And I

523
00:26:30,440 --> 00:26:32,320
Speaker 16: would note as well that in the House, the Chair

524
00:26:32,320 --> 00:26:34,879
Speaker 16: of the Financial Services Committee, Patrick McHenry, along with the

525
00:26:34,960 --> 00:26:37,439
Speaker 16: ranking member Maxine Waters, are working on a stable coin

526
00:26:37,480 --> 00:26:38,240
Speaker 16: effort of their own.

527
00:26:38,800 --> 00:26:41,600
Speaker 3: Yeah, I mean already meeting with Senate to Majority Leader

528
00:26:41,800 --> 00:26:46,639
Speaker 3: Chuck Schumer about this and Kayleie more broadly, how necessary

529
00:26:46,760 --> 00:26:49,200
Speaker 3: is is for the industry to have some sort of

530
00:26:49,560 --> 00:26:52,560
Speaker 3: robust set of rules of god rails. We've already been

531
00:26:52,600 --> 00:26:55,120
Speaker 3: speaking with the European Commissioner, for example, about how they've

532
00:26:55,119 --> 00:26:57,680
Speaker 3: been trying to enforce regulation, and at least it's clarity

533
00:26:57,760 --> 00:26:58,320
Speaker 3: of some sort.

534
00:27:00,080 --> 00:27:02,880
Speaker 16: Clarity is what this industry is often asking for, Caroline,

535
00:27:02,920 --> 00:27:04,960
Speaker 16: and certainly this probably does not go as far as

536
00:27:05,000 --> 00:27:06,960
Speaker 16: the industry would like, but it would address us a

537
00:27:07,160 --> 00:27:09,360
Speaker 16: certain part of it. We've already heard from some industry

538
00:27:09,600 --> 00:27:12,640
Speaker 16: lobbying groups, like the Blockchain Associations that they are supportive

539
00:27:12,920 --> 00:27:14,800
Speaker 16: of the stable coin effort, and when I've talked to

540
00:27:14,960 --> 00:27:17,120
Speaker 16: people here in Washington, a lot of them have expressed

541
00:27:17,160 --> 00:27:20,640
Speaker 16: much more optimism that stable coin legislation is the most

542
00:27:20,760 --> 00:27:23,359
Speaker 16: likely thing to get through this Congress, versus more wide

543
00:27:23,400 --> 00:27:27,320
Speaker 16: sweeping legislation that would actually perhaps delineate what, for example,

544
00:27:27,400 --> 00:27:30,639
Speaker 16: the SEC would have control over versus the CFTC. That

545
00:27:30,760 --> 00:27:33,600
Speaker 16: has proven a lot more difficult to do. On Capitol Hill,

546
00:27:33,920 --> 00:27:36,280
Speaker 16: we have seen an effort get through the Financial Services

547
00:27:36,359 --> 00:27:39,560
Speaker 16: and Agricultural committees in the House that would delineate that

548
00:27:39,680 --> 00:27:44,360
Speaker 16: regulatory land, if you will, territory for those two different agencies,

549
00:27:44,400 --> 00:27:46,760
Speaker 16: but that has not moved forward further. And that really

550
00:27:47,440 --> 00:27:50,280
Speaker 16: is what would be required much more sweepingly to provide

551
00:27:50,359 --> 00:27:52,560
Speaker 16: clarity for the industry. And in the meantime, of course,

552
00:27:52,640 --> 00:27:54,840
Speaker 16: is you all probably know well based on your crypto

553
00:27:54,880 --> 00:27:58,159
Speaker 16: conversations here on Bloomberg Technology, the industry really sees that

554
00:27:58,280 --> 00:28:00,719
Speaker 16: at this point it's regulation buy horsemen, and it may

555
00:28:00,760 --> 00:28:03,960
Speaker 16: stay that way. Intel Congress actually moves forward more substantially.

556
00:28:04,320 --> 00:28:07,639
Speaker 3: Katie Lyon's just so thorough with the analysis and getting

557
00:28:07,720 --> 00:28:09,800
Speaker 3: us up to speed. We thank you so much over

558
00:28:09,880 --> 00:28:12,000
Speaker 3: there in Washington. Meanwhile, well, let's just talk about the

559
00:28:12,119 --> 00:28:14,520
Speaker 3: SEC a little bit more. It's blocked third party messaging

560
00:28:14,600 --> 00:28:18,560
Speaker 3: apps and texts from employees work mobile phones. Now it's

561
00:28:18,600 --> 00:28:21,439
Speaker 3: bringing its own practices closer to the standards it's enforcing

562
00:28:21,480 --> 00:28:24,520
Speaker 3: for the industry, and that includes what'sapp and I Message. Now,

563
00:28:24,560 --> 00:28:28,000
Speaker 3: the SEC says it will quote lower risk that their systems.

564
00:28:27,640 --> 00:28:31,480
Speaker 6: Could be compromised and enhance record keeping. Interesting one head.

565
00:28:32,280 --> 00:28:34,040
Speaker 5: Yeah, let's get to another story out of Europe.

566
00:28:34,119 --> 00:28:36,920
Speaker 4: The CEO of Atos, which has been struggling with tumbling

567
00:28:37,000 --> 00:28:40,080
Speaker 4: shares and a wall of debt, is confident that the

568
00:28:40,200 --> 00:28:45,320
Speaker 4: French IT company will be say CEO Paul Seller, spoke

569
00:28:45,400 --> 00:28:49,240
Speaker 4: in an exclusive interview with Bloomberg's Caroline Connen in Paris.

570
00:28:49,320 --> 00:28:49,960
Speaker 5: Have listened to this.

571
00:28:51,440 --> 00:28:55,160
Speaker 13: We are actually working with our creditors, both our banks

572
00:28:55,280 --> 00:28:59,520
Speaker 13: and our bondholders for a solution to our high level

573
00:28:59,560 --> 00:29:02,440
Speaker 13: of debt, and the maturities are coming due in the

574
00:29:02,560 --> 00:29:07,280
Speaker 13: next eighteen months and the dialogue is very very positive.

575
00:29:08,280 --> 00:29:11,040
Speaker 13: We're operating under the consoliation, as you have mentioned, is

576
00:29:11,120 --> 00:29:16,080
Speaker 13: like a mediator is helping through those discussions, and I

577
00:29:16,320 --> 00:29:20,560
Speaker 13: do believe that everybody is aligned. In fact, we recently

578
00:29:20,760 --> 00:29:26,840
Speaker 13: had secured some liquidity from those same bondholders and banks,

579
00:29:27,480 --> 00:29:29,120
Speaker 13: again an indication.

580
00:29:28,800 --> 00:29:31,240
Speaker 2: That everybody is really aligned to find a solution.

581
00:29:31,720 --> 00:29:34,440
Speaker 14: So what else could be part of the proposal apart

582
00:29:34,600 --> 00:29:37,440
Speaker 14: from this group of bondholders. Are we going to see,

583
00:29:38,240 --> 00:29:42,480
Speaker 14: for example, some asset disposal announced before the end of

584
00:29:42,600 --> 00:29:45,880
Speaker 14: next week. Is that a possibility or do you totally

585
00:29:46,440 --> 00:29:46,960
Speaker 14: exclude this.

586
00:29:47,800 --> 00:29:51,040
Speaker 13: I think what we have presented is a comprehensive plan

587
00:29:51,320 --> 00:29:54,959
Speaker 13: that included all the assets of ATOS. Now we're going

588
00:29:55,000 --> 00:29:57,480
Speaker 13: to have to see what proposals are going to come through.

589
00:29:57,720 --> 00:30:00,680
Speaker 13: Some of them will just keep the whole the whole

590
00:30:01,880 --> 00:30:05,280
Speaker 13: of the company together. One of those proposal is likely

591
00:30:05,360 --> 00:30:08,400
Speaker 13: to come from one point, one of our largest shareholders,

592
00:30:08,840 --> 00:30:11,400
Speaker 13: and others will come in from our bondholders who seem

593
00:30:11,440 --> 00:30:14,240
Speaker 13: to also be interested in keeping the whole company. But

594
00:30:14,400 --> 00:30:18,560
Speaker 13: we can't just really ago any potential proposal that may

595
00:30:18,640 --> 00:30:21,360
Speaker 13: come in and may entail a different mix of ideas

596
00:30:21,560 --> 00:30:23,360
Speaker 13: such as asset disposal or not.

597
00:30:24,200 --> 00:30:27,600
Speaker 14: Will this whole pen be able to save Atos?

598
00:30:28,360 --> 00:30:30,680
Speaker 13: Actually ATOS is a great company. As I mentioned to you,

599
00:30:30,840 --> 00:30:33,360
Speaker 13: it's just really is going to be you know, I

600
00:30:33,480 --> 00:30:35,120
Speaker 13: do believe that will come up with a plan and

601
00:30:35,200 --> 00:30:38,680
Speaker 13: the company will succeed long term, So we'll be saved.

602
00:30:39,000 --> 00:30:39,800
Speaker 8: It will be saved.

603
00:30:41,240 --> 00:30:43,960
Speaker 6: Carolyn Conan speaking with the h CEO called Sally.

604
00:30:44,640 --> 00:30:46,440
Speaker 3: Now coming up, we're going to be joined my Meno

605
00:30:46,520 --> 00:30:49,560
Speaker 3: Ventures partner Matt Murphy on the firm's new playbook for

606
00:30:49,680 --> 00:30:51,600
Speaker 3: AI applications.

607
00:30:51,760 --> 00:30:52,000
Speaker 13: Or on that.

608
00:30:52,120 --> 00:30:53,360
Speaker 6: Next, this is Blue Big Technology.

609
00:31:07,000 --> 00:31:09,360
Speaker 3: Now it's today's VC Spotlight, and we're going to be

610
00:31:09,400 --> 00:31:11,400
Speaker 3: taking a look at some of the winning strategies the

611
00:31:11,520 --> 00:31:16,040
Speaker 3: formulas being used by early AI native enterprise companies, because

612
00:31:16,120 --> 00:31:18,640
Speaker 3: that's the focus of Meno ventures latest AI paybook.

613
00:31:19,000 --> 00:31:21,600
Speaker 6: Here to discuss. This is Meno Ventures partner Matt Murphy,

614
00:31:21,800 --> 00:31:22,719
Speaker 6: and you've put out.

615
00:31:22,760 --> 00:31:26,840
Speaker 3: Basically, there's seven Golden rules for generative AI apps. So

616
00:31:26,960 --> 00:31:29,520
Speaker 3: this isn't the foundational models, but this is the applications

617
00:31:29,520 --> 00:31:31,480
Speaker 3: which can be built upon these models that can be

618
00:31:31,520 --> 00:31:35,720
Speaker 3: then used cross industry or across sales or marketing. What

619
00:31:35,880 --> 00:31:38,160
Speaker 3: have you learned about the similarities some of these breakout

620
00:31:38,200 --> 00:31:39,360
Speaker 3: successes we've already seen.

621
00:31:40,600 --> 00:31:44,240
Speaker 17: Sure, yeah, so I think basically this is the fastest

622
00:31:44,320 --> 00:31:49,480
Speaker 17: innovation cycle we've seen across many like mobile, cloud, et cetera.

623
00:31:49,880 --> 00:31:52,440
Speaker 17: And so what really has to happen to create the

624
00:31:52,520 --> 00:31:57,600
Speaker 17: foundation for application innovators to move as quickly as they

625
00:31:57,640 --> 00:31:59,440
Speaker 17: want to as you need the infrastructure side to be

626
00:31:59,520 --> 00:32:01,600
Speaker 17: built out. So you've got kind of the brain, the

627
00:32:01,680 --> 00:32:04,240
Speaker 17: heart and soul and something like anthropic and then a

628
00:32:04,280 --> 00:32:07,960
Speaker 17: lot of surrounding tooling to get data into the model,

629
00:32:08,040 --> 00:32:11,200
Speaker 17: such as an unstructured or a clean lab or pine

630
00:32:11,280 --> 00:32:13,240
Speaker 17: cone which kind of help you curate and get data

631
00:32:13,480 --> 00:32:16,560
Speaker 17: into the model. So that kind of is well underway,

632
00:32:16,680 --> 00:32:18,280
Speaker 17: and we feel like we're at a kind of tipping

633
00:32:18,360 --> 00:32:21,600
Speaker 17: point where the infrastructure stack is pretty well solidified and

634
00:32:21,720 --> 00:32:24,040
Speaker 17: now we're really going to see a ten year innovation

635
00:32:24,200 --> 00:32:27,640
Speaker 17: cycle around applications that's going to move faster and faster,

636
00:32:27,720 --> 00:32:29,640
Speaker 17: and now that that tooling infrastructure is in place.

637
00:32:29,920 --> 00:32:31,080
Speaker 1: So what we really took a look.

638
00:32:30,960 --> 00:32:34,080
Speaker 17: At in this report is what are some of the

639
00:32:34,360 --> 00:32:37,400
Speaker 17: early breakout applications, what have they done differently? And so

640
00:32:37,520 --> 00:32:41,000
Speaker 17: we came up with the seven Golden rules. So for example,

641
00:32:41,360 --> 00:32:44,640
Speaker 17: things like having an almost infinite content loop. If you

642
00:32:44,680 --> 00:32:48,200
Speaker 17: think about a lot of applications, before you basically had

643
00:32:48,240 --> 00:32:53,480
Speaker 17: to have a designer, design creative for each potential campaign.

644
00:32:53,720 --> 00:32:55,800
Speaker 1: You would then test that, take it back, iterate.

645
00:32:56,040 --> 00:32:58,360
Speaker 17: The way generative AI works is it can make end

646
00:32:58,440 --> 00:33:01,440
Speaker 17: different campaigns and actually kind of close the loop and

647
00:33:01,560 --> 00:33:04,760
Speaker 17: have this constant feedback system. So one of the companies

648
00:33:04,800 --> 00:33:07,520
Speaker 17: that does that really well is a company called Typeface,

649
00:33:07,560 --> 00:33:12,400
Speaker 17: which is the former CTO of Adobe and based recreating

650
00:33:12,440 --> 00:33:13,600
Speaker 17: the Adobe Creative.

651
00:33:13,320 --> 00:33:18,920
Speaker 1: Cloud with generative AI. So that's one very concrete example that's.

652
00:33:18,760 --> 00:33:21,840
Speaker 3: Obviously one of your portfolio companies start to jump in there.

653
00:33:21,920 --> 00:33:26,200
Speaker 3: But what's so interesting is what sets that particular business

654
00:33:26,280 --> 00:33:30,280
Speaker 3: apart from others that you're pitched. I mean you also say, look,

655
00:33:30,320 --> 00:33:33,520
Speaker 3: you want to see within these AI apps target work

656
00:33:33,640 --> 00:33:36,400
Speaker 3: that is high value, high volume, or facing labor shortages.

657
00:33:36,480 --> 00:33:39,920
Speaker 3: So basically, where is AI going to have the biggest impact.

658
00:33:40,440 --> 00:33:42,000
Speaker 3: What are some of the winning formulas there?

659
00:33:43,200 --> 00:33:45,760
Speaker 1: Yeah, sure, so what you want to do? Basically, there's

660
00:33:45,800 --> 00:33:48,040
Speaker 1: two ways that an a generator model can work.

661
00:33:48,080 --> 00:33:50,000
Speaker 17: It can work as a kind of an assistant, call

662
00:33:50,080 --> 00:33:54,040
Speaker 17: that a copilot, where it makes a worker smarter, better,

663
00:33:54,160 --> 00:33:57,360
Speaker 17: more efficient at their job. And then sometimes it'll actually

664
00:33:57,400 --> 00:33:59,960
Speaker 17: be able to replace that job and allow a work

665
00:34:00,200 --> 00:34:03,440
Speaker 17: to do something else, like a higher value, higher skilled activity.

666
00:34:03,840 --> 00:34:06,440
Speaker 17: Things like customer service are a great example of that,

667
00:34:07,000 --> 00:34:09,439
Speaker 17: and you kind of look for these pattern based, high

668
00:34:09,520 --> 00:34:12,799
Speaker 17: volume workflows. On the other end of the spectrum, there

669
00:34:12,840 --> 00:34:17,400
Speaker 17: are these high value, lower maybe amount of people workflows

670
00:34:17,600 --> 00:34:20,160
Speaker 17: like let's say a paralegal who spends all this time

671
00:34:20,320 --> 00:34:24,000
Speaker 17: digging through documents and matching terms and things like that,

672
00:34:24,120 --> 00:34:26,200
Speaker 17: when they could really do the analytic work if a

673
00:34:26,280 --> 00:34:28,919
Speaker 17: system built that for them. So that's kind of what's

674
00:34:28,960 --> 00:34:32,000
Speaker 17: being reimagined here with general AI is how do you

675
00:34:32,080 --> 00:34:35,360
Speaker 17: kind of take the bulk work and allow workers to

676
00:34:35,480 --> 00:34:37,680
Speaker 17: do more higher value add activities.

677
00:34:37,719 --> 00:34:40,960
Speaker 4: On top of that, Matt, I'd like to discuss how

678
00:34:41,280 --> 00:34:44,800
Speaker 4: backward or forward looking your report is and how crowded

679
00:34:44,880 --> 00:34:47,640
Speaker 4: the field of activity is in this space, I would

680
00:34:47,640 --> 00:34:52,600
Speaker 4: say that. Caroline and I talk about companies and platforms

681
00:34:52,640 --> 00:34:58,319
Speaker 4: targeting developers and apps quite regularly, an example being Salesforce.

682
00:34:57,840 --> 00:34:58,680
Speaker 5: And the app Exchange.

683
00:34:58,760 --> 00:35:01,839
Speaker 4: Right, they have brought in generative AI layer to their

684
00:35:01,880 --> 00:35:02,880
Speaker 4: existing products.

685
00:35:03,200 --> 00:35:05,000
Speaker 5: Just how busy is it right now in what you

686
00:35:05,120 --> 00:35:05,840
Speaker 5: see happening?

687
00:35:07,280 --> 00:35:09,080
Speaker 1: Yeah, I mean it's extremely busy, right.

688
00:35:09,120 --> 00:35:11,640
Speaker 17: I mean so if you look even anecdotally at our

689
00:35:11,760 --> 00:35:15,600
Speaker 17: portfolio over fifty companies early half of last year, maybe

690
00:35:15,680 --> 00:35:18,480
Speaker 17: twenty percent of companies were doing something with the generative AI.

691
00:35:18,880 --> 00:35:20,880
Speaker 17: Now it's pushing up against one hundred percent, and I

692
00:35:20,920 --> 00:35:22,920
Speaker 17: think that's a good proxy for what's going on in

693
00:35:22,960 --> 00:35:26,600
Speaker 17: the market. If you're an existing software company, it's not

694
00:35:26,719 --> 00:35:29,320
Speaker 17: doing something with generative AI, you're falling behind.

695
00:35:29,400 --> 00:35:30,799
Speaker 1: So that's one wave. But frankly, the.

696
00:35:30,840 --> 00:35:33,440
Speaker 17: Wave that we're most excited about is people kind of

697
00:35:33,719 --> 00:35:37,000
Speaker 17: rebuilding from the get go now with new capabilities of

698
00:35:37,080 --> 00:35:39,880
Speaker 17: generative AI, like a couple that I mentioned, Because the

699
00:35:39,920 --> 00:35:43,200
Speaker 17: most creative and innovative way to disrupt applications is to

700
00:35:43,360 --> 00:35:45,800
Speaker 17: kind of start with a founding team that has the

701
00:35:45,920 --> 00:35:48,960
Speaker 17: DNA around AI and thinks about how to build the

702
00:35:49,120 --> 00:35:50,880
Speaker 17: application from the bottoms up.

703
00:35:50,920 --> 00:35:53,239
Speaker 1: That way, whenever you bolt something on later.

704
00:35:54,120 --> 00:35:56,120
Speaker 17: It's beneficial, it's additive, it's going to be great for

705
00:35:56,239 --> 00:35:59,080
Speaker 17: Salesforce and other companies. But when a new company comes

706
00:35:59,120 --> 00:36:01,920
Speaker 17: in and says, hey, you could just this application shouldn't

707
00:36:01,920 --> 00:36:04,040
Speaker 17: even exist without generative AI.

708
00:36:04,280 --> 00:36:06,160
Speaker 1: I can give you an example if you'd like.

709
00:36:06,239 --> 00:36:10,279
Speaker 17: For example, take a company like Elios, which is doing

710
00:36:10,600 --> 00:36:13,880
Speaker 17: kind of a generative AI for a therapist conversation, so

711
00:36:13,960 --> 00:36:16,800
Speaker 17: you can listen to the conversation, use your own model

712
00:36:17,080 --> 00:36:20,560
Speaker 17: to kind of parse that and give doctor notes as

713
00:36:20,600 --> 00:36:23,719
Speaker 17: well as a potential diagnosis, and use generat AI to

714
00:36:23,880 --> 00:36:27,160
Speaker 17: package that up and curate the content that comes.

715
00:36:26,960 --> 00:36:27,279
Speaker 4: Out of that.

716
00:36:27,400 --> 00:36:29,879
Speaker 17: These are kind of capabilities that a model was never

717
00:36:29,960 --> 00:36:33,360
Speaker 17: able to process before, nor synthesize it down as something

718
00:36:33,440 --> 00:36:36,040
Speaker 17: as precise and important as a diagnosis.

719
00:36:37,080 --> 00:36:40,320
Speaker 4: Matt, one of your bullets is build where incumbents aren't, can't,

720
00:36:40,520 --> 00:36:43,080
Speaker 4: or won't. But you know, in the history of Silicon

721
00:36:43,200 --> 00:36:47,719
Speaker 4: Valley often something gets built because a founder realizes there's

722
00:36:47,760 --> 00:36:49,960
Speaker 4: a problem because they were at another tech company where

723
00:36:50,000 --> 00:36:52,680
Speaker 4: they were frustrated by something. Do you have any sense

724
00:36:52,719 --> 00:36:56,160
Speaker 4: that the end markets actually know what their use cases

725
00:36:56,160 --> 00:36:58,680
Speaker 4: are yet how they're supposed to be using generative AI

726
00:36:59,080 --> 00:37:02,080
Speaker 4: in whatever it is the business does well.

727
00:37:02,080 --> 00:37:03,680
Speaker 17: I think that's kind of my point is, like, I

728
00:37:03,719 --> 00:37:07,080
Speaker 17: think everyone knows that generative AI is a great writer.

729
00:37:07,360 --> 00:37:09,960
Speaker 17: It can take all the content you have, synthesize it

730
00:37:10,080 --> 00:37:13,640
Speaker 17: down and help people get through it really quickly. If

731
00:37:13,680 --> 00:37:15,640
Speaker 17: you've got a graph or a set of output, you

732
00:37:15,719 --> 00:37:17,520
Speaker 17: can distill all that down and it makes it so

733
00:37:17,880 --> 00:37:21,000
Speaker 17: much more effective for the user to consume the output.

734
00:37:20,680 --> 00:37:23,240
Speaker 1: Of your application. But that's kind of table stakes.

735
00:37:23,320 --> 00:37:26,239
Speaker 17: The things that again we're most interested in is how

736
00:37:26,320 --> 00:37:30,200
Speaker 17: are you using that technology to the make the application

737
00:37:30,320 --> 00:37:35,400
Speaker 17: completely different than what the incumbent has done and in

738
00:37:35,520 --> 00:37:35,920
Speaker 17: the past.

739
00:37:35,960 --> 00:37:37,759
Speaker 1: And that's really the opportunity lies here.

740
00:37:38,120 --> 00:37:39,959
Speaker 17: Seeing a bunch of examples of that in the legal

741
00:37:40,080 --> 00:37:42,800
Speaker 17: vertical that's a fast mower out of the gate and

742
00:37:42,920 --> 00:37:46,120
Speaker 17: areas like that. I already mentioned customer service. So it's

743
00:37:46,239 --> 00:37:49,400
Speaker 17: just different when you say let's change the entire workflow

744
00:37:49,600 --> 00:37:52,560
Speaker 17: versus bolt on the capability after.

745
00:37:52,440 --> 00:37:57,280
Speaker 4: The fact Menlovench has partner Matt Murphy. Thank you very much, Caroline.

746
00:37:58,080 --> 00:38:00,120
Speaker 3: Yeah, let's have a quick check on talking to he

747
00:38:00,360 --> 00:38:03,800
Speaker 3: first up to Shiba is seeking to cut five thousand jobs,

748
00:38:03,880 --> 00:38:05,600
Speaker 3: or roughly ten percent of its head count in Japan.

749
00:38:05,880 --> 00:38:08,640
Speaker 6: That's what the NIK is currently reporting now. The move could.

750
00:38:08,480 --> 00:38:11,640
Speaker 3: Initiate one hu Jupan's biggest rounds of staff reductions this

751
00:38:11,760 --> 00:38:13,600
Speaker 3: year in a country that has some of the world's

752
00:38:13,600 --> 00:38:18,239
Speaker 3: strictest worker protection laws. Plus, Apple is wearing a possibility

753
00:38:18,280 --> 00:38:20,440
Speaker 3: of making some of its gadgets in Indonesia now, the

754
00:38:20,520 --> 00:38:23,480
Speaker 3: iPhone maker has been exploring production bases beyond its longtime

755
00:38:23,520 --> 00:38:26,640
Speaker 3: stronghold of China. Of course, that to minimize geopolitical risks

756
00:38:26,719 --> 00:38:29,680
Speaker 3: as tensions they rise between the world's two biggest superpowers.

757
00:38:30,200 --> 00:38:32,279
Speaker 6: And business software start up Rippling.

758
00:38:32,440 --> 00:38:34,400
Speaker 3: Well, it's a reporter that's in talks to raise new

759
00:38:34,480 --> 00:38:37,600
Speaker 3: funding and evaluation between thirteen and fourteen billion dollars and

760
00:38:37,719 --> 00:38:39,920
Speaker 3: the company we understand is planning to raise about five

761
00:38:39,960 --> 00:38:42,640
Speaker 3: hundred million in a deal. According to sources, the talks

762
00:38:42,680 --> 00:38:45,480
Speaker 3: with investors are ongoing, with plans to finalize terms within

763
00:38:45,560 --> 00:38:47,719
Speaker 3: a few weeks. It might be said that Rippling so

764
00:38:47,840 --> 00:38:57,920
Speaker 3: far has denied some of those reports. So the Bloomberg

765
00:38:58,080 --> 00:39:00,680
Speaker 3: NEF Summit, it's in full swing here in New York,

766
00:39:00,719 --> 00:39:04,440
Speaker 3: convenient leaders and energy, industry, transport, technology, finance, and government

767
00:39:04,880 --> 00:39:07,160
Speaker 3: to discuss just how to shape a cleaner and more

768
00:39:07,200 --> 00:39:10,120
Speaker 3: competitive future. I'm pleased to say we're speaking to a

769
00:39:10,239 --> 00:39:14,360
Speaker 3: person who's doing just that. Cuna, your CEO, Spante Fresh

770
00:39:14,480 --> 00:39:17,640
Speaker 3: Office panel from the summit and Claude. What's so interesting

771
00:39:17,800 --> 00:39:21,680
Speaker 3: is you are providing the technology, the filters, the ability

772
00:39:21,800 --> 00:39:27,160
Speaker 3: for companies such as say manufacturing makers to carbon capture.

773
00:39:27,400 --> 00:39:28,520
Speaker 8: How well.

774
00:39:28,600 --> 00:39:31,320
Speaker 18: We kind of focus on a problem that we stated

775
00:39:31,440 --> 00:39:34,840
Speaker 18: the one hundred and thirty five years ago by fellow

776
00:39:34,880 --> 00:39:38,440
Speaker 18: called Venti Ernius, and we took the name Vante similar

777
00:39:38,520 --> 00:39:41,920
Speaker 18: to Tesla Fuish on the EV. So we're unicorn and

778
00:39:42,120 --> 00:39:46,319
Speaker 18: focusing on carbon management by providing solution to avoid SEE

779
00:39:46,440 --> 00:39:48,640
Speaker 18: two going out in the atmosphere in the first place,

780
00:39:49,040 --> 00:39:54,640
Speaker 18: industrial factory, cement pulp on paper plants, or even removing

781
00:39:54,719 --> 00:39:57,640
Speaker 18: the SU two from the atmosphere that's sorry above the ground.

782
00:39:57,760 --> 00:39:59,880
Speaker 18: So we are working in that case with the company

783
00:40:00,040 --> 00:40:03,120
Speaker 18: called clim Works and we provide the filters inside the

784
00:40:03,239 --> 00:40:05,759
Speaker 18: contactor that they have to collect the C two from

785
00:40:05,800 --> 00:40:06,480
Speaker 18: the atmosphere.

786
00:40:07,320 --> 00:40:09,960
Speaker 3: You built a lot of partnerships, You're working with a

787
00:40:10,000 --> 00:40:13,400
Speaker 3: lot of companies and emitter is how long a timeframe

788
00:40:13,480 --> 00:40:16,600
Speaker 3: is it to build such you know, to manufacture to

789
00:40:16,719 --> 00:40:19,040
Speaker 3: actually get this going when you know that one of

790
00:40:19,080 --> 00:40:20,480
Speaker 3: your clients wants indeed.

791
00:40:20,200 --> 00:40:23,800
Speaker 18: Action this so for a legacy is that we're sixteen

792
00:40:23,880 --> 00:40:27,640
Speaker 18: years in research, in developing the technology, the product, product,

793
00:40:27,719 --> 00:40:30,880
Speaker 18: proving out in the market, and raising all these partnership

794
00:40:30,920 --> 00:40:33,520
Speaker 18: to get there. We've raised five hundred million dollars to

795
00:40:33,600 --> 00:40:35,800
Speaker 18: get to the point where we are, and we're currently

796
00:40:35,880 --> 00:40:38,560
Speaker 18: building a factory to make the filters that we'll be

797
00:40:38,600 --> 00:40:41,840
Speaker 18: able to equip the equivalent of ten project of a

798
00:40:41,960 --> 00:40:44,920
Speaker 18: million ton. The world needs to get to ten thousand

799
00:40:45,080 --> 00:40:47,359
Speaker 18: plant of a million ton in the next thirty years.

800
00:40:47,600 --> 00:40:47,799
Speaker 2: Wow.

801
00:40:47,920 --> 00:40:50,920
Speaker 18: So this is factory number one for us, and we're

802
00:40:50,960 --> 00:40:53,920
Speaker 18: almost there in terms of building up the capacity to

803
00:40:54,040 --> 00:40:57,120
Speaker 18: deliver projects. So now when you look at the specific

804
00:40:57,239 --> 00:41:00,400
Speaker 18: project with one a meter, normally it takes about almost

805
00:41:00,520 --> 00:41:04,600
Speaker 18: two years of discussions studies to evaluate how we're going

806
00:41:04,680 --> 00:41:08,160
Speaker 18: to integrate the capture project into let's say a cement

807
00:41:08,280 --> 00:41:10,680
Speaker 18: factory or a pulp and paper plant, and then it

808
00:41:10,760 --> 00:41:13,480
Speaker 18: takes two years to construct. So these projects are to

809
00:41:13,560 --> 00:41:16,480
Speaker 18: take a certain amount of time to deploy, and imagine,

810
00:41:16,520 --> 00:41:18,160
Speaker 18: you know, we need to deploy at the rate of

811
00:41:18,280 --> 00:41:21,440
Speaker 18: two capture plant every week for the next thirty.

812
00:41:21,239 --> 00:41:25,440
Speaker 4: Years clause your twenty twenty two Series E three hundred

813
00:41:25,440 --> 00:41:28,440
Speaker 4: and eighty million dollars led by Chevron's venture arm I

814
00:41:28,520 --> 00:41:31,279
Speaker 4: remember twenty twenty one, twenty twenty two, there was a

815
00:41:31,320 --> 00:41:34,840
Speaker 4: lot of energy, pardon the expression behind your industry and

816
00:41:34,920 --> 00:41:39,080
Speaker 4: financial backing. Had things slowed down in terms of corporate

817
00:41:39,160 --> 00:41:42,839
Speaker 4: interest and public commitment to what you're trying to do, well,

818
00:41:42,920 --> 00:41:43,040
Speaker 4: you have?

819
00:41:43,200 --> 00:41:46,319
Speaker 18: Your question is two fold one. The corporate commitment has

820
00:41:46,400 --> 00:41:50,400
Speaker 18: been there and it's growing as well. I think the

821
00:41:50,520 --> 00:41:53,640
Speaker 18: support we got from the audio gas industry and primarily

822
00:41:53,800 --> 00:41:58,360
Speaker 18: led by Chevron, is instrumental in us being here today

823
00:41:58,480 --> 00:42:02,400
Speaker 18: be able to talk about about management. But you know

824
00:42:02,520 --> 00:42:05,240
Speaker 18: what's missing now to grow the business at the scale

825
00:42:05,280 --> 00:42:07,760
Speaker 18: that needs to be done is we need the private

826
00:42:07,840 --> 00:42:11,160
Speaker 18: equity infrastructure funds to come to play, and so far

827
00:42:11,320 --> 00:42:14,120
Speaker 18: they never come to the value of debt of early

828
00:42:14,239 --> 00:42:17,960
Speaker 18: start startup companies and help them to deploy. So this

829
00:42:18,120 --> 00:42:20,560
Speaker 18: is something that we're working on these days to bring

830
00:42:20,719 --> 00:42:23,520
Speaker 18: the two together because it's the sum of the financial

831
00:42:23,880 --> 00:42:28,239
Speaker 18: sector plus the corporate that will make this thing work. Now,

832
00:42:28,800 --> 00:42:32,080
Speaker 18: from a consumer point of view or the public acceptance,

833
00:42:32,920 --> 00:42:35,800
Speaker 18: I think that it's a slow process. And the analogy

834
00:42:35,840 --> 00:42:39,200
Speaker 18: I can give you is we've been throwing see you

835
00:42:39,280 --> 00:42:41,760
Speaker 18: two in the atmosphere freely for the last one hundred

836
00:42:41,880 --> 00:42:46,239
Speaker 18: years with no consequences. In the minds of people, the

837
00:42:46,320 --> 00:42:48,719
Speaker 18: analogy is the waste management. At the same time you

838
00:42:48,760 --> 00:42:52,600
Speaker 18: know you're rubbish at home. The waste management somebody collects

839
00:42:52,640 --> 00:42:56,760
Speaker 18: and transport and recycle and store your rubbish. That's because

840
00:42:56,800 --> 00:42:59,399
Speaker 18: it smells and you can see it. You cannot see

841
00:42:59,480 --> 00:43:02,359
Speaker 18: and smell Coe two. So it's very difficult for people

842
00:43:02,480 --> 00:43:06,040
Speaker 18: to associate that the common management industry needs to exist.

843
00:43:06,719 --> 00:43:10,960
Speaker 18: And the price of collecting your rubbish is about one

844
00:43:11,040 --> 00:43:14,320
Speaker 18: hundred and fifty dollars per per ton of SE two equivalent.

845
00:43:14,480 --> 00:43:17,919
Speaker 6: So Claude, we will share more time. Claude Latuna, your CEO.

846
00:43:18,040 --> 00:43:20,600
Speaker 6: A savante that does it for this edition of BLOEMG

847
00:43:20,640 --> 00:43:21,120
Speaker 6: Technology