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

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

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Speaker 1: and Eva Low in San Francisco.

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Speaker 2: This is Bloomberg Tech coming up. Elon Muskin Chance Investors

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Speaker 2: with his vision of the future, has questions about the SPACEXIPO,

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Speaker 2: a brushed aside at an investor event. Plus coming in

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Speaker 2: Hot Job Surgeon May. But techtoks are under pressure as

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Speaker 2: investors reassessed the path for interest rates and three is

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Speaker 2: the biggest names in AI. At Bloomberg Tech San Francisco.

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Speaker 2: We're going to bring you those conversations throughout the programs.

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Speaker 2: SpaceX's IPO is absolutely dominating in the news cycle, but

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Speaker 2: there is so much happening in financial markets and a

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Speaker 2: lot with the tech sector. The s and P five

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Speaker 2: hundred is down a percentage point that really the underperformance

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Speaker 2: is in chip socks, with down four point seven percent

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Speaker 2: on the socks. Interestingly, we're still positive on the week,

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Speaker 2: but everyone kind of talking about chips and saying we

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Speaker 2: are due a correction. Actually, outside of chips, the rest

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Speaker 2: of the AI trade is getting hammered. Those jobs numbers

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Speaker 2: for May were hot US ten year yield four point

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Speaker 2: five percent. So bad news, guys. I'm sorry, I know

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Speaker 2: it's Friday. The S and P five hundred is down

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Speaker 2: on the week, and that means that it will not,

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Speaker 2: as it stands, hit a historic tenth week of games.

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Speaker 2: It will stop short at nine straight weeks again. The

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Speaker 2: SpaceX IPO, the imminency of the pricing next Thursday and

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Speaker 2: the trade following that is a big factor in the market.

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Speaker 2: It is going to be some form of catalysts and

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Speaker 2: volatility is being very closely watched. Let's get to our

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Speaker 2: top story. Elon Musk in chants investors.

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Speaker 3: Why SpaceX public now? Because you had choices, you didn't

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Speaker 3: have to Why.

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Speaker 2: Now We're embarking on a massive new growth phase and

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Speaker 2: you capital for that.

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Speaker 4: Okay.

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Speaker 3: Number two.

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Speaker 2: Another thing is that the revenue also feel pretty good

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Speaker 2: about like the revenue projections. But I mean, like like before,

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Speaker 2: like lib reveue was a little unstable. Elon Musk still

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Speaker 2: has the power to sell a vision that a SpaceX

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Speaker 2: investor event yesterday hosted by Jamie Dimond at JP Morgan's headquarters,

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Speaker 2: talk of the company's future overshadowed questions about the IPO itself.

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Speaker 2: The absolute latest this morning is Bloomberg reporting that this

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Speaker 2: IPO is already way over subscribed. There are a number

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Speaker 2: of other pieces of news you have to get to

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Speaker 2: Bloomberg's Cragdrey hours with us. I actually want to start

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Speaker 2: just with last night. You were manning the desk, managing

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Speaker 2: the team of like, okay, what is new and what

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Speaker 2: Elon Musk is saying here? What's the big takeaway that

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Speaker 2: we need to know about in what's an unusual pre

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Speaker 2: IPO pitch on stage so to speak?

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Speaker 5: I have phones last note's LoveFest between Diamond and Muscas.

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Speaker 3: You know, fascinating.

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Speaker 5: It's not often that you see Jamie me Diamond struggle

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Speaker 5: to get a word in, and yet we saw a

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Speaker 5: little bit of that where you know, it took time

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Speaker 5: for for from Us to get through his answers and

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Speaker 5: he was quite long winded and they had some fun

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Speaker 5: about that, as we saw in that clip. I think also,

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Speaker 5: you know, Musk was pretty forthcoming about this notion. That's

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Speaker 5: what's what's driving this is that he needs capital. And

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Speaker 5: you know we saw this week as well. You know

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Speaker 5: even at Alphabet, this this notion that a company of

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Speaker 5: that size, that is that cash generative still is going

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Speaker 5: out to raise eighty billion dollars with the backing of

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Speaker 5: Berkshire Hathaway. So I think this is part of you know,

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Speaker 5: this broader you know movement by by tech companies and

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Speaker 5: by you know, AI hyperscalers to raise it absolutely as

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Speaker 5: much money as they possibly can, given just how intensive

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Speaker 5: this race has become.

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Speaker 2: If you're watching back Tech and you coming to this

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Speaker 2: story fresh, that's a surprise. But basically SpaceX is on

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Speaker 2: a roadshow right now and they plan to final pricing

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Speaker 2: of the IPO next Thursday. They've already said one hundred

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Speaker 2: and thirty five dollars a share, raising more than seventy

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Speaker 2: five billion dollars at a one point seven seven trillion

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Speaker 2: dollar valuation, this will be the biggest IPO of all time. Craig,

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Speaker 2: you're the managing editor that kind of leads coverage of

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Speaker 2: the company. I appreciate that. But there is some kind

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Speaker 2: of market mechanics things that are really important in the

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Speaker 2: news cycle, one being that SpaceX and these other IPOs

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Speaker 2: wedding in the wings will get fast tracked into the

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Speaker 2: S and P five hundred. Can you talk a little

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Speaker 2: bit about that and why that matters.

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Speaker 3: Yeah, I think that's a setback.

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Speaker 5: But I think it's also the case that the S

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Speaker 5: and P you know, is deciding differently from some other indices,

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Speaker 5: and so you know, the Nasdaq has taken a different approach,

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Speaker 5: and so perhaps there will be you know, some indexes

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Speaker 5: you know where you and I, if we're invested in

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Speaker 5: index funds, you know, will get a piece of this company.

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Speaker 5: And it's just a matter of some sort of spacing

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Speaker 5: out of when we'll see demand for these shares from

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Speaker 5: from various you know, institutional investors.

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Speaker 3: And perhaps that's actually for the best.

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Speaker 5: Given that there is so much concern about all of

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Speaker 5: these sort of market mechanics and just how disruptive this

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Speaker 5: will be, given how how small afloat, you know, we're

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Speaker 5: seeing this company list here.

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Speaker 2: Another quick story for me this morning, China and Hong

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Speaker 2: Kong vestors are being told or the odd the rights

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Speaker 2: being told that they can't take orders from investors in

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Speaker 2: China Hong Kong's security. There's also something going on with

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Speaker 2: Tesla and sell side coverage that's relevant. Just very quickly

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Speaker 2: summed that up for us.

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Speaker 3: It's fascinating to me.

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Speaker 5: We saw last night Jamie Diamond and Elon Musk yucking

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Speaker 5: it up. These two haven't always been so close, and

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Speaker 5: yet the morning after we see JP Morgan go from

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Speaker 5: one of the most bearish banks on Tesla shares to

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Speaker 5: a new analyst taking over coverage and increasing the price

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Speaker 5: target by more than two hundred percent.

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Speaker 3: So very notable in light of last night's event.

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Speaker 2: Who Boss, Craig, you though, thank you very much has

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Speaker 2: certified Samsung, s k Heinitz and Micron to supply HBM

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Speaker 2: forour memory for its next generation AI chips, and video

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Speaker 2: CEO Jensen one confirm that move just after arriving in Soul.

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Speaker 6: All three vendors have been qualified, All three vendors are

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Speaker 6: in production, and.

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Speaker 3: They're all racing. They're racing all to support Vera.

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Speaker 2: Rubino, the very Rubin platform now in full production, head

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Speaker 2: a third quarter deliveries with systems combining in video CPUs

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Speaker 2: GPUs with those HBM for memory ships. Again today, right

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Speaker 2: now in the markets, chips under pressure. The labor market

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Speaker 2: keeps refusing to crack. Today's jobs report came in stronger

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Speaker 2: than expected, but in the market, tech stocks are under pressure.

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Speaker 2: Investors basically reassessing the path for interest rates for more.

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Speaker 2: We're joined by Martha Gimball, executive director of VL Budget Lab.

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Speaker 2: I always enjoy having you on the show, Martha. I

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Speaker 2: think you make what's happening in the US economy understandable,

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Speaker 2: digestable for a broad portion of people. What did you

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Speaker 2: see in the data this morning? What was the story

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Speaker 2: it was telling you?

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Speaker 3: I mean, it's interesting.

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Speaker 7: This is one of those reports where as an economist

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Speaker 7: you kind of open it up, look at it and go, wow,

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Speaker 7: this is really great, not a ton going on here,

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Speaker 7: and then close it and go get to go get

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Speaker 7: your coffee. So, you know, I think this is a

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Speaker 7: report that looks like what it is. Job growth is

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Speaker 7: really strong. You know, some lags on the wage side,

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Speaker 7: particularly compared to inflation. But if you're thinking about where

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Speaker 7: the FED goes from here, this is certainly giving them

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Speaker 7: room to try to do some action on inflation with

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Speaker 7: interest rates.

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Speaker 2: There is an AI story here. But when we spoke

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Speaker 2: to San Francisco Fair President Mary Daily yesterday, we had

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Speaker 2: to get to where is policy right now? I just

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Speaker 2: want to play you a SoundBite from what she said.

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Speaker 6: Right now, policies in a good place. We are prepared

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Speaker 6: to respond either way whatever the economy brings. But I

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Speaker 6: think giving more forward guidance about what's possible could be

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Speaker 6: misguiding in the end, because we just have to wait

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Speaker 6: for the economy to revolve. Everybody wants to resolve the

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Speaker 6: uncertainty today. But I think that's a mistake because it

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Speaker 6: will close off our mind about what we really have

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Speaker 6: to look at.

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Speaker 2: I think that's pretty standard interim fed speech. Let's say,

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Speaker 2: but one of the things we tried to get at,

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Speaker 2: is AI showing up in productivity data? Or are you

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Speaker 2: seeing anything that maybe suggests in the labor market companies

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Speaker 2: big and small holding off hiring because they just don't

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Speaker 2: know what AI will or won't do for them. I

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Speaker 2: wonder if you see any evidence at all.

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Speaker 7: I really am not seeing any evidence of major AI

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Speaker 7: impacts in the economic data at this time.

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Speaker 2: I should say.

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Speaker 7: You know, obviously on the investment side and things like that,

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Speaker 7: it is making a difference, But if it doesn't seem

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Speaker 7: to be holding at companies from hiring at this point,

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Speaker 7: it doesn't really seem to be showing up in the

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Speaker 7: productivity data.

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Speaker 3: You know, in some ways we wouldn't.

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Speaker 2: Expect it to.

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Speaker 7: I know I keep saying this and I'm a broken

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Speaker 7: but it is still early in this It is really

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Speaker 7: really early in this technology, and in some ways we're

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Speaker 7: holding it to an impossible standard to say, wow, it

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Speaker 7: must have already be impacting the labor market.

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Speaker 2: Martha. I mean you're not alone in saying that. I

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Speaker 2: think that's really, you know, important to point out. People

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Speaker 2: come on bloombug Tech every week and say this very early.

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Speaker 2: One thing I really want to try and get deep

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Speaker 2: into is the impact of the capital expenditure. So you know,

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Speaker 2: the numbers are just ginormous, and as you just mentioned,

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Speaker 2: like in the capital markets, whether it's IPO or what

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Speaker 2: Alphabet's doing in its equity offering, is all of that

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Speaker 2: inflationary or is it disinflationary? Like some of the utilities

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Speaker 2: would even argue that, like if the hyperscales take the

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Speaker 2: burden of these big projects, it may cause energy prices

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Speaker 2: to come down, and it's always been an argument that

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Speaker 2: we found hard to track.

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Speaker 7: So first of all, I think it's important to say

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Speaker 7: that the most important inflationary impact on the energy side

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Speaker 7: right now is what's going on in the Middle East.

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Speaker 7: That's going to trump anything else. I do think the

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Speaker 7: evidence is clear if you're looking at the inflation data

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Speaker 7: that there is some upward impact from what is going

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Speaker 7: on in the AI space. You know, if we change

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Speaker 7: what's happening on the utility side and how these things

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Speaker 7: are financed, that might start working in the other direction.

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Speaker 7: But I don't think that's happening quite yet.

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Speaker 2: Yeah, I just you know, people sometimes have short memories.

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Speaker 2: I go back, I think it was two quarters ago

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Speaker 2: where Meta said one reason we're raising capex is not

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Speaker 2: because we need to spend more, is because the cost

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Speaker 2: of what we're trying to do is higher because of

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Speaker 2: the environment. And what I'm trying to work out is

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Speaker 2: how circular that is, how much that feeds back into inflation.

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Speaker 7: I mean, I think right now this is a sector

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Speaker 7: that people really want to invest in. It's also an

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Speaker 7: incredibly expensive sector to invest in. These models are not free,

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Speaker 7: the data centers are not free, and so as you're

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Speaker 7: having money pile into that sector, it's going to drive

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Speaker 7: prices up, and then if you want to keep investing,

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Speaker 7: they're going to have to hey those prices.

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Speaker 2: Martha Gimbo of the el Budget Lab Hot Jobs Data.

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Speaker 2: In May, we talked a lot about inflation though as well.

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Speaker 2: Thank you very much, And now coming up and fropits

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Speaker 2: co founder and explains why the company needs to go

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Speaker 2: public a really big conversation. Next this is Bloomberg Tech,

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Speaker 2: Andthropic is warning that the technology is involving faster than anticipated,

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Speaker 2: raising the risk of AI systems autonomously building their own successors.

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Speaker 2: In a new lengthy log post, the company says it

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Speaker 2: would be good for the world to have the option

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Speaker 2: to show or temporarily pause development and projects that become

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Speaker 2: too dangerous, and Probics says it will meet with global

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Speaker 2: policymakers and rival labs in the coming months to coordinate

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Speaker 2: safety thresholds, all while the startup prepares for its highly

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Speaker 2: anticipated IPO. And At a Bloomberg Tech event in San

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Speaker 2: Francisco yesterday, and Probits co founder and president Danielle A.

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Speaker 2: M O'Day was on stage explaining why the company needs

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Speaker 2: to go public in this race against OpenAI.

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Speaker 8: Listened to this speaking for ours, and I think ideally

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Speaker 8: probably really for the AI industry more broadly, it's a

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Speaker 8: very capital intensitive business to train AI models. I think

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Speaker 8: the sort of core set of companies that are working

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Speaker 8: to advance the frontier are just going to need access

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Speaker 8: to capital. And I think the public market is very

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Speaker 8: well suited to that.

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Speaker 2: I want to get deep into this conversation with Bloomberg Shringafari,

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Speaker 2: who spoke to Zanya Amaday on stage yesterday. You know,

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Speaker 2: it's a pretty simple explanation. Why do companies go public?

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Speaker 2: They need money, And you actually got a little bit

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Speaker 2: deeper into anthropics strategy for security and compute relative to

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Speaker 2: open aies. I think that's a really interesting place to

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Speaker 2: jump in.

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Speaker 3: Yeah, that's right.

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Speaker 9: Open Ai was really early to making some splashy announcements

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Speaker 9: about how big they wanted to go on compute, spending

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Speaker 9: up to trillion, you know, or around that on these

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Speaker 9: massive projects like Stargate, and at the time that was

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Speaker 9: on it all something that was considered normal. A lot

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Speaker 9: of people critiqued it and thought that was overly ambitious.

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Speaker 9: Now we're seeing Anthropic be quite upfront about the fact

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Speaker 9: that they need a lot more compute and that that

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Speaker 9: is really one of the main, if not one of

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Speaker 9: the main motivating factors for going public at this time.

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Speaker 2: Very quickly on answering stream, what was your other big takeaway?

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Speaker 2: What are you going to be writing about next?

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Speaker 9: There's a lot more about just how these companies are

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Speaker 9: managing the timeline of these IPOs. On the one hand, yes,

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Speaker 9: you have access to more capital, but on the other wall,

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Speaker 9: Street's going to be scrutinizing those numbers if they do

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Speaker 9: flip public on the financials and so these companies are

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Speaker 9: going to have to ask themselves if they're really ready

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Speaker 9: at this exact moment.

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Speaker 2: In and amongst it all, Sreen you and I also

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Speaker 2: broke a story that Brian Cheski, the CEO of Airbnb,

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Speaker 2: is starting a UAI company. What do we need to know?

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Speaker 3: That's right?

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Speaker 9: So Brian Chesky, co founder and CEO of Airbnb, starting

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Speaker 9: and lab, a sort of neo lab if you will.

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Speaker 9: He will remain CEO of a abnb and new starts

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Speaker 9: to say, he will also not be CEO of this

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Speaker 9: new lab. But this is a sign, and this is

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Speaker 9: a bigger trend we're seeing where some of the existing

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Speaker 9: tech founders who still want to go all in on

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Speaker 9: the AI race, if they're not directly doing an AI

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Speaker 9: only company, may get involved in these sort of side

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Speaker 9: projects or side companies. So it's an interesting one and

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Speaker 9: it may end up having a design and UI focus,

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Speaker 9: which is something that Cheskey is quite passionate about.

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Speaker 2: Bloomberg Sheren Gafari, Top Conversations, Top Reporting, Thank you very much.

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Speaker 2: I want to take a quick look at the shares

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Speaker 2: of Broadcom over five days. But remember Broacon reported its

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Speaker 2: earnings Wednesday night, traded into Thursday. We're down ten eleven

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Speaker 2: percent on the week. There was a big negative reaction

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Speaker 2: in that Thursday session to the outlook for the current

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Speaker 2: period as it relates to AI chip sales. Brocom CEO

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Speaker 2: Hoc Tan was one of the biggest speakers at Bloomberg

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Speaker 2: Tech yesterday. He sat down with our executive editor for Tech,

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Speaker 2: Tom Giles, and talks a lot about potential M and A.

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Speaker 2: Listen to this and it's an interesting question.

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Speaker 10: It's a very valid question because across my mind and

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Speaker 10: that on my boards on an ongoing basis not regularly

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Speaker 10: as do we do M and A. So I'll be

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Speaker 10: very flip and give you a straight answer. In the

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Speaker 10: last two years, between twenty four and twenty six, this year,

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Speaker 10: I would be doubling my revenues. I would create over

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Speaker 10: fifty billion dollars per year, and alys on revenue, I'm

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Speaker 10: looking around what can I buy? They even come close

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Speaker 10: to that, And that's the tricky part. I mean it's

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Speaker 10: a distraction. All MNA's are distraction to acquire, go to regulators,

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Speaker 10: further distraction to integrate another year. Meanwhile, organically, this phenomenon

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Speaker 10: we call generative AI, and our ability to ship ship

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Speaker 10: them picks and shovels the compute capacity into this demand,

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Speaker 10: which is almost insatiable, makes it very hard to choose

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Speaker 10: MNA over focusing and succeeding in generative AI compute.

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Speaker 11: Is there any area where you think you might want

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Speaker 11: to sort of make an exception to this, whether it's

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Speaker 11: maybe photonics just to name one.

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Speaker 10: Area, photonics or you mean optics. I'm just throwing anything. Yeah,

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Speaker 10: we ran the business. I ran a business morel twenty years.

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Speaker 10: I tried to very hard, I know, try to avoid

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Speaker 10: bright shiny objects.

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Speaker 2: That was broke from CEO Hocktan speaking with Bloomberg's Tom Giles.

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Speaker 12: Versus a AI.

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Speaker 3: What are they unleashing right now?

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Speaker 12: They're unleashing an incredible CAPEX boom and to this audience

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Speaker 12: and to the topic of the show in an incredible

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Speaker 12: wealth effect. Right, So when you see that that is

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Speaker 12: leading to a higher degree of economic growth than anybody

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Speaker 12: expected from those two facets, right, You've got an incredible

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Speaker 12: surge in investment spend coming out of this AI demand

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Speaker 12: for investment and a wealth effect that's powering consumption.

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Speaker 2: That was Jeffrey Rosenberg of Black Rocks speaking with Tom

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Speaker 2: Keane and Scarlet Food an exclusive event for Bloomberg dot

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Speaker 2: Com subscribers in New York earlier this week. That conversation

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Speaker 2: coming ahead of the launch of Bloomberg Money Today, a

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Speaker 2: new weekly show hosted by Tom and Scarlet airing every

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Speaker 2: Friday twelve pm New York time. Tom Keane's here with

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Speaker 2: us Bloomberg Money. The vertical So interesting what he was saying,

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Speaker 2: because in our world over here it's a wealth creation

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Speaker 2: story from AI right and on the Bloomberg terminal, people

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Speaker 2: have always read about personal finance the wealthful success of

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Speaker 2: their peers. What do you want to do with this show, Tom.

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Speaker 4: Well, what we want to do with the reps into

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Speaker 4: what you're doing at Ludlow is go downstream from the

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Speaker 4: fancy people you talked to out on the West Coast.

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Speaker 4: I talked today to a gentleman who about a company

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Speaker 4: in Bismarck, North Dakota, which is directly prosperous from everything

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Speaker 4: that's going on. With Bloomberg technology, there's a huge follow

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Speaker 4: on effect that Jeff Rosenberg was talking about, and it

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Speaker 4: goes into the general sense of personal finance. We want

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Speaker 4: to lift up that conversation, but it goes to this

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Speaker 4: dream of retirement within an America with huge retirement instability,

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Speaker 4: and most importantly getting to the wealth management, being in

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Speaker 4: the wealth management and what do you do when you

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Speaker 4: finally made it.

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Speaker 2: All of that is so relevant to like now this week,

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Speaker 2: next week the coverage of SpaceX IPO and the clamoring

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Speaker 2: of basically wealth advisors on behalf of their clients. But also,

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Speaker 2: like what I'm interested in, Tommy, is like how much

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Speaker 2: you think private markets will come into it. There's a

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Speaker 2: lot of an aspiration from people around the world to

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Speaker 2: get involved in private companies, but the mechanisms aren't there.

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Speaker 2: Sometimes we've written a lot about SPVs, for example, and

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Speaker 2: trying to get in on the secondary market.

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Speaker 4: And the problem with a little bit of lineage is

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Speaker 4: you learn the private markets means ill liquidity somewhere down

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Speaker 4: the road. You do that better than menad But I

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Speaker 4: would say it's a big part of what we're doing,

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Speaker 4: but we're really looking much more towards the more prosaic

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Speaker 4: personal finance stories, but also that new access when you

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Speaker 4: see four one ks and retirement plans that have ability

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Speaker 4: in bitcoin, have ability in any kind of alternative investments.

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Speaker 4: That is a whole new world after all, and we're

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Speaker 4: going to focus on that.

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Speaker 2: I'm conscious like in the moment, what one session of

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Speaker 2: market does not make, But actually we're seeing declines extend

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Speaker 2: quite significantly on the NAST one hundred now that the

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Speaker 2: Philadelphia Semiconductrin excel socks is down significantly, although part of

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Speaker 2: that is like we were due a correction having been

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Speaker 2: up more than eighty percent year. Today you go to

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Speaker 2: air in thirty five minutes time. I don't want to

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Speaker 2: frun run it, but just give me an overview of

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Speaker 2: today's show.

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Speaker 4: I gotta ask first, ed, have you ever enjoyed a correction?

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Speaker 4: Have you ever enjoyed a bear market? Is it a

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Speaker 4: whole new thing?

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Speaker 2: You know? That's why I always say one session of

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Speaker 2: market does not make stocks go up and stocks go down.

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Speaker 2: And as you taught me over a number of years,

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Speaker 2: zoom out on the chart, don't look at the red

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Speaker 2: flashing on the streets.

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Speaker 4: Zoom out over the chart and look at American prosperity,

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Speaker 4: which is booming right now in the Caroline Hyde and

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Speaker 4: that Love Little World. We're going to talk to liz

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Speaker 4: Ayne Sanders. She is on fire about the betting, the

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Speaker 4: speculation in the market. Gabrielo Santos has one single sentence involved,

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Speaker 4: you have to say more. That'll be two of the

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Speaker 4: big focal points.

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Speaker 2: We'll look at Bloomberg's Tom Kane. Thank you very much,

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Speaker 2: and please don't miss the premiere of Bloomberg Money today

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Speaker 2: twelve pm New York time. Okay, welcome back to Bloomberg Tech.

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Speaker 2: We have to talk about financial markets. Then. That's that

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Speaker 2: one hundred down two and a half percent, the socks

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Speaker 2: down six percent. There's a lot of nervousness in the market.

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Speaker 2: The AI trade is getting hammered on all sides. I

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Speaker 2: would still observe that chip stocks are still up on

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Speaker 2: the week. For the s and P five hundred is

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Speaker 2: actually down on the week. That means that after nine

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Speaker 2: straight weeks of gains, we fall short of a historic tenth.

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Speaker 2: Economic data is playing a role here. Jobs data for

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Speaker 2: May came in hot. We're looking at wage data and

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Speaker 2: that's why you see that move in the US ten

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Speaker 2: year yield. There's also some digestion of broad COM's earnings

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Speaker 2: which were Wednesday night, but that outlook for the current

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Speaker 2: period for chip sales AI chip sales that did put

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Speaker 2: some nervousness into the market. And a SpaceX ip around

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Speaker 2: the corner. There's a lot on the Bloomberg terminal about

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Speaker 2: how that feeds into volatility. Speaking of today's big number one,

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Speaker 2: one hundred gigawatts that's the annual AI compute capacity. SpaceX

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Speaker 2: says it ultimately wants to deploy an orbit, a lofty

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Speaker 2: goal made no easier by the challenges of building and

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Speaker 2: maintaining networks of thousands, even millions of these specialized spacecraft

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Speaker 2: in a harsh, harsh orbital environment. What does a space

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Speaker 2: based data center look like? Bloomberg get a mock up

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Speaker 2: of how these craft would work. So this is the

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Speaker 2: body inside radiation tolerant AI chips right in here around them,

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Speaker 2: networking layer, power, thermal management, flight control systems. Then for communications,

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Speaker 2: the satellites could rely on laser links around here rather

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Speaker 2: than your traditional radio frequencies. That's kind of the swiggly

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Speaker 2: line beaming back down to Earth, handling that enormous flow

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Speaker 2: of data to power these systems absolutely massive solar array

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Speaker 2: panels as well as batteries to store that energy when

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Speaker 2: the Sun's not there, right when it's blocked. This up

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Speaker 2: here is the radiator. On Earth, data centers use air

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Speaker 2: or they use water for cooling. In space, radiators disperse

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Speaker 2: the heat into deep space, and they're going to need

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Speaker 2: to handle far more heat than today's current designs. Out

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Speaker 2: of this world. Yes, our next guest has already started

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Speaker 2: down this complicated road. Philip Johnson. It's the CEO and

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Speaker 2: co founder of star Cloud, which launched the first Nvidia

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Speaker 2: H one hundred chip aboard its satellite this past November.

437
00:23:43,840 --> 00:23:46,840
Speaker 2: Put myself on the spot here. You tell me what

438
00:23:46,960 --> 00:23:50,359
Speaker 2: was my explanation of the basics of orbit or data center? Like,

439
00:23:50,880 --> 00:23:51,600
Speaker 2: did we get it right?

440
00:23:52,400 --> 00:23:53,800
Speaker 13: Excellent, excellent explanation.

441
00:23:55,200 --> 00:23:59,120
Speaker 2: So for you this is real, right and H one

442
00:23:59,240 --> 00:24:04,520
Speaker 2: hundred inside the form factor of a satellite. Explain how

443
00:24:04,560 --> 00:24:08,399
Speaker 2: you got to that point, the engineering challenge, the deployment.

444
00:24:09,600 --> 00:24:12,080
Speaker 13: Yeah, for sure. So we as we rightly mentioned, we

445
00:24:12,280 --> 00:24:15,640
Speaker 13: launched the first in video H one hundred in November

446
00:24:15,680 --> 00:24:18,760
Speaker 13: last year. Actually, we launched a satellite which has five

447
00:24:18,840 --> 00:24:21,680
Speaker 13: GPUs three from in video three from arm, but the

448
00:24:21,800 --> 00:24:24,640
Speaker 13: H one hundred was the most part, the most important

449
00:24:24,640 --> 00:24:27,919
Speaker 13: and interesting one. And the way we got there is

450
00:24:27,960 --> 00:24:29,600
Speaker 13: we started the coming about two and a half years

451
00:24:29,600 --> 00:24:33,320
Speaker 13: ago and we had a very quick development cycle for

452
00:24:33,359 --> 00:24:35,720
Speaker 13: that first place craft. And I think on the screen

453
00:24:35,720 --> 00:24:37,439
Speaker 13: here actually you can see one of our early renders

454
00:24:37,480 --> 00:24:40,359
Speaker 13: of a very large four kilometer by four kilometer solo

455
00:24:40,359 --> 00:24:42,119
Speaker 13: panel with a five gig or what cluster in the

456
00:24:42,119 --> 00:24:45,640
Speaker 13: middle that would be for something like training. We're more

457
00:24:45,680 --> 00:24:48,480
Speaker 13: going to be doing things like right now, we're very

458
00:24:48,480 --> 00:24:51,479
Speaker 13: focused on these small inference notes, so we'll be launching

459
00:24:51,680 --> 00:24:53,600
Speaker 13: We just filed with the SEC for a constellation of

460
00:24:53,640 --> 00:24:56,639
Speaker 13: eighty eight thousand of them, which enables us to deploy

461
00:24:56,640 --> 00:25:00,240
Speaker 13: about twenty giga whatts of compute.

462
00:24:59,240 --> 00:25:01,480
Speaker 2: And Philip, you know we're zooming out now that this

463
00:25:01,600 --> 00:25:04,600
Speaker 2: is a case study of a five giga what data center.

464
00:25:05,160 --> 00:25:07,760
Speaker 2: Could you just reiterate what you said about the scale

465
00:25:07,760 --> 00:25:10,080
Speaker 2: of the solar arrays? What were the numbers on that?

466
00:25:11,000 --> 00:25:12,960
Speaker 13: Yeah, so this this soarray on the screen is about

467
00:25:12,960 --> 00:25:15,280
Speaker 13: four kilometers by four kilometers with about a one kilmeter

468
00:25:15,359 --> 00:25:19,320
Speaker 13: by four kilometer radiated down the back there. I would

469
00:25:19,359 --> 00:25:22,080
Speaker 13: say that renders from a couple of years ago, when

470
00:25:22,119 --> 00:25:26,720
Speaker 13: most AI workloads were for training, and it seemed more

471
00:25:26,960 --> 00:25:28,760
Speaker 13: you know, it seems we wanted to show that if

472
00:25:28,800 --> 00:25:30,440
Speaker 13: you wanted to do any kind of workload, you could,

473
00:25:30,440 --> 00:25:31,600
Speaker 13: and so we showed the hardest thing.

474
00:25:31,960 --> 00:25:32,080
Speaker 14: You know.

475
00:25:32,200 --> 00:25:34,600
Speaker 13: Actually, now it looks like ninety nine percent of all

476
00:25:34,640 --> 00:25:37,040
Speaker 13: AI workloads will very soon be inference, and so we

477
00:25:37,040 --> 00:25:38,919
Speaker 13: don't need to dock together a large structure like that.

478
00:25:39,000 --> 00:25:41,879
Speaker 13: And that's the reason we're now doing this distributed constellation

479
00:25:41,960 --> 00:25:43,320
Speaker 13: of much smaller satellites.

480
00:25:44,280 --> 00:25:47,040
Speaker 2: So, you know, Philip, I want to be audience honest

481
00:25:47,040 --> 00:25:49,000
Speaker 2: with the Bloomberg Tech audience. I invited you onto the

482
00:25:49,000 --> 00:25:54,359
Speaker 2: program because this is SpaceX's pitch, right a vast network

483
00:25:54,440 --> 00:25:58,800
Speaker 2: of orbital data centers. And I guess that we in

484
00:25:58,880 --> 00:26:02,280
Speaker 2: SpaceX's case, we're still waiting for the designs. And Elon

485
00:26:02,359 --> 00:26:04,159
Speaker 2: Musk has said that, you know, in the coming weeks

486
00:26:04,200 --> 00:26:07,600
Speaker 2: he will show his hand on that. You're a company

487
00:26:07,600 --> 00:26:11,080
Speaker 2: that is already doing this, yes, at right now, limited

488
00:26:11,119 --> 00:26:14,560
Speaker 2: and small scale. I just wondered if you'd account for that,

489
00:26:14,760 --> 00:26:17,240
Speaker 2: you know, the idea that SpaceX plans to do this,

490
00:26:17,880 --> 00:26:21,280
Speaker 2: and you know how star Cloud will fit in with that.

491
00:26:23,080 --> 00:26:23,640
Speaker 14: Yeah, for sure.

492
00:26:23,720 --> 00:26:27,359
Speaker 13: I mean, I think we really are talking about potentially

493
00:26:28,119 --> 00:26:31,080
Speaker 13: the largest market opportunity ever. We're talking about trillions dollars

494
00:26:31,080 --> 00:26:33,359
Speaker 13: per year of Catholics will be deployed in space, and

495
00:26:33,440 --> 00:26:36,040
Speaker 13: so everybody's going to have to have a solution for

496
00:26:36,359 --> 00:26:39,000
Speaker 13: space compute. Some of that will be using SpaceX, but

497
00:26:39,040 --> 00:26:40,600
Speaker 13: you know, certainly lots of other people are going to

498
00:26:41,240 --> 00:26:45,480
Speaker 13: want more independent clouds, and so that's the customer base

499
00:26:45,520 --> 00:26:49,200
Speaker 13: we're going after. Initially or so, we'll be serving workloads

500
00:26:49,240 --> 00:26:53,400
Speaker 13: to you know, other spacecraft, providing edge and cloud services

501
00:26:53,400 --> 00:26:54,919
Speaker 13: for other spacecrafts.

502
00:26:55,840 --> 00:26:59,560
Speaker 2: What about the deployment of your satellites. Are you a

503
00:26:59,600 --> 00:27:02,240
Speaker 2: customer of SpaceX on the ride share program or how

504
00:27:02,320 --> 00:27:02,920
Speaker 2: does that work?

505
00:27:04,000 --> 00:27:06,399
Speaker 13: Yes, we are a very happy customer of SpaceX on

506
00:27:06,720 --> 00:27:10,359
Speaker 13: both the right share program and potentially for dedicated TALC

507
00:27:10,400 --> 00:27:14,320
Speaker 13: and nine launchers. So I don't want to speak too soon,

508
00:27:14,359 --> 00:27:17,080
Speaker 13: but we should have some interesting things to say about

509
00:27:17,080 --> 00:27:17,360
Speaker 13: that soon.

510
00:27:18,160 --> 00:27:20,280
Speaker 2: You come back on the show when you're ready. Let's

511
00:27:20,359 --> 00:27:23,760
Speaker 2: end here. What's the ripple effect of this SpaceX IPO

512
00:27:23,880 --> 00:27:26,000
Speaker 2: going to be for you? Right? Is there going to

513
00:27:26,000 --> 00:27:27,200
Speaker 2: be trickle down?

514
00:27:28,359 --> 00:27:31,679
Speaker 13: Yeah, we're already seeing it. I mean, there's just enormous

515
00:27:31,880 --> 00:27:35,000
Speaker 13: interest in the space industry that wasn't there before. So

516
00:27:35,040 --> 00:27:38,480
Speaker 13: we raised actually the fastest Unicorn round coming out of

517
00:27:38,520 --> 00:27:41,960
Speaker 13: vicely ever, So in seventeen months we went from basically

518
00:27:42,080 --> 00:27:44,639
Speaker 13: zero to one point one billion dollar evaluation with one

519
00:27:44,680 --> 00:27:46,679
Speaker 13: hundred and seventy million dollar raise. I think a lot

520
00:27:46,720 --> 00:27:49,960
Speaker 13: of that is fueled by people now understanding space, realizing

521
00:27:50,000 --> 00:27:52,840
Speaker 13: there's a huge market opportunity there, and that's in large

522
00:27:52,840 --> 00:27:55,080
Speaker 13: part because of the SpaceX IPO.

523
00:27:56,520 --> 00:27:59,720
Speaker 2: Star Cloud CEO and co founder Philip Johnson, really grateful

524
00:27:59,760 --> 00:28:01,919
Speaker 2: for your explanation of the technology, but also to hear

525
00:28:01,960 --> 00:28:04,240
Speaker 2: about what you guys are working on. Comeback. Thank you.

526
00:28:04,600 --> 00:28:07,760
Speaker 2: The challenge of satellites reaching orbit has mainly been solved

527
00:28:08,200 --> 00:28:11,879
Speaker 2: through reusable launch vehicles i e. SpaceX, but one critical

528
00:28:11,960 --> 00:28:16,560
Speaker 2: bottleneck remains scaled satellite manufacturing. That's partly what SpaceX needs

529
00:28:16,600 --> 00:28:20,800
Speaker 2: cash for, not just SpaceX. Startup Apex has just raised

530
00:28:20,840 --> 00:28:22,560
Speaker 2: more than two hundred million dollars is it works to

531
00:28:22,600 --> 00:28:26,080
Speaker 2: increase production of its satellite platforms for commercial firms and

532
00:28:26,119 --> 00:28:30,280
Speaker 2: government work, including an involvement in President Trump's Golden Dome

533
00:28:30,640 --> 00:28:36,120
Speaker 2: missile defense shield. APEX CEO Ian Cinnamon joins us more Ian.

534
00:28:36,200 --> 00:28:38,880
Speaker 2: Congratulations on the round and thank you for being here.

535
00:28:38,960 --> 00:28:42,600
Speaker 2: It's interesting what we're doing. We're trying to encompass all

536
00:28:42,640 --> 00:28:48,240
Speaker 2: of this future economy, the bottleneck of satellite manufacturing. How

537
00:28:48,280 --> 00:28:51,000
Speaker 2: severe is that bottleneck? Try and quantify it.

538
00:28:50,960 --> 00:28:56,000
Speaker 15: For me, satellites have been around for seventy years. Satellites

539
00:28:56,040 --> 00:28:58,920
Speaker 15: themselves are not new. But to your point, SpaceX has

540
00:28:59,040 --> 00:29:02,600
Speaker 15: really solved the key part of the space ecosystem, which

541
00:29:02,600 --> 00:29:05,400
Speaker 15: is how do we deliver the satellites from Earth to space?

542
00:29:06,040 --> 00:29:08,040
Speaker 15: And to give you a sense of how severe the

543
00:29:08,080 --> 00:29:11,520
Speaker 15: new bottleneck is, which is actually the spacecraft production. The

544
00:29:11,640 --> 00:29:14,120
Speaker 15: facility that I'm standing in right now, we call it

545
00:29:14,200 --> 00:29:17,840
Speaker 15: APEX Factory one, is capable of producing over two hundred

546
00:29:17,920 --> 00:29:21,600
Speaker 15: satellites per year. That is more satellites than the US

547
00:29:21,720 --> 00:29:26,040
Speaker 15: government launched last year, and the demand is growing sky high.

548
00:29:26,200 --> 00:29:29,400
Speaker 15: So for US, we are seeing an enormous shift in

549
00:29:29,440 --> 00:29:34,040
Speaker 15: the market away from saying, let's launch one large, bespoke,

550
00:29:34,200 --> 00:29:38,360
Speaker 15: exquisite satellite and shifting to how do we launch constellations

551
00:29:38,480 --> 00:29:40,800
Speaker 15: fleets of satellites that are actually able to work together.

552
00:29:41,200 --> 00:29:43,560
Speaker 15: And that has led APEX to becoming really that go

553
00:29:43,600 --> 00:29:46,240
Speaker 15: to spacecraft platform company in the industry.

554
00:29:46,360 --> 00:29:50,200
Speaker 2: In the regular audience of Bit of a Tech has

555
00:29:50,320 --> 00:29:52,120
Speaker 2: kind of heard this story before. There's a company in

556
00:29:52,160 --> 00:29:57,040
Speaker 2: Bulgaria called Endurosat, which basically pitches itself as like the

557
00:29:57,080 --> 00:30:02,600
Speaker 2: TSMC of Bulgaria, bit similar contract manufacturer for satellites of

558
00:30:02,680 --> 00:30:06,040
Speaker 2: third parties. Is that an accurate way to also describe

559
00:30:06,040 --> 00:30:08,440
Speaker 2: what Apex is doing right now in the United States?

560
00:30:09,480 --> 00:30:12,000
Speaker 15: So, and Duriside is a great company, but fundamentally a

561
00:30:12,040 --> 00:30:13,600
Speaker 15: different business model from us.

562
00:30:13,760 --> 00:30:15,480
Speaker 14: So what we do is you could, if you want.

563
00:30:15,400 --> 00:30:17,600
Speaker 15: To make an analogy here right, I would actually say

564
00:30:17,600 --> 00:30:21,000
Speaker 15: we were the Ford of satellites. So we go ahead

565
00:30:21,080 --> 00:30:22,800
Speaker 15: and we say we're going to have a set number

566
00:30:22,840 --> 00:30:25,560
Speaker 15: of products, a small satellite, a medium one, a large one,

567
00:30:25,560 --> 00:30:28,600
Speaker 15: I think, a sedan and suv, a pickup truck. We

568
00:30:28,680 --> 00:30:32,120
Speaker 15: design that upfront, so we're not contract manufacturing someone else's design.

569
00:30:32,240 --> 00:30:35,080
Speaker 14: It's ours, and we go and produce them in the factory.

570
00:30:35,120 --> 00:30:37,960
Speaker 15: I'm standing in right now ahead of any customer demand.

571
00:30:38,160 --> 00:30:40,560
Speaker 15: So when somebody says I need one hundred satellites, or

572
00:30:40,600 --> 00:30:43,000
Speaker 15: someone like star Cloud wants to go launch a constellation,

573
00:30:43,480 --> 00:30:46,000
Speaker 15: We're able to rapidly supply them in a matter of

574
00:30:46,040 --> 00:30:49,600
Speaker 15: weeks or months instead of years with a standard off

575
00:30:49,640 --> 00:30:50,600
Speaker 15: the shelf design.

576
00:30:52,720 --> 00:30:55,400
Speaker 2: Let's talk about the money, two hundred million dollar raise.

577
00:30:55,760 --> 00:30:57,960
Speaker 2: You know, what is the priority for you to get

578
00:30:57,960 --> 00:31:00,880
Speaker 2: scaling here? What is the thing you're at on most quickly?

579
00:31:02,040 --> 00:31:05,280
Speaker 15: Space is expensive, right, Space is full of hardware, It's

580
00:31:05,320 --> 00:31:09,560
Speaker 15: full of you know, expensive launches, buying Falcon nines, buying other.

581
00:31:09,440 --> 00:31:10,720
Speaker 14: Launch vehicles and so on.

582
00:31:11,120 --> 00:31:14,080
Speaker 15: But APEX is in a very fortunate position where we're

583
00:31:14,120 --> 00:31:17,440
Speaker 15: actually able to operate as a real long term, sustainable business.

584
00:31:17,720 --> 00:31:20,520
Speaker 15: We have now raised three back to back two hundred

585
00:31:20,560 --> 00:31:23,400
Speaker 15: million dollar rounds over the last fourteen months, and we've

586
00:31:23,480 --> 00:31:25,720
Speaker 15: raised all of them from a position of strength where

587
00:31:25,720 --> 00:31:28,040
Speaker 15: we don't actually leave the capital, but it's simply a

588
00:31:28,080 --> 00:31:30,360
Speaker 15: way for us to speed up and actually go faster.

589
00:31:30,800 --> 00:31:34,760
Speaker 15: So for us, that means increased hiring, increased production, and

590
00:31:34,800 --> 00:31:38,080
Speaker 15: more deliveries to our customers for programs like we announced

591
00:31:38,120 --> 00:31:40,560
Speaker 15: earlier this week with our partnership with North of Grumman

592
00:31:40,640 --> 00:31:41,960
Speaker 15: on space based interceptors.

593
00:31:43,600 --> 00:31:48,240
Speaker 2: Next week, SpaceX will conduct the biggest IPO in history.

594
00:31:48,960 --> 00:31:51,800
Speaker 2: How is that going to impact you in the immediate

595
00:31:51,880 --> 00:31:53,200
Speaker 2: and and also in the future.

596
00:31:54,320 --> 00:31:56,080
Speaker 14: It's incredibly synergistic right.

597
00:31:56,160 --> 00:31:59,920
Speaker 15: SpaceX has really opened up the space ecosystem and really

598
00:32:00,120 --> 00:32:03,880
Speaker 15: paved the way for different customers, whether it be US government,

599
00:32:04,000 --> 00:32:08,640
Speaker 15: Allied nations, commercial customers to ship their architectures from large,

600
00:32:09,040 --> 00:32:12,920
Speaker 15: expensive bespoke satellites to fleet to proliferated systems.

601
00:32:13,280 --> 00:32:15,120
Speaker 14: We supply those proliferated systems.

602
00:32:15,160 --> 00:32:17,960
Speaker 15: So for US, SpaceX has been a massive tail wind

603
00:32:18,000 --> 00:32:20,880
Speaker 15: in our favor that's actually helping the industry move forward

604
00:32:20,920 --> 00:32:23,760
Speaker 15: at a faster pace. We are a great customer at SpaceX.

605
00:32:23,800 --> 00:32:26,280
Speaker 15: We have a wonderful relationship with them, and we are

606
00:32:26,400 --> 00:32:28,240
Speaker 15: very excited for their IPO next.

607
00:32:28,040 --> 00:32:32,400
Speaker 2: Week in Cinramon Apex CEO, the company raising its lates

608
00:32:32,440 --> 00:32:35,360
Speaker 2: two undred million dollar found around, Thank you very much. Indeed,

609
00:32:35,760 --> 00:32:37,280
Speaker 2: coming up on the program, we're going to hear from

610
00:32:37,320 --> 00:32:43,080
Speaker 2: Thinking Machines Lab CEO, the former Open AIICTO, Miramaradi on

611
00:32:43,080 --> 00:32:46,400
Speaker 2: what her new startup is trying to achieve. That conversation

612
00:32:46,440 --> 00:32:49,200
Speaker 2: from the Bloomberg Tech event coming up next, I have

613
00:32:49,240 --> 00:32:52,360
Speaker 2: to check back on markets. Look, this is where we're at.

614
00:32:52,360 --> 00:32:54,160
Speaker 2: There's a lot of red on that screen this Friday.

615
00:32:54,800 --> 00:32:58,960
Speaker 2: Tech is underperforming and that's probably putting it mildly or lightly,

616
00:32:59,160 --> 00:33:01,320
Speaker 2: and that's like one hundred down now almost three percent.

617
00:33:01,640 --> 00:33:03,960
Speaker 2: The Philadelphia Semi conduct To Index is down six and

618
00:33:04,000 --> 00:33:07,720
Speaker 2: a half percent, its biggest drops since October. There is

619
00:33:07,920 --> 00:33:09,720
Speaker 2: a lot of chatter out there that while we're kind

620
00:33:09,720 --> 00:33:12,640
Speaker 2: of due a correction in chips, the index up more

621
00:33:12,680 --> 00:33:15,160
Speaker 2: than eighty percent year today. Anyway, the S and P

622
00:33:15,280 --> 00:33:18,200
Speaker 2: five hundred is snapping as it stands a nine straight

623
00:33:18,240 --> 00:33:20,920
Speaker 2: week of gains run and yields a higher on the

624
00:33:20,920 --> 00:33:23,240
Speaker 2: ECO data. We're going to keep checking that. It's important.

625
00:33:23,280 --> 00:33:30,960
Speaker 2: This is Bloomberg Tech. Humans should stay in the loop

626
00:33:31,280 --> 00:33:35,719
Speaker 2: for AI, says Thinking Machines Lab CEO and founder Mira Muradi.

627
00:33:35,800 --> 00:33:38,120
Speaker 2: She spoke at the Bloomberg Tech event last night with

628
00:33:38,200 --> 00:33:39,320
Speaker 2: Bloomberg's Emily Chang.

629
00:33:39,360 --> 00:33:43,040
Speaker 16: Listen to this the most advanced system, so that the

630
00:33:43,120 --> 00:33:46,840
Speaker 16: most incredible tools for thought that humanity can ever have.

631
00:33:47,640 --> 00:33:50,640
Speaker 16: And so how can this change the way that we

632
00:33:51,160 --> 00:33:56,960
Speaker 16: think and where we're still thinking? But it's changing the

633
00:33:57,040 --> 00:34:01,840
Speaker 16: nature of thought what we're thinking about. And this part

634
00:34:02,000 --> 00:34:04,720
Speaker 16: I don't think is actually this part is familiar to us.

635
00:34:05,280 --> 00:34:09,319
Speaker 16: You know since the beginning of time, like technologies have

636
00:34:10,840 --> 00:34:17,359
Speaker 16: deep technologies have changed what we think about like language, writing, numerals.

637
00:34:17,560 --> 00:34:19,960
Speaker 16: I think this is the opportunity ahead of us, this

638
00:34:20,080 --> 00:34:26,879
Speaker 16: possibility to expand what we think about and have new

639
00:34:27,000 --> 00:34:31,560
Speaker 16: tangible things that we think about. And this requires but

640
00:34:31,680 --> 00:34:37,600
Speaker 16: this requires very intentional research work and product work in

641
00:34:37,640 --> 00:34:38,200
Speaker 16: this direction.

642
00:34:39,360 --> 00:34:42,439
Speaker 17: When I interviewed you was early twenty twenty three chat

643
00:34:42,480 --> 00:34:46,600
Speaker 17: GBT had just changed everything. You were CTO of open AI.

644
00:34:47,000 --> 00:34:49,040
Speaker 17: Few thoughts I talked to you said, you basically ran

645
00:34:49,080 --> 00:34:55,240
Speaker 17: the place. Say when you left and founded thinking machines.

646
00:34:55,520 --> 00:34:58,920
Speaker 17: Were you running towards something or away from something?

647
00:35:01,600 --> 00:35:06,520
Speaker 16: I most definitely was running towards something once I figured

648
00:35:06,520 --> 00:35:14,400
Speaker 16: out what that thing was. But I had an incredible

649
00:35:14,800 --> 00:35:17,399
Speaker 16: I mean, I had an incredible experience at Opening Eye.

650
00:35:17,480 --> 00:35:21,400
Speaker 16: I was so incredibly lucky to work with some of

651
00:35:21,440 --> 00:35:26,160
Speaker 16: the most dedicated and most talented people in the world.

652
00:35:26,280 --> 00:35:30,040
Speaker 16: And that's that's incredibly special and I'm very grateful for

653
00:35:30,120 --> 00:35:35,520
Speaker 16: that experience. And you know, eventually I had my own

654
00:35:36,200 --> 00:35:41,680
Speaker 16: view of a very strong view of how I think

655
00:35:41,840 --> 00:35:46,080
Speaker 16: this technology ought to be developed. And it's very rare

656
00:35:46,320 --> 00:35:52,000
Speaker 16: to start something from scratch once you have developed such

657
00:35:52,000 --> 00:35:55,680
Speaker 16: a strong perspective on it, and that's that's a rare

658
00:35:55,760 --> 00:36:00,080
Speaker 16: privilege to have. And I think having a company like

659
00:36:00,160 --> 00:36:06,120
Speaker 16: Thinking Machines gives us an opportunity to focus on where

660
00:36:06,120 --> 00:36:09,719
Speaker 16: we have the highest conviction and build an orient the

661
00:36:09,840 --> 00:36:11,920
Speaker 16: entire company around that conviction.

662
00:36:13,360 --> 00:36:16,799
Speaker 2: That was Thinking Machines Labs CEO Mirror Murti, along with

663
00:36:16,960 --> 00:36:22,080
Speaker 2: Emily Chang. Software has dominated a bench capital for years,

664
00:36:22,120 --> 00:36:26,360
Speaker 2: but as AI moves beyond the digital world and into manufacturing, robotics,

665
00:36:26,400 --> 00:36:29,960
Speaker 2: industrial systems, investors are betting that that next trillion dollar

666
00:36:30,000 --> 00:36:33,600
Speaker 2: opportunity is wrapping software into the physical world. There's also

667
00:36:33,719 --> 00:36:36,839
Speaker 2: the small matter of SpaceX's IPO and the ripple effects

668
00:36:36,840 --> 00:36:40,480
Speaker 2: for builders in and outside of Earth's atmosphere. Joining us

669
00:36:40,520 --> 00:36:43,560
Speaker 2: now is Nina Shaddin, partner at Index Ventures, an early

670
00:36:43,600 --> 00:36:47,520
Speaker 2: backer of companies including anthropic but physical intelligence service Titan,

671
00:36:48,040 --> 00:36:50,400
Speaker 2: and I'm so glad you're here. With the thesis, I

672
00:36:50,400 --> 00:36:52,400
Speaker 2: would just point out there's a lot going on in

673
00:36:52,400 --> 00:36:58,520
Speaker 2: public markets right now, but actually, you know, private markets

674
00:36:59,600 --> 00:37:03,080
Speaker 2: are active in physical AI. You know, I was tracking

675
00:37:03,080 --> 00:37:06,319
Speaker 2: the data pitchbook like twenty nineteen to last year. It's

676
00:37:06,400 --> 00:37:07,759
Speaker 2: going up. What are you.

677
00:37:07,680 --> 00:37:10,080
Speaker 11: Saying, Yeah, Well, thanks for having me ed, It's great

678
00:37:10,080 --> 00:37:12,800
Speaker 11: to be here. And of course Index and other folks

679
00:37:12,800 --> 00:37:14,560
Speaker 11: in the valley have been spending a lot of time

680
00:37:14,640 --> 00:37:17,839
Speaker 11: investing in the fundamentals of AI, which have been really

681
00:37:17,840 --> 00:37:20,480
Speaker 11: based on LLLMS. So at Index we've done everything from

682
00:37:20,840 --> 00:37:24,560
Speaker 11: foundation model of anthropic to inference layer fireworks, and then

683
00:37:24,560 --> 00:37:27,040
Speaker 11: a bunch of application companies. But if you take a

684
00:37:27,080 --> 00:37:30,600
Speaker 11: step back, actually most of the disruption in software has

685
00:37:30,640 --> 00:37:33,920
Speaker 11: really been around the knowledge worker, and that's where a

686
00:37:33,920 --> 00:37:35,680
Speaker 11: lot of the investment has gone. But there's a big

687
00:37:35,719 --> 00:37:37,759
Speaker 11: white space, which is what does AI do in the

688
00:37:37,760 --> 00:37:39,600
Speaker 11: physical world, And that's where we've been spending a lot

689
00:37:39,640 --> 00:37:39,919
Speaker 11: of time.

690
00:37:40,000 --> 00:37:42,840
Speaker 2: Yeah, you know, for a really long time, in many cycles,

691
00:37:43,320 --> 00:37:46,239
Speaker 2: software is eating the world, and now people say, well,

692
00:37:46,239 --> 00:37:50,400
Speaker 2: hardware is eating the world. You're basically saying both, you know,

693
00:37:51,400 --> 00:37:52,280
Speaker 2: they are joint.

694
00:37:52,800 --> 00:37:54,880
Speaker 11: Yeah, well, I think if you look at it, there's

695
00:37:55,080 --> 00:38:00,000
Speaker 11: all of these functions electrical engineers, mechanical engineers, aerospace engineers,

696
00:38:00,480 --> 00:38:03,840
Speaker 11: and for decades they have been using the same software stack.

697
00:38:04,200 --> 00:38:05,960
Speaker 11: And a lot of these incumbents were built in the

698
00:38:06,080 --> 00:38:09,120
Speaker 11: nineteen eighties, and there's this huge race, not just the

699
00:38:09,200 --> 00:38:11,480
Speaker 11: race to get to space, but the race to put

700
00:38:11,560 --> 00:38:14,680
Speaker 11: better things in the physical world. And unlike some of

701
00:38:14,680 --> 00:38:17,040
Speaker 11: the things in the digital world. If you get one

702
00:38:17,280 --> 00:38:20,759
Speaker 11: character wrong in these code, something could actually blow up,

703
00:38:20,800 --> 00:38:23,000
Speaker 11: like a nuclear reactor or a rocket. And so we're

704
00:38:23,040 --> 00:38:25,160
Speaker 11: really interested in companies that are trying to solve this

705
00:38:25,200 --> 00:38:27,760
Speaker 11: deeply technical, very domain specific.

706
00:38:27,320 --> 00:38:31,759
Speaker 2: Problem, most multimodal, trying to ground the model in real

707
00:38:31,840 --> 00:38:35,360
Speaker 2: world physics. You know, in the case of robotics, you know,

708
00:38:35,600 --> 00:38:41,680
Speaker 2: in my study of software, the vcs that come on

709
00:38:41,680 --> 00:38:44,680
Speaker 2: the shows would say, we have conviction about this because

710
00:38:44,680 --> 00:38:48,120
Speaker 2: there's a public market proxy, an example we can point

711
00:38:48,160 --> 00:38:51,120
Speaker 2: to on how we value this company or the market

712
00:38:51,120 --> 00:38:55,040
Speaker 2: opportunity for it. In humanoid robotics and other physical eye

713
00:38:55,080 --> 00:38:58,279
Speaker 2: domains much harder to do. Is SpaceX going to be

714
00:38:58,320 --> 00:38:59,600
Speaker 2: a good proxy for that?

715
00:38:59,719 --> 00:39:01,799
Speaker 11: Well, I think that's why all eyes are on the

716
00:39:01,840 --> 00:39:04,799
Speaker 11: SpaceX IPO, and I think that it's really opened the

717
00:39:04,880 --> 00:39:07,880
Speaker 11: eyes to folks of just how compelling this market is

718
00:39:08,560 --> 00:39:10,960
Speaker 11: and how lucrative it could be, and that's drawing a

719
00:39:11,000 --> 00:39:13,560
Speaker 11: lot of dollars into the private sector in companies that

720
00:39:13,600 --> 00:39:14,960
Speaker 11: are doing similar things.

721
00:39:15,640 --> 00:39:18,360
Speaker 2: Yesterday I was on stage with Tray Stevens from Founder's Fund.

722
00:39:19,080 --> 00:39:22,319
Speaker 2: He made a very simple outline of a thesis on

723
00:39:22,360 --> 00:39:25,000
Speaker 2: why SpaceX is going to work, just we'll play the SoundBite.

724
00:39:25,920 --> 00:39:29,120
Speaker 18: We have a SpaceX investment in almost every fund, both

725
00:39:29,160 --> 00:39:31,680
Speaker 18: across our venture funds and our growth funds, so there's

726
00:39:31,719 --> 00:39:34,080
Speaker 18: a ton of exposure for LPs that were with us

727
00:39:34,080 --> 00:39:36,040
Speaker 18: since the very beginning, and even the ones that joined

728
00:39:36,120 --> 00:39:39,840
Speaker 18: us very recently. I think, you know, the core lesson

729
00:39:39,880 --> 00:39:43,359
Speaker 18: that everyone has learned from this experience has never bet

730
00:39:43,360 --> 00:39:44,000
Speaker 18: against Eelon.

731
00:39:44,239 --> 00:39:48,879
Speaker 2: It's just a bad idea, pretty simple. Don't best bet

732
00:39:48,880 --> 00:39:51,719
Speaker 2: against Eelon Musk. It's a bad idea. I don't know,

733
00:39:51,800 --> 00:39:54,080
Speaker 2: like how deeply you read the s one and you

734
00:39:54,160 --> 00:39:57,560
Speaker 2: were and the world that is envisioned for the future

735
00:39:57,840 --> 00:40:00,440
Speaker 2: or out of this world, and how how much that

736
00:40:00,480 --> 00:40:02,320
Speaker 2: gave you conviction in your own thesis.

737
00:40:02,400 --> 00:40:04,920
Speaker 11: Yeah. Well, I think the way that we have played

738
00:40:04,920 --> 00:40:07,960
Speaker 11: it is that Elon is a huge talent attractor. And

739
00:40:08,000 --> 00:40:10,800
Speaker 11: so actually two of the most recent investments I've made

740
00:40:10,960 --> 00:40:13,960
Speaker 11: are both in former SpaceX founders. So one is Scott

741
00:40:14,000 --> 00:40:17,640
Speaker 11: Morton from Revel. He spent ten years at SpaceX, that's right,

742
00:40:17,680 --> 00:40:21,239
Speaker 11: doing launch control, and his role there really exposed him

743
00:40:21,280 --> 00:40:23,080
Speaker 11: to how big of a need there is for better

744
00:40:23,120 --> 00:40:25,920
Speaker 11: software for hardware, which is why he's now built Revel,

745
00:40:26,200 --> 00:40:29,279
Speaker 11: and then Sergei Nesarenko, who's the founder of Quilter, spent

746
00:40:29,400 --> 00:40:31,799
Speaker 11: six years at SpaceX where he was really frustrated that

747
00:40:31,840 --> 00:40:34,160
Speaker 11: he had to wait for a human to manually lay

748
00:40:34,160 --> 00:40:37,400
Speaker 11: out a printed circuit board. Of course, printed circuit boards

749
00:40:37,440 --> 00:40:40,120
Speaker 11: are really important to everything that's happening in ships, so

750
00:40:40,160 --> 00:40:43,759
Speaker 11: he started Quilter to use AI to autonomously layout PCBs.

751
00:40:43,840 --> 00:40:45,960
Speaker 2: We just have thirty seconds, you know. I think that

752
00:40:46,080 --> 00:40:49,720
Speaker 2: we make the point that whatever happens with the IPO,

753
00:40:50,080 --> 00:40:53,120
Speaker 2: it will cause some volatility in public markets one way

754
00:40:53,160 --> 00:40:56,240
Speaker 2: or another. But from your the bench capital industry standpoint,

755
00:40:56,239 --> 00:40:58,520
Speaker 2: I won't ask you to speak specific on anathropic How

756
00:40:58,600 --> 00:41:01,480
Speaker 2: much do do you guys need some big IPOs to

757
00:41:01,560 --> 00:41:02,640
Speaker 2: get the world moving well.

758
00:41:02,680 --> 00:41:04,840
Speaker 11: I think VC is always rooting for the IPO window

759
00:41:04,920 --> 00:41:07,880
Speaker 11: to be open. It creates more options for our founders

760
00:41:07,880 --> 00:41:10,959
Speaker 11: for capital and liquidity, and it's really exciting to see

761
00:41:11,320 --> 00:41:13,000
Speaker 11: the ones that are lined up today, and so I

762
00:41:13,000 --> 00:41:14,800
Speaker 11: think folks will be watching them very closely.

763
00:41:15,320 --> 00:41:18,040
Speaker 2: Nino Shadren of Index Mension Partners here in SF really

764
00:41:18,080 --> 00:41:23,160
Speaker 2: appreciate the thesis on software into physical AI and hardware

765
00:41:23,320 --> 00:41:26,760
Speaker 2: that does it for this edition of Bloomberg Tech Crazy Week.

766
00:41:26,880 --> 00:41:28,040
Speaker 2: This is what markets look like.

767
00:41:28,400 --> 00:41:28,640
Speaker 3: Look.

768
00:41:28,719 --> 00:41:31,439
Speaker 2: Tech is down. Part of it is Eco data. Part

769
00:41:31,480 --> 00:41:34,160
Speaker 2: of it was coming recap the show on the podcast

770
00:41:34,200 --> 00:41:37,439
Speaker 2: you Know Where to find it. Really top conversations from

771
00:41:37,480 --> 00:41:41,560
Speaker 2: the last twenty four hours. Iheartspotify and on Apple. This

772
00:41:41,600 --> 00:41:42,399
Speaker 2: is Bloomberg Tech