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Speaker 1: From Marhart. We're Innovation, Money and Power Collie in Silicon

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Speaker 1: Vallet NBN. This is Bloomberg Technology with Caroline Hyde and

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Speaker 1: Ed Ludlow.

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Speaker 2: Ed Ludlow here in San Francisco. Caroline Hyde off today.

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Speaker 2: This is Bloomberg Technology. Massive show coming out. Full coverage

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Speaker 2: on artificial intelligence. We speak to someone who testified before

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Speaker 2: lawmakers just twenty four hours ago on the risks of

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Speaker 2: the technology, as well as the CEO of character AI

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Speaker 2: and Dina Trace. Last, we bring you the biggest takeaways

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Speaker 2: from the Tesla AGM and break it all down with

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Speaker 2: Tasha Kini of ARC invest and we'll discuss the state

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Speaker 2: of venture capital investing and go life to sol Y

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Speaker 2: Connections Forum focus on global finance, tech and public policy.

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Speaker 3: But first it's going to check on these markets.

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Speaker 2: There's a lot of attention from Wall Street on what's

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Speaker 2: happening in DC and the debt ceiling, a smaller group

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Speaker 2: trying to accelerate negotiations. There's fighting talk from the President

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Speaker 2: and Chuck Schumer as well.

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Speaker 3: How that looks in markets?

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Speaker 2: Now's that one hundred up half a percentage point outperformance

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Speaker 2: in semiconductors. You look at names, I can video AMD

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Speaker 2: a number of names in that basket up in the

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Speaker 2: high single digits, some of it AI related, I am sure.

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Speaker 2: In terms of the bomb market, US Tenure, Yeald three

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Speaker 2: point five six percent a little higher, and Bitcoin interesting.

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Speaker 2: We're back now below twenty seven thousand US dollars per token.

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Speaker 2: We'll bring you some details of newsflow in the crypto

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Speaker 2: space later in the program in terms of specific names

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Speaker 2: and movers. One piece of news out this Wednesday morning

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Speaker 2: is Amazon with new Echo devices, a real emphasis on

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Speaker 2: Alexa and part of the narrative around generative AI tools.

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Speaker 2: I guess in the voice context of what they'll do

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Speaker 2: to push back in some of the advances of their

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Speaker 2: peers like Alphabet Dina, Trace now down one point four percent,

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Speaker 2: have been much higher in pre market lease on strong

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Speaker 2: earnings beating the quarter gone in a full year outlook,

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Speaker 2: there was above expectations. We will speak to the CEO

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Speaker 2: letter later in the program. Then Tesla up four percent,

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Speaker 2: now really accelerating in terms of its gains. The big

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Speaker 2: takeaways out of the annual general meeting. They will now

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Speaker 2: look at advertising in a limited way. Jbi Strawbell added

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Speaker 2: to the board and Elon Musk is there to stay.

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Speaker 3: He will remain as CEO.

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Speaker 2: He's going to really focus now a lot more on Tesla,

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Speaker 2: having of course been distracted by Twitter in recent weeks

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Speaker 2: and months. Let's stick with a Tesla story. Yesterday, CEO

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Speaker 2: Elon Musk held that company and your shareholder meeting, and

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Speaker 2: while taking questions from investors, he excited the crowd basically

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Speaker 2: by saying this.

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Speaker 4: And although there's there's obviously a lot of people that

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Speaker 4: follow like to say the Tesla count and the you

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Speaker 4: know my account whatever on Twitter, to some degree, it

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Speaker 4: is preaching to the choir, and the choir is already convinced.

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Speaker 4: So I think what you're saying does does have some merits,

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Speaker 4: and you know what, I believe in taking taking suggestions.

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Speaker 4: So we'll try a little advertising and see how it goes.

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Speaker 2: Let's get into Tesla and his ad shift with Tasha

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Speaker 2: Kini Ark invest director of Investment Analysis. That's choir as

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Speaker 2: convinced Tasha, does advertising convince others that aren't in the choir?

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Speaker 5: Yeah, I mean, I think it's certainly the point that

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Speaker 5: everyone is harping on here. You know, I think generally

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Speaker 5: it's not a bad idea to at least try advertising,

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Speaker 5: right I mean from our research, we know that on

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Speaker 5: both a sticker price basis now and a total cost

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Speaker 5: of ownership basis, evise are better cars. Right, They're cheaper,

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Speaker 5: they're more performant. So you know, if you're not buying

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Speaker 5: an electric vehicle, the question is why. Maybe you know

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Speaker 5: that information just hasn't reached you yet. Maybe an ad will.

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Speaker 5: So I don't think it's a bad idea there, you know,

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Speaker 5: I'd say what I was most excited about from last

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Speaker 5: night was all the talk around autonomy. You know, Elon

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Speaker 5: said he thinks this will be the greatest asset unlock

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Speaker 5: in human history, and i'd agree. You know, we've modeled

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Speaker 5: that autonomous driving could add an additional twenty six trillion

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Speaker 5: dollars to GDP in the next ten years. It is

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Speaker 5: going to totally change touslis business model and we're e's

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Speaker 5: ated for it.

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Speaker 2: I want to get into a five year forecast. But

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Speaker 2: let's react to the other announcements, which is JB. Strubble

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Speaker 2: being added to the board. And also you know the

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Speaker 2: comments from Elon Musk about his focus on Tesla remaining

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Speaker 2: as CEO, particularly to get the company through its next

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Speaker 2: focus on artificial intelligence. What did you make of that, Tasha, Yeah,

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Speaker 2: you know, I think JV.

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Speaker 5: Strubble is a great addition to the board, right you know,

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Speaker 5: right now he's working with his own company on battery cycling.

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Speaker 5: We know that's crucial to scale the battery industry as

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Speaker 5: a whole. They're seeing great efficiency, I think over ninety

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Speaker 5: percent and recycling. You know, we've always thought that it's

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Speaker 5: important for Elon to stay on as CEO as they

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Speaker 5: reach full autonomy again because we think that this is

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Speaker 5: the next greatest milestone for Tesla. And you know, arguably,

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Speaker 5: you know, any automaker out out there, this is what

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Speaker 5: they really should be going for. So we think that

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Speaker 5: autonomous driving or autonomous ridehill, which is how we expected

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Speaker 5: to play out, will constitute roughly two thirds of Tesla's

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Speaker 5: enterprise value in the next five years. So this is huge.

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Speaker 5: Tesla has an enormous data advantage and you know you

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Speaker 5: shouldn't miss it.

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Speaker 2: Investors or shareholders rejected a proposal for Tesla to publish

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Speaker 2: a key person risk report, and Tessa's argument was, well,

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Speaker 2: if we put all of our talent in the shop window,

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Speaker 2: our competitors will try and take them. But do you

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Speaker 2: still have a key man risk concern around Elon Musk.

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Speaker 2: I guess, you know, a new CEO at Twitter goes

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Speaker 2: some way to allaying those concerns.

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Speaker 5: Yeah, you know, I think there's a lot of focus

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Speaker 5: on this question. What I what I'd broadly say is

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Speaker 5: I'd be a lot more concerned if he said he

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Speaker 5: was stepping away from Tesla. Right again, I think he's

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Speaker 5: crucial to cross that autonomy finish line. You know, autonomous

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Speaker 5: driving lowers the cost per mile of ride hill significantly

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Speaker 5: invites a lot of people that are not currently in

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Speaker 5: the red hill market into it. You know, it's going

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Speaker 5: to be a multi trillion dollar industry over the next

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Speaker 5: five years. So, you know, and on Elon's time. You know,

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Speaker 5: we've heard this question come up so many times over

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Speaker 5: the past five years. I mean, Tesla's SpaceX. You know,

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Speaker 5: he was one of the founders of Open AI. He's

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Speaker 5: clearly demonstrated that he can juggle a lot of tasks

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Speaker 5: and ultimately we're just focused on his execution, right and

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Speaker 5: I think given that Tesla has the best you know,

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Speaker 5: cost relative to performance of any electric vehicle maker out there,

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Speaker 5: you know, he's already proved sort of that that he

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Speaker 5: can juggle multiple things and succeed at it.

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Speaker 2: There was a demonstration of progress, apparent progress in Optimus

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Speaker 2: that the humanoid.

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Speaker 3: Bolts.

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Speaker 2: When you saw that, did it give you any feeling

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Speaker 2: that they are making progress in the field of AI

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Speaker 2: in that use case at least?

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Speaker 3: Yes?

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Speaker 5: I think Optimist is really interesting. So it's definitely out

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Speaker 5: of our five year forecasting window. You know, our our

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Speaker 5: price target for Teslas that our expected value is roughly

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Speaker 5: two thousand dollars per share in twenty twenty seven. That

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Speaker 5: actually doesn't include Optimists because I think that you know,

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Speaker 5: by the time it's a meaningful contributor. Again, it could

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Speaker 5: be further into the future. But I think the important

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Speaker 5: thing to look at here is you know why Tesla,

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Speaker 5: Why why humanoid robot? Well, given that they have this

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Speaker 5: massive data advantage from autonomous driving. You know, they have

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Speaker 5: millions of cars on the road, they're collecting over a million,

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Speaker 5: they have over a million miles driven in FSD daylight daily,

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Speaker 5: which they can then pull data from. You know, that's

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Speaker 5: an order of magnitude more than competitors. So I think

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Speaker 5: this sets them up well to base create robots that

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Speaker 5: move through the physical world and the autonomous car will

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Speaker 5: be the first version of that, but they can you know,

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Speaker 5: poort that knowledge over into other robots, and I think

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Speaker 5: the Optimist robot is a good example. You know, we

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Speaker 5: talk a lot at ARC about backwards integration. I think

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Speaker 5: the fact that this is a humanoid robot and it

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Speaker 5: can move through spaces that were already built for humans

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Speaker 5: is big. And I think this, you know, in the future, yeah,

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Speaker 5: could be a major productivity enhancer like all AI. You know,

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Speaker 5: we'll see you know, non market labor activity turn into

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Speaker 5: market labor activity, and I think the productivity of the

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Speaker 5: individual worker will ultimately increase because of this. So we're

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Speaker 5: excited about it.

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Speaker 2: So your twenty twenty seven coal is two thousand dollars

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Speaker 2: per share, which I'll give you an opportunity to explain.

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Speaker 2: But the question is, did anything in that presentation or

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Speaker 2: in the interview with CNBC afterwards move the needle for

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Speaker 2: you guys change the course of where you see this

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Speaker 2: company going.

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Speaker 5: Yeah, I think again, since we're so focused on the

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Speaker 5: long term horizon, that five year view, you know, I

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Speaker 5: would stick to that two thousand dollars per share estimate

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Speaker 5: that we put out, And you know, I think again,

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Speaker 5: what I'm more looking forward to is everyone else realizing

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Speaker 5: the opportunity and autonomous driving, because I actually think that

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Speaker 5: Tesla does not get enough questions about this opportunity, given

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Speaker 5: how monumental it will be. So I was glad that

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Speaker 5: we heard Elon talk about that last night again because

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Speaker 5: I think that most don't truly understand how big of

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Speaker 5: a productivity unlock and a cash flow unlock, because it

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Speaker 5: will be because we think autonomous ride hill could have

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Speaker 5: software like Margins, and we heard affirmation of that last night.

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Speaker 5: So again, it's a recurring revenue stream software like Margins,

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Speaker 5: So you know, it's up to let's say, roughly ten

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Speaker 5: thousand dollars in cash flower car per year. I mean

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Speaker 5: that's unheard of, and no other traditional automaker is, you know,

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Speaker 5: close to the data advantage that Tesla has at least,

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Speaker 5: so they're in a major position there.

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Speaker 2: Yeah, it's interesting because ahead of you coming on, a

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Speaker 2: number of people tweeted at me saying, actually they wanted

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Speaker 2: to hear a little bit more about the plan for

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Speaker 2: autonomous driving or at least the robotaxi vision of the

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Speaker 2: future are Thanks to Tasha Kini Promark invest for that reaction,

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Speaker 2: to Tesla's AGM before a Senate Judiciary subcommittee on Tuesday.

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Speaker 2: Open AI CEO Sam Outman praised AI's potential, but warned

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Speaker 2: that the emerging technology is powerful enough to change society

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Speaker 2: in unpredictable ways. Joining us now as someone who also

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Speaker 2: testified on the Hill alongside him, NYU Professor emeritus and

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Speaker 2: Geometric Intelligence founder Gary Marcus. Of course, you're also the

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Speaker 2: host of the podcast Humans Versus Machines, which is kind

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Speaker 2: kind of the debate that we're having as a nation

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Speaker 2: and globally. Right now, let me ask you this, what

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Speaker 2: good came out of yesterday's hearing?

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Speaker 3: What was the net result?

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Speaker 6: I thought the hearing was actually fantastic. It far exceeded

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Speaker 6: my own expectations that I think probably most others. It

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Speaker 6: was really bipartisan, and I think we all agreed there.

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Speaker 6: Almost everybody except the IBM executive all agreed that we

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Speaker 6: need to have some kind of national agency governing AI,

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Speaker 6: and probably want some global agency doing that. Sam Altman

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Speaker 6: was supportive of that. That's an idea I've been pushing

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Speaker 6: for the last month or two. So it was wonderful

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Speaker 6: to have his endorsement and the government or the Senators

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Speaker 6: as a whole were pretty positive towards it. I think

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Speaker 6: the notion is that the United States should try to

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Speaker 6: lead the way if we're going to do something global,

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Speaker 6: and I hope that will happen. And there was also

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Speaker 6: strong support for having something like FDA kind of regulations

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Speaker 6: where you have a sufficiently large model, you need to

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Speaker 6: show that it is sufficiently safe.

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Speaker 1: You can't just release something to one hundred million people.

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Speaker 6: So there's lots to discuss, but I thought it was

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Speaker 6: a very positive atmosphere, very bipartisan, and people recognized how

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Speaker 6: serious the problems were.

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Speaker 1: And also I think there was a lot of.

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Speaker 6: Sense from the senators that they feel like they didn't

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Speaker 6: do the right thing yes with Section two thirty in

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Speaker 6: the Internet, and that they wanted to do better this

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Speaker 6: time in there here a lot faster. You know, it

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Speaker 6: took them like fourteen years after social media before they

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Speaker 6: really did anything, and you know, it's only six months

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Speaker 6: after shat GPT made it big that they're trying to

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Speaker 6: yes handle it. So I saw a lot of seriousness

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Speaker 6: on the part of the senators.

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Speaker 3: Gary.

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Speaker 2: I invited our audience to put forward questions. A lot

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Speaker 2: of people interested on you coming on the program, We'll

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Speaker 2: give you a second to take on some water. There

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Speaker 2: are many that tweeted at me saying that what you

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Speaker 2: were doing was scare mongering. Is that a fair accusation

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Speaker 2: that they've led at you.

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Speaker 1: I don't think so.

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Speaker 6: I mean, it's true that I'm trying to raise alarms

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Speaker 6: about things that I think are genuinely risky. But I

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Speaker 6: think scare mongering is if you don't actually think that

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Speaker 6: there's a risk.

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Speaker 1: And you're just trying to manipulate people. And I think

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Speaker 1: the real risk.

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Speaker 6: I've put all my own effort, I'm not getting paid

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Speaker 6: for this into trying to help us address those risks

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Speaker 6: because I think they're real. Sam thought they were real too,

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Speaker 6: you know. Sam agreed with me. Yes, there were risks

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Speaker 6: to our elections and possibly much graver risks in the

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Speaker 6: long term if we don't figure out how to control

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Speaker 6: our AI systems.

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Speaker 2: One of the proposals that Sam put forward was to establish,

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Speaker 2: at least here in the United States, an agency to

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Speaker 2: license or have some sort of license seeing system for

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Speaker 2: the development of AI. There are many that believe that

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Speaker 2: would basically centralize activity control power among the biggest tech companies.

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Speaker 3: What's your response to that, I mean, just to clarify.

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Speaker 6: Even before this meeting, I wrote our ed and Economist,

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Speaker 6: and I gave a TED talk on April eighteenth about

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Speaker 6: having an international agency to regulate AI, and.

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Speaker 1: Sam was supportive of that.

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Speaker 6: He emphasized licensing, and he emphasized excuse me, sorry about

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Speaker 6: the cough. I've been doing so many interviews. He emphasized

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Speaker 6: licensing for large scale models, not necessarily for small models.

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Speaker 6: I think we all agree that we don't want to

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Speaker 6: cut off research. We don't want to cut off small

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Speaker 6: companies from having a role. But the larger the model,

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Speaker 6: the larger the impact, the more we might need licensing.

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Speaker 2: One of the questions from our audience is for you

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Speaker 2: to explain what is the difference between fooling a human

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Speaker 2: with artificial intelligence and fooling a system with artificial intelligence.

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Speaker 6: I'm not sure what you mean by fooling a system

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Speaker 6: with artificial intelligence, but I think.

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Speaker 2: What I mean is that if you're a consumer and

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Speaker 2: you're confronted by information from a generative a tool and

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Speaker 2: it's false, you have fooled. But you can also use

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Speaker 2: llms and foundation models to automate all kinds of processes

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Speaker 2: from SaaS through to internal workflow, whatever it will be.

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Speaker 2: And I think the root of that question is what

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Speaker 2: is the greater risk about human interaction with AI or

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Speaker 2: broadly automation that comes from those tools.

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Speaker 6: That's a good question, I guess. You know, there are

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Speaker 6: different risks of different timescales. The thing that I'm most

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Speaker 6: currently worried about is the risk to democracy, and the

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Speaker 6: risk to democracy actually comes both from kind of accidental

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Speaker 6: mistakes from these systems. We know they can fabulate, or

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Speaker 6: some people call it hallucinate. They do that automatically without

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Speaker 6: human intervention. They're just not a very reliable technology, and

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Speaker 6: then bad actors can deliberately use them to make enormous

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Speaker 6: amounts of misinformation that's incredibly plausible. They can do that

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Speaker 6: with deep fix for images, they can do it with

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Speaker 6: TEX and so they're both the fact that the systems

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Speaker 6: are unreliable, they don't know what they're talking about, can't

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Speaker 6: verify what they're saying, and they can be abused. So

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Speaker 6: it's kind of like both similarly, in terms of long

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Speaker 6: term risk, you can think of deliberate scenarios where people

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Speaker 6: try to manipulate the markets and things go wrong and

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Speaker 6: there's violence because people misattribute things that are happening, or

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Speaker 6: maybe machines do things that are entirely different from what

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Speaker 6: we program them do. So I don't know if you

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Speaker 6: can neatly dichotomize it them that way. The reality is

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Speaker 6: the machines are not reliable, and the machines are not

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Speaker 6: very well controlled, and so that leads to all kinds

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Speaker 6: of risks.

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Speaker 2: Gary, why is the large language model approach not the

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Speaker 2: right approach to achieve AGI in your opinion?

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Speaker 6: Well, I just saw other words on your screen safer

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Speaker 6: and more relied, more aligned. Excuse me, These systems aren't

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Speaker 6: that safe. They're not that sophisticated. They don't have a

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Speaker 6: model of the world. They don't understand what's going on,

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Speaker 6: and so, for example, they make stuff up all the time.

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Speaker 6: They up a sexual harassment charge. They said Elon Musk

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Speaker 6: was dead when he's alive. I mean, all kinds of craziness.

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Speaker 3: They're just not.

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Speaker 6: Systematic, trustworthy bits of AI. You know, when we look

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Speaker 6: back twenty years ago, I mean twenty years from now,

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Speaker 6: it'll be like looking back at cell phones from twenty

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Speaker 6: years ago. It's like they had a phone that big.

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Speaker 6: We're going to say they used AI that was that unreliable?

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Speaker 1: Like, what were they.

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Speaker 2: Thinking, Gary, what do you want to happen next? You know,

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Speaker 2: you actually quite praising or complementary of the hearing itself

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Speaker 2: it being bipartisan. But if there is a concrete step

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Speaker 2: for law makers or regulators to take, what does it

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Speaker 2: look like to your mind?

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Speaker 6: I think the next step is to actually figure out

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Speaker 6: what regulation would look like. I think we probably need

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Speaker 6: a cabinet level agency and I think we should start

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Speaker 6: drafting plans for how that would work. And there's lots

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Speaker 6: of complications in terms of how it would work with

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Speaker 6: existing agencies, how it would work international. I think we

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Speaker 6: should start, you know, taking the consensus that we've got

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Speaker 6: and try to represent what that might look as actual legislation.

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Speaker 2: And finally, Gary, you talked about the need for a

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Speaker 2: global regulator or a global agency. Do you recognize a

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Speaker 2: sort of multi speed approach what Europe is doing, what

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Speaker 2: China is doing when it comes to the regulation and

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Speaker 2: development of AI technology.

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Speaker 6: So I think we have an opportunity here to do

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Speaker 6: something rational rather than just sort of arbitrary. In Balkani

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Speaker 6: so it's not actually in the interest of the AI

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Speaker 6: companies if we have like one hundred and ninety three

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Speaker 6: or one hundred and ninety five rivers. You know, many

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Speaker 6: different places where you have to train your own language model.

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Speaker 6: So as you probably well know, it's very expensive to

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Speaker 6: train these models. It's very costly in terms of climate impact.

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Speaker 6: And so if everybody is requiring their own set of rules,

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Speaker 6: their own set of giant language model that takes you know,

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Speaker 6: millions of dollars to train and you know a certain

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Speaker 6: number of jet flights in terms of emissions and so forth,

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Speaker 6: that's not a great thing.

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Speaker 1: And so I think the.

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Speaker 6: Companies themselves would like some kind of alignment here, some

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Speaker 6: kind of systematic.

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Speaker 1: Way of doing business.

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Speaker 6: And then you know, there's all this talk about global

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Speaker 6: tension and stuff like that, and some of it is

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Speaker 6: of course real, but in terms of AI, like, no

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Speaker 6: country wants their citizens to be completely overwhelmed by misinformation,

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Speaker 6: nobody wants to be completely overwhelmed by cybercrime, and nobody wants,

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Speaker 6: you know, robots to take over the world, which is

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Speaker 6: not an immediate concern, but in the long term we

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Speaker 6: do have to make sure we get that right. So

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Speaker 6: I think even if you know some countries are going

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Speaker 6: to do some things differently. I think there's a lot

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Speaker 6: of intersection between what different countries want and even the companies,

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Speaker 6: you know, want some alignment here. So I think there's

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Speaker 6: a real opportunity, even though, of course the politics are difficult.

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Speaker 2: M YU Professor Emeritis Gary Marcus, also the host, of course,

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Speaker 2: of the podcast Humans Versus Machines, were very grateful for

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Speaker 2: your time.

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Speaker 3: Thank you. Out of Washington, d C.

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Speaker 2: Now sticking with AI, Goldman Sachs says artificial intelligence offers

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Speaker 2: the biggest potential long term support for US profit margins.

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Speaker 2: AI can boost net margins by nearly four hundred basis

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Speaker 2: points over a decade, but the Goldman team notes that

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Speaker 2: predicting AI's impact is tricky due to the large number

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Speaker 2: of unknown factors surrounding it, such as as we just

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Speaker 2: discussed regulation. There have been about sixteen hundred mentions of

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Speaker 2: AI by US and European firms in the first quarter

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Speaker 2: earning's conference calls alone, of course, a record number now

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Speaker 2: coming up. How Kim Kardashian is using her social reach

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Speaker 2: to attract investors for a private equity fund. Keeping our

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Speaker 2: eye also on shares of Cisco. We get their earnings

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Speaker 2: after the bell. Another name to what's we're hired by

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Speaker 2: around seven tenths of one percent. Again, would we be

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Speaker 2: surprised if AI is a key term when it comes

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Speaker 2: to them, but more I guess on the networking side.

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Speaker 3: This is Bloomberg.

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Speaker 2: Welcome back to Bloomberg Technology, imed Love in San Francisco.

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Speaker 2: It's got a quick check in on the markets as

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Speaker 2: you want to pay pretty close attention to bitcoin. We're

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Speaker 2: down now below twenty seven thousand US dollars per token,

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Speaker 2: off six tens of a percent in a session.

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Speaker 3: Remember we're trading twenty four seven.

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Speaker 2: When it comes to bitcoin, there is some technology news

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Speaker 2: out there in terms of engagement between platforms.

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Speaker 3: No real news driver.

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Speaker 2: There isn't really a close correlation between the trading we

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Speaker 2: see in bitcoin and other risk assets, particularly as it

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Speaker 2: relates to the debt ceiling.

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Speaker 3: Although I would say.

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Speaker 2: We've kind of traded in this range from twenty six

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Speaker 2: to twenty eight thousand US dollars per token over the

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Speaker 2: last couple of weeks or so.

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Speaker 3: In terms of the.

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Speaker 2: Specific equity moves, there is newsflow that is driving particular names.

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Speaker 2: We're thinking of course about Amazon dot Com. They're out

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Speaker 2: with a new range of eco device and we'll give

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Speaker 2: you those details in just a moment. But when we

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Speaker 2: consider AI, a lot of commentary right now from the

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Speaker 2: companies themselves about how they're taking the R and D

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Speaker 2: side of what they've done in the field of afterivisial

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Speaker 2: intelligence and putting it into products. Alphabet continuing to see momentum.

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Speaker 3: We're actually now.

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Speaker 2: Flat on the stock, but it has seen a lot

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Speaker 2: of gains in recent sessions based on the announcements that

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Speaker 2: were made.

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Speaker 3: At Google io I teased it.

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Speaker 2: Let's talk a little bit about Amazon introducing an updated

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Speaker 2: slate of e co devices and pledging to bring chat

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Speaker 2: GPT style AI to Alexa powered gadgets. Amazon Senior vice

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Speaker 2: president of Devices and Services Dave Limp said the new

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Speaker 2: more conversational capabilities will roll out incrementally with a few

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Speaker 2: things to solve along the way. Now it's not just Alexa,

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Speaker 2: and it's not Alexa, but it does offer personalized AI assistance.

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Speaker 2: I'm talking about Character Ai, a platform launched last September

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Speaker 2: which is now reaching two hundred million platform visits per month.

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Speaker 2: Noam Shazir, founder and CEO of Character Ai, and of

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Speaker 2: of course, former Google Brain team member, joins us now

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Speaker 2: for more.

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Speaker 3: Noah, welcome to the program.

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Speaker 2: We wanted to have you on Bloomberg Technology for a

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Speaker 2: little while. It's interesting to see the engagement with character RAI,

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Speaker 2: and I start by asking you this. You have a

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Speaker 2: history and a story in the development of AI. But

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Speaker 2: character AI is a pretty simple tool. Why did you

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Speaker 2: start it?

440
00:23:18,359 --> 00:23:21,680
Speaker 7: Well, I mean, I've been involved in inventing a lot

441
00:23:21,680 --> 00:23:26,560
Speaker 7: of the technology behind large language models. But like, this

442
00:23:26,640 --> 00:23:29,720
Speaker 7: is a technology that has like a billion use cases,

443
00:23:29,840 --> 00:23:32,639
Speaker 7: and you know it's something where you will no longer

444
00:23:32,680 --> 00:23:35,960
Speaker 7: need a developer to invent like a billion new applications.

445
00:23:36,040 --> 00:23:38,320
Speaker 7: Users can just talk to the thing and come up

446
00:23:38,320 --> 00:23:41,320
Speaker 7: with new value and so, like the most important thing

447
00:23:41,800 --> 00:23:45,120
Speaker 7: is get it to the users, like right right now.

448
00:23:45,160 --> 00:23:47,920
Speaker 7: So we just wanted to do that as quickly as

449
00:23:47,960 --> 00:23:51,639
Speaker 7: possible and let people figure out what it's good for it.

450
00:23:53,000 --> 00:23:57,359
Speaker 2: Okay, so I've been using character AI in recent weeks.

451
00:23:57,480 --> 00:23:59,359
Speaker 2: You have the choice, right, you can use a pre

452
00:23:59,400 --> 00:24:02,280
Speaker 2: created app Baitar, which we'll show an example of in

453
00:24:02,320 --> 00:24:04,040
Speaker 2: just a second, or you can create your own. But

454
00:24:04,080 --> 00:24:08,159
Speaker 2: it's interesting. You offer an avatar in the likeness of

455
00:24:08,240 --> 00:24:11,520
Speaker 2: Elon Musk and you can interact with it. You can

456
00:24:11,560 --> 00:24:14,719
Speaker 2: ask questions. We're showing that on the screen right now.

457
00:24:15,119 --> 00:24:17,480
Speaker 2: He starts by saying, you're wasting my time. I literally

458
00:24:17,600 --> 00:24:20,000
Speaker 2: rule the world. You ask if you could go back

459
00:24:20,000 --> 00:24:23,240
Speaker 2: in time, where, when and where would you go? Just

460
00:24:23,359 --> 00:24:27,639
Speaker 2: explain what one could use character AI for.

461
00:24:30,280 --> 00:24:31,920
Speaker 7: Well, it's not our job to tell you what to

462
00:24:32,040 --> 00:24:34,640
Speaker 7: use it for. Like our job is to put out

463
00:24:34,640 --> 00:24:37,000
Speaker 7: something general and have users figure it out. And what

464
00:24:37,000 --> 00:24:40,480
Speaker 7: we're seeing is a lot of fun, a lot of entertainment,

465
00:24:40,600 --> 00:24:43,600
Speaker 7: and the huge amount of emotional support. We see testimonials

466
00:24:43,600 --> 00:24:46,240
Speaker 7: of people saying like I have no friends, I was

467
00:24:46,280 --> 00:24:50,000
Speaker 7: depressed to save my life, like all kinds of wonderful

468
00:24:50,000 --> 00:24:54,400
Speaker 7: stuff that we just had never imagined and and it's happening.

469
00:24:55,200 --> 00:24:58,680
Speaker 2: And I should point out again that is a generative

470
00:24:58,720 --> 00:25:03,040
Speaker 2: AI avatar is not the real Elon Musk, But therein

471
00:25:03,160 --> 00:25:06,960
Speaker 2: lies the point of the platform. Does this show the

472
00:25:07,000 --> 00:25:11,280
Speaker 2: limitations of where we are with large language models? You know, respectfully,

473
00:25:12,040 --> 00:25:16,280
Speaker 2: the character AI is a platform is a simple interaction

474
00:25:16,400 --> 00:25:21,520
Speaker 2: for the user. Right is that where large language models are?

475
00:25:21,640 --> 00:25:21,800
Speaker 5: Right?

476
00:25:21,840 --> 00:25:22,119
Speaker 3: Now?

477
00:25:23,119 --> 00:25:25,400
Speaker 7: Yeah, I mean this is what it's good for now,

478
00:25:25,440 --> 00:25:27,879
Speaker 7: So let's let people use it for now, Like we

479
00:25:27,960 --> 00:25:30,920
Speaker 7: have on every page it says. Everything the characters say

480
00:25:31,000 --> 00:25:33,800
Speaker 7: is made up, so users understand that this is fiction,

481
00:25:33,880 --> 00:25:37,359
Speaker 7: but it's still bringing a huge amount of value from

482
00:25:37,400 --> 00:25:40,760
Speaker 7: what is really the very very beginnings of this technology.

483
00:25:40,840 --> 00:25:44,120
Speaker 7: This is like iteration, like zero point zero point one,

484
00:25:44,600 --> 00:25:47,960
Speaker 7: you know, relative to to you know to what's coming next.

485
00:25:48,000 --> 00:25:50,159
Speaker 7: And we're just going to keep making this thing better.

486
00:25:50,200 --> 00:25:53,160
Speaker 7: But at the same time, like, let let's let's let

487
00:25:53,160 --> 00:25:53,800
Speaker 7: people use it.

488
00:25:54,920 --> 00:25:56,600
Speaker 3: So how do you monetize this platform?

489
00:25:56,680 --> 00:26:02,119
Speaker 7: Now, Well, we are we are starting with you know,

490
00:26:02,119 --> 00:26:06,439
Speaker 7: with the freemium model, but you know what we you know,

491
00:26:06,440 --> 00:26:09,320
Speaker 7: we're convinced that the real value is to consumers and

492
00:26:09,400 --> 00:26:13,440
Speaker 7: end users, and so we will continue to as things

493
00:26:13,440 --> 00:26:16,439
Speaker 7: get better, you know, monetized to users.

494
00:26:17,640 --> 00:26:19,960
Speaker 2: You you were at Google Brain and I know you've

495
00:26:19,960 --> 00:26:23,000
Speaker 2: talked about this idea of the twenty percenters, in other words,

496
00:26:23,040 --> 00:26:26,040
Speaker 2: people who were kicking around Menlo Park at the time

497
00:26:26,080 --> 00:26:30,040
Speaker 2: and working on AI in their spare time in the

498
00:26:30,080 --> 00:26:33,440
Speaker 2: working day, right, And I wondered when you saw Google

499
00:26:33,600 --> 00:26:37,159
Speaker 2: at Google Io make all of these product announcements and

500
00:26:37,200 --> 00:26:39,560
Speaker 2: put their work in AI into the real world, what

501
00:26:39,600 --> 00:26:40,520
Speaker 2: your reaction.

502
00:26:40,440 --> 00:26:44,680
Speaker 7: Was, Oh, that's one, It's wonderful. Google's an incredible company.

503
00:26:44,840 --> 00:26:48,879
Speaker 7: Google has been bringing trillions of dollars of value to

504
00:26:48,920 --> 00:26:52,280
Speaker 7: the world, you know, directly to consumers for you know,

505
00:26:52,320 --> 00:26:55,640
Speaker 7: for decades, and very excited to see that continue.

506
00:26:57,560 --> 00:27:01,320
Speaker 2: What did you make of yesterday's here of Sam Outman,

507
00:27:01,480 --> 00:27:03,560
Speaker 2: Gary Marcus and the conversations that we.

508
00:27:03,640 --> 00:27:09,520
Speaker 7: Had, Well, I mean, I mean, like we don't even

509
00:27:09,560 --> 00:27:11,760
Speaker 7: know what the best use cases are. It's it's the

510
00:27:11,800 --> 00:27:15,680
Speaker 7: actual users, like the individual like every individual person on earth,

511
00:27:15,680 --> 00:27:19,679
Speaker 7: who can actually unlock the value in this stuff. So

512
00:27:20,359 --> 00:27:24,719
Speaker 7: I am kind of dubious about the ability of the

513
00:27:24,720 --> 00:27:28,480
Speaker 7: federal government to you know, to regulate and to tell

514
00:27:28,480 --> 00:27:30,879
Speaker 7: people what the thing is good for, because you know,

515
00:27:31,080 --> 00:27:32,480
Speaker 7: they just don't have the capacity.

516
00:27:33,720 --> 00:27:36,119
Speaker 2: All right, No, I'm She's a founder and CEO of

517
00:27:36,200 --> 00:27:39,480
Speaker 2: Character AI, at one time a member of Google Brain.

518
00:27:39,520 --> 00:27:42,399
Speaker 3: Thank you so much for your time. Thank you, ed Now.

519
00:27:42,680 --> 00:27:45,600
Speaker 2: In other news that we're following, Elizabeth Holmes lost her

520
00:27:45,640 --> 00:27:49,480
Speaker 2: final request to remain free on bail while she appeals

521
00:27:49,840 --> 00:27:53,040
Speaker 2: her fraud conviction. The ruling means the Pharaenos founder will

522
00:27:53,080 --> 00:27:55,720
Speaker 2: soon have to report to prison to begin her more

523
00:27:55,760 --> 00:27:59,600
Speaker 2: than eleven year sentence after being convicted of defrauding investors

524
00:27:59,720 --> 00:28:04,720
Speaker 2: last November former farahno's president Ramesh Sunny Balwani's similar request

525
00:28:04,840 --> 00:28:08,120
Speaker 2: was also denied, and he reported to prison last month

526
00:28:08,400 --> 00:28:14,040
Speaker 2: to begin his thirteen year sentence. Coming up, we'll discuss

527
00:28:14,119 --> 00:28:18,800
Speaker 2: the globalization of VC money and opportunities with companies that

528
00:28:18,880 --> 00:28:22,400
Speaker 2: have ties with China US or next with Patrick John

529
00:28:22,520 --> 00:28:23,920
Speaker 2: from M thirty one Capital.

530
00:28:24,200 --> 00:28:25,000
Speaker 3: This is Bloomberg.

531
00:28:33,480 --> 00:28:36,400
Speaker 2: Let's head out to Sault Eye Connections. The Global Finance,

532
00:28:36,480 --> 00:28:38,880
Speaker 2: Tech and Public Policy Forum happen over in New York

533
00:28:38,960 --> 00:28:42,280
Speaker 2: right now Bloomberg. Shnali Bassak there with our next guest,

534
00:28:42,360 --> 00:28:45,680
Speaker 2: M thirty one Capital founding partner Patrick Jong, just off

535
00:28:45,680 --> 00:28:51,120
Speaker 2: a panel way discussed globalization of bench capital and tech entrepreneurship.

536
00:28:51,280 --> 00:28:56,000
Speaker 8: Shnali, thank you Ed, and thank you Patrick for joining

537
00:28:56,080 --> 00:28:59,000
Speaker 8: us because I understand that this is your first trip

538
00:28:59,280 --> 00:29:01,640
Speaker 8: back to New York since COVID.

539
00:29:02,160 --> 00:29:03,360
Speaker 3: What has that been.

540
00:29:03,360 --> 00:29:06,360
Speaker 8: Like and what has the reopening been like in China?

541
00:29:07,280 --> 00:29:08,920
Speaker 3: Well, it's been a long time.

542
00:29:09,080 --> 00:29:12,960
Speaker 9: I used to before COVID, I travel as much as

543
00:29:12,960 --> 00:29:16,520
Speaker 9: like one hundred and ninety seventy six days a year globally,

544
00:29:17,280 --> 00:29:22,080
Speaker 9: but COVID and kind of hold everybody's back and in

545
00:29:22,160 --> 00:29:24,480
Speaker 9: China right now, I think on the ground, there are

546
00:29:24,480 --> 00:29:27,680
Speaker 9: a lot of activities going on. I think the consumer

547
00:29:27,800 --> 00:29:31,760
Speaker 9: is very eager to you know, embrace the world, embrace

548
00:29:31,880 --> 00:29:32,400
Speaker 9: new life.

549
00:29:32,480 --> 00:29:33,760
Speaker 3: And so there have been.

550
00:29:34,200 --> 00:29:38,080
Speaker 9: Kind of doing all kinds of interesting activities. And so

551
00:29:38,200 --> 00:29:40,400
Speaker 9: let's just see, you know, what's going to happen and

552
00:29:40,600 --> 00:29:44,240
Speaker 9: in the second half of the year and for the economy.

553
00:29:44,280 --> 00:29:46,880
Speaker 9: But so far, I think everybody is so eager to

554
00:29:46,920 --> 00:29:47,400
Speaker 9: go back.

555
00:29:47,600 --> 00:29:50,360
Speaker 8: You know, it's interesting when you think about globalization, the

556
00:29:50,400 --> 00:29:52,520
Speaker 8: panel that you were just on, there's a lot of

557
00:29:52,600 --> 00:29:56,160
Speaker 8: questions about how quickly the world may be deglobalizing in

558
00:29:56,160 --> 00:29:59,120
Speaker 8: the wake of China, in the wake of geopolitical tensions,

559
00:29:59,160 --> 00:30:02,080
Speaker 8: in the wake of COVID. What are you seeing in

560
00:30:02,160 --> 00:30:07,000
Speaker 8: terms of technology companies in particular, is it more competition

561
00:30:07,320 --> 00:30:10,560
Speaker 8: or are you seeing just little flights of cooperation here?

562
00:30:11,720 --> 00:30:16,160
Speaker 9: You know, I think everybody to be frank uh. You know,

563
00:30:16,240 --> 00:30:18,040
Speaker 9: a lot of my friends had talked to being the

564
00:30:18,040 --> 00:30:23,120
Speaker 9: investor entrepreneurs, they're all worried. And it's actually the same

565
00:30:23,200 --> 00:30:27,800
Speaker 9: here with American investors and entrepreneurs as well between the

566
00:30:27,840 --> 00:30:30,840
Speaker 9: two countries. But I have to say this, I think

567
00:30:30,840 --> 00:30:34,360
Speaker 9: the world is truly interlinked. And you know, I've been

568
00:30:34,400 --> 00:30:38,640
Speaker 9: talking to a think tank in America and they actually

569
00:30:38,640 --> 00:30:40,840
Speaker 9: said to me, they said, you know, if there is

570
00:30:40,880 --> 00:30:45,880
Speaker 9: a complete the leakage between China and the US American companies,

571
00:30:45,960 --> 00:30:49,680
Speaker 9: we potentially could lose half of its market cap just

572
00:30:49,720 --> 00:30:52,040
Speaker 9: because of cost will go up so significantly.

573
00:30:52,640 --> 00:30:56,600
Speaker 8: Another interesting aspect of this is kind of the competition

574
00:30:56,840 --> 00:31:00,800
Speaker 8: to develop technologies faster, harder, stronger than the other. And

575
00:31:00,840 --> 00:31:02,600
Speaker 8: I'm wondering how you see that playing out in the

576
00:31:02,640 --> 00:31:05,040
Speaker 8: world of AI. A lot of American investors here are

577
00:31:05,040 --> 00:31:08,120
Speaker 8: talking about how AI will push the market so much faster.

578
00:31:08,360 --> 00:31:10,880
Speaker 8: Is there anything China is doing when it comes to

579
00:31:10,960 --> 00:31:14,000
Speaker 8: artificial intelligence that the US is not particularly doing.

580
00:31:14,440 --> 00:31:16,760
Speaker 9: First of all, I think if you talk to the

581
00:31:16,800 --> 00:31:20,120
Speaker 9: AI community, I mean, there's certainly a community in the US,

582
00:31:20,440 --> 00:31:23,760
Speaker 9: there's a community in China, there's a community in Europe.

583
00:31:23,920 --> 00:31:26,880
Speaker 9: And this guy's they work in the virtual world. They

584
00:31:26,920 --> 00:31:30,280
Speaker 9: actually talk to each other quite a lot. And I

585
00:31:30,320 --> 00:31:32,680
Speaker 9: wouldn't say it's a competition. If it's a competition, it's

586
00:31:32,680 --> 00:31:38,000
Speaker 9: a competition amount different technologies, or a competition among different entrepreneurs,

587
00:31:38,160 --> 00:31:42,320
Speaker 9: different businesses. But I still see pretty optimistic. I think

588
00:31:42,360 --> 00:31:45,120
Speaker 9: people wanted to work with each other to give you

589
00:31:45,160 --> 00:31:49,080
Speaker 9: an example, and China for the mobile Internet. That's sort

590
00:31:49,080 --> 00:31:53,480
Speaker 9: of the last generation digital economy. I mean, China was

591
00:31:53,480 --> 00:31:57,200
Speaker 9: the lead in the world. And you know, I advise

592
00:31:57,280 --> 00:32:01,480
Speaker 9: some of the European very large multinational company CEOs. They

593
00:32:01,520 --> 00:32:04,640
Speaker 9: actually said to me the consumer experience on the digital

594
00:32:04,760 --> 00:32:07,880
Speaker 9: side in China was the best for their old reasons.

595
00:32:08,120 --> 00:32:11,680
Speaker 9: US was number two and Europe was number three. So

596
00:32:12,440 --> 00:32:15,360
Speaker 9: and China had a lot of talents, you know, I

597
00:32:15,360 --> 00:32:19,160
Speaker 9: mean so many engineers, and they're battle tested. They work

598
00:32:19,200 --> 00:32:24,000
Speaker 9: on very large scale consumer operations. So I actually see

599
00:32:24,040 --> 00:32:26,520
Speaker 9: a lot of interesting innovations going to come.

600
00:32:26,360 --> 00:32:26,840
Speaker 3: Out of it.

601
00:32:27,320 --> 00:32:29,640
Speaker 9: By the way, when we think about internet, how many

602
00:32:29,680 --> 00:32:32,880
Speaker 9: of us remember who actually invented the internet? How many

603
00:32:32,920 --> 00:32:38,080
Speaker 9: of us, you know, kind of remembered who invented refrigerator.

604
00:32:38,520 --> 00:32:40,960
Speaker 9: But it is the Amazon, the world is Coca Cola.

605
00:32:41,040 --> 00:32:45,720
Speaker 9: The world benefited from this great invention. So the bottom

606
00:32:45,840 --> 00:32:48,800
Speaker 9: line is, I think a great entrepreneurs when they have

607
00:32:49,160 --> 00:32:52,920
Speaker 9: this kind of new technology wave, they're going to leverage

608
00:32:52,960 --> 00:32:56,880
Speaker 9: it to create something very exciting for consumer or businesses.

609
00:32:58,440 --> 00:33:01,560
Speaker 2: Patrick, thank you for your time. I'm here on Bloomberg Technology.

610
00:33:01,600 --> 00:33:06,680
Speaker 2: There is a debate about US domicile vcs putting money

611
00:33:07,320 --> 00:33:11,480
Speaker 2: into Chinese technology companies. What are you seeing in terms

612
00:33:11,480 --> 00:33:17,360
Speaker 2: of the LP appetite US institutional money and LPs wanting

613
00:33:17,880 --> 00:33:20,320
Speaker 2: to invest in China technology companies.

614
00:33:22,880 --> 00:33:25,840
Speaker 9: I think for right now, I feel like everybody kind

615
00:33:25,840 --> 00:33:29,560
Speaker 9: of put it on hold because of the duo political concerns.

616
00:33:30,040 --> 00:33:33,040
Speaker 9: You know, as I said, listen, I mean this is

617
00:33:34,160 --> 00:33:37,720
Speaker 9: the first trip I did to America since COVID. I

618
00:33:37,720 --> 00:33:41,520
Speaker 9: think many of the LPs in America, many of the

619
00:33:41,600 --> 00:33:46,600
Speaker 9: company are CEOs in America, haven't traveled to China. And

620
00:33:46,760 --> 00:33:49,160
Speaker 9: they used to travel to China like once a quarter,

621
00:33:49,280 --> 00:33:52,520
Speaker 9: but they haven't been back since COVID. Just give you

622
00:33:52,560 --> 00:33:57,560
Speaker 9: a number the flights between US and China today in

623
00:33:57,680 --> 00:34:02,800
Speaker 9: Q one It was only five percent that capacity.

624
00:34:01,880 --> 00:34:03,480
Speaker 3: Prior to COVID.

625
00:34:03,840 --> 00:34:07,560
Speaker 9: So there's a lack of interactions, lack of communication.

626
00:34:08,120 --> 00:34:09,520
Speaker 3: So people kind of tend.

627
00:34:09,440 --> 00:34:12,920
Speaker 9: To think everything in a very abstract way when you

628
00:34:13,000 --> 00:34:14,400
Speaker 9: actually don't meet, don't talk.

629
00:34:16,400 --> 00:34:19,839
Speaker 2: When you were at Wellington, you held some of the

630
00:34:19,960 --> 00:34:24,960
Speaker 2: top China tech names as an institutional investor. How attractive

631
00:34:25,040 --> 00:34:27,440
Speaker 2: right now are the USA drs for some of these

632
00:34:27,520 --> 00:34:28,680
Speaker 2: China tech names.

633
00:34:28,920 --> 00:34:29,520
Speaker 3: You know, there's been a.

634
00:34:29,520 --> 00:34:32,719
Speaker 2: Lot of back and forth on the listing delisting, but

635
00:34:32,760 --> 00:34:34,920
Speaker 2: they are still giants of technology globally.

636
00:34:37,760 --> 00:34:41,520
Speaker 9: Yes, you know, I think they are still working very hard.

637
00:34:41,760 --> 00:34:45,680
Speaker 9: And by the way, and the China or the companies

638
00:34:45,760 --> 00:34:49,080
Speaker 9: for the last twenty years, they have accumulated a lot

639
00:34:49,120 --> 00:34:54,440
Speaker 9: of nohas. They have a world class engineering force working there.

640
00:34:55,040 --> 00:34:58,720
Speaker 9: And you know, I think the world, to be honest,

641
00:34:59,080 --> 00:35:02,839
Speaker 9: is a or sad because we do politics, people don't

642
00:35:02,880 --> 00:35:03,640
Speaker 9: talk to each other.

643
00:35:04,000 --> 00:35:05,480
Speaker 3: But I feel these.

644
00:35:05,280 --> 00:35:08,200
Speaker 9: Companies that are doing real work, and they're ones who

645
00:35:08,280 --> 00:35:12,480
Speaker 9: come up with real and exciting products, especially writing on

646
00:35:12,560 --> 00:35:15,640
Speaker 9: this the next wave of AI. And again I think

647
00:35:15,719 --> 00:35:20,879
Speaker 9: on the application side, I think the Chinese companies potentially

648
00:35:20,920 --> 00:35:22,360
Speaker 9: can do really really well.

649
00:35:22,680 --> 00:35:25,440
Speaker 8: For a couple of months, there were fears there from

650
00:35:25,600 --> 00:35:29,400
Speaker 8: US investors investing in Chinese check giants because of crackdowns

651
00:35:29,480 --> 00:35:33,359
Speaker 8: regulatory crackdowns. How much is that a concern for you

652
00:35:33,400 --> 00:35:34,279
Speaker 8: as a local.

653
00:35:34,000 --> 00:35:39,520
Speaker 9: Investor, So I think, you know, we're venture capital investors,

654
00:35:39,760 --> 00:35:42,520
Speaker 9: so we have to think everything a little bit longer term,

655
00:35:43,200 --> 00:35:47,200
Speaker 9: and we look at the technology, how the company is

656
00:35:47,239 --> 00:35:49,480
Speaker 9: sort of organized. We compare them to the best of

657
00:35:49,560 --> 00:35:52,680
Speaker 9: companies in the world, into the companies in Silicon Valley.

658
00:35:53,080 --> 00:35:56,520
Speaker 9: We actually see some of the companies they're really really competitive,

659
00:35:57,200 --> 00:36:00,360
Speaker 9: and so it's investors job to help them to bridged

660
00:36:00,360 --> 00:36:05,400
Speaker 9: the gap and helped them actually even grow globally.

661
00:36:05,560 --> 00:36:08,799
Speaker 8: Patrick, thank you so much for your tank. Ed back

662
00:36:08,840 --> 00:36:09,000
Speaker 8: to you.

663
00:36:09,280 --> 00:36:12,439
Speaker 2: Yep, that was thirty one Capital founding partner Patrick Johng

664
00:36:12,480 --> 00:36:25,040
Speaker 2: and of course INVOTIONALI BASSEG infrastructure software company diner trace

665
00:36:25,080 --> 00:36:28,840
Speaker 2: out with earnings, beating expectations and offering an upbeat forecast

666
00:36:28,920 --> 00:36:32,239
Speaker 2: which came in above analyst expectations for the full year.

667
00:36:32,280 --> 00:36:35,480
Speaker 2: Dina Tray CEO Rick McConnell with us now that full

668
00:36:35,600 --> 00:36:39,480
Speaker 2: year guidance coming in strong, Rick, how much of that

669
00:36:39,960 --> 00:36:42,400
Speaker 2: was to do with euphoria around AI?

670
00:36:43,960 --> 00:36:46,480
Speaker 10: Well, first, thanks so much for having us again, I

671
00:36:46,520 --> 00:36:47,200
Speaker 10: appreciate it.

672
00:36:47,640 --> 00:36:48,480
Speaker 3: Actually, none of it.

673
00:36:49,520 --> 00:36:54,560
Speaker 10: We see generative AI as a fascinating and very compelling technology,

674
00:36:55,000 --> 00:36:57,560
Speaker 10: but we didn't factor any of that into our guide

675
00:36:57,560 --> 00:36:58,520
Speaker 10: for half way twenty four.

676
00:36:59,480 --> 00:37:00,359
Speaker 3: It's all side.

677
00:37:01,520 --> 00:37:05,520
Speaker 2: That's a quite candid and fresh response rate, because what

678
00:37:05,560 --> 00:37:08,760
Speaker 2: we've heard for weeks is the AI is everything that said.

679
00:37:08,920 --> 00:37:11,360
Speaker 2: You know, it seems like there is actually some potential upside.

680
00:37:11,400 --> 00:37:14,680
Speaker 2: Just what are you doing to integrate generative AI tools

681
00:37:14,719 --> 00:37:18,000
Speaker 2: to boost your infrastructure offering as it is well.

682
00:37:18,040 --> 00:37:21,279
Speaker 10: We do think that generative AI is highly synergistic with

683
00:37:21,320 --> 00:37:25,320
Speaker 10: the observability space, the fifty billion dollars observability and application

684
00:37:25,400 --> 00:37:28,279
Speaker 10: security space in which we dine a Trace participate in lead.

685
00:37:29,719 --> 00:37:30,359
Speaker 3: And the way that.

686
00:37:30,400 --> 00:37:33,880
Speaker 10: Generative AI is going to foster itself in this environment

687
00:37:33,960 --> 00:37:38,000
Speaker 10: is it is going to generate massive gains in productivity.

688
00:37:38,520 --> 00:37:41,399
Speaker 10: And that productivity isn't just from writing text, it's also

689
00:37:41,440 --> 00:37:45,879
Speaker 10: from writing source code. And more code means more productivity,

690
00:37:46,000 --> 00:37:51,359
Speaker 10: means more applications, more workloads, more cloud and stantiations. As

691
00:37:51,400 --> 00:37:55,080
Speaker 10: you get through all of that, you need more observability capabilities,

692
00:37:55,120 --> 00:37:58,120
Speaker 10: which is precisely what we do to make sure that

693
00:37:58,120 --> 00:37:59,920
Speaker 10: that environment works perfectly.

694
00:38:00,239 --> 00:38:02,000
Speaker 5: And that's what Dina trace is all about.

695
00:38:02,360 --> 00:38:04,920
Speaker 10: So huge synergy and generate.

696
00:38:04,640 --> 00:38:07,360
Speaker 5: Of AI with the capabilities from Dynatris.

697
00:38:08,400 --> 00:38:11,680
Speaker 2: You know, the kind of enterprise cloud area is really interesting.

698
00:38:11,719 --> 00:38:14,160
Speaker 2: Barkley's had a note out and response to your numbers

699
00:38:14,480 --> 00:38:18,239
Speaker 2: saying that it hints at improving macro conditions. Are you

700
00:38:18,320 --> 00:38:20,560
Speaker 2: hearing that from your customers that things are getting better

701
00:38:20,640 --> 00:38:21,040
Speaker 2: out there?

702
00:38:22,040 --> 00:38:24,879
Speaker 10: We didn't say that in our comments today. In fact,

703
00:38:24,920 --> 00:38:28,160
Speaker 10: our guidance assumes no change in the macro environment through

704
00:38:28,320 --> 00:38:31,160
Speaker 10: FA twenty four for US, which just began on April first,

705
00:38:31,520 --> 00:38:34,880
Speaker 10: so we are not factoring that into our guidance. Again,

706
00:38:35,000 --> 00:38:40,040
Speaker 10: any macro upside would be an opportunity potentially for accelerated

707
00:38:40,080 --> 00:38:41,360
Speaker 10: growth in our performance.

708
00:38:41,400 --> 00:38:42,600
Speaker 5: Turning off way twenty four.

709
00:38:43,560 --> 00:38:47,600
Speaker 2: You know you've talked about what the potential generative AI is.

710
00:38:47,840 --> 00:38:51,160
Speaker 2: How are you hiring to ensure that you can harness

711
00:38:51,239 --> 00:38:51,880
Speaker 2: that potential?

712
00:38:53,280 --> 00:38:57,759
Speaker 10: Well, we continue to bring new engineer design globally to

713
00:38:57,880 --> 00:39:02,000
Speaker 10: take advantage of inordinate increase is an opportunity in our market.

714
00:39:03,239 --> 00:39:06,520
Speaker 10: As we look at the hyperscalers aws as your GCP

715
00:39:07,120 --> 00:39:10,920
Speaker 10: one hundred and seventy five billion dollars of annualized revenue

716
00:39:10,960 --> 00:39:15,080
Speaker 10: they just reported in their latest quarters. All of these

717
00:39:15,120 --> 00:39:19,880
Speaker 10: cloud and santiations really benefit from observability. So the observability

718
00:39:19,880 --> 00:39:24,279
Speaker 10: attach raate to overall cloud deployments should be substantial and

719
00:39:24,480 --> 00:39:27,799
Speaker 10: as such we continue to grow our business. We just

720
00:39:27,880 --> 00:39:30,400
Speaker 10: reported a quarter with twenty nine percent adjust Today are

721
00:39:30,440 --> 00:39:34,399
Speaker 10: our growth twenty nine percent, subscription revenue growth, twenty nine

722
00:39:34,440 --> 00:39:38,520
Speaker 10: percent pre cash flow margin. These are exceptional results with

723
00:39:38,640 --> 00:39:43,040
Speaker 10: a balance model of revenue and profitability that we couldn't

724
00:39:43,080 --> 00:39:44,319
Speaker 10: be more enthusiastic about.

725
00:39:44,360 --> 00:39:49,360
Speaker 2: As we look to f y twenty four, quickly, geographically,

726
00:39:49,480 --> 00:39:51,799
Speaker 2: where is the most activity for your business right now?

727
00:39:51,800 --> 00:39:53,280
Speaker 3: Where is the strength globally?

728
00:39:54,320 --> 00:39:56,520
Speaker 10: It's interesting, Ed, as you and I talked about last

729
00:39:56,560 --> 00:39:59,279
Speaker 10: quarter we had we had an answer that we were

730
00:39:59,320 --> 00:40:03,120
Speaker 10: seeing more track in the America as last order, more traction.

731
00:40:02,840 --> 00:40:04,200
Speaker 3: In Europe the quarter before that.

732
00:40:04,719 --> 00:40:09,040
Speaker 10: We really saw a very balanced geographic distribution with really

733
00:40:09,040 --> 00:40:12,319
Speaker 10: strong growth in each of our geos in year every

734
00:40:12,360 --> 00:40:16,360
Speaker 10: year arr last quarter. So we were really pleased across

735
00:40:16,400 --> 00:40:18,320
Speaker 10: the board with the results.

736
00:40:19,320 --> 00:40:22,120
Speaker 2: All Right, Rick McConnell, Dinah Trace CEO, good to catch up.

737
00:40:22,160 --> 00:40:24,359
Speaker 2: Another quarter in the bag. Thank you for your time,

738
00:40:24,880 --> 00:40:27,319
Speaker 2: Thanks to you, Ed, Thank you. That does it for

739
00:40:27,360 --> 00:40:30,640
Speaker 2: this edition of Bloomberg Technology. Don't forget to check out

740
00:40:30,640 --> 00:40:34,640
Speaker 2: our podcast. We are only three days into a monster week,

741
00:40:34,800 --> 00:40:39,960
Speaker 2: so so much to consider. Wherever you get your podcasts, Apple, Spotify, iHeart.

742
00:40:39,960 --> 00:40:44,720
Speaker 2: There's been a huge focus on artificial intelligence in this program,

743
00:40:44,719 --> 00:40:46,160
Speaker 2: but you look at the news flow, you look at

744
00:40:46,200 --> 00:40:48,680
Speaker 2: the markets, that is where we've been.

745
00:40:49,280 --> 00:40:50,799
Speaker 3: This is Bloomberg