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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 sent frances Go.

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Speaker 2: This is Bloomberg Tech coming up in video strikes a

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Speaker 2: deal to invest two billion dollars in chip design software

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Speaker 2: makers Synopsis, one of its own suppliers.

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Speaker 3: Plus crypto concerns continue amid a wide ranging sell off

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Speaker 3: that has Strategy and stable coin Tether in the crosshairs.

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Speaker 2: An open Ai is taking an ownership stake in five holdings,

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Speaker 2: adding to a growing list of circular deals involving the

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Speaker 2: chat GBT maker and its backers.

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Speaker 3: Oh, we're gonna be talking circular deals today, that m

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Speaker 3: video deal with open Ai. But now we look at

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Speaker 3: the border market, said, and we're under pressure once again.

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Speaker 3: We're only off by three percent on the overall Nasdaq

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Speaker 3: one hundred, but remember it had a brutal month of November,

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Speaker 3: the worst.

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Speaker 4: Set off that we've seen since March.

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Speaker 3: And more broadly, we are starting to reassess these ongoing

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Speaker 3: narratives of AI, momentum trades, crypto and the like. We're

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Speaker 3: seeing Bitcoin off by another seven percent. We trade at

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Speaker 3: eighty four six hundred and seventy eight. We're going to

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Speaker 3: deep dive into that. But if you're going to deep

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Speaker 3: dive on some single.

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Speaker 2: Names right now, yeah, are we going to be talking

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Speaker 2: about circular financing? Maybe in Video shares higher, Synoptis shares

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Speaker 2: higher but off their session highs. In video investing two

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Speaker 2: billion dollars buying into Synopsis at four hundred and fourteen

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Speaker 2: dollars seventy nine cents a share, you can see we're

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Speaker 2: now pretty far beyond that. What will happen is Synopsis

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Speaker 2: will take in videos GPUs and its software library Kuda

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Speaker 2: X and basically integrated into the number one tool for

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Speaker 2: designing and validating chips. But on a webcast just now,

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Speaker 2: Jensen one and Video CEO saying there is no requirement

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Speaker 2: that Synopsis buy GPUs as part of the deal, and

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Speaker 2: actually any chip maker that already does business with Synopsis

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Speaker 2: can do so freely. There's no exclusivity here. Still, that

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Speaker 2: was the reaction.

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Speaker 3: Circular financing it is, And let's dig into the rationale

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Speaker 3: a little bit more deeply. Now, Man Singh is here

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Speaker 3: with us covers semiconductors of call some Bloomberg intelligence, Mandy,

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Speaker 3: what is in it? To solidify a partnership there was

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Speaker 3: already very strong.

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Speaker 4: Why take an equity state, do you think?

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Speaker 5: Well, at this point, I think in Vidia is strengthening

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Speaker 5: its relationships with all its suppliers, whether it's TSMC and

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Speaker 5: the purchase commitments it has with TSMC or for that matter,

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Speaker 5: with Synopsis which provides the EDA software. And look, the

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Speaker 5: ACIC threat, which wasn't really that big of a thread before,

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Speaker 5: has really kind of come to the forefront now with

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Speaker 5: Google TPUs and you know, Broadcom has talked about how

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Speaker 5: it has got three or more customers that are taping

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Speaker 5: out their new chips. So from that perspective, you know,

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Speaker 5: the suppliers of Nvidia come to the focus. And on

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Speaker 5: the DA side, it's really an oligopoly with you know, Canaan,

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Speaker 5: SYNOPSI and Semens. So from that perspective, it's not surprising

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Speaker 5: to see, you know, Nvidia really investing in one of

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Speaker 5: their suppliers.

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Speaker 2: I'd like to dig into that last part a little

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Speaker 2: bit more. Men deep I mean listening to Jensen Wong

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Speaker 2: and the Synopsis CEA on the call, Basically they're explaining

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Speaker 2: that engineering tools still largely run on CPU based systems, right,

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Speaker 2: and EDA Electronic design automation is kind of the next

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Speaker 2: frontier right for improvement with AI. Is that something you

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Speaker 2: see as being real here?

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Speaker 5: I mean, look, I mean you can talk about how

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Speaker 5: you know, developers may benefit from, you know, a coding

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Speaker 5: agent type of functionality when it comes to EDA software tools.

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Speaker 5: But to my mind, you know, you don't need a

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Speaker 5: big training cluster for EDIA software. That's not the I

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Speaker 5: think the sense of what they're doing here. It's more

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Speaker 5: to do with just strengthening the relationship with their suppliers.

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Speaker 5: And some of these could a players, I mean, who knows,

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Speaker 5: And that's where I think having STIGs just give them

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Speaker 5: some skin in the game.

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Speaker 2: And to be clear, Jensen one saying in a webcast

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Speaker 2: that's ongoing, there is no requirement that Synopsis uses the

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Speaker 2: funds from the investment to buying video GPUs Man deep

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Speaker 2: seeing the Bloomberg Intelligence.

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Speaker 6: Appreciate you kicking the show off with that.

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Speaker 2: Let's get more on what's happening with financial markets right

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Speaker 2: now for Ernis and coders. Senior analysts from City Index

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Speaker 2: joined us. It is the first day of December, and

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Speaker 2: actually a couple of hours ago we started things looking

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Speaker 2: pretty bleak and video is turned a corner which has

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Speaker 2: kind of changed a little bit the tone, but basically

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Speaker 2: tech lower, crypto lower, and we started December and risk

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Speaker 2: off mode.

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Speaker 7: Why yeah, I mean, we had a really solid rally

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Speaker 7: last week, you know, on those expectations that the Federal

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Speaker 7: Reserve will be cutting interest rates in December. But as

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Speaker 7: we start off this new week, there's been a turnaround

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Speaker 7: in that sentiment. I think the catalyst for that for

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Speaker 7: me came from Japan. We heard Bank of Japan Governor

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Speaker 7: Huaida adopting a more hawkish tone, really ramping up sort

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Speaker 7: of expectations for a December rate hike. Now what does

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Speaker 7: this mean and why is it important? Basically, there are

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Speaker 7: concerns now that we might see the unwinding of the

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Speaker 7: carry trade. So that's when we sort of have institutions

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Speaker 7: investors borrowing in the low interest yen in order to

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Speaker 7: invest in higher yielding, riskier assets. Now, if we're seeing

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Speaker 7: the Bank of Japan raise interest rates, we've got yields

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Speaker 7: in Japan tenure yields at seventeen year high. There is

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Speaker 7: this concern that we're going to start see the repatriation

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Speaker 7: of those funds back to Japan. Now, this is important

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Speaker 7: because liquidity changes and those sort of sectors of the

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Speaker 7: market such as crypto, such as tech, which are really

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Speaker 7: sensitive to liquidity, or where we're seeing that sort of

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Speaker 7: risk off.

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Speaker 2: Come through December historically strong, particularly for technology, right and

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Speaker 2: when I was on vacation last week, what a week

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Speaker 2: to pick and we had the Thanksgiving holiday. Circular financing

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Speaker 2: was a factor in video in particular weighing and the

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Speaker 2: index level. You just heard the reporting about how the

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Speaker 2: Synopsis in Video deal is not circular financing. To the

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Speaker 2: mind of the leaders of those companies, do you buy it?

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Speaker 2: And how big a factor is that in this market

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Speaker 2: right now?

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Speaker 7: Really good question. I mean this is front and central

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Speaker 7: I think in a lot of investors' minds at the moment,

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Speaker 7: particularly as soon as we hear the name and Video

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Speaker 7: and Vida's been investing in firms that you know are

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Speaker 7: or will be major customers of its products. So you

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Speaker 7: know that has raised concerns within sort of the markets,

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Speaker 7: and we've seen that play out in previous weeks where

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Speaker 7: and Video really has come under a lot of pressure.

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Speaker 7: It does feel like that that is sort of starting

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Speaker 7: to turn a little bit of a corner. Obviously, those

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Speaker 7: comments today, I think will help a little bit to

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Speaker 7: ease those concerns. But at the end of the day,

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Speaker 7: there is this sort of worries surrounding circular financing, and

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Speaker 7: they're not going to disappear overnight. And that's because circular

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Speaker 7: financing makes an ecosystem fragile. You know, it just takes

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Speaker 7: one disappointment for the whole loop to fall apart. So

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Speaker 7: I think it is right that the market does question

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Speaker 7: question whether this circular financing could be a problem.

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Speaker 3: Meanwhile, Fion, you could think that these are companies that

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Speaker 3: are strategically thinking of how they can ride extra upside

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Speaker 3: should the ai wins really come to bear. We're going

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Speaker 3: to be digging into how open aiye is backing a

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Speaker 3: fund of its own VC partner that is looking at

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Speaker 3: integrating its technology into portfolio companies, into service companies. For example,

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Speaker 3: it wins not only by adoption of its technology, but

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Speaker 3: on seeing some equity upside, Fion, It's what do you

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Speaker 3: need to see to vindicate that the productivity gains are there?

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Speaker 7: Yeah, this is why it's so difficult because I think

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Speaker 7: sometimes or often, if you're in that bubble and it's

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Speaker 7: falling apart, you won't often realize when it's actually too late.

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Speaker 7: So you know, I think the numbers that we been

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Speaker 7: seeing from Nvidia have been encouraging. You know, the latest

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Speaker 7: the reports earnings report was extremely positive. There weren't really

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Speaker 7: any faults there. I think this is just a nervousness

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Speaker 7: that we're seeing.

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Speaker 4: But I think at the end of.

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Speaker 7: The day, we know that AI technology is solid, we

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Speaker 7: know that it exists, it's going to increase productivity. There

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Speaker 7: are obviously concerns over monetization, circular funding, revenue, lagging investment,

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Speaker 7: but I think, you know, longer term, this technology does

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Speaker 7: have a broad use, and I think that's why over

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Speaker 7: the longer term this trade is still one that has upside.

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Speaker 7: You know, if we just think about the horizontally at automation,

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Speaker 7: R and D customer service, vertically different sectors, and an

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Speaker 7: infrastructure level as well. So this is a very broad,

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Speaker 7: broad technology which which sort of helps ease those concerns

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Speaker 7: over the bubble fears.

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Speaker 4: I guess what about crypto?

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Speaker 7: Yeah, crypto, I mean what every Monday I feel I

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Speaker 7: wake up and something else is happening in crypto. You know,

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Speaker 7: we saw that rejection at that ninety two there just

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Speaker 7: above that ninety two thousand level, which was a pretty

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Speaker 7: key level. And I think again this is that unwinding

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Speaker 7: or fears over the unwinding of the yen carry trade.

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Speaker 7: Crypto is very sensitive to liquidity, and that fear that

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Speaker 7: there might be a slight reduction in liquidity if that

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Speaker 7: winding of the carry trade happens is what I see

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Speaker 7: pulling on crypto. Institutional demand is still very weak. You know,

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Speaker 7: we saw November ETFs they saw outflows of three point

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Speaker 7: four eight billion, so huge outflows, I think, second largest,

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Speaker 7: second worst month that we've seen so far. So that

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Speaker 7: really does need to turn around before we can expect

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Speaker 7: to see any sort of recovery or solid recovery in

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Speaker 7: the bitcoin price or crypto prices.

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Speaker 3: Talking about the broad application of AI and perhaps the

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Speaker 3: less broad application in the here and now of crypto,

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Speaker 3: Fiona Sinkotta, it's great to have some time with the

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Speaker 3: senior analyst over at City Index. Meanwhile, let's talk about

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Speaker 3: data centers for a moment, but when it comes to

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Speaker 3: financial prices, because data centers do support the CME, for example,

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Speaker 3: one of the world's largest derivatives exchanges. But we know

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Speaker 3: that now the data center behind the CME has bolstered

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Speaker 3: its own backup cooling capacity after overheating last Friday. CME's

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Speaker 3: markets were out for more than ten hours after a

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Speaker 3: failure and cooling system at the facility run by privately

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Speaker 3: owned cyrus Wan.

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Speaker 2: Okay, coming up, we're going to dig more into crypto

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Speaker 2: markets and what's behind this week's long seller.

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Speaker 6: That's next, This is Bloomberg Tech.

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Speaker 2: Cryptocurrency is fell again today, Bitcoin dipping below eighty four

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Speaker 2: thousand US dollars per token at one point, Ether dropping

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Speaker 2: more than seven percent to below twenty eight hundred dollars,

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Speaker 2: joining us now in the crypto markets weeks long sell off.

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Speaker 2: Bloomberg's senior Digital finance editor, Anna Irrera, we're writing on

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Speaker 2: the Bloomberg terminal about how we've started December in risk

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Speaker 2: off mode, but actually trade is in the crypto space

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Speaker 2: embracing for even bigger moves lower.

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Speaker 6: What are the factors behind that?

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Speaker 8: So, as you know, cryptos not immune to big price swings,

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Speaker 8: and you know we've had these before, we're not a

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Speaker 8: stranger to it. But what's different this time? It seems

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Speaker 8: that there's you know, there's not been any big crypto

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Speaker 8: event like you know, fraud or something that's very dramatic

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Speaker 8: that's typical to crypto. It seems like cryptos may be

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Speaker 8: starting to mature in a way, and it's starting to

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Speaker 8: follow more the macro environment and starting to behave a

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Speaker 8: bit more like risk assets. So one of the factors

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Speaker 8: that today was sort of the Bank of Japan, which

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Speaker 8: you were talking about before, and how that trade is

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Speaker 8: unwinding and people are concerned that there will be less

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Speaker 8: liquidity and of course than less money to spend on

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Speaker 8: risk your assets like crypto.

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Speaker 3: There are some idiosyncratic issues at play here though, Anna,

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Speaker 3: one of them being perhaps standing a pause, putting out

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Speaker 3: a bit of a risk warning on tether, the stable

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Speaker 3: coin most widely used one, worrying about its collateralization.

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Speaker 4: But then we looked at what's happening in strategy.

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Speaker 3: Of course, a big purchaser a bitcoin that shook the

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Speaker 3: market by potentially saying it could even sell off that

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Speaker 3: bitcoin if.

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Speaker 4: Really forced to do so.

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Speaker 3: But now they try and come in and temper those

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Speaker 3: fears when news of well, really a reserve fund. Why

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Speaker 3: isn't it working, Why isn't pushing strategy back higher again?

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Speaker 8: I think, I think again, perhaps everything in crypto tends

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Speaker 8: to move together. Still. You know, we had it with

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Speaker 8: Coinbase for a while when it was the only listed company.

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Speaker 8: You tend to react to bigcoin even before the ETFs, right,

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Speaker 8: it was kind of a gauge on the broader crypto

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Speaker 8: market away investors could go in. So I think perhaps

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Speaker 8: the mood is just risk off in general, and so

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Speaker 8: no matter what strategy is saying, investors are still a

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Speaker 8: bit concerned, a bit cautious. And again, you know we

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Speaker 8: mentioned you mentioned before sort of the big withdrawals and

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Speaker 8: redemptions on the ETFs. Again, until that changes and there

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Speaker 8: is more investment in the ATFS, it's hard to see

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Speaker 8: how you know the mark the crypto market will pick

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Speaker 8: up again. But you know, crypto's also very strange to predict,

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Speaker 8: so you never know.

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Speaker 6: Tomorrow might be another day in very.

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Speaker 3: Different well, said Bloomberg's Ana Arera. We thank you very much.

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Speaker 3: Let's keep the discussion going. Jilak Jovin Futro is with

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Speaker 3: us managing partner at Future Perfect Ventures. It's an early

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Speaker 3: stage firm. We focus on blockchain technology, crypto assets and more.

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Speaker 4: For you, you're about the builders.

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Speaker 3: And the companies that are trying to use the underlying technology, Jilac.

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Speaker 3: But just paint the macro picture right now, what does

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Speaker 3: bring institutional retail money back into the ETFs or to

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Speaker 3: the crypto as they stand?

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Speaker 9: Sure, I mean your previous guests have mentioned the Bank

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Speaker 9: of Japan news which was significant for bitcoin as well

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Speaker 9: as the broader crypto markets. And we've seen crypto since

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Speaker 9: the ETFs have been issued, and we've seen more institutional

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Speaker 9: involvement move more in line with other risk assets. Before,

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Speaker 9: you know, we saw a decoupling of the price behavior,

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Speaker 9: but now you know, we're looking at the macro outlook

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Speaker 9: and it's challenging for risk assets right now. We are

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Speaker 9: also have the added challenge of the bitcoin treasuries that

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Speaker 9: have been built up, and if we start to see selling,

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Speaker 9: as you know Strategy mentioned they may do, that's going

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Speaker 9: to create more downward pressure. However, you know, we still

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Speaker 9: believe that there's great global demand for bitcoin specifically, and

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Speaker 9: we're going to continue to see ETFs and new products

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Speaker 9: develop on bitcoin. That's just going to happen in the

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Speaker 9: next year or so.

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Speaker 3: I mean, you're all about the ecosystem, blockshoined, blockchain, dot com, bicco.

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Speaker 4: Also thinking of course, how you've been in moon pay.

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Speaker 3: But when you see warries around stable coins or when

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Speaker 3: there's a broader macro downward pressure, does that impact those

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Speaker 3: that are trying to build or do they shake it off?

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Speaker 9: Well, it certainly impacts in the short term, but a

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Speaker 9: lot of these entrepreneurs have been building for years towards

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Speaker 9: a rewiring of the financial system that is faster, cheaper,

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Speaker 9: offers access to different types of financial assets, and we

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Speaker 9: have seen over the years that that trend is not

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Speaker 9: going away. There's going to be short term blips as

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Speaker 9: we're seeing now, but stable coins are going to be

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Speaker 9: an important part of the new financial order, and we're

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Speaker 9: seeing companies like Stripe and others start to adopt access

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Speaker 9: to stable coins.

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Speaker 2: That is the state of play that you've just summarized.

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Speaker 2: Everyone is bullish on the long term about the role

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Speaker 2: of these technologies in the global financial system, and in

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Speaker 2: the near term we're in risk off mode because of

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Speaker 2: worries about interest rates and the direction for interest rates,

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Speaker 2: and that in the moment is moving markets.

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Speaker 6: How long does that carry on for?

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Speaker 9: Well, you know, there's a great amount of uncertainty. I

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Speaker 9: think through the end of the year and we're you know,

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Speaker 9: i'd say, I'm looking towards the early next year.

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Speaker 6: To have that certainty back.

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Speaker 9: We have several companies in our portfolio lined up to

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Speaker 9: go public on NASDAC. We're probably going to see, you know,

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Speaker 9: some of the short term delay on that until market

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Speaker 9: conditions improve, but I do believe that they will improve

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Speaker 9: early next year.

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Speaker 6: This can't help myself.

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Speaker 2: What is your twenty twenty six year end bitcoin prediction?

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Speaker 9: Well, I don't believe we're going to see massive kind

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Speaker 9: of price movements. I've been invested in the sector, as

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Speaker 9: you know, since twenty thirteen, specifically in bitcoin, and we

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Speaker 9: see massive you know upswings, down swings. What we're seeing now.

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Speaker 9: I think there'll be some tight trading. I can see

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Speaker 9: it go you know, up to two hundred. I'm not

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Speaker 9: one of those that's going to predict that it's going

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Speaker 9: to go to a million.

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Speaker 2: The reason I asked that I was trying to marry

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Speaker 2: those two points, the long term bullishness on the underlying

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Speaker 2: technology in the short term price volatility. I do find

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Speaker 2: it interesting reading the Bloomberg Terminal that the data set

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Speaker 2: that is giving most concern is lack of inflows into

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Speaker 2: the ETFs, which you've discussed what would change the psychology

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Speaker 2: of people to push inflows in a positive direction.

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Speaker 9: Yeah. I think it goes back to certainty. We want

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Speaker 9: to see where the interest rates are headed on a

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Speaker 9: global basis. You know, the October sell off in the

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Speaker 9: crypto sector was due to the tariff uncertainty around China.

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Speaker 9: We also saw people taking profits when bitcoin went up

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Speaker 9: to one hundred and twenty six thousand, So in the

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Speaker 9: span of three months or less than three months, we've

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Speaker 9: seen over a thirty percent drop. That being said, I

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Speaker 9: don't see a downward spiral because there are going to

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Speaker 9: be people who want to get access to the asset.

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Speaker 4: I mean, we just came off of.

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Speaker 9: Thanksgiving in the US and dinner table conversation was all

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Speaker 9: around crypto. Of course AI also, but crypto was a

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Speaker 9: big part of it. So there's still a lot of

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Speaker 9: retail interest and you know, I think they get concerned

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Speaker 9: when you just see these big price drops. But we'll

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Speaker 9: start to see that reverse.

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Speaker 2: J lak Jobum plus sure from Future Perfect Ventures. Great

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Speaker 2: to have you back on the show. Thank you, Caroy.

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Speaker 3: Yeah, now it's time for talking tech and first up,

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Speaker 3: well we're looking at elon Musk expanding snail Brook, Texas,

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Speaker 3: the company town tied to the boring company. Correspondence between

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Speaker 3: muscles and executives and local officials show plans for new housing,

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Speaker 3: a science center, and a gym.

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Speaker 4: It comes as the must tunnel.

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Speaker 3: Building company negotiates with a county over tax breaks and

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Speaker 3: environmental concerns. Plus, Australia will become the first democracy to

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Speaker 3: ban its youth from popular services like TikTok and Instagram.

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Speaker 4: That a law requiring platforms to block.

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Speaker 3: Under sixteens takes effect to some of the tenth companies

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Speaker 3: don't comply, they could be facing fines as high as

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Speaker 3: three thirty two million dollars, and SoftBank founder Masayoshi Son

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Speaker 3: says the company would have held onto its Nvidia shares

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Speaker 3: if it had unlimited.

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Speaker 4: Cash for its next AI investments.

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Speaker 3: Soft Bank has doubled down on AI, backing the Stargate

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Speaker 3: Data Center project, buying chip designer Ampere, and it plans

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Speaker 3: to invest more in open Ai by the end of

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Speaker 3: the year ed open Ai Well, It's agreed to take

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Speaker 3: an ownership stake in Thrive Holdings, an investment vehicle of

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Speaker 3: its own VC backer Thrive Capital. It's all about accelerating

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Speaker 3: enterprise AI adoption, we're told Opening I says it plans

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Speaker 3: to boost speed, accuracy, cost efficiency among the companies within

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Speaker 3: Thrive Holdings.

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Speaker 4: Newberg Seth Hman joins us now and.

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Speaker 3: The latest that some will say again circular AI deals.

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Speaker 4: How is this going to be a win win for

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Speaker 4: open Ai.

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Speaker 10: Yeah, it does have the appearance of circularity. We'll figure

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Speaker 10: out more of the details soon. But OPENINGI is going

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Speaker 10: to help the businesses their Thrive Holdings invest in by

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Speaker 10: embegging its own teams within those businesses to help them

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Speaker 10: speed up and become more efficient in AI deployment. And

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Speaker 10: of course, Thrive Holdings is part of Thrive Capital, which

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Speaker 10: played a really pivotal role in backing Opening I over

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Speaker 10: the last couple of years in multiple rounds and making

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Speaker 10: it what it goes today.

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Speaker 6: So mutual.

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Speaker 2: So I think we're both hearing in sort of pushback

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Speaker 2: to whether this is or is not circular financing that

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Speaker 2: there is no exchange of money or funds between open

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Speaker 2: AI and the holdingspit, which makes it confusing. Basically, Therefore,

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Speaker 2: it kind of is like a venture capitalist who's an operator.

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Speaker 2: It sounds like open AI comes in and goes, we'll

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Speaker 2: help you of all these things for zero dollars.

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Speaker 10: Yeah, I think we're trying to figure out what the

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Speaker 10: precise deal terms are here. But regardless, this is opening

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Speaker 10: I that was primarily packed by Thrive Capital over multiple

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Speaker 10: investment rounds, now having a stake and investing interest in

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Speaker 10: seeing this other arm of Thrive, you know, Thrive and

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Speaker 10: its businesses do well. What dollars changed hands, we don't

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Speaker 10: know yet, but it fits into the larger mold here

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Speaker 10: of a lot of these non traditional arrangements between vcs,

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Speaker 10: chip makers, cloud providers, and the up and coming AI

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Speaker 10: developers who are all kind of bound tightly together. And

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Speaker 10: I think the industry would say, well, this ensures that

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Speaker 10: they all kind of rise up, they all help each other,

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Speaker 10: and they all do well. But I think those those

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Speaker 10: interwoven agreements are also a real cause for concern right now.

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Speaker 3: It's also about how VC itself is pushing boundaries and

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Speaker 3: new types of vehicles. Josh Kushner, who found a Thrive

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Speaker 3: Capital all the way back in twenty ten, is now

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Speaker 3: only recently in April, I think, founded this Thrive Holdings,

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Speaker 3: and it's about buying stakes or even growing accounting companies.

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Speaker 10: Now, yeah, I think you know, Thrive at one time

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Speaker 10: was more into smaller investments, and now they're kind of

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Speaker 10: doing the private equity thing. You are either launching or

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Speaker 10: acquiring bigger firms, and they see AI as a real

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Speaker 10: opportunity to expand their portfolio and the way that maybe

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Speaker 10: the traditional VC arm would not have done. And Open

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Speaker 10: AI seems very eager and willing to come along for

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Speaker 10: that ride.

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Speaker 2: So Bloomberg seth Figgerman out in New York City on

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Speaker 2: the Thrive Holdings Open Aideal. Thank you, Welcome back to

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Speaker 2: Bloomberg Tech. We have a deal of sorts this Monday,

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Speaker 2: and that's in video and sting two billion dollars into Synopsis,

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Speaker 2: a leading name in electronic design automation software or EDA,

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Speaker 2: basically the software tools that help you lay out a

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Speaker 2: chips design in video, buying in at four hundred and

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Speaker 2: forteen dollars seventy nine cents a share, Synopsis currently trading

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Speaker 2: at four hundred and thirty two dollars fifty three cents.

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Speaker 2: It's an engineering agreement in video tech integrated into the

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Speaker 2: Snopsis platform. But what it does not include is any

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Speaker 2: mandatory requirement for Synopsis to buy in video GPUs. Here

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Speaker 2: is in Vidia CEO Jensen One speaking in.

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Speaker 11: Arago, there's no purchasing relationship between the investment and anything else.

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Speaker 11: Synapsis is already a customer of videos, and in the future,

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Speaker 11: of course, as we move into the world of accelerated

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Speaker 11: computing and AI computing, is much larger customer of video.

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Speaker 11: I recognize that none of this is exclusive. Synopsis has

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Speaker 11: a lot of chip partnerships, are going to continue to

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Speaker 11: nurture and continue to advance, and Vidia has a lot

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Speaker 11: of partnerships with Cadence and Siemens and to sew that

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Speaker 11: we're going to continue to nurture invents.

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Speaker 3: From aideals to holiday shopping deals. Consumer spending is showing

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Speaker 3: some resilience, even if discounts this year haven't been as

438
00:23:08,480 --> 00:23:10,840
Speaker 3: steep as in the past. So what are we seeing

439
00:23:10,880 --> 00:23:13,760
Speaker 3: this Cyber Monday? And in e commerce sales more broadly,

440
00:23:14,040 --> 00:23:16,639
Speaker 3: remerg Expenser, Sopa joins us. Now the stats please for

441
00:23:16,680 --> 00:23:17,520
Speaker 3: this Cyber Monday.

442
00:23:20,119 --> 00:23:23,159
Speaker 12: Yes, still very early. You know, Cyber Monday is one

443
00:23:23,160 --> 00:23:26,400
Speaker 12: of those days where people really load up their carts

444
00:23:26,600 --> 00:23:30,840
Speaker 12: all weekend and then sometimes wait for the final minute,

445
00:23:31,119 --> 00:23:33,480
Speaker 12: you know, to push the buy button. So the activity

446
00:23:33,560 --> 00:23:35,560
Speaker 12: is really going to pick up this evening, you know,

447
00:23:36,720 --> 00:23:40,840
Speaker 12: before before midnight, and the deals the deals close. But

448
00:23:40,960 --> 00:23:43,440
Speaker 12: so far, we are seeing that patience is paying off

449
00:23:43,760 --> 00:23:47,439
Speaker 12: for people who waited for Cyber Monday discounts averaging I

450
00:23:47,440 --> 00:23:51,200
Speaker 12: think thirty one percent according to Salesforce on Cyber Monday,

451
00:23:51,880 --> 00:23:54,439
Speaker 12: and that compares to about twenty seven twenty eight percent

452
00:23:54,840 --> 00:23:57,760
Speaker 12: on Black Friday, So people who waited a little longer

453
00:23:57,760 --> 00:24:00,359
Speaker 12: are getting a little bit better deal spender.

454
00:24:00,400 --> 00:24:02,000
Speaker 2: I think it's there's a lot of value for the

455
00:24:02,000 --> 00:24:04,919
Speaker 2: bloom Bell Tech audience to hear from you which data

456
00:24:04,960 --> 00:24:08,480
Speaker 2: sets you're tracking, which platforms we play most attention to,

457
00:24:09,200 --> 00:24:11,080
Speaker 2: and throughout the last few days, if there's any kind

458
00:24:11,080 --> 00:24:14,480
Speaker 2: of bigger picture consumer behavior trends that you've spotted.

459
00:24:16,119 --> 00:24:20,919
Speaker 12: Yeah, that's a great question. And actually there's a lot

460
00:24:20,960 --> 00:24:23,359
Speaker 12: of talk about consumers being cautious. I think even data

461
00:24:23,359 --> 00:24:26,480
Speaker 12: providers are being a little more cautious because we are

462
00:24:26,520 --> 00:24:29,040
Speaker 12: getting the data a little more slowly. This is our

463
00:24:29,080 --> 00:24:33,639
Speaker 12: first year, our first holiday shopping season with you know,

464
00:24:34,119 --> 00:24:37,359
Speaker 12: since the tariffs were announced, we've and also we had

465
00:24:37,359 --> 00:24:39,480
Speaker 12: the big government shutdowns, so there's a lot of mixed

466
00:24:39,480 --> 00:24:42,359
Speaker 12: signals going in. There's still pretty rosy projections, things like

467
00:24:42,400 --> 00:24:47,240
Speaker 12: the NRF and Adobe predicting. You know, another record year

468
00:24:48,160 --> 00:24:51,760
Speaker 12: for the holiday. But you know that a lot of

469
00:24:51,800 --> 00:24:54,280
Speaker 12: those predictions were made a little bit earlier, and even

470
00:24:54,320 --> 00:24:58,560
Speaker 12: before the government shutdown was lagging. So I'm curious to

471
00:24:58,560 --> 00:25:00,840
Speaker 12: see how it plays out. There's a lot of mixed

472
00:25:00,840 --> 00:25:03,919
Speaker 12: signals going into this holiday shopping season, even more so

473
00:25:04,000 --> 00:25:05,439
Speaker 12: than a typical one.

474
00:25:05,560 --> 00:25:06,040
Speaker 6: Bloomberg.

475
00:25:06,080 --> 00:25:09,560
Speaker 2: Spencer Sopa, thank you very much. Okay, what's next. Let's

476
00:25:09,600 --> 00:25:12,879
Speaker 2: stay on the topic of holiday shopping because despite some

477
00:25:12,920 --> 00:25:16,920
Speaker 2: of that economic uncertainty, Spencer was talking about disappointment over

478
00:25:16,960 --> 00:25:20,120
Speaker 2: the size of discounts, US shoppers did open their wallets

479
00:25:20,160 --> 00:25:22,439
Speaker 2: on Black Friday. So what does that mean for the

480
00:25:22,480 --> 00:25:26,400
Speaker 2: month ahead. Harley Fingalstem, president of global commerce company Shopify,

481
00:25:27,000 --> 00:25:30,320
Speaker 2: is here with some good data, some good insights. I

482
00:25:30,320 --> 00:25:34,720
Speaker 2: mean where we ended it there with Spencer, Harley, the forecasters,

483
00:25:35,520 --> 00:25:39,040
Speaker 2: the predictors say that this will be another record shopping

484
00:25:39,080 --> 00:25:42,080
Speaker 2: season in aggregate the data available to you.

485
00:25:42,359 --> 00:25:44,320
Speaker 6: What are you seeing? What are the trends?

486
00:25:44,960 --> 00:25:47,240
Speaker 13: Yeah, you know, I heard what Spencer said there. I mean, look,

487
00:25:47,359 --> 00:25:50,720
Speaker 13: Friday was another record breaking Black Friday weekend. We saw

488
00:25:50,720 --> 00:25:53,959
Speaker 13: about six point two billion dollars in sales for Black Friday.

489
00:25:54,080 --> 00:25:57,360
Speaker 13: That's about twenty five percent from last year. We saw

490
00:25:57,480 --> 00:26:00,720
Speaker 13: peak sales of five point one million dollars per minute

491
00:26:00,720 --> 00:26:03,239
Speaker 13: on Friday that happened around noon on Friday. That's up

492
00:26:03,240 --> 00:26:06,120
Speaker 13: from four point six million last year as well. And

493
00:26:06,400 --> 00:26:09,159
Speaker 13: if you pull up actually anyone can see this BFCM

494
00:26:09,359 --> 00:26:12,440
Speaker 13: dot shop, you can actually see global commerce happening right

495
00:26:12,520 --> 00:26:15,520
Speaker 13: now in real time. We are currently seeing about two

496
00:26:15,600 --> 00:26:19,159
Speaker 13: point five million dollars per sales and sales per minute,

497
00:26:19,640 --> 00:26:23,280
Speaker 13: about twenty five thousand orders per minute, and we've seen

498
00:26:23,440 --> 00:26:27,080
Speaker 13: just shy of seventy million unique shoppers buy from Shopify stores.

499
00:26:27,119 --> 00:26:29,520
Speaker 13: So it's certainly shipping up to be another great day.

500
00:26:29,560 --> 00:26:31,439
Speaker 13: And happy to go into some of the merchants and

501
00:26:31,440 --> 00:26:32,600
Speaker 13: some of the trends that we're seeing.

502
00:26:32,880 --> 00:26:34,760
Speaker 6: Crush Oapiva, Hollie.

503
00:26:34,760 --> 00:26:36,480
Speaker 2: What I'd like to do is actually take a sort

504
00:26:36,520 --> 00:26:39,520
Speaker 2: of geographic breakdown. Yeah, because in the limited data that

505
00:26:39,720 --> 00:26:41,879
Speaker 2: we went through, his Spenser, I think we're recognizing different

506
00:26:41,960 --> 00:26:46,080
Speaker 2: speeds of great sarticula Us versus Europe.

507
00:26:46,119 --> 00:26:46,920
Speaker 6: Could we start there?

508
00:26:47,520 --> 00:26:47,720
Speaker 9: Yeah?

509
00:26:47,760 --> 00:26:49,359
Speaker 13: Sure, I mean, look, if you look at top selling

510
00:26:49,400 --> 00:26:52,119
Speaker 13: countries across the world where shopflay seals. We have millions

511
00:26:52,119 --> 00:26:54,520
Speaker 13: of stores in places like the US. We power twelve

512
00:26:54,600 --> 00:26:56,760
Speaker 13: percent of all e commerce, so we have a really

513
00:26:56,760 --> 00:27:00,320
Speaker 13: great view of things. US, UK, Australia, Germany and Canada

514
00:27:00,520 --> 00:27:01,280
Speaker 13: are the top five.

515
00:27:01,480 --> 00:27:02,280
Speaker 6: Look at top.

516
00:27:02,160 --> 00:27:05,400
Speaker 13: Three selling cities, you see La New York and London.

517
00:27:06,040 --> 00:27:09,160
Speaker 13: In US alone, La New York and San Francisco were

518
00:27:09,200 --> 00:27:11,480
Speaker 13: the top three. The other thing that we also saw

519
00:27:11,560 --> 00:27:15,600
Speaker 13: is cross border, but seventeen percent of orders were cross

520
00:27:15,600 --> 00:27:18,720
Speaker 13: border orders, so shipped to a different country as well.

521
00:27:19,000 --> 00:27:21,679
Speaker 13: And then in terms of top trending merchants that we saw,

522
00:27:21,840 --> 00:27:23,919
Speaker 13: we saw Hatch which is sort of this it's be

523
00:27:23,960 --> 00:27:26,280
Speaker 13: called the Restore three, which is this phone free alarm

524
00:27:26,359 --> 00:27:29,520
Speaker 13: for kind of morning routines. Crunch Labs has the sort

525
00:27:29,520 --> 00:27:33,159
Speaker 13: of kids build it yourself monthly toy subscription, brook linnen

526
00:27:33,840 --> 00:27:36,119
Speaker 13: on their super plush robe, and then Bass has their

527
00:27:36,119 --> 00:27:39,160
Speaker 13: weekend or travel bag as well. So we're seeing across

528
00:27:39,240 --> 00:27:41,600
Speaker 13: a bunch of different verticals do really well. But a

529
00:27:41,600 --> 00:27:43,840
Speaker 13: big thing actually that we're seeing now is last year

530
00:27:43,880 --> 00:27:46,639
Speaker 13: seemed to be a lot of talk about getting outside

531
00:27:46,680 --> 00:27:49,280
Speaker 13: gifts were about the products being sold. We're about you know,

532
00:27:49,320 --> 00:27:49,720
Speaker 13: things like.

533
00:27:49,680 --> 00:27:51,760
Speaker 6: Skiing or hiking, or camping.

534
00:27:52,119 --> 00:27:54,280
Speaker 13: This year seems to be that home is really you know,

535
00:27:54,320 --> 00:27:56,960
Speaker 13: the winner here where people are buying things for their home,

536
00:27:57,080 --> 00:28:00,760
Speaker 13: whether it's kitchens except for the kitchen, or puzzles or blankets.

537
00:28:01,200 --> 00:28:04,200
Speaker 13: But generally consumers are buying from brands they love, and

538
00:28:04,280 --> 00:28:06,000
Speaker 13: we're fortunate those brands are on Shopify.

539
00:28:06,520 --> 00:28:10,640
Speaker 3: What does that cross border stat that's seventeen percent signal

540
00:28:10,720 --> 00:28:11,000
Speaker 3: to you?

541
00:28:11,200 --> 00:28:11,920
Speaker 4: Is that growing?

542
00:28:12,119 --> 00:28:14,680
Speaker 3: I mean, how does that stand up to last year

543
00:28:14,720 --> 00:28:16,359
Speaker 3: when perhaps Taris went such a headwind.

544
00:28:17,280 --> 00:28:19,720
Speaker 13: Yeah, I mean it's what I think it means is

545
00:28:19,760 --> 00:28:22,560
Speaker 13: that consumers generally are somewhat geographically agnostic.

546
00:28:22,600 --> 00:28:24,080
Speaker 6: They want to buy from their favorite brands.

547
00:28:24,119 --> 00:28:25,760
Speaker 13: You know, we heard from Jim Shark they had their

548
00:28:25,760 --> 00:28:29,360
Speaker 13: biggest Black Friday sale ever online ag one Athletic Greens

549
00:28:29,480 --> 00:28:32,240
Speaker 13: was a fifty percent brunt workwhere got an order every

550
00:28:32,240 --> 00:28:35,320
Speaker 13: five seconds. So if you think about what where consumers

551
00:28:35,320 --> 00:28:37,720
Speaker 13: are purchasing, they're buying from brands they love, the Voris,

552
00:28:37,960 --> 00:28:41,480
Speaker 13: the figs, the alo Yogas, the on runnings of the world,

553
00:28:41,840 --> 00:28:43,960
Speaker 13: and I think they're agnostic to where those are coming from.

554
00:28:44,120 --> 00:28:45,840
Speaker 13: The Other thing that we're seeing, which is really interesting

555
00:28:45,920 --> 00:28:48,120
Speaker 13: is that this really wasn't just about discounts. This year,

556
00:28:48,240 --> 00:28:51,560
Speaker 13: people shop brands, not channels. We're almost worrying living in

557
00:28:51,600 --> 00:28:54,000
Speaker 13: sort of a post world channel where you know, the

558
00:28:54,160 --> 00:28:57,200
Speaker 13: consumers bounced from a TikTok video to an AI agent,

559
00:28:57,280 --> 00:28:59,520
Speaker 13: to a website to a store. Even in video games

560
00:28:59,560 --> 00:29:02,800
Speaker 13: like Roadblo were Shopify powers commerce. The brands that really

561
00:29:02,840 --> 00:29:06,600
Speaker 13: won the weekend made the experience feel the same everywhere

562
00:29:06,640 --> 00:29:09,400
Speaker 13: they were, and that continues today.

563
00:29:10,280 --> 00:29:13,320
Speaker 3: Let's talk holly about the inevitable AI question.

564
00:29:13,520 --> 00:29:14,480
Speaker 4: Because you are.

565
00:29:14,440 --> 00:29:18,360
Speaker 3: Like the global commerce company, you're helping people be digitally native.

566
00:29:18,400 --> 00:29:20,880
Speaker 3: How are they interacting therefore in this world of brands

567
00:29:21,360 --> 00:29:23,800
Speaker 3: with various AI agents, whether they their own or whether

568
00:29:23,840 --> 00:29:25,160
Speaker 3: they're external third party.

569
00:29:25,960 --> 00:29:27,959
Speaker 13: Yeah, I mean, look, it's still very very early, but

570
00:29:28,000 --> 00:29:30,840
Speaker 13: since January we've actually seen AI driven traffic on to

571
00:29:30,920 --> 00:29:34,000
Speaker 13: Shopify stores up about six x. But we've been preparing

572
00:29:34,400 --> 00:29:36,600
Speaker 13: for this for years. We've been laying the rails for

573
00:29:36,840 --> 00:29:39,720
Speaker 13: agentik where already we've already anounced deals with Perplexity and

574
00:29:39,760 --> 00:29:43,200
Speaker 13: more recently with chat GBT, where brands on Shopify can

575
00:29:43,240 --> 00:29:47,000
Speaker 13: sell directly in those conversations, no links, no redirects, just

576
00:29:47,000 --> 00:29:50,080
Speaker 13: sort of this very seamless experience directly in the chat,

577
00:29:50,120 --> 00:29:51,400
Speaker 13: and so I think we're going to see a lot

578
00:29:51,440 --> 00:29:55,120
Speaker 13: more of that. Now what permutation eventually wins, We're not

579
00:29:55,160 --> 00:29:57,080
Speaker 13: sure yet, but we'll be ready for it. But the

580
00:29:57,080 --> 00:29:59,240
Speaker 13: other side of it is really interesting, which is that brands,

581
00:29:59,400 --> 00:30:02,080
Speaker 13: the merchants and selves are using AI to support them

582
00:30:02,080 --> 00:30:04,760
Speaker 13: on the biggest shopping season weekend of the year. So

583
00:30:05,040 --> 00:30:07,560
Speaker 13: we have something called Sidekick, which is the AI assistant

584
00:30:07,600 --> 00:30:10,680
Speaker 13: built into Shopify, which knows everything about merchant stores, knows

585
00:30:10,680 --> 00:30:13,480
Speaker 13: everything about Shopify, and we've seen over one hundred million

586
00:30:13,520 --> 00:30:16,960
Speaker 13: conversations with Sidekick. So merchants are able to do things

587
00:30:17,000 --> 00:30:20,800
Speaker 13: like you know, design, copyright market create these incredible analytics

588
00:30:20,880 --> 00:30:23,080
Speaker 13: dashboards where they can decide where to spend in a

589
00:30:23,120 --> 00:30:26,240
Speaker 13: much more sophisticated way. And so it's giving smaller merchants

590
00:30:26,280 --> 00:30:28,560
Speaker 13: a real leg up against the bigger guys because this

591
00:30:28,640 --> 00:30:30,280
Speaker 13: AI empowers them.

592
00:30:30,920 --> 00:30:32,640
Speaker 2: I want to go back to tariffs, Holly, but with

593
00:30:32,720 --> 00:30:35,400
Speaker 2: a slightly different way of asking the question. Sure, you

594
00:30:35,440 --> 00:30:38,200
Speaker 2: can look at overall spend, but the question we're asking

595
00:30:38,200 --> 00:30:40,640
Speaker 2: ourselves in the bloom Bag news room is our consumer

596
00:30:40,720 --> 00:30:44,320
Speaker 2: is still spending and getting less in return. So what

597
00:30:44,400 --> 00:30:47,080
Speaker 2: are the data sets that you're tracking the evidence that

598
00:30:47,200 --> 00:30:51,000
Speaker 2: impact of tariffs either through a diminished spend or same

599
00:30:51,080 --> 00:30:53,560
Speaker 2: level of spend, but you just get less for it.

600
00:30:54,400 --> 00:30:57,240
Speaker 13: We actually measure you know, both consumer confidence but also

601
00:30:57,480 --> 00:31:00,640
Speaker 13: consumer's appetite to spend at checkout. So far, I mean

602
00:31:00,680 --> 00:31:02,960
Speaker 13: as we speak right now, ed two point five million

603
00:31:03,000 --> 00:31:05,239
Speaker 13: dollars are going through check with every single minute. So

604
00:31:05,320 --> 00:31:08,239
Speaker 13: we've operated through all types of macro environments and our

605
00:31:08,280 --> 00:31:10,280
Speaker 13: focus is always the same, which is support merchants.

606
00:31:10,520 --> 00:31:11,480
Speaker 6: And I think what we're.

607
00:31:11,320 --> 00:31:13,440
Speaker 13: Seeing though is that, you know, whether during the pandemic,

608
00:31:13,480 --> 00:31:16,480
Speaker 13: for example, we introduce tools like you know, contact list

609
00:31:16,760 --> 00:31:19,680
Speaker 13: delivery back in twenty twenty. Well, now we're creating tools

610
00:31:19,680 --> 00:31:23,080
Speaker 13: to help merchants navigate cross border tariffs, navigate sort of

611
00:31:23,280 --> 00:31:25,840
Speaker 13: some global uncertainty, to make sure they have everything they need.

612
00:31:26,080 --> 00:31:29,160
Speaker 13: So we're not complacent about this. We're tracking macro conditions,

613
00:31:29,280 --> 00:31:33,600
Speaker 13: but so far Shopify merchants consistently outperform the overall market,

614
00:31:33,680 --> 00:31:35,959
Speaker 13: and we're seeing that that they're they're you know, they

615
00:31:36,000 --> 00:31:37,000
Speaker 13: have a lot of buyers right now.

616
00:31:37,560 --> 00:31:39,560
Speaker 3: I think we'll see great to have some time with

617
00:31:39,600 --> 00:31:40,720
Speaker 3: the president of Shopify.

618
00:31:41,160 --> 00:31:42,680
Speaker 4: What a weekend it's been for you. I'm sure.

619
00:31:43,200 --> 00:31:46,840
Speaker 3: Now coming up, we speak with Runway CEO Christoval Venez

620
00:31:46,960 --> 00:31:50,160
Speaker 3: Valenzuela and as of course, the company's launching its latest

621
00:31:50,200 --> 00:31:52,520
Speaker 3: text and video model is already top of the leaderboards

622
00:31:52,640 --> 00:31:53,600
Speaker 3: as a blue bag tech.

623
00:32:03,960 --> 00:32:06,120
Speaker 4: Run Way and it's announced.

624
00:32:05,600 --> 00:32:09,040
Speaker 3: The launch of its latest AI model, Gen four point five,

625
00:32:09,360 --> 00:32:12,040
Speaker 3: which aims to deliver the best in the world text

626
00:32:12,080 --> 00:32:14,520
Speaker 3: to video. Joining us out to break down why it's that.

627
00:32:14,560 --> 00:32:17,960
Speaker 3: It's Christoph Valnezuela, Runway CEO. Last time I checked on

628
00:32:17,960 --> 00:32:20,240
Speaker 3: the leaderboards, some of them already have you at the

629
00:32:20,320 --> 00:32:23,640
Speaker 3: number one spot, Cristoval, What is it that yours offers

630
00:32:23,720 --> 00:32:26,200
Speaker 3: versus Sora two versus the latest Google offering?

631
00:32:27,200 --> 00:32:30,040
Speaker 14: Yeah, that's true. So we just released a Runway Gen

632
00:32:30,120 --> 00:32:33,080
Speaker 14: four point five, our latest video model, and it tops

633
00:32:33,400 --> 00:32:36,720
Speaker 14: the charts in terms of performance and vetchmarks across all

634
00:32:36,760 --> 00:32:39,480
Speaker 14: other models, which is kind of like a big deal

635
00:32:39,520 --> 00:32:44,400
Speaker 14: within research. Is the first time a company has lead

636
00:32:44,720 --> 00:32:47,960
Speaker 14: led the leaderboards in this company not being basically a

637
00:32:48,000 --> 00:32:52,280
Speaker 14: large research lab. It's a model that surpasses pretty much

638
00:32:52,280 --> 00:32:56,360
Speaker 14: all other models with incredible consistency, really good realistic results

639
00:32:56,360 --> 00:33:00,280
Speaker 14: and just like across the board, amazing creative results. Really

640
00:33:00,320 --> 00:33:02,320
Speaker 14: excited to get this modell owed and have people use

641
00:33:02,360 --> 00:33:02,760
Speaker 14: it now.

642
00:33:02,760 --> 00:33:05,600
Speaker 3: You had it out there on the leader boards with

643
00:33:05,680 --> 00:33:09,560
Speaker 3: a pseudonym before it was launched in public, Crystabal, and

644
00:33:09,600 --> 00:33:14,480
Speaker 3: you had it called David. Is that a Goliath David construct?

645
00:33:14,560 --> 00:33:18,440
Speaker 3: How are you competing against these vast generous AI companies.

646
00:33:19,360 --> 00:33:21,000
Speaker 14: Yeah, it was a little bit of a play with

647
00:33:21,280 --> 00:33:23,480
Speaker 14: that with David and Goliath. I think we've we've managed

648
00:33:23,520 --> 00:33:26,880
Speaker 14: to outcompete the largest research labs by being very focused.

649
00:33:27,080 --> 00:33:30,280
Speaker 14: I think it's the era of both research and efficiency.

650
00:33:30,880 --> 00:33:33,800
Speaker 14: And if you're able to maintain the focus as a team,

651
00:33:34,600 --> 00:33:37,280
Speaker 14: you're able to deliver kind of ground vacant results. And

652
00:33:37,280 --> 00:33:39,080
Speaker 14: we're proving this. This is the first time I think

653
00:33:39,080 --> 00:33:41,920
Speaker 14: we're again anyone has kind of topped the leader boards,

654
00:33:42,000 --> 00:33:46,200
Speaker 14: not being a large, well funded research lab. And I

655
00:33:46,240 --> 00:33:48,360
Speaker 14: think part of it is really like the team, and

656
00:33:48,400 --> 00:33:50,160
Speaker 14: it's really also the vision. We've been working on this

657
00:33:50,160 --> 00:33:52,920
Speaker 14: for almost seven years. We start working on video models

658
00:33:52,920 --> 00:33:54,760
Speaker 14: when the world and even like the other boards to

659
00:33:54,840 --> 00:33:57,280
Speaker 14: start with, and I think eventually you build some sort

660
00:33:57,320 --> 00:34:00,200
Speaker 14: of intuition and really good like momentum as to to

661
00:34:00,240 --> 00:34:02,640
Speaker 14: improve these models over time. And look this is still

662
00:34:02,680 --> 00:34:04,920
Speaker 14: like the worst the models will ever be, and so

663
00:34:04,960 --> 00:34:06,480
Speaker 14: we have a bunch of more releases coming up that

664
00:34:06,520 --> 00:34:09,879
Speaker 14: I think will further improve both pre training and post training,

665
00:34:09,920 --> 00:34:11,040
Speaker 14: and so we're very excited for that.

666
00:34:12,800 --> 00:34:14,080
Speaker 6: Chris Belle, you're not that small.

667
00:34:14,320 --> 00:34:17,360
Speaker 2: You raised three hundred million dollars in April at the

668
00:34:17,440 --> 00:34:20,760
Speaker 2: foremost four billion dollar or three point three billion dollar valuation.

669
00:34:21,160 --> 00:34:23,279
Speaker 2: I do think there's some value in you explaining what

670
00:34:23,360 --> 00:34:26,640
Speaker 2: was different this time around in the training of Gen

671
00:34:26,719 --> 00:34:29,239
Speaker 2: four point five and the data set. What is it

672
00:34:29,280 --> 00:34:32,839
Speaker 2: that you did differently and that has allowed you to

673
00:34:32,880 --> 00:34:34,440
Speaker 2: release such a competitive model.

674
00:34:35,840 --> 00:34:37,920
Speaker 14: I think there's a lot of different things. On the

675
00:34:37,960 --> 00:34:41,360
Speaker 14: one end, pre training has been one of our focus

676
00:34:41,360 --> 00:34:44,080
Speaker 14: for a long time making sure that both the algorithmic

677
00:34:44,160 --> 00:34:46,759
Speaker 14: improvements are there, but also the way we caption, we

678
00:34:46,880 --> 00:34:50,160
Speaker 14: structure data, we build the models themselves and test them

679
00:34:50,800 --> 00:34:52,440
Speaker 14: the best way. We need to think about a lot

680
00:34:52,440 --> 00:34:55,680
Speaker 14: of the researchers. You need to conduct multiple experiments through

681
00:34:55,760 --> 00:34:58,759
Speaker 14: multiple months, and there's a lot of learnings within what

682
00:34:58,840 --> 00:35:01,200
Speaker 14: you how you run those experiments, and I think what

683
00:35:01,239 --> 00:35:05,319
Speaker 14: we're kind of proving is that infinite resources. I mean,

684
00:35:05,360 --> 00:35:08,560
Speaker 14: you're right, we're definitely not in the smallest side of

685
00:35:09,680 --> 00:35:12,000
Speaker 14: our company, but we're not a trillion dollar or four

686
00:35:12,040 --> 00:35:15,080
Speaker 14: trillion dollar company yet still managing to old compete. The

687
00:35:15,120 --> 00:35:17,640
Speaker 14: research with of those companies is kind of it's kind

688
00:35:17,680 --> 00:35:19,680
Speaker 14: of insane, to be honest, and I think a lot

689
00:35:19,680 --> 00:35:21,560
Speaker 14: of it has to do with the experiments and the

690
00:35:21,640 --> 00:35:24,560
Speaker 14: research taste, which is, if you're running all these experiments,

691
00:35:24,800 --> 00:35:27,080
Speaker 14: how do you make sure they're effective and efficient? And

692
00:35:27,120 --> 00:35:29,319
Speaker 14: I think that's I think something we've done really, really well,

693
00:35:29,320 --> 00:35:31,160
Speaker 14: which is focusing a lot on pre training.

694
00:35:31,800 --> 00:35:35,400
Speaker 2: Gen point four four point four point five apologies has

695
00:35:35,440 --> 00:35:36,960
Speaker 2: been released to your enterprise customers.

696
00:35:37,000 --> 00:35:39,560
Speaker 6: Straight away, what's the business model for it?

697
00:35:39,920 --> 00:35:42,120
Speaker 2: You know, how do you guys monetize on top of

698
00:35:42,160 --> 00:35:43,800
Speaker 2: that You've just talked a lot about research.

699
00:35:45,160 --> 00:35:47,960
Speaker 14: I mean, it's pretty straightforward. We have subscriptions and we

700
00:35:48,040 --> 00:35:50,200
Speaker 14: have credits that people can buy to use the model.

701
00:35:50,280 --> 00:35:54,320
Speaker 14: We're releasing this model to gaming companies, to studios, to brands,

702
00:35:54,320 --> 00:35:56,560
Speaker 14: to production companies to create it from the world. We

703
00:35:56,640 --> 00:36:00,319
Speaker 14: have tens of millions of users actively using Runway. And again,

704
00:36:00,320 --> 00:36:02,399
Speaker 14: the efficiency side is not only coming from the pre

705
00:36:02,440 --> 00:36:04,640
Speaker 14: training or the training set of things. We've also been

706
00:36:04,680 --> 00:36:08,320
Speaker 14: incredibly efficient and deplined the models. We partner with Nvidia

707
00:36:08,440 --> 00:36:10,799
Speaker 14: for a lot of this work and we managed to

708
00:36:10,840 --> 00:36:13,480
Speaker 14: get really good performance of inference, and so we actually

709
00:36:13,560 --> 00:36:16,000
Speaker 14: make money every time you use the model. And that's

710
00:36:16,160 --> 00:36:18,680
Speaker 14: I think a remarkable feed not only on how we

711
00:36:18,800 --> 00:36:20,719
Speaker 14: think about the research that needs to be done, but

712
00:36:20,760 --> 00:36:23,760
Speaker 14: also the market and deploying this. So the union economics

713
00:36:23,760 --> 00:36:25,880
Speaker 14: makes sense because.

714
00:36:25,680 --> 00:36:28,960
Speaker 2: That's about sorry, just real quick. So you're saying you're

715
00:36:29,000 --> 00:36:31,120
Speaker 2: profitable running you.

716
00:36:31,120 --> 00:36:34,279
Speaker 14: Know we're making I'm saying we're making money by every

717
00:36:34,280 --> 00:36:36,000
Speaker 14: time you use the model. We have a good margin

718
00:36:36,040 --> 00:36:38,319
Speaker 14: on the model itself and how you use it. We

719
00:36:38,400 --> 00:36:41,239
Speaker 14: managed to deliver a really good performance on inference, so

720
00:36:41,400 --> 00:36:44,080
Speaker 14: the model is still like cheap to use compared to

721
00:36:44,120 --> 00:36:47,480
Speaker 14: other models, while still being the best model in the category.

722
00:36:47,560 --> 00:36:51,200
Speaker 14: And that's incredibly hard to do. Again, it just goes

723
00:36:51,239 --> 00:36:53,239
Speaker 14: back to the focus. The team has sad for quittent time.

724
00:36:54,360 --> 00:36:56,920
Speaker 2: Christ About Valenzuela Runway CEO, It's great to have you

725
00:36:56,960 --> 00:37:05,960
Speaker 2: back on bloom Back Tech. It was a landmark opening

726
00:37:06,000 --> 00:37:10,040
Speaker 2: for Disney's Zootopia two. The sequel delivered the biggest global

727
00:37:10,080 --> 00:37:13,080
Speaker 2: opening ever for an animated film, grossing five hundred and

728
00:37:13,120 --> 00:37:16,040
Speaker 2: fifty six million dollars worldwide. The strong debut is a

729
00:37:16,080 --> 00:37:20,200
Speaker 2: welcome boost for Disney's animation unit after several lackluster releases.

730
00:37:20,200 --> 00:37:23,279
Speaker 2: Over the Thanksgiving weekend, Zootopia pulled in one hundred and

731
00:37:23,320 --> 00:37:26,000
Speaker 2: fifty six million dollars in the US and Canada.

732
00:37:26,239 --> 00:37:28,840
Speaker 3: Carot can't wait to watch it mean while staying in media,

733
00:37:28,920 --> 00:37:32,680
Speaker 3: it's a final day for second round bids from Comcast, Paramount,

734
00:37:32,719 --> 00:37:35,960
Speaker 3: Sky Nance and Netflix in the episuite for Warner Brothers Discovery.

735
00:37:36,120 --> 00:37:38,759
Speaker 3: Here to break it all down, Felix Jollett, Look, are

736
00:37:38,760 --> 00:37:39,720
Speaker 3: we likely to see.

737
00:37:39,600 --> 00:37:41,160
Speaker 4: Sweetened deals coming on here?

738
00:37:41,440 --> 00:37:42,840
Speaker 3: And how are we going to know who's in the

739
00:37:43,080 --> 00:37:44,080
Speaker 3: running for first place?

740
00:37:44,080 --> 00:37:45,640
Speaker 4: Because some of them want pot of the business, some

741
00:37:45,640 --> 00:37:46,279
Speaker 4: of them want all of it.

742
00:37:46,320 --> 00:37:48,879
Speaker 15: Yeah, it's not exactly in Apple's Apples comparison.

743
00:37:49,280 --> 00:37:49,480
Speaker 2: You know.

744
00:37:49,520 --> 00:37:54,120
Speaker 15: Obviously, Netflix and Comcast want just the stream in the studios,

745
00:37:54,320 --> 00:37:57,560
Speaker 15: whereas Paramount is seeking the whole company. You know, I

746
00:37:57,560 --> 00:37:59,759
Speaker 15: think the big question is Ken David Zaslav gat the

747
00:37:59,760 --> 00:38:02,399
Speaker 15: third dollars to share he's after you know, there's been

748
00:38:02,440 --> 00:38:04,840
Speaker 15: some skepticism that the bids will come in that high.

749
00:38:05,200 --> 00:38:07,560
Speaker 15: I think all of these companies, you know, they're looking

750
00:38:07,600 --> 00:38:10,239
Speaker 15: at the future. It's going to be an incredibly competitive landscape.

751
00:38:10,280 --> 00:38:13,640
Speaker 15: They feel like they need these assets, uh, to compete

752
00:38:13,640 --> 00:38:17,279
Speaker 15: with all the other distractions online, the you know, YouTube's,

753
00:38:17,320 --> 00:38:20,560
Speaker 15: the tiktoks everything. So, yeah, it's going to be a

754
00:38:20,560 --> 00:38:23,839
Speaker 15: big deal, and I think it remains to be seen

755
00:38:24,120 --> 00:38:27,479
Speaker 15: will they meet what zablab wants do?

756
00:38:27,480 --> 00:38:30,120
Speaker 2: Do we know what they meant by sweetened bids? What

757
00:38:30,160 --> 00:38:30,920
Speaker 2: it is they wanted.

758
00:38:30,960 --> 00:38:34,160
Speaker 15: In addition, yeah, I think there's you know, part of

759
00:38:34,160 --> 00:38:36,880
Speaker 15: it is just how they're going to finance it. Also

760
00:38:37,280 --> 00:38:40,480
Speaker 15: apparently they want more cash, you know, versus stock.

761
00:38:41,360 --> 00:38:43,480
Speaker 6: You know, will there be breakup fees?

762
00:38:44,040 --> 00:38:47,440
Speaker 15: All of these companies face pretty you know, big regulatory

763
00:38:47,680 --> 00:38:50,360
Speaker 15: questions about whether or not these deals. I mean, Netflix

764
00:38:50,400 --> 00:38:53,960
Speaker 15: has the number one streamer. You'd be getting HBO Max assets,

765
00:38:54,120 --> 00:38:56,239
Speaker 15: which would be you know, the number three streamer in

766
00:38:56,280 --> 00:38:59,960
Speaker 15: the market. You know, with NBC, they have a broad

767
00:39:00,120 --> 00:39:01,799
Speaker 15: cast license. What do you do with that if you

768
00:39:01,840 --> 00:39:04,680
Speaker 15: need to transfer. We've seen all these FCC issues come

769
00:39:04,760 --> 00:39:11,120
Speaker 15: up over broadcast issues. And with Paramount you know they've

770
00:39:11,280 --> 00:39:14,920
Speaker 15: just bought you know, they're still working on the previous

771
00:39:14,920 --> 00:39:17,640
Speaker 15: deal with Skydans and Paramounts. So there are issues facing

772
00:39:17,680 --> 00:39:21,560
Speaker 15: all these companies. You know, Paramount's been very aggressive about saying,

773
00:39:21,640 --> 00:39:25,520
Speaker 15: oh we have you know, Trump's blessing, and so it's

774
00:39:25,520 --> 00:39:27,680
Speaker 15: going to be we have the easiest, clearest pathway to

775
00:39:27,680 --> 00:39:28,680
Speaker 15: get this deal done.

776
00:39:29,400 --> 00:39:32,360
Speaker 3: I think has been a very busy weekend for many lawyers,

777
00:39:32,400 --> 00:39:34,919
Speaker 3: at least on an m and a strategist of the deal,

778
00:39:35,040 --> 00:39:36,400
Speaker 3: Bloomberg's Felix Jillette.

779
00:39:36,440 --> 00:39:37,160
Speaker 4: Great breakdown.

780
00:39:37,480 --> 00:39:42,000
Speaker 3: Now turning to Amazon's AWS Reinvent conference kicks off today

781
00:39:42,000 --> 00:39:44,040
Speaker 3: in Las Vegas, where tech leaders are gathering for the

782
00:39:44,080 --> 00:39:47,320
Speaker 3: latest innovations in cloud and networking technology. Let's get to

783
00:39:47,360 --> 00:39:49,840
Speaker 3: what we can expect down Ragrana. He's Blenberg Intelligence senior

784
00:39:49,880 --> 00:39:52,319
Speaker 3: tech analysts. So it's been a busy weekend and run up.

785
00:39:52,360 --> 00:39:55,160
Speaker 3: I'm sure for Amazon employeese too. I really want to

786
00:39:55,160 --> 00:39:57,000
Speaker 3: hear about trainum three. Is that what we're going to

787
00:39:57,040 --> 00:40:00,000
Speaker 3: be hearing about about the competition in chips?

788
00:40:00,000 --> 00:40:02,000
Speaker 16: I think they have to because you know, Google's really

789
00:40:02,040 --> 00:40:04,440
Speaker 16: put down the gauntlet for all the risk of the

790
00:40:04,480 --> 00:40:07,160
Speaker 16: hyper scale clout providers to say that you know, we

791
00:40:07,280 --> 00:40:10,239
Speaker 16: have this chip that's doing really well. What can Amazon do?

792
00:40:10,280 --> 00:40:12,080
Speaker 16: In fact, what can Microsoft do? A lot down the

793
00:40:12,120 --> 00:40:15,200
Speaker 16: road in that framework, because at the end of the day,

794
00:40:15,360 --> 00:40:17,800
Speaker 16: if you can build your own chip, it's much cheaper

795
00:40:17,840 --> 00:40:20,919
Speaker 16: in terms of margins and you know your workloads, et cetera.

796
00:40:22,280 --> 00:40:26,520
Speaker 2: You know, reinvent is a developers or industry conference, right

797
00:40:26,520 --> 00:40:28,520
Speaker 2: there's going to be things specific to that. But actually,

798
00:40:28,840 --> 00:40:31,240
Speaker 2: Anna Rag, I think you agree, there's some very general

799
00:40:31,360 --> 00:40:36,040
Speaker 2: questions the AWS need to answer about capacity planning, their

800
00:40:36,080 --> 00:40:39,320
Speaker 2: exposure to one type of GPU or accelerator versus another.

801
00:40:39,800 --> 00:40:41,640
Speaker 2: What would be top of mind for you just to

802
00:40:41,680 --> 00:40:43,360
Speaker 2: get the basics from AWS on.

803
00:40:44,600 --> 00:40:46,640
Speaker 16: So one of the things we'll be hearing for is

804
00:40:47,480 --> 00:40:49,960
Speaker 16: what is their expansion plans right now? What are their

805
00:40:50,000 --> 00:40:52,799
Speaker 16: customers doing in terms of deploying AI. One of the

806
00:40:52,800 --> 00:40:55,200
Speaker 16: most important parts of AWS story that I don't think

807
00:40:55,239 --> 00:40:59,279
Speaker 16: people understand. It's like need deep into enterprises and that's

808
00:40:59,320 --> 00:41:01,480
Speaker 16: where a lot of the data resides. So for the

809
00:41:01,520 --> 00:41:03,640
Speaker 16: next level of growth for us, it's not going to

810
00:41:03,640 --> 00:41:06,200
Speaker 16: come from consumer apps. It's going to come from enterprise

811
00:41:06,239 --> 00:41:09,759
Speaker 16: AI adoption, and a large portion of that is on AWS.

812
00:41:09,960 --> 00:41:11,719
Speaker 16: What are they doing with it, what's kind of their

813
00:41:11,760 --> 00:41:14,640
Speaker 16: partnership with open AI right now? How many models do

814
00:41:14,680 --> 00:41:17,640
Speaker 16: they have on their services and what kind of adoption

815
00:41:17,719 --> 00:41:18,640
Speaker 16: rate you're seeing.

816
00:41:19,600 --> 00:41:23,320
Speaker 2: Started twenty twenty five asking what would happen between AWS

817
00:41:23,560 --> 00:41:26,560
Speaker 2: and open ai kind of know a bit more now,

818
00:41:26,960 --> 00:41:28,719
Speaker 2: How interested are you in that relationship?

819
00:41:29,719 --> 00:41:31,560
Speaker 16: I think it's a very important one because at the

820
00:41:31,640 --> 00:41:33,719
Speaker 16: end of the day, OpenAI will not be able to

821
00:41:34,480 --> 00:41:38,439
Speaker 16: get into enterprises at that same level as it has

822
00:41:38,480 --> 00:41:41,399
Speaker 16: on the consumer side if it's not on AWS, because

823
00:41:41,440 --> 00:41:42,920
Speaker 16: at the end of the day, you know, if you

824
00:41:43,040 --> 00:41:46,120
Speaker 16: look at their relative size of AWS in terms of

825
00:41:46,160 --> 00:41:49,480
Speaker 16: revenue compared to the others, it's still much bigger. The

826
00:41:49,840 --> 00:41:51,680
Speaker 16: issue that they are facing is, you know, they don't

827
00:41:51,680 --> 00:41:55,640
Speaker 16: have a consumer app like chat GPT that runs on AWS.

828
00:41:55,960 --> 00:41:59,120
Speaker 16: But when it comes to enterprise workloads, they're still the

829
00:41:59,200 --> 00:41:59,719
Speaker 16: place to go.

830
00:42:01,280 --> 00:42:04,000
Speaker 2: And Arek Rana of Bloomberg Intelligence teeing us up. Thank

831
00:42:04,040 --> 00:42:06,960
Speaker 2: you very much. That does it for this edition of

832
00:42:06,960 --> 00:42:10,320
Speaker 2: Bloomberg Tech. Tune in to Bloomberg Television tomorrow Carrow for

833
00:42:10,480 --> 00:42:12,759
Speaker 2: more out of AWS to bring you a live conversation

834
00:42:13,160 --> 00:42:17,279
Speaker 2: with aws's CEO Matt Garman and actually many other executives

835
00:42:17,400 --> 00:42:18,359
Speaker 2: at AWS two.

836
00:42:18,480 --> 00:42:18,960
Speaker 6: Don't miss it.

837
00:42:19,040 --> 00:42:20,439
Speaker 4: You're going to be racing there. I mean, well, don't

838
00:42:20,480 --> 00:42:22,080
Speaker 4: forget to check out our podcast.

839
00:42:22,360 --> 00:42:24,960
Speaker 3: Find it on the terminal as well as online on Apple, Spotify,

840
00:42:25,040 --> 00:42:27,719
Speaker 3: and iHeart plenty to digest in the latest aideals.

841
00:42:27,719 --> 00:42:31,520
Speaker 4: Are these circular? This is just a virtuous cycle from

842
00:42:31,560 --> 00:42:33,800
Speaker 4: New York for San Francisco. This is Bloomberg Tech