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Speaker 1: And joins us shortly, this is Bloomberg Technology coming up.

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Speaker 1: We'll break down earnings from sales voice that disappoints and

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Speaker 1: C three AI, the artificial intelligence high pits reality with

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Speaker 1: disappointing earnings from that company too. But let's stick on

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Speaker 1: AI in the here and then now with Palenteer, we

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Speaker 1: go live to Palo Alto, California, where our own Ed

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Speaker 1: Ludlow is sitting down for an exclusive conversation with the

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Speaker 1: CEO but us.

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Speaker 2: We'll have more on.

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Speaker 1: Guess what artificial intelligence, how Wall Street is using it,

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Speaker 1: how the technology is replacing our jobs, and even how

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Speaker 1: AI is replacing ourselves.

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Speaker 2: We'll have more throughout the hour.

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Speaker 1: First, set's check in to an audience where we're going

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Speaker 1: to be discussing so much more the future of AI,

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Speaker 1: where the likes of one key executive who wants to

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Speaker 1: take the whole market on that. I want to welcome

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Speaker 1: our Bloomberg TV and our radio audiences. We want to

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Speaker 1: send it over now to our one Ed Ludlow, who's

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Speaker 1: sitting down with an exclusive interview the CEO of Palenteer, Ed. Yeah.

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Speaker 3: We're joined by Alex Karp, the CEO of Paneteer at

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Speaker 3: aip CON Artificial Intelligence Platform CON a chance for you

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Speaker 3: to talk with customers about some of what you told

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Speaker 3: us three weeks ago. And on that note, three weeks ago,

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Speaker 3: you said that Palente's plan.

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Speaker 4: For AI was quote, just take the whole market. How's

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Speaker 4: that going?

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Speaker 1: Well?

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Speaker 5: You know, unlike most people, we've been involved in what

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Speaker 5: people call AI for the last five six seven years

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Speaker 5: in the classified environment, building systems that will allow you

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Speaker 5: to identify adversarial positions.

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Speaker 6: And in that.

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Speaker 5: Context, we've built proprietary technology that will allow you to

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Speaker 5: work with large language models, securely, enhance them, roll them

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Speaker 5: across your whole enterprise. You know, I've been at this

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Speaker 5: for about twenty years, and I know it will take

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Speaker 5: everyone else four or five years to build this.

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Speaker 6: We're rolling it out. Our customer base.

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Speaker 5: Is large, and we have you know, usually we wait

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Speaker 5: for we have to go out and find people. Now

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Speaker 5: we have customers, especially in the US, just calling us

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Speaker 5: every day.

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Speaker 4: You said, the demand is huge, can you quantify it?

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Speaker 5: And so you know usually again we've had a number

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Speaker 5: of inbound call a year that we usually have in

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Speaker 5: a year, in like a month, and then if.

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Speaker 6: We're at a conference, if you go next door.

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Speaker 5: There are customers showing potential customers how to use our product.

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Speaker 5: Its sense well, it's on real data, it's it's it's

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Speaker 5: things that they've done so right now, the whole world

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Speaker 5: is hungry for something that it understands as AI, which

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Speaker 5: is really AI or large language models. We are actually

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Speaker 5: have customers using our products showing other customers how to

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Speaker 5: do it.

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Speaker 6: I mean this is like you release a song and

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Speaker 6: everyone else playing it. So okay, great, We're very happy.

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Speaker 5: And you know, the thing is, the US market is

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Speaker 5: just hungry for innovation. It's hungry for things. It's now beginning.

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Speaker 5: It needs to needs, you know, beginning to understand. It

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Speaker 5: needs like an ability to map a l M on

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Speaker 5: your enterprise securely, ability to enhance the output of a

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Speaker 5: large language model, and ability and and what's the output,

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Speaker 5: better margins, better safety.

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Speaker 6: You can change your enterprise in weeks.

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Speaker 3: I just want to jump in and ask a very

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Speaker 3: basic question, AIP is it built on GPT four?

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Speaker 4: We different foundation. We as the underlying.

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Speaker 5: Tech, the underlying we are completely agnostic to whatever large

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Speaker 5: language model you want to use. Large language models have

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Speaker 5: certain attributes, like they can give you reasoning, but you

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Speaker 5: can't import that reasoning into your enterprise. What AIP does

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Speaker 5: is allow you to take the benefits of the large

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Speaker 5: language model, enhance them with ILL algorithms that we help

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Speaker 5: you build and roll it securely across your whole enterprise.

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Speaker 5: And what does that mean. It means you get all

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Speaker 5: the benefits of a large language model in your enterprise today,

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Speaker 5: not in five years. Not something that writes poetry. We're

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Speaker 5: not offering people poetry writing in their enterprise. We're offering

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Speaker 5: things that are so powerful that really, in reality, I'm

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Speaker 5: not sure we should even sell this to some of

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Speaker 5: our clients like national security.

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Speaker 3: Who are those clients? Well, who are those clients proportionately?

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Speaker 3: When you think about demand, how much is coming from

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Speaker 3: the defense use case? Since you kind of gave more

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Speaker 3: flesh to the AI bones at that during that earning.

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Speaker 5: Look, what's driving the demand for our product and defense

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Speaker 5: is simply what is what people have seen on the battlefield,

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Speaker 5: and that's very sensitive and very classified. But the demand

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Speaker 5: for that is very large, it's going to get larger.

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Speaker 5: Why is it going to get larger? Because America is

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Speaker 5: the best at software. Software that's built in a product

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Speaker 5: is in high demand and defense? Why is it also

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Speaker 5: in demand because until two years ago everyone thought this

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Speaker 5: was a joke. We are building systems over the last

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Speaker 5: five years that are deadly that those deadly systems have

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Speaker 5: changed the course of history.

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Speaker 6: It's no longer mad man saying this.

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Speaker 5: You see it on the battlefield in US commercial US

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Speaker 5: commercial industry is the most adaptive in the world, and

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Speaker 5: they are hungry. Our clients are hungry for things that

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Speaker 5: will give them a disapportionate advantage on margins, on safety,

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Speaker 5: on secure use of LMS, on making sure this is

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Speaker 5: not just some poetry recreating what somebody said, but actually

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Speaker 5: can create actual tangible difference.

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Speaker 6: And we are rolling it out and we're very happy.

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Speaker 3: For our global Bloomberg television and radio audiences. We are

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Speaker 3: at aip COM. We're joined by the CEO of Palenteer,

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Speaker 3: Alex carp. During that earning school you said we have

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Speaker 3: no pricing strategy. We're going to create a lot of value.

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Speaker 3: We're going to get hundreds of customers, and we will

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Speaker 3: price it as we go. Have you made any progress

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Speaker 3: on pricing strategy since that No.

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Speaker 4: I'm so relaxed about it.

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Speaker 5: Because if you it's like one of these things like

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Speaker 5: when you go to a bar.

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Speaker 6: You know everyone wants to meet you. Do you have

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Speaker 6: a pricing strategy when you go to the bar?

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Speaker 5: No, you're like, Oh, I'm cool. I know we have

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Speaker 5: the best product on the market.

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Speaker 6: I know customers will pay us fairly.

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Speaker 5: I know that it's much more valuable than anyone will

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Speaker 5: understand till they install it, and we will sort out

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Speaker 5: there's to make it like slightly academic. I believe in

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Speaker 5: prey too optimization. We are going to create a lot

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Speaker 5: of value, and we're going to get some portion of

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Speaker 5: that value. And customers are smart, they'll pay you some

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Speaker 5: portion of the value. Why should I just as an

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Speaker 5: actually metic thing. If you have a software product, you

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Speaker 5: always want to get paid after you deliver value.

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Speaker 7: If you've got a.

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Speaker 5: PowerPoint, something that doesn't work, something that's not valuable, you

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Speaker 5: want to get.

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Speaker 6: Paid before you create value.

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Speaker 5: We know it's valuable, we know it's much more valuable

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Speaker 5: than people understand. We know we're going to continue to

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Speaker 5: augment that value, and we're going to get paid along

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Speaker 5: the way.

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Speaker 4: Well, the counter consideration is how much you invest in

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Speaker 4: the product.

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Speaker 5: But we've already invested billions of building these things in

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Speaker 5: various components over the last twenty years, and we have

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Speaker 5: the we have the IP and we're basically sewing it

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Speaker 5: together and adding things on top of it. And so

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Speaker 5: we know we have the IP, we're certain of the value.

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Speaker 5: Why would we So again I come to you, I'm like, hey,

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Speaker 5: I'm certain this is very valuable.

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Speaker 6: Pay me ten million dollars. What are you going to.

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Speaker 4: Say, Well, I don't have ten many.

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Speaker 6: Until well, okay, give me your British charm.

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Speaker 3: Well and they say, well for your British charm, my

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Speaker 3: British charm. If I were a customer, would say, this

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Speaker 3: is a really hard environment. If I think about cloud exactly,

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Speaker 3: customers are looking for value at the lowest price.

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Speaker 5: Now. I but if you believe you have the best

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Speaker 5: product in the world, where are you going to say? Okay, great,

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Speaker 5: let's not even have that discussion.

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Speaker 6: I'll create the value. You tell me how much value created?

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Speaker 5: By the way, if you don't want to pay me,

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Speaker 5: then I'll go to someone else who will. You can

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Speaker 5: just you can have different margins and the personal payment,

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Speaker 5: you can have a different.

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Speaker 6: Safety profile and the paper. You can have a different.

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Speaker 5: Ability to control your whole business from your laptop than

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Speaker 5: someone else because the person who valued it paid me.

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Speaker 6: We don't want to pay me.

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Speaker 3: Great Alex Carpcio Palenteer Technologies. This morning, the Bear Cave,

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Speaker 3: a subset based newsletter, put out a negative report into it.

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Speaker 4: Let me just read one of the claims.

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Speaker 3: The Bear Cave believes Palenteer is an ai imposta engaging

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Speaker 3: in spurious games to inflate its books and obfuscate its

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Speaker 3: less sexy role as an over height data consultant.

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Speaker 4: What is your response to that.

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Speaker 6: The bear Cave is a bear cave. They can stay

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Speaker 6: in the bear cave. We're a profitable software company.

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Speaker 5: Those are interesting critiques of us, and you know we

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Speaker 5: have the best products in the market and that's why

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Speaker 5: they're profitable and we will win.

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Speaker 4: Wanted to give you the right response.

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Speaker 3: That was all the core of Palente's pitch, right, is

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Speaker 3: that you have this experience in managing sensitive classified often

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Speaker 3: data networks or closed network How does that work in

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Speaker 3: the training of.

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Speaker 4: The l lms that are going into a I P.

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Speaker 3: Is it difficult when they have that kind of restriction

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Speaker 3: on the data source.

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Speaker 5: It's a very very very important technical question to work

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Speaker 5: to use l l ms at scale in a classified

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Speaker 5: in sensitive environment. You have to have something like a

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Speaker 5: data model that trains the data model, and something like

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Speaker 5: branching and and and and and access control. Those products

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Speaker 5: take decades to build.

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Speaker 6: We have them.

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Speaker 5: But if you have those products, you can segment in

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Speaker 5: real time what what.

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Speaker 4: The already built.

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Speaker 6: They're already built.

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Speaker 5: We're already they're already part of all of our core

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Speaker 5: fied they're part of PG, they're part of foundry.

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Speaker 6: This is they're.

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Speaker 5: About we roll them out to our current customers. Many

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Speaker 5: of our customers have not needed to use these, They

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Speaker 5: now need to use them.

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Speaker 6: Why do they need to use them?

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Speaker 5: Because if you in any environment, you're gonna have records

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Speaker 5: that you're gonna have, you're gonna have data and insights

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Speaker 5: you're not going to share. With the large language model,

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Speaker 5: you're gonna have data insights you do want to share,

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Speaker 5: and you're gonna have a hybrid and that requires a segmenting,

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Speaker 5: branching architecture. And one of the things we built over

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Speaker 5: the last ten years, randomly because we thought it would

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Speaker 5: be valuable someday.

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Speaker 3: Was that for our Bloomberg radio and television audiences worldwide.

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Speaker 3: We are with doctor Alex Karp, the CEO of Palenter.

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Speaker 3: An interesting case study is Ukraine. You've deepened your relationship

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Speaker 3: and activity in Ukraine using AI in one case to

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Speaker 3: help with reconstruction.

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Speaker 4: How else given just.

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Speaker 6: Well, by the way, my real answer to the short

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Speaker 6: people is ask the Russians what do you mean by this?

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Speaker 5: Like we can't say, ask the Ukrainians, Ask people who

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Speaker 5: are in the battlefield, ask people who have been subject.

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Speaker 5: You're talking about the effective effectiveness of our product. Okay,

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Speaker 5: So it's like there's very little we can say. You

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Speaker 5: can read what the Ukrainians are saying. They use targeting,

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Speaker 5: according to reports, has gone up given the use of

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Speaker 5: AI by products potentially ours from like by twenty to

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Speaker 5: fifty x. These products have changed the course of history

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Speaker 5: and they will continue to.

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Speaker 6: Can't change the course of history, and super proud of that.

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Speaker 3: Do we already have in the military use case, an

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Speaker 3: arms race between powers like the US, Russia, China specifically

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Speaker 3: in the field of artificial intelligence.

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Speaker 5: Yes, and we have an advantage and if we don't

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Speaker 5: get get out of our own way, we might actually

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Speaker 5: continue to.

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Speaker 4: When you say we gets out of our own way,

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Speaker 4: what do you mean by that?

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Speaker 5: Well, you know in America has the best best software

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Speaker 5: companies in the world. The software companies largely come from

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Speaker 5: a sliver of America. They produce products. We need to

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Speaker 5: get to a point where one percent of our spend

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Speaker 5: on defense goes to products that have been proven on

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Speaker 5: the battlefield, not power points. And so like in the

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Speaker 5: large language model and the generalizable AI. We are far ahead,

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Speaker 5: call it a year or two, but we must actually implant.

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Speaker 5: And there's a huge debate. Part of the debate, of course,

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Speaker 5: is these things are very dangerous. If we didn't have

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Speaker 5: vicious adversaries, we should we should slow it down, but

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Speaker 5: we do. But we also have lots of people who

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Speaker 5: don't want to roll this out because they have nothing

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Speaker 5: to roll out, and so there's like the debate machine

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Speaker 5: about rolling this out. Is partly for legitimate reasons because

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Speaker 5: this could be dangerous, partly for security reasons you brought up.

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Speaker 5: But there are architectures that will allow you to deal

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Speaker 5: with this as a product like Pounder and hopefully someday others.

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Speaker 5: But there's also the debate machine because there are only

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Speaker 5: three or four companies in the world with anything to sell,

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Speaker 5: and everyone else wants to debate why should we do this,

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Speaker 5: how should we do this.

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Speaker 6: Can we play catch up? Can we talk about this

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Speaker 6: in five years?

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Speaker 5: That really plays into our adversary's hands, and we really

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Speaker 5: have to avoid that.

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Speaker 3: The long term concern that came up twenty four hours

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Speaker 3: ago or forty hours ago is an existential threat from

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Speaker 3: a you talk at panting about bending AI to a

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Speaker 3: collective will, Well, do you share the concern though about

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Speaker 3: an extinction level?

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Speaker 5: Well, there's a lot going on. There are these things dangerous.

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Speaker 5: Could they become potentially dangerous? Could they become Yes? But

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Speaker 5: what these debates ignore is either we will wield them

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Speaker 5: or our adversaries will will them. It is much better

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Speaker 5: if we wield the technology than our adversaries who do

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Speaker 5: not respect our norms, do not respect the rule of law,

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Speaker 5: and do not respect the way we want to live

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Speaker 5: in freedom.

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Speaker 6: So that's point one. Point two.

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Speaker 5: In the near term, what penalteer will allow you to

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Speaker 5: do is make these things really, really valuable commercially and

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Speaker 5: in the military context. And we in commercial context, you

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Speaker 5: have to do it because if you don't buy our product,

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Speaker 5: your competition will. In the military context, we have to

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Speaker 5: do it because our adversaries will build those products.

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Speaker 3: You toys about the competitive landscape. I actually wanted to

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Speaker 3: ask you about C three AI as an example, because

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Speaker 3: you come up in bidding processes with them.

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Speaker 5: Actually we look, this is a massive market. We actually

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Speaker 5: don't come up with bidding processes anyway. And I'll tell

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Speaker 5: you what I think about everyone.

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Speaker 4: See, you don't have any competition.

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Speaker 5: Let me just let me just tell you about this

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Speaker 5: competition thing that while street analysts love, it's complete ps you.

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Speaker 5: This is an infinite market. Basically, try what we're doing,

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Speaker 5: and try what everyone else is doing, and buy the

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Speaker 5: thing that creates the most.

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Speaker 3: Value on an infinite market. Blom Bag Intelligence put out

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Speaker 3: this research report this morning that says generative AI as

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Speaker 3: a market will be one point three trillion in twenty

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Speaker 3: to thirty two. That requires compound annual growth of about

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Speaker 3: forty percent a year from this point over a decade.

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Speaker 3: Do you see that as really well, what I see,

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Speaker 3: I don't know.

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Speaker 4: This based on the markets you operate.

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Speaker 6: Look, these experts just make stuff up.

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Speaker 5: But you know, what we know is this is a large,

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Speaker 5: basically impossible to measure market.

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Speaker 6: And what we also know.

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Speaker 5: Is everybody in the in the US is going to

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Speaker 5: find ways to become more efficient and better using software,

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Speaker 5: and a lot of that software is going to be

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Speaker 5: AI driven.

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Speaker 6: We also know they're.

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Speaker 5: Going to the market over time, not in a quarter,

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Speaker 5: will end up picking the best products.

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Speaker 6: That's all we know.

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Speaker 3: In the United Kingdom, my home country, the ft is

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Speaker 3: reporting that within the NHS as a case study, there

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Speaker 3: is some concern about deepening the data relationship with palenteer.

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Speaker 3: What would be your answer to those concerns?

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Speaker 5: Look outside of America and in the UK, there are

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Speaker 5: legitimate questions that get asked, where's the data going to go?

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Speaker 5: How is it moved too touches it? Does it get

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Speaker 5: exported to the US? Can we verify how is used,

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Speaker 5: what context? And can we make sure that the underprivileged

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Speaker 5: p people of the UK actually get the same treatment

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Speaker 5: as the privileged people, including not just in treatment but

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Speaker 5: future treatment which is a huge issue in the UK

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Speaker 5: because there's a backlog, So how do you deal with

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Speaker 5: the backlog equitably? Talentaer provides the most robust transparent software

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Speaker 5: in the world, which is part of the reason we're

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Speaker 5: having an AI bonanza because to make AI work you

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Speaker 5: have to show how it works. How did the transform work,

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Speaker 5: how does the branching work, how does the ontology work.

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Speaker 6: How does it map to on This.

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Speaker 5: Is exactly what you have to show in a hospital context.

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Speaker 5: Who worked with the patient, under what condition, what doctor

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Speaker 5: was it was the person equitably and fairly treated.

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Speaker 6: What happens to backlog by the way, we've proven we

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Speaker 6: can do this as a.

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Speaker 5: Product safely, efficiently and under the hardest conditions in the UK,

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Speaker 5: and I really hope we win that for this for

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Speaker 5: our sake, but also for the sake of our UK

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Speaker 5: employees and others that we greatly respect, and because it'll

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Speaker 5: lead transparency leads to the fairest, most ethical and justified

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Speaker 5: outcomes you can get.

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Speaker 3: Is Alix carp CEO of Palented, thank you for having

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Speaker 3: us at AIP can't hear in palawawle.

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Speaker 4: Thank you back to you, Take care.

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Speaker 1: Ed, absolutely fascinating conversation and AI bonanza. We're going to

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Speaker 1: deep dive into all things artificial intelligence throughout the show.

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Speaker 1: Coming up, we break down the earnings of C three AI.

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Speaker 1: Apparently the market is infinite. Well, why is Dan i'ves

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Speaker 1: gone outperformed this stock?

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Speaker 2: Is that why he's from Webush of course.

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Speaker 1: Get his thoughts as well as what's happening in the

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Speaker 1: future for Apple, and it's a our path, it's a Bloomberg.

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Speaker 1: Let's get back to some of these earnings, the earnings

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Speaker 1: reactions because Salesforce tumbling after the software company signaled it

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Speaker 1: isn't growing as fast as well it used to, while

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Speaker 1: of course shifting its focus to generating higher profits. Let's

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Speaker 1: get into the risk reward here with Bloomberg's Brodie Ford,

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Speaker 1: and it was a notable drop. We'm now studying a

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Speaker 1: little bit, but ultimately this is a company that's having

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Speaker 1: to do layoffs, having to tighten its overall expenses. Why

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Speaker 1: the sales slow down there?

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Speaker 8: Yeah, so last quarter Salesforce said we're going to focus

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Speaker 8: on profit now, and the market said, yeah, finally, like

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Speaker 8: we know, we couldn't be more excited, and this quarner

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Speaker 8: that gave us more of that. But then the market

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Speaker 8: started saying, oh, wait a second, but we you guys

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Speaker 8: are a growth company and we want to make sure

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Speaker 8: we keep seeing further revenue growth. And so it's one

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Speaker 8: of those funny situations where really almost all the metrics

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Speaker 8: who are beat or at least a meat, but just

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Speaker 8: a slight deceleration and sales has people saying, oh, man,

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Speaker 8: are these cost cuts going to weigh on their ability

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Speaker 8: to really keep growing in the way they have been

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Speaker 8: over the last decade.

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Speaker 1: And it's a similar theme that perhaps we saw with

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Speaker 1: C three AI as well, is that a company that

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Speaker 1: has significant growth well Salesforce at the best performing stock

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Speaker 1: in the s and P five hundred this year, C

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Speaker 1: three AI is tripled in its market valuation, and yet

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Speaker 1: the growth that they're guiding.

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Speaker 2: To just do isn't living up to expectation.

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Speaker 4: Yeah, when it.

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Speaker 8: Comes to C three, So if salesforce is one of

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Speaker 8: the best in the SMP, C three is the best

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Speaker 8: tech stock performance. I mean, it's up three hundred percent, right.

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Speaker 8: There's been so much hype, and the big question is

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Speaker 8: is it just hype?

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Speaker 9: Right?

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Speaker 8: Is it have a real robust AI ability to grow

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Speaker 8: or are people just buying the ticker because it says AI,

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Speaker 8: you know. And so when it rallied three hundred percent

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Speaker 8: this year, a lot of people say this looks about like,

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Speaker 8: you know, Game Stop in twenty twenty one or something. Yeah,

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Speaker 8: So I think when the figures last night came in

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Speaker 8: even a little bit light, people kind of panicked and said, oh, man,

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Speaker 8: is this hype? Are we getting a pull down the string, you.

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Speaker 1: Know, Yeah, and you're someone who's perhaps been laying there.

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Speaker 1: Some of the arguments as to why it's hype some

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Speaker 1: great writing coming from Bradie forty Gunn.

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Speaker 2: Check out his reporting.

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Speaker 1: We thank him for bringing us up to speed on

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Speaker 1: the latest on C three AI. But one person still

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Speaker 1: likes the hype around the stock that i'ves In fact

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Speaker 1: from Webush senior equity analyst, you rose to an outperform

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Speaker 1: rating and a fifty dollars.

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Speaker 2: Price target on the stock.

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Speaker 1: Right, So talk to me about why how are you

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Speaker 1: seeing this company capitalize on artificial intelligence?

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Speaker 10: In my opinion, I mean they're going through a model

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Speaker 10: transition on the consumption side and on the other side,

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Speaker 10: they're on their way to what's going to be five

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Speaker 10: hundred million of rev and going because this is an

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Speaker 10: eight hundred billion dollar market opportunity in terms of AI.

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Speaker 10: And when you look at how Seebull's position is despite

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Speaker 10: all the controversies, I think from a platform perspective, they're

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Speaker 10: just going use case by use case, continuing to increase

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Speaker 10: their tentacles.

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Speaker 11: And I think the.

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Speaker 10: Stock relative to where it could ultimately lead, you know,

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Speaker 10: will we be buyers here in this dip, which is

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Speaker 10: why we upgrade.

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Speaker 1: It's interesting that Alex Kart from Palenteer just hearing saying

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Speaker 1: this isn't an infinite market. But I am going to

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Speaker 1: ask the competition question anyway, because that is some of

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Speaker 1: the worry here. The worry that they're very focused on

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Speaker 1: perhaps offering their services to energy companies, they haven't really

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Speaker 1: diversified out of that that successfully thus far, and BlueBag Intelligence.

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Speaker 1: They're kind of worried about large application software platforms. They're

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Speaker 1: worried about other cloud vendors getting in on the space.

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Speaker 10: Yeah, and look, no doubt, I mean this is a

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Speaker 10: game of Thrones playing out in AI. You know, as

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Speaker 10: Alex talked about Pound Tier being one of the core

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Speaker 10: AI players. You look at what we've seen from Microsoft

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Speaker 10: in the video. That's really the start of it. But

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Speaker 10: in terms of second third derivative, there's gonna be many

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Speaker 10: winners here. And when I look at C three in

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Speaker 10: terms of what they built, I think it just speaks

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Speaker 10: to there's gonna be many companies that even though right

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Speaker 10: now you're not seeing it from a revenue perspective, in

445
00:19:31,000 --> 00:19:33,920
Speaker 10: terms of how they got it, I think three four

446
00:19:34,000 --> 00:19:36,040
Speaker 10: quarters from that, we look back at this is more

447
00:19:36,080 --> 00:19:39,240
Speaker 10: of an inflection point rather than the start of some

448
00:19:39,280 --> 00:19:39,920
Speaker 10: sort of frad.

449
00:19:40,800 --> 00:19:44,639
Speaker 1: It is heavily shorted, and there have been some notes

450
00:19:44,680 --> 00:19:48,120
Speaker 1: coming from short sellers on the stock worried about overpromising

451
00:19:48,240 --> 00:19:52,800
Speaker 1: under delivering. You hinted at the controversy there. What makes

452
00:19:52,880 --> 00:19:54,960
Speaker 1: you confident in the leadership of this business.

453
00:19:56,040 --> 00:19:58,160
Speaker 10: Yeah, Look, and obviously the shorts have done a ton

454
00:19:58,200 --> 00:20:00,000
Speaker 10: of work. I mean, if you look at the bears,

455
00:20:00,040 --> 00:20:02,280
Speaker 10: they spend a lot of time in the story. But

456
00:20:02,320 --> 00:20:04,199
Speaker 10: that's the sense of what makes the market right. In

457
00:20:04,240 --> 00:20:07,800
Speaker 10: other words, it comes down to can they execute? And

458
00:20:07,880 --> 00:20:11,800
Speaker 10: I believe in terms of the relationships with hyperscale players

459
00:20:12,280 --> 00:20:14,920
Speaker 10: within the Beltway, and you look with Sebele.

460
00:20:14,680 --> 00:20:15,400
Speaker 11: Sort of built here.

461
00:20:15,440 --> 00:20:17,440
Speaker 10: Look, if I could go back five six years ago,

462
00:20:17,840 --> 00:20:20,439
Speaker 10: many thought that, you know, that this was something that

463
00:20:20,520 --> 00:20:23,000
Speaker 10: was never going to come to fruition when they were private,

464
00:20:23,080 --> 00:20:24,640
Speaker 10: and you look at how they built it.

465
00:20:25,320 --> 00:20:28,200
Speaker 11: I think they're going through a transition on the consumption model.

466
00:20:28,240 --> 00:20:31,280
Speaker 11: And now next three to four quarters it's an execution story.

467
00:20:31,320 --> 00:20:35,120
Speaker 10: We're betting that it's going to be positive execution, and

468
00:20:35,160 --> 00:20:35,879
Speaker 10: that's why you.

469
00:20:35,880 --> 00:20:38,480
Speaker 11: Know, ultimately, I think this is a situation that you're seeing.

470
00:20:38,240 --> 00:20:42,000
Speaker 10: Across AI because I believe it's a revolution in terms

471
00:20:42,040 --> 00:20:42,520
Speaker 10: of this is.

472
00:20:42,480 --> 00:20:44,920
Speaker 11: Not a hype theme in my opinion, in terms of

473
00:20:44,960 --> 00:20:45,600
Speaker 11: broader AI.

474
00:20:45,960 --> 00:20:49,000
Speaker 1: Okay, we've got one minute, Dan, Broader AI and Apple.

475
00:20:49,160 --> 00:20:50,800
Speaker 1: You're expecting much on Monday.

476
00:20:51,760 --> 00:20:55,040
Speaker 11: Oh, I think you from Cooper Tino clearly, you.

477
00:20:55,000 --> 00:20:57,680
Speaker 10: Know, as Germans talked about an ar VR that will

478
00:20:57,720 --> 00:21:00,760
Speaker 10: be front and center. AI will be a theme and

479
00:21:00,800 --> 00:21:04,200
Speaker 10: the keynote from Cook. We believe it's about the developers.

480
00:21:04,520 --> 00:21:08,080
Speaker 10: There's a battle right now. Battle feel from developers from Google,

481
00:21:08,160 --> 00:21:11,879
Speaker 10: Microsoft and Apple. You know, I view that as a

482
00:21:11,960 --> 00:21:14,600
Speaker 10: key opportunity for them to go out there in terms

483
00:21:14,600 --> 00:21:17,919
Speaker 10: of building AI on the app Store and really the

484
00:21:17,960 --> 00:21:20,280
Speaker 10: start of what's going to be a multi year and

485
00:21:20,320 --> 00:21:25,000
Speaker 10: I think massive growth opportunity that's being underestimated within Kuper.

486
00:21:24,800 --> 00:21:28,159
Speaker 1: Tina, Dana ie a web Bush, thanks for all the thoughts.

487
00:21:28,160 --> 00:21:29,000
Speaker 2: Great to have you on some.

488
00:21:28,960 --> 00:21:38,960
Speaker 1: Of these earnings and these movers. Welcome back to Blue

489
00:21:39,000 --> 00:21:41,040
Speaker 1: Bow Technology. I'm Caroline Hide in New York. Let's talk

490
00:21:41,080 --> 00:21:43,600
Speaker 1: about really the AI hype that we continue to live

491
00:21:43,640 --> 00:21:46,480
Speaker 1: and die by. At the moment, the finance industry moving quickly.

492
00:21:46,560 --> 00:21:50,480
Speaker 1: We understand to use artificial intelligence in productive and innovative ways,

493
00:21:50,520 --> 00:21:53,240
Speaker 1: but there are still times when it makes more sense.

494
00:21:53,400 --> 00:21:57,320
Speaker 2: To actually use human brain power. It makes a Shagani reports.

495
00:21:57,800 --> 00:22:01,000
Speaker 12: Time because some real talk about AI. Not only is

496
00:22:01,040 --> 00:22:04,560
Speaker 12: it sometimes worse than humans, it can also be more expensive.

497
00:22:04,840 --> 00:22:09,320
Speaker 9: It costs GPT four around fourteen dollars to answer one

498
00:22:09,400 --> 00:22:14,359
Speaker 9: question on one one hundred thousand word loan document. An

499
00:22:14,400 --> 00:22:17,760
Speaker 9: example of a question might be what are the downgrade.

500
00:22:17,240 --> 00:22:18,360
Speaker 4: Triggers for this loan?

501
00:22:18,800 --> 00:22:21,479
Speaker 9: And the reason why it costs fourteen dollars are simply

502
00:22:21,560 --> 00:22:25,800
Speaker 9: down to the extreme compute costs required for open AI

503
00:22:26,119 --> 00:22:29,040
Speaker 9: to operate its large language models, so it passes that

504
00:22:29,080 --> 00:22:32,440
Speaker 9: compute costs onto the software vendor or onto the user.

505
00:22:32,560 --> 00:22:35,280
Speaker 9: At the same time, it only costs around six to

506
00:22:35,280 --> 00:22:39,600
Speaker 9: seven dollars for a human being just opening up Adobe

507
00:22:39,600 --> 00:22:43,399
Speaker 9: Acrobat and Microsoft Exel to answer that specific question.

508
00:22:43,640 --> 00:22:47,840
Speaker 12: News company sells data and answers technology to financial institutions.

509
00:22:48,240 --> 00:22:50,680
Speaker 12: It's an industry that is rapidly adopting AI.

510
00:22:51,040 --> 00:22:53,760
Speaker 9: Banks need to be far more strategic in their way

511
00:22:53,800 --> 00:22:57,120
Speaker 9: of leveraging AI, both from a cost perspective and from

512
00:22:57,160 --> 00:22:58,280
Speaker 9: a risk management.

513
00:22:57,960 --> 00:23:02,800
Speaker 12: Perspective, so well is using large language models to analyze

514
00:23:02,840 --> 00:23:07,560
Speaker 12: regulatory data to make recommendations to clients. France's BNP Powabus

515
00:23:07,600 --> 00:23:11,240
Speaker 12: meanwhile uses AI powered chatbots for customer service as well

516
00:23:11,280 --> 00:23:11,920
Speaker 12: as using the.

517
00:23:11,880 --> 00:23:13,240
Speaker 2: Tech in fraud detection.

518
00:23:13,480 --> 00:23:16,240
Speaker 12: And JP Morgan, the biggest US bank, is on a

519
00:23:16,320 --> 00:23:19,680
Speaker 12: hiring spree. It advertised for a massive three and a

520
00:23:19,760 --> 00:23:23,760
Speaker 12: half thousand AI related roles in the three months through Eppril.

521
00:23:24,000 --> 00:23:26,520
Speaker 12: These are some of the early applications at a time

522
00:23:26,560 --> 00:23:31,080
Speaker 12: when costs remain relatively high that's likely to change. Knowing

523
00:23:31,119 --> 00:23:34,960
Speaker 12: the technology's current limits also lets companies focus on the

524
00:23:35,040 --> 00:23:37,680
Speaker 12: real opportunities.

525
00:23:38,880 --> 00:23:39,680
Speaker 2: We just heard it there.

526
00:23:39,760 --> 00:23:42,560
Speaker 1: JP Morgan absolutely leading the pack when it comes to

527
00:23:42,640 --> 00:23:46,159
Speaker 1: experimentation or indeed hiring of AI talent. We want to

528
00:23:46,160 --> 00:23:48,639
Speaker 1: dig into this in Bloomberg Sally Bakewell, who covers all

529
00:23:48,680 --> 00:23:52,560
Speaker 1: things wall Stream, and just remind us why for banks

530
00:23:52,560 --> 00:23:55,520
Speaker 1: at the moment generator AI, AI in general is going

531
00:23:55,560 --> 00:23:56,520
Speaker 1: to be such a winner for them.

532
00:23:56,800 --> 00:23:59,880
Speaker 13: So yes, Wall Street is racing to use AI basically

533
00:24:00,080 --> 00:24:02,840
Speaker 13: ways that make money, that saves money, and that prevents

534
00:24:02,920 --> 00:24:03,879
Speaker 13: nefarious money.

535
00:24:04,040 --> 00:24:05,680
Speaker 2: Now why is AI useful to banks?

536
00:24:05,720 --> 00:24:09,040
Speaker 13: Well, banks are these complex machines of reams of data,

537
00:24:09,080 --> 00:24:14,480
Speaker 13: of risk modeling of decisions underpinned by vast quantities of information,

538
00:24:14,880 --> 00:24:17,800
Speaker 13: and so if AI can make that more efficient or

539
00:24:17,840 --> 00:24:21,040
Speaker 13: cut some of the manpower involved, that is a huge

540
00:24:21,080 --> 00:24:22,119
Speaker 13: win for Wall Street.

541
00:24:22,400 --> 00:24:23,480
Speaker 2: Now what is it doing?

542
00:24:23,840 --> 00:24:28,080
Speaker 13: As we just heard banks like Deutsche banker deploying deeper

543
00:24:28,160 --> 00:24:31,280
Speaker 13: learning so that they can help clients analyze whether they

544
00:24:31,280 --> 00:24:34,520
Speaker 13: are too heavily invested in a particular asset. JP Morgan

545
00:24:34,560 --> 00:24:37,600
Speaker 13: has filed a patent for some sort of chat GPT

546
00:24:37,760 --> 00:24:42,160
Speaker 13: device that might help investors select equities and BNP paribad Well,

547
00:24:42,160 --> 00:24:44,880
Speaker 13: it's using chatbots to answer client questions.

548
00:24:44,880 --> 00:24:47,320
Speaker 1: Interesting, so that's sort of a serving to the customer.

549
00:24:47,480 --> 00:24:50,480
Speaker 1: Very much clear how that would work. I'm interested in

550
00:24:50,520 --> 00:24:53,520
Speaker 1: some of the risk analysis they're doing around this as well, because.

551
00:24:53,280 --> 00:24:54,320
Speaker 2: Some are being more cautious than another.

552
00:24:54,400 --> 00:24:56,280
Speaker 1: Does it feels like Morgan Stanley's having a bit more

553
00:24:56,280 --> 00:24:59,880
Speaker 1: of an experimental just within the confines Therefore, Walls take

554
00:25:00,880 --> 00:25:03,000
Speaker 1: Some had actually banned the use of CHATCHBT by their

555
00:25:03,000 --> 00:25:06,000
Speaker 1: own employees. So how do you see they're putting in

556
00:25:06,040 --> 00:25:08,480
Speaker 1: place the right guardrails.

557
00:25:08,040 --> 00:25:11,200
Speaker 13: Exactly, And we've seen some blow ups in the world

558
00:25:11,200 --> 00:25:13,880
Speaker 13: of advanced technology. Is you know, crypto and blockchain, those

559
00:25:13,880 --> 00:25:16,840
Speaker 13: have been hugely problematic, and so banks are indeed being

560
00:25:16,920 --> 00:25:19,520
Speaker 13: very very cautious. And we have heard from you know,

561
00:25:19,600 --> 00:25:22,720
Speaker 13: Warren Buffett who has said that you once it's out there,

562
00:25:22,760 --> 00:25:24,879
Speaker 13: you can't uninvent it, and so you know, the genie

563
00:25:24,880 --> 00:25:26,919
Speaker 13: out the bottle could propose a bit of a problem.

564
00:25:26,920 --> 00:25:29,840
Speaker 13: And Moynihan too has said Bank of America Chief Executive

565
00:25:29,840 --> 00:25:32,600
Speaker 13: Moynihan has also said that you know, you don't know

566
00:25:32,680 --> 00:25:33,480
Speaker 13: what are going.

567
00:25:33,280 --> 00:25:34,560
Speaker 2: Into a lot of these decisions.

568
00:25:34,600 --> 00:25:37,080
Speaker 13: If you don't know the inputs, you should potentially be

569
00:25:37,160 --> 00:25:39,679
Speaker 13: concerned about the outputs. And then, of course, you know,

570
00:25:39,720 --> 00:25:44,040
Speaker 13: banks have a fiduciary duty not to trade on unreliable information,

571
00:25:44,640 --> 00:25:47,040
Speaker 13: which also begs the question of the sources of data

572
00:25:47,040 --> 00:25:48,800
Speaker 13: that are pulled in when it.

573
00:25:48,760 --> 00:25:49,359
Speaker 2: Comes to AI.

574
00:25:50,040 --> 00:25:53,439
Speaker 1: How much are they building themselves or how much are

575
00:25:53,480 --> 00:25:54,360
Speaker 1: they doing plugins?

576
00:25:54,400 --> 00:25:55,440
Speaker 2: Do you know how much they're.

577
00:25:55,280 --> 00:25:58,600
Speaker 1: Looking to other AI tech outside of their world.

578
00:25:58,800 --> 00:26:01,760
Speaker 13: I think they are doing any and all. Some are

579
00:26:01,960 --> 00:26:03,800
Speaker 13: tried to do it in house and some are using

580
00:26:03,840 --> 00:26:07,359
Speaker 13: external consultancies. And you know, we had that great data

581
00:26:07,400 --> 00:26:10,720
Speaker 13: point about talent, because talent is very much at the

582
00:26:10,760 --> 00:26:13,560
Speaker 13: heart of this and of all the banks that are

583
00:26:13,560 --> 00:26:18,399
Speaker 13: really embracing AI. About forty percent of their open jobs

584
00:26:18,520 --> 00:26:22,520
Speaker 13: are AI related. That's for quants, that's for ethics or

585
00:26:22,560 --> 00:26:26,280
Speaker 13: governance or analysts and so and JP Morgan is very

586
00:26:26,359 --> 00:26:28,560
Speaker 13: much at the forefront of that, accounting for more than

587
00:26:28,560 --> 00:26:31,520
Speaker 13: three thousand, six hundred of those jobs. So very much

588
00:26:31,520 --> 00:26:34,000
Speaker 13: at the heart of this race is, as always on Wall.

589
00:26:33,840 --> 00:26:37,119
Speaker 1: Street, the battle for talent, and that talent's expensive as well.

590
00:26:37,240 --> 00:26:39,399
Speaker 1: I mean, it's not bad timing that a lot of

591
00:26:39,400 --> 00:26:41,320
Speaker 1: the big tech companies have been letting go of some

592
00:26:41,440 --> 00:26:43,280
Speaker 1: key talent, and I'm sure they'll be sucked up into

593
00:26:43,280 --> 00:26:45,480
Speaker 1: the world of finance. But is there any reskilling that

594
00:26:45,560 --> 00:26:47,399
Speaker 1: goes on within the banks or is it always just

595
00:26:47,440 --> 00:26:48,560
Speaker 1: looking out externally?

596
00:26:48,800 --> 00:26:50,760
Speaker 13: Again, I think it's probably a bit of both. And

597
00:26:50,800 --> 00:26:53,560
Speaker 13: indeed the talent will be very expensive, you know, when

598
00:26:53,560 --> 00:26:57,359
Speaker 13: combined with wage inflation and inflation in general, and the

599
00:26:57,480 --> 00:27:00,800
Speaker 13: costs of AI can also be expensive. We included a

600
00:27:00,800 --> 00:27:03,280
Speaker 13: stat in the Big Takes story that costs of using

601
00:27:03,400 --> 00:27:07,160
Speaker 13: large language models could be about fourteen dollars per hour,

602
00:27:07,200 --> 00:27:09,480
Speaker 13: which compares to six dollars per hour when it's a

603
00:27:09,480 --> 00:27:11,200
Speaker 13: good old human human lawyer.

604
00:27:11,600 --> 00:27:14,800
Speaker 1: Well, we'll see as and when that cost point comes down.

605
00:27:15,080 --> 00:27:17,080
Speaker 1: That all comes down to compute costs. But we thank

606
00:27:17,119 --> 00:27:19,920
Speaker 1: you so much. What great big take, Sally Berquill running

607
00:27:19,960 --> 00:27:21,480
Speaker 1: us through it. Go check it out on dot com

608
00:27:21,560 --> 00:27:23,320
Speaker 1: or indeed, if you're lucky enough to have a terminal

609
00:27:23,880 --> 00:27:26,680
Speaker 1: the Meanwhile, let's just talk a little bit about digital avatars.

610
00:27:27,000 --> 00:27:30,320
Speaker 1: The market is another faction of the AI space, expected

611
00:27:30,359 --> 00:27:32,159
Speaker 1: to see wrapping both in the next few years. But

612
00:27:32,200 --> 00:27:35,160
Speaker 1: the most Tom McKenzie spoke about the ethics behind using

613
00:27:35,200 --> 00:27:35,879
Speaker 1: the technology.

614
00:27:36,200 --> 00:27:39,040
Speaker 2: We're in his very own digital twin take a look.

615
00:27:39,760 --> 00:27:43,399
Speaker 14: Joining us now was a very special, slightly weird guest.

616
00:27:43,440 --> 00:27:47,240
Speaker 14: It is, in fact, my own digital avatar, Tom McKenzie,

617
00:27:47,280 --> 00:27:48,320
Speaker 14: Tom Digital Tom.

618
00:27:48,680 --> 00:27:49,480
Speaker 4: Welcome to the show.

619
00:27:49,720 --> 00:27:52,560
Speaker 15: Thank you, Tom. It's a pleasure to be here on

620
00:27:52,600 --> 00:27:56,320
Speaker 15: Bloomberg TV. I am an AI powered digital avatar design

621
00:27:56,400 --> 00:27:59,800
Speaker 15: to provide insights and information on a wide range of topics.

622
00:28:00,040 --> 00:28:02,760
Speaker 14: Okay, this is all very odd, all right? What exactly

623
00:28:03,480 --> 00:28:04,480
Speaker 14: are avatars?

624
00:28:05,160 --> 00:28:09,879
Speaker 15: Digital avatars powered by generative AI are virtual representations of

625
00:28:09,920 --> 00:28:14,920
Speaker 15: individuals that can mimic human behavior, appearance, and interactions. They

626
00:28:14,960 --> 00:28:18,119
Speaker 15: work by utilizing AI algorithms to analyze and learn from

627
00:28:18,240 --> 00:28:23,400
Speaker 15: vast amounts of data including facial expressions, gestures, and speech patterns.

628
00:28:23,960 --> 00:28:28,480
Speaker 15: This enables them to generate realistic and responsive virtual characters. Okay,

629
00:28:28,480 --> 00:28:31,119
Speaker 15: so avatars aren't just gimmicks then, but what are the

630
00:28:31,160 --> 00:28:36,000
Speaker 15: real world's implications I'm thinking across the economy, across different industries,

631
00:28:36,080 --> 00:28:38,800
Speaker 15: what are the impacts actually likely to be? They can

632
00:28:38,840 --> 00:28:44,719
Speaker 15: revolutionize industries such as entertainment, gaming, and virtual communication. Avatars

633
00:28:44,760 --> 00:28:49,240
Speaker 15: have the power to enhance user experiences, enabling immersive virtual

634
00:28:49,320 --> 00:28:55,000
Speaker 15: interactions and personalized content. They can facilitate remote collaboration, telet presence,

635
00:28:55,200 --> 00:28:56,959
Speaker 15: and even assist with customer service.

636
00:28:57,120 --> 00:28:57,800
Speaker 6: Okay, there have.

637
00:28:57,760 --> 00:29:00,360
Speaker 14: To be some downsides though, So what are the discs

638
00:29:00,600 --> 00:29:01,760
Speaker 14: of this kind of technology?

639
00:29:01,960 --> 00:29:06,040
Speaker 15: As AI becomes more pervasive, ethical concerns arise, including issues

640
00:29:06,080 --> 00:29:10,000
Speaker 15: related to bias and fairness in AI algorithms, transparency and

641
00:29:10,120 --> 00:29:14,400
Speaker 15: explainability of AI decision making, data, privacy and security, and

642
00:29:14,440 --> 00:29:17,680
Speaker 15: the potential FAII to perpetuate existing social inequalities.

643
00:29:19,960 --> 00:29:24,320
Speaker 1: Tom McKenzie and AI. Tom McKenzie, let's just bring you

644
00:29:24,440 --> 00:29:27,520
Speaker 1: some headlines. We're currently seeing that the Treasury may delay

645
00:29:27,560 --> 00:29:31,120
Speaker 1: three month and six month bill sales due to the

646
00:29:31,160 --> 00:29:34,400
Speaker 1: debt cap. We are understanding, of course, are still conversations

647
00:29:34,440 --> 00:29:36,600
Speaker 1: going about the debt ceiling, how that's going to be

648
00:29:36,600 --> 00:29:39,240
Speaker 1: agreed Currently in the app passed by the House waiting

649
00:29:39,240 --> 00:29:41,720
Speaker 1: for the Senate, we understand that the US is tentatively

650
00:29:41,760 --> 00:29:44,880
Speaker 1: planning three month and six month bill auctions on June

651
00:29:44,880 --> 00:29:47,920
Speaker 1: the fifth, but the Treasury may delay those auction sales

652
00:29:48,240 --> 00:29:49,360
Speaker 1: due to the debt.

653
00:29:49,160 --> 00:29:49,960
Speaker 2: Cap at the moment.

654
00:29:49,960 --> 00:29:53,080
Speaker 1: Will bring you any further news on the debt sealing negotiations.

655
00:29:53,160 --> 00:29:55,680
Speaker 1: I Meanwhile, coming up, we'll talk about the unique opportunities

656
00:29:55,720 --> 00:29:58,920
Speaker 1: in artificial intelligence and legal tech and fintech and much

657
00:29:59,000 --> 00:30:01,560
Speaker 1: much more Cammas bench back. Ellen's going to be joining

658
00:30:01,640 --> 00:30:04,200
Speaker 1: us next. Meanwhile, let's just have a little look what's

659
00:30:04,200 --> 00:30:07,959
Speaker 1: having in the world retail. Macy's shows actually really managing

660
00:30:08,000 --> 00:30:10,239
Speaker 1: to bounce back from what was pretty ugly sell off

661
00:30:10,440 --> 00:30:13,240
Speaker 1: in pre market this after the numbers came in well

662
00:30:13,520 --> 00:30:14,280
Speaker 1: less than expected.

663
00:30:14,320 --> 00:30:15,840
Speaker 2: The Ford looking guid and it's having to pull back

664
00:30:16,120 --> 00:30:17,880
Speaker 2: their overall outlook.

665
00:30:17,880 --> 00:30:21,000
Speaker 1: For their business as the consumer dials back, particularly from

666
00:30:21,000 --> 00:30:24,360
Speaker 1: the Macy's brand rather than Bloomingdale's and Indie Blue Mercy.

667
00:30:24,360 --> 00:30:25,720
Speaker 1: Who are We saw a bounce back, But let's just

668
00:30:25,760 --> 00:30:27,080
Speaker 1: have a look at what the CEO told me a

669
00:30:27,120 --> 00:30:29,760
Speaker 1: little bit earlier. We sat down with Jeff Gonnett and

670
00:30:30,720 --> 00:30:33,600
Speaker 1: his talking about his role within artificial intelligence, saying, when

671
00:30:33,640 --> 00:30:35,800
Speaker 1: we look at AI more broadly, where our team can

672
00:30:35,840 --> 00:30:39,640
Speaker 1: build more customer products discovery, we are on the vanguard

673
00:30:39,800 --> 00:30:41,080
Speaker 1: to continue to deploy that.

674
00:30:41,440 --> 00:30:44,240
Speaker 2: So still an area growth, it's a bloomberg.

675
00:30:54,480 --> 00:30:56,920
Speaker 11: All the way to like accounting and operations.

676
00:30:56,960 --> 00:31:00,160
Speaker 8: I think it's going to completely revolutionize and transfer from

677
00:31:00,160 --> 00:31:04,360
Speaker 8: our industry, and we're investing very, very heavily into the

678
00:31:04,520 --> 00:31:06,600
Speaker 8: development of new capabilities ANAI.

679
00:31:07,120 --> 00:31:09,840
Speaker 1: Henri Que Lubergrass, the rex CO CEO, was with us

680
00:31:09,920 --> 00:31:12,560
Speaker 1: yesterday talking about AI's impact on fintech.

681
00:31:12,960 --> 00:31:15,760
Speaker 2: And now let's stand to a fintech investor who's not totally.

682
00:31:15,480 --> 00:31:18,120
Speaker 1: Convinced that AI has the will breakout use case in

683
00:31:18,160 --> 00:31:19,959
Speaker 1: the space quite yet, Rebecca Lyn, I'm place to say

684
00:31:19,960 --> 00:31:22,440
Speaker 1: it's joining us co founder and general partner of Canvas Ventures,

685
00:31:22,640 --> 00:31:25,320
Speaker 1: so firm specializing in fintech and AI, among other things

686
00:31:25,400 --> 00:31:27,640
Speaker 1: health as well, one hundred and thirty five million dollars

687
00:31:27,640 --> 00:31:31,280
Speaker 1: in assetsunder management, and fascinating to have you with us, Rebecca,

688
00:31:31,320 --> 00:31:34,000
Speaker 1: as to why maybe fintech isn't the first and foremost

689
00:31:34,000 --> 00:31:35,280
Speaker 1: place you'd be putting AI to work.

690
00:31:36,840 --> 00:31:38,680
Speaker 16: Yeah, I think there are a lot of interesting places

691
00:31:38,800 --> 00:31:41,440
Speaker 16: to put AI to work. You know, fintech is not

692
00:31:41,800 --> 00:31:44,520
Speaker 16: the absolute top of my list. I think companies that

693
00:31:44,960 --> 00:31:49,720
Speaker 16: have true transformative capabilities using AI are really where I'd

694
00:31:49,760 --> 00:31:51,880
Speaker 16: focus first. And I can give an example in my

695
00:31:51,960 --> 00:31:55,920
Speaker 16: portfolio with a company called case Text. So case Text

696
00:31:56,040 --> 00:31:58,920
Speaker 16: is in the legal in the legal world, they help

697
00:31:59,040 --> 00:32:02,959
Speaker 16: lawyers really put together in legal research, write their briefs,

698
00:32:03,200 --> 00:32:08,920
Speaker 16: do discovery. And the technology of AI, especially GPT four,

699
00:32:09,160 --> 00:32:13,040
Speaker 16: has supercharged that company and really transformed it and taken

700
00:32:13,120 --> 00:32:14,040
Speaker 16: it to the next level.

701
00:32:14,360 --> 00:32:15,000
Speaker 2: How do I think.

702
00:32:15,400 --> 00:32:18,960
Speaker 1: Comfortable are potential customers with the offering from case Texts.

703
00:32:19,000 --> 00:32:22,680
Speaker 1: For example, we saw the news and some smirked somewhat

704
00:32:22,760 --> 00:32:26,200
Speaker 1: that two New York lawyers are potentially facing well some

705
00:32:26,560 --> 00:32:29,920
Speaker 1: not only backlash but penalties because they used CHATCHPT four

706
00:32:30,040 --> 00:32:32,200
Speaker 1: to be able to put forward case studies that actually

707
00:32:32,240 --> 00:32:32,840
Speaker 1: didn't exist.

708
00:32:33,000 --> 00:32:36,080
Speaker 2: It hallucinated them. And I'm wondering what I mean.

709
00:32:36,160 --> 00:32:38,360
Speaker 1: It seems funny, but I mean this has real connotations

710
00:32:38,400 --> 00:32:40,960
Speaker 1: when people need to start saying that they're using AI within.

711
00:32:40,880 --> 00:32:43,240
Speaker 2: This work, right, oh, one hundred percent?

712
00:32:43,320 --> 00:32:45,840
Speaker 16: I mean, so I will tell you people have been

713
00:32:45,880 --> 00:32:48,640
Speaker 16: so comfortable with case texts that the company wrapped up

714
00:32:49,080 --> 00:32:52,800
Speaker 16: five million, additional five million dollars in additional arr in

715
00:32:52,880 --> 00:32:56,960
Speaker 16: forty five days after the launch of their product called

716
00:32:57,040 --> 00:33:01,040
Speaker 16: co Council. And regarding that story, you know what has

717
00:33:01,160 --> 00:33:03,440
Speaker 16: to happen even if I'm an attorney as well. Actually,

718
00:33:03,560 --> 00:33:06,400
Speaker 16: and so if you're an attorney, you need to read

719
00:33:06,480 --> 00:33:07,320
Speaker 16: the work, whether that.

720
00:33:07,440 --> 00:33:10,320
Speaker 2: Be of your associate or of your AI.

721
00:33:10,560 --> 00:33:14,280
Speaker 16: And what's happening is that case Tex is effectively standing

722
00:33:14,360 --> 00:33:17,360
Speaker 16: in the shoes of an associate. And so as a

723
00:33:17,440 --> 00:33:20,200
Speaker 16: partner at a firm, you would be required, of course

724
00:33:20,320 --> 00:33:24,320
Speaker 16: to review that work. And I can pretty much guarantee

725
00:33:24,320 --> 00:33:25,360
Speaker 16: you they weren't.

726
00:33:25,120 --> 00:33:28,880
Speaker 2: Using case Tex. And basically that's kind of what you've

727
00:33:28,920 --> 00:33:29,560
Speaker 2: got to do right now.

728
00:33:29,600 --> 00:33:31,680
Speaker 1: You've got to sort the wheat from the chaff, what's real,

729
00:33:31,800 --> 00:33:34,520
Speaker 1: what's not, what's hype, what's reality? And ultimately, how are

730
00:33:34,520 --> 00:33:37,240
Speaker 1: you doing that when you're looking at I'm sure hundreds

731
00:33:37,360 --> 00:33:40,120
Speaker 1: of messages pouring into your inbox trying to sell you

732
00:33:40,480 --> 00:33:42,880
Speaker 1: the AI vision that they've suddenly bolted onto their company.

733
00:33:43,840 --> 00:33:46,719
Speaker 16: Yeah, it's funny, I tell everyone, my inbox is more

734
00:33:46,840 --> 00:33:48,840
Speaker 16: like a Twitter stream at this point in time, right,

735
00:33:49,400 --> 00:33:51,640
Speaker 16: So there's a lot of sorting, I will tell you,

736
00:33:51,840 --> 00:33:53,480
Speaker 16: but there's always a lot of sorting for us. And

737
00:33:53,720 --> 00:33:55,640
Speaker 16: quite frankly, that's what we're paid to do and venture

738
00:33:55,720 --> 00:33:57,920
Speaker 16: is to really sort of see around the corner and

739
00:33:58,360 --> 00:34:01,080
Speaker 16: really start with the the with what's next, like what

740
00:34:01,320 --> 00:34:03,520
Speaker 16: is it that this world needs, what is it that

741
00:34:04,360 --> 00:34:06,800
Speaker 16: is really going to be happening, you know, in the

742
00:34:06,920 --> 00:34:10,120
Speaker 16: next ten, fifteen, twenty years, and then work backwards and

743
00:34:10,280 --> 00:34:12,240
Speaker 16: so you know, just sorting through what comes an email

744
00:34:12,320 --> 00:34:15,440
Speaker 16: is very reactionary. And you know it's taught very early

745
00:34:15,640 --> 00:34:19,200
Speaker 16: by probably one of the most respected people inventor Bill Gurly,

746
00:34:19,360 --> 00:34:22,160
Speaker 16: that the best deals are really the outbound deals. So

747
00:34:22,440 --> 00:34:25,440
Speaker 16: our firm and our and our thesis is really outbound

748
00:34:26,040 --> 00:34:28,839
Speaker 16: macro and then you work backwards from there. So what's early,

749
00:34:28,920 --> 00:34:33,400
Speaker 16: what's transformative and just tagging AI to something it doesn't

750
00:34:33,440 --> 00:34:35,520
Speaker 16: really help. I mean, I think AI will be helpful

751
00:34:35,560 --> 00:34:39,359
Speaker 16: in almost every business, and we really want to see,

752
00:34:39,920 --> 00:34:42,799
Speaker 16: you know, not what's just helpful, but what's transformative, Right,

753
00:34:42,920 --> 00:34:45,359
Speaker 16: what's really going to create the next you know, one

754
00:34:45,400 --> 00:34:47,640
Speaker 16: billion dollar, ten billion dollar, you know, one trillion dollar

755
00:34:47,719 --> 00:34:48,319
Speaker 16: company out there?

756
00:34:48,800 --> 00:34:52,919
Speaker 1: What are valuations like when you're looking at those outbound opportunities.

757
00:34:54,080 --> 00:34:57,279
Speaker 16: Yeah, so evaluations we invest in the Series A and

758
00:34:57,440 --> 00:35:01,920
Speaker 16: B are real core asset in our firm is our

759
00:35:02,000 --> 00:35:04,320
Speaker 16: go to market capability. And so what we like to

760
00:35:04,400 --> 00:35:08,440
Speaker 16: do is come in and invest when companies are Series

761
00:35:08,560 --> 00:35:10,960
Speaker 16: A or B, and what we like to do is

762
00:35:11,200 --> 00:35:14,080
Speaker 16: sort of do the late A early B. So really

763
00:35:14,200 --> 00:35:18,600
Speaker 16: before somebody is you know, technically fundraising, go in, come

764
00:35:18,680 --> 00:35:20,920
Speaker 16: in a little premptively and help, you know, help them

765
00:35:21,000 --> 00:35:23,000
Speaker 16: get to that next level. You know, when you talk

766
00:35:23,040 --> 00:35:27,439
Speaker 16: about valuations, the evaluations across the boarder down. They're down

767
00:35:27,880 --> 00:35:30,120
Speaker 16: less for the Series A than they are for a

768
00:35:30,239 --> 00:35:34,080
Speaker 16: growth stage. The growth stage valuations have just taken a noseedive.

769
00:35:34,160 --> 00:35:36,120
Speaker 16: They're down eighty percent, I would.

770
00:35:35,880 --> 00:35:36,480
Speaker 2: Say right now.

771
00:35:37,320 --> 00:35:41,600
Speaker 16: Early stage valuations are down forty on the average. However,

772
00:35:42,280 --> 00:35:44,279
Speaker 16: there is the case of the have and the have nots.

773
00:35:44,880 --> 00:35:47,680
Speaker 16: I have one company in particular that was not even

774
00:35:47,760 --> 00:35:51,279
Speaker 16: in a process and now is sitting on four term sheets, right,

775
00:35:51,520 --> 00:35:54,000
Speaker 16: and so it really is this case.

776
00:35:53,800 --> 00:35:56,560
Speaker 1: Of a have and have not when you're looking at

777
00:35:56,600 --> 00:36:00,400
Speaker 1: the late stage with valuations on the downside, having to

778
00:36:00,440 --> 00:36:02,080
Speaker 1: buckle up. They're going to have to ride out the

779
00:36:02,120 --> 00:36:04,479
Speaker 1: next couple of years as the economy recovers, as people

780
00:36:04,520 --> 00:36:07,239
Speaker 1: get risk appetite once again, and they're also having to

781
00:36:07,320 --> 00:36:09,719
Speaker 1: maybe pivot or indeed ensure that they're not having their

782
00:36:09,800 --> 00:36:12,040
Speaker 1: lunch eaten by other new AI players on the scene.

783
00:36:12,160 --> 00:36:14,880
Speaker 1: How are you making sure your portfolio is robust for

784
00:36:15,000 --> 00:36:19,040
Speaker 1: this complete change in vanguard moment as many.

785
00:36:18,920 --> 00:36:21,080
Speaker 2: Want to call it in the world of artificial intelligence.

786
00:36:22,040 --> 00:36:23,879
Speaker 16: Yeah, I mean this is a moment that we haven't

787
00:36:23,920 --> 00:36:27,760
Speaker 16: really seen since really when the wild Garden Wild Garden

788
00:36:27,880 --> 00:36:30,880
Speaker 16: came down with the introduced introduction of the iPhone. That happened,

789
00:36:30,920 --> 00:36:32,839
Speaker 16: you know, about fifteen years ago when I first came

790
00:36:32,880 --> 00:36:36,040
Speaker 16: into venture. And I think our firm is unique and

791
00:36:36,160 --> 00:36:39,319
Speaker 16: that we have seen multiple cycles. This isn't our first rodeo, right,

792
00:36:39,760 --> 00:36:42,919
Speaker 16: and so we've been through this before and in every

793
00:36:43,000 --> 00:36:46,560
Speaker 16: cycle like this, the advice is the same. It's cut

794
00:36:46,640 --> 00:36:48,640
Speaker 16: your burn, you know, cut your burn and cut your burn,

795
00:36:49,200 --> 00:36:52,120
Speaker 16: plan to get there on your own oxygen, and don't

796
00:36:52,200 --> 00:36:54,759
Speaker 16: be afraid to pivot. I mean there are in the

797
00:36:54,880 --> 00:36:58,239
Speaker 16: land of AI, there are huge opportunities in front of companies.

798
00:36:58,560 --> 00:37:01,880
Speaker 16: You know, take a beat, hut your burn, and you

799
00:37:02,080 --> 00:37:04,800
Speaker 16: live to fight the next fight and take advantage of

800
00:37:04,920 --> 00:37:08,360
Speaker 16: what the capabilities of AI can offer you to really

801
00:37:08,719 --> 00:37:11,640
Speaker 16: rethink your company and think out of the box and

802
00:37:11,680 --> 00:37:12,040
Speaker 16: get there.

803
00:37:12,400 --> 00:37:13,080
Speaker 4: And the problem a.

804
00:37:13,080 --> 00:37:15,600
Speaker 16: Lot of these companies have is they're sitting at such

805
00:37:15,640 --> 00:37:19,160
Speaker 16: a high valuation on their post of their last round

806
00:37:19,920 --> 00:37:22,879
Speaker 16: that they have to get profitable because they're not going

807
00:37:22,960 --> 00:37:25,040
Speaker 16: to be able to get that next round without a

808
00:37:25,080 --> 00:37:28,000
Speaker 16: big down round or even a recap.

809
00:37:29,040 --> 00:37:32,160
Speaker 1: Canvas Ventures co founder and general partner telling it straight,

810
00:37:32,200 --> 00:37:33,560
Speaker 1: Rebecca Lyn, really great to have you.

811
00:37:33,920 --> 00:37:36,680
Speaker 2: Thank you so much for our VC spotlight. Meanwhile, it's

812
00:37:36,719 --> 00:37:37,080
Speaker 2: time for.

813
00:37:37,200 --> 00:37:40,200
Speaker 1: Talking tech, and first up, Tether's stabile coin has recovered

814
00:37:40,239 --> 00:37:42,720
Speaker 1: all of the roughly twenty billion dollars in market value

815
00:37:42,920 --> 00:37:46,880
Speaker 1: it lost following the collapse of the algorithmic rival terror USD.

816
00:37:47,040 --> 00:37:48,719
Speaker 1: It's a little over a year ago, of course, and

817
00:37:48,920 --> 00:37:51,280
Speaker 1: it's even topping its previous record of eighty three billion

818
00:37:51,320 --> 00:37:52,880
Speaker 1: dollars set back in May twenty twenty two.

819
00:37:52,880 --> 00:37:55,600
Speaker 2: It's calling to a live track up published bright Tether.

820
00:37:56,360 --> 00:37:58,680
Speaker 1: Meanwhile, Apple is testing a pair of new high end

821
00:37:58,760 --> 00:38:02,080
Speaker 1: max and they're accompanying processes ahead of its worldwide Developers

822
00:38:02,080 --> 00:38:04,440
Speaker 1: conference that's next week. This is part of an effort

823
00:38:04,520 --> 00:38:07,239
Speaker 1: to overhaul the backline and the tracks consumers during It's

824
00:38:07,280 --> 00:38:09,160
Speaker 1: like a stretch for the computer industry.

825
00:38:09,600 --> 00:38:12,920
Speaker 2: Glass and Video CEO Jessin Huang is heading to China, we.

826
00:38:12,960 --> 00:38:15,200
Speaker 1: Understand, to meet the tech executives in the world's biggest

827
00:38:15,280 --> 00:38:18,440
Speaker 1: chip market. It's despite rising tensions, of course, between Washington

828
00:38:18,480 --> 00:38:18,920
Speaker 1: and Beijing.

829
00:38:19,120 --> 00:38:19,920
Speaker 2: It's sort of according to.

830
00:38:19,960 --> 00:38:31,680
Speaker 7: Sources, are you ready to be replaced? Hello?

831
00:38:31,840 --> 00:38:32,000
Speaker 4: There?

832
00:38:32,719 --> 00:38:33,560
Speaker 7: As you listen to me.

833
00:38:33,640 --> 00:38:36,960
Speaker 17: Speak and raise my eyebrows, you're probably noticing something a

834
00:38:37,000 --> 00:38:40,320
Speaker 17: bit off about me. I'm an avatar of Parmi Olsen,

835
00:38:40,640 --> 00:38:44,839
Speaker 17: a technology columnist with Bloomberg Opinion. Parmi spent about two

836
00:38:44,920 --> 00:38:47,680
Speaker 17: hours in a TV studio speaking into a camera and

837
00:38:47,840 --> 00:38:51,000
Speaker 17: microphone so that an AI model could be trained to

838
00:38:51,080 --> 00:38:53,360
Speaker 17: clone her into what you see in front of you.

839
00:38:54,120 --> 00:38:56,359
Speaker 17: Maybe in a year or two, I'll look a lot

840
00:38:56,440 --> 00:38:59,840
Speaker 17: more real and a little less glitchy, making people like

841
00:38:59,880 --> 00:39:03,000
Speaker 17: you and par me easier to replace in videos.

842
00:39:04,040 --> 00:39:06,520
Speaker 2: Wow, isn't it all the rage these AI avatars?

843
00:39:06,640 --> 00:39:06,680
Speaker 13: That?

844
00:39:06,840 --> 00:39:09,320
Speaker 1: Of course the BlueBag Opinions parame Elsen there, and we

845
00:39:09,440 --> 00:39:12,560
Speaker 1: speaking of AI imitating us better and better and threatening

846
00:39:12,680 --> 00:39:16,759
Speaker 1: to replace our jobs. You might actually disproportionately replace jobs

847
00:39:16,800 --> 00:39:18,200
Speaker 1: typically held by women.

848
00:39:18,560 --> 00:39:18,640
Speaker 13: Now.

849
00:39:18,680 --> 00:39:22,280
Speaker 1: It's according to HR analytics firm Velio Labs. An economists

850
00:39:22,320 --> 00:39:24,920
Speaker 1: at the firm says, quote, the distribution of genders across

851
00:39:24,960 --> 00:39:28,920
Speaker 1: occupations reflects the biases deeply rooted in our society, with

852
00:39:29,080 --> 00:39:32,560
Speaker 1: women often being confined to roles such as administrative assistance and.

853
00:39:32,600 --> 00:39:36,280
Speaker 2: Set the AAI food.

854
00:39:36,560 --> 00:39:40,640
Speaker 1: Along general lines from Revelio Labs identified jobs more likely

855
00:39:40,719 --> 00:39:45,759
Speaker 1: to be REPLCEDI and generally how by women such as

856
00:39:45,880 --> 00:39:49,960
Speaker 1: bill and count collectors, payroll clerks, executive secretaries, and more. Now,

857
00:39:50,000 --> 00:39:53,520
Speaker 1: the firm says, providing we train opportunities will be key

858
00:39:53,600 --> 00:39:56,040
Speaker 1: for women to navigate this evolving job landscape.

859
00:39:56,520 --> 00:39:56,880
Speaker 2: Now does it?

860
00:39:56,880 --> 00:39:59,000
Speaker 1: If there's a edittional BlueBag technology, If we get to

861
00:39:59,080 --> 00:40:01,440
Speaker 1: check out our podcast, I confined on the terminal as

862
00:40:01,520 --> 00:40:05,160
Speaker 1: well as online at Apples, Spotify, and iHeart this Supreme

863
00:40:05,200 --> 00:40:05,399
Speaker 1: Bank