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Speaker 1: Bloomberg Audio Studios, podcasts, radio news from Marhard.

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Speaker 2: We're Innovation, Money and Power Collie in Silicon Valley, NBN.

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Speaker 3: This is Bloomberg Technology with Caroline Hyde and Ed lud Love.

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Speaker 4: I'm Caroline Heyde a Bloomberg's World headquarters in New York,

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Speaker 4: and I'm Ed Lodlow in San Francisco.

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Speaker 5: This is Bloomberg Technology coming up.

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Speaker 4: Full earnings coverage ahead. We've got Cisco. It's sliding because

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Speaker 4: analysts dub its outlook conservative.

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Speaker 6: We'll hear from the CFO Plust.

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Speaker 7: We dig into Walmart's results as the commerce giant posts

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Speaker 7: a big jump in its online business.

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Speaker 4: Only We're just sitting down with the CEO of hot

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Speaker 4: Ai search startup Perplexity to discuss growing competition in the

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Speaker 4: search space and a new set of board advisors in

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Speaker 4: the here and then now is to dig in to

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Speaker 4: investor sentiment more broadly. We can do that across geographies,

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Speaker 4: across industry groups, but really to really tackle these record highs,

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Speaker 4: where with Principal Asset Management Chief Global Strategist Sema Shah

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Speaker 4: joining us and look, I mean game on today from

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Speaker 4: a macro perspective and will it carry on into the

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Speaker 4: tech outperformance.

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Speaker 3: Do you think hi creator the honesty. Look, it's clearly

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Speaker 3: it's a good risk one day. I think there's fundamental

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Speaker 3: reasons for that. You know, of course there's a discussion

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Speaker 3: about rape cuts coming back onto the agenda. But I

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Speaker 3: think more fundamentally, we can see that from the economic

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Speaker 3: data it's slowing, but we're not talking about a weak

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Speaker 3: economy as yet at least, so this is still a

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Speaker 3: fundamentally strong economy, which means it and should be fairly

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Speaker 3: solid as we go through THEA and that should translate

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Speaker 3: into a broadening of the rally, which is why we're

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Speaker 3: seeing so many of them.

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Speaker 8: Disease reach record highs.

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Speaker 7: As Sebahad dead in San Francisco. Looking at all the

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Speaker 7: index level energy so far yere today, right and you

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Speaker 7: look at the NAZAQ one hundred s and P five hundred.

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Speaker 7: I can't believe we showed the Dow Jones Industrial average

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Speaker 7: and the top of this program today. But I think

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Speaker 7: what I'm trying to find out is if there's any

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Speaker 7: commonality in the direction of the market and strip out

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Speaker 7: the mag seven, because I know that that's top of

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Speaker 7: mind for so many people. Is there something in common

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Speaker 7: for this market, or is it still dominated by our

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Speaker 7: view of those single technology names.

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Speaker 3: So I think those single technous they are clearly still

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Speaker 3: dominating the market.

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Speaker 8: They're driving a lot.

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Speaker 3: But what you have seen from the most recent earning

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Speaker 3: season is that this good performance is spreading out to

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Speaker 3: other parts of the economy.

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Speaker 8: Other sectors have done pretty well. So if that can

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Speaker 8: be maintained, and particularly if you.

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Speaker 3: Do actually start adding in those ratee puts towards the

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Speaker 3: last part of the year, that will help the story,

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Speaker 3: particularly for certain segments of the market. I mean, even

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Speaker 3: if you just thinking about small caps, they need those

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Speaker 3: rate cuts. But overall this is a broad good story.

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Speaker 3: We still probably need to see a little bit more

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Speaker 3: evidence in order to secure that perspective, but at least

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Speaker 3: for now, the evidence that is coming in suggests that

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Speaker 3: this isn't just a year for the mag seven but

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Speaker 3: it's going to be a year for the broad act market.

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Speaker 4: What about the valuations of well, they're fewer than seven

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Speaker 4: now when they're magnificent, But more broadly, let's call it

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Speaker 4: chip stocks, let's call it the winning formula of Nvidia,

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Speaker 4: of Well, Alphabet, even Apple getting some lift again.

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Speaker 6: Now, are those valuations out of whack.

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Speaker 8: It's interesting.

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Speaker 3: I was traveling across the US last week and I

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Speaker 3: was with investors everywhere, and that was a question that

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Speaker 3: still keeps coming up, is you know, do we think

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Speaker 3: of this as overvalued and is a time for a correction?

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Speaker 3: So we would definitely push back on that. Yes, it's

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Speaker 3: a little bit frothy, but do we make comparisons to

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Speaker 3: the dot com bubble?

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Speaker 8: Absolutely not. These earnings are somewhat believable, maybe a.

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Speaker 3: Little bit frothy, as I said, but there's actually something

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Speaker 3: fundamental in those valuations.

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Speaker 8: So it doesn't really concern us.

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Speaker 3: Inevitably with this kind of market, when there's so much

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Speaker 3: good news priced in, there's always going to be risk

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Speaker 3: of a bit of a pullback, But we look at

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Speaker 3: the tech story as a long term play, and from

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Speaker 3: a long term play, we still it's still one of

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Speaker 3: our favored areas of the market.

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Speaker 5: It talks about the good news that's priced in. Seema.

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Speaker 7: I don't want to bring the mood down, but something

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Speaker 7: we've been talking a lot about recently on the show

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Speaker 7: is the relationship between the US and China, whether it's

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Speaker 7: constructive or otherwise. Is that a headwind you're factoring in

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Speaker 7: right now, particularly when you look at the technology sector,

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Speaker 7: so Ineffe, it's.

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Speaker 3: Something that we do to consider, especially when you're going

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Speaker 3: into the presidential election, where China is going to be

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Speaker 3: top of the news as we get close from closer

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Speaker 3: to that point. So it is something we consider, but

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Speaker 3: I think it's very much on a single stock name basis,

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Speaker 3: because there's some which are going to be particularly exposed

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Speaker 3: and others which are not so much. The good news

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Speaker 3: from a China stundpoint, though, is from the most recent

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Speaker 3: data that we're seeing come in there does seem like

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Speaker 3: it's a bit of a sickle club term, nothing to

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Speaker 3: rave on about, but something at least to lift at

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Speaker 3: least expectations. If that is carried through then certainly for

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Speaker 3: a lot of those bigger multinational tech companies which have

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Speaker 3: got so much exposure to China, it does suggest.

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Speaker 8: That they are a little bit less at risk than

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Speaker 8: we would have considered.

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Speaker 3: But certainly the geopolitics is something we always have to watch,

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Speaker 3: but it's not yet at that point where it's changing

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Speaker 3: up perspective on positivity about the sector.

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Speaker 4: I like that we go global because look, it's not

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Speaker 4: just US stocks have done particularly well of late Japanese

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Speaker 4: stocks on a tear, European stocks at Sandraman and at

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Speaker 4: a record as well. Where are you liking other geographies

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Speaker 4: right now?

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Speaker 3: Yeah, And it's a great question because at the moment,

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Speaker 3: I mean, at least for the last couple of weeks,

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Speaker 3: what we've been challenged with in the market is that

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Speaker 3: although I think a lot of people continue to believe

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Speaker 3: in use exceptionalism, they have been confounded by the fact

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Speaker 3: that the rat debate continues. We're not very sure about

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Speaker 3: where that's going. When you think about the growth story,

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Speaker 3: you're suddenly the economic downside surprises in the US, and

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Speaker 3: then of course there's evaluation. So then this is probably

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Speaker 3: a good time to start thinking outside of the US,

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Speaker 3: and there are some good stories.

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Speaker 8: So if you think about Europe, which.

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Speaker 3: Is the perennial disappointment, but now you're seeing that we

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Speaker 3: know what the ECB is going to do, we have

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Speaker 3: I think pretty much good confidence that there's going to

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Speaker 3: be cut in June, and then thereafter we know that

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Speaker 3: we're starting to see a cyclical economic upside upside surprise,

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Speaker 3: not an acceleration, but at least we're seeing some good

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Speaker 3: news come through, and then valuations more attractive.

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Speaker 8: So if you look across Europe, that's fairly good news.

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Speaker 3: And even in China, as I said, given where sentiment was,

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Speaker 3: you couldn't get much worse than that. And now with

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Speaker 3: the economic data seeing some signs of brightness, that is

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Speaker 3: looking like a more attractive part of the market too.

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Speaker 7: A Seema, you're a regular on Bloomberg Television. You're a

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Speaker 7: regular here on Bloomberg Technology away from the market, So

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Speaker 7: I wanted to ask you about how you're using AI

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Speaker 7: at work or at home. You know we're going to

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Speaker 7: be talking later about AI driven search. What have you

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Speaker 7: been up to in the world of AI.

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Speaker 3: Look, I mean, I think any company out there has

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Speaker 3: to be embracing AI and trying to figure out where

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Speaker 3: they can.

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Speaker 8: Use it in any parts of working home.

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Speaker 3: So from a work perspective, of course, trying to adopt

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Speaker 3: AI into our models, trying to analyze the companies from

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Speaker 3: a single sector, single stock perspective. That is going to

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Speaker 3: be the easiest way to try and do full analysis

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Speaker 3: and get a full complete level of information.

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Speaker 8: So that is something that we're trying to bring into work.

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Speaker 3: I have some more children. AI is the stuff that

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Speaker 3: they talk about it at school. As well, so you

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Speaker 3: can see that this is everywhere. It's not just dominated

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Speaker 3: by the finance news.

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Speaker 7: Princevill Asset Management, Chief Global Strategy as semashark Ai everything

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Speaker 7: all the time and markets.

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Speaker 5: What more can you ask for? On Bloombo Technology.

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Speaker 7: Coming up on the show, we're going to hear from

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Speaker 7: the Cisco CFO Scott Heron is the company's slides and

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Speaker 7: its outlook is being seen as conservative as coming up next,

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Speaker 7: Stick with us because we'll be right back.

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Speaker 5: This is Bloombo Technology.

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Speaker 6: Got to talk.

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Speaker 4: Cisco because it's been quite the volatile trade after hours

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Speaker 4: and in today's training. We're now off by one point

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Speaker 4: seven percent, moving lower well despite the forecast actually showing

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Speaker 4: a return of customer spending of networking gear, analysts dubbing

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Speaker 4: the outlook perhaps a little conservative. Wiliam had got the

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Speaker 4: chance to discuss all of the results earlier with Cisco

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Speaker 4: CFO Scott Heron Tickulasen.

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Speaker 9: So this was our fiscal third quarter that we announced.

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Speaker 9: Then normally I wouldn't guide fiscal twenty five until we

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Speaker 9: get to the fourth quarter earnings call. But you know,

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Speaker 9: we just acquired Splunk. So the big news for us

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Speaker 9: is we acquired Splunk. We got a closed mid quarter,

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Speaker 9: and I think the analysts are all trying to adjust

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Speaker 9: now to what does the combined company look like.

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Speaker 2: So what I wanted to do is kind of.

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Speaker 9: Get ahead of that with fiscal twenty five and without

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Speaker 9: doing a fulsome guide, just get enough information out there

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Speaker 9: so that they could narrow down their expectations for fiscal

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Speaker 9: twenty five conservative. I think it's a first view of

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Speaker 9: where that's going, you know. I think the encouraging thing

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Speaker 9: for us is we've worked through the supply chain disruptions

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Speaker 9: that we had that caused us to build up a

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Speaker 9: huge backlog, and then we're able to clear as we

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Speaker 9: got the components in, we're able to clear that backlog,

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Speaker 9: revenue spiked, and now we're having to compare to those

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Speaker 9: to those compare points, so the year on years are

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Speaker 9: a little bit difficult to actually discern what's happening in

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Speaker 9: the underlying business. We spent some time trying to unpack

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Speaker 9: that for the for investors yesterday, and we'll do some

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Speaker 9: more of that with the byside later. So first view,

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Speaker 9: but really just trying to give them enough insight so

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Speaker 9: that the models would start to converge on what we think.

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Speaker 2: Is the right set of numbers.

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Speaker 1: So it sounds like you're not saying no, but making

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Speaker 1: the point that it's very early. I mean, twenty twenty five,

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Speaker 1: who knows what will happen. But let's talk a little

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Speaker 1: bit more about inventory.

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Speaker 10: You make the point that it feels like you've turned

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Speaker 10: a corner when it comes to a lot of those

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Speaker 10: supply chain disruptions, and what does that say about corporate spending.

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Speaker 10: A lot of people looked at this report and said,

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Speaker 10: there's screenshoots there, there's a bit of an uptick.

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Speaker 1: That's the case. Where is that coming from? What kind

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Speaker 1: of customers are we seeing that demand from?

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Speaker 9: You know, what's really interesting for us? And of course

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Speaker 9: we've got Splunk added in for just half a quarter.

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Speaker 9: So if you net that out and look at what

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Speaker 9: the core business apples to Apple's basis, looks like. Actually

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Speaker 9: orders were flat overall, which is an improvement from where

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Speaker 9: it's been as customers have been really trying to implement

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Speaker 9: the huge amount of product that we shipped out to

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Speaker 9: them in three consecutive quarters.

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Speaker 2: We see that ending.

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Speaker 9: We see them getting through implementing all of the product

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Speaker 9: that we ship to them. By about the end of

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Speaker 9: this fiscal quarter, which for US will end at the

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Speaker 9: end of July, and as that happens, we're already starting

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Speaker 9: to see demand return and product orders flat, I think

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Speaker 9: encouragingly within that overall flat growth in security orders, growth

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Speaker 9: in collaboration orders, but most importantly growth in data center

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Speaker 9: networking orders, which has been a headwind for some time,

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Speaker 9: and growth in campus switching. And I think the campus

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Speaker 9: switching one surprised a lot of people that we're seeing

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Speaker 9: growth there with the sense that you know, there's more

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Speaker 9: vacant office space than there's ever been. How can campus

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Speaker 9: switching be growing? So I think it's an encouraging sign.

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Speaker 1: So some tailwinds there, and it took me through questions,

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Speaker 1: but we.

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Speaker 10: Have to talk about AI because of course a detail

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Speaker 10: that caught a lot of people's eyes was the fact

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Speaker 10: that you have about a billion dollars on AI infrastructure

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Speaker 10: orders in sight. And I guess my question there is

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Speaker 10: is that for a particular line of business, where would

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Speaker 10: that spending actually be taking.

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Speaker 1: Place within Cisco.

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Speaker 9: Yeah, it's a great question. That's not a new data

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Speaker 9: point we put that out there. It's in the back end.

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Speaker 9: So if you think of how these AI models get

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Speaker 9: trained and then get used, which is called inferencing. It's

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Speaker 9: in the back end, so it's networking and optics in

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Speaker 9: the back end. A lot of that going to the big,

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Speaker 9: large public clouds that you'd expect in the Tier two

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Speaker 9: AI infrastructure build out is what drives them.

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Speaker 7: Cisco CFO Scott Heron giving Frankly a classic class on

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Speaker 7: being a CFO for a technology company. Let's get some

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Speaker 7: clarity on Cisco It's technology and its endings. Efering Bloombergsy

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Speaker 7: and King. There was a lot of CFO speaking on

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Speaker 7: pick it for us and explain the quarter that's just gone.

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Speaker 11: Yeah, I think the best way to view it is

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Speaker 11: through the lens of what happened with the share price.

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Speaker 11: We had an initial spike yesterday and after hours right

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Speaker 11: exactly and after hours trading, and today we're sort of

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Speaker 11: down moderately a little bit more sober.

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Speaker 2: What really happened and Scott kind.

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Speaker 11: Of alluded to that was the expectation for orders was

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Speaker 11: that they would be down possibly as much as ten percent,

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Speaker 11: maybe even.

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Speaker 2: More in the quarter that they just reported came in.

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Speaker 11: It's kind of okay, maybe up slightly when you include

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Speaker 11: this new acquisition. So there was like, oh, things aren't

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Speaker 11: as bad as we had feared. Kind of reaction that

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Speaker 11: happened in the after hours. Then when we go through

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Speaker 11: the numbers and listen to the call, we've seen a

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Speaker 11: reaction this morning which is more like, yeah, okay, it's okay.

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Speaker 11: But bearing in mind they've just done this massive acquisition,

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Speaker 11: maybe they're not getting quite the kick from that.

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Speaker 4: Yeah, I mean, we have to take Splunk into consideration here,

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Speaker 4: and there's gonna be new role of president for example,

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Speaker 4: in But what about some of the other partnerships natural

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Speaker 4: M and A. But you know, they had this big

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Speaker 4: fanfare of an end video deal and partnership has that

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Speaker 4: bringing in any much needed revenue streams.

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Speaker 11: Well that is still in the early day, so we

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Speaker 11: don't really know. But you know, back to the conversation

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Speaker 11: that was had with Scott and the big question of

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Speaker 11: this billion dollar of AI related orders. Obviously Cisco wants

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Speaker 11: to go to the party too at the moment though,

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Speaker 11: and there were a lot of questions on the call yesterday, well,

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Speaker 11: what exactly do you mean by a billion dollars?

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Speaker 2: Who's giving you these orders?

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Speaker 11: How sure are you that these are real orders and

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Speaker 11: not just an aspirational goal, and there was a lot

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Speaker 11: of to and fro with a leadership team about you

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Speaker 11: know what that really means and whether this is real

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Speaker 11: and really the bottom line is that investors aren't quite

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Speaker 11: yet ready to let Cisco go into that AI party.

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Speaker 7: I and you broke a story literally as we started

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Speaker 7: the show and before you walked on set about ampare

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Speaker 7: the chip company teaming up with to make AI technology

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Speaker 7: AI infrastructure. I didn't know that Qualcom was in that

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Speaker 7: line of business. I do now give us the details.

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Speaker 11: Yeah, I mean what's going on. There is Umpair, who

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Speaker 11: we had at our tech conference flash It. They're an

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Speaker 11: armed server chip company trying to get into all of

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Speaker 11: the big hyperscalers. They're teaming up with Qualcomm to bring

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Speaker 11: in this accelerator to try to basically bring a cheaper

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Speaker 11: alternative to the kind of thing that Nvidia is selling

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Speaker 11: for many tens.

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Speaker 2: Of thousands of dollars. We'll see how well that goes.

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Speaker 2: We'll see how well that performs.

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Speaker 4: I mean, we sat with Renee James, both you and

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Speaker 4: I individually at various points throughout that event last week.

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Speaker 4: I and she has a brother fresh air in many

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Speaker 4: ways to the area of computing and the startup side

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Speaker 4: of things. But it looks like there's kind of a

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Speaker 4: trifecta going on. They can't just do it alone. They've

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Speaker 4: got to be working whether super Micro's involved, super Microcomputer

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Speaker 4: alongside Qualcom, alongside a pair. But the key refrain coming

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Speaker 4: from everyone is energy right efficiency.

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Speaker 11: Here, Yeah, as you as you point out, Reno is

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Speaker 11: pretty fired up about this, and she's making a point

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Speaker 11: which many agree, which is we cannot carry on like this.

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Speaker 11: The cost of AI in terms of power, in terms

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Speaker 11: of money is just too great right now. We need

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Speaker 11: technology answers to that problem. We need more efficient chips,

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Speaker 11: we need cheaper chips, and she's trying to offer that

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Speaker 11: her company as a solution to that problem.

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Speaker 4: Well, certainly managing to come out to the market with

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Speaker 4: a combination offering at the moment, Ian, it's brilliant that

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Speaker 4: you got to break it for us.

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Speaker 6: Thank you, Blue Meg's Ian King. Time now for talking tech.

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Speaker 4: First up, Microsoft is said to have asked hundreds of

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Speaker 4: its China based AI staff to consider relocating.

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Speaker 6: That's all, according to the Wall Street Journal.

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Speaker 4: Now, the tech giant has asked its employees based in

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Speaker 4: China to move to a different country, perhaps US, but Ireland,

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Speaker 4: Australia and New Zealand. It's all a growing tensions between

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Speaker 4: the US and China on technology. Those who choose not

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Speaker 4: to transfer could remain working in the country for now. Plus,

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Speaker 4: Michael Varie's investment firm is doubling down on its Chinese

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Speaker 4: investments JD dot Com, Ali barber Now. According to thirteen

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Speaker 4: f filings, JD was the firm's top holding after boosting

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Speaker 4: its stake by eighty percent, with Ali Barba coming in second.

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Speaker 4: Laberia has been making a return to the Chinese tech

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Speaker 4: sector after exiting them at one point in twenty twenty three,

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Speaker 4: and revenue for Baydo grew at actually its slowest pace

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Speaker 4: in more than a year. Chinese internet search leader look

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Speaker 4: At struggling to turn its investments into generative AI into actually.

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Speaker 6: Real revenue real earnings.

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Speaker 4: They came in at four point four billion for the

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Speaker 4: three months ending in March. Meanwhile, JD dot Com reported

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Speaker 4: well better than expected seven percent rise in revenue, this

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Speaker 4: after the Beijing based retailer actually cut prices ramped up

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Speaker 4: customer perks to counter us on that competition. JD dot

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Speaker 4: COM's results are seen as one of the key bellweathers

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Speaker 4: of Chinese consumption, which look a struggle to recover since

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Speaker 4: the country lifted COVID curbs.

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Speaker 7: Yeah, there's going to be a big global e commerce

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Speaker 7: theme in today's show because Walmart's also out with its

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Speaker 7: results this morning. It showed a twenty two percent jump

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Speaker 7: year over year in its online business. This is e

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Speaker 7: commerce growth, principally in the US. Let's get out to

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Speaker 7: Bloomberg's Jay one Com who covers Walmart for US, and

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Speaker 7: that's the story.

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Speaker 5: But what's behind it?

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Speaker 7: What's Walmart been doing with its digital presence?

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Speaker 12: Yeah, so e commerce has been a really important business

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Speaker 12: for Walmart. It's you know, definitely one of the factors

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Speaker 12: of growth. And what they've been doing is, you know,

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Speaker 12: really adding capabilities like delivering orders super early in the morning.

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Speaker 12: They're also working to deliver their items faster so you know,

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Speaker 12: the same day, you know, or in a couple of hours.

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Speaker 8: They're also improving what.

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Speaker 12: The company calls a perfect order, which refers to different

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Speaker 12: metrics like you know, when orders arrive or how complete

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Speaker 12: the orders are, and they've been working on improving all

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Speaker 12: of those things while cutting down costs associated with delivery.

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Speaker 6: The numbers are phenomenal.

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Speaker 4: And when you're looking at what four billion items in

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Speaker 4: the last twelve months delivered by in the same day

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Speaker 4: or next day, that's comparable to Amazon. What's also comparable

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Speaker 4: to Amazon is some of the teething problems that they

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Speaker 4: have back in the day when they started having third

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Speaker 4: party sellers. Look, I was just sat down in makeup

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Speaker 4: and make a part of saying, look, have you seen

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Speaker 4: that there's like some fake handbags being sold on Walmart?

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Speaker 6: Now, I had an.

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Speaker 4: Issue with my son's gift arriving during Christmas because it

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Speaker 4: was from a third party vendor and that bender didn't

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Speaker 4: seem to live up to the expectations.

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Speaker 6: What's going on in terms of teething issues.

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Speaker 12: So Marketplace has also been one of the factors of

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Speaker 12: growth for Walmart, and it's been trying to you know,

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Speaker 12: add sellers and increase the number of items that it

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Speaker 12: sells on on Marketplace in the US and abroad. And

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Speaker 12: a huge benefit of that business is the fact that

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Speaker 12: you know, the company can really grow the range of

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Speaker 12: items that it selves online and they you know, see

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Speaker 12: a lot of opportunities with general merchandise in particular. So

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Speaker 12: you know, this morning they talked about items like pets

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Speaker 12: and beauty. They're you know, going up in sales by

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Speaker 12: more than thirty percent on marketplace, and you know, at

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Speaker 12: the same time it is it is a more decent.

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Speaker 8: Business compared to selling items in stores.

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Speaker 12: And so you know we'll see the different puts in

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Speaker 12: takes that they'll you know, test start with that business for.

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Speaker 4: Now, and thing's looking very strong. Thank you so much,

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Speaker 4: Joy and Kang on All Things warm Up. Welcome back

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Speaker 4: to Blue Bag Technology and Karin Hide in New York.

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Speaker 5: And I'm id love Low in San Francisco.

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Speaker 4: But for now, there's news from Perplexity, the developer of

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Speaker 4: an AI based search engine platform. It's creating brand new

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Speaker 4: advice board that will include Mikhale Parakin, the former head

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Speaker 4: of Microsoft being researched, of course, Rich Minor, Google Advisor,

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Speaker 4: Android co founder, and Emil Michael, former.

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Speaker 6: Chief business officer over al Uber.

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Speaker 4: Here for more on this news is Perplexity CEO Aravin Shunibas.

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Speaker 4: So we have this new set of advisors. I'm going

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Speaker 4: to start with the obvious. Having your look to the pictures.

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Speaker 4: You're four male co founders and you've got three men

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Speaker 4: on your advisory board. You are a man of color.

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Speaker 4: I don't need you to preach to the converted. Whereas

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Speaker 4: the women, where's a diversity.

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Speaker 13: So one of our board directors Cacile Hem from IVP,

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Speaker 13: she's a woman. We also have another board observer and

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Speaker 13: board 'ski from Anya who's also a woman. And Susan Wojitski, farmer,

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Speaker 13: YouTube CEOs and investor in US and she's also very

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Speaker 13: helpful in advice. So we have a lot of people

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Speaker 13: from many different diverse backgrounds helping us right now.

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Speaker 4: Okay, so one female board member, one observer, one investor

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Speaker 4: that you name. That doesn't feel like a lot, but

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Speaker 4: I get it. You feel this diversity enough. Many would

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Speaker 4: quibble with that, but let's talk about what these three

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Speaker 4: people bring from a across the board, across experience.

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Speaker 6: What is it that you need right now in terms

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Speaker 6: of advice.

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Speaker 13: Yeah, So as a startup, we've been fortunate to scale

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Speaker 13: up so much in such a short period of time.

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Speaker 13: The next step for us is to really think hard

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Speaker 13: about strategy, both in terms of search, how to build

440
00:21:27,040 --> 00:21:29,320
Speaker 13: our own index and how to like scale it up

441
00:21:29,359 --> 00:21:32,240
Speaker 13: to like even more users, and in terms of how

442
00:21:32,440 --> 00:21:36,280
Speaker 13: our mobile and distribution strategy. It was right, So search

443
00:21:36,400 --> 00:21:39,240
Speaker 13: is all about distribution. They all say soon their purchases

444
00:21:39,280 --> 00:21:42,919
Speaker 13: CEO Google because he really cracked the distribution code to Chrome.

445
00:21:44,040 --> 00:21:46,640
Speaker 13: So we are working with Email who is the farmer

446
00:21:46,720 --> 00:21:49,639
Speaker 13: business officer of Uber, who is so aggressive on growth

447
00:21:49,680 --> 00:21:52,040
Speaker 13: in terms of getting such a large user base, So

448
00:21:52,240 --> 00:21:54,800
Speaker 13: we need to think have similar ideas here.

449
00:21:55,680 --> 00:21:57,600
Speaker 6: And in terms of mobile, I would say it was

450
00:21:57,640 --> 00:21:58,200
Speaker 6: too aggressive.

451
00:21:58,960 --> 00:22:01,040
Speaker 14: Yeah, we'll try to tone it down, sure, but.

452
00:22:02,800 --> 00:22:06,439
Speaker 13: Look, startups have to be aggressive in terms of competing

453
00:22:06,480 --> 00:22:09,720
Speaker 13: against incumbents who already have like billion users or Opening

454
00:22:09,840 --> 00:22:11,160
Speaker 13: I has one hundred million users.

455
00:22:11,240 --> 00:22:13,240
Speaker 14: We don't have that today, so we have to be

456
00:22:13,560 --> 00:22:14,840
Speaker 14: it's on us to do that.

457
00:22:15,480 --> 00:22:19,160
Speaker 13: And it requires a good mobile strategy to a great

458
00:22:19,240 --> 00:22:22,199
Speaker 13: mobile experience, so rich from Android it would be very

459
00:22:22,240 --> 00:22:25,119
Speaker 13: helpful there. And of course the core thing is search,

460
00:22:25,440 --> 00:22:28,520
Speaker 13: and no one better than Mikail who used to head bing.

461
00:22:29,119 --> 00:22:33,240
Speaker 13: He rolled out being chat co pilot and was previously

462
00:22:33,800 --> 00:22:36,520
Speaker 13: doing all the search infrastructure for yandex. So he's going

463
00:22:36,600 --> 00:22:38,520
Speaker 13: to advise us on building a lot of in house

464
00:22:38,560 --> 00:22:39,479
Speaker 13: infrastructure for us.

465
00:22:40,680 --> 00:22:41,720
Speaker 5: Aravin. Thanks.

466
00:22:41,800 --> 00:22:44,040
Speaker 7: Coming back on the program, I've got to ask you

467
00:22:44,119 --> 00:22:49,040
Speaker 7: about Google Io and the real emphasis frankly that Google

468
00:22:49,160 --> 00:22:53,119
Speaker 7: put on integration of AI into search specifically, it's like this.

469
00:22:53,240 --> 00:22:54,760
Speaker 5: Idea of AI overviews.

470
00:22:54,880 --> 00:22:57,440
Speaker 7: Right, you know, you search for something, it gives you

471
00:22:58,200 --> 00:23:01,280
Speaker 7: a text answer as opposed to link. A lot of

472
00:23:01,320 --> 00:23:05,240
Speaker 7: people want to know what is Aravin Shinabas's reaction to

473
00:23:05,359 --> 00:23:07,200
Speaker 7: what you saw out a Google IO.

474
00:23:07,960 --> 00:23:10,120
Speaker 13: That's the same reaction to what I saw last year

475
00:23:10,320 --> 00:23:14,000
Speaker 13: when they called it Search Generative Experience. It's just the

476
00:23:14,080 --> 00:23:16,480
Speaker 13: exact same thing called it is a different name. There's

477
00:23:16,520 --> 00:23:20,200
Speaker 13: literally no change to it. And sure it's being rolled

478
00:23:20,240 --> 00:23:22,159
Speaker 13: out to more people, but it's not going to be

479
00:23:22,240 --> 00:23:22,720
Speaker 13: rolled out for.

480
00:23:22,760 --> 00:23:24,480
Speaker 14: Every single query you type on Google.

481
00:23:25,000 --> 00:23:28,280
Speaker 13: And the most important thing when someone uses a product

482
00:23:28,400 --> 00:23:29,480
Speaker 13: is they should know what's.

483
00:23:29,320 --> 00:23:29,879
Speaker 14: Going to happen.

484
00:23:30,760 --> 00:23:34,480
Speaker 13: When people go to Google, they hate the latency being bad.

485
00:23:35,000 --> 00:23:37,680
Speaker 13: They expect instant and the links to render. So the

486
00:23:37,760 --> 00:23:41,440
Speaker 13: moment you start making people wonder what's going to happen,

487
00:23:41,480 --> 00:23:43,399
Speaker 13: whether that's going to be a streaming of tokens for

488
00:23:43,480 --> 00:23:45,200
Speaker 13: an answer, or is this going to be links, or

489
00:23:45,200 --> 00:23:46,760
Speaker 13: whether it's going to be ads, or whether it's going

490
00:23:46,840 --> 00:23:50,280
Speaker 13: to be some other panels and a UI that's so

491
00:23:50,440 --> 00:23:53,800
Speaker 13: cluttered with all these elements at one face versus a

492
00:23:53,920 --> 00:23:57,000
Speaker 13: single UI just focus for a question answering. That's what

493
00:23:57,400 --> 00:24:00,560
Speaker 13: users are going to decide between perplexity and Google AI overviews.

494
00:24:00,600 --> 00:24:03,680
Speaker 13: And I think like users like simple, minimum, clean products,

495
00:24:03,760 --> 00:24:06,520
Speaker 13: so we are pretty confident in our own direction.

496
00:24:08,080 --> 00:24:10,840
Speaker 7: Aravin, Do you have any information or even just sense

497
00:24:10,960 --> 00:24:15,760
Speaker 7: that this was Google responding directly to perplexity, pulsa and

498
00:24:16,520 --> 00:24:20,200
Speaker 7: possible right? And how do you plan to respond yourself?

499
00:24:20,760 --> 00:24:24,480
Speaker 13: But being a better product by actually working on categories

500
00:24:24,560 --> 00:24:27,920
Speaker 13: of searches that they're definitely going to be not incentivized to,

501
00:24:28,280 --> 00:24:30,800
Speaker 13: for example, shopping. If it's still like I'm on the

502
00:24:30,920 --> 00:24:34,320
Speaker 13: SGE experiment already, and when I still type on shoes,

503
00:24:34,359 --> 00:24:36,280
Speaker 13: I don't get any of the AI overviews. I just

504
00:24:36,359 --> 00:24:39,040
Speaker 13: get all the Google Shopping clutter UI, so I still

505
00:24:39,040 --> 00:24:42,479
Speaker 13: get ads. So there are like query categories, commercially intended

506
00:24:42,560 --> 00:24:48,160
Speaker 13: query categories insurance, travel, shopping, or like education, the universities

507
00:24:48,680 --> 00:24:53,280
Speaker 13: from different fution programs, taxes, all these different categories where

508
00:24:53,320 --> 00:24:55,360
Speaker 13: all these advertisers are screaming.

509
00:24:54,960 --> 00:24:56,520
Speaker 14: At you to click on them.

510
00:24:57,320 --> 00:24:59,920
Speaker 13: There's just no incentive for them to put an AI

511
00:25:00,080 --> 00:25:02,560
Speaker 13: overview for those querries and like lose their revenue share.

512
00:25:03,080 --> 00:25:06,440
Speaker 4: What's interesting though, is you actually are built in some ways.

513
00:25:06,480 --> 00:25:11,080
Speaker 4: You use claw three, you also use open aiyes underlying

514
00:25:11,119 --> 00:25:13,480
Speaker 4: large language model. And there's rumors that open aie got

515
00:25:13,560 --> 00:25:15,840
Speaker 4: to come out with some sort of search. That worry

516
00:25:15,880 --> 00:25:16,560
Speaker 4: a little bit more.

517
00:25:17,240 --> 00:25:18,960
Speaker 14: Not really though.

518
00:25:19,000 --> 00:25:21,640
Speaker 13: Their strategy is cleaner than what Google is doing. They're

519
00:25:21,640 --> 00:25:24,280
Speaker 13: not actually just changing chat GPT to put in search.

520
00:25:24,680 --> 00:25:27,679
Speaker 13: They're building out a separate product. But by building out

521
00:25:27,680 --> 00:25:30,639
Speaker 13: a separate product, you're competing for users from you know,

522
00:25:30,920 --> 00:25:33,320
Speaker 13: in the same way as like Google rolling out Gemini

523
00:25:33,480 --> 00:25:35,879
Speaker 13: or Bard. You're not actually taking advantage of the existing

524
00:25:35,960 --> 00:25:41,200
Speaker 13: user base. And we are confident that like our existing

525
00:25:41,320 --> 00:25:44,320
Speaker 13: work on using many models and many indexes and all

526
00:25:44,359 --> 00:25:46,680
Speaker 13: the orchestration that we do will still keep us a

527
00:25:46,760 --> 00:25:49,800
Speaker 13: superior product. But it's a good competition, Like we're definitely

528
00:25:49,840 --> 00:25:52,360
Speaker 13: going to watch what they're going to release and make

529
00:25:52,400 --> 00:25:54,480
Speaker 13: sure we execute even faster than what we're doing.

530
00:25:54,960 --> 00:25:59,600
Speaker 4: It's competition for talent, it is competition for dollars because

531
00:25:59,640 --> 00:26:01,879
Speaker 4: this is not a cheap process to be answering all

532
00:26:01,960 --> 00:26:04,000
Speaker 4: our questions, are you fundraising again?

533
00:26:04,040 --> 00:26:06,560
Speaker 6: As rumors that you are are there rumors?

534
00:26:06,920 --> 00:26:07,720
Speaker 14: I mean, I don't know.

535
00:26:07,960 --> 00:26:11,119
Speaker 13: Look, all I'll say is that we are trying to

536
00:26:11,280 --> 00:26:14,680
Speaker 13: be as efficient as possible and not going and raising

537
00:26:14,760 --> 00:26:18,119
Speaker 13: like a billion dollar round or something like that. I

538
00:26:18,200 --> 00:26:20,719
Speaker 13: would say, yes, I agree with you, this is going

539
00:26:20,800 --> 00:26:23,959
Speaker 13: to be expensive. We would have to fundraise again at

540
00:26:24,000 --> 00:26:26,920
Speaker 13: some point. But our goal is to get our revenue

541
00:26:27,000 --> 00:26:29,800
Speaker 13: in good shape so that as we scale in terms

542
00:26:29,800 --> 00:26:32,080
Speaker 13: of a number of people, that's a good revenue per

543
00:26:32,160 --> 00:26:34,560
Speaker 13: employee and we're a healthy business too.

544
00:26:36,400 --> 00:26:40,480
Speaker 7: Aravins, Welcome to Bloomberg Technology. I'm glad that you're in

545
00:26:40,520 --> 00:26:43,680
Speaker 7: the studio with us today. When we say that you're

546
00:26:43,680 --> 00:26:45,280
Speaker 7: coming on the show, we get a lot of questions

547
00:26:45,320 --> 00:26:49,280
Speaker 7: from the audience. Unsurprisingly, there are many perplexity users out

548
00:26:49,280 --> 00:26:51,639
Speaker 7: there right. What always strikes me is how simple the

549
00:26:51,720 --> 00:26:53,920
Speaker 7: questions are. They just want to know from you, what

550
00:26:54,040 --> 00:26:57,040
Speaker 7: your daily active user metrics are, the latest on the

551
00:26:57,240 --> 00:26:59,919
Speaker 7: user base geographically where these people are.

552
00:27:01,000 --> 00:27:03,600
Speaker 13: Yeah, so we actually care more about the number of

553
00:27:03,720 --> 00:27:06,399
Speaker 13: queries we get every single day. That's a better metric

554
00:27:06,440 --> 00:27:08,679
Speaker 13: for us because that's more directly tied to expanding our

555
00:27:08,720 --> 00:27:11,639
Speaker 13: index and getting our models in better shape. So we

556
00:27:11,680 --> 00:27:13,879
Speaker 13: are pretty close to ten million daily queries of this

557
00:27:14,000 --> 00:27:17,720
Speaker 13: fine somewhere in the late single digits of millions, So

558
00:27:17,880 --> 00:27:20,439
Speaker 13: that's actually a really good progress from where we started.

559
00:27:20,800 --> 00:27:23,240
Speaker 13: Every single month we are basically serving close to like

560
00:27:23,240 --> 00:27:27,320
Speaker 13: one hundred and ninety million queries, and this is only expanding.

561
00:27:27,560 --> 00:27:30,600
Speaker 13: We started like one and a half years ago and

562
00:27:32,440 --> 00:27:35,199
Speaker 13: one looking forward like we can definitely for example, bing

563
00:27:35,320 --> 00:27:39,360
Speaker 13: chat served like a billion queries in twenty twenty three,

564
00:27:39,640 --> 00:27:42,360
Speaker 13: and we sell five hundred million this year itself. We've

565
00:27:42,400 --> 00:27:44,840
Speaker 13: already surpassed the entirety of twenty twenty three in the

566
00:27:44,880 --> 00:27:48,080
Speaker 13: first three months of four months. So looking forward, if

567
00:27:48,119 --> 00:27:51,119
Speaker 13: we keep sustaining these growth rates, we're headed for a

568
00:27:51,160 --> 00:27:51,760
Speaker 13: great future.

569
00:27:53,200 --> 00:27:55,320
Speaker 7: Aravin I wrote on Monday in The Tech Daily that

570
00:27:55,480 --> 00:28:01,600
Speaker 7: consolidations coming among AI startups essentially, have you held talks

571
00:28:01,720 --> 00:28:05,280
Speaker 7: with a bigger technology company about the potential of being

572
00:28:05,359 --> 00:28:09,280
Speaker 7: acquired or do you see yourselves as an acquirer of

573
00:28:09,400 --> 00:28:11,439
Speaker 7: some of the other smaller names or teams that are

574
00:28:11,480 --> 00:28:11,840
Speaker 7: out there.

575
00:28:12,560 --> 00:28:15,440
Speaker 13: Yeah, so I agree with you on the broader point

576
00:28:15,520 --> 00:28:18,080
Speaker 13: of consolidation. It's already happening. Like a lot of us

577
00:28:18,119 --> 00:28:20,280
Speaker 13: startups that used to be there no longer there, and

578
00:28:20,440 --> 00:28:23,399
Speaker 13: like the fewer ones that exist continue to keep getting

579
00:28:23,440 --> 00:28:25,879
Speaker 13: more and more powerful in terms of user based or

580
00:28:27,320 --> 00:28:31,400
Speaker 13: market share and APIs. So we've had interests in the past,

581
00:28:31,440 --> 00:28:34,000
Speaker 13: I'm not going to deny that. And we've also looked

582
00:28:34,000 --> 00:28:37,920
Speaker 13: into acquiring a few smaller startups, and in fact we've

583
00:28:37,960 --> 00:28:41,080
Speaker 13: made one active hire already, like a company called spell Wise,

584
00:28:41,520 --> 00:28:45,360
Speaker 13: which helped revamp our mobile team. And we're always looking

585
00:28:45,440 --> 00:28:48,320
Speaker 13: for like new hungry founders who are like you know,

586
00:28:48,840 --> 00:28:52,800
Speaker 13: even earlier stage than we are, faster at executing than

587
00:28:52,840 --> 00:28:55,960
Speaker 13: like typical engineers from big tech, and we're always excited

588
00:28:56,000 --> 00:28:57,440
Speaker 13: about like talking to more people like that.

589
00:28:58,600 --> 00:29:01,680
Speaker 7: An arab intion Abatis pop CEO, thank you for coming

590
00:29:01,720 --> 00:29:05,000
Speaker 7: back on the show in Bluebot Technology and joining.

591
00:29:04,800 --> 00:29:06,400
Speaker 5: Us at the New York studio. Appreciate it.

592
00:29:06,760 --> 00:29:10,240
Speaker 7: Sticking with AI, the Senates by Partners in AI working Group,

593
00:29:10,280 --> 00:29:13,520
Speaker 7: which is led by Majority leader Chuck Schumer, finally released

594
00:29:13,560 --> 00:29:18,720
Speaker 7: its highly anticipated roadmap yesterday to provide guidance on congressional

595
00:29:18,840 --> 00:29:22,880
Speaker 7: efforts to harness AI's benefits but mitigate potential risks. The

596
00:29:22,920 --> 00:29:27,800
Speaker 7: blueprint recommends thirty two billion dollars in annual government spending

597
00:29:28,160 --> 00:29:31,720
Speaker 7: to support non defense AI research and development efforts, in

598
00:29:31,800 --> 00:29:34,600
Speaker 7: part to stay ahead of rivals like China.

599
00:29:34,880 --> 00:29:38,440
Speaker 4: Caroline long time coming, a fur bit of criticism from

600
00:29:38,520 --> 00:29:41,040
Speaker 4: some lobby years over there. But now we're going to

601
00:29:41,080 --> 00:29:44,000
Speaker 4: take the conversation forward and actually maybe divert into the

602
00:29:44,040 --> 00:29:44,920
Speaker 4: world of web three.

603
00:29:44,800 --> 00:29:45,240
Speaker 6: For a moment.

604
00:29:45,880 --> 00:29:48,120
Speaker 4: Segey Nazarrov's gonna be with US co found and Chainlink

605
00:29:48,200 --> 00:29:52,280
Speaker 4: to discuss the trend in asset tokenizations from New York,

606
00:29:53,000 --> 00:29:55,800
Speaker 4: where we're also looking at meta shares, talking of all

607
00:29:55,880 --> 00:29:59,040
Speaker 4: things social media. Look, we're currently under pressure just by

608
00:29:59,080 --> 00:30:01,720
Speaker 4: one point four percent, and the day interesting risk off

609
00:30:01,760 --> 00:30:04,040
Speaker 4: tone given that the rest of the market is pushing

610
00:30:04,200 --> 00:30:08,200
Speaker 4: higher measure being suspected by the EU of hooking kids

611
00:30:08,480 --> 00:30:11,160
Speaker 4: on Facebook and Instagram. Look, this is yet another investigation,

612
00:30:11,240 --> 00:30:13,760
Speaker 4: this time coming under the Digital Services Act that could

613
00:30:13,880 --> 00:30:16,680
Speaker 4: lead to some finds from New York. For San Francisco,

614
00:30:16,720 --> 00:30:17,800
Speaker 4: this is a Bloomberg technology.

615
00:30:32,680 --> 00:30:36,240
Speaker 7: Let's talk crypto an assets ognization with chain Link, a

616
00:30:36,320 --> 00:30:39,760
Speaker 7: company that connects blockchains and brings real world data on

617
00:30:40,000 --> 00:30:42,760
Speaker 7: chain and which now works with the likes of BNP,

618
00:30:43,280 --> 00:30:45,960
Speaker 7: Bank of America and Citybanking. Just in the last few

619
00:30:46,040 --> 00:30:49,480
Speaker 7: moments has had some pretty significant breaking news. Sergey Nazarov

620
00:30:49,760 --> 00:30:52,600
Speaker 7: is the co founder of chain Link and joins US

621
00:30:52,720 --> 00:30:54,360
Speaker 7: now let's start with that news.

622
00:30:54,480 --> 00:30:55,200
Speaker 5: DTCC.

623
00:30:55,720 --> 00:30:59,240
Speaker 7: You have a new arrangement with them, a new relationship,

624
00:30:59,320 --> 00:31:00,000
Speaker 7: explain it to us.

625
00:31:00,040 --> 00:31:03,240
Speaker 5: Yes, yeah, sure.

626
00:31:03,360 --> 00:31:05,440
Speaker 15: So we've been working with them for some time now

627
00:31:05,640 --> 00:31:09,240
Speaker 15: on how to create high quality on chain digital assets

628
00:31:09,800 --> 00:31:13,080
Speaker 15: that are driven by data. And so the report that's

629
00:31:13,160 --> 00:31:16,680
Speaker 15: just come out today shows how data can flow into

630
00:31:16,800 --> 00:31:20,840
Speaker 15: assets on chain and prove critical things about the assets

631
00:31:21,400 --> 00:31:24,320
Speaker 15: on an ongoing basis, which is very different from how

632
00:31:24,400 --> 00:31:27,239
Speaker 15: the current financial system works, where the ownership of an

633
00:31:27,240 --> 00:31:31,520
Speaker 15: asset doesn't give you access to data that becomes your responsibility.

634
00:31:32,120 --> 00:31:35,200
Speaker 15: So the BTCC report was around something called smart now

635
00:31:36,040 --> 00:31:38,600
Speaker 15: where chain link was used to put critical pieces of

636
00:31:38,680 --> 00:31:44,000
Speaker 15: data on chain around funds tokenized funds, and the collaboration

637
00:31:44,160 --> 00:31:48,560
Speaker 15: included big firms like JP Morgan, Franklin, Templeton, BNYML and

638
00:31:48,680 --> 00:31:49,560
Speaker 15: State Street.

639
00:31:49,360 --> 00:31:51,200
Speaker 5: And others were part of the pilot with them.

640
00:31:51,880 --> 00:31:54,160
Speaker 15: And I think what this shows is that real world

641
00:31:54,200 --> 00:31:59,640
Speaker 15: asset to organization is taking a more mainstream institutional interest level,

642
00:32:00,320 --> 00:32:03,880
Speaker 15: especially the real world assets that are backed by data,

643
00:32:04,480 --> 00:32:08,200
Speaker 15: because the big difference here is that the asset can

644
00:32:08,280 --> 00:32:13,280
Speaker 15: have critical information about it continually updated, and the holder

645
00:32:13,320 --> 00:32:15,800
Speaker 15: of the asset or the buyer of the asset doesn't

646
00:32:15,800 --> 00:32:17,800
Speaker 15: have to search for that data and they don't need

647
00:32:17,840 --> 00:32:20,880
Speaker 15: to have any special expertise. And also the data can

648
00:32:20,960 --> 00:32:23,280
Speaker 15: move across chains. That was the other big part of

649
00:32:23,360 --> 00:32:26,760
Speaker 15: the collaboration is how does the data and the asset

650
00:32:26,840 --> 00:32:30,280
Speaker 15: end up moving across chains. But this is just some

651
00:32:30,440 --> 00:32:32,880
Speaker 15: of our initial work together, and I'm hopeful there will

652
00:32:32,920 --> 00:32:36,440
Speaker 15: be more with them and other large csds.

653
00:32:36,920 --> 00:32:39,360
Speaker 4: It's interesting, of course, because there are many vocal critics

654
00:32:39,480 --> 00:32:45,560
Speaker 4: of some of the actual coins and investment opportunities when

655
00:32:45,560 --> 00:32:48,840
Speaker 4: it comes to crypto, but they've always all together now said,

656
00:32:49,280 --> 00:32:52,960
Speaker 4: but we like the underlying technology. We like blockchain CEO

657
00:32:53,120 --> 00:32:56,520
Speaker 4: Jamie Diamond for example of JP Morgan. How does this

658
00:32:56,680 --> 00:32:59,960
Speaker 4: disintermediate longer term financial institute?

659
00:33:02,720 --> 00:33:05,840
Speaker 15: I think financial institutions have a fundamental role to play

660
00:33:06,000 --> 00:33:09,440
Speaker 15: in terms of compliance and in terms of allowing users

661
00:33:10,000 --> 00:33:13,840
Speaker 15: to use digital asset products in a compliant way, in

662
00:33:13,960 --> 00:33:17,280
Speaker 15: relation to their identity, in relation to ways that regulators

663
00:33:17,720 --> 00:33:22,560
Speaker 15: and csds and central banks find acceptable. So I don't

664
00:33:22,600 --> 00:33:25,280
Speaker 15: think banks are going away. I don't think things like

665
00:33:25,360 --> 00:33:28,240
Speaker 15: the DTCC are going away. I don't think central banks

666
00:33:28,240 --> 00:33:30,840
Speaker 15: are going away. I think they're going to use the

667
00:33:30,920 --> 00:33:34,040
Speaker 15: technology I think there will actually be the biggest net

668
00:33:34,240 --> 00:33:37,480
Speaker 15: users of blockchain technology, and they're going to generate the

669
00:33:37,520 --> 00:33:41,320
Speaker 15: most assets, they're going to generate the most payments, and importantly,

670
00:33:41,400 --> 00:33:43,480
Speaker 15: they're going to do it in a way that complies

671
00:33:43,560 --> 00:33:46,520
Speaker 15: with legal requirements. We're kind of at a place in

672
00:33:46,560 --> 00:33:48,360
Speaker 15: the crypto industry where if you're a two and a

673
00:33:48,400 --> 00:33:51,160
Speaker 15: half trillion maybe you can go to as high as

674
00:33:51,240 --> 00:33:54,360
Speaker 15: ten trillion off of hedge funds and prop traders in

675
00:33:54,400 --> 00:33:55,400
Speaker 15: the retail community.

676
00:33:55,920 --> 00:33:57,320
Speaker 5: But in my opinion, if you want to go.

677
00:33:57,360 --> 00:34:01,160
Speaker 15: To the hundreds of trillions that will be the blockchain format,

678
00:34:01,640 --> 00:34:04,800
Speaker 15: you need the global c as these the central banks,

679
00:34:05,040 --> 00:34:08,960
Speaker 15: the big commercial banks, to basically adopt the technology for

680
00:34:09,239 --> 00:34:12,560
Speaker 15: its value. And that's what this collaboration with the DTCC

681
00:34:12,680 --> 00:34:16,319
Speaker 15: has been about, is providing that technical value in ways

682
00:34:16,400 --> 00:34:20,880
Speaker 15: that benefits both the traditional financial system and consumers and

683
00:34:21,080 --> 00:34:23,120
Speaker 15: the Web three blockchain community.

684
00:34:24,000 --> 00:34:26,400
Speaker 4: This is about, you, say, accelo, to the investment fund

685
00:34:26,600 --> 00:34:30,960
Speaker 4: tokenization movement, which particular real world assets we're ultimately going

686
00:34:31,000 --> 00:34:34,680
Speaker 4: to see being put forward on the blockchain. First, it's

687
00:34:34,680 --> 00:34:37,400
Speaker 4: already being done, already being experimented with. But for the

688
00:34:37,560 --> 00:34:40,080
Speaker 4: audience out there who also happen to be training equities

689
00:34:40,160 --> 00:34:43,360
Speaker 4: or in a commodity space, maybe they're upond syndicate desk.

690
00:34:43,880 --> 00:34:46,040
Speaker 4: Where are they going to see their assets at HIT first?

691
00:34:49,480 --> 00:34:52,400
Speaker 15: So it's actually going to vary by institution and what

692
00:34:52,560 --> 00:34:56,320
Speaker 15: institutions are good at tokenizing or good at turning into securities.

693
00:34:56,680 --> 00:34:58,200
Speaker 5: There are basically two categories.

694
00:34:58,320 --> 00:35:00,839
Speaker 15: There are the core financial pro products of the current

695
00:35:00,880 --> 00:35:06,200
Speaker 15: financial system, treasuries, money market funds, bonds. These are being

696
00:35:06,280 --> 00:35:09,680
Speaker 15: tokenized by bigger institutions, and the benefits of tokenizing them

697
00:35:09,760 --> 00:35:13,520
Speaker 15: are around collateral management, and they're around diversifying your on

698
00:35:13,760 --> 00:35:17,239
Speaker 15: chain holdings so that you're not just holding cryptocurrencies on chain,

699
00:35:17,280 --> 00:35:20,560
Speaker 15: you're holding treasuries on chain. Then you have what are

700
00:35:20,640 --> 00:35:23,960
Speaker 15: called assets at the edges. These are assets that have

701
00:35:24,080 --> 00:35:29,120
Speaker 15: traditionally not been tokenized. So these are assets like carbon credits,

702
00:35:29,560 --> 00:35:33,800
Speaker 15: real estate, private equity. These are the assets that have

703
00:35:34,000 --> 00:35:37,719
Speaker 15: not been securitized that are now being securitized in the

704
00:35:37,840 --> 00:35:40,960
Speaker 15: form of a tokenized product. These are the things that

705
00:35:41,080 --> 00:35:44,120
Speaker 15: people have more press releases about. They hear more about

706
00:35:44,520 --> 00:35:48,280
Speaker 15: because they are more exciting kind of net new product.

707
00:35:49,000 --> 00:35:51,840
Speaker 15: And I think that there is tens of trillions of

708
00:35:51,880 --> 00:35:55,280
Speaker 15: dollars in value just in the net new product category.

709
00:35:55,600 --> 00:35:58,319
Speaker 15: But I am actually seeing both of them happening at

710
00:35:58,360 --> 00:36:01,279
Speaker 15: the same time. I'm seeing the toe organization of treasuries

711
00:36:01,680 --> 00:36:06,040
Speaker 15: and money markets and more foundational financial products and very

712
00:36:06,320 --> 00:36:09,239
Speaker 15: foundational parts of the financial system. And then I'm also

713
00:36:09,280 --> 00:36:14,600
Speaker 15: seeing the more advanced kind of securitization two point zero

714
00:36:14,680 --> 00:36:18,359
Speaker 15: waves of tokenized products. The ones that come to mind

715
00:36:18,400 --> 00:36:19,920
Speaker 15: are private equity, real.

716
00:36:19,840 --> 00:36:23,840
Speaker 5: Estate, and carbon credits. So again, Nazrov co founder Chainling,

717
00:36:23,920 --> 00:36:25,279
Speaker 5: great to have you on the show. Thank you.

718
00:36:33,840 --> 00:36:33,880
Speaker 2: So.

719
00:36:34,040 --> 00:36:36,440
Speaker 4: Service Now is beefing up, It's increasing its workforce, its

720
00:36:36,480 --> 00:36:38,040
Speaker 4: expanding in enterprise software.

721
00:36:38,400 --> 00:36:42,040
Speaker 6: How pretty simple. The hiring from a larger.

722
00:36:41,800 --> 00:36:44,880
Speaker 4: Competitor salesforce me to Brodie Ford is here with a

723
00:36:45,040 --> 00:36:47,200
Speaker 4: very well read story today because everyone loves to hear

724
00:36:47,280 --> 00:36:50,200
Speaker 4: about people pinching people from workplaces, and this is.

725
00:36:50,200 --> 00:36:50,640
Speaker 6: What's going on.

726
00:36:51,080 --> 00:36:54,880
Speaker 16: We all love a rivalry, The tech industry loves a rivalry,

727
00:36:54,960 --> 00:36:57,319
Speaker 16: and we're seeing one here. Service Now is a very

728
00:36:57,440 --> 00:37:01,640
Speaker 16: fast growing software company. A lot of other software companies

729
00:37:01,719 --> 00:37:05,239
Speaker 16: have seen this real almost oppressive environment. They've managed to

730
00:37:05,320 --> 00:37:08,759
Speaker 16: keep growing quick. They're expanding into new product categories. Where

731
00:37:08,800 --> 00:37:11,520
Speaker 16: does that leave them? Edging up? Into Salesforce, right, and

732
00:37:11,600 --> 00:37:13,600
Speaker 16: so what do you do? You poach a couple hundred people.

733
00:37:14,000 --> 00:37:16,120
Speaker 16: And that's what we're seeing here, right. They have hired

734
00:37:16,200 --> 00:37:20,560
Speaker 16: pretty aggressively from Salesforce, their CEOs taking some potshots at them.

735
00:37:21,120 --> 00:37:23,600
Speaker 16: I mean they even stopped using Slack once Slack got

736
00:37:23,680 --> 00:37:26,120
Speaker 16: bought by Salesforce. So we have kind of one of

737
00:37:26,160 --> 00:37:28,200
Speaker 16: these fun little rivalries bubbling up here.

738
00:37:28,239 --> 00:37:28,400
Speaker 8: You know.

739
00:37:30,040 --> 00:37:33,239
Speaker 5: Emily Changs can be speaking to Benioff later today. I'm

740
00:37:33,239 --> 00:37:35,160
Speaker 5: speaking to Bill McDermott on Monday.

741
00:37:35,280 --> 00:37:35,480
Speaker 14: Yeah.

742
00:37:35,520 --> 00:37:37,600
Speaker 5: I wonder if it come I wonder if it comes up.

743
00:37:39,560 --> 00:37:40,280
Speaker 5: Put that aside.

744
00:37:40,360 --> 00:37:43,440
Speaker 7: Put that aside for a second. Competition is not new

745
00:37:43,640 --> 00:37:45,800
Speaker 7: in this place, this town. Is it salaries?

746
00:37:46,000 --> 00:37:47,560
Speaker 5: Is it stock? How they're doing it?

747
00:37:48,520 --> 00:37:51,719
Speaker 16: Yeah, I would say part of it is that there's

748
00:37:51,760 --> 00:37:54,000
Speaker 16: been disruption at Salesforce, right, I mean they had the

749
00:37:54,040 --> 00:37:57,000
Speaker 16: big layoffs a little over a year ago. A lot

750
00:37:57,080 --> 00:37:59,520
Speaker 16: of it I hear is that salespeople got laid off

751
00:37:59,600 --> 00:38:02,640
Speaker 16: over there. Service Now said hey, we actually need more

752
00:38:02,680 --> 00:38:04,839
Speaker 16: salespeople with your kind of experience or come on over.

753
00:38:05,320 --> 00:38:07,360
Speaker 16: But it's not at the executive level too, right, I

754
00:38:07,440 --> 00:38:11,279
Speaker 16: mean Service Now's new chief marketing officer came from Salesforce,

755
00:38:11,840 --> 00:38:15,040
Speaker 16: so I imagine there are some pretty aggressive offers happening here,

756
00:38:15,640 --> 00:38:17,800
Speaker 16: and it's really across the board. So it's interesting to

757
00:38:17,880 --> 00:38:20,160
Speaker 16: see what this kind of new infusion of talent does

758
00:38:20,239 --> 00:38:22,799
Speaker 16: for them. I mean, they've grown their headcount maybe three

759
00:38:22,920 --> 00:38:26,279
Speaker 16: thousand over the last year, which most companies have cut

760
00:38:26,360 --> 00:38:28,239
Speaker 16: by that amount, so it'll be interesting to see how

761
00:38:28,280 --> 00:38:29,040
Speaker 16: that impacts them.

762
00:38:29,560 --> 00:38:31,959
Speaker 4: I love that Phil Patterson is giving you the quote

763
00:38:32,000 --> 00:38:35,200
Speaker 4: of basically that imitation is the best form of flattery.

764
00:38:35,280 --> 00:38:37,840
Speaker 4: So it's quite flattering, honestly that they happen to aspire

765
00:38:37,880 --> 00:38:38,680
Speaker 4: to be more like us.

766
00:38:38,880 --> 00:38:41,040
Speaker 16: The larger company doesn't usually want to engage with this

767
00:38:41,160 --> 00:38:43,359
Speaker 16: kind of thing because then it validates it, right, So yeah,

768
00:38:43,360 --> 00:38:45,880
Speaker 16: they they kind of poo poo to the competition.

769
00:38:46,360 --> 00:38:49,680
Speaker 4: Oh well, we'll see how they handle some of the questions. Yeah, Emily,

770
00:38:50,160 --> 00:38:51,920
Speaker 4: and from our own end a little bit later, Bridie

771
00:38:51,920 --> 00:38:55,280
Speaker 4: Ford always great writing, great explaining. He love having on Meanwhile,

772
00:38:55,400 --> 00:38:57,160
Speaker 4: that does it for this edition of BlueBag Technology.

773
00:38:57,239 --> 00:39:00,759
Speaker 7: Yet what an addition of BlueBag Technology was check out

774
00:39:00,800 --> 00:39:03,399
Speaker 7: the pod. Lots of chat about the pod all the time,

775
00:39:03,440 --> 00:39:06,479
Speaker 7: you know exactly where to find it. One day left

776
00:39:06,520 --> 00:39:09,800
Speaker 7: in this fantastic week. Keep with us from San Francisco

777
00:39:09,840 --> 00:39:11,640
Speaker 7: and New York. This is Bloomberg Technology,