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Speaker 1: From Mahart.

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

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Speaker 1: This is Bloomberg Technology with Caroline Hyde and Ed Ludlow.

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Speaker 3: I'm Carolin Hyde at Bloomberg's world headquarters in New York.

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Speaker 4: And I met Lovelow in San Francisco. This is Bloomberg

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Speaker 4: Technology coming up.

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Speaker 3: The race to regulate artificial intelligence. It's underway in Washington

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Speaker 3: as his Open AI CEO Sam Altman lays out the

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Speaker 3: benefits and the risks to senators, and.

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Speaker 4: The CEO of Andresen backed Hippocratic AI joins us in

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Speaker 4: studio to discuss their generative AI healthcare tech.

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Speaker 3: An X, the parent company of Twitter, has made as

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Speaker 3: first acquisition a tech talent recruiting service called Laski. We

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Speaker 3: discussed why verse four almostlet's get to these markets because

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Speaker 3: once again we're actually seeing big tech on top lackluster

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Speaker 3: trading day muted as we worry about the debt ceiling.

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Speaker 3: The arguments still abound between left and right. We're seeing

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Speaker 3: that's that one hundred and five tens percent big tech

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Speaker 3: scene as some sort of haven at the moment. We'll

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Speaker 3: talk about the individual names with Ed, but all country

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Speaker 3: world indext as I show the world is more downpat

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Speaker 3: on markets at the moment. Bank of America really showing

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Speaker 3: how much risk a version there is coming from traders

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Speaker 3: at the moment, and we're seeing down by three tenths

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Speaker 3: of a percent, people not wanting to add to the

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Speaker 3: stock market.

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Speaker 5: Right now.

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Speaker 3: Across the world, we're seeing tenure yield actually up six

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Speaker 3: bass points, even as we see in the individual retail

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Speaker 3: data coming out showing you and I are still willing

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Speaker 3: to spend in this inflationary data.

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Speaker 5: It seems as though.

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Speaker 3: Some of that federal reserves speak coming from Loretta Mesta

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Speaker 3: from Barkin seem to be hinting the look that they're

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Speaker 3: still all.

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Speaker 5: Eyes on inflation.

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Speaker 3: Let's someone look at what's happening in terms of bitcoin

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Speaker 3: as well. Because the dollar is out performing, that means

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Speaker 3: bitcoin's on the downside. We're off by about a percentage

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Speaker 3: point in the day, still languishing around twenty seven thousand.

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Speaker 3: ED dig into some of the big movers because there

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Speaker 3: are points in the up side from big tech today.

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Speaker 4: It's like a market that's treading water when you think

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Speaker 4: about the macro, but when you think about the micro,

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Speaker 4: there are some stories out there Tesla up one point

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Speaker 4: six percent the AGM after the bell today, but Bloomberg

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Speaker 4: reporting overnight, according to sources, Shanghai is moving towards trial

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Speaker 4: production of an updated Model three that's supporting the stock. Baidu,

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Speaker 4: speaking of China, also up three point three percent. Strong earnings,

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Speaker 4: a beat on the top and bottom line. It's like

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Speaker 4: a post Chinese New Year rebound, a reopening of that economy,

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Speaker 4: and it is boosting China tech. We get Ali Barber

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Speaker 4: later in the week, so we're going to continue to

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Speaker 4: watch China tech, especially the US listed shares of those names.

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Speaker 4: Ai Ai is Everything. I just put a Twitter out

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Speaker 4: there on video. All it said was Ai is everything.

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Speaker 4: And then if you look at equity markets, we've discussed

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Speaker 4: and we looked at all the column inches dedicated to

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Speaker 4: how a lot of the momentum in the start of

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Speaker 4: twenty twenty three originates for investor enthusiasm for Ai. Amazon

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Speaker 4: and Alphabet pair and of Google, both big movers to

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Speaker 4: the upside, both big points gainers on the Nasdaq one hundred.

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Speaker 4: We'll give you the detail and why later in the show.

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Speaker 4: But Bloomberg reporting that if you look at the signs

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Speaker 4: Amazon might be bringing a trat GPT style bot to

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Speaker 4: Amazon dot Com, which is something we've been waiting for.

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Speaker 4: But that's what the talk is about this Tuesday morning.

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Speaker 4: Artificial intelligence it is.

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Speaker 5: And how to regulate it.

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Speaker 3: And right now Sam Altman, the CEO of Open ai,

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Speaker 3: really telling law makers on Capitol Hill about perhaps steps

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Speaker 3: necessary to put in place rules around AI technology. He

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Speaker 3: says is so powerful that he's worried it could have

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Speaker 3: repercussions quote an a level far beyond anything we're prepared for.

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Speaker 3: He also said, look, it's going to choose the labor market.

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Speaker 6: Take a listen, GBT four A will I think entirely

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Speaker 6: ontomate away some jobs and it will create new ones

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Speaker 6: that we believe will be much better.

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Speaker 3: Pleased to say, we've just pulled out of that hearing

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Speaker 3: ed Anna Edgerton, who's been really listening into not just

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Speaker 3: Sam Altman, but we've also heard from representatives from IBM who,

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Speaker 3: well we know that IBM is already saying they're cutting

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Speaker 3: their back office staff most likely calls of AI and

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Speaker 3: scientists represent in too.

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Speaker 5: What's the key takeaway thus far?

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Speaker 7: It really has been all focused on regulation. You have

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Speaker 7: Sam Altman of open AI and Christina Montgomery, the chief

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Speaker 7: Privacy and Trust Officer for IBM, almost pleading with lawmakers

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Speaker 7: to regulate this space. They want guardrails and they want

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Speaker 7: certainty to protect against the most dangerous abuses of this

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Speaker 7: powerful technology. And we hear it from the senators themselves.

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Speaker 7: They say, we know this is a space that needs

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Speaker 7: new rules, but they are the first to recognize that

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Speaker 7: Congress has not been able to regulate the technology we

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Speaker 7: already have. Social media has been a big failure for

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Speaker 7: the US Congress, and they are looking to do a

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Speaker 7: better job as they learn more about artificial intelligence.

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Speaker 4: While you join us from the hill and some outman

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Speaker 4: continues to speak, and he's saying that the US should

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Speaker 4: form an agency to license some AI efforts. A lot

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Speaker 4: of bench capitalists and founders I know do not agree

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Speaker 4: with the idea of licensing. They think it's basically a

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Speaker 4: block to innovation. When you have hearings like this, also

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Speaker 4: depends on the questions that get asked. Sometimes these lawmakers

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Speaker 4: go way off topic. How much have they asked about

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Speaker 4: US leadership in the field of artificial intelligence Outside of

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Speaker 4: the regulatory debate.

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Speaker 7: There have been some thoughtful questions on this and He's

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Speaker 7: had some thoughtful answers as well. He made a really

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Speaker 7: interesting point when he said, you know, the US can

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Speaker 7: leverage its leadership in the development of microelectronics and ships

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Speaker 7: that are needed to run.

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Speaker 5: These powerful AI systems.

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Speaker 7: The US can leverage that leadership to encourage global governance

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Speaker 7: so that not only are we setting rules here in

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Speaker 7: the United States, but he made the point that we're

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Speaker 7: not the only ones developing this technology. This is something

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Speaker 7: that has a potential to impact the whole world, and

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Speaker 7: for when it comes to governance of this new technology,

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Speaker 7: it really needs to happen on a global level.

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Speaker 3: To that end, many will be unsurprised to know that

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Speaker 3: the EU has kind of been leading the regulatory charge.

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Speaker 3: They're already proposing potential laws in the next month that'll

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Speaker 3: be eyed by.

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Speaker 5: The entire parity of the European Commission.

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Speaker 3: I'm interested in how much you think we'll make is

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Speaker 3: all seeing what Europe is looking at all thinking about

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Speaker 3: the global ability to regulate.

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Speaker 7: Well, certainly US lawmakers are casting and their counterparts over

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Speaker 7: in Europe. And Christina Montgomery of IBM had an interesting

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Speaker 7: little comment in her opening statement. She said, there's been

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Speaker 7: a lot of hype around generative AI, but that doesn't

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Speaker 7: mean we should move away from the risk based approach

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Speaker 7: that they started taking in Europe. And that was kind

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Speaker 7: of a reference to what we see happening around Europe's

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Speaker 7: AI Act, where they had this really well thought out

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Speaker 7: regulation based on the risk of the use of AI

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Speaker 7: rather than the actual development of this technology. And now

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Speaker 7: you have chat GP exploding onto the scene and regulators

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Speaker 7: thinking well, maybe we need include this kind of general

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Speaker 7: use tool in this regulation as well. And she's trying

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Speaker 7: to argue, that's what we shouldn't do. We shouldn't look

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Speaker 7: at the hype around chat GPT, these other generative AI

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Speaker 7: products and kind of project that onto the rest of

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Speaker 7: the artificial intelligence ecosystem, which includes the kind of enterprise

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Speaker 7: AI that is offered by companies like IBM.

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Speaker 3: Yeah, it really is about application for many at the moment,

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Speaker 3: and Juton, we thank you so much to get her

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Speaker 3: back onto that hearing at the moment and fantastic analysis

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Speaker 3: coming from Capitol Hill. What about those that used to

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Speaker 3: advise governments, what about those that are thinking about the

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Speaker 3: application thus far of regulation. Lindsay Gorman's one of them,

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Speaker 3: Senior fellow for emerging technologies of the German Marshall Funds,

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Speaker 3: Alliance for Securing Democracy and Lizzie.

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Speaker 5: Thus far are.

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Speaker 3: The right guardrails being thought about here from a global perspective.

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Speaker 1: So I do think that the right issues and problems

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Speaker 1: are being fought about. But I will caveat that by

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Speaker 1: saying that the problems that we can anticipate today might

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Speaker 1: not be the problems that end up causing us the

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Speaker 1: most consternation once these technologies become more widespread. But I

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Speaker 1: do think the level of literacy when it comes to

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Speaker 1: what the real concerns are and also what the possibilities

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Speaker 1: are being thought about, concerns around elections and disinformation and

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Speaker 1: disruption of the democratic process, concerns about workforce and job displacement,

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Speaker 1: and concerns about how we interact with systems and what

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Speaker 1: morals and values get put into these systems on the forefront.

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Speaker 1: So I do think the right questions are being asked.

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Speaker 1: Of course, we can only know what we know now,

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Speaker 1: and ten years from now we might say that we

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Speaker 1: were completely looking at the problems from a different era Linzee.

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Speaker 3: When we look at what the EU, for example, is

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Speaker 3: thinking of putting in place, they want to produce risk assessments.

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Speaker 3: They want to see a summarize of copyrighted material that

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Speaker 3: models have been trained on. They also want it to

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Speaker 3: be flagged when you're looking at AI in general, but

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Speaker 3: most notably if you're looking at deep fakes.

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Speaker 5: Does that, to your point of view, go far enough?

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Speaker 3: And ultimately, are we just in a game of whack

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Speaker 3: a mole all over again?

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Speaker 1: I certainly hope not. I think the EU has obviously

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Speaker 1: taken one of the most aggressive approaches to thoughtful regulation

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Speaker 1: of AI with the EUAI Act and the risk based

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Speaker 1: approach now and the USNST has also put out a

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Speaker 1: risk based framework on AI that's the National Institute of

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Speaker 1: Standards and Technology, but it's completely voluntary. One thing that

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Speaker 1: I think the EU has kind of in its pocket

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Speaker 1: as perhaps an advantage in coming up with thoughtful regulation

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Speaker 1: is that it already has existing frameworks around data privacy.

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Speaker 1: Today at the hearing, you've heard multiple senators talk about

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Speaker 1: the need for protecting data that's training these AI models,

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Speaker 1: because ultimately, if you're training the data on certain models,

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Speaker 1: that those biases and the biases and the data are

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Speaker 1: going to be propagate and the values in the data

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Speaker 1: are going to be propagated through.

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Speaker 5: For the models.

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Speaker 1: Now, the EU has the General Data Privacy Framework, the

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Speaker 1: US doesn't have that, and it's a little bit concerning that.

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Speaker 1: Despite the enthusiasm with which lawmakers are tackling the AI issue,

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Speaker 1: which is a positive, that we're still not able to

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Speaker 1: pass federal data privacy legislation. So I do think the

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Speaker 1: has a leg up in applying their existing frameworks, and

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Speaker 1: that's why we've seen countries such as Italy apply the

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Speaker 1: General Data Protection Regulation to chat GPT and have that basis,

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Speaker 1: what are the things that need to be demanded, the

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Speaker 1: common uses, the justifiability of that which Maltman alluded to today.

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Speaker 5: It is notable, isn't it.

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Speaker 3: Every conversation we have tends to involve regulation. Everyone seems

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Speaker 3: to agree it's needed and not just self regulation, but

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Speaker 3: the applications of so doing is still sort of a

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Speaker 3: black box here.

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Speaker 8: Yeah.

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Speaker 4: The difference our guests notifires off on this show f Lindsey,

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Speaker 4: is that if you regulate the deep learning part, the

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Speaker 4: training of the models, you might kill innovation. There is

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Speaker 4: a difference between regulating inference or in other words, the

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Speaker 4: use of artificial and technology the tool and the development

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Speaker 4: of it. Where do you think we should focus our

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Speaker 4: regulatory efforts.

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Speaker 1: Yeah, this is really the debate that you think you

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Speaker 1: heard in the hearing, which is do we take an

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Speaker 1: approach of only regulating at the point where technology meets society,

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Speaker 1: ie the applications, or do we need to regulate in

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Speaker 1: the model development? And I think both have some merits. Clearly,

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Speaker 1: we need to regulate in the former case and focus

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Speaker 1: on the actual harms that are being caused in society

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Speaker 1: or that could be caused.

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Speaker 8: But that said, those.

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Speaker 1: Harms are not technology neutral. They are not applicable regardless

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Speaker 1: of how you train the model. So I think that's

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Speaker 1: where it gets a little bit complicated, where actually the

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Speaker 1: inputs to the training do make a difference on how

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Speaker 1: the harms could get propagated once technology meets society. And

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Speaker 1: this idea, though that lawmakers have said today that we

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Speaker 1: had the Section two thirty where we said companies just

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Speaker 1: go develop and we'll give you the space and not regulate.

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Speaker 1: I don't think that's going to fly now in the

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Speaker 1: AI era.

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Speaker 4: Do you agree with Sam Autman that the US should

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Speaker 4: establish an agency to license AI development.

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Speaker 1: I would like to hear more arguments on both sides.

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Speaker 1: To be honest, I think we've just gotten to the

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Speaker 1: scratching the surface on whether we need a whole new agency.

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Speaker 1: But the point that I really do agree with that

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Speaker 1: Sam and others made is that we definitely need much

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Speaker 1: more resources, whether that's with existing agencies or in fact

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Speaker 1: a new agency. The idea that we could keep up

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Speaker 1: with these developments and that regulators and policymakers could keep

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Speaker 1: up with these developments without growing significantly more resources into

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Speaker 1: just hiring technical experts to understand and to craft policy

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Speaker 1: solutions on them, I think is completely completely crazy. So

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Speaker 1: whether that's a new agency or significantly beefing up of

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Speaker 1: existing agencies, we definitely need a significant resource investment.

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Speaker 4: Should point out as well that it's not just Sam Outman,

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Speaker 4: We've been talking a lot about him, but IBM's Chief

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Speaker 4: Privacy and Trust Officer Christina Montgomery is also testifying in

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Speaker 4: that Caroline and Lindsay Linda Gorman, Senior Fellow for Emerging

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Speaker 4: Technologies at the German Marshal Fund, that thank You Grinder

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Speaker 4: reporting first quarter earnings with revenue of fifty five point

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Speaker 4: eight million dollars, up from forty three point five million

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Speaker 4: dollars a year earlier around twenty eight percent gain year

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Speaker 4: on year, still shares down after reporting a net loss

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Speaker 4: of thirty two point nine million dollars. Let's bring in

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Speaker 4: Grinder CEO George Arrison for more so kind of growth

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Speaker 4: on the top line and I think actually a decline

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Speaker 4: year on year on the bottom.

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Speaker 8: Line or an EPs.

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Speaker 4: But what is the difference in the environment at the

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Speaker 4: beginning of this year versus last year for people interacting

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Speaker 4: with one another?

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Speaker 9: Yeah, so ook, I've only been on here for six months,

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Speaker 9: so I can only speak about a beginning of last

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Speaker 9: year per se.

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Speaker 8: But this was and no one looks backward looking.

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Speaker 9: And this year was really good and we grew really well.

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Speaker 9: Our EBIDA was really strong as well, at thirty nine percent.

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Speaker 9: Both growth and EBITA numbers exceeded our Follier guidance. The

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Speaker 9: I think on the EPs side, there's just some stuff

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Speaker 9: with warrants that makes that number look very different because

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Speaker 9: of how you do work accounting. But that doesn't really

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Speaker 9: impact how our business is doing, which is extremely well.

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Speaker 9: Users are obviously very engaged and our overall average quarterly

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Speaker 9: users also up quarter over quarter.

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Speaker 4: So for what's driving the engagement, what is it that

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Speaker 4: people are turning to Grinder for what is the behavior

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Speaker 4: you see.

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Speaker 9: Grinder has always had a lot of success in getting

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Speaker 9: each new generation of people to come on the platform

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Speaker 9: so on. Like a lot of other social media platforms

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Speaker 9: that lose users in the new generation, we've actually not

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Speaker 9: had that. We've kept users coming in and I think

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Speaker 9: it frankly, the fact that all the users are there

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Speaker 9: is another reason why people come back right like that,

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Speaker 9: That engagement is really strong. And also Grinder's very authentic.

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Speaker 9: Grinder was a company built by gay people for gay people,

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Speaker 9: so it's very authentic to user base and I think

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Speaker 9: that helps us as well. Obviously, we know users want

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Speaker 9: more innovation the product, and that's something that we're very

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Speaker 9: actively working on.

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Speaker 5: Let's talk about the innovation. And in some ways it

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Speaker 5: feels a bit old school. George, you've got Grinder Web.

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Speaker 3: Talk to me about why you listened and heard from

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Speaker 3: your user basis that's what they wanted totally.

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Speaker 9: So yeah, we just launched Grinder Web. Kind of launch

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Speaker 9: it this month in anticipation of Pride next month. And

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Speaker 9: the thinking is that there are a bunch of features

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Speaker 9: you can launch on the web that you can't really

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Speaker 9: launch anywhere else because of limitations that the app store

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Speaker 9: has put on you, and that's something that our uses

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Speaker 9: very much want. So Grinder Web that we launch now,

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Speaker 9: which is still in data and we help users try

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Speaker 9: that out, is a way to set ourselves up for

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Speaker 9: the future to launch additional features that are more specific

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Speaker 9: to the use cases that our users want that we

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Speaker 9: can't really do on the app.

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Speaker 3: Monetization one are ways in which you're thinking about people

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Speaker 3: using it paying for it in a different, more seamless

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Speaker 3: way on the app itself and indeed on web totally.

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Speaker 9: So what we've heard from users on kind of our

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Speaker 9: subscription basis is they want two things. Number one, they

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Speaker 9: want a slightly higher so a slightly lower cost model

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Speaker 9: where we have an entry point that's not nineteen nine nine,

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Speaker 9: but something lower than that with less features obviously because

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Speaker 9: we do offer a very broad base of features in

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Speaker 9: our nineteen nine nine tier, so we're working on that

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Speaker 9: lower priced tier. And then they also want us to

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Speaker 9: build more features on the higher end and are willing

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Speaker 9: to pay for those. And obviously a lot of the

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Speaker 9: add ons that overdating apps offer that Blender doesn't yet

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Speaker 9: have aurur card we are building, and then we think

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Speaker 9: there's a lot of opportunity to build functionality that supports

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Speaker 9: activity that's already happening in the app, such as dating

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Speaker 9: that we don't have functionally. For as far as building

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Speaker 9: on grinder Web, we do have a big portion of

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Speaker 9: users that are discrete, meaning they can't really be out

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Speaker 9: for a variety of reasons, whether it's where they live

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Speaker 9: or family situations, et cetera. And for them, grinder Web

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Speaker 9: is a really great, just stinct building option that's not

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Speaker 9: available yet, but it's coming because we do want to

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Speaker 9: facilidate that. Based on what the users have asces used.

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Speaker 4: For this past week, one Elon Musk said that Twitter

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Speaker 4: or x the everything out adding some dating functionality was

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Speaker 4: an interesting idea.

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Speaker 8: How seriously do you take that threat?

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Speaker 9: I mean, let's see what he does. I think it'll

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Speaker 9: be interesting how he approaches things. And look, you never

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Speaker 9: better against even musk rat. Nobody else has done what

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Speaker 9: he's done in terms of how many companies is built.

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Speaker 9: But we feel really good about our user base and

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Speaker 9: the fact that our users very engaged with us. I

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Speaker 9: think our engagement numbers are are frankly, very unusual. Nearly

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Speaker 9: an hour of spending the app last year one hundred

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Speaker 9: and eleven billion messages sent. So I'm not really worried

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Speaker 9: about that from our user base perspective, but more broadly,

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Speaker 9: certainly interesting grind.

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Speaker 4: The CEO, George Harrison here in San Francisco, Thanks for

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Speaker 4: your time, Carol.

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Speaker 3: Yeah, let's just stick on the awning scene for a

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Speaker 3: moment and look at the Chinese giant by Do shares

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Speaker 3: traded here in the US.

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Speaker 5: Actually, you have been performing really well.

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Speaker 3: You see up three and a half percent there or thereabouts,

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Speaker 3: and looks as though the company really managed to beat expectations.

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Speaker 3: Stronger than expected revenue ten percent, it grew after its

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Speaker 3: advertising in cloud businesses. This seemed to be benefiting from

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Speaker 3: China's post pandemic reopening overall, so some strength there ed.

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Speaker 5: Meanwhile, coming up that we've got to talk.

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Speaker 3: About some warries post Silicon Valley bank collapse. The former

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Speaker 3: CEO Greg Becker currently testifying before the US Senate committee.

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Speaker 5: They're busy up on the hill today.

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Speaker 3: He has to say, of course, about the focus the

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Speaker 3: failure and what is next for regional banks more generally.

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Speaker 5: We can dig into that in a minute. This is Bloomberg.

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Speaker 3: We of course are dissecting everything that has been happening

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Speaker 3: across in Washington, not only the AI focus of some outman,

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Speaker 3: but Silicon Valley Bank focus Greg Becker. He's been testifying

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Speaker 3: in front of the Senate Banking Committee today blaming social

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Speaker 3: media in fact, for the bank's collapse. A slipe deeper

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Speaker 3: into it all without one to no nationalibascond Sanni, he's

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Speaker 3: gonna have a tough crowd. What ultimately do you think

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Speaker 3: he's saying? Is it landing the way he hopes it too?

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Speaker 5: Do you think what is.

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Speaker 10: Interesting among the lawmakers here is you're hearing kind of

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Speaker 10: two sides of a story. One is a set of

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Speaker 10: lawmakers that are extraordinarily frustrated by the way SVB was

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Speaker 10: managed and how much Greg Becker was paid. He declined

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Speaker 10: to say whether he would give back some of his

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Speaker 10: bonuses as a result of the bank's failure. And the

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Speaker 10: ultimate you know, blamed mismanagement. There's a sense here also

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Speaker 10: among the separate set of lawmakers that believe that a

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Speaker 10: lot of this was brought on by the Fed's mismanagement

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Speaker 10: as well as the fast rise in interest rates. You

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Speaker 10: heard some lawmakers really attack the Biden administration here and

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Speaker 10: the inflationary environment that had led to.

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Speaker 5: These horror It is becoming bipartisan.

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Speaker 10: It is becoming certainly very very very political on on

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Speaker 10: tip of and we know also for its own worth here,

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Speaker 10: we know that FED officials are also being grilled in

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Speaker 10: Washington this week, and they have also outlined some series

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Speaker 10: of their own failure of oversight for some of these

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Speaker 10: firms as well, and.

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Speaker 4: The FDI see of course, Sinnati, we have been listening

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Speaker 4: into that, Harry. Let's take a listen to just some

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Speaker 4: of what greg Becca had to say.

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Speaker 11: I believe that svb's failure was brought about by a

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Speaker 11: series of unprecedented events. Despite stark differences in our business models,

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Speaker 11: news reports and investors wrongly lumped SVB and silver Gate together.

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Speaker 11: Rumors and misconceptions quickly spread online, culminating on March ninth

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Speaker 11: with the first ever social media bank run, leading to

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Speaker 11: forty two billion in deposits being withdrawn from SVB in

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Speaker 11: ten hours, or roughly one million dollars every second.

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Speaker 4: He claims different facts as SVB Silvergate, that did we

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Speaker 4: hear any sort of mission? Shinnati quickly on him taking

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Speaker 4: responsibility for what happened.

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Speaker 10: I don't think he took responsibility for all of it,

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Speaker 10: and I think that's the important part here. Remember this

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Speaker 10: idea of a social media fuel bank run. There's a

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Speaker 10: lot still to be understood about how that happened, because

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Speaker 10: remember every bank CEO across the country and every private

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Speaker 10: equity from trying to back them is now having that

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Speaker 10: same question can this happen again? That's why these hearings

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Speaker 10: are still so important, because what kind of changes need

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Speaker 10: to be made to the banking system coming out of this,

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Speaker 10: beyond the politics that we're obviously seeing.

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Speaker 4: Clay out today, all right, bloomboch Naali Bask on the

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Speaker 4: Wall Street Beat, but a story that really hit here

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Speaker 4: the heart of Silicon Valley.

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Speaker 3: Welcome back to bloembog Technology. I'm Caroline hind and yelks

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Speaker 3: Ed Lovelow.

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Speaker 4: In San Francisco. All about AI. But let's get a

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Speaker 4: check in on the market. I think there's still a

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Speaker 4: lot of lingering concern care about the debt ceiling and

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Speaker 4: the progress that we're seeing or lack ofb in Washington,

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Speaker 4: DC and AWS that one hundred outperformance in tech up

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Speaker 4: half a percentage point.

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Speaker 8: We've been talking about do having strong earnings.

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Speaker 4: That is an outperformer in terms of the US listed

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Speaker 4: shares of China tech. Ali Barber reports later in the week.

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Speaker 4: But there are some movers like ten Cent to the downside,

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Speaker 4: the NAS that Golden Dragon China Index, that basket of

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Speaker 4: US listed China shares down half percentage point, yields climbing

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Speaker 4: higher six basis points three point six percent ish on

443
00:22:19,480 --> 00:22:21,560
Speaker 4: the US ten year yield where we were kind of

444
00:22:21,600 --> 00:22:25,560
Speaker 4: in March, Bitcoin back down towards twenty seven thousand US

445
00:22:25,680 --> 00:22:26,720
Speaker 4: dollars per token.

446
00:22:26,760 --> 00:22:28,080
Speaker 8: In terms of the individual.

447
00:22:27,720 --> 00:22:29,520
Speaker 4: Movers, I talked a little bit about bay Do that

448
00:22:29,640 --> 00:22:32,200
Speaker 4: moving to the upside, strong earnings beat top and bottom line,

449
00:22:32,440 --> 00:22:35,479
Speaker 4: the story about a rebound in China after the Chinese

450
00:22:35,560 --> 00:22:38,600
Speaker 4: New Year easing of restrictions, travels stargning back up. Also

451
00:22:38,680 --> 00:22:42,240
Speaker 4: the ad business doing well and AI AI everything all

452
00:22:42,280 --> 00:22:42,920
Speaker 4: the time AI.

453
00:22:43,240 --> 00:22:44,840
Speaker 8: There's no sort of main catalyst.

454
00:22:44,920 --> 00:22:47,359
Speaker 4: But you know, Alphabet, parent of Google, has seen gained

455
00:22:47,400 --> 00:22:49,720
Speaker 4: since Google Io when we learn more about it's offering.

456
00:22:50,080 --> 00:22:53,760
Speaker 4: Amazon Bloomberg reporting making moves bring us some of those details,

457
00:22:53,760 --> 00:22:55,280
Speaker 4: because that is what the real mover is.

458
00:22:55,280 --> 00:22:58,359
Speaker 3: To the upside is oh perfect segue ed into talking

459
00:22:58,440 --> 00:23:00,560
Speaker 3: tech first up, Alphabet, you were just talking about making

460
00:23:00,680 --> 00:23:04,600
Speaker 3: back market cap ground as its artificial intelligence gains. As

461
00:23:04,640 --> 00:23:06,640
Speaker 3: you back on, remember it seemed to be lagging behind

462
00:23:06,720 --> 00:23:09,640
Speaker 3: its peers, wasn't it, in the race to deploy generative

463
00:23:09,640 --> 00:23:10,440
Speaker 3: AI products.

464
00:23:10,640 --> 00:23:13,000
Speaker 5: Abundance of caution many would call it, But Google's parent.

465
00:23:12,880 --> 00:23:14,720
Speaker 3: Company is added more than one hundred and fifteen billion

466
00:23:14,720 --> 00:23:17,439
Speaker 3: dollars in market value. Some some veiling plans for its

467
00:23:17,520 --> 00:23:20,800
Speaker 3: EI tools last week at the event you were at. Meanwhile,

468
00:23:21,000 --> 00:23:24,040
Speaker 3: if you've been scouring the job boards for AI related jobs,

469
00:23:24,440 --> 00:23:27,080
Speaker 3: Amazon I have some listed. The e commerce giant is

470
00:23:27,160 --> 00:23:31,240
Speaker 3: planning to add chatchipt style search function to its online storm.

471
00:23:31,320 --> 00:23:33,600
Speaker 5: It's scorning to job posts reviewed by Blue Vegnus.

472
00:23:33,680 --> 00:23:36,240
Speaker 3: Of course, as part of a larger effort to rival

473
00:23:36,280 --> 00:23:40,520
Speaker 3: efforts by Microsoft, by Google tweet these generative AI elements

474
00:23:40,600 --> 00:23:42,200
Speaker 3: into its own search engine set.

475
00:23:43,800 --> 00:23:44,119
Speaker 8: All right.

476
00:23:44,280 --> 00:23:48,600
Speaker 4: Healthcare startup Hippocratic Ai staking its claim in the large

477
00:23:48,680 --> 00:23:51,000
Speaker 4: language model boom with its tool, which it hopes to

478
00:23:51,040 --> 00:23:54,320
Speaker 4: make medical care more accessible with The Palo Alto based

479
00:23:54,359 --> 00:23:57,760
Speaker 4: company launched out of stealth today, raising fifty million dollars

480
00:23:57,800 --> 00:24:00,159
Speaker 4: in a seed round from Andres and horror Itz and

481
00:24:00,280 --> 00:24:04,200
Speaker 4: General Catalyst. Here in the studio with me Sinjo Munjawsha, sorry,

482
00:24:04,320 --> 00:24:09,240
Speaker 4: CEO of Hippocratic AI. Fifty million dollars for a seed round.

483
00:24:09,600 --> 00:24:12,840
Speaker 4: That's a lot of money. Tell me, let me just

484
00:24:12,920 --> 00:24:15,520
Speaker 4: ask what is the valuation on this. You've come out

485
00:24:15,600 --> 00:24:19,160
Speaker 4: with a large language model, you say is commercially ready, right,

486
00:24:19,480 --> 00:24:20,280
Speaker 4: what's your valuation?

487
00:24:21,200 --> 00:24:24,520
Speaker 12: You know, we're not announcing that today, but we really

488
00:24:24,640 --> 00:24:28,399
Speaker 12: felt that generator of AI has really captured the imagination

489
00:24:28,560 --> 00:24:31,520
Speaker 12: of really the world right, and it's captured the zeitgeist.

490
00:24:32,240 --> 00:24:35,400
Speaker 12: And when you think about its application to healthcare, you realize,

491
00:24:35,440 --> 00:24:39,240
Speaker 12: you know, there's three million missing healthcare workers in this country.

492
00:24:39,400 --> 00:24:42,280
Speaker 12: We do not have enough people after the pandemic, so

493
00:24:42,440 --> 00:24:45,359
Speaker 12: many burned out and there's really no way to close

494
00:24:45,400 --> 00:24:48,320
Speaker 12: that gap except using technologies like Generative AI.

495
00:24:48,240 --> 00:24:48,520
Speaker 7: To do that.

496
00:24:48,680 --> 00:24:51,440
Speaker 4: But the misconception out there this morning is that you

497
00:24:51,560 --> 00:24:55,520
Speaker 4: have released a chat gp T style bot that replaces

498
00:24:55,560 --> 00:24:57,240
Speaker 4: the doctor the physician.

499
00:24:57,320 --> 00:24:58,600
Speaker 8: That's not the case at all, though.

500
00:24:58,520 --> 00:25:00,520
Speaker 12: Now now you know, we actually, I don't think the

501
00:25:00,600 --> 00:25:04,240
Speaker 12: Generative AI is ready to do diagnoses. We think diagnoses

502
00:25:04,359 --> 00:25:06,600
Speaker 12: needs to come much later when these models are safe.

503
00:25:06,680 --> 00:25:09,240
Speaker 12: We think that there's a set of applications, you know,

504
00:25:09,320 --> 00:25:12,159
Speaker 12: healthcare is way bigger than just the doctor. There are

505
00:25:12,240 --> 00:25:13,120
Speaker 12: so many people.

506
00:25:12,920 --> 00:25:15,720
Speaker 8: Who work out back office, and not just back office.

507
00:25:15,840 --> 00:25:20,280
Speaker 12: You have registered dietitians, you have genetic counselors, you have

508
00:25:21,320 --> 00:25:25,680
Speaker 12: many other roles that are supporting roles and supporting actors

509
00:25:25,880 --> 00:25:29,920
Speaker 12: in the healthcare system that really could benefit from genitive AI.

510
00:25:30,800 --> 00:25:36,000
Speaker 3: I'm interested in what you see as the regulatory environment

511
00:25:36,080 --> 00:25:38,119
Speaker 3: with which you put this. People are going to be

512
00:25:38,200 --> 00:25:42,080
Speaker 3: fearful of medical advice plus chatchipt, many already knowing that

513
00:25:42,119 --> 00:25:44,240
Speaker 3: you shouldn't. You're not going to get an abundance of

514
00:25:45,040 --> 00:25:47,879
Speaker 3: advice when you go into open AI's product, when you

515
00:25:48,040 --> 00:25:50,480
Speaker 3: go into bard And interestingly, we're hearing from some Oltmann

516
00:25:50,480 --> 00:25:53,119
Speaker 3: at the moment thinking about regulator pressure, saying that for

517
00:25:53,240 --> 00:25:55,399
Speaker 3: open ai, for Google to take on board, but they

518
00:25:55,400 --> 00:25:57,760
Speaker 3: don't want to slow down smaller startups. As a man

519
00:25:57,800 --> 00:26:02,800
Speaker 3: who's been in AI for throughout your learning experience, when

520
00:26:02,800 --> 00:26:06,000
Speaker 3: you're at university, when you went on to further your education,

521
00:26:06,080 --> 00:26:08,760
Speaker 3: when you've gone on to build companies, is a regulation

522
00:26:09,080 --> 00:26:10,440
Speaker 3: going to help or hinder you?

523
00:26:12,160 --> 00:26:12,320
Speaker 1: You know?

524
00:26:12,440 --> 00:26:14,640
Speaker 12: I think that this is an area that does need

525
00:26:15,560 --> 00:26:19,720
Speaker 12: a regulatory framework and one that allows us all to

526
00:26:19,880 --> 00:26:24,320
Speaker 12: create safe large language models in safe generative AI. You know,

527
00:26:24,440 --> 00:26:28,399
Speaker 12: we decided as we were building Hippocratic AI that we

528
00:26:28,520 --> 00:26:31,320
Speaker 12: wanted to be safety first. Like this was the foundation

529
00:26:31,520 --> 00:26:33,840
Speaker 12: of how we designed the company and how we designed

530
00:26:33,880 --> 00:26:36,879
Speaker 12: the product. I mean, you know, our name is hippocratic,

531
00:26:37,000 --> 00:26:40,280
Speaker 12: like the hippocratic ohth Our tagline is do no harm.

532
00:26:40,400 --> 00:26:43,040
Speaker 12: This is our number one focus as a company, and

533
00:26:43,440 --> 00:26:45,920
Speaker 12: we focused on a set of key features to be

534
00:26:45,960 --> 00:26:47,359
Speaker 12: able to do that, which I'm happy to.

535
00:26:47,320 --> 00:26:50,159
Speaker 3: Share with Yeah, it's delve in because we understand your

536
00:26:50,240 --> 00:26:54,160
Speaker 3: startup has the AI is possible that one hundred healthcare

537
00:26:54,240 --> 00:26:57,879
Speaker 3: certifications and outperformed open AI. What is the data that

538
00:26:57,920 --> 00:27:00,440
Speaker 3: you're training on and what are the safety prints you've

539
00:27:00,440 --> 00:27:01,080
Speaker 3: wound into that.

540
00:27:02,240 --> 00:27:04,399
Speaker 12: Yeah, so we built in four or five things to

541
00:27:04,440 --> 00:27:06,240
Speaker 12: really make this safer. I mean, I think the first

542
00:27:06,400 --> 00:27:08,520
Speaker 12: was we certified it. We didn't just say, hey, let's

543
00:27:08,600 --> 00:27:12,280
Speaker 12: pass the us mL the medical licensing exam, where some

544
00:27:12,480 --> 00:27:15,760
Speaker 12: of the language models have posted their results on. We said,

545
00:27:15,880 --> 00:27:20,080
Speaker 12: let's also look at the naeclex, the nursing exam, the

546
00:27:21,119 --> 00:27:25,160
Speaker 12: pharmacy exam, the naplex, and we just said, hey, let's

547
00:27:25,200 --> 00:27:27,119
Speaker 12: go through all of these and we did one hundred

548
00:27:27,119 --> 00:27:29,639
Speaker 12: and fourteen of these different exams, grouped them to groups

549
00:27:29,760 --> 00:27:32,480
Speaker 12: like all the dental exams together, all the physician exams,

550
00:27:32,520 --> 00:27:33,000
Speaker 12: and said.

551
00:27:32,800 --> 00:27:34,200
Speaker 8: Hey, it's important to be certified.

552
00:27:34,240 --> 00:27:36,240
Speaker 12: These are actually the exact exams you see at the

553
00:27:36,359 --> 00:27:39,119
Speaker 12: end of your nurses name on her badge.

554
00:27:39,200 --> 00:27:40,399
Speaker 8: She has all those little letters.

555
00:27:40,440 --> 00:27:42,160
Speaker 5: We made sure we had all of those.

556
00:27:42,480 --> 00:27:45,800
Speaker 12: Those same certifications used to hire healthcare workers, or the

557
00:27:45,840 --> 00:27:50,960
Speaker 12: SA certifications we used and tested on our language model. Second,

558
00:27:51,200 --> 00:27:56,520
Speaker 12: we actually recruited those exact same healthcare workers. So pediatric

559
00:27:56,680 --> 00:28:00,560
Speaker 12: nurses came in and gave our system feedback on how

560
00:28:00,640 --> 00:28:03,520
Speaker 12: it was doing on those questions. Our dietitians came in

561
00:28:03,920 --> 00:28:05,960
Speaker 12: and did the same. And so we believe who's best

562
00:28:06,040 --> 00:28:08,479
Speaker 12: to judge the accuracy of a healthcare of a language

563
00:28:08,520 --> 00:28:10,280
Speaker 12: model than the people doing those.

564
00:28:10,119 --> 00:28:11,200
Speaker 5: Exact jobs today.

565
00:28:11,359 --> 00:28:11,600
Speaker 7: And so.

566
00:28:13,359 --> 00:28:15,440
Speaker 12: You know, those are two of about four or five

567
00:28:15,520 --> 00:28:18,720
Speaker 12: different things that we've done to really make this a

568
00:28:19,400 --> 00:28:20,040
Speaker 12: safer system.

569
00:28:20,119 --> 00:28:22,080
Speaker 4: Let's do a quick far around when do you make

570
00:28:22,160 --> 00:28:23,840
Speaker 4: the LM open to the public.

571
00:28:24,640 --> 00:28:28,600
Speaker 12: You know, we have decided on a threshold based launch

572
00:28:28,640 --> 00:28:31,920
Speaker 12: strategy than a time based land strategy. We're saying when

573
00:28:32,080 --> 00:28:35,040
Speaker 12: those professionals that I just told you about. When we

574
00:28:35,200 --> 00:28:37,720
Speaker 12: have the dieticians using it and they say this is

575
00:28:37,840 --> 00:28:39,320
Speaker 12: ready to go out, that's when.

576
00:28:39,240 --> 00:28:39,800
Speaker 5: It'll go out.

577
00:28:39,920 --> 00:28:41,280
Speaker 8: How do you monetize.

578
00:28:42,280 --> 00:28:44,360
Speaker 12: We will figure that out after we make sure that

579
00:28:44,440 --> 00:28:47,640
Speaker 12: we build a safe and ready language model. I think again,

580
00:28:47,840 --> 00:28:49,720
Speaker 12: you can't say your safety first and be like I'm

581
00:28:49,760 --> 00:28:52,240
Speaker 12: launching on this date. You have to say I'm launching

582
00:28:52,400 --> 00:28:56,800
Speaker 12: when the language model is ready and the professionals who

583
00:28:56,840 --> 00:28:58,440
Speaker 12: do that task today say it's ready.

584
00:28:58,800 --> 00:29:03,560
Speaker 3: Minchelle, you said, and I'm sure you didn't. It was

585
00:29:03,600 --> 00:29:05,800
Speaker 3: a sort of a comment that just comes out, you said.

586
00:29:06,240 --> 00:29:11,200
Speaker 3: Nurses she and I'm in many ways when we say yeah,

587
00:29:11,200 --> 00:29:13,560
Speaker 3: a lot on urs is our female. But therein lies

588
00:29:13,640 --> 00:29:16,520
Speaker 3: some of the issue and the concern around bias within

589
00:29:16,840 --> 00:29:19,200
Speaker 3: these sorts of AI models and the data that's run.

590
00:29:19,440 --> 00:29:20,240
Speaker 5: I'm sure you're.

591
00:29:20,120 --> 00:29:23,920
Speaker 3: Thinking deeply about how, particularly from a medical perspective, biases

592
00:29:23,920 --> 00:29:27,240
Speaker 3: aren't built in. How do you counteract for that going forward?

593
00:29:28,560 --> 00:29:32,080
Speaker 12: You know we've already begun testing the bias of the model.

594
00:29:32,240 --> 00:29:34,720
Speaker 12: You can actually go on our side at hippocraticai dot

595
00:29:34,800 --> 00:29:38,960
Speaker 12: com slash benchmarks and you can take a look at

596
00:29:39,200 --> 00:29:42,440
Speaker 12: our first pass of assessing the bias of our model

597
00:29:42,520 --> 00:29:46,080
Speaker 12: on a bunch of different dimensions, including certain ethnic biases

598
00:29:46,160 --> 00:29:51,280
Speaker 12: and certain gender biases. And you know, so far, you know,

599
00:29:51,360 --> 00:29:54,480
Speaker 12: we were able to show less bias than GPT four.

600
00:29:55,480 --> 00:29:57,320
Speaker 8: But that's just the beginning.

601
00:29:57,400 --> 00:30:00,480
Speaker 12: That's our first installment, our down payment on really just

602
00:30:00,520 --> 00:30:02,280
Speaker 12: trying to say, hey, look, we just launched, but we're

603
00:30:02,280 --> 00:30:04,600
Speaker 12: already testing this, we already care about this, and it's

604
00:30:04,640 --> 00:30:06,480
Speaker 12: something that we're going to continue to work on each

605
00:30:06,520 --> 00:30:07,000
Speaker 12: and every day.

606
00:30:07,120 --> 00:30:09,200
Speaker 8: I just put out to our audience. I wrote about

607
00:30:09,240 --> 00:30:10,040
Speaker 8: this this morning.

608
00:30:11,040 --> 00:30:16,480
Speaker 4: Hippocratic AI they benchmarked, were tested against Chat GPT in

609
00:30:16,520 --> 00:30:19,160
Speaker 4: one hundred and fourteen certifications. I went to open ai

610
00:30:19,760 --> 00:30:22,480
Speaker 4: and asked for them to comment on that performance relative

611
00:30:22,520 --> 00:30:26,160
Speaker 4: to Hippocratic AI. Open ai did not reply, just putting

612
00:30:26,200 --> 00:30:29,160
Speaker 4: that out there to our globalance Caroline, thanks to munjow

613
00:30:29,200 --> 00:30:31,520
Speaker 4: Shar of course, CEO of Hippocratic AI. On the day,

614
00:30:31,560 --> 00:30:34,080
Speaker 4: they raised fifty million dollars in a seed from two

615
00:30:34,640 --> 00:30:37,200
Speaker 4: big names now turning to m and a Twitter parent company,

616
00:30:37,280 --> 00:30:41,120
Speaker 4: ex Corp, has acquired a tech talent recruiting service called

617
00:30:41,480 --> 00:30:45,400
Speaker 4: Laski that, according to a Bloomberg source full disclosure Bloomberg

618
00:30:45,480 --> 00:30:49,240
Speaker 4: Beta part of Bloomberg LP was an investor in Laski.

619
00:30:49,320 --> 00:30:53,040
Speaker 4: Bloomberg's ashaccounts has US joined us with more Asia Laski.

620
00:30:53,120 --> 00:30:54,000
Speaker 8: What is it and why.

621
00:30:55,240 --> 00:30:58,440
Speaker 13: Laski is a early It was early stage startup that

622
00:30:58,760 --> 00:31:03,240
Speaker 13: does recruiting, so it matches employers with candidates very straightforward.

623
00:31:03,680 --> 00:31:07,200
Speaker 13: It's not exactly clear why ex corporate Twitter was interested

624
00:31:07,240 --> 00:31:09,880
Speaker 13: in buying this company, but Musk has talked about this

625
00:31:10,040 --> 00:31:12,360
Speaker 13: idea of creating an everything app, so it could be

626
00:31:12,440 --> 00:31:13,800
Speaker 13: a part of that broader vision.

627
00:31:14,840 --> 00:31:16,640
Speaker 3: So we're all left kind of trying to fill in

628
00:31:16,720 --> 00:31:20,040
Speaker 3: the dots. Meanwhile, Lasky doesn't seem to be operating anymore

629
00:31:20,080 --> 00:31:22,480
Speaker 3: online and I'm waiting for the latest tweet out of

630
00:31:22,520 --> 00:31:27,000
Speaker 3: their pretty active tweeter in chief fir CEO.

631
00:31:27,000 --> 00:31:28,280
Speaker 5: Does a lot online.

632
00:31:29,200 --> 00:31:32,640
Speaker 3: What remind us of the overall vision of the X

633
00:31:32,880 --> 00:31:36,120
Speaker 3: product because many would say that's why the new CEO

634
00:31:36,200 --> 00:31:37,880
Speaker 3: of Twitter, for example, has come.

635
00:31:37,840 --> 00:31:39,560
Speaker 5: On board, right.

636
00:31:39,680 --> 00:31:41,960
Speaker 13: That's one thing that Musk has said when he announced

637
00:31:42,000 --> 00:31:45,320
Speaker 13: that the hiring of Linda Yakarina officially on his Twitter account,

638
00:31:45,400 --> 00:31:46,960
Speaker 13: he said, this is going to be part of the

639
00:31:47,080 --> 00:31:50,080
Speaker 13: vision to create this everything app or this ex app,

640
00:31:50,480 --> 00:31:51,960
Speaker 13: and so it must has been pretty vocal about this.

641
00:31:52,240 --> 00:31:54,720
Speaker 13: He sees it as an app where you can do

642
00:31:54,880 --> 00:31:58,680
Speaker 13: everything from maybe making payments like booking a ticket or

643
00:31:58,880 --> 00:32:02,400
Speaker 13: sending money to a friend, and he hasn't exactly explained

644
00:32:02,560 --> 00:32:05,840
Speaker 13: what it is, but he's taking ideas from things like

645
00:32:06,000 --> 00:32:08,160
Speaker 13: Uber where you can order food and also order a

646
00:32:08,280 --> 00:32:10,880
Speaker 13: cab or even we chat in China. So he's talked

647
00:32:10,920 --> 00:32:13,680
Speaker 13: about Twitter being a place where you can do that,

648
00:32:13,760 --> 00:32:15,440
Speaker 13: where you can do anything that you might be able

649
00:32:15,440 --> 00:32:18,360
Speaker 13: to imagine, and adding in the payments infrastructure as well.

650
00:32:19,120 --> 00:32:21,040
Speaker 5: Asia. It's great to couch ut with you. Thank you,

651
00:32:21,120 --> 00:32:21,800
Speaker 5: Asha Counts.

652
00:32:22,160 --> 00:32:25,280
Speaker 3: Meanwhile, we've got our VC Spotlight next Sad and it's

653
00:32:25,320 --> 00:32:27,720
Speaker 3: with someone who well knows Eno Musk and is back

654
00:32:27,760 --> 00:32:30,280
Speaker 3: to him and some of those other companies from New York,

655
00:32:30,280 --> 00:32:31,000
Speaker 3: from San Francisco.

656
00:32:31,200 --> 00:32:31,920
Speaker 5: It's a bloomberg.

657
00:32:40,840 --> 00:32:43,800
Speaker 2: The last ten years of financial services has seen an

658
00:32:44,160 --> 00:32:50,800
Speaker 2: extraordinary change. The advent of digital banking, mobile banking, and

659
00:32:50,920 --> 00:32:53,800
Speaker 2: all of the technology enabled services that we have now

660
00:32:53,960 --> 00:32:57,520
Speaker 2: come to consider to be table stakes. The next ten

661
00:32:57,640 --> 00:33:00,680
Speaker 2: years is going to take that technology impact and expand

662
00:33:00,760 --> 00:33:01,800
Speaker 2: it exponentially.

663
00:33:02,000 --> 00:33:04,320
Speaker 5: There are enablers to that that the primary driver.

664
00:33:04,320 --> 00:33:07,800
Speaker 2: Is going to be related to the deployment of artificial intelligence.

665
00:33:08,840 --> 00:33:12,160
Speaker 3: Former JP Morgan Executive Live Masters. They're speaking about the

666
00:33:12,280 --> 00:33:16,040
Speaker 3: big innovations that will reshape fintech space in particular, but

667
00:33:16,160 --> 00:33:19,360
Speaker 3: actually every industry when it comes to AI. Let's bring

668
00:33:19,440 --> 00:33:22,440
Speaker 3: in Andrea Lamari from Manhattan Ventures Partners.

669
00:33:22,120 --> 00:33:24,360
Speaker 5: For more on the world of investing.

670
00:33:24,440 --> 00:33:27,520
Speaker 3: The world of well in fact, your own portfolio is

671
00:33:27,560 --> 00:33:30,600
Speaker 3: fascinating discord of course in many ways AI driven. But

672
00:33:30,720 --> 00:33:33,600
Speaker 3: Klana one of those companies of fintech business that's already

673
00:33:33,720 --> 00:33:36,760
Speaker 3: used the chat GPT plug in and open AI plug in.

674
00:33:37,240 --> 00:33:39,080
Speaker 3: How are you thinking about the ethical way in which

675
00:33:39,120 --> 00:33:42,600
Speaker 3: these companies do adopt and let this generative AI run

676
00:33:42,680 --> 00:33:44,480
Speaker 3: loose on their own proprietary data.

677
00:33:46,000 --> 00:33:49,560
Speaker 14: Thanks Carolyn, So overall we are really thinking about generative

678
00:33:49,600 --> 00:33:52,360
Speaker 14: AI as a tool for good. But what it is

679
00:33:52,440 --> 00:33:55,440
Speaker 14: showing to be is that companies are learning so much

680
00:33:55,480 --> 00:33:58,800
Speaker 14: more about their consumer base and allowing their consumers to

681
00:33:58,960 --> 00:34:02,560
Speaker 14: spend time showing trends in a much more vulnerable way,

682
00:34:03,000 --> 00:34:05,560
Speaker 14: which we find fascinating. The way that Klarna is engaging

683
00:34:05,600 --> 00:34:07,760
Speaker 14: with their customers on a new level, in a way

684
00:34:07,800 --> 00:34:10,560
Speaker 14: that they're gathering the data to prove that there are

685
00:34:10,680 --> 00:34:14,560
Speaker 14: ways to use and harness the data to really provide

686
00:34:14,640 --> 00:34:18,040
Speaker 14: better products and services to consumers from a lending perspective

687
00:34:18,400 --> 00:34:20,560
Speaker 14: and then from a spend perspective. And it seems as

688
00:34:20,640 --> 00:34:23,680
Speaker 14: though consumers are really engaging with the AI in a

689
00:34:23,719 --> 00:34:25,560
Speaker 14: way that just seems so much more real and authentic

690
00:34:25,640 --> 00:34:26,160
Speaker 14: than they ever.

691
00:34:26,160 --> 00:34:30,600
Speaker 4: Have if financial conditions are getting tighter, and it's hard

692
00:34:30,719 --> 00:34:35,640
Speaker 4: out there for founders all bench capitals being pushed into

693
00:34:35,719 --> 00:34:38,960
Speaker 4: making AI related investments that in any other economy or

694
00:34:39,080 --> 00:34:41,800
Speaker 4: environment they just wouldn't normally make because of all of

695
00:34:41,880 --> 00:34:42,280
Speaker 4: the hype.

696
00:34:43,200 --> 00:34:46,960
Speaker 14: Overall, what's so interesting about the vcs in the space

697
00:34:47,120 --> 00:34:50,480
Speaker 14: making AI bets is that so many companies we're already

698
00:34:50,680 --> 00:34:55,480
Speaker 14: utilizing AI functionality as a way to harness data and

699
00:34:55,560 --> 00:34:58,600
Speaker 14: then use insights of that artificial intelligence data to build

700
00:34:58,680 --> 00:35:02,040
Speaker 14: better products. But what's funny is that vcs today are

701
00:35:02,120 --> 00:35:04,120
Speaker 14: having a hard time determining what's actually going to make

702
00:35:04,160 --> 00:35:06,719
Speaker 14: money at so some of these companies are really taking

703
00:35:06,719 --> 00:35:10,560
Speaker 14: a push towards open sourcing the technology versus actually making

704
00:35:10,640 --> 00:35:14,279
Speaker 14: it a repeatable, subscription based business. And I think that's

705
00:35:14,320 --> 00:35:16,239
Speaker 14: the big debate within VC world, is what's going to

706
00:35:16,280 --> 00:35:17,080
Speaker 14: actually make money.

707
00:35:17,520 --> 00:35:18,400
Speaker 8: I'm going to jump on that.

708
00:35:18,560 --> 00:35:22,560
Speaker 4: What's actually going to make money you invest in SpaceX?

709
00:35:23,360 --> 00:35:25,880
Speaker 4: When is SpaceX going to make money? Is SpaceX ever

710
00:35:25,960 --> 00:35:28,040
Speaker 4: going to IPO or spin off Starlink?

711
00:35:28,680 --> 00:35:31,120
Speaker 14: Well, overall, SpaceX does have a lot of cash and

712
00:35:31,200 --> 00:35:33,480
Speaker 14: they do make money by way of the Starlink spaceships

713
00:35:33,560 --> 00:35:37,120
Speaker 14: and satellite services. Because overall, what's fascinating is a lot

714
00:35:37,200 --> 00:35:39,840
Speaker 14: of people across the world now that it's in every continent,

715
00:35:39,960 --> 00:35:42,359
Speaker 14: are spending money on Starlink and it's generating a ton

716
00:35:42,400 --> 00:35:45,040
Speaker 14: of revenue. But what we do think is that Starlink

717
00:35:45,160 --> 00:35:47,200
Speaker 14: is big enough as a business that they could spin

718
00:35:47,280 --> 00:35:50,320
Speaker 14: it off eventually, right, And I do see an independent IPO.

719
00:35:51,480 --> 00:35:55,239
Speaker 14: If we just see that muskwor to integrate Generative AI

720
00:35:55,320 --> 00:35:57,120
Speaker 14: in to SpaceX, it might just be a done deal.

721
00:35:57,200 --> 00:35:57,359
Speaker 7: Though.

722
00:35:57,719 --> 00:36:00,839
Speaker 5: If that were the case, talk to us.

723
00:36:00,840 --> 00:36:04,000
Speaker 3: A little bit about tomorrow's IPOs today, which is what

724
00:36:04,120 --> 00:36:07,680
Speaker 3: Manhattan Ventures Partner's sort of tagline is. You're all about

725
00:36:07,680 --> 00:36:09,440
Speaker 3: the secondary market in many ways, and just what is

726
00:36:09,480 --> 00:36:12,800
Speaker 3: the secondary market like for an E on back company.

727
00:36:12,880 --> 00:36:15,160
Speaker 5: I mean, whether it be the vision of.

728
00:36:15,520 --> 00:36:17,640
Speaker 3: X, whether it's SpaceX, so whether it's any of the

729
00:36:17,680 --> 00:36:19,600
Speaker 3: companies you have in your portfolio right now and people

730
00:36:19,680 --> 00:36:22,000
Speaker 3: willing and able and wanting to buy the secondary market.

731
00:36:23,480 --> 00:36:26,920
Speaker 14: So right now, it is absolutely fascinating because you're getting

732
00:36:27,040 --> 00:36:30,480
Speaker 14: to see that the secondary market is driving the true

733
00:36:30,600 --> 00:36:34,520
Speaker 14: value of every single private company. And overall, I think

734
00:36:34,560 --> 00:36:37,920
Speaker 14: it's the real indicator for where investors are willing to

735
00:36:38,000 --> 00:36:40,759
Speaker 14: buy and shareholders are looking to sell. So I think

736
00:36:40,800 --> 00:36:44,359
Speaker 14: we've never seen a more clear opportunity to dive into

737
00:36:44,400 --> 00:36:48,560
Speaker 14: the secondary market. And what's fascinating is companies themselves are

738
00:36:48,640 --> 00:36:50,680
Speaker 14: coming to us and coming to many others in the

739
00:36:50,719 --> 00:36:54,000
Speaker 14: secondary market space and saying, we don't really know what

740
00:36:54,160 --> 00:36:56,600
Speaker 14: the value of our company is in the current market

741
00:36:56,640 --> 00:36:59,160
Speaker 14: because we last raise a big round of funding in

742
00:36:59,320 --> 00:37:02,440
Speaker 14: mid twenty two, twenty one. And so the top indicator,

743
00:37:02,600 --> 00:37:05,800
Speaker 14: the leading indicator we're seeing is the secondary market to

744
00:37:05,960 --> 00:37:10,440
Speaker 14: price companies at their true asset value. So it's fascinating

745
00:37:10,480 --> 00:37:13,840
Speaker 14: to see it, and we ourselves are seeing the opportunities

746
00:37:13,880 --> 00:37:17,319
Speaker 14: are growing exponentially in a way that the companies are

747
00:37:17,719 --> 00:37:21,040
Speaker 14: thankful that there's a real indicator that goes beyond a

748
00:37:21,120 --> 00:37:22,520
Speaker 14: new round of primary financing.

749
00:37:23,360 --> 00:37:25,640
Speaker 3: I mean, isn't it ed at the moment when we're

750
00:37:25,640 --> 00:37:27,360
Speaker 3: ever talking about primary round and financing.

751
00:37:27,440 --> 00:37:29,359
Speaker 5: It tends to be AI related in some way shape

752
00:37:29,400 --> 00:37:29,680
Speaker 5: or forward.

753
00:37:29,760 --> 00:37:31,080
Speaker 8: Does Yeah.

754
00:37:31,280 --> 00:37:33,040
Speaker 4: I think that's the part that we all want to

755
00:37:33,120 --> 00:37:37,359
Speaker 4: understand better, Andrea, which is just forget a new cycle

756
00:37:37,400 --> 00:37:40,839
Speaker 4: or a hype cycle. What are you doing to wake

757
00:37:40,920 --> 00:37:42,400
Speaker 4: up each day and say, Okay, here's where we're going

758
00:37:42,440 --> 00:37:45,439
Speaker 4: to deploy capital, here's our plan for twenty twenty three.

759
00:37:45,920 --> 00:37:47,759
Speaker 4: You know, what I'm trying to understand is what is

760
00:37:47,920 --> 00:37:50,560
Speaker 4: driving investment visis for vcs right now.

761
00:37:51,280 --> 00:37:54,600
Speaker 14: What's interesting about the thesis driven approach with all vcs

762
00:37:54,680 --> 00:37:57,600
Speaker 14: is that all the companies that are approaching us and

763
00:37:57,760 --> 00:38:00,640
Speaker 14: we are approaching them for capital raising, is that they're

764
00:38:00,719 --> 00:38:03,719
Speaker 14: really looking to preserve the valuation that they last had

765
00:38:03,960 --> 00:38:08,120
Speaker 14: and it is almost a valuation at all at all,

766
00:38:08,200 --> 00:38:12,040
Speaker 14: stake at all, you know, permutations of evaluation. So what's

767
00:38:12,160 --> 00:38:15,960
Speaker 14: interesting is that from a thesis perspective, we're determining whether

768
00:38:16,080 --> 00:38:19,400
Speaker 14: or not to invest in companies that have investor sweeteners

769
00:38:19,760 --> 00:38:22,680
Speaker 14: involved in the next round of funding. Or maybe it

770
00:38:22,800 --> 00:38:26,480
Speaker 14: is that the best entry price is that secondary market value.

771
00:38:27,239 --> 00:38:31,040
Speaker 14: Maybe that next round of funding is cluttered with really

772
00:38:31,120 --> 00:38:35,640
Speaker 14: strong investor provisions that allow for protection and upside and

773
00:38:35,760 --> 00:38:37,520
Speaker 14: so I think a lot of investors in our space,

774
00:38:37,640 --> 00:38:40,439
Speaker 14: especially in the growth and late stage, are determining whether

775
00:38:40,560 --> 00:38:43,839
Speaker 14: or not that next round of funding is attractive enough

776
00:38:43,920 --> 00:38:46,200
Speaker 14: to go into relative to secondary.

777
00:38:46,239 --> 00:38:50,719
Speaker 3: Andrew, after Linda Yakarino news came on Twitter, did that

778
00:38:50,960 --> 00:38:54,040
Speaker 3: bump the valuation you've been seeing for Twitter? I know

779
00:38:54,080 --> 00:38:57,120
Speaker 3: it's part of your portfolio post it going private? Is

780
00:38:57,200 --> 00:38:59,840
Speaker 3: that something that you think is getting a clearer destination,

781
00:39:00,400 --> 00:39:01,760
Speaker 3: getting investors more interested.

782
00:39:03,120 --> 00:39:05,719
Speaker 14: Having Linda involved, we think is a very strong indicator

783
00:39:05,920 --> 00:39:09,239
Speaker 14: of where we believe Elon is playing the sea level

784
00:39:09,320 --> 00:39:11,840
Speaker 14: shuffle what we like to call right, so bringing in

785
00:39:11,960 --> 00:39:14,080
Speaker 14: the strong adults in the room to really bring in

786
00:39:14,480 --> 00:39:17,680
Speaker 14: a background in advertising and media, which overall that is

787
00:39:17,800 --> 00:39:21,000
Speaker 14: what Twitter really is focused on, right, advertising, media and

788
00:39:21,080 --> 00:39:21,760
Speaker 14: content driven.

789
00:39:22,239 --> 00:39:24,600
Speaker 5: So I think it's a strong single Linda is.

790
00:39:24,640 --> 00:39:27,319
Speaker 14: A phenomenal executive with an incredible background that I think

791
00:39:27,360 --> 00:39:29,719
Speaker 14: many of us in the industry are very impressed with

792
00:39:29,800 --> 00:39:33,440
Speaker 14: their ability to land. So I think generally we consider

793
00:39:33,480 --> 00:39:34,480
Speaker 14: that a massive positive.

794
00:39:35,400 --> 00:39:39,000
Speaker 4: Manhattan Ventures partner Andrea Lamari, who's able Caroline to talk

795
00:39:39,040 --> 00:39:43,040
Speaker 4: about every news item this week and has investments related

796
00:39:43,080 --> 00:39:45,680
Speaker 4: to every sub sector that we discuss, so thank you

797
00:39:45,880 --> 00:39:48,440
Speaker 4: so much for your time. In other news, East Ventures,

798
00:39:48,520 --> 00:39:52,000
Speaker 4: south East Age's most active early stage tech investment firm,

799
00:39:52,080 --> 00:39:54,560
Speaker 4: raise two hundred and fifty million for its twelveth fund,

800
00:39:54,640 --> 00:39:57,520
Speaker 4: a rare sign of confidence in the global tech sector

801
00:39:57,840 --> 00:40:00,640
Speaker 4: during a tumultuous year. The Indonesia Focus says it will

802
00:40:00,680 --> 00:40:04,120
Speaker 4: allocate the money as follow on investments toward growth portfolio

803
00:40:04,239 --> 00:40:07,320
Speaker 4: companies that demonstrate strong potential.

804
00:40:15,080 --> 00:40:19,480
Speaker 3: Going viral is the unusual gift from Google co founder

805
00:40:19,560 --> 00:40:22,000
Speaker 3: so I gave Brin. In a filing that we saw Monday,

806
00:40:22,080 --> 00:40:24,480
Speaker 3: it shows that he gifted Alphabet shares worth six hundred

807
00:40:24,520 --> 00:40:26,400
Speaker 3: million dollars last Thursday.

808
00:40:26,760 --> 00:40:27,480
Speaker 5: It's actually kind of.

809
00:40:27,520 --> 00:40:30,360
Speaker 3: Unclear who's got the five point two million shares. We

810
00:40:30,520 --> 00:40:33,520
Speaker 3: of course know that it could be perhaps to charity

811
00:40:33,680 --> 00:40:36,680
Speaker 3: or to a trust or another financial instrument. But ultimately

812
00:40:36,760 --> 00:40:38,320
Speaker 3: this is on a week where Brinn and his co

813
00:40:38,400 --> 00:40:41,760
Speaker 3: founder Larry Page saw their wealth are combined eighteen billion

814
00:40:41,840 --> 00:40:42,759
Speaker 3: dollars and we know why.

815
00:40:43,280 --> 00:40:44,160
Speaker 5: It's because of AI.

816
00:40:45,080 --> 00:40:45,239
Speaker 8: Yeah.

817
00:40:45,320 --> 00:40:48,120
Speaker 4: Look, it all comes out of momentum from Google Io

818
00:40:48,320 --> 00:40:52,360
Speaker 4: where we finally understood how Alphabet takes its competence and

819
00:40:52,480 --> 00:40:55,279
Speaker 4: puts it into its tools. But I was there right

820
00:40:55,320 --> 00:40:58,960
Speaker 4: and I would say they stressed deliberate, slow roll out

821
00:40:59,480 --> 00:41:01,319
Speaker 4: safety guardrails.

822
00:41:01,600 --> 00:41:03,480
Speaker 8: And now we have Altman speaking on Capitol Hill.

823
00:41:03,719 --> 00:41:06,840
Speaker 5: Yeah, who's again been stressing safety.

824
00:41:07,000 --> 00:41:09,880
Speaker 3: We know that it's wrapped up now the Senate hearing

825
00:41:10,239 --> 00:41:12,839
Speaker 3: that features some Oltman, CEO of Open AI, as well

826
00:41:12,880 --> 00:41:16,279
Speaker 3: as IBM, as well as scientists, But ultimately he's really saying, look,

827
00:41:16,280 --> 00:41:19,520
Speaker 3: there are no plans for chat GPT five training in

828
00:41:19,600 --> 00:41:20,640
Speaker 3: the next six months.

829
00:41:20,719 --> 00:41:22,560
Speaker 5: This seems to be sort of under some juriss we'd

830
00:41:22,560 --> 00:41:23,200
Speaker 5: heard that from some of.

831
00:41:23,239 --> 00:41:25,360
Speaker 3: His colleagues on Twitter, saying, look, whenever you might be

832
00:41:25,440 --> 00:41:30,120
Speaker 3: hearing GPT five isn't currently underway. But notably, everyone wants

833
00:41:30,120 --> 00:41:31,640
Speaker 3: to know what this has on the startup culture, what

834
00:41:31,719 --> 00:41:33,520
Speaker 3: this has in terms of society.

835
00:41:33,120 --> 00:41:36,320
Speaker 4: Too, And again he Altman says the pressure should be

836
00:41:36,320 --> 00:41:38,719
Speaker 4: on the leaders open AI and Google. The name check

837
00:41:38,800 --> 00:41:42,719
Speaker 4: that was Richard Blumenthal, the Connecticut Democrat who chairs that

838
00:41:42,800 --> 00:41:45,000
Speaker 4: Southern Committee, and he also had questions about the US

839
00:41:45,160 --> 00:41:46,680
Speaker 4: leadership in this field.

840
00:41:47,000 --> 00:41:48,000
Speaker 5: Yeah, Visa v.

841
00:41:48,360 --> 00:41:51,279
Speaker 3: China something that we continue to discuss. How can you

842
00:41:51,360 --> 00:41:55,520
Speaker 3: regulate without offsetting innovation? And what a thoroughly deep dive

843
00:41:55,960 --> 00:41:57,480
Speaker 3: show we had today that does it for this edition,

844
00:41:57,480 --> 00:42:00,600
Speaker 3: though on BlueBag technology. Tomorrow, well, we've got Patrick Zong

845
00:42:00,680 --> 00:42:04,160
Speaker 3: Fanning partner of thirty one from Salt Eye Connections in

846
00:42:04,280 --> 00:42:04,640
Speaker 3: New York.

847
00:42:04,680 --> 00:42:05,400
Speaker 5: You don't want to miss it.