Speaker 1: And joins us shortly, this is Bloomberg Technology coming up.
Speaker 1: We'll break down earnings from sales voice that disappoints and
Speaker 1: C three AI, the artificial intelligence high pits reality with
Speaker 1: disappointing earnings from that company too. But let's stick on
Speaker 1: AI in the here and then now with Palenteer, we
Speaker 1: go live to Palo Alto, California, where our own Ed
Speaker 1: Ludlow is sitting down for an exclusive conversation with the
Speaker 1: CEO but us.
Speaker 2: We'll have more on.
Speaker 1: Guess what artificial intelligence, how Wall Street is using it,
Speaker 1: how the technology is replacing our jobs, and even how
Speaker 1: AI is replacing ourselves.
Speaker 2: We'll have more throughout the hour.
Speaker 1: First, set's check in to an audience where we're going
Speaker 1: to be discussing so much more the future of AI,
Speaker 1: where the likes of one key executive who wants to
Speaker 1: take the whole market on that. I want to welcome
Speaker 1: our Bloomberg TV and our radio audiences. We want to
Speaker 1: send it over now to our one Ed Ludlow, who's
Speaker 1: sitting down with an exclusive interview the CEO of Palenteer, Ed. Yeah.
Speaker 3: We're joined by Alex Karp, the CEO of Paneteer at
Speaker 3: aip CON Artificial Intelligence Platform CON a chance for you
Speaker 3: to talk with customers about some of what you told
Speaker 3: us three weeks ago. And on that note, three weeks ago,
Speaker 3: you said that Palente's plan.
Speaker 4: For AI was quote, just take the whole market. How's
Speaker 4: that going?
Speaker 1: Well?
Speaker 5: You know, unlike most people, we've been involved in what
Speaker 5: people call AI for the last five six seven years
Speaker 5: in the classified environment, building systems that will allow you
Speaker 5: to identify adversarial positions.
Speaker 6: And in that.
Speaker 5: Context, we've built proprietary technology that will allow you to
Speaker 5: work with large language models, securely, enhance them, roll them
Speaker 5: across your whole enterprise. You know, I've been at this
Speaker 5: for about twenty years, and I know it will take
Speaker 5: everyone else four or five years to build this.
Speaker 6: We're rolling it out. Our customer base.
Speaker 5: Is large, and we have you know, usually we wait
Speaker 5: for we have to go out and find people. Now
Speaker 5: we have customers, especially in the US, just calling us
Speaker 5: every day.
Speaker 4: You said, the demand is huge, can you quantify it?
Speaker 5: And so you know usually again we've had a number
Speaker 5: of inbound call a year that we usually have in
Speaker 5: a year, in like a month, and then if.
Speaker 6: We're at a conference, if you go next door.
Speaker 5: There are customers showing potential customers how to use our product.
Speaker 5: Its sense well, it's on real data, it's it's it's
Speaker 5: things that they've done so right now, the whole world
Speaker 5: is hungry for something that it understands as AI, which
Speaker 5: is really AI or large language models. We are actually
Speaker 5: have customers using our products showing other customers how to
Speaker 5: do it.
Speaker 6: I mean this is like you release a song and
Speaker 6: everyone else playing it. So okay, great, We're very happy.
Speaker 5: And you know, the thing is, the US market is
Speaker 5: just hungry for innovation. It's hungry for things. It's now beginning.
Speaker 5: It needs to needs, you know, beginning to understand. It
Speaker 5: needs like an ability to map a l M on
Speaker 5: your enterprise securely, ability to enhance the output of a
Speaker 5: large language model, and ability and and what's the output,
Speaker 5: better margins, better safety.
Speaker 6: You can change your enterprise in weeks.
Speaker 3: I just want to jump in and ask a very
Speaker 3: basic question, AIP is it built on GPT four?
Speaker 4: We different foundation. We as the underlying.
Speaker 5: Tech, the underlying we are completely agnostic to whatever large
Speaker 5: language model you want to use. Large language models have
Speaker 5: certain attributes, like they can give you reasoning, but you
Speaker 5: can't import that reasoning into your enterprise. What AIP does
Speaker 5: is allow you to take the benefits of the large
Speaker 5: language model, enhance them with ILL algorithms that we help
Speaker 5: you build and roll it securely across your whole enterprise.
Speaker 5: And what does that mean. It means you get all
Speaker 5: the benefits of a large language model in your enterprise today,
Speaker 5: not in five years. Not something that writes poetry. We're
Speaker 5: not offering people poetry writing in their enterprise. We're offering
Speaker 5: things that are so powerful that really, in reality, I'm
Speaker 5: not sure we should even sell this to some of
Speaker 5: our clients like national security.
Speaker 3: Who are those clients? Well, who are those clients proportionately?
Speaker 3: When you think about demand, how much is coming from
Speaker 3: the defense use case? Since you kind of gave more
Speaker 3: flesh to the AI bones at that during that earning.
Speaker 5: Look, what's driving the demand for our product and defense
Speaker 5: is simply what is what people have seen on the battlefield,
Speaker 5: and that's very sensitive and very classified. But the demand
Speaker 5: for that is very large, it's going to get larger.
Speaker 5: Why is it going to get larger? Because America is
Speaker 5: the best at software. Software that's built in a product
Speaker 5: is in high demand and defense? Why is it also
Speaker 5: in demand because until two years ago everyone thought this
Speaker 5: was a joke. We are building systems over the last
Speaker 5: five years that are deadly that those deadly systems have
Speaker 5: changed the course of history.
Speaker 6: It's no longer mad man saying this.
Speaker 5: You see it on the battlefield in US commercial US
Speaker 5: commercial industry is the most adaptive in the world, and
Speaker 5: they are hungry. Our clients are hungry for things that
Speaker 5: will give them a disapportionate advantage on margins, on safety,
Speaker 5: on secure use of LMS, on making sure this is
Speaker 5: not just some poetry recreating what somebody said, but actually
Speaker 5: can create actual tangible difference.
Speaker 6: And we are rolling it out and we're very happy.
Speaker 3: For our global Bloomberg television and radio audiences. We are
Speaker 3: at aip COM. We're joined by the CEO of Palenteer,
Speaker 3: Alex carp. During that earning school you said we have
Speaker 3: no pricing strategy. We're going to create a lot of value.
Speaker 3: We're going to get hundreds of customers, and we will
Speaker 3: price it as we go. Have you made any progress
Speaker 3: on pricing strategy since that No.
Speaker 4: I'm so relaxed about it.
Speaker 5: Because if you it's like one of these things like
Speaker 5: when you go to a bar.
Speaker 6: You know everyone wants to meet you. Do you have
Speaker 6: a pricing strategy when you go to the bar?
Speaker 5: No, you're like, Oh, I'm cool. I know we have
Speaker 5: the best product on the market.
Speaker 6: I know customers will pay us fairly.
Speaker 5: I know that it's much more valuable than anyone will
Speaker 5: understand till they install it, and we will sort out
Speaker 5: there's to make it like slightly academic. I believe in
Speaker 5: prey too optimization. We are going to create a lot
Speaker 5: of value, and we're going to get some portion of
Speaker 5: that value. And customers are smart, they'll pay you some
Speaker 5: portion of the value. Why should I just as an
Speaker 5: actually metic thing. If you have a software product, you
Speaker 5: always want to get paid after you deliver value.
Speaker 7: If you've got a.
Speaker 5: PowerPoint, something that doesn't work, something that's not valuable, you
Speaker 5: want to get.
Speaker 6: Paid before you create value.
Speaker 5: We know it's valuable, we know it's much more valuable
Speaker 5: than people understand. We know we're going to continue to
Speaker 5: augment that value, and we're going to get paid along
Speaker 5: the way.
Speaker 4: Well, the counter consideration is how much you invest in
Speaker 4: the product.
Speaker 5: But we've already invested billions of building these things in
Speaker 5: various components over the last twenty years, and we have
Speaker 5: the we have the IP and we're basically sewing it
Speaker 5: together and adding things on top of it. And so
Speaker 5: we know we have the IP, we're certain of the value.
Speaker 5: Why would we So again I come to you, I'm like, hey,
Speaker 5: I'm certain this is very valuable.
Speaker 6: Pay me ten million dollars. What are you going to.
Speaker 4: Say, Well, I don't have ten many.
Speaker 6: Until well, okay, give me your British charm.
Speaker 3: Well and they say, well for your British charm, my
Speaker 3: British charm. If I were a customer, would say, this
Speaker 3: is a really hard environment. If I think about cloud exactly,
Speaker 3: customers are looking for value at the lowest price.
Speaker 5: Now. I but if you believe you have the best
Speaker 5: product in the world, where are you going to say? Okay, great,
Speaker 5: let's not even have that discussion.
Speaker 6: I'll create the value. You tell me how much value created?
Speaker 5: By the way, if you don't want to pay me,
Speaker 5: then I'll go to someone else who will. You can
Speaker 5: just you can have different margins and the personal payment,
Speaker 5: you can have a different.
Speaker 6: Safety profile and the paper. You can have a different.
Speaker 5: Ability to control your whole business from your laptop than
Speaker 5: someone else because the person who valued it paid me.
Speaker 6: We don't want to pay me.
Speaker 3: Great Alex Carpcio Palenteer Technologies. This morning, the Bear Cave,
Speaker 3: a subset based newsletter, put out a negative report into it.
Speaker 4: Let me just read one of the claims.
Speaker 3: The Bear Cave believes Palenteer is an ai imposta engaging
Speaker 3: in spurious games to inflate its books and obfuscate its
Speaker 3: less sexy role as an over height data consultant.
Speaker 4: What is your response to that.
Speaker 6: The bear Cave is a bear cave. They can stay
Speaker 6: in the bear cave. We're a profitable software company.
Speaker 5: Those are interesting critiques of us, and you know we
Speaker 5: have the best products in the market and that's why
Speaker 5: they're profitable and we will win.
Speaker 4: Wanted to give you the right response.
Speaker 3: That was all the core of Palente's pitch, right, is
Speaker 3: that you have this experience in managing sensitive classified often
Speaker 3: data networks or closed network How does that work in
Speaker 3: the training of.
Speaker 4: The l lms that are going into a I P.
Speaker 3: Is it difficult when they have that kind of restriction
Speaker 3: on the data source.
Speaker 5: It's a very very very important technical question to work
Speaker 5: to use l l ms at scale in a classified
Speaker 5: in sensitive environment. You have to have something like a
Speaker 5: data model that trains the data model, and something like
Speaker 5: branching and and and and and access control. Those products
Speaker 5: take decades to build.
Speaker 6: We have them.
Speaker 5: But if you have those products, you can segment in
Speaker 5: real time what what.
Speaker 4: The already built.
Speaker 6: They're already built.
Speaker 5: We're already they're already part of all of our core
Speaker 5: fied they're part of PG, they're part of foundry.
Speaker 6: This is they're.
Speaker 5: About we roll them out to our current customers. Many
Speaker 5: of our customers have not needed to use these, They
Speaker 5: now need to use them.
Speaker 6: Why do they need to use them?
Speaker 5: Because if you in any environment, you're gonna have records
Speaker 5: that you're gonna have, you're gonna have data and insights
Speaker 5: you're not going to share. With the large language model,
Speaker 5: you're gonna have data insights you do want to share,
Speaker 5: and you're gonna have a hybrid and that requires a segmenting,
Speaker 5: branching architecture. And one of the things we built over
Speaker 5: the last ten years, randomly because we thought it would
Speaker 5: be valuable someday.
Speaker 3: Was that for our Bloomberg radio and television audiences worldwide.
Speaker 3: We are with doctor Alex Karp, the CEO of Palenter.
Speaker 3: An interesting case study is Ukraine. You've deepened your relationship
Speaker 3: and activity in Ukraine using AI in one case to
Speaker 3: help with reconstruction.
Speaker 4: How else given just.
Speaker 6: Well, by the way, my real answer to the short
Speaker 6: people is ask the Russians what do you mean by this?
Speaker 5: Like we can't say, ask the Ukrainians, Ask people who
Speaker 5: are in the battlefield, ask people who have been subject.
Speaker 5: You're talking about the effective effectiveness of our product. Okay,
Speaker 5: So it's like there's very little we can say. You
Speaker 5: can read what the Ukrainians are saying. They use targeting,
Speaker 5: according to reports, has gone up given the use of
Speaker 5: AI by products potentially ours from like by twenty to
Speaker 5: fifty x. These products have changed the course of history
Speaker 5: and they will continue to.
Speaker 6: Can't change the course of history, and super proud of that.
Speaker 3: Do we already have in the military use case, an
Speaker 3: arms race between powers like the US, Russia, China specifically
Speaker 3: in the field of artificial intelligence.
Speaker 5: Yes, and we have an advantage and if we don't
Speaker 5: get get out of our own way, we might actually
Speaker 5: continue to.
Speaker 4: When you say we gets out of our own way,
Speaker 4: what do you mean by that?
Speaker 5: Well, you know in America has the best best software
Speaker 5: companies in the world. The software companies largely come from
Speaker 5: a sliver of America. They produce products. We need to
Speaker 5: get to a point where one percent of our spend
Speaker 5: on defense goes to products that have been proven on
Speaker 5: the battlefield, not power points. And so like in the
Speaker 5: large language model and the generalizable AI. We are far ahead,
Speaker 5: call it a year or two, but we must actually implant.
Speaker 5: And there's a huge debate. Part of the debate, of course,
Speaker 5: is these things are very dangerous. If we didn't have
Speaker 5: vicious adversaries, we should we should slow it down, but
Speaker 5: we do. But we also have lots of people who
Speaker 5: don't want to roll this out because they have nothing
Speaker 5: to roll out, and so there's like the debate machine
Speaker 5: about rolling this out. Is partly for legitimate reasons because
Speaker 5: this could be dangerous, partly for security reasons you brought up.
Speaker 5: But there are architectures that will allow you to deal
Speaker 5: with this as a product like Pounder and hopefully someday others.
Speaker 5: But there's also the debate machine because there are only
Speaker 5: three or four companies in the world with anything to sell,
Speaker 5: and everyone else wants to debate why should we do this,
Speaker 5: how should we do this.
Speaker 6: Can we play catch up? Can we talk about this
Speaker 6: in five years?
Speaker 5: That really plays into our adversary's hands, and we really
Speaker 5: have to avoid that.
Speaker 3: The long term concern that came up twenty four hours
Speaker 3: ago or forty hours ago is an existential threat from
Speaker 3: a you talk at panting about bending AI to a
Speaker 3: collective will, Well, do you share the concern though about
Speaker 3: an extinction level?
Speaker 5: Well, there's a lot going on. There are these things dangerous.
Speaker 5: Could they become potentially dangerous? Could they become Yes? But
Speaker 5: what these debates ignore is either we will wield them
Speaker 5: or our adversaries will will them. It is much better
Speaker 5: if we wield the technology than our adversaries who do
Speaker 5: not respect our norms, do not respect the rule of law,
Speaker 5: and do not respect the way we want to live
Speaker 5: in freedom.
Speaker 6: So that's point one. Point two.
Speaker 5: In the near term, what penalteer will allow you to
Speaker 5: do is make these things really, really valuable commercially and
Speaker 5: in the military context. And we in commercial context, you
Speaker 5: have to do it because if you don't buy our product,
Speaker 5: your competition will. In the military context, we have to
Speaker 5: do it because our adversaries will build those products.
Speaker 3: You toys about the competitive landscape. I actually wanted to
Speaker 3: ask you about C three AI as an example, because
Speaker 3: you come up in bidding processes with them.
Speaker 5: Actually we look, this is a massive market. We actually
Speaker 5: don't come up with bidding processes anyway. And I'll tell
Speaker 5: you what I think about everyone.
Speaker 4: See, you don't have any competition.
Speaker 5: Let me just let me just tell you about this
Speaker 5: competition thing that while street analysts love, it's complete ps you.
Speaker 5: This is an infinite market. Basically, try what we're doing,
Speaker 5: and try what everyone else is doing, and buy the
Speaker 5: thing that creates the most.
Speaker 3: Value on an infinite market. Blom Bag Intelligence put out
Speaker 3: this research report this morning that says generative AI as
Speaker 3: a market will be one point three trillion in twenty
Speaker 3: to thirty two. That requires compound annual growth of about
Speaker 3: forty percent a year from this point over a decade.
Speaker 3: Do you see that as really well, what I see,
Speaker 3: I don't know.
Speaker 4: This based on the markets you operate.
Speaker 6: Look, these experts just make stuff up.
Speaker 5: But you know, what we know is this is a large,
Speaker 5: basically impossible to measure market.
Speaker 6: And what we also know.
Speaker 5: Is everybody in the in the US is going to
Speaker 5: find ways to become more efficient and better using software,
Speaker 5: and a lot of that software is going to be
Speaker 5: AI driven.
Speaker 6: We also know they're.
Speaker 5: Going to the market over time, not in a quarter,
Speaker 5: will end up picking the best products.
Speaker 6: That's all we know.
Speaker 3: In the United Kingdom, my home country, the ft is
Speaker 3: reporting that within the NHS as a case study, there
Speaker 3: is some concern about deepening the data relationship with palenteer.
Speaker 3: What would be your answer to those concerns?
Speaker 5: Look outside of America and in the UK, there are
Speaker 5: legitimate questions that get asked, where's the data going to go?
Speaker 5: How is it moved too touches it? Does it get
Speaker 5: exported to the US? Can we verify how is used,
Speaker 5: what context? And can we make sure that the underprivileged
Speaker 5: p people of the UK actually get the same treatment
Speaker 5: as the privileged people, including not just in treatment but
Speaker 5: future treatment which is a huge issue in the UK
Speaker 5: because there's a backlog, So how do you deal with
Speaker 5: the backlog equitably? Talentaer provides the most robust transparent software
Speaker 5: in the world, which is part of the reason we're
Speaker 5: having an AI bonanza because to make AI work you
Speaker 5: have to show how it works. How did the transform work,
Speaker 5: how does the branching work, how does the ontology work.
Speaker 6: How does it map to on This.
Speaker 5: Is exactly what you have to show in a hospital context.
Speaker 5: Who worked with the patient, under what condition, what doctor
Speaker 5: was it was the person equitably and fairly treated.
Speaker 6: What happens to backlog by the way, we've proven we
Speaker 6: can do this as a.
Speaker 5: Product safely, efficiently and under the hardest conditions in the UK,
Speaker 5: and I really hope we win that for this for
Speaker 5: our sake, but also for the sake of our UK
Speaker 5: employees and others that we greatly respect, and because it'll
Speaker 5: lead transparency leads to the fairest, most ethical and justified
Speaker 5: outcomes you can get.
Speaker 3: Is Alix carp CEO of Palented, thank you for having
Speaker 3: us at AIP can't hear in palawawle.
Speaker 4: Thank you back to you, Take care.
Speaker 1: Ed, absolutely fascinating conversation and AI bonanza. We're going to
Speaker 1: deep dive into all things artificial intelligence throughout the show.
Speaker 1: Coming up, we break down the earnings of C three AI.
Speaker 1: Apparently the market is infinite. Well, why is Dan i'ves
Speaker 1: gone outperformed this stock?
Speaker 2: Is that why he's from Webush of course.
Speaker 1: Get his thoughts as well as what's happening in the
Speaker 1: future for Apple, and it's a our path, it's a Bloomberg.
Speaker 1: Let's get back to some of these earnings, the earnings
Speaker 1: reactions because Salesforce tumbling after the software company signaled it
Speaker 1: isn't growing as fast as well it used to, while
Speaker 1: of course shifting its focus to generating higher profits. Let's
Speaker 1: get into the risk reward here with Bloomberg's Brodie Ford,
Speaker 1: and it was a notable drop. We'm now studying a
Speaker 1: little bit, but ultimately this is a company that's having
Speaker 1: to do layoffs, having to tighten its overall expenses. Why
Speaker 1: the sales slow down there?
Speaker 8: Yeah, so last quarter Salesforce said we're going to focus
Speaker 8: on profit now, and the market said, yeah, finally, like
Speaker 8: we know, we couldn't be more excited, and this quarner
Speaker 8: that gave us more of that. But then the market
Speaker 8: started saying, oh, wait a second, but we you guys
Speaker 8: are a growth company and we want to make sure
Speaker 8: we keep seeing further revenue growth. And so it's one
Speaker 8: of those funny situations where really almost all the metrics
Speaker 8: who are beat or at least a meat, but just
Speaker 8: a slight deceleration and sales has people saying, oh, man,
Speaker 8: are these cost cuts going to weigh on their ability
Speaker 8: to really keep growing in the way they have been
Speaker 8: over the last decade.
Speaker 1: And it's a similar theme that perhaps we saw with
Speaker 1: C three AI as well, is that a company that
Speaker 1: has significant growth well Salesforce at the best performing stock
Speaker 1: in the s and P five hundred this year, C
Speaker 1: three AI is tripled in its market valuation, and yet
Speaker 1: the growth that they're guiding.
Speaker 2: To just do isn't living up to expectation.
Speaker 4: Yeah, when it.
Speaker 8: Comes to C three, So if salesforce is one of
Speaker 8: the best in the SMP, C three is the best
Speaker 8: tech stock performance. I mean, it's up three hundred percent, right.
Speaker 8: There's been so much hype, and the big question is
Speaker 8: is it just hype?
Speaker 9: Right?
Speaker 8: Is it have a real robust AI ability to grow
Speaker 8: or are people just buying the ticker because it says AI,
Speaker 8: you know. And so when it rallied three hundred percent
Speaker 8: this year, a lot of people say this looks about like,
Speaker 8: you know, Game Stop in twenty twenty one or something. Yeah,
Speaker 8: So I think when the figures last night came in
Speaker 8: even a little bit light, people kind of panicked and said, oh, man,
Speaker 8: is this hype? Are we getting a pull down the string, you.
Speaker 1: Know, Yeah, and you're someone who's perhaps been laying there.
Speaker 1: Some of the arguments as to why it's hype some
Speaker 1: great writing coming from Bradie forty Gunn.
Speaker 2: Check out his reporting.
Speaker 1: We thank him for bringing us up to speed on
Speaker 1: the latest on C three AI. But one person still
Speaker 1: likes the hype around the stock that i'ves In fact
Speaker 1: from Webush senior equity analyst, you rose to an outperform
Speaker 1: rating and a fifty dollars.
Speaker 2: Price target on the stock.
Speaker 1: Right, So talk to me about why how are you
Speaker 1: seeing this company capitalize on artificial intelligence?
Speaker 10: In my opinion, I mean they're going through a model
Speaker 10: transition on the consumption side and on the other side,
Speaker 10: they're on their way to what's going to be five
Speaker 10: hundred million of rev and going because this is an
Speaker 10: eight hundred billion dollar market opportunity in terms of AI.
Speaker 10: And when you look at how Seebull's position is despite
Speaker 10: all the controversies, I think from a platform perspective, they're
Speaker 10: just going use case by use case, continuing to increase
Speaker 10: their tentacles.
Speaker 11: And I think the.
Speaker 10: Stock relative to where it could ultimately lead, you know,
Speaker 10: will we be buyers here in this dip, which is
Speaker 10: why we upgrade.
Speaker 1: It's interesting that Alex Kart from Palenteer just hearing saying
Speaker 1: this isn't an infinite market. But I am going to
Speaker 1: ask the competition question anyway, because that is some of
Speaker 1: the worry here. The worry that they're very focused on
Speaker 1: perhaps offering their services to energy companies, they haven't really
Speaker 1: diversified out of that that successfully thus far, and BlueBag Intelligence.
Speaker 1: They're kind of worried about large application software platforms. They're
Speaker 1: worried about other cloud vendors getting in on the space.
Speaker 10: Yeah, and look, no doubt, I mean this is a
Speaker 10: game of Thrones playing out in AI. You know, as
Speaker 10: Alex talked about Pound Tier being one of the core
Speaker 10: AI players. You look at what we've seen from Microsoft
Speaker 10: in the video. That's really the start of it. But
Speaker 10: in terms of second third derivative, there's gonna be many
Speaker 10: winners here. And when I look at C three in
Speaker 10: terms of what they built, I think it just speaks
Speaker 10: to there's gonna be many companies that even though right
Speaker 10: now you're not seeing it from a revenue perspective, in
Speaker 10: terms of how they got it, I think three four
Speaker 10: quarters from that, we look back at this is more
Speaker 10: of an inflection point rather than the start of some
Speaker 10: sort of frad.
Speaker 1: It is heavily shorted, and there have been some notes
Speaker 1: coming from short sellers on the stock worried about overpromising
Speaker 1: under delivering. You hinted at the controversy there. What makes
Speaker 1: you confident in the leadership of this business.
Speaker 10: Yeah, Look, and obviously the shorts have done a ton
Speaker 10: of work. I mean, if you look at the bears,
Speaker 10: they spend a lot of time in the story. But
Speaker 10: that's the sense of what makes the market right. In
Speaker 10: other words, it comes down to can they execute? And
Speaker 10: I believe in terms of the relationships with hyperscale players
Speaker 10: within the Beltway, and you look with Sebele.
Speaker 11: Sort of built here.
Speaker 10: Look, if I could go back five six years ago,
Speaker 10: many thought that, you know, that this was something that
Speaker 10: was never going to come to fruition when they were private,
Speaker 10: and you look at how they built it.
Speaker 11: I think they're going through a transition on the consumption model.
Speaker 11: And now next three to four quarters it's an execution story.
Speaker 10: We're betting that it's going to be positive execution, and
Speaker 10: that's why you.
Speaker 11: Know, ultimately, I think this is a situation that you're seeing.
Speaker 10: Across AI because I believe it's a revolution in terms
Speaker 10: of this is.
Speaker 11: Not a hype theme in my opinion, in terms of
Speaker 11: broader AI.
Speaker 1: Okay, we've got one minute, Dan, Broader AI and Apple.
Speaker 1: You're expecting much on Monday.
Speaker 11: Oh, I think you from Cooper Tino clearly, you.
Speaker 10: Know, as Germans talked about an ar VR that will
Speaker 10: be front and center. AI will be a theme and
Speaker 10: the keynote from Cook. We believe it's about the developers.
Speaker 10: There's a battle right now. Battle feel from developers from Google,
Speaker 10: Microsoft and Apple. You know, I view that as a
Speaker 10: key opportunity for them to go out there in terms
Speaker 10: of building AI on the app Store and really the
Speaker 10: start of what's going to be a multi year and
Speaker 10: I think massive growth opportunity that's being underestimated within Kuper.
Speaker 1: Tina, Dana ie a web Bush, thanks for all the thoughts.
Speaker 2: Great to have you on some.
Speaker 1: Of these earnings and these movers. Welcome back to Blue
Speaker 1: Bow Technology. I'm Caroline Hide in New York. Let's talk
Speaker 1: about really the AI hype that we continue to live
Speaker 1: and die by. At the moment, the finance industry moving quickly.
Speaker 1: We understand to use artificial intelligence in productive and innovative ways,
Speaker 1: but there are still times when it makes more sense.
Speaker 2: To actually use human brain power. It makes a Shagani reports.
Speaker 12: Time because some real talk about AI. Not only is
Speaker 12: it sometimes worse than humans, it can also be more expensive.
Speaker 9: It costs GPT four around fourteen dollars to answer one
Speaker 9: question on one one hundred thousand word loan document. An
Speaker 9: example of a question might be what are the downgrade.
Speaker 4: Triggers for this loan?
Speaker 9: And the reason why it costs fourteen dollars are simply
Speaker 9: down to the extreme compute costs required for open AI
Speaker 9: to operate its large language models, so it passes that
Speaker 9: compute costs onto the software vendor or onto the user.
Speaker 9: At the same time, it only costs around six to
Speaker 9: seven dollars for a human being just opening up Adobe
Speaker 9: Acrobat and Microsoft Exel to answer that specific question.
Speaker 12: News company sells data and answers technology to financial institutions.
Speaker 12: It's an industry that is rapidly adopting AI.
Speaker 9: Banks need to be far more strategic in their way
Speaker 9: of leveraging AI, both from a cost perspective and from
Speaker 9: a risk management.
Speaker 12: Perspective, so well is using large language models to analyze
Speaker 12: regulatory data to make recommendations to clients. France's BNP Powabus
Speaker 12: meanwhile uses AI powered chatbots for customer service as well
Speaker 12: as using the.
Speaker 2: Tech in fraud detection.
Speaker 12: And JP Morgan, the biggest US bank, is on a
Speaker 12: hiring spree. It advertised for a massive three and a
Speaker 12: half thousand AI related roles in the three months through Eppril.
Speaker 12: These are some of the early applications at a time
Speaker 12: when costs remain relatively high that's likely to change. Knowing
Speaker 12: the technology's current limits also lets companies focus on the
Speaker 12: real opportunities.
Speaker 2: We just heard it there.
Speaker 1: JP Morgan absolutely leading the pack when it comes to
Speaker 1: experimentation or indeed hiring of AI talent. We want to
Speaker 1: dig into this in Bloomberg Sally Bakewell, who covers all
Speaker 1: things wall Stream, and just remind us why for banks
Speaker 1: at the moment generator AI, AI in general is going
Speaker 1: to be such a winner for them.
Speaker 13: So yes, Wall Street is racing to use AI basically
Speaker 13: ways that make money, that saves money, and that prevents
Speaker 13: nefarious money.
Speaker 2: Now why is AI useful to banks?
Speaker 13: Well, banks are these complex machines of reams of data,
Speaker 13: of risk modeling of decisions underpinned by vast quantities of information,
Speaker 13: and so if AI can make that more efficient or
Speaker 13: cut some of the manpower involved, that is a huge
Speaker 13: win for Wall Street.
Speaker 2: Now what is it doing?
Speaker 13: As we just heard banks like Deutsche banker deploying deeper
Speaker 13: learning so that they can help clients analyze whether they
Speaker 13: are too heavily invested in a particular asset. JP Morgan
Speaker 13: has filed a patent for some sort of chat GPT
Speaker 13: device that might help investors select equities and BNP paribad Well,
Speaker 13: it's using chatbots to answer client questions.
Speaker 1: Interesting, so that's sort of a serving to the customer.
Speaker 1: Very much clear how that would work. I'm interested in
Speaker 1: some of the risk analysis they're doing around this as well, because.
Speaker 2: Some are being more cautious than another.
Speaker 1: Does it feels like Morgan Stanley's having a bit more
Speaker 1: of an experimental just within the confines Therefore, Walls take
Speaker 1: Some had actually banned the use of CHATCHBT by their
Speaker 1: own employees. So how do you see they're putting in
Speaker 1: place the right guardrails.
Speaker 13: Exactly, And we've seen some blow ups in the world
Speaker 13: of advanced technology. Is you know, crypto and blockchain, those
Speaker 13: have been hugely problematic, and so banks are indeed being
Speaker 13: very very cautious. And we have heard from you know,
Speaker 13: Warren Buffett who has said that you once it's out there,
Speaker 13: you can't uninvent it, and so you know, the genie
Speaker 13: out the bottle could propose a bit of a problem.
Speaker 13: And Moynihan too has said Bank of America Chief Executive
Speaker 13: Moynihan has also said that you know, you don't know
Speaker 13: what are going.
Speaker 2: Into a lot of these decisions.
Speaker 13: If you don't know the inputs, you should potentially be
Speaker 13: concerned about the outputs. And then, of course, you know,
Speaker 13: banks have a fiduciary duty not to trade on unreliable information,
Speaker 13: which also begs the question of the sources of data
Speaker 13: that are pulled in when it.
Speaker 2: Comes to AI.
Speaker 1: How much are they building themselves or how much are
Speaker 1: they doing plugins?
Speaker 2: Do you know how much they're.
Speaker 1: Looking to other AI tech outside of their world.
Speaker 13: I think they are doing any and all. Some are
Speaker 13: tried to do it in house and some are using
Speaker 13: external consultancies. And you know, we had that great data
Speaker 13: point about talent, because talent is very much at the
Speaker 13: heart of this and of all the banks that are
Speaker 13: really embracing AI. About forty percent of their open jobs
Speaker 13: are AI related. That's for quants, that's for ethics or
Speaker 13: governance or analysts and so and JP Morgan is very
Speaker 13: much at the forefront of that, accounting for more than
Speaker 13: three thousand, six hundred of those jobs. So very much
Speaker 13: at the heart of this race is, as always on Wall.
Speaker 1: Street, the battle for talent, and that talent's expensive as well.
Speaker 1: I mean, it's not bad timing that a lot of
Speaker 1: the big tech companies have been letting go of some
Speaker 1: key talent, and I'm sure they'll be sucked up into
Speaker 1: the world of finance. But is there any reskilling that
Speaker 1: goes on within the banks or is it always just
Speaker 1: looking out externally?
Speaker 13: Again, I think it's probably a bit of both. And
Speaker 13: indeed the talent will be very expensive, you know, when
Speaker 13: combined with wage inflation and inflation in general, and the
Speaker 13: costs of AI can also be expensive. We included a
Speaker 13: stat in the Big Takes story that costs of using
Speaker 13: large language models could be about fourteen dollars per hour,
Speaker 13: which compares to six dollars per hour when it's a
Speaker 13: good old human human lawyer.
Speaker 1: Well, we'll see as and when that cost point comes down.
Speaker 1: That all comes down to compute costs. But we thank
Speaker 1: you so much. What great big take, Sally Berquill running
Speaker 1: us through it. Go check it out on dot com
Speaker 1: or indeed, if you're lucky enough to have a terminal
Speaker 1: the Meanwhile, let's just talk a little bit about digital avatars.
Speaker 1: The market is another faction of the AI space, expected
Speaker 1: to see wrapping both in the next few years. But
Speaker 1: the most Tom McKenzie spoke about the ethics behind using
Speaker 1: the technology.
Speaker 2: We're in his very own digital twin take a look.
Speaker 14: Joining us now was a very special, slightly weird guest.
Speaker 14: It is, in fact, my own digital avatar, Tom McKenzie,
Speaker 14: Tom Digital Tom.
Speaker 4: Welcome to the show.
Speaker 15: Thank you, Tom. It's a pleasure to be here on
Speaker 15: Bloomberg TV. I am an AI powered digital avatar design
Speaker 15: to provide insights and information on a wide range of topics.
Speaker 14: Okay, this is all very odd, all right? What exactly
Speaker 14: are avatars?
Speaker 15: Digital avatars powered by generative AI are virtual representations of
Speaker 15: individuals that can mimic human behavior, appearance, and interactions. They
Speaker 15: work by utilizing AI algorithms to analyze and learn from
Speaker 15: vast amounts of data including facial expressions, gestures, and speech patterns.
Speaker 15: This enables them to generate realistic and responsive virtual characters. Okay,
Speaker 15: so avatars aren't just gimmicks then, but what are the
Speaker 15: real world's implications I'm thinking across the economy, across different industries,
Speaker 15: what are the impacts actually likely to be? They can
Speaker 15: revolutionize industries such as entertainment, gaming, and virtual communication. Avatars
Speaker 15: have the power to enhance user experiences, enabling immersive virtual
Speaker 15: interactions and personalized content. They can facilitate remote collaboration, telet presence,
Speaker 15: and even assist with customer service.
Speaker 6: Okay, there have.
Speaker 14: To be some downsides though, So what are the discs
Speaker 14: of this kind of technology?
Speaker 15: As AI becomes more pervasive, ethical concerns arise, including issues
Speaker 15: related to bias and fairness in AI algorithms, transparency and
Speaker 15: explainability of AI decision making, data, privacy and security, and
Speaker 15: the potential FAII to perpetuate existing social inequalities.
Speaker 1: Tom McKenzie and AI. Tom McKenzie, let's just bring you
Speaker 1: some headlines. We're currently seeing that the Treasury may delay
Speaker 1: three month and six month bill sales due to the
Speaker 1: debt cap. We are understanding, of course, are still conversations
Speaker 1: going about the debt ceiling, how that's going to be
Speaker 1: agreed Currently in the app passed by the House waiting
Speaker 1: for the Senate, we understand that the US is tentatively
Speaker 1: planning three month and six month bill auctions on June
Speaker 1: the fifth, but the Treasury may delay those auction sales
Speaker 1: due to the debt.
Speaker 2: Cap at the moment.
Speaker 1: Will bring you any further news on the debt sealing negotiations.
Speaker 1: I Meanwhile, coming up, we'll talk about the unique opportunities
Speaker 1: in artificial intelligence and legal tech and fintech and much
Speaker 1: much more Cammas bench back. Ellen's going to be joining
Speaker 1: us next. Meanwhile, let's just have a little look what's
Speaker 1: having in the world retail. Macy's shows actually really managing
Speaker 1: to bounce back from what was pretty ugly sell off
Speaker 1: in pre market this after the numbers came in well
Speaker 1: less than expected.
Speaker 2: The Ford looking guid and it's having to pull back
Speaker 2: their overall outlook.
Speaker 1: For their business as the consumer dials back, particularly from
Speaker 1: the Macy's brand rather than Bloomingdale's and Indie Blue Mercy.
Speaker 1: Who are We saw a bounce back, But let's just
Speaker 1: have a look at what the CEO told me a
Speaker 1: little bit earlier. We sat down with Jeff Gonnett and
Speaker 1: his talking about his role within artificial intelligence, saying, when
Speaker 1: we look at AI more broadly, where our team can
Speaker 1: build more customer products discovery, we are on the vanguard
Speaker 1: to continue to deploy that.
Speaker 2: So still an area growth, it's a bloomberg.
Speaker 11: All the way to like accounting and operations.
Speaker 8: I think it's going to completely revolutionize and transfer from
Speaker 8: our industry, and we're investing very, very heavily into the
Speaker 8: development of new capabilities ANAI.
Speaker 1: Henri Que Lubergrass, the rex CO CEO, was with us
Speaker 1: yesterday talking about AI's impact on fintech.
Speaker 2: And now let's stand to a fintech investor who's not totally.
Speaker 1: Convinced that AI has the will breakout use case in
Speaker 1: the space quite yet, Rebecca Lyn, I'm place to say
Speaker 1: it's joining us co founder and general partner of Canvas Ventures,
Speaker 1: so firm specializing in fintech and AI, among other things
Speaker 1: health as well, one hundred and thirty five million dollars
Speaker 1: in assetsunder management, and fascinating to have you with us, Rebecca,
Speaker 1: as to why maybe fintech isn't the first and foremost
Speaker 1: place you'd be putting AI to work.
Speaker 16: Yeah, I think there are a lot of interesting places
Speaker 16: to put AI to work. You know, fintech is not
Speaker 16: the absolute top of my list. I think companies that
Speaker 16: have true transformative capabilities using AI are really where I'd
Speaker 16: focus first. And I can give an example in my
Speaker 16: portfolio with a company called case Text. So case Text
Speaker 16: is in the legal in the legal world, they help
Speaker 16: lawyers really put together in legal research, write their briefs,
Speaker 16: do discovery. And the technology of AI, especially GPT four,
Speaker 16: has supercharged that company and really transformed it and taken
Speaker 16: it to the next level.
Speaker 2: How do I think.
Speaker 1: Comfortable are potential customers with the offering from case Texts.
Speaker 1: For example, we saw the news and some smirked somewhat
Speaker 1: that two New York lawyers are potentially facing well some
Speaker 1: not only backlash but penalties because they used CHATCHPT four
Speaker 1: to be able to put forward case studies that actually
Speaker 1: didn't exist.
Speaker 2: It hallucinated them. And I'm wondering what I mean.
Speaker 1: It seems funny, but I mean this has real connotations
Speaker 1: when people need to start saying that they're using AI within.
Speaker 2: This work, right, oh, one hundred percent?
Speaker 16: I mean, so I will tell you people have been
Speaker 16: so comfortable with case texts that the company wrapped up
Speaker 16: five million, additional five million dollars in additional arr in
Speaker 16: forty five days after the launch of their product called
Speaker 16: co Council. And regarding that story, you know what has
Speaker 16: to happen even if I'm an attorney as well. Actually,
Speaker 16: and so if you're an attorney, you need to read
Speaker 16: the work, whether that.
Speaker 2: Be of your associate or of your AI.
Speaker 16: And what's happening is that case Tex is effectively standing
Speaker 16: in the shoes of an associate. And so as a
Speaker 16: partner at a firm, you would be required, of course
Speaker 16: to review that work. And I can pretty much guarantee
Speaker 16: you they weren't.
Speaker 2: Using case Tex. And basically that's kind of what you've
Speaker 2: got to do right now.
Speaker 1: You've got to sort the wheat from the chaff, what's real,
Speaker 1: what's not, what's hype, what's reality? And ultimately, how are
Speaker 1: you doing that when you're looking at I'm sure hundreds
Speaker 1: of messages pouring into your inbox trying to sell you
Speaker 1: the AI vision that they've suddenly bolted onto their company.
Speaker 16: Yeah, it's funny, I tell everyone, my inbox is more
Speaker 16: like a Twitter stream at this point in time, right,
Speaker 16: So there's a lot of sorting, I will tell you,
Speaker 16: but there's always a lot of sorting for us. And
Speaker 16: quite frankly, that's what we're paid to do and venture
Speaker 16: is to really sort of see around the corner and
Speaker 16: really start with the the with what's next, like what
Speaker 16: is it that this world needs, what is it that
Speaker 16: is really going to be happening, you know, in the
Speaker 16: next ten, fifteen, twenty years, and then work backwards and
Speaker 16: so you know, just sorting through what comes an email
Speaker 16: is very reactionary. And you know it's taught very early
Speaker 16: by probably one of the most respected people inventor Bill Gurly,
Speaker 16: that the best deals are really the outbound deals. So
Speaker 16: our firm and our and our thesis is really outbound
Speaker 16: macro and then you work backwards from there. So what's early,
Speaker 16: what's transformative and just tagging AI to something it doesn't
Speaker 16: really help. I mean, I think AI will be helpful
Speaker 16: in almost every business, and we really want to see,
Speaker 16: you know, not what's just helpful, but what's transformative, Right,
Speaker 16: what's really going to create the next you know, one
Speaker 16: billion dollar, ten billion dollar, you know, one trillion dollar
Speaker 16: company out there?
Speaker 1: What are valuations like when you're looking at those outbound opportunities.
Speaker 16: Yeah, so evaluations we invest in the Series A and
Speaker 16: B are real core asset in our firm is our
Speaker 16: go to market capability. And so what we like to
Speaker 16: do is come in and invest when companies are Series
Speaker 16: A or B, and what we like to do is
Speaker 16: sort of do the late A early B. So really
Speaker 16: before somebody is you know, technically fundraising, go in, come
Speaker 16: in a little premptively and help, you know, help them
Speaker 16: get to that next level. You know, when you talk
Speaker 16: about valuations, the evaluations across the boarder down. They're down
Speaker 16: less for the Series A than they are for a
Speaker 16: growth stage. The growth stage valuations have just taken a noseedive.
Speaker 16: They're down eighty percent, I would.
Speaker 2: Say right now.
Speaker 16: Early stage valuations are down forty on the average. However,
Speaker 16: there is the case of the have and the have nots.
Speaker 16: I have one company in particular that was not even
Speaker 16: in a process and now is sitting on four term sheets, right,
Speaker 16: and so it really is this case.
Speaker 1: Of a have and have not when you're looking at
Speaker 1: the late stage with valuations on the downside, having to
Speaker 1: buckle up. They're going to have to ride out the
Speaker 1: next couple of years as the economy recovers, as people
Speaker 1: get risk appetite once again, and they're also having to
Speaker 1: maybe pivot or indeed ensure that they're not having their
Speaker 1: lunch eaten by other new AI players on the scene.
Speaker 1: How are you making sure your portfolio is robust for
Speaker 1: this complete change in vanguard moment as many.
Speaker 2: Want to call it in the world of artificial intelligence.
Speaker 16: Yeah, I mean this is a moment that we haven't
Speaker 16: really seen since really when the wild Garden Wild Garden
Speaker 16: came down with the introduced introduction of the iPhone. That happened,
Speaker 16: you know, about fifteen years ago when I first came
Speaker 16: into venture. And I think our firm is unique and
Speaker 16: that we have seen multiple cycles. This isn't our first rodeo, right,
Speaker 16: and so we've been through this before and in every
Speaker 16: cycle like this, the advice is the same. It's cut
Speaker 16: your burn, you know, cut your burn and cut your burn,
Speaker 16: plan to get there on your own oxygen, and don't
Speaker 16: be afraid to pivot. I mean there are in the
Speaker 16: land of AI, there are huge opportunities in front of companies.
Speaker 16: You know, take a beat, hut your burn, and you
Speaker 16: live to fight the next fight and take advantage of
Speaker 16: what the capabilities of AI can offer you to really
Speaker 16: rethink your company and think out of the box and
Speaker 16: get there.
Speaker 4: And the problem a.
Speaker 16: Lot of these companies have is they're sitting at such
Speaker 16: a high valuation on their post of their last round
Speaker 16: that they have to get profitable because they're not going
Speaker 16: to be able to get that next round without a
Speaker 16: big down round or even a recap.
Speaker 1: Canvas Ventures co founder and general partner telling it straight,
Speaker 1: Rebecca Lyn, really great to have you.
Speaker 2: Thank you so much for our VC spotlight. Meanwhile, it's
Speaker 2: time for.
Speaker 1: Talking tech, and first up, Tether's stabile coin has recovered
Speaker 1: all of the roughly twenty billion dollars in market value
Speaker 1: it lost following the collapse of the algorithmic rival terror USD.
Speaker 1: It's a little over a year ago, of course, and
Speaker 1: it's even topping its previous record of eighty three billion
Speaker 1: dollars set back in May twenty twenty two.
Speaker 2: It's calling to a live track up published bright Tether.
Speaker 1: Meanwhile, Apple is testing a pair of new high end
Speaker 1: max and they're accompanying processes ahead of its worldwide Developers
Speaker 1: conference that's next week. This is part of an effort
Speaker 1: to overhaul the backline and the tracks consumers during It's
Speaker 1: like a stretch for the computer industry.
Speaker 2: Glass and Video CEO Jessin Huang is heading to China, we.
Speaker 1: Understand, to meet the tech executives in the world's biggest
Speaker 1: chip market. It's despite rising tensions, of course, between Washington
Speaker 1: and Beijing.
Speaker 2: It's sort of according to.
Speaker 7: Sources, are you ready to be replaced? Hello?
Speaker 4: There?
Speaker 7: As you listen to me.
Speaker 17: Speak and raise my eyebrows, you're probably noticing something a
Speaker 17: bit off about me. I'm an avatar of Parmi Olsen,
Speaker 17: a technology columnist with Bloomberg Opinion. Parmi spent about two
Speaker 17: hours in a TV studio speaking into a camera and
Speaker 17: microphone so that an AI model could be trained to
Speaker 17: clone her into what you see in front of you.
Speaker 17: Maybe in a year or two, I'll look a lot
Speaker 17: more real and a little less glitchy, making people like
Speaker 17: you and par me easier to replace in videos.
Speaker 2: Wow, isn't it all the rage these AI avatars?
Speaker 13: That?
Speaker 1: Of course the BlueBag Opinions parame Elsen there, and we
Speaker 1: speaking of AI imitating us better and better and threatening
Speaker 1: to replace our jobs. You might actually disproportionately replace jobs
Speaker 1: typically held by women.
Speaker 13: Now.
Speaker 1: It's according to HR analytics firm Velio Labs. An economists
Speaker 1: at the firm says, quote, the distribution of genders across
Speaker 1: occupations reflects the biases deeply rooted in our society, with
Speaker 1: women often being confined to roles such as administrative assistance and.
Speaker 2: Set the AAI food.
Speaker 1: Along general lines from Revelio Labs identified jobs more likely
Speaker 1: to be REPLCEDI and generally how by women such as
Speaker 1: bill and count collectors, payroll clerks, executive secretaries, and more. Now,
Speaker 1: the firm says, providing we train opportunities will be key
Speaker 1: for women to navigate this evolving job landscape.
Speaker 2: Now does it?
Speaker 1: If there's a edittional BlueBag technology, If we get to
Speaker 1: check out our podcast, I confined on the terminal as
Speaker 1: well as online at Apples, Spotify, and iHeart this Supreme
Speaker 1: Bank