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Speaker 1: From Marhart where Innovation, Money and Power Collie in Silicon

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Speaker 1: Vallet NBN.

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Speaker 2: This is Bloomberg Technology with Caroline Hyde and Ed loud Love.

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Speaker 3: And Caroline Heide Bloomberg's world headquarters in New York and

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Speaker 3: AURM Ed Lodlow in San Francisco. This is Bloomberg Technology

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Speaker 3: coming up.

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Speaker 4: Amazon CEO Annie Jase He says that general to AI

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Speaker 4: boom is going to be.

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Speaker 5: Built on Amazon Web Services.

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Speaker 4: Or we need the takeaways from his annual letters.

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Speaker 3: To shareholders class technology heavyweights to send on Washington for

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Speaker 3: the White House steak dinner with big names like Jeff

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Speaker 3: Bezos and Tim Kirk attending a lavish event and.

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Speaker 4: Open AI CEO Sam Altman pictures a global AI coalition

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Speaker 4: on his visit to the Middle East.

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Speaker 5: Next stop.

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Speaker 4: Maybe we see him over on Capitol Hill too. We'll

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Speaker 4: discuss that and so much more throughout this hour. But Ed,

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Speaker 4: we start on these markets up a quarter of a

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Speaker 4: percentage point currently NASDAK managing.

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Speaker 5: To put itself off of its lows.

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Speaker 4: This is we once again really tackle inflationary pressures. Whether

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Speaker 4: it's a PPI number coming after the CPI print yesterday,

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Speaker 4: both showing inflationary pressures just start dialing up that little bit.

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Speaker 4: And we're seeing though, managing the NASDAK to outperform as bomb.

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Speaker 5: Markets actually pull back a little bit. I'm looking at

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Speaker 5: the two year yield.

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Speaker 4: We're outperforming on the front end of the curve, whereas

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Speaker 4: the back end, the tenure yield, for example, you'll still

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Speaker 4: pushing higher. But really the big move was yesterday, and

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Speaker 4: looking at what's happening in the euro it's not just

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Speaker 4: all about the Federal Reserve. It's about global world banks

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Speaker 4: at the moment, central.

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Speaker 5: Banks that is.

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Speaker 4: And we're looking at what the ECB said, look staying

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Speaker 4: pat as many had anticipated in terms of interest rates,

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Speaker 4: but signaling that inflationary pressures are dialing down across the Atlantic,

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Speaker 4: and therefore could we see cuts coming or off by

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Speaker 4: three tens percent versus the US dollar.

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Speaker 5: Moving on, look at what else is performing versus the

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Speaker 5: US dollar. Crypto I'm down.

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Speaker 4: By two tens percent on bitcoin at the moment, just

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Speaker 4: sub that seventy thousand dollars level.

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Speaker 5: But what are you watching on the micro.

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Speaker 3: There are a lot of stories to fit in today.

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Speaker 3: Apple's a name that we're going to look at later

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Speaker 3: in the program with Bloomberg's Mark German rough eight tens

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Speaker 3: one percent. Early unionization efforts are kind of the main headline.

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Speaker 3: But there is a note from JP Morgan analysts that

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Speaker 3: say that hedge funds are now looking at this stock

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Speaker 3: and considering what happens next with artificial intelligence in the

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Speaker 3: context of value add iOS and the handset. Hard to

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Speaker 3: know what the driver is, if any at all, but

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Speaker 3: we hire eight tens to one percent, and given its

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Speaker 3: market capitalization, we always pay attention to Apple because if

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Speaker 3: its waiting on major indices. There is an annual shareholder

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Speaker 3: letter plus AI theme in today's show. I am very

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Speaker 3: excited about it. JP Morgan is down nine tens to

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Speaker 3: one percent Monday night. I know I wasn't here earlier

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Speaker 3: in the week. Put the shareholder letter out and says AI,

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Speaker 3: and we're going to talk about the numbers behind that.

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Speaker 3: I think it's really interesting, so to Amazon up three

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Speaker 3: tens to one percent Andy Jasse's annual letter. The focus

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Speaker 3: about AI is interesting specific aws. Amazon is going to

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Speaker 3: be a facilitator, a place where AI is built by others,

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Speaker 3: maybe not building so much generator of AI themselves. That's

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Speaker 3: the story you and I have told quite a lot

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Speaker 3: in recent weeks and months.

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Speaker 5: We are long shareholder letters.

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Speaker 4: Let's dig in with punam Call and BLUEBG Intelligence and

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Speaker 4: Ed just puts it so eloquently there. Ultimately, they want

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Speaker 4: to be at the very heart the infrastructure of generative AI. Yes,

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Speaker 4: they will build a substantial number of GENAI applications ourselves,

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Speaker 4: but they're looking to others to build.

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Speaker 6: Yes, you're absolutely right.

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Speaker 7: You know, right now Amazon lags Microsoft when it comes

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Speaker 7: to AI because Microsoft has access to the open AI. However,

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Speaker 7: as they build these building blocks going forward, they can

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Speaker 7: close that gap over time. So we think the investment

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Speaker 7: is right. We think the time is right, and generator

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Speaker 7: of AI will not only help fueld the AWS business,

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Speaker 7: but also it's e commerce marketplace business where it will

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Speaker 7: help drive sales higher.

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Speaker 3: Pun and what we're talking about specifically is I think

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Speaker 3: and you can go to Bedrock if you're a very

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Speaker 3: large enterprise player or a smaller startup and build your

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Speaker 3: own large language model, you can take advantage of a

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Speaker 3: third party large language model. Caroline and I have spoken

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Speaker 3: to Anthropic, for example Daniella m O'Day, and she talks

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Speaker 3: about all the.

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Speaker 6: Business they're winning through AWS.

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Speaker 3: Bedrock What I don't know is Amazon's cloud business AWS

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Speaker 3: actually gaining new customers, gaining growth or market share because

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Speaker 3: of their strategy with Bedrock.

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Speaker 7: Yeah, I think you know, over time they will gain

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Speaker 7: new customers. It's just a matter of getting these large

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Speaker 7: language models developed and them getting the customers interested. But

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Speaker 7: we do think their scope for Amazon to continue to

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Speaker 7: gain in that vertical.

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Speaker 4: What's interesting is well Google's event earlier this week, Cloud Event,

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Speaker 4: and they were really saying that it's startups that are

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Speaker 4: coming to them for cloud. How much, ultimately do companies.

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Speaker 5: Diversify, not go all in with one player.

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Speaker 7: I think diversification will happen across the board. It depends

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Speaker 7: on what you're using and where you want to be.

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Speaker 7: Amazon has done a great job in attracting not just

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Speaker 7: the startups but also established businesses, So.

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Speaker 5: We think the scope is wide.

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Speaker 7: We do think they're in the early innings of this,

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Speaker 7: and we do think right now they lag, but that

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Speaker 7: doesn't mean that they'll lag for a long time.

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Speaker 6: Let's talk about Andy Jesse.

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Speaker 3: He comes from AWS, that was his world, but the

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Speaker 3: story since he's taken the helm of Amazon has been

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Speaker 3: about cost discipline. In fact, don't even call it discipline pooa,

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Speaker 3: let's call it what it is, cutting cost lowering CAPEX.

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Speaker 3: And when I read the letter, he seems committed to that.

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Speaker 6: Still, yes, he does.

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Speaker 7: In fact, you know, they've been highlighting since last quarter

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Speaker 7: about the forty five cent reduction that they've had per

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Speaker 7: unit in their cost basis, and they think they can

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Speaker 7: press the pedal harder on that this year, largely as

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Speaker 7: they base GLEE streamline their inbound fulfillment, and they parse

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Speaker 7: out packages more efficiently to those regional distribution centers that

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Speaker 7: allow for SIND delivery.

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Speaker 3: Clunamgyle of Bloomberg Intelligence one of our analysts that leaves

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Speaker 3: coverage of this stock, and we're really grateful to have

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Speaker 3: you in reaction. Meanwhile, Amazon founder Jeff Bezos joined tech

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Speaker 3: heavyweights last night at the White House state dinner hosted

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Speaker 3: by President Biden for Japan's Prime Minister Fumiyo Kashida. Attendees

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Speaker 3: included Bezos's fiance Lawrence Sanchez, Apple CEO Tim Cook and

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Speaker 3: executive Lisa Jackson. Microsoft president Brad Smith was Intendants as

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Speaker 3: a well, and actor Robert de Niro. Even vent capitalists

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Speaker 3: including Robert Stavius of Bessemer Venture Partners. You also had

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Speaker 3: Jamie Diamond, Massa Sun and John Gray of Blackstone and Caro.

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Speaker 3: For me, the story is pretty clear here. Japan important

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Speaker 3: for technology and the economy and more.

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Speaker 4: Broadly for big get togethers of some important players, no

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Speaker 4: matter where they tend to be based. I mean, this

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Speaker 4: is where certain conversations had, deals are done. How many

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Speaker 4: are having intimate conversations with the Prime Minister of Japan.

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Speaker 4: But more broadly, I liked his Star Trek reference as well.

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Speaker 4: If you heard some of the commentary coming out of

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Speaker 4: the Leader of Japan, he was sort of going to

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Speaker 4: go boldly where no one has been before in terms

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Speaker 4: of deepening the US Japanese relationship.

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Speaker 5: But this is more broad, isn't as well?

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Speaker 4: I mean, there's a delightful menu that they managed to

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Speaker 4: be having triage Raby state not bad. But this is

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Speaker 4: also about deepening ties. But not just from an economic perspective.

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Speaker 4: We've got to think of this from a geopolitical perspective,

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Speaker 4: and notably what's happening with the Philippines coming in at

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Speaker 4: the moment, and indeed what this means for China US relations.

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Speaker 3: Yeah, I go straight to China when the CEOs turn up.

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Speaker 3: You know that that market is important, right, whether it's

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Speaker 3: for supply chain or the economy direct.

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Speaker 6: So let's bring up menu back up.

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Speaker 3: That is contemporary modern American Japanese fusion.

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Speaker 6: That looks great.

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Speaker 3: But there's also a political element to this as well,

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Speaker 3: that Biden had a lot of big names in the

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Speaker 3: White House in an election year.

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Speaker 4: Character Yeah, I think that is really notable and ultimately

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Speaker 4: trying to ensure that the economy is what Biden can

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Speaker 4: hang his hat on as well. Many worrying about the

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Speaker 4: inflationary pressures that were garnered yesterday and they did what

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Speaker 4: this really means for Biden Bidenomics and the lack thereof

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Speaker 4: but tech heavyweight really clear and present when it comes

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Speaker 4: to relationships with US and Japan.

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Speaker 5: JP Morgan.

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Speaker 4: It kicks off earning season Banks they are at the

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Speaker 4: start of them hammers tomorrow and earlier this week. Just

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Speaker 4: remember we heard from the CEO Jamie Diamond in his

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Speaker 4: annual letter to shareholders where he focused mainly on artificial

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Speaker 4: intelligence joining us now to discuss how banksy utilizing the

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Speaker 4: technology investing in it.

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Speaker 5: Evidence CEO Alexandra was a Visita is with us.

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Speaker 4: Evidence is an intelligent platform that really benchmarks attracts AI

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Speaker 4: adoption across financial services sector.

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Speaker 5: And JP Morgan is the standout performer.

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Speaker 4: Alexandra, you think that's in some way akin to the

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Speaker 4: performance of the bank.

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Speaker 8: Can it stop more broadly, Yeah, well, thank you so

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Speaker 8: much for having me on Caroline and ED. But its

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Speaker 8: performance is incredibly strong in terms of its AI maturity,

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Speaker 8: its head and shoulders above other banks. It was early

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Speaker 8: to the game. Jamie Diamond made AI a focus for

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Speaker 8: the banks six years ago, where he made clear that

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Speaker 8: it was going to be an AI first organization and

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Speaker 8: from that followed a lot of initiatives like establishing a

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Speaker 8: research lab, you know, doubling down on hiring AI talent,

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Speaker 8: reorganizing the EXAC team, and that really has paid off

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Speaker 8: and you see, well, according to our measurements, you can

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Speaker 8: see that they sort of really stand out against other

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Speaker 8: banks and are incredibly strong on AI capabilities.

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Speaker 3: We've been looking at the data behind JP Morgan's AI hiring.

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Speaker 3: You just made a really interesting point, which is six

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Speaker 3: years ago this all started.

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Speaker 6: Let's show the chart.

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Speaker 3: There's the AI hype of twenty two, twenty twenty three,

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Speaker 3: but if you track the.

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Speaker 6: Hiring and what's so interesting.

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Speaker 3: The title of that chart is JP Morgan slows AI hiring.

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Speaker 3: But look how elevated it was in twenty twenty two

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Speaker 3: and it started back in twenty twenty. What do you

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Speaker 3: make of that about how ahead of the curve or not?

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Speaker 3: So to speak, JP Morgan.

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Speaker 8: Is Yeah, I mean, I mean, first of all, it's

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Speaker 8: a really important point that you make it in terms

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Speaker 8: of the hype in twenty two and twenty three of

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Speaker 8: AI and sort of what we're heading into is more

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Speaker 8: of a let's sort of see what gets through production.

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Speaker 2: It's much harder.

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Speaker 8: Than we think, and sort of the use cases going

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Speaker 8: from ideation to production just takes a little longer, and

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Speaker 8: some of that slow down and hiring is also a

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Speaker 8: little bit of a let's what talent do we actually need?

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Speaker 8: But when you look at that chart, you can see

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Speaker 8: that really being on a hiring tear from for the

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Speaker 8: last sort of six seven years. And you know, the

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Speaker 8: proportion of AI talent and the caliber of AI talent

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Speaker 8: in the bank is second to none. So right now

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Speaker 8: a lot of the banks are looking at like where

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Speaker 8: you know, the onset of using you know, genitive AI,

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Speaker 8: Where are we using it in the bank? Which which

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Speaker 8: platforms are we going to use, whether it's open AI

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Speaker 8: or Mistral or hugging face, which one of those, How

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Speaker 8: does it fit into our particular needs and how do

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Speaker 8: we refine it so so it gives us the best

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Speaker 8: output possible. So there is a bit of a let's

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Speaker 8: see what we can use internally of our AI talent

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Speaker 8: that can you know, can help us, you know, refine

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Speaker 8: that and before we sort of accelerate again, uh, and

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Speaker 8: think about what we hire externally. But it should should

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Speaker 8: also be noted that when you sort of look across

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Speaker 8: the banking sector, even the banks that have cut employees

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Speaker 8: have sort of tried to keep the AI talent sort

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Speaker 8: of relatively steady, if not increasing.

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Speaker 4: Talking of talent, of course, within that shareholder letter was

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Speaker 4: the demonstration and their commitment to elevating almost exs level

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Speaker 4: the discussion around AI, and they mention the importance in

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Speaker 4: creating that new position Chief Data and Analytics Officer that

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Speaker 4: sits in the operating committee. That person over at JP

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Speaker 4: Morgan is indeed Teresa heightens Ritter, who I was lucky

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Speaker 4: enough to speak to at an evident event, and it's

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Speaker 4: notable that she has been galvanizing the focus on sort

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Speaker 4: of from top down ensuring that all talent are online

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Speaker 4: with where AI can really bring about a change in productivity,

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Speaker 4: Where are they seeing it and drive down in costs?

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Speaker 5: Where are they passing this on to the user of

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Speaker 5: JP Morgan.

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Speaker 8: Yeah, when it comes to well, first of all, when

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Speaker 8: it comes to driving the use of AI and where

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Speaker 8: you sort of look at the letter as really talking

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Speaker 8: about gaining efficiencies and really making the banks sort of

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Speaker 8: strong and lean and robust forwithstanding sort of any macro

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Speaker 8: challenges on you know, in the future, So looking at

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Speaker 8: driving down the costs and bringing up efficiency and bringing

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Speaker 8: up productivity, and AI being used to also see where

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Speaker 8: you can elevate revenues.

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Speaker 5: Is it's whereas that it's sort of.

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Speaker 8: In every newk and cranny of the bank looking at

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Speaker 8: sort of the ways that it can create that return

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Speaker 8: on investment. And then you know, with the AI tools

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Speaker 8: that then gets passed on to the customer and you

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Speaker 8: know in better customer management, you know experiences if time and.

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Speaker 3: To Alexandra, do you think Jamie Diamond has a good

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Speaker 3: understanding of artificial intelligence?

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Speaker 6: He himself, he is.

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Speaker 8: Nick He saw it very early. He knew it was

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Speaker 8: going to be very impactful. He was one of the

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Speaker 8: first CEOs bank CEOs to go out and and say

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Speaker 8: we are changing. We are going to be an AI

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Speaker 8: company or an AI first company, where many banks would

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Speaker 8: be we're going to be great banks and using AI

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Speaker 8: to become a little bit better bank. He was a

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Speaker 8: visionary with that. I think he has a good understanding.

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Speaker 3: Yes, he's more upbeat about it than he is about Bitcoin.

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Speaker 3: Alex and Mussa Visita. It's great to have you, of evident.

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Speaker 3: Of course, now EU's top tech regulator, Margareta ves Thea

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Speaker 3: says AI could be as disruptive as the atomic bomb.

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Speaker 3: The Executive Vice President of the European Commission joined Bloomberg

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Speaker 3: earlier today to talk about the current state of AI.

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Speaker 9: It's top of mind for us to make sure that

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Speaker 9: the market stays competitive. When everything gets fueled by artificial intelligence,

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Speaker 9: it's going to change the marketplace.

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Speaker 10: Do you have a timeline at all, Commissioner, when we

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Speaker 10: could potentially see.

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Speaker 11: Some movement on this.

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Speaker 6: No, we don't have a fixed timeline.

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Speaker 9: Of course, we want to produce results as fast as possible,

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Speaker 9: also for the involved companies for their benefit. We'll be

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Speaker 9: getting there soon, I think.

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Speaker 12: So we're talking about Microsoft and open Ai, but of

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Speaker 12: course they're not the only players. To hear, you have

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Speaker 12: Amazon actually with a big almost three billion dollar investment

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Speaker 12: into Anthropic, which of course competes with OpenAI.

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Speaker 11: What do you make of that?

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Speaker 12: Is that something that you're looking at as well?

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Speaker 9: Well? We will be having, you know, a vigilant, keen

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Speaker 9: attention to what is happening in this field. We see

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Speaker 9: a lot of entrenched market power when it comes to technology,

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Speaker 9: and it's really important that now when we have technology

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Speaker 9: which is not just a new technology, it's it's basically

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Speaker 9: a new world that we're looking into, that we make

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Speaker 9: sure that it's a competitive new world.

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Speaker 10: You just recently under the DMA went after three big

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Speaker 10: US tech companies, and to be honest, Commissioner, three is

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Speaker 10: a crowd. I'm really curious to know which of these

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Speaker 10: you are honing in on. And because a lot of

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Speaker 10: people really seem to think it's Apple, where the EU

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Speaker 10: has a lot of problems with.

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Speaker 9: Well, what's the point of the Digital Markets Act is

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Speaker 9: to open the market to make sure that that more

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Speaker 9: businesses can can get to their customers so customers have

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Speaker 9: more choice. So obviously we are looking at getting out

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Speaker 9: of self preferencing so that you do not if your

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Speaker 9: for instance, have a Google search and you do not

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Speaker 9: just get a Google products. And we're looking at how

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Speaker 9: businesses can get a real relationship with their customers. So

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Speaker 9: to get rid of the ends is staring that we

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Speaker 9: see quite a lot and people have choice is they

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Speaker 9: want to stay in the Apple payment environment or they

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Speaker 9: want to have a direct and sometimes cheaper relationship with

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Speaker 9: their service provider.

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Speaker 12: Well among those three Apple, Google and Meta, do you

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Speaker 12: have a priority target?

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Speaker 9: Well, we have opened these five cases because we think

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Speaker 9: these are very important.

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Speaker 11: We may have more cases in the.

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Speaker 9: Pipeline, so we have given retaining orders for what may

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Speaker 9: be evidence once we're moving forward. These five cases, they

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Speaker 9: are all priority. We think that they are absolutely key

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Speaker 9: if our suspicions are confirmed that we get compliance because

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Speaker 9: this is this is what opens the market, and that's

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Speaker 9: the basic idea. Are the Digital Markets Act for many

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Speaker 9: more businesses to have a fair chance to get to

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Speaker 9: their customers.

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Speaker 6: Okay, time for talking tech and first up.

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Speaker 3: KKR is said to be reviewing options of a sale

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Speaker 3: or an IPO for BMC software, which could be worth

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Speaker 3: as much as fifteen billion dollars including debt. That's according

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Speaker 3: to sources. While no final decisions have been made, KKR

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Speaker 3: is favoring an IPO of BMC, which filed confidentially last

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Speaker 3: year with the SEC for a listing. Plus French chip

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Speaker 3: materials company Seutech is said to be considering building a

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Speaker 3: factory in the United States, following in the footsteps of

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Speaker 3: one of its biggest customers, TSMC, as it expands from

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Speaker 3: Arizona to Texas. With government incentives, The consideration would mean

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Speaker 3: that Stech would add to its global factory facilities alongside Singapore, Belgium,

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Speaker 3: and France, and positions the company for growth outside of

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Speaker 3: China and Saudi Arabia and the UAE are racing for

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Speaker 3: AI dominance, rushing to build out expensive data center infrastructure

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Speaker 3: to help support the technology. Both countries lag behind Western

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Speaker 3: Europe in terms of data center capacity, with hopes of

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Speaker 3: closing that gap in.

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Speaker 11: A few years.

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Speaker 3: In a recent report by PwC, it estimated that AI

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Speaker 3: will contribute nearly ninety six billion dollars to the UAE

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Speaker 3: and one hundred and thirty five billion to Saudi Arabia's

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Speaker 3: economy by twenty thirty.

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Speaker 5: Caroline That was a great business sweep piece.

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Speaker 4: Meanwhile, let's just talk about employees over in an Apple

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Speaker 4: store in Shorthills, New Jersey, just petitioned to unionize. It's

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Speaker 4: marking the first lutch effort that we've seen it in

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Speaker 4: about a year long lull of news around this. Bloomberg's

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Speaker 4: Mark German just going to remind us of the context

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Speaker 4: when it comes to Apple, because they're not the first

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Speaker 4: store that's looking to unionize.

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Speaker 13: Yeah, no, that's a great point.

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Speaker 14: The Apple unionization efforts really kicked into high gear right

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Speaker 14: in the.

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Speaker 13: Middle of the COVID pandemic. In twenty twenty two.

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Speaker 14: You saw their retail store in the Towson area of

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Speaker 14: Maryland petition to unionize.

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Speaker 13: They successfully unionized.

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Speaker 14: Are now recognize is a unionized Apple retail store. The

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Speaker 14: Penn Station store and a mall in Oklahoma City, Oklahoma

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Speaker 14: is a unionized store. So those two are the only

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Speaker 14: two unionized stores. But there have been a few other

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Speaker 14: stores that attempted, one in Saint Louis, Missouri, one in Atlanta, Georgia.

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Speaker 13: Those fell apart.

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Speaker 14: One of those unions even chastised the union company and

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Speaker 14: ended up siding with Apple. Now you have another store

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Speaker 14: joining in and that is the Short Hills mall store

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Speaker 14: and upscale area in New Jersey. And at this point

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Speaker 14: it's still early. They've petitioned, they have one hundred and

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Speaker 14: four employees that would be part of the union. There

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Speaker 14: still needs to be a vote. They still need to

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Speaker 14: get to the bargaining table with Apple.

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Speaker 13: But this is the process.

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Speaker 14: And so this was a major development petitioning publicly stating hey,

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Speaker 14: we are trying to unionize.

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Speaker 13: So this is a big deal. The fifth store that

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Speaker 13: we know of to attempt.

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Speaker 3: They are trying to unionize. This is a path trodden

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Speaker 3: by Amazon. You know, Caroline and I've kind of been

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Speaker 3: through a similar story arc with that company.

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Speaker 6: But I guess the next question.

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Speaker 3: At all smart, what is Apple's attitude towards unionization the

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Speaker 3: company's approach.

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Speaker 14: Well, the company is completely against unionization. They've been holding

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Speaker 14: roundtables at the Short Hills, New Jersey store for months

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Speaker 14: now pushing back on the idea of that team unionizing.

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Speaker 14: They don't want to have to change their perks, their benefits,

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Speaker 14: their pay strategy. You know, Apple is a company that

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Speaker 14: wants things to be consistent and completely under their control.

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Speaker 13: Unionization upsets that.

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Speaker 14: Finely tuned balance the company has enjoyed for as long

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Speaker 14: as it has under Tim Cook and Steve Jobs before him,

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Speaker 14: So certainly this is not something that Apple wants. I

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Speaker 14: think they're fairly happy that out of the two hundred

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Speaker 14: and seventy and change retail stores in the US, only

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Speaker 14: two have successfully unionized, and of those two, they haven't

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Speaker 14: given them anything.

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Speaker 6: Blamebak's chief correspondent Motgum and thank you.

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Speaker 3: Welcome back to blame Bags ten Lovelow here in San

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Speaker 3: Francisco and Caroline Heid and New York.

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Speaker 4: I've got to check on the markets for you, Ed,

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Speaker 4: because right now, no, we're actually seeing NAZAC and tech

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Speaker 4: stocks outperform on a day where bon yields are still

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Speaker 4: selling off on the end of the curve, so ten

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Speaker 4: yure yields are seeing four basis points to the higher.

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Speaker 5: We are still trying to.

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Speaker 4: Debate the inflatory pressures we have PPI today, CPI yesterday,

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Speaker 4: still showing inflation is kind of going the wrong direction

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Speaker 4: for the Federal Reserve. If banking on a cup come June,

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Speaker 4: people starting to take those bets off the table. We're

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Speaker 4: seeing Bitcoin under pressure just by two tens percent, where

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Speaker 4: of sixty nine thousand s.

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Speaker 5: It's around sixty five.

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Speaker 4: We're basically kind of treading water ahead of earning season

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Speaker 4: as well, the banks kicking off tomorrow. Moving on to

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Speaker 4: some individual movers, I mean interesting on the upside, A

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Speaker 4: lot of the key names are used to Nvidia, Apple, indeed,

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Speaker 4: even Amazon and a new record high today after its

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Speaker 4: shareholder letter.

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Speaker 5: I want to shine on an Atlassian.

439
00:21:45,480 --> 00:21:47,879
Speaker 4: It's getting an upgrade from analysts thinking that now is

440
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Speaker 4: the time to be buying into this enterprise focus software company.

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Speaker 5: So up more than three percent. Tesla though once again languishing,

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Speaker 5: pulling back.

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Speaker 4: In fact, you noted that Rivian shares performing as much

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Speaker 4: as seven percent to a record lows. Some reports that

445
00:21:58,960 --> 00:22:02,400
Speaker 4: Ford is cutting its evil prices. Elon Musk apparently looking

446
00:22:02,400 --> 00:22:05,280
Speaker 4: at India. Maybe we'll get some updates on supply and

447
00:22:05,640 --> 00:22:08,080
Speaker 4: in team demand coming from that country. Adobe, though, want

448
00:22:08,160 --> 00:22:11,520
Speaker 4: to watch interesting reporting coming out on basically how expensive

449
00:22:11,640 --> 00:22:14,919
Speaker 4: it is sometimes to be training your large language models

450
00:22:14,920 --> 00:22:18,520
Speaker 4: and text to video in particular, and where you're getting

451
00:22:18,600 --> 00:22:19,640
Speaker 4: some of those videos from.

452
00:22:19,880 --> 00:22:20,880
Speaker 5: We want to dig in on Nado.

453
00:22:20,960 --> 00:22:23,040
Speaker 4: We've just done a pressure by about percentage point today,

454
00:22:23,280 --> 00:22:25,000
Speaker 4: but looking like it is really trying to take on

455
00:22:25,040 --> 00:22:27,600
Speaker 4: the likes of Saura. Of course open aiyes product when

456
00:22:27,640 --> 00:22:30,600
Speaker 4: it comes to AI video generation, apparently offering it's network

457
00:22:30,640 --> 00:22:32,560
Speaker 4: of photographers and artists get this one hundred and twenty

458
00:22:32,600 --> 00:22:36,720
Speaker 4: dollars to submit videos of people engaged in everyday actions, walking,

459
00:22:36,840 --> 00:22:41,400
Speaker 4: expressing emotions, joy, anger, according to documents all seen by Bloomberg.

460
00:22:41,480 --> 00:22:43,760
Speaker 4: Let's just get more of our own reporter Brady Ford,

461
00:22:43,800 --> 00:22:46,159
Speaker 4: who helped break this story. And it is notable that

462
00:22:46,200 --> 00:22:47,880
Speaker 4: they're playing a bit of catch up here with text

463
00:22:47,920 --> 00:22:50,280
Speaker 4: to video. But where they don't have to play so

464
00:22:50,400 --> 00:22:53,200
Speaker 4: much catch up is where they train their data.

465
00:22:53,280 --> 00:22:54,879
Speaker 5: You'd have thought, but maybe they have to pay a

466
00:22:54,880 --> 00:22:55,280
Speaker 5: bit extra.

467
00:22:55,880 --> 00:22:57,240
Speaker 11: Yeah, it's a funny dynamic.

468
00:22:57,359 --> 00:23:00,880
Speaker 15: So they have this giant repository of they've been using

469
00:23:00,960 --> 00:23:04,080
Speaker 15: to train their photos. They got like three hundred million photos,

470
00:23:04,359 --> 00:23:06,359
Speaker 15: not as big of a deal when it comes to

471
00:23:06,440 --> 00:23:08,399
Speaker 15: the video. They don't have quite as much of that

472
00:23:08,480 --> 00:23:10,600
Speaker 15: in the stock libraries. So we see now that they're

473
00:23:10,600 --> 00:23:12,960
Speaker 15: starting to procure data to be able to train on.

474
00:23:13,640 --> 00:23:15,879
Speaker 15: And it's really funny kind of videos, right. It's like

475
00:23:16,560 --> 00:23:19,680
Speaker 15: some of the sample videos is people smiling, people frowning,

476
00:23:19,880 --> 00:23:23,000
Speaker 15: picking up weights, right, training these AI models on how

477
00:23:23,040 --> 00:23:24,720
Speaker 15: people really interact with the world.

478
00:23:24,920 --> 00:23:26,280
Speaker 5: I like them thing we're all really fit.

479
00:23:26,720 --> 00:23:28,480
Speaker 15: Yes, they think we're all very fit, or they don't

480
00:23:28,480 --> 00:23:29,719
Speaker 15: even have any videos of us doing it so they

481
00:23:29,760 --> 00:23:32,399
Speaker 15: know we're not right. But yeah, so they really need

482
00:23:32,480 --> 00:23:35,200
Speaker 15: these videos to help their model understand how the world

483
00:23:35,320 --> 00:23:37,040
Speaker 15: works and so that they can, you know, not get

484
00:23:37,080 --> 00:23:38,280
Speaker 15: smoked by open AI.

485
00:23:39,840 --> 00:23:44,800
Speaker 3: Mate two dollars sixty two cents per minute or as

486
00:23:44,880 --> 00:23:48,440
Speaker 3: high as seven dollars five cents per minute, based on

487
00:23:48,520 --> 00:23:52,360
Speaker 3: your reporting good reporting. If you're the CFO or COO

488
00:23:52,600 --> 00:23:56,720
Speaker 3: of Adobe, and that's before you facturing compute costs, you're

489
00:23:56,800 --> 00:24:00,600
Speaker 3: not going to be thrilled about the cost of training

490
00:24:00,640 --> 00:24:04,080
Speaker 3: a model like the Economic Sphere are actually newsworthy.

491
00:24:04,160 --> 00:24:05,040
Speaker 6: This is a surprise.

492
00:24:05,800 --> 00:24:09,359
Speaker 15: So it's not quite as high as it may sound like, right,

493
00:24:09,400 --> 00:24:12,679
Speaker 15: because it's one hundred and twenty dollars for one hundred

494
00:24:12,760 --> 00:24:13,520
Speaker 15: short videos.

495
00:24:13,640 --> 00:24:13,760
Speaker 9: Right.

496
00:24:13,840 --> 00:24:15,880
Speaker 11: I was trying to do the math of if I wanted.

497
00:24:15,680 --> 00:24:17,680
Speaker 15: To create this, it's going to take you a good

498
00:24:17,840 --> 00:24:21,600
Speaker 15: five six hours to make all these videos doing push ups,

499
00:24:21,680 --> 00:24:25,200
Speaker 15: then cooking and smiling and frowning. I mean, the actual

500
00:24:25,240 --> 00:24:27,440
Speaker 15: amount they're paying ends up being pretty low. And you

501
00:24:27,520 --> 00:24:30,159
Speaker 15: could say that all. I mean, they could scrape the

502
00:24:30,240 --> 00:24:33,280
Speaker 15: open Internet for free, but we know that there are

503
00:24:33,359 --> 00:24:36,679
Speaker 15: issues there with tagging the videos correctly getting the exact

504
00:24:36,760 --> 00:24:39,399
Speaker 15: quality you need. So I mean these are things they

505
00:24:39,480 --> 00:24:41,639
Speaker 15: might need to procure once, but they can continue to

506
00:24:41,720 --> 00:24:44,480
Speaker 15: train on. So, Yes, it is a material amount of

507
00:24:44,560 --> 00:24:47,960
Speaker 15: money in the grand scheme. It's not like they're selling

508
00:24:48,000 --> 00:24:49,920
Speaker 15: out a ton to every creator though.

509
00:24:51,200 --> 00:24:53,879
Speaker 4: I mean all of this comes within the context of

510
00:24:54,640 --> 00:24:57,360
Speaker 4: Sora and where it got data, right, yeah.

511
00:24:57,480 --> 00:24:57,600
Speaker 13: Right.

512
00:24:57,760 --> 00:25:01,600
Speaker 15: We also have that viral clip of the open Aic saying, oh,

513
00:25:01,760 --> 00:25:03,680
Speaker 15: we don't know where it was trained, but right, we

514
00:25:03,720 --> 00:25:05,480
Speaker 15: all kind of know where it was trained, right, And

515
00:25:05,520 --> 00:25:08,360
Speaker 15: I mean that's been Adoby's whole marketing pitch that we're

516
00:25:08,440 --> 00:25:10,080
Speaker 15: going to try to do things the right way.

517
00:25:10,240 --> 00:25:12,320
Speaker 11: We're going to only use sources that we have true

518
00:25:12,400 --> 00:25:13,000
Speaker 11: access to.

519
00:25:13,480 --> 00:25:15,560
Speaker 5: You're not going to get done in the courts. Basically,

520
00:25:15,600 --> 00:25:16,400
Speaker 5: you're going to get sued.

521
00:25:16,400 --> 00:25:16,600
Speaker 13: You're not.

522
00:25:16,920 --> 00:25:19,520
Speaker 15: They literally say, yeah, if you get sued, we're going

523
00:25:19,600 --> 00:25:20,119
Speaker 15: to be there with you.

524
00:25:20,200 --> 00:25:21,800
Speaker 11: We'll see you in court, we'll back you up.

525
00:25:22,760 --> 00:25:25,080
Speaker 15: Will that end up holding them back? Will that end

526
00:25:25,160 --> 00:25:27,520
Speaker 15: up casting more than it needs to? It is yet

527
00:25:27,600 --> 00:25:30,520
Speaker 15: to be seen. But that's at least their public message

528
00:25:30,520 --> 00:25:31,000
Speaker 15: at this time.

529
00:25:32,200 --> 00:25:34,240
Speaker 3: Bloomberg's Brady Ford, I think you're always on top of

530
00:25:34,280 --> 00:25:36,960
Speaker 3: it when it comes to Adobe, Thank you now. Open

531
00:25:37,040 --> 00:25:40,720
Speaker 3: AI CEO Sam Outman has been working to build a

532
00:25:40,840 --> 00:25:46,000
Speaker 3: global coalition among government and industry leaders to support boosting

533
00:25:46,080 --> 00:25:50,040
Speaker 3: the supply of chips, energy and data center capacity that's

534
00:25:50,119 --> 00:25:54,080
Speaker 3: needed to develop artificial intelligence technology. That's according to our

535
00:25:54,160 --> 00:25:57,399
Speaker 3: sources and Bloomberg Shreen Gafari broke that story with me

536
00:25:57,960 --> 00:25:59,919
Speaker 3: and joined me on set. This is kind of an

537
00:26:00,000 --> 00:26:03,000
Speaker 3: evolution of our existing reporting, right. It started with this

538
00:26:03,200 --> 00:26:06,400
Speaker 3: idea that Sam Outman was worried about the supply long

539
00:26:06,560 --> 00:26:10,560
Speaker 3: term of AI accelerators or the GPUs, the train models,

540
00:26:10,880 --> 00:26:13,240
Speaker 3: and later inference it's bigger.

541
00:26:13,320 --> 00:26:15,600
Speaker 6: Now, just recap for audience what.

542
00:26:15,640 --> 00:26:18,360
Speaker 16: You and I have learned, right, So we know now

543
00:26:18,600 --> 00:26:22,680
Speaker 16: that Sam Maltman has been in the UAE this week

544
00:26:23,080 --> 00:26:26,840
Speaker 16: meeting with government officials, with investors, and the pitch is

545
00:26:26,920 --> 00:26:29,720
Speaker 16: to build some kind of global coalition that goes beyond

546
00:26:29,880 --> 00:26:34,119
Speaker 16: just manufacturing chips, but actually into also things like energy production,

547
00:26:34,280 --> 00:26:37,240
Speaker 16: which is a huge and growing resource for them that

548
00:26:37,240 --> 00:26:38,560
Speaker 16: they're quite worried about securing.

549
00:26:39,240 --> 00:26:42,399
Speaker 3: What's interesting here is the players. You know, we have

550
00:26:42,600 --> 00:26:48,040
Speaker 3: some understanding that Sam met with UAE officials. G forty

551
00:26:48,080 --> 00:26:50,280
Speaker 3: two is a name that gets bandied round, But do

552
00:26:50,400 --> 00:26:52,560
Speaker 3: we know who's in this coalition so far?

553
00:26:53,520 --> 00:26:55,720
Speaker 5: So we know the pitch is that it's worldwide.

554
00:26:56,560 --> 00:27:01,160
Speaker 16: Sam is traveling to Washington, DC today right to meet

555
00:27:01,280 --> 00:27:05,520
Speaker 16: with people on Capitol Hill, national security leaders, so we

556
00:27:05,600 --> 00:27:08,040
Speaker 16: know that, you know, he's talking to US leaders as well,

557
00:27:08,960 --> 00:27:12,080
Speaker 16: but we also know that he's talking to He has

558
00:27:12,119 --> 00:27:15,359
Speaker 16: in the past talking to leaders of several Western countries

559
00:27:15,440 --> 00:27:17,960
Speaker 16: and democracies, and so the pitch goes beyond just the

560
00:27:18,040 --> 00:27:18,600
Speaker 16: Middle East.

561
00:27:18,680 --> 00:27:21,840
Speaker 4: For sure, it's going to resonate with law makers. It's

562
00:27:21,880 --> 00:27:24,920
Speaker 4: got to resonate with the supply chain more sure. And

563
00:27:25,640 --> 00:27:27,440
Speaker 4: we're seeing a lot of money being spent by the

564
00:27:27,520 --> 00:27:32,600
Speaker 4: US government trying a law offshore chip makers manufacturers fabs

565
00:27:32,720 --> 00:27:34,200
Speaker 4: ultimately coming from TSMC.

566
00:27:34,320 --> 00:27:36,280
Speaker 5: There's much reporting on whether Samsung is going to.

567
00:27:36,280 --> 00:27:37,120
Speaker 6: Be here as well.

568
00:27:37,200 --> 00:27:40,600
Speaker 4: Digging in deeper, how much is he talking to Corporate

569
00:27:40,920 --> 00:27:43,600
Speaker 4: America and corporations more broadly as well?

570
00:27:43,760 --> 00:27:46,399
Speaker 16: Just because this doesn't happen even night right, I mean,

571
00:27:46,440 --> 00:27:49,880
Speaker 16: I think this conversation is going to go beyond open

572
00:27:49,920 --> 00:27:51,920
Speaker 16: an eye to other industries. I don't think you know,

573
00:27:52,680 --> 00:27:54,520
Speaker 16: you don't want the pitch to sort of just focus

574
00:27:54,600 --> 00:27:58,919
Speaker 16: on one company, but rather on building out the capabilities

575
00:27:58,960 --> 00:28:01,360
Speaker 16: for industry as a whole. So that's what I'm under

576
00:28:01,400 --> 00:28:03,560
Speaker 16: seeing the discussion to be at these times.

577
00:28:04,400 --> 00:28:07,119
Speaker 4: It's a brilliant reporting from both you and ED and

578
00:28:07,240 --> 00:28:09,600
Speaker 4: we'll see what that conversation ends up looking like on

579
00:28:09,720 --> 00:28:10,600
Speaker 4: Capitol Hill as well.

580
00:28:10,920 --> 00:28:13,119
Speaker 5: I'm sure you're reporting on that too, Sharon Guffrey. We

581
00:28:13,160 --> 00:28:13,840
Speaker 5: thank you so much.

582
00:28:13,960 --> 00:28:16,040
Speaker 4: I meanwhile, coming up, pitch Book out with its latest

583
00:28:16,080 --> 00:28:17,720
Speaker 4: report into the health of the VC industry.

584
00:28:17,960 --> 00:28:20,840
Speaker 5: May readging into the pitch Book analyst Kyle Stamford that's next.

585
00:28:21,320 --> 00:28:44,560
Speaker 4: This is Blue made Technology. Let's just talk about private

586
00:28:44,600 --> 00:28:46,960
Speaker 4: markets a little bit. Fundraising they're in beginning of twenty

587
00:28:47,040 --> 00:28:49,840
Speaker 4: twenty four met with some actual residual optimism, but that

588
00:28:49,960 --> 00:28:53,440
Speaker 4: did not translate into meaningful growth in VC activity. That's

589
00:28:53,440 --> 00:28:56,080
Speaker 4: all according to Pitchbook's latest Venture Monitor report, and it

590
00:28:56,160 --> 00:28:59,960
Speaker 4: highlights the competition for capital remains a fierce two years

591
00:29:00,240 --> 00:29:02,680
Speaker 4: into a slowdown, and while stronger companies might still be

592
00:29:02,720 --> 00:29:04,240
Speaker 4: able to compel investment, the same is.

593
00:29:04,280 --> 00:29:06,640
Speaker 5: Not true for those that are kind of struggling.

594
00:29:06,840 --> 00:29:09,880
Speaker 4: Carl Stamford with please to say, leads US Venture capital

595
00:29:09,920 --> 00:29:12,440
Speaker 4: research over at Pitchbook and joins us. And so it

596
00:29:12,560 --> 00:29:14,320
Speaker 4: really feels like a tailor that haves and the have not.

597
00:29:14,520 --> 00:29:16,360
Speaker 4: If you're an AI dialing, you're able to reap it in.

598
00:29:16,480 --> 00:29:19,440
Speaker 4: If you're not, and you're not managing to grow revenue

599
00:29:19,520 --> 00:29:22,120
Speaker 4: and indeed profitability and count yourself out.

600
00:29:22,520 --> 00:29:24,320
Speaker 1: Yeah, but I mean at some point that's where this

601
00:29:24,520 --> 00:29:27,840
Speaker 1: market should be, right, I mean, we're looking at high

602
00:29:27,920 --> 00:29:31,000
Speaker 1: risk investments, companies that should be growing fast, being able

603
00:29:31,080 --> 00:29:34,600
Speaker 1: to get their their product and market and eventually exit

604
00:29:34,680 --> 00:29:35,240
Speaker 1: down the line.

605
00:29:36,320 --> 00:29:38,080
Speaker 2: Fifty five thousand companies.

606
00:29:37,760 --> 00:29:40,520
Speaker 1: Are currently VC backed in the US market, you know,

607
00:29:40,960 --> 00:29:43,720
Speaker 1: one hundred and twenty thousand or so globally. That's a

608
00:29:43,840 --> 00:29:47,600
Speaker 1: huge number of companies that still are fighting through similar trajectory,

609
00:29:47,640 --> 00:29:50,200
Speaker 1: similar sectors, and fighting for that capital that's just not

610
00:29:50,360 --> 00:29:50,880
Speaker 1: there anymore.

611
00:29:51,320 --> 00:29:51,560
Speaker 6: Okay.

612
00:29:52,040 --> 00:29:55,680
Speaker 3: I am hearing and seeing things that the data doesn't show.

613
00:29:55,880 --> 00:29:59,080
Speaker 3: For example, twenty four hours ago, we had Toyota Ventures

614
00:29:59,160 --> 00:30:03,440
Speaker 3: on the show. They are a single LP fund granted Toyota,

615
00:30:03,880 --> 00:30:06,400
Speaker 3: but they raised even more money from that LP to

616
00:30:06,560 --> 00:30:10,160
Speaker 3: invest in physical stuff I'm hearing lots about funds that

617
00:30:10,240 --> 00:30:16,640
Speaker 3: are being raised to invest in manufacturing industrialization. Why is

618
00:30:16,720 --> 00:30:17,960
Speaker 3: that not showing up in the data?

619
00:30:18,040 --> 00:30:19,920
Speaker 1: Kyle Right, Well, I think first if you start with

620
00:30:20,000 --> 00:30:23,960
Speaker 1: Toyota Adventures or these corporate LPs, they have a much

621
00:30:24,040 --> 00:30:26,800
Speaker 1: different return profile or ability to get these different returns

622
00:30:26,840 --> 00:30:30,360
Speaker 1: than a attraditional VC autritional LP might be looking for. Right,

623
00:30:30,440 --> 00:30:36,479
Speaker 1: there's a return on products or ability to integrate new

624
00:30:36,560 --> 00:30:40,680
Speaker 1: products into their cars down the road. As the attritional LP, though,

625
00:30:40,720 --> 00:30:42,880
Speaker 1: they're looking for that cash on cash return, and we

626
00:30:43,000 --> 00:30:45,480
Speaker 1: have not seen that by any means. I mean, all

627
00:30:45,560 --> 00:30:49,880
Speaker 1: the last quarter, the entire story was about Reddit and Esterilabs,

628
00:30:49,880 --> 00:30:53,440
Speaker 1: which were solid IPOs, but in total in the US

629
00:30:53,520 --> 00:30:55,560
Speaker 1: it was just eighteen point six billion dollars in exit

630
00:30:55,600 --> 00:30:57,440
Speaker 1: value that was generated. And so if you're an LP

631
00:30:57,680 --> 00:31:00,840
Speaker 1: without those returns and those distributions is coming back to you,

632
00:31:01,440 --> 00:31:04,880
Speaker 1: there's less incentive to put money back into the market now,

633
00:31:05,360 --> 00:31:06,400
Speaker 1: or there's not an ability.

634
00:31:06,560 --> 00:31:06,640
Speaker 16: Right.

635
00:31:06,720 --> 00:31:09,000
Speaker 1: They're looking for that capital would come back to recycle

636
00:31:09,040 --> 00:31:11,600
Speaker 1: into new funds and new commitments and make sure that

637
00:31:11,640 --> 00:31:13,480
Speaker 1: they're in all these vintage years. But if that's not

638
00:31:13,600 --> 00:31:16,120
Speaker 1: coming back, there's just no way for these traditional LPs

639
00:31:16,440 --> 00:31:18,560
Speaker 1: to get or to put money back into the venture

640
00:31:18,600 --> 00:31:19,120
Speaker 1: market right.

641
00:31:19,040 --> 00:31:21,760
Speaker 4: Now, and you say traditional LPs, and it has been

642
00:31:21,840 --> 00:31:25,600
Speaker 4: fascinating to see almost the domination of corporate VC in

643
00:31:25,640 --> 00:31:31,160
Speaker 4: the area of AI, certainly compared to the venture capital

644
00:31:31,640 --> 00:31:34,680
Speaker 4: arena right now. Who are a new guard of LPs

645
00:31:34,960 --> 00:31:38,520
Speaker 4: that aren't corporate. Are we seeing bench capital companies managing

646
00:31:38,560 --> 00:31:41,040
Speaker 4: to lure in different types of investors to secure their

647
00:31:41,080 --> 00:31:41,560
Speaker 4: next funds?

648
00:31:42,000 --> 00:31:43,880
Speaker 2: Do you think the investor of the LP bas is

649
00:31:43,920 --> 00:31:45,280
Speaker 2: going to always be wide ranging?

650
00:31:45,360 --> 00:31:45,440
Speaker 9: Right?

651
00:31:45,560 --> 00:31:48,400
Speaker 1: Pensions obviously are in that you sell CalPERS, read up

652
00:31:48,560 --> 00:31:52,280
Speaker 1: their commitment and increase their commitment to VC or the

653
00:31:52,360 --> 00:31:52,880
Speaker 1: last quarter.

654
00:31:53,560 --> 00:31:55,000
Speaker 2: I mean, corporates obviously are huge.

655
00:31:55,040 --> 00:31:57,280
Speaker 1: You look at the Japanese corporations and there's trillions of

656
00:31:57,320 --> 00:31:59,440
Speaker 1: dollars on their balance sheet they're able to put into

657
00:31:59,720 --> 00:32:04,400
Speaker 1: put to work in venture into new businicists. The large

658
00:32:04,920 --> 00:32:07,680
Speaker 1: megacaps in the US as well, huge amounts of cash

659
00:32:07,720 --> 00:32:09,040
Speaker 1: to be able to put it put to work, and

660
00:32:09,080 --> 00:32:10,960
Speaker 1: we've seen that in some of their AI invest some

661
00:32:11,080 --> 00:32:12,840
Speaker 1: of the past few years. I think where it really

662
00:32:12,880 --> 00:32:15,720
Speaker 1: gets down to struggling for LPs when you look at

663
00:32:15,720 --> 00:32:18,040
Speaker 1: the smaller ones, right the high net worth individuals that

664
00:32:18,160 --> 00:32:21,640
Speaker 1: maybe are feeling still very wealthy, but not nearly what

665
00:32:21,760 --> 00:32:25,240
Speaker 1: they were in twenty twenty one. You look at foundations

666
00:32:25,360 --> 00:32:28,560
Speaker 1: or endowments that are finding ways to put capital to work,

667
00:32:28,640 --> 00:32:32,880
Speaker 1: but maybe are not as able to take that ten

668
00:32:32,960 --> 00:32:35,160
Speaker 1: years of illiquidity that DC is going to bring to

669
00:32:36,240 --> 00:32:39,000
Speaker 1: to their cash. So there is still a broad range

670
00:32:39,440 --> 00:32:43,560
Speaker 1: in investors. Gps are being very strategic with who they're

671
00:32:43,560 --> 00:32:46,480
Speaker 1: going after and how they're positioning their strategy, and BC

672
00:32:46,640 --> 00:32:48,959
Speaker 1: fund to those LPs make sure it works for them

673
00:32:49,000 --> 00:32:49,280
Speaker 1: as well.

674
00:32:50,640 --> 00:32:56,520
Speaker 3: Kyle, it's been relatively exciting recently, relatively in the IPO market.

675
00:32:57,160 --> 00:33:01,080
Speaker 3: You know Reddit as an example. Is there a relationship

676
00:33:01,160 --> 00:33:05,600
Speaker 3: between upstream downstream? So when LPs and the firms that

677
00:33:05,600 --> 00:33:08,120
Speaker 3: they give money to see activity in the IPO market,

678
00:33:08,520 --> 00:33:12,200
Speaker 3: so they regain faith that actually putting something into a

679
00:33:12,320 --> 00:33:15,440
Speaker 3: five or ten year horizon is a good idea.

680
00:33:15,920 --> 00:33:17,760
Speaker 1: I mean, I think relative excitement is a really good

681
00:33:17,800 --> 00:33:20,840
Speaker 1: way to position Q one, right. I mean, those two

682
00:33:21,000 --> 00:33:23,480
Speaker 1: IPOs were great, but there's seven hundred and twenty unicorns

683
00:33:23,520 --> 00:33:26,160
Speaker 1: in the market right now or private in the US,

684
00:33:26,560 --> 00:33:29,320
Speaker 1: or fourteen hundred globally. Globally, there's a four point eight

685
00:33:29,400 --> 00:33:33,320
Speaker 1: trillion dollar market cap on these unicorns, and so a

686
00:33:33,400 --> 00:33:36,440
Speaker 1: couple IPOs is great, but until those distributions actually get

687
00:33:36,560 --> 00:33:39,400
Speaker 1: going back to LPs again, it's going to be a

688
00:33:39,440 --> 00:33:41,280
Speaker 1: slow fundraising market. It's going to be a slow deal

689
00:33:41,320 --> 00:33:46,840
Speaker 1: making market. We have our fun distribution data is showing

690
00:33:46,880 --> 00:33:49,880
Speaker 1: that distributions over the past four quarters have been as

691
00:33:50,000 --> 00:33:52,520
Speaker 1: low as they were in the global financial crisis. And

692
00:33:52,640 --> 00:33:56,760
Speaker 1: so again, there is definitely some positives that come out

693
00:33:56,800 --> 00:33:59,760
Speaker 1: of a few companies going to public, but there needs to.

694
00:33:59,760 --> 00:34:00,520
Speaker 2: Be many more.

695
00:34:00,640 --> 00:34:02,680
Speaker 1: And the M and A market again too, where a

696
00:34:02,720 --> 00:34:05,240
Speaker 1: lot of these returns are also going to get generated,

697
00:34:05,440 --> 00:34:08,160
Speaker 1: is non existing. Many of the deals we have from

698
00:34:08,160 --> 00:34:10,920
Speaker 1: an M and A perspective this quarter were had no

699
00:34:11,000 --> 00:34:14,000
Speaker 1: deal of value attached, right, so their immaterial to the

700
00:34:14,080 --> 00:34:16,640
Speaker 1: corporate growth. And so that's a very difficult spot when

701
00:34:16,640 --> 00:34:17,759
Speaker 1: you look at returns down the road.

702
00:34:18,320 --> 00:34:21,200
Speaker 3: Hey, Carl, let's tap into your relative excitement. What are

703
00:34:21,239 --> 00:34:24,600
Speaker 3: you relatively excited about thematically? Subsector wise?

704
00:34:25,440 --> 00:34:27,840
Speaker 1: Sure, subsector wise, I mean, obviously AI is going to

705
00:34:27,920 --> 00:34:30,879
Speaker 1: be the major area going forward. I think we've seen

706
00:34:30,920 --> 00:34:33,760
Speaker 1: a lot of unique companies and you need this models

707
00:34:33,760 --> 00:34:36,120
Speaker 1: getting started. I think It's interesting dynamic too, with the

708
00:34:36,200 --> 00:34:39,960
Speaker 1: incumbents and Microsoft and AI and Microsoft and Google and

709
00:34:40,000 --> 00:34:42,640
Speaker 1: Amazon being so heavy into it. I think it's going

710
00:34:42,719 --> 00:34:46,239
Speaker 1: to be very quick to what we're talking about, the

711
00:34:46,280 --> 00:34:48,400
Speaker 1: bifurcation and have and have nots. Then there's gonna be

712
00:34:48,400 --> 00:34:50,040
Speaker 1: a lot of money made in AI and there's a

713
00:34:50,080 --> 00:34:51,920
Speaker 1: lot of money loss, and I think it's be relatively

714
00:34:52,040 --> 00:34:54,520
Speaker 1: quick over the next few years because of the incumbents

715
00:34:54,560 --> 00:34:55,719
Speaker 1: and how they're playing in the market.

716
00:34:57,360 --> 00:34:58,200
Speaker 2: You know, Tech in.

717
00:34:58,280 --> 00:35:01,359
Speaker 1: General, I think is still really strong. Even though it's

718
00:35:01,360 --> 00:35:04,279
Speaker 1: a slow market. Like Karen said, strong companies are getting funded.

719
00:35:04,320 --> 00:35:06,680
Speaker 1: There's money out there for these companies, and so tech

720
00:35:07,080 --> 00:35:11,120
Speaker 1: is going to continue pushing forward in We like what

721
00:35:11,200 --> 00:35:13,480
Speaker 1: we've seen, but it's in me a difficult market, I

722
00:35:13,560 --> 00:35:15,000
Speaker 1: think for every sector down the road.

723
00:35:15,239 --> 00:35:18,400
Speaker 3: Carl Stamford, lead US Bench Capital Research Analysts at Pitchburg,

724
00:35:18,440 --> 00:35:19,279
Speaker 3: Thank you very much.

725
00:35:26,400 --> 00:35:29,360
Speaker 4: More protection for teens on Instagram is coming now. The

726
00:35:29,440 --> 00:35:32,160
Speaker 4: social platform, run of course by Meta, is turning to

727
00:35:32,400 --> 00:35:36,400
Speaker 4: AI to blur out nude images sent via direct message now.

728
00:35:36,480 --> 00:35:39,480
Speaker 4: Instagram announced in a blog post today that the update

729
00:35:39,520 --> 00:35:42,400
Speaker 4: aims to protect users from unwanted photos as well as

730
00:35:42,440 --> 00:35:46,200
Speaker 4: from potential sextortion scammers. This comes, of course, as US

731
00:35:46,239 --> 00:35:49,400
Speaker 4: politicians have been continuing to accuse a platform of damage

732
00:35:49,480 --> 00:35:50,840
Speaker 4: to youth mental health.

733
00:35:52,440 --> 00:35:53,480
Speaker 6: Let's stay with Instagram.

734
00:35:53,560 --> 00:35:58,000
Speaker 3: It's recently changed its policies for political content and it

735
00:35:58,120 --> 00:36:01,919
Speaker 3: has caused a stir from some users. The move, which

736
00:36:02,080 --> 00:36:06,280
Speaker 3: is coming during an election year, has prompted some pushback,

737
00:36:06,440 --> 00:36:10,120
Speaker 3: with some like Republican Senator Marsha Blackburn even accusing the

738
00:36:10,200 --> 00:36:13,120
Speaker 3: company of censorship. You're looking at the posts that she

739
00:36:13,280 --> 00:36:16,520
Speaker 3: made on x Let's break it all down with el Rochford,

740
00:36:16,600 --> 00:36:19,760
Speaker 3: senior data senior data and policy specialists with the Crime

741
00:36:19,800 --> 00:36:23,480
Speaker 3: and Justice Institute, as well as Jess Natali, creator of

742
00:36:23,600 --> 00:36:28,200
Speaker 3: the So Informed Instagram page. Jess, Let's start with you.

743
00:36:28,320 --> 00:36:32,000
Speaker 3: You are a user, a well followed user of Instagram.

744
00:36:32,719 --> 00:36:35,160
Speaker 3: The policy and its rationale are clear.

745
00:36:35,280 --> 00:36:36,400
Speaker 6: What is your response to it?

746
00:36:36,800 --> 00:36:40,400
Speaker 17: I mean, I think it's quite alarming and quite transparent,

747
00:36:40,520 --> 00:36:44,200
Speaker 17: the timing in which this is being rolled out, and

748
00:36:44,320 --> 00:36:48,839
Speaker 17: as small things like preparing people for the election, giving

749
00:36:48,880 --> 00:36:53,480
Speaker 17: them the educational tools all the way to a genocide

750
00:36:53,520 --> 00:36:58,719
Speaker 17: being documented on their platform. I think it's taking away

751
00:36:58,960 --> 00:37:02,600
Speaker 17: the authority through people to consume what they want to

752
00:37:02,640 --> 00:37:06,520
Speaker 17: be consuming, which is important information, it's news.

753
00:37:08,080 --> 00:37:13,080
Speaker 4: I'm interested in the academic perspective here, l because your

754
00:37:13,120 --> 00:37:15,719
Speaker 4: PhD in sociology of the per dou you're focused on

755
00:37:15,840 --> 00:37:19,480
Speaker 4: reproductive justice, on racial justice movements in particular. And I'm

756
00:37:19,520 --> 00:37:23,200
Speaker 4: wondering if when ultimately Instagram is saying we are not

757
00:37:23,360 --> 00:37:25,720
Speaker 4: trying to clamp down on what you have actively decided

758
00:37:25,800 --> 00:37:27,960
Speaker 4: you want to watch those that you follow, but it's

759
00:37:28,040 --> 00:37:30,200
Speaker 4: more people that you don't follow sort of suddenly getting

760
00:37:30,239 --> 00:37:31,520
Speaker 4: into your.

761
00:37:31,480 --> 00:37:33,000
Speaker 5: Reels, into your streams more poorly.

762
00:37:33,480 --> 00:37:36,680
Speaker 4: How much do you think that this does limit, you know, ultimately,

763
00:37:36,719 --> 00:37:38,719
Speaker 4: people not wanting to be bombarded with things that they

764
00:37:38,760 --> 00:37:40,680
Speaker 4: do want to be seeing, or indeed, does it cut

765
00:37:40,760 --> 00:37:44,239
Speaker 4: off an ability to see outside your own bubble right now?

766
00:37:45,120 --> 00:37:51,120
Speaker 18: Sure, I think that's a great question. Opting into seeing

767
00:37:51,160 --> 00:37:56,400
Speaker 18: political content is a little bit tricky because a lot

768
00:37:56,440 --> 00:37:58,560
Speaker 18: of users weren't aware of this future was being rolled

769
00:37:58,560 --> 00:38:02,320
Speaker 18: out at all, so they weren't aware that this rollout

770
00:38:02,360 --> 00:38:04,520
Speaker 18: would impact what they were seeing or not seeing.

771
00:38:05,160 --> 00:38:08,480
Speaker 5: But it seems a bit odd the selection criteria.

772
00:38:08,680 --> 00:38:11,600
Speaker 18: So social topics are being filtered out, but how is

773
00:38:11,680 --> 00:38:19,680
Speaker 18: that being defined? Social media offers essential infrastructure informational infrastructure

774
00:38:20,160 --> 00:38:24,239
Speaker 18: to users and so a lot of young people are

775
00:38:24,360 --> 00:38:27,600
Speaker 18: getting their news from social media and this will impact

776
00:38:27,640 --> 00:38:29,759
Speaker 18: what they're seeing and what they're not seeing, and they

777
00:38:29,800 --> 00:38:32,200
Speaker 18: may not even be aware that there are things being

778
00:38:32,280 --> 00:38:33,800
Speaker 18: filtered from their viewing.

779
00:38:34,600 --> 00:38:37,279
Speaker 4: So before we dig in with you all a little

780
00:38:37,280 --> 00:38:39,600
Speaker 4: bit more about where the lines are drawn and where

781
00:38:39,640 --> 00:38:41,920
Speaker 4: a blurry is to what is seen as political content.

782
00:38:42,040 --> 00:38:44,400
Speaker 4: I just want to go to you, Jess, because your

783
00:38:44,480 --> 00:38:47,520
Speaker 4: numbers just give us a breakdown. People who opt in

784
00:38:47,719 --> 00:38:50,240
Speaker 4: to see your content because they want to be politically

785
00:38:50,320 --> 00:38:52,880
Speaker 4: informed by you, are they seeing less of.

786
00:38:52,920 --> 00:38:53,960
Speaker 5: Your content on the back of this.

787
00:38:54,200 --> 00:38:56,840
Speaker 4: You still getting the amount of video engagement, still getting

788
00:38:56,840 --> 00:38:58,719
Speaker 4: the amount of people who want to come and build

789
00:38:58,800 --> 00:38:59,400
Speaker 4: up your profile.

790
00:38:59,480 --> 00:39:04,479
Speaker 17: Ultimately, there is absolutely a dramatic decrease and the people

791
00:39:04,560 --> 00:39:07,440
Speaker 17: I'm reaching and the people who are engaging, which you know,

792
00:39:07,640 --> 00:39:11,520
Speaker 17: those go hand in hand. Prior to this being rolled out,

793
00:39:11,600 --> 00:39:15,279
Speaker 17: it was also there were censorship in place before this,

794
00:39:15,440 --> 00:39:17,440
Speaker 17: and I want to make that clear. Instagram with the

795
00:39:17,480 --> 00:39:21,319
Speaker 17: sensoring content well before this new rollout, but this has

796
00:39:21,440 --> 00:39:24,600
Speaker 17: caused the dramatic decrease. I have three point one million followers,

797
00:39:25,160 --> 00:39:27,120
Speaker 17: so three point one million people who have opted in

798
00:39:27,200 --> 00:39:30,080
Speaker 17: to see what I'm posting, and I have been reaching

799
00:39:30,200 --> 00:39:33,080
Speaker 17: somewhere between six and eight percent of my own followers

800
00:39:33,320 --> 00:39:34,200
Speaker 17: since this went.

801
00:39:35,040 --> 00:39:39,040
Speaker 3: Okay, well, I'm an Instagram user, Caroline's Instagram user. That

802
00:39:39,239 --> 00:39:41,279
Speaker 3: there is the policy side, and then there's just the

803
00:39:41,360 --> 00:39:44,839
Speaker 3: technology the platform, And I think many people would say

804
00:39:45,360 --> 00:39:48,920
Speaker 3: Instagram is not a place you go for news or politics.

805
00:39:49,320 --> 00:39:52,360
Speaker 3: Many would just say it's for entertainment value. It is

806
00:39:52,480 --> 00:39:55,520
Speaker 3: to share what you're interested in from a passion point

807
00:39:55,560 --> 00:39:58,480
Speaker 3: of view. That is an experience I think many people have.

808
00:39:59,000 --> 00:40:02,080
Speaker 3: What is your research tell you about the majority of

809
00:40:02,320 --> 00:40:05,440
Speaker 3: use case for why people go to Instagram and the

810
00:40:05,600 --> 00:40:07,000
Speaker 3: type of content they're trying to get.

811
00:40:07,840 --> 00:40:08,759
Speaker 11: Absolutely so.

812
00:40:08,960 --> 00:40:11,319
Speaker 18: I think there's also many people who would say they

813
00:40:11,360 --> 00:40:13,799
Speaker 18: don't go to Instagram to see food content, that they're

814
00:40:13,840 --> 00:40:17,120
Speaker 18: sick of seeing food blogs and recipes. Right, So, for

815
00:40:17,239 --> 00:40:19,759
Speaker 18: every user who doesn't want to see political content, there

816
00:40:19,800 --> 00:40:22,799
Speaker 18: are users who are seeking out political content and news

817
00:40:22,920 --> 00:40:27,680
Speaker 18: content on Instagram. The perception that Instagram is not a

818
00:40:27,719 --> 00:40:31,560
Speaker 18: political platform is just that it's a perception. There are

819
00:40:31,760 --> 00:40:37,120
Speaker 18: plenty of users who use Instagram to follow journalists, to

820
00:40:37,320 --> 00:40:42,600
Speaker 18: follow news outlets, to follow political organizations and political organizations

821
00:40:42,800 --> 00:40:46,520
Speaker 18: are very active on the platform, so this is going

822
00:40:46,600 --> 00:40:51,360
Speaker 18: to harm those organizations in all directions across the political spectrum.

823
00:40:52,480 --> 00:40:55,120
Speaker 18: And I think it's a bit shortsighted to say that

824
00:40:55,200 --> 00:40:58,080
Speaker 18: Instagram isn't a political platform and that that's not what

825
00:40:58,320 --> 00:41:01,080
Speaker 18: users want to see. We have plenty of evidence that

826
00:41:01,280 --> 00:41:04,520
Speaker 18: activists are online and that Instagram is being used for

827
00:41:04,719 --> 00:41:10,800
Speaker 18: political organization and for entertainment purposes. A lot of political

828
00:41:10,880 --> 00:41:14,080
Speaker 18: content is entertaining for people, although that might not be

829
00:41:14,200 --> 00:41:17,360
Speaker 18: their primary source of entertainment and often.

830
00:41:17,200 --> 00:41:19,759
Speaker 5: Their passion throughout. You're bringing both of your passions today.

831
00:41:19,880 --> 00:41:22,600
Speaker 4: L Rochford, of course, and Jess Nattel we wish we

832
00:41:22,640 --> 00:41:23,560
Speaker 4: had along with both of you.

833
00:41:23,840 --> 00:41:24,960
Speaker 5: This is broom big technology