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

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Speaker 1: from the heart of Silicon Valley with Ed Lao in

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Speaker 1: San Francisco.

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Speaker 2: This is Bloomberg Tech coming up. The global AI race

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Speaker 2: is accelerating, as Chinese startup Moonshot says its latest model

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Speaker 2: can compete with the best from Open Ai and m

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Speaker 2: propic plus, Netflix shares tumble up for the streaming giant

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Speaker 2: warns of slowing sales growth for a second straight quarter,

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Speaker 2: and Graylock, one of the oldest venture firms, just raised

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Speaker 2: one point five billion dollars for its eighteenth fund. We

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Speaker 2: speak with partner some modern medi Let's get straight to

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Speaker 2: our top story. Technology stocks are under pressure all around

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Speaker 2: the world this Friday, and the catalyst is a surprise

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Speaker 2: out of China. AI startup Moonshot has unveiled Kimmy K three,

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Speaker 2: a two point eight trillion parameter model it says can

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Speaker 2: compete with the best from Open Ai and Anthropic. It

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Speaker 2: charges about three dollars per million input tokens and fifteen

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Speaker 2: dollars per million output tokens, basically dramatically cheaper to use

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Speaker 2: than many leading AI models. Despite performing at the frontier,

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Speaker 2: investors are drawing parallels with last year's deepseek moment and

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Speaker 2: questioning whether the AI industry is enormous spending spree is

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Speaker 2: becoming harder to justify. Here's what's moving, and here's where

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Speaker 2: the pressure is. Right now, we're off session lows, but

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Speaker 2: the NASDAK one hundred down one point seven percent, the

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Speaker 2: Socks down almost three percent. The Socks is on track

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Speaker 2: for its biggest weekly decline since April of twenty twenty five.

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Speaker 2: Right now, it's trading at its lowest level since about

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Speaker 2: early to mid May. Then, as that one hundred is

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Speaker 2: not far off that weekly drop milestone either, about a

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Speaker 2: percentage point away from its biggest drop since April of

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Speaker 2: twenty twenty five. The individual movers, their compute names that

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Speaker 2: we're looking at one point, Apple is now down significantly

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Speaker 2: modestly two tens percent. It is very close, and at

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Speaker 2: one point did overtaking Video as Well's most valuable company.

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Speaker 2: But basically the market's under pressure generally. Let's get to

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Speaker 2: Bloomberg Senior Tech editor Mike Shephard, and let's talk about

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Speaker 2: China and AI. I mean, let's start with Moonshot and

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Speaker 2: Kimmy K three. I will try to get as much

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Speaker 2: of the detail of this release as possible and the

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Speaker 2: market reactions clear. But what else do we know?

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Speaker 3: Well, what we know is that this really does put

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Speaker 3: China on the map once again with the model that

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Speaker 3: rivals some of the best coming from Silicon Valley, and

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Speaker 3: it also echoes in a way if you ask around

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Speaker 3: town here in Washington, certainly among some of the companies

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Speaker 3: here in the US, that some of these models that

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Speaker 3: China is putting forth may have been built on the

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Speaker 3: backs of this practice known as distillation, where results are

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Speaker 3: harvested from the outs of American AI models to build

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Speaker 3: this rival generation of chatbots in China. On the chief,

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Speaker 3: China has rejected a lot of those claims, and yet

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Speaker 3: it certainly is a friction point between the US and

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Speaker 3: China in this broader global race to dominate the emerging

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Speaker 3: technology of AI.

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Speaker 2: Chinese President Jujingpang has been out staking his claim of

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Speaker 2: the leadership in the field of AI, talking about China's

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Speaker 2: domestic prowess and AI, but also kind of taking policy

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Speaker 2: action to support what we see from Moonshalt. What do

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Speaker 2: we need to know about what President g has been

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Speaker 2: saying this week.

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Speaker 3: Well, really earlier today, he made a debut appearance, his

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Speaker 3: first ever at the Shanghai World AI Forum. This is

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Speaker 3: an event held annually there, but this really signified his

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Speaker 3: personal stamp on China's push to lead in AI. Earlier

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Speaker 3: this year, the government there had agreed to put as

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Speaker 3: much as two hundred and ninety five billion dollars toward

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Speaker 3: building a nationwide interconnected network of computing hubs data centers

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Speaker 3: to really power the nationwide push to ensure that there

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Speaker 3: is enough compute to go around to support all of

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Speaker 3: these startups that are developing models like Kimmy and others

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Speaker 3: that we have been talking about on this program. And

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Speaker 3: then she is also making this not just a message

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Speaker 3: about China, but about the world. He is calling for

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Speaker 3: AI to be something distributed more equitably around the world.

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Speaker 3: He pitched this message to the Global South, saying he

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Speaker 3: wants to see AI for all countries, not just the

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Speaker 3: richest and most powerful, and that really played to concerns

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Speaker 3: that we have heard from decades from developing nations that

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Speaker 3: they are the last to bear the fruits of any

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Speaker 3: big technological advance and AI. Many countries in the Global

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Speaker 3: South have been watching advances by the US and by

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Speaker 3: China wondering when they would get a p the action,

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Speaker 3: and she is making an offer to them that look,

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Speaker 3: if you sign up with us, you will you will

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Speaker 3: get your fair share. He is also calling for some

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Speaker 3: guardrails on the technology. He sees just like officials here

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Speaker 3: in the US do some of the security risks potentially

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Speaker 3: not only to China's national security, but the global security.

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Speaker 2: But he is also calling for cooperation with.

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Speaker 3: The United States and other countries when it comes to

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Speaker 3: development of AI and promoting it worldwide.

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Speaker 2: Ed BLUEBGGS, Mike Shepherd in DC, thank you very much.

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Speaker 2: The surprise release of Kimmi K three. It is a

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Speaker 2: massive market story and throughout the hour we're going to

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Speaker 2: get different perspectives on what this means for American AI.

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Speaker 2: Will continue to track the market moves, but right now

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Speaker 2: you see the Nazak one hundred and in particular semiconductors

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Speaker 2: under pressure. That's in the equity markets. The AI trade

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Speaker 2: isn't just hitting stocks, it's weighing on bonds too. Debt

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Speaker 2: sold by tech giants to fund massive AI investments is

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Speaker 2: under performing as investors grow more cautious about the sectors

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Speaker 2: spending spree. Bloomberg's Tasos Vossos joins us for more like

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Speaker 2: look the bond market. This predates the headline overnight right

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Speaker 2: on Kimik three. But if you look at portfolio performance,

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Speaker 2: we made a lot of news headlines about the issuance

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Speaker 2: of the debt, the need for capital. Tell us how

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Speaker 2: those that's gone for those bonds in the interim period.

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Speaker 4: And that's the thing. A few months ago people were

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Speaker 4: already warning the debt is building too fast within portfolios,

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Speaker 4: within indices. What if something actually goes wrong, And as

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Speaker 4: you said before, it's not driven by the same thing,

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Speaker 4: which actually makes it rather confusing for investors who may

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Speaker 4: have both equities and bonds in their portfolio, some of

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Speaker 4: them issued by the same AI company at Hyperscala, for example.

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Speaker 2: Because of the credit market.

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Speaker 4: The main concern now is whether the debt is being

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Speaker 4: built up too fast and by too much more than

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Speaker 4: the market can actually absorb, because obviously the AI races

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Speaker 4: is heating up and you cannot possibly lose out. You

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Speaker 4: have to spend a load of money and you have

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Speaker 4: to actually raise it from every corner of the all

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Speaker 4: you can find, which is why now that we're seeing

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Speaker 4: a downturn in bonds that are leading losses is actually

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Speaker 4: happening across the world and not just in the United States.

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Speaker 2: Tells us, I'm going to read you some of the

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Speaker 2: data from your own report, if that's okay. Seventy nine

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Speaker 2: percent of the sector's bonds sold since early twenty twenty

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Speaker 2: five are now currently indicated at a wider spread compared

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Speaker 2: to the first day of trading. Explain that to us.

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Speaker 2: But the other point being that price moving inversity yield,

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Speaker 2: the price have fallen from the issue price on average

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Speaker 2: across that that bucket. What's going on?

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Speaker 4: Well, the reason why we're using so many metrics is

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Speaker 4: because different investors may be using different metrics to benchmark

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Speaker 4: themselves and explain to their you know, their investors whether

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Speaker 4: they are actually performing well or not. But whichever metric

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Speaker 4: you use on the air AI bonds now, whether it's spread,

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Speaker 4: whether it's surprized, whether it's total return that looks into

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Speaker 4: interest income as well, they're all negative. And what that

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Speaker 4: shows is that whichever your approach has been towards that

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Speaker 4: sector actually hasn't worked out. Whether you try to eliminate

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Speaker 4: one one risk or another, either way you're in the red.

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Speaker 4: So what the number of active investors have been telling

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Speaker 4: us is that, actually, do you know what, I don't

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Speaker 4: even want to hold them at all, because unlike stocks,

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Speaker 4: where if the AI future is as bright as some

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Speaker 4: people pull tend to be and you end up holding

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Speaker 4: an AI winner, then the stock can go through the roof.

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Speaker 4: The problem is with the bonds because of the mechanics

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Speaker 4: getting paid face value at part that's not going to happen,

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Speaker 4: So the upside is actually rather limited by the downside

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Speaker 4: could be really significant.

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Speaker 2: Bloomberg tass rs us with the credit market some of

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Speaker 2: what happening in AI and hyperscalers, thank you very much. Indeed,

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Speaker 2: coming up another top story, Netflix is under pressure. The

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Speaker 2: streaming giant is warning of slower sales growth and we're

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Speaker 2: going to speak with hell Wang of Philip Securities to

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Speaker 2: try and work out what the story is here with Netflix.

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Speaker 2: That's next. This is Bloomberg Tech.

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Speaker 5: We believe it takes a great artists to make something great,

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Speaker 5: and AI is not changing that. AI will give creators

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Speaker 5: better tools to bring their visions to life. Movies are

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Speaker 5: being made by people who make movies. AI provides them

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Speaker 5: with better tools to make them even better.

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Speaker 2: That was Netflix co CEO Ted Serrandos, speaking on yesterday's

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Speaker 2: earnings call, look at Netflix shares at one point in

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Speaker 2: the session, We're on track for our biggest drop in

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Speaker 2: four years, down more than eight percent. The company outlined

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Speaker 2: a bigger role for AI and pointed to advertising as

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Speaker 2: its next major growth engine, But investors are beginning to

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Speaker 2: worry that the company's breakneck growth is starting to slow. Specifically,

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Speaker 2: it's slowing for a second straight quarter with decelerating revenue gains.

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Speaker 2: Joining us now is Helena Wong. She has a by

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Speaker 2: rating on Netflix. They're looking for a story beyond subscriber growth, right.

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Speaker 2: It was so interesting to go through how they'll use AI, advertising,

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Speaker 2: live content. We'll go over all three. But for you, Helena,

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Speaker 2: what was the main takeaway from this Netflix print and

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Speaker 2: what was said on the call?

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Speaker 6: Well, I guess if you just look at a quarter

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Speaker 6: results itself, I will say it's still overall broadly in line.

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Speaker 6: So membership trends has been really healthy. They recently increased

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Speaker 6: their price again earlier this year. In the markets like

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Speaker 6: the US, Mexico and Spain. It continues to be well accepted.

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Speaker 6: The air revenue continues to suspend. It's on track to

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Speaker 6: double its revenue this year, so that helps us the

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Speaker 6: money tandation. So margins are holding up fairly well as well,

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Speaker 6: so I would say the results is actually pretty healthy.

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Speaker 2: Yeah, you know how long did Netflix say, judge us

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Speaker 2: by traditional financial metrics and you look at those metrics

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Speaker 2: pretty good. They're talking about how Netflix will be different

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Speaker 2: as well, right the platform, I mean more live content,

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Speaker 2: live sports in particular, video podcasts, creators, cloud gaming. I'm

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Speaker 2: looking at that and saying, I watch Netflix in the evening,

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Speaker 2: sit down, stream a show, a film. What about the

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Speaker 2: rest of the day. To me, it seems like Netflix

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Speaker 2: is thinking about how do we get into that dressable

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Speaker 2: market for people's eyeballs morning through evening. Is that what

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Speaker 2: you see?

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Speaker 7: Yeah?

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Speaker 6: I think another challenge that they're seeing is they're facing

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Speaker 6: a lot of competition that is not just from the

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Speaker 6: traditional streaming platform, So they are competing a lot of

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Speaker 6: the screen time with short video subscribers, so services like

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Speaker 6: YouTube shorts and TikTok videos, because consumer behavior is now changing,

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Speaker 6: so now every time you go home, you're not just

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Speaker 6: oning a TV, You're spending a lot of time on

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Speaker 6: your phone as well. So they're definitely competing with this

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Speaker 6: limited screen time that people have, so for them, they

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Speaker 6: are also they're trying to strengthen their engagement. They're trying

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Speaker 6: to do a lot of the life events, a lot

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Speaker 6: of the podcasts, and a lot of the mobile features.

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Speaker 6: But it's definitely going to be a long term challenge

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Speaker 6: for them.

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Speaker 2: They're talking about using AI in three different ways, improving

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Speaker 2: the product, lowering costs, and expanding margins. You talked about

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Speaker 2: the margins point earlier for the stock. What's most meaningful

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Speaker 2: for Netflix to communicate on the on their use of AI.

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Speaker 6: I think the use of AI is inevidable because it's

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Speaker 6: now just expanding to all aspects of the industry. I

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Speaker 6: think the reason that the market is reacting very negatively

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Speaker 6: is mainly still because that they're giving a relatively softer

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Speaker 6: guidance for their startcolder revenue, and I would say it's

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Speaker 6: still considered pretty healthy growth. It just compared to the

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Speaker 6: growth that we haven't seen earlier, it is considered slightly softer.

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Speaker 6: Some Netflix is a business where everything is going right

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Speaker 6: lay the investor's expectation for it is just relatively high,

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Speaker 6: so they're really not tolerating any of the minor disappointment

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Speaker 6: that coms it. I think another reason that it's not

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Speaker 6: doing well is because the content slate is not as

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Speaker 6: compelling compared to last year. So this year they have

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Speaker 6: some exciting.

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Speaker 2: Sorry to interrupt you, Helena, I'm so glad you went

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Speaker 2: to the content slate, right. I get you have to

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Speaker 2: model on certain metrics the growth of this business, but

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Speaker 2: like what are you watching right now? It still comes

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Speaker 2: down to content, is king? How's Netflix doing on that front?

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Speaker 6: Yes, I would say their content slate is definitely not

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Speaker 6: exciting when you're compared to last year. So this year

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Speaker 6: they do have some exciting coming up, like seventy two

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Speaker 6: Hours with Kevin Hart, and they have some NFL games

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Speaker 6: lining up, But compared to last year, they had Wednesday,

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Speaker 6: they had Strangers since they had all those exciting contents,

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Speaker 6: they had Squeaking, so that was all. The are very

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Speaker 6: popular franchise with a lot of hardcore fans, so they

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Speaker 6: actually managed to bring a lot of subscribers onto the

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Speaker 6: platform because of that. So this year, compared to that,

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Speaker 6: it is just not very exciting. And for Netflix, it's

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Speaker 6: a company that has a lot of high expectations, so

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Speaker 6: investors are constantly looking for science that there's going to

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Speaker 6: be reacceleration in the business. So for this sector, we're

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Speaker 6: not really.

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Speaker 2: Seeing that, Helena. We just showed Lucas Shaw, who leads

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Speaker 2: our coverage of screen time the Entertainment Industry's column prior

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Speaker 2: to earnings, and he was writing about how Netflix can't

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Speaker 2: get its audience to stick with the show, so they

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Speaker 2: start and then they don't stick with it. Why does

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Speaker 2: that matter?

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Speaker 6: Well, it does matter because at the end of the day,

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Speaker 6: it really comes down to your ability to persuade your

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Speaker 6: customer to stay on your platform. But I will say

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Speaker 6: right now, I don't see very clear evidence that their

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Speaker 6: engagement is deteriorating. Actually, their engagement is improving compared to

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Speaker 6: twenty twenty five according to their Engagement report, So I

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Speaker 6: would say right now, there's no clear sign that they're

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Speaker 6: losing on that, and there's still very strong pricing power.

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Speaker 6: So overall, I would still say we're very very positive

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Speaker 6: on Netflix, especially with the reason price correction. So previously

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Speaker 6: the premium valuation largely limit the upside that we have

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Speaker 6: in our vieuse, So right now is the stock foolback.

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Speaker 6: We do think it is a good opportunity to actually

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Speaker 6: start runing a.

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Speaker 2: Pollution hell and along with Philip Securities back on Bloomberg Tech.

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Speaker 2: Thank you very much, all things Netflix coming up. There's

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Speaker 2: a setback for Google's AI ambitions. Why Gemini's latest delay

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Speaker 2: has invested this question in whether the company is losing

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Speaker 2: ground in the AI race. This is Bloomberg Tech.

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Speaker 8: It's time now for talking tech. I'm you Hira on

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Speaker 8: on first step. Ze to Ai, also known as Jipu,

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Speaker 8: is on track to be China's first AI firm, hitting

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Speaker 8: one billion dollars in annual sales. The AI startup achieved

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Speaker 8: its full year sales target in July, marketing a milestone

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Speaker 8: for a company aiming to distinguish itself as a provider

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Speaker 8: of AI to companies and enterprises. Plus, Data Bricks is

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Speaker 8: in its second round of financing this year and is

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Speaker 8: seeking capital from investors led by Coaching Managements at a

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Speaker 8: one hundred and eighty eight billion dollar valuation that would

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Speaker 8: mark a forty percent jump from the one hundred and

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Speaker 8: thirty four billion it achieved earlier this year. Data Bricks,

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Speaker 8: of course, has long been regarded as an attractive candidate

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Speaker 8: to IPO and Chinese Sorry Japanese memory chip maker Kiyoxia

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Speaker 8: has now lost more than half its market value from

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Speaker 8: its June peak. At that point, the company was Japan's

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Speaker 8: highest valued firm. The sell off coming on growing concerns

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Speaker 8: AI driven rally has gone too far, something we are

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Speaker 8: seeing again today.

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Speaker 2: Ed okay, thank you. The AI race is moving so

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Speaker 2: fast that even a few months can make a big difference.

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Speaker 2: Bloomberg's reporting Google is months behind on the release of

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Speaker 2: its next flagship AI model, Gemini three point five, pro

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Speaker 2: raising new questions about whether it's falling behind rivals Open

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Speaker 2: Ai and Amthropic. Bloomberg Intelligence analyst man Deep Singh, writing

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Speaker 2: a reaction piece to the report that this could be

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Speaker 2: a sign that recent high profile departures have hurt frontier

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Speaker 2: model development, and Mandeep joins us Now, it was an

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Speaker 2: interesting and detailed report from Bloomberg News, but I like

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Speaker 2: the thesis presented in the react go a little bit

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Speaker 2: deeper into it. Well, we've seen you.

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Speaker 7: Fabrile five and the latest OpenAI model clearly have had

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Speaker 7: a step up in terms of functionality and coding agencies,

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Speaker 7: where Google seems to have missed the boat with Gemini.

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Speaker 7: So I would have expected Gemini three to be released sooner,

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Speaker 7: and now we're talking about Gimmy three and how you

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Speaker 7: know that head.

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Speaker 2: And a step. I'm going to jump in here, guys,

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Speaker 2: because I think the man Deep's zoom is hitting some

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Speaker 2: technical issues and maybe the control room just hit mute

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Speaker 2: on him for a second. Sorry about that, Man Deep.

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Speaker 2: We'll come back to the story that the race and

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Speaker 2: model development. Obviously today with Kimmy K three, it's our

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Speaker 2: top story. Let's get to Apple. Apple's product refresh is

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Speaker 2: far from over. The company is now preparing a sweeping

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Speaker 2: update to its iPad lineup. Bloomberg's learned Apples planning its

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Speaker 2: biggest iPad Mini overhaul in five years, complete with an

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Speaker 2: O lead display. Bloomberg's Apple and Consumer Tech Managing editor

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Speaker 2: Mark Duman joins us with the details spare of forought

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Speaker 2: for iPad Mini. It's been a while since I think

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Speaker 2: you and I have talked about that product line a

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Speaker 2: lot of detail in the new strategy. What do we

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Speaker 2: need to know?

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Speaker 9: Yeah, Apple's got a jam packed product portfolio over the

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Speaker 9: next few years, and part of that is a big

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Speaker 9: overhaul of the iPad lineup. And so this fall you're

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Speaker 9: going to see one of the biggest ever updates to

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Speaker 9: the iPad Mini. It's getting an OLED screen, which means

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Speaker 9: the display quality is now going to be up there

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Speaker 9: with an iPhone or an iPad pro. This has been

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Speaker 9: a long time coming. This is something that tech enthusiasts

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Speaker 9: have been clamoring for.

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Speaker 2: You're also going to.

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Speaker 9: See updates to all the other iPads next year that

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Speaker 9: includes faster iPad pros, new iPad airs in a new

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Speaker 9: entry level iPad. In the backdrop here is that Apple

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Speaker 9: has made all of the iPads more expensive in recent

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Speaker 9: weeks because of the memory shortage. Right, some of them

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Speaker 9: went up by one hundred dollars, some of them went

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Speaker 9: up one hundred and fifty, some of them went up

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Speaker 9: by two hundred dollars. So now you need upgrades to

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Speaker 9: the product to make them more worthwhile in this competitive

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Speaker 9: environment where you have great tablets from other companies like Amazon,

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Speaker 9: Samsung and the.

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Speaker 2: Like marks this is the first opportunity to you. And

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Speaker 2: I've had to discuss Apple's AI plan for China. There

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Speaker 2: was some developments this week in how they're able to

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Speaker 2: put an AI product into that market. Could you explain

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Speaker 2: the basics of it to us?

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Speaker 9: So right now, Apple Intelligence inside of the United States

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Speaker 9: and the rest of the world for the most part,

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Speaker 9: uses models from Apple on device, models from Apple in

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Speaker 9: the cloud, and then there's partners for some features that

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Speaker 9: Apple doesn't do so well, so search they work with Google,

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Speaker 9: and then they work with open Ai as sort of

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Speaker 9: a fallback for Siri. If you ask it's something it

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Speaker 9: doesn't know the answer to, it'll query chat GBT if

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Speaker 9: you allow it to. So for China, there's a few nuances.

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Speaker 9: For search, they're working with Baidu for that chatbot, the

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Speaker 9: open Ai replacement, they're working with Baidu. Obviously open Ai

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Speaker 9: Chat gybt is not available in that part of the world.

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Speaker 9: But then there's the Ali baba component, and so the

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Speaker 9: phones in China will continue to use the on device

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Speaker 9: Apple models, but there will be a layer on top

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Speaker 9: using Ali Baba ai technology that I call a censorship filter.

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Speaker 9: And so what this will do is we'll keep the

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Speaker 9: phone in constant connection with the Chinese government, so as

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Speaker 9: there's updated models that are hitting the iPhone, Apple of

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Speaker 9: course is always updating its models in the background. Those

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Speaker 9: will need to be approved every so often with the

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Speaker 9: Chinese government. And this type of censorship filter is what

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Speaker 9: other companies in China who operate have to do show

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Speaker 9: me Huiwei the other phone providers with AI technology on

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Speaker 9: their phones.

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Speaker 2: All right, most mark German with everything that's happened this week,

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Speaker 2: Actually not everything. There was a lot more with Apple too,

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Speaker 2: but thank you very much. Coming up, just a little

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Speaker 2: over a month after its blockbuster IPOs, SpaceX is experiencing

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Speaker 2: a few setbacks. There was a scrub start ship launch

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Speaker 2: last night. Stock reacted, but the slock generally is under pressure.

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Speaker 2: We can have more in that story next. A reminder. Overnight,

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Speaker 2: China's Moonshot released Kimmi K three, an open weighted model

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Speaker 2: that has top benchmarks. But there's a big focus on

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Speaker 2: training costs and token economics with this as well. Right now,

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Speaker 2: the market's asking itself, do we need to reconsider all

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Speaker 2: of the money that is going into AI from an

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Speaker 2: American perspective? And do we need to think again about

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Speaker 2: what the MOTI is for some of these American companies.

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Speaker 2: We're going to have that conversation next. Now, if I'm lucky,

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Speaker 2: We're going to cut some beautiful pictures of San Francisco.

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Speaker 2: It's half time. We'll be right back. This is Bloomberg

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Speaker 2: Tech Welcome back to Bloomberg Tech. All across the AI trade.

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Speaker 2: US technology stocks are under pressure then, as that one

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Speaker 2: hundred and Socks both down in the session significantly, but

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Speaker 2: on the week the Socks is headed for its biggest

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Speaker 2: weekly drop since April of twenty twenty five. The NATSAK

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Speaker 2: one hundred is not far off that about a percentage

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Speaker 2: point from being in the realm of the same milestone

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Speaker 2: China's moonshot with a surprise release of Kimmy K three.

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Speaker 2: The market recalculating a lot of the spending that has

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Speaker 2: is and will happen on AI development and the infrastructure

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Speaker 2: side in video biggest points drag Intel down significantly. One

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Speaker 2: story which is kind of chopped and changed is that

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Speaker 2: at one moment in the session Apple overtook in video

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Speaker 2: as the world's most valuable company. We're going to do

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Speaker 2: the math on that a little bits time. It's close,

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Speaker 2: but again we're a few hours from the session being

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Speaker 2: closed overnight. Space SpaceX's Starship rocket mission failed to launch yesterday.

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Speaker 2: It would have been its thirteenth Starship test flight. Right

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Speaker 2: at liftoff, blooms of smoke or steam from the launch tower.

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Speaker 2: There was an engine failure. This isn't the first time.

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Speaker 2: Starship previously had delays and malfunctions. But what CEO Elam

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Speaker 2: Musk said on X is that the next launch attempt

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Speaker 2: would hopefully come in the next few days. What was

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Speaker 2: interesting about it in the moment in after ours trading,

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Speaker 2: the stock immediately reacted and there was a decline of

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Speaker 2: about four percent already in the session yesterday, SpaceX's shares

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Speaker 2: a dropped below its IPO price. The company's facing turbulence

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Speaker 2: just a little over a month after its historic IPO

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Speaker 2: and dampening euphoria potentially for newly public companies joining US now.

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Speaker 2: Is Bloomberg's Anthony Hughes. This is where we stand. We're

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Speaker 2: showing it on the screen June twelfth to present day.

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Speaker 2: What has been the market's digestion of the biggest IPO

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Speaker 2: of all time?

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Speaker 10: Yes, I mean, after all, the hyperspace is going public

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Speaker 10: last month. I think we've seen a bit of a

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Speaker 10: hangover in the IPO market really, with the momentum trade

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Speaker 10: also coming off here at the same time the AI

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Speaker 10: trade coming off, you know, but it's not unusual that

436
00:25:06,960 --> 00:25:11,280
Speaker 10: a big IPO might sort of pull back here given that,

437
00:25:11,840 --> 00:25:17,000
Speaker 10: you know, some of the technical dynamics. You know, once

438
00:25:17,800 --> 00:25:21,120
Speaker 10: the price price sort of settles down and the market

439
00:25:21,200 --> 00:25:23,800
Speaker 10: settles down, that the stocks come back. So I mean,

440
00:25:24,680 --> 00:25:28,480
Speaker 10: obviously it's a bit concerning for investors that SpaceX is

441
00:25:28,520 --> 00:25:30,440
Speaker 10: already trading at a discount.

442
00:25:30,440 --> 00:25:31,679
Speaker 2: I think it's about three percent.

443
00:25:31,440 --> 00:25:33,240
Speaker 10: To the IPO price, because we still have all this

444
00:25:33,280 --> 00:25:35,480
Speaker 10: stock to come out from out of lock up as well.

445
00:25:35,600 --> 00:25:39,480
Speaker 10: So you know, but overall, the IPO market has lost

446
00:25:39,520 --> 00:25:42,720
Speaker 10: a lot of steam here because of mainly because of SpaceX,

447
00:25:42,720 --> 00:25:45,960
Speaker 10: and you know, the average return from IPOs this year

448
00:25:45,960 --> 00:25:47,639
Speaker 10: has really evaporated to zero.

449
00:25:48,760 --> 00:25:50,880
Speaker 2: I would just again meet the point that SpaceX being

450
00:25:50,920 --> 00:25:53,240
Speaker 2: down five percent, and this Friday session is in part

451
00:25:53,280 --> 00:25:58,159
Speaker 2: because of the scrubbed Starship test thirteen last night. You know,

452
00:25:58,200 --> 00:26:00,919
Speaker 2: we're measuring the success from IPO based on the first

453
00:26:00,960 --> 00:26:04,719
Speaker 2: thirty days of trade. But for the bankers, you know,

454
00:26:04,800 --> 00:26:09,360
Speaker 2: and and the investors that participated, this was a success, right, Anthony,

455
00:26:09,400 --> 00:26:11,680
Speaker 2: that they would say that they pulled this off.

456
00:26:11,720 --> 00:26:15,000
Speaker 10: Well, yeah, I think at the time when SpaceX went public,

457
00:26:15,040 --> 00:26:16,960
Speaker 10: I think it was it was it was a very

458
00:26:17,720 --> 00:26:21,920
Speaker 10: well executed process. I think that you know, as as

459
00:26:22,000 --> 00:26:24,239
Speaker 10: time goes on, the stock's going to trade more on

460
00:26:24,560 --> 00:26:28,080
Speaker 10: the you know, the news, the fundamentals and news news

461
00:26:28,080 --> 00:26:30,679
Speaker 10: that the news flow here, and obviously there's been some

462
00:26:30,720 --> 00:26:33,320
Speaker 10: bad news flow initially here. But you know, it's a

463
00:26:33,359 --> 00:26:36,400
Speaker 10: long for a lot of the a lot of investors

464
00:26:36,400 --> 00:26:38,639
Speaker 10: that got behind SpaceX. It's a long term proposition, and

465
00:26:39,240 --> 00:26:41,919
Speaker 10: you know, anyone who's familiar with what happened with Tesla

466
00:26:42,040 --> 00:26:44,000
Speaker 10: knows that there's plenty of ups and downs along along

467
00:26:44,040 --> 00:26:46,320
Speaker 10: the way. But obviously the investors will be hoping that

468
00:26:46,400 --> 00:26:49,879
Speaker 10: longer term the prices performs.

469
00:26:49,480 --> 00:26:53,560
Speaker 2: A lot better. Bloomberg's Anthony Hughes on SpaceX. Thank you

470
00:26:53,680 --> 00:26:57,719
Speaker 2: very much from SpaceX. So the next generation of space startups.

471
00:26:57,800 --> 00:27:02,360
Speaker 2: Texas is fast becoming the capitol of the commercial space industry.

472
00:27:02,440 --> 00:27:05,280
Speaker 2: So what's driving the boom and what does it mean

473
00:27:05,320 --> 00:27:08,640
Speaker 2: for the state's economy. Bloomberg originals has the story.

474
00:27:10,760 --> 00:27:15,240
Speaker 11: For most of space white history, NASA and government or

475
00:27:15,440 --> 00:27:20,760
Speaker 11: nation states were really the biggest players in charge. That

476
00:27:20,880 --> 00:27:22,160
Speaker 11: has recently shifted.

477
00:27:22,600 --> 00:27:23,520
Speaker 2: This was in front here.

478
00:27:23,560 --> 00:27:25,760
Speaker 12: We really believed for a long time it was only

479
00:27:25,800 --> 00:27:27,800
Speaker 12: a place for governments to build and innovate.

480
00:27:28,160 --> 00:27:31,879
Speaker 6: What we're seeing is a democratized space sector with a

481
00:27:31,920 --> 00:27:34,760
Speaker 6: lot of smaller players and a lot of larger commercial

482
00:27:34,800 --> 00:27:35,680
Speaker 6: players as well.

483
00:27:36,000 --> 00:27:39,280
Speaker 13: With the global space economy currently valued it over six

484
00:27:39,400 --> 00:27:43,200
Speaker 13: hundred and fifty billion dollars, one American state in particular

485
00:27:43,480 --> 00:27:46,400
Speaker 13: is betting big on this rapidly growing sector.

486
00:27:46,760 --> 00:27:51,280
Speaker 3: Governor Abbat, coming to you from the great Data Technis Texas.

487
00:27:50,880 --> 00:27:54,080
Speaker 11: Is a really good snapshot of the new direction that

488
00:27:54,119 --> 00:27:59,040
Speaker 11: the space industry is taking. The biggest tenant in Texas

489
00:27:59,240 --> 00:28:02,120
Speaker 11: is Space, so they have set up shop with their

490
00:28:02,160 --> 00:28:05,359
Speaker 11: Star based facility on the southern tip of Texas. Another

491
00:28:05,440 --> 00:28:09,240
Speaker 11: big tenant is Blue Origin in West Texas outside of

492
00:28:09,320 --> 00:28:12,760
Speaker 11: Van Horn. And then you also have a big cluster

493
00:28:13,000 --> 00:28:16,439
Speaker 11: of companies in Houston, the home of NASAs Johnson Space Center,

494
00:28:16,640 --> 00:28:20,040
Speaker 11: so you have Axiom Space, you have Intuitive Machines. And

495
00:28:20,080 --> 00:28:24,240
Speaker 11: then in Central Texas, Firefly Aerospace has its rocket facility

496
00:28:24,720 --> 00:28:25,919
Speaker 11: and development center.

497
00:28:28,440 --> 00:28:31,320
Speaker 13: Last year, the Texas Space Commission gave out one hundred

498
00:28:31,320 --> 00:28:34,840
Speaker 13: and fifty million dollars in grants to support space business

499
00:28:34,840 --> 00:28:37,800
Speaker 13: in the state. It's due to double that by the

500
00:28:37,880 --> 00:28:38,520
Speaker 13: end of this year.

501
00:28:40,400 --> 00:28:42,800
Speaker 14: What we wanted to do is make it well known

502
00:28:43,240 --> 00:28:46,320
Speaker 14: that those companies who had already decided to be set

503
00:28:46,400 --> 00:28:49,160
Speaker 14: up here in Texas that they didn't leave, and that

504
00:28:49,200 --> 00:28:52,680
Speaker 14: they are also helped to expand their existing footprint and.

505
00:28:52,880 --> 00:28:56,880
Speaker 9: Lift off the crew of Artimists two now bound for

506
00:28:56,920 --> 00:28:57,320
Speaker 9: the room.

507
00:28:58,040 --> 00:29:02,000
Speaker 14: From a NASA standpoint, because of that competition, they're lowering

508
00:29:02,120 --> 00:29:05,400
Speaker 14: the overall costs that allows us to be able to

509
00:29:05,480 --> 00:29:06,000
Speaker 14: do more.

510
00:29:06,320 --> 00:29:08,640
Speaker 5: Humanity's next great voyage begins.

511
00:29:11,400 --> 00:29:14,000
Speaker 2: Or you can watch the full Bloomberg original story online

512
00:29:14,040 --> 00:29:16,320
Speaker 2: and of course on the Bloomberg terminal. Let's get back

513
00:29:16,360 --> 00:29:19,920
Speaker 2: to Apple, who at one point surpassed and Video this

514
00:29:20,000 --> 00:29:23,840
Speaker 2: morning is the world's most valuable company, likely benefiting from

515
00:29:23,840 --> 00:29:27,920
Speaker 2: this broader rotation in the tech sector. Out of the

516
00:29:27,960 --> 00:29:31,280
Speaker 2: big spenders on AI joining us as Bloombos, Ryan Verslica.

517
00:29:31,320 --> 00:29:34,280
Speaker 2: I keep glancing down at my terminal. I think, you know,

518
00:29:34,560 --> 00:29:36,560
Speaker 2: we're changing hands a few times here, but I think

519
00:29:36,600 --> 00:29:39,480
Speaker 2: in Video is now slightly back ahead. But that's been

520
00:29:39,520 --> 00:29:43,920
Speaker 2: the story, right. Apple doesn't have the big capital expenditure story.

521
00:29:44,280 --> 00:29:47,760
Speaker 2: It doesn't have the big dollar story around AI. Video

522
00:29:47,800 --> 00:29:50,280
Speaker 2: has been the principal beneficiary of everything in the last

523
00:29:50,320 --> 00:29:51,120
Speaker 2: four years.

524
00:29:52,080 --> 00:29:55,080
Speaker 12: That's what's very interesting about this trade rate. Now what

525
00:29:55,240 --> 00:29:57,880
Speaker 12: used to be seen as a real risk for Apple

526
00:29:58,000 --> 00:30:01,040
Speaker 12: that it wasn't aggressively pursuing AI and the way that

527
00:30:01,120 --> 00:30:03,800
Speaker 12: some of the other big cap tech companies are. That's

528
00:30:03,840 --> 00:30:06,440
Speaker 12: now become a real asset. It doesn't have the sort

529
00:30:06,440 --> 00:30:09,840
Speaker 12: of capex risk that's being priced into Microsoft.

530
00:30:09,840 --> 00:30:10,680
Speaker 2: To give sort of the.

531
00:30:12,160 --> 00:30:15,479
Speaker 12: Typical example there, it's really been benefiting sort of as

532
00:30:15,520 --> 00:30:18,120
Speaker 12: a safe haven as we see the chips face unwind

533
00:30:18,160 --> 00:30:20,960
Speaker 12: as we see more questions right the hyperscaler side of

534
00:30:21,000 --> 00:30:23,720
Speaker 12: the market, it's really been pushing higher. I think it's

535
00:30:23,800 --> 00:30:25,960
Speaker 12: up more than twenty percent in a couple of weeks.

536
00:30:26,000 --> 00:30:28,600
Speaker 12: That's a huge gain for a company of this size,

537
00:30:28,600 --> 00:30:31,280
Speaker 12: and it's really because of its own unique path that

538
00:30:31,320 --> 00:30:34,360
Speaker 12: it's charting within AI. I would also add that it

539
00:30:34,400 --> 00:30:37,080
Speaker 12: does have the foldable iPhone that's expected to come out

540
00:30:37,160 --> 00:30:40,080
Speaker 12: later on this year. It does have the agentic AI

541
00:30:40,240 --> 00:30:43,960
Speaker 12: SERI Digital Assistant. There's been some disappointments with that, but

542
00:30:44,000 --> 00:30:46,080
Speaker 12: there is optimism that we are going to start to

543
00:30:46,080 --> 00:30:49,680
Speaker 12: see a big iPhone upgrade cycle going up that's going

544
00:30:49,760 --> 00:30:53,480
Speaker 12: to help overshadow the increase in high memory chip prices.

545
00:30:53,560 --> 00:30:55,800
Speaker 12: But in general, it is seem like the fact that

546
00:30:55,840 --> 00:30:58,520
Speaker 12: Apple isn't participating in AI in the same way that

547
00:30:58,560 --> 00:31:01,360
Speaker 12: some of its peers are a real positive and thing

548
00:31:01,440 --> 00:31:03,440
Speaker 12: that is distinguishing the company.

549
00:31:04,800 --> 00:31:07,560
Speaker 2: In the here and now. I see the Philadelphia Semiconductor

550
00:31:07,640 --> 00:31:09,720
Speaker 2: Index on the week, on track for its biggest weekly

551
00:31:09,800 --> 00:31:13,440
Speaker 2: drops since April twenty twenty five. We've talked a lot

552
00:31:13,440 --> 00:31:15,480
Speaker 2: about Moonshot and Kimmy K three, A lot more to

553
00:31:15,480 --> 00:31:17,040
Speaker 2: come in the show on that, But you and the

554
00:31:17,080 --> 00:31:20,600
Speaker 2: team writing that the SOCKS is set to fall into

555
00:31:20,640 --> 00:31:22,240
Speaker 2: a bear market. That's big.

556
00:31:23,280 --> 00:31:26,000
Speaker 12: Yeah, there's been a real reversal in momentum in the

557
00:31:26,080 --> 00:31:28,520
Speaker 12: chip space. I think there are some concerns about you

558
00:31:28,560 --> 00:31:30,560
Speaker 12: know this. The index is up. I think at some

559
00:31:30,560 --> 00:31:33,240
Speaker 12: point that was nearly double year today, so a huge

560
00:31:33,280 --> 00:31:36,080
Speaker 12: gain there and still up quite significantly for this year,

561
00:31:36,120 --> 00:31:38,120
Speaker 12: even though it has pulled back some. But I think

562
00:31:38,120 --> 00:31:39,760
Speaker 12: there is a little bit of profit taking, a little

563
00:31:39,800 --> 00:31:42,920
Speaker 12: bit of reversal in momentum. And as we are discussing

564
00:31:43,480 --> 00:31:47,240
Speaker 12: you know, this whole question about hyperscale or ROI, that

565
00:31:47,360 --> 00:31:49,600
Speaker 12: is really feeding into the chip space as well, because

566
00:31:49,760 --> 00:31:51,719
Speaker 12: if the companies that are doing all of the spending

567
00:31:51,760 --> 00:31:54,959
Speaker 12: on AI, if they're not seeing a dramatic return on that,

568
00:31:55,000 --> 00:31:58,160
Speaker 12: if it's not flowing through to their revenue. The issue

569
00:31:58,200 --> 00:31:59,880
Speaker 12: is are they going to start pulling back on spin?

570
00:32:00,680 --> 00:32:03,480
Speaker 12: Is the peak earnings being priced into the chip space

571
00:32:03,560 --> 00:32:06,040
Speaker 12: right now? And where do they go from there? If

572
00:32:06,160 --> 00:32:09,640
Speaker 12: the hyperscervis are pulling back chip stocks, likely we'll see

573
00:32:09,680 --> 00:32:11,680
Speaker 12: weaker growth going forward, is the concern.

574
00:32:12,520 --> 00:32:16,080
Speaker 2: Bloomberg's Ryan Fastelica on it. Thank you very much. Indeed,

575
00:32:16,520 --> 00:32:18,920
Speaker 2: coming up, let's talk a little bit more about private markets.

576
00:32:18,920 --> 00:32:21,280
Speaker 2: One of the oldest venture firms in the world just

577
00:32:21,360 --> 00:32:25,160
Speaker 2: raised one point five billion dollars for its eighteenth fund.

578
00:32:25,720 --> 00:32:28,360
Speaker 2: There's also a lot of relevance here to what's happening

579
00:32:28,360 --> 00:32:31,520
Speaker 2: around the world. Some modern EDI general partner at Greylock

580
00:32:31,760 --> 00:32:34,960
Speaker 2: here on Bloomberg Tech, stay with us. This is Bloomberg Tech.

581
00:32:45,280 --> 00:32:48,040
Speaker 2: Greylock Partners just raised one point five billion dollars for

582
00:32:48,120 --> 00:32:51,400
Speaker 2: its eighteenth fund, dedicated to partnering with founders at the

583
00:32:51,400 --> 00:32:56,600
Speaker 2: earliest stages of AI across sectors like infrastructure, cybersecurity, and fintech.

584
00:32:56,720 --> 00:33:00,160
Speaker 2: Joining us now is Greylock General Partner. So modermedian. It's

585
00:33:00,240 --> 00:33:03,280
Speaker 2: good and time need to have a deep private markets

586
00:33:03,480 --> 00:33:08,000
Speaker 2: and venure caps with conversation, It's so interesting because I

587
00:33:08,000 --> 00:33:11,640
Speaker 2: think in aggregate we see a lot of the most

588
00:33:11,720 --> 00:33:16,160
Speaker 2: established firms raising funds with some regularity. This is the

589
00:33:16,160 --> 00:33:19,680
Speaker 2: eighteenth fund at some scale, So it's an early stage

590
00:33:19,680 --> 00:33:23,040
Speaker 2: fund one point five billion dollars. What do we infer

591
00:33:23,120 --> 00:33:23,440
Speaker 2: from that?

592
00:33:23,960 --> 00:33:26,000
Speaker 15: Well, thanks for having us on the show. And as

593
00:33:26,040 --> 00:33:28,160
Speaker 15: you mentioned, we just launched our eighteenth fund. It's a

594
00:33:28,200 --> 00:33:31,680
Speaker 15: billion and a half dollars dedicated to early stage AI entrepreneurs.

595
00:33:31,880 --> 00:33:34,280
Speaker 15: Greylock over the last six decades has been partnering with

596
00:33:34,400 --> 00:33:38,720
Speaker 15: entrepreneurs when companies get started, companies like Airbnb, Facebook, palad

597
00:33:38,720 --> 00:33:41,280
Speaker 15: Alto Networks. And we're excited with this new fund to

598
00:33:41,320 --> 00:33:43,880
Speaker 15: back a new generation of AI entrepreneurs. And as you know,

599
00:33:43,960 --> 00:33:46,960
Speaker 15: we've partnered with many of the early AI leaders dating

600
00:33:47,000 --> 00:33:48,680
Speaker 15: back many years before AI was obvious.

601
00:33:48,800 --> 00:33:50,560
Speaker 2: So we rewind the clock to twenty nineteen.

602
00:33:50,840 --> 00:33:53,120
Speaker 15: We backed Base ten based ten today as the leader

603
00:33:53,160 --> 00:33:55,720
Speaker 15: in AI in prints with you know, serving some of

604
00:33:55,720 --> 00:33:59,160
Speaker 15: the leading application companies like Cursor, a Bridge and others.

605
00:33:59,360 --> 00:34:02,560
Speaker 15: We backed AI, one of the leaders in customer service AI.

606
00:34:02,640 --> 00:34:04,840
Speaker 2: If you call United or Maria today.

607
00:34:04,680 --> 00:34:07,240
Speaker 15: You're talking to a crust AI agent. And of course

608
00:34:07,240 --> 00:34:09,319
Speaker 15: we're partners with the companies like Anthropic and Open AI,

609
00:34:09,480 --> 00:34:12,200
Speaker 15: powering the foundation model layer that's driving this whole economy.

610
00:34:12,440 --> 00:34:13,799
Speaker 15: But at the same time, I think one of the

611
00:34:13,840 --> 00:34:16,279
Speaker 15: benefits of our history is we've seen many of these

612
00:34:16,280 --> 00:34:19,000
Speaker 15: technology waves, and when we think about the iwave and

613
00:34:19,040 --> 00:34:20,919
Speaker 15: you connect it back to let's say, the mobile wave,

614
00:34:21,160 --> 00:34:22,680
Speaker 15: we're in the equivalent of two thousand and eight or

615
00:34:22,680 --> 00:34:24,680
Speaker 15: two thousand and nine. We're one to two years after

616
00:34:24,719 --> 00:34:26,960
Speaker 15: the iPhone moment, and I think we're going to look

617
00:34:27,000 --> 00:34:29,520
Speaker 15: forward and many of the defining AI companies of this

618
00:34:29,640 --> 00:34:32,080
Speaker 15: generation have yet to get started, and this new fund

619
00:34:32,120 --> 00:34:34,399
Speaker 15: is all about finding those entrepreneurs and helping them build

620
00:34:34,400 --> 00:34:34,840
Speaker 15: those compies.

621
00:34:34,840 --> 00:34:37,240
Speaker 2: And I think it's interesting what those entrepreneurs are doing

622
00:34:37,440 --> 00:34:40,600
Speaker 2: and what kind of companies that they're forming. You as

623
00:34:40,600 --> 00:34:43,160
Speaker 2: an example, so you're investors in open AI and Anthropic.

624
00:34:43,280 --> 00:34:46,600
Speaker 2: You got into Anthropic at Series F in the later rounds,

625
00:34:47,120 --> 00:34:52,600
Speaker 2: those are the leading frontier labs. With this new eighteenth fund,

626
00:34:53,239 --> 00:34:57,120
Speaker 2: you are not necessarily going after the model layer. What

627
00:34:57,239 --> 00:34:58,239
Speaker 2: is it you're focused on.

628
00:34:58,560 --> 00:35:00,279
Speaker 15: We think of it as three layers, and we will

629
00:35:00,320 --> 00:35:02,480
Speaker 15: continue to invest in all three. So there's the model layer.

630
00:35:02,680 --> 00:35:05,160
Speaker 15: As you mentioned, we're investors in both Anthropic and OpenAI,

631
00:35:05,440 --> 00:35:08,760
Speaker 15: we're seeing new companies get built around new data domains

632
00:35:08,800 --> 00:35:11,000
Speaker 15: or new approaches, and we continue to look for and

633
00:35:11,040 --> 00:35:13,719
Speaker 15: we'll back new companies at that layer. There's a very

634
00:35:13,719 --> 00:35:17,160
Speaker 15: big opportunity and infrastructure, the entire stock's going to get

635
00:35:17,160 --> 00:35:20,359
Speaker 15: rewritten around agents. Today we have companies like base ten

636
00:35:20,480 --> 00:35:24,360
Speaker 15: and Inference, brain Trust and Observability, Snorkel and Data but

637
00:35:24,440 --> 00:35:26,080
Speaker 15: we believe we're going to see a whole new agent

638
00:35:26,160 --> 00:35:29,600
Speaker 15: cloud get created. And just like we saw happen with

639
00:35:29,719 --> 00:35:31,560
Speaker 15: the rise of the cloud and the hyper scalers and

640
00:35:31,560 --> 00:35:34,400
Speaker 15: companies like data Dog and Mango, deb and Snowflake, we're

641
00:35:34,440 --> 00:35:37,120
Speaker 15: going to see similar purpose build services for these new

642
00:35:37,160 --> 00:35:40,400
Speaker 15: workloads and that's an amazing opportunity for entrepreneurs. And then,

643
00:35:40,440 --> 00:35:43,520
Speaker 15: of course, on top of that are applications, and within applications,

644
00:35:43,520 --> 00:35:46,640
Speaker 15: we already have companies like Cresstown Customer Service or Abnormal

645
00:35:46,800 --> 00:35:49,400
Speaker 15: and Cybersecurity, but we're going to see a proliferation of

646
00:35:49,440 --> 00:35:53,040
Speaker 15: application companies, especially now that agents actually work. We're six

647
00:35:53,080 --> 00:35:57,040
Speaker 15: months into the models that can power long running agents

648
00:35:57,200 --> 00:35:59,640
Speaker 15: and we're seeing amazing results. For example, you know, we're

649
00:35:59,680 --> 00:36:02,640
Speaker 15: investor in a company called RESOLVEI. They built agentic on

650
00:36:02,760 --> 00:36:06,080
Speaker 15: call engineers in the last six months at companies like Coinbase,

651
00:36:06,440 --> 00:36:09,920
Speaker 15: Door dash Fireworks, engineers don't have to wake up in

652
00:36:09,920 --> 00:36:11,719
Speaker 15: the middle of the night when there's an incident because

653
00:36:11,719 --> 00:36:14,680
Speaker 15: the resolve agent can autonomously handle that incident. That's an

654
00:36:14,719 --> 00:36:17,719
Speaker 15: example of agentic AI having huge impact. And we're just

655
00:36:17,719 --> 00:36:19,520
Speaker 15: getting started in that era of technology.

656
00:36:20,280 --> 00:36:23,279
Speaker 2: We need to talk about Moonshot and Kimmy K three.

657
00:36:24,120 --> 00:36:27,640
Speaker 2: You know, the public markets is where you see the drama.

658
00:36:28,160 --> 00:36:33,000
Speaker 2: But what is your interpretation of that and why a

659
00:36:33,400 --> 00:36:38,400
Speaker 2: twenty billion dollar startup from China topping a benchmark and

660
00:36:38,480 --> 00:36:41,400
Speaker 2: people looking at their economics has caused this reaction.

661
00:36:42,000 --> 00:36:44,520
Speaker 15: Well, I'd start by saying it was an amazing release yesterday,

662
00:36:44,640 --> 00:36:46,960
Speaker 15: largest open weights model to ever be released, two point

663
00:36:47,000 --> 00:36:50,240
Speaker 15: eight trillion parameters. As you said, really strong initial results,

664
00:36:50,680 --> 00:36:52,839
Speaker 15: and they also showed a lot of interesting techniques from

665
00:36:52,840 --> 00:36:55,160
Speaker 15: an efficiency perspective and how they built that model. That's

666
00:36:55,200 --> 00:36:58,480
Speaker 15: really impressive and I think it's great for that. Models

667
00:36:58,520 --> 00:37:00,239
Speaker 15: like that, models like the one that came out from

668
00:37:00,239 --> 00:37:03,279
Speaker 15: Thinking Machines the day prior, continue to give vibrancy to

669
00:37:03,360 --> 00:37:06,080
Speaker 15: the open source ecosystem, which is an important part of

670
00:37:06,080 --> 00:37:09,040
Speaker 15: our overall AI economy. At the same time, I think

671
00:37:09,040 --> 00:37:11,600
Speaker 15: the reaction over the last twenty four hours is perhaps

672
00:37:11,640 --> 00:37:13,759
Speaker 15: a little bit premature, and I would say, you know,

673
00:37:13,800 --> 00:37:15,400
Speaker 15: a couple of points. It reminds me a little bit

674
00:37:15,400 --> 00:37:18,040
Speaker 15: of when the deep Seek moment happened last year, and

675
00:37:18,080 --> 00:37:19,879
Speaker 15: when you look at this new Kimmi model, there's maybe

676
00:37:19,880 --> 00:37:21,840
Speaker 15: a few things, you know, the audience should consider. The

677
00:37:21,880 --> 00:37:25,359
Speaker 15: first is benchmarks are imperfect, right. You know, you can

678
00:37:25,360 --> 00:37:27,080
Speaker 15: take a model and make it very very strong at

679
00:37:27,120 --> 00:37:29,120
Speaker 15: a particular set of benchmarks. I don't think it's until

680
00:37:29,120 --> 00:37:31,120
Speaker 15: that model has had time to percolate in the real

681
00:37:31,120 --> 00:37:33,279
Speaker 15: world that we can get a true sense for the

682
00:37:33,320 --> 00:37:35,680
Speaker 15: trade offs that the models incurred and what its real

683
00:37:35,680 --> 00:37:38,840
Speaker 15: world performance looks like. The second is this whole discussion

684
00:37:38,840 --> 00:37:42,359
Speaker 15: around cost. I think the discussion misses the point. It's

685
00:37:42,480 --> 00:37:45,879
Speaker 15: very focused on token cost. Yes, but we think about

686
00:37:45,880 --> 00:37:49,080
Speaker 15: things more in terms of task cost. Not every token

687
00:37:49,160 --> 00:37:50,719
Speaker 15: is equal, and so I'd make two points as it

688
00:37:50,760 --> 00:37:53,359
Speaker 15: relates to the cost profile of Kimmy. The first that's

689
00:37:53,360 --> 00:37:56,000
Speaker 15: actually interesting is Kimmy K three is much more expensive

690
00:37:56,000 --> 00:37:59,040
Speaker 15: on a per token basis than KIMMYK two, which is

691
00:37:59,120 --> 00:38:00,839
Speaker 15: sort of contra to the narrative we.

692
00:38:00,840 --> 00:38:03,000
Speaker 2: Do not know what it costs to train can be

693
00:38:03,080 --> 00:38:05,160
Speaker 2: K three. We have an idea on K two. I

694
00:38:05,280 --> 00:38:08,160
Speaker 2: just want to put that out there. Absolutely absolutely correct.

695
00:38:08,360 --> 00:38:10,840
Speaker 15: So the per token cost is more, but even more importantly,

696
00:38:10,840 --> 00:38:13,800
Speaker 15: it's not particularly token efficient. And so what that means

697
00:38:13,880 --> 00:38:16,719
Speaker 15: is for a given task, it actually uses many more

698
00:38:16,719 --> 00:38:19,520
Speaker 15: tokens than an open AI or anthropic model. And you're

699
00:38:19,520 --> 00:38:21,960
Speaker 15: seeing that already in the early cost beunchmarking. And so

700
00:38:22,000 --> 00:38:23,880
Speaker 15: I think this conclusion that it's going to lead to

701
00:38:23,960 --> 00:38:26,560
Speaker 15: price erosion for the frontier is perhaps a bit of

702
00:38:26,600 --> 00:38:29,120
Speaker 15: pretty in this case, the frontier, let's say it's anthropic

703
00:38:29,200 --> 00:38:29,759
Speaker 15: and open AI.

704
00:38:30,120 --> 00:38:32,799
Speaker 2: Right. You know some people are making the argument, well,

705
00:38:32,840 --> 00:38:35,680
Speaker 2: hold on a minute, if a twenty billion dollar valuation

706
00:38:35,800 --> 00:38:38,080
Speaker 2: Chinese startup, we don't know the training costs, but they're

707
00:38:38,120 --> 00:38:42,040
Speaker 2: basically saying three million, three dollars per million tokens on

708
00:38:42,080 --> 00:38:45,040
Speaker 2: the input side fifteen dollars per million tokens on the

709
00:38:45,040 --> 00:38:48,880
Speaker 2: output side. If they can do that, why are we

710
00:38:49,000 --> 00:38:51,560
Speaker 2: valuing anthropic at nearly a trillion dollars? Like, what's the

711
00:38:51,680 --> 00:38:53,600
Speaker 2: moat that anthropic has?

712
00:38:54,600 --> 00:38:57,000
Speaker 15: I think both in thropic opening, I have multiple modes.

713
00:38:57,080 --> 00:38:57,680
Speaker 2: The firs is.

714
00:38:57,760 --> 00:39:00,160
Speaker 15: They have very significant revenues. These are the fact to

715
00:39:00,320 --> 00:39:03,040
Speaker 15: growing companies in human history, and those revenues are not

716
00:39:03,080 --> 00:39:04,840
Speaker 15: just on the back of their models and their amazing

717
00:39:04,880 --> 00:39:08,080
Speaker 15: API businesses, but their first party products. Right and I

718
00:39:08,080 --> 00:39:09,920
Speaker 15: think relative the last time I was on the show.

719
00:39:10,160 --> 00:39:12,080
Speaker 15: You look at the success that Anthropic has had with

720
00:39:12,120 --> 00:39:16,200
Speaker 15: cloud Code, Cloud Cowork most recently, claud tag It and

721
00:39:16,320 --> 00:39:18,919
Speaker 15: open Ai are full stack AI company.

722
00:39:19,120 --> 00:39:20,839
Speaker 2: Can I just say one thing? I think for two

723
00:39:20,960 --> 00:39:22,880
Speaker 2: years and it's been too long since you've been on

724
00:39:22,920 --> 00:39:25,720
Speaker 2: the show, but we've basically assumed the best model wins.

725
00:39:26,480 --> 00:39:28,040
Speaker 2: Is it as simple as that you seem to be

726
00:39:28,080 --> 00:39:28,600
Speaker 2: saying it's.

727
00:39:28,520 --> 00:39:32,279
Speaker 15: Not, Well, it depends on how you define the best model, right,

728
00:39:32,320 --> 00:39:34,440
Speaker 15: and I think I would be again, I'd be careful

729
00:39:34,440 --> 00:39:36,960
Speaker 15: to jump to the conclusion that Kimmy is now competitive

730
00:39:36,960 --> 00:39:39,880
Speaker 15: with the best, certainly on some of the benchmarks that

731
00:39:39,880 --> 00:39:43,480
Speaker 15: they release, it's competitive with i'd say one generation prior.

732
00:39:43,800 --> 00:39:46,400
Speaker 15: And of course we don't know what's coming out from

733
00:39:46,400 --> 00:39:48,640
Speaker 15: Anthropic and OPENINGI in the coming weeks and coming months,

734
00:39:48,640 --> 00:39:51,359
Speaker 15: but it's been rumored and reported that they're significant new

735
00:39:51,360 --> 00:39:53,120
Speaker 15: releases coming out from those models, So we have a

736
00:39:53,120 --> 00:39:56,040
Speaker 15: particular checkpoint in time that we're comparing it to. I

737
00:39:56,920 --> 00:39:58,360
Speaker 15: think we're going to see a lot of really exciting

738
00:39:58,400 --> 00:40:00,640
Speaker 15: releases in the coming months, but maybe, and if I can,

739
00:40:00,920 --> 00:40:03,480
Speaker 15: I want to make one broader point, which is if

740
00:40:03,480 --> 00:40:05,320
Speaker 15: you just think about the overall size of the token

741
00:40:05,320 --> 00:40:08,080
Speaker 15: economy and where we're going. Right in twenty twenty three,

742
00:40:08,160 --> 00:40:12,160
Speaker 15: Opening Eyes API was processing per day about thirty million tokens.

743
00:40:12,480 --> 00:40:14,640
Speaker 15: In March of twenty twenty six, they announced they were

744
00:40:14,640 --> 00:40:18,120
Speaker 15: doing fifteen billion, so just in less than three years,

745
00:40:18,360 --> 00:40:21,359
Speaker 15: a growth of fifty times. I think when we are

746
00:40:21,360 --> 00:40:24,200
Speaker 15: talking in twenty thirty, the overall token economy will be

747
00:40:24,280 --> 00:40:26,880
Speaker 15: two orders of magnitude larger than it is today, and

748
00:40:26,880 --> 00:40:29,440
Speaker 15: so there's going to be plenty of opportunity for the

749
00:40:29,480 --> 00:40:32,319
Speaker 15: best leading closed frontier models to grow, for the open

750
00:40:32,320 --> 00:40:35,480
Speaker 15: source economy to grow, and for the application layer to grow.

751
00:40:35,520 --> 00:40:38,560
Speaker 15: I think the only mistake one can make is underestimating

752
00:40:38,600 --> 00:40:39,799
Speaker 15: the size of this overall wave.

753
00:40:40,600 --> 00:40:43,319
Speaker 2: Gray Luck, General partner, some monomenty back on Bloomberg Tech.

754
00:40:43,600 --> 00:40:46,480
Speaker 2: I hope to have you back with some regularity. Congratulations

755
00:40:46,480 --> 00:40:49,080
Speaker 2: on the fund and then your success at the firm

756
00:40:49,400 --> 00:40:54,560
Speaker 2: coming up forgets Superheroes director chrispher Nolan is becoming a

757
00:40:54,680 --> 00:40:56,560
Speaker 2: franchise all on his own. We're going to take a

758
00:40:56,600 --> 00:40:59,760
Speaker 2: very quick look at the blockbuster business behind the opposite

759
00:40:59,760 --> 00:41:10,359
Speaker 2: odd that's next. This is Bloomberg Tech. Let's go back

760
00:41:10,400 --> 00:41:13,200
Speaker 2: to Netflix. Bloomberg Intelligence is out with the react saying

761
00:41:13,200 --> 00:41:16,720
Speaker 2: the results quote reinforce the bearish narrative with a muted

762
00:41:16,800 --> 00:41:20,040
Speaker 2: third quarter in twenty twenty six, guide singingly a slowdown

763
00:41:20,040 --> 00:41:24,160
Speaker 2: in revenue growth while plateauing engagement keeps sentiments subdued. Keita

764
00:41:24,239 --> 00:41:27,560
Speaker 2: ranging up and of Bloomberg Intelligence, the author of that research.

765
00:41:27,800 --> 00:41:29,359
Speaker 2: You know, you and I in real time went over

766
00:41:29,400 --> 00:41:32,520
Speaker 2: Netflix yesterday. But the main point you want to hammer.

767
00:41:32,239 --> 00:41:36,319
Speaker 16: Home, Yeah, the main point is they somehow have to

768
00:41:36,360 --> 00:41:40,480
Speaker 16: turn the narrative around ed on this whole deceleration in

769
00:41:40,520 --> 00:41:43,240
Speaker 16: revenue growth. I mean, we've seen it significantly come down,

770
00:41:43,480 --> 00:41:46,120
Speaker 16: you know, from sixteen percent in the first quarter to

771
00:41:46,600 --> 00:41:48,759
Speaker 16: fourteen percent in the second quarter and now eleven point

772
00:41:48,840 --> 00:41:52,080
Speaker 16: seven percent guidance for the third quarter. And really the

773
00:41:52,120 --> 00:41:55,439
Speaker 16: whole of you know, narrative here is anchored by those

774
00:41:55,880 --> 00:41:59,640
Speaker 16: double digit, solid double digit mid teens gains in revenue

775
00:41:59,640 --> 00:42:03,280
Speaker 16: growth until and unless we see that picking up along

776
00:42:03,320 --> 00:42:06,200
Speaker 16: with a pickup in engagement and subscriber growth. I think

777
00:42:06,200 --> 00:42:08,759
Speaker 16: it's going to be really hard to get behind this,

778
00:42:09,480 --> 00:42:10,880
Speaker 16: you know, the whole Netflix thesis.

779
00:42:12,120 --> 00:42:14,840
Speaker 2: Straight off to the show, Bloomberg Tech producer Justin Lowes

780
00:42:14,920 --> 00:42:17,719
Speaker 2: running to an Imax somewhere in New York City to

781
00:42:17,800 --> 00:42:20,200
Speaker 2: watch The Odyssey. Is this going to be the big

782
00:42:20,200 --> 00:42:20,839
Speaker 2: one of the year.

783
00:42:21,840 --> 00:42:24,560
Speaker 16: It absolutely is. I mean we've you know, believe it

784
00:42:24,640 --> 00:42:28,040
Speaker 16: or not, the tickets went on sale for this ed

785
00:42:28,120 --> 00:42:30,920
Speaker 16: over a year ago and they sold out within minutes.

786
00:42:31,040 --> 00:42:33,680
Speaker 16: For you know, all the Imax and the premium formats.

787
00:42:33,880 --> 00:42:35,839
Speaker 16: This one is going to be huge. We just got

788
00:42:35,880 --> 00:42:40,160
Speaker 16: the numbers for the Thursday preview releases highest ever this year,

789
00:42:40,200 --> 00:42:43,200
Speaker 16: beating even Toy Story five. So I think this, you know,

790
00:42:43,280 --> 00:42:45,319
Speaker 16: combined with all of the buzz, the fact that it

791
00:42:45,400 --> 00:42:47,799
Speaker 16: was shot in an Imax camera, and that you know,

792
00:42:47,880 --> 00:42:50,480
Speaker 16: everybody wants to I mean, Christopher Nolan is always great

793
00:42:50,480 --> 00:42:53,160
Speaker 16: at eventizing a film, and I think we're going to

794
00:42:53,239 --> 00:42:55,800
Speaker 16: see that really translate into some very very strong numbers

795
00:42:55,800 --> 00:42:56,799
Speaker 16: in the box office.

796
00:42:58,200 --> 00:43:00,400
Speaker 2: I really want to see this one too. Media and

797
00:43:00,520 --> 00:43:03,919
Speaker 2: Entertainment ANALYSISKI that Arang an athen of Bloomberg Intelligence. Thank

798
00:43:03,960 --> 00:43:06,440
Speaker 2: you very much. That does it for this edition of

799
00:43:06,440 --> 00:43:11,000
Speaker 2: Bloomberg Tech. Again, the picture in financial markets, in equity

800
00:43:11,080 --> 00:43:14,760
Speaker 2: markets is there's a lot of red. We're off session lows,

801
00:43:14,760 --> 00:43:19,480
Speaker 2: significantly off session lows. But the catalyst was China's moonshot

802
00:43:19,480 --> 00:43:23,120
Speaker 2: releasing Kimmy K three, and that causing concerns that, well,

803
00:43:23,120 --> 00:43:25,360
Speaker 2: why are we spending all this money in America on

804
00:43:25,440 --> 00:43:28,640
Speaker 2: developing AI if a startup in China can do it

805
00:43:29,120 --> 00:43:32,000
Speaker 2: at a much cheaper level with different economics. There is

806
00:43:32,000 --> 00:43:33,920
Speaker 2: some dispute of that. We went over in the show.

807
00:43:34,080 --> 00:43:37,040
Speaker 2: In video and Intel down Apple lower. At one point,

808
00:43:37,160 --> 00:43:40,239
Speaker 2: Apple was the world's most valuable company, pipping in video.

809
00:43:40,600 --> 00:43:42,640
Speaker 2: Right now, it's not We're going up for more tech

810
00:43:42,680 --> 00:43:45,080
Speaker 2: earnings next week. This is your calendar, this is what

811
00:43:45,120 --> 00:43:48,520
Speaker 2: we're bracing for. It really starts in earnest over the

812
00:43:48,560 --> 00:43:51,239
Speaker 2: next seven days. Don't forget to recap the show on

813
00:43:51,280 --> 00:43:52,960
Speaker 2: the podcast. You know where to find it on the

814
00:43:53,000 --> 00:43:57,279
Speaker 2: Bloomberg platforms, but also online on Apple, Spotify, and iHeart.

815
00:43:57,320 --> 00:44:00,080
Speaker 2: Have a great weekend from San Francisco. This isl and

816
00:44:00,120 --> 00:44:00,640
Speaker 2: bag Tack