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Speaker 1: I'm Caroline Hyde, Bloomberg's world headquarters in New York. Ludlow

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Speaker 1: were three days into our New York reunion. This is

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Speaker 1: Bloomberg Technology coming up this hour. Ed we're grounded the

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Speaker 1: f A a halted flight departures nationwide after a key

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Speaker 1: pilot information system failed. What technology exactly failed us? We

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Speaker 1: have the details and more Apple supply changes first chips,

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Speaker 1: now screens, plus some plans for a touch screen mac book.

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Speaker 1: Bloomberg's Mark German on all of this week's scoops, and well,

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Speaker 1: where are the best companies to work for other than

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Speaker 1: with us? Right here? Glass Door just came out with

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Speaker 1: their annual list, but some of the Silicon Valley darlings

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Speaker 1: they did not make the cut this time. The story

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Speaker 1: of the day was flights grounded throughout the nation. Pre

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Speaker 1: market we were lower. Most of the airlines actually, look

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Speaker 1: at that screen now so much green. Many of the

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Speaker 1: airlines recovering at the market open on Wednesday. Just a

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Speaker 1: single name down in the road with Southwest. You know

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Speaker 1: a lot of problems to Southwest coming out of that

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Speaker 1: storm and busy holiday period, down seven tents of one percent.

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Speaker 1: There's so much to discuss here. What on Earth went wrong.

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Speaker 1: I mean, it's a technology story and it's very hard

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Speaker 1: ed because the systems of Love, as the ticker is

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Speaker 1: known Southwest, have been a key thorn in this side.

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Speaker 1: But now the Federal Aviation Administration halting thousands of flights nationwide,

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Speaker 1: as you just saying. It all happened early Wednesday, and

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Speaker 1: it was a pilot notification system that exactly failed. But

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Speaker 1: now we've got lawmakers worrying about investigating this and they

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Speaker 1: want to see how this is going to influence course

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Speaker 1: some major upcoming aviation bills that are going to be

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Speaker 1: assessed by these lawmakers. It's bringing George Ferguson, who's our

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Speaker 1: senior airline analyst over at Bloomberg Intelligence, and George just

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Speaker 1: tell us exactly what failed, what went wrong from a

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Speaker 1: tech perspective here, and so the no term system noticed.

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Speaker 1: The Airman system is a system that does so many

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Speaker 1: the information to flight crew, lets them know what what

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Speaker 1: outages there might be at the airports they're arriving at, right,

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Speaker 1: they might arrive at an airport that doesn't have an

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Speaker 1: instrument approach that's working quite right, lets them know airspace restrictions.

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Speaker 1: So it's I mean it's all about safety. And so

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Speaker 1: that system seemed to be broke down earlier today. And

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Speaker 1: when that happens, I think out of an abundance of caution,

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Speaker 1: they stop all flights because they can't be sure they're

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Speaker 1: feeding flight crews the right information to go into uh

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Speaker 1: you know, airports all over the country. And so that

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Speaker 1: was the breakdown, probably a technology breakdown. I don't think

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Speaker 1: it's people. I think it's you know, they collect this

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Speaker 1: information from flight service stations around the country, uh, and

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Speaker 1: then they disseminate it back to the pilot. So it's

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Speaker 1: a big sort of collection and dissemination uh, you know

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Speaker 1: process system, right. Um, So I think that's what probably

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Speaker 1: broke down. George, I'm one of those guys, you know,

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Speaker 1: I walk out into the street, I look up in

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Speaker 1: the sky, look at the airplanes overhead. I marvel about

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Speaker 1: what's happening every day of the year, hundreds thousands of

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Speaker 1: flights all around the world. Old. Is it just that

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Speaker 1: we rely on an archaic system to manage all of this?

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Speaker 1: I mean we're talking here about this technology as somebody

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Speaker 1: that researches looks at the fundamentals of this industry. Are

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Speaker 1: you really concerned was this just an unfortunate glitch. I

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Speaker 1: think it was an unfortunate glitch. I think I think, Um,

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Speaker 1: we won't continue to see problems like this, but we'll

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Speaker 1: always see tech problems in this business, right because the

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Speaker 1: tech is kind of always catching up with the business.

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Speaker 1: I mean, we've we've been flying airplanes for over a

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Speaker 1: hundred years now. Really commercial air travels come, you know,

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Speaker 1: come into its golden age mid last century. You built systems.

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Speaker 1: Those systems need to migrate over time. People that know

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Speaker 1: I T systems know it's hard to plug the newest

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Speaker 1: technology sometimes into the old technology. That migration is a challenge. Um.

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Speaker 1: I've heard that there was some work being done in

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Speaker 1: the no TAM system, so maybe that was part of

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Speaker 1: the challenge here. But it's always a constant migration of

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Speaker 1: bringing old technology to new bringing the best to the front.

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Speaker 1: It takes time in the government doing it, and they

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Speaker 1: don't always have the most minded to do it, so

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Speaker 1: it probably moves on a slower scale than business. George

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Speaker 1: ferguson bloom meg Intelligence, we thank you. Edit Do you

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Speaker 1: actually do that? Do you actually like walk out and

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Speaker 1: go wow? I do I'm a business traveler. I'm somebody

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Speaker 1: that you know, I'm fascinated by the underlying technology of

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Speaker 1: something that every single day you look up and when

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Speaker 1: something like this happens, is a consumer is a technology?

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Speaker 1: Does you ask what we just started? George? How on

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Speaker 1: Earth or something like that happened? His answer, probably a glitch,

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Speaker 1: you know, It's just it's incredible. Have they tried turning

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Speaker 1: it on and off again? Right? Moved to the cloud? Whatever?

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Speaker 1: I love her to Your Eneggie just brings me so

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Speaker 1: much joy. Meanwhile, let's let's kick out a little bit

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Speaker 1: more because we've got to talk Apple and it's planning

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Speaker 1: to start using its own custom displays and mobile devices

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Speaker 1: as early as and on top of that, maybe in

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Speaker 1: the future those devices with those screens you can touch

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Speaker 1: them even on a Mac computer. Let's bring in on

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Speaker 1: the scoop Generator. That's Mark German and Mark, I mean

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Speaker 1: you're out with another one. That start with the Macnews

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Speaker 1: because I thought that Tim Cook was going to follow

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Speaker 1: in his predecessors viewpoint on this and not want to

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Speaker 1: have touch screens on computers. This is very significant. Now

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Speaker 1: they won't fly, so ED won't be able to marvel

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Speaker 1: at them in the air, but they will get touch

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Speaker 1: screens right. Apple has a project. They have multiple teams

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Speaker 1: working on effort to bring touch displays to the Mac

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Speaker 1: for the first time. This is the first time where

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Speaker 1: they're actually serious about this. They've explored in the past,

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Speaker 1: but work has ramped up on these new machines and

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Speaker 1: this is a big u turn from what Apple has

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Speaker 1: done in the past. They've talked about how the iPad

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Speaker 1: is the best experience for touch. They talked about how

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Speaker 1: mac os, the Mac operating system doesn't work well with touch.

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Speaker 1: They talked about the ergonomic concerns about that, but the

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Speaker 1: reality is is that consumers want touch. The Mac can

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Speaker 1: run iPhone and iPad apps now, but they don't have

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Speaker 1: a touch screen, so they don't work very well. Kids

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Speaker 1: these days have grown up on touch screens, iPhones, iPads,

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Speaker 1: Apple watches, Amazon, Kindled table, It's you name it right.

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Speaker 1: The next generation of people who are going to get

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Speaker 1: laptops and desktops, get computers, get max they are used

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Speaker 1: to touch. So Apple has to think about that new

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Speaker 1: generation and that learning curve and what this new generation

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Speaker 1: of potential buyers is used to. So touch displays are

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Speaker 1: important to the future of the Mac, and that's why

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Speaker 1: Apple is going to go down this road in a

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Speaker 1: few years. Let's pivots your other big scoop, which is

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Speaker 1: the starting with high end Apple Watch and later the

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Speaker 1: iPhone handsets, Apple wants to move to in house technology

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Speaker 1: for the displays. Uh, you know the currently l G

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Speaker 1: and Samsung are the big supplies when it comes to

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Speaker 1: those displays. What have you learned in the course of

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Speaker 1: your reporting? So right now, the iPhone and the Apple Watch,

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Speaker 1: both you use what is known as an ol end display.

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Speaker 1: Those are supplied primarily by Samsung. They get some from

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Speaker 1: Japan Display LG. They're also working with a company called

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Speaker 1: BOE in China and source displays from there too. But

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Speaker 1: that underlying technology that's made by those manufacturing companies that

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Speaker 1: make those displays, right, Apple can tweak and customize them.

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Speaker 1: The underlying technology has not been developed by Apple. Now

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Speaker 1: they're developing the underlying technology and that manufacturing process. So

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Speaker 1: now no longer will Apple be competing with Samsung but

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Speaker 1: also sourcing parts from Samsung for one of the most

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Speaker 1: important components inside of their products. All right, bloombergs Mark German,

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Speaker 1: thank you very much. Well, let's talk about some progress

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Speaker 1: being made when it comes to executive searches, Walt Disney

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Speaker 1: in fact electing a new independent director, Mark Parker, as

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Speaker 1: the chairman and the board. Now. Parker already been on

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Speaker 1: the board for seven years now and he was, of

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Speaker 1: course still executive chairman of Nike. Says some big roles

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Speaker 1: he's got under his belt. He's succeeding Susan Arnold, who

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Speaker 1: will not be standing for re election. Student too, what

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Speaker 1: is a fifteen year term in it under Disney's board

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Speaker 1: tenure policy, shares likinginess. I mean he had a cool,

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Speaker 1: storied career over at Disney, I mean over at Nike now,

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Speaker 1: says a chair. But his main priority, ed, he says,

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Speaker 1: is to be getting a new CEO. Who is that

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Speaker 1: going to be? He says? Already the searchers upon it.

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Speaker 1: There is a so a deeper story here, which is

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Speaker 1: that Nelson Pelts and Tree and an activist investor we

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Speaker 1: found out in November eight million dollars state they were

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Speaker 1: trying to get a board seat and the board rejecting

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Speaker 1: that idea. But the reason why is that Nelson Pelts

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Speaker 1: and Tree and they did not want Bob Iger to

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Speaker 1: return as CEO, which again was a shock in itself

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Speaker 1: when those headlines broke. So there's things happening behind the

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Speaker 1: scenes here at Disney, but at least they have some

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Speaker 1: certainty now about the new board chair chat GPT. Yes,

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Speaker 1: we're talking in again and there's a lot of fanfare

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Speaker 1: around the AI powered chat pot that's gone viral in

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Speaker 1: recent weeks. But there's some negative connotations here potentially posing

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Speaker 1: propaganda hacking risks. Now, this is all according to a

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Speaker 1: report published by researchers on Wednesday. Here's the thing. Two

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Speaker 1: of those authored the study actually worked at open ai,

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Speaker 1: and one of them, however, has since left there. Not

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Speaker 1: for profit and blog accompanying the report stated quote, Our

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Speaker 1: bottom line judgment is that language models will be useful

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Speaker 1: for propagandists and will likely transform online influence operations. Joining

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Speaker 1: us now to discuss the risks as well as the positives,

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Speaker 1: the use cases, the future of the technology. Street Or Ramaswamy,

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Speaker 1: co founder CEO of the ad free private search engine Neiva,

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Speaker 1: and we're going to go into the what Neiva does

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Speaker 1: and the way in which you use natural language in

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Speaker 1: a moment, you know, but just talk to us first

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Speaker 1: and foremost about what you made of this discussion about

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Speaker 1: the fear of propaganda about misinformation being used within AI

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Speaker 1: because also on the flip side, there's a lot of

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Speaker 1: positives the way in which you can actually uncover who's

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Speaker 1: behind certain cyber attacks at least two. So large language

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Speaker 1: models are one of the most exciting developments in the

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Speaker 1: last few years. These are models that have been trained

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Speaker 1: pretty much on everything that's been written down, everything that's

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Speaker 1: on that page. Um. The results of that is that

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Speaker 1: they have enormous fluency. Can ask chat DPD too write

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Speaker 1: a look form or a story and it will do

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Speaker 1: an amazing job. Um. It doesn't know right from wrong

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Speaker 1: or truth from falsehood. So you know, the word that

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Speaker 1: we use for that is halstening um. But it is

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Speaker 1: incredibly prolific. I think of these large language models as savants. Um.

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Speaker 1: They can say very convincing things. They can say things

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Speaker 1: in the voice of somebody like you're writing if you

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Speaker 1: give it enough of a sample. Um. So it's not

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Speaker 1: surprising obviously that they can be used for propaganda or misinformation.

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Speaker 1: That's a problem that we face online anyway. I know

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Speaker 1: what I see something I always look to see, well,

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Speaker 1: is this from Boomberg? Is this reputable? So I think

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Speaker 1: those kinds of risks are going to go up, but

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Speaker 1: I think there's also a lot of positive potential that

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Speaker 1: come from these models. Do you know what's interesting is

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Speaker 1: we went to our own audience earlier in the day

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Speaker 1: using Twitter a pole, and we asked them, look to

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Speaker 1: these sorts of reports about the ability to incite propaganda

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Speaker 1: put you off from using chat gypt Interestingly, most still say, look,

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Speaker 1: they're going to be continuing to use it despite these reports.

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Speaker 1: But about said, no way, I'm not going to be

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Speaker 1: using it from your perspective. Is this where you step

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Speaker 1: in Because one of the fixes perhaps that neither has

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Speaker 1: for chat GPT is by allowing us to know what

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Speaker 1: the sourcing of the information is that we're reading. Yeah,

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Speaker 1: that was one of our big goals. We wanted to

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Speaker 1: integrate the fluency of these large language models into search.

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Speaker 1: Right now, search is pretty hard to put in a keyword.

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Speaker 1: You have to go click on a link, figured out

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Speaker 1: what's there, um, and then come back maybe it goes

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Speaker 1: somewhere at else um. So we wanted the fluent answers

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Speaker 1: of chat GPT, but we wanted to be done in

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Speaker 1: an authentic way. So we set out to make sure

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Speaker 1: that every sentence he wrote had its source clearly put there,

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Speaker 1: so there's a citation. UM. We also wanted to make

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Speaker 1: sure that we could take into our count real time information.

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Speaker 1: This is what we launched earlier UM last week, and

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Speaker 1: it's been exceptionally well received because two of the biggest

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Speaker 1: problems that GPT has old information it's sort of hallucinate,

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Speaker 1: sort of makes a fact or addressed to a large

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Speaker 1: degree by our ability to combine a search engine, which

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Speaker 1: is all about finding you authentic, believable information UM with

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Speaker 1: the fluency of a large language model. So we are

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Speaker 1: at the very much the beginning inning of how large

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Speaker 1: language models are going to be integrated into various functions,

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Speaker 1: including search engines, so they can be put to very

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Speaker 1: very positive views. Just like the Internet. The Internet is

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Speaker 1: great for finding things, but obviously a lot of misinformation

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Speaker 1: and bad things also happened, so we should think of

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Speaker 1: these language models as being the same. But it's a tool. Hey, Sea,

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Speaker 1: we're so grateful for you joining us from Munich, Germany

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Speaker 1: or attending d l D. I know you're surrounded by

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Speaker 1: people who understand the underlying technology, but there are so

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Speaker 1: many people out there that do not, And I know

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Speaker 1: that it's not straightforward, but could you try and explain

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Speaker 1: to our audience what large language models are in Layman's terms,

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Speaker 1: how do they work? Yeah, so language models understand the

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Speaker 1: structure of how we write and speak um, and you

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Speaker 1: should really think of them as this intermediarly. Just like

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Speaker 1: the keyboard takes the strokes that you put on it

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Speaker 1: and produces sentences, these language models can take your input, um,

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Speaker 1: but they're able to respond back to you um with

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Speaker 1: writings off their own because they've been trained on so

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Speaker 1: much text. UM. But these writings by themselves, like don't

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Speaker 1: always have meaning. If you ask you to write a poem,

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Speaker 1: you know, should it will write a poem. It will

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Speaker 1: be fun. On the other hand, it might not know

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Speaker 1: true from false, or like what is a fact and

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Speaker 1: what is believable and things like that. So this technology

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Speaker 1: is very much developing, but right now you should think

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Speaker 1: of this the same way, um you do. You think

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Speaker 1: of like any interface or you know, like an interpreter

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Speaker 1: um that can listen to you in English and translate

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Speaker 1: it into into German. They have that kind of a skill,

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Speaker 1: but they need to be augmented with you know, other

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Speaker 1: things from real world to be truly useful and those

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Speaker 1: things are going to come up sew. We're talking about

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Speaker 1: chat GPT for a reason, right because of the news

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Speaker 1: cycle Bloomberg reporting that Microsoft is looking at investing maybe

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Speaker 1: ten billion dollars into open Ai and chat GPT over

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Speaker 1: a series of years. And it seems like, you know,

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Speaker 1: the common sense conclusion is that they would use chat

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Speaker 1: GPT to improve being as a search engine, make it

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Speaker 1: more competitive against Google as somebody that operates in search.

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Speaker 1: You know, what's your read on that piece of news. Absolutely,

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Speaker 1: you know, being as clearly a number two in the

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Speaker 1: search space, and they want to leave pragu over, you know,

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Speaker 1: just like we want to leave prob over and create

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Speaker 1: a better search experience. It absolutely makes sense that Microsoft

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Speaker 1: would want to do that. Um. But we should remember

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Speaker 1: that Microsoft actually a juggernaut in things like work communication,

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Speaker 1: personal communication. Um. And these language models are going to

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Speaker 1: be helping any time we write an email, any time

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Speaker 1: we want to consume a document, auto web page. So

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Speaker 1: the use cases for the language models are truly massive

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Speaker 1: and clearly microsof us being very smart by making a

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Speaker 1: big early bet in the space. Should you make bets

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Speaker 1: for living to your venture partner at Greylock as well

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Speaker 1: as of course co founder of this business. Neither talk

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Speaker 1: to us about where the money is being made in

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Speaker 1: your business if you're not going to have ads, and

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Speaker 1: and if we're trying to visualize what a language format

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Speaker 1: would be in terms of search that gives you a

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Speaker 1: perfect answer and not only the links to click on

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Speaker 1: what what's the what's the money's been ahead? Well, so

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Speaker 1: this is real. It actually gets really exciting for Neiva

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Speaker 1: because we have a subscription search engine, be said, or

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Speaker 1: to create a search engine that was all about you,

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Speaker 1: the user. UM, And we have a premium model. You know,

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Speaker 1: people pay for premium features like being able to connect

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Speaker 1: their email or works like to them, so our model

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Speaker 1: is perfectly aligned. UM. I can imagine a scenario in which, yes,

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Speaker 1: there is a chat like interface but some of the

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Speaker 1: sentences are sponsored. Maybe, UM, but I think the one

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Speaker 1: answer format it doesn't really jive all that well with

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Speaker 1: with advertising. But there will be other cases. You know,

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Speaker 1: people are going to use these models to create like

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Speaker 1: personalized advertising for each and every one of us. So

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Speaker 1: you're going to lose some there are other places where

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Speaker 1: you're going to live in as well. In advertisement. All right,

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Speaker 1: Suda Ramaswami really smart analysis of this. Nascent Space co

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Speaker 1: founder and CEO of Neiva, thank you so much for joining.

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Speaker 1: Now coming up, what's new in well Elon Musk's world,

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Speaker 1: from new potential deals to being grand favorite at Goldman Sacks.

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Speaker 1: Will have the latest and everything Tesla everything you need

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Speaker 1: to know. This is Bloomberg. Now let's get to the

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Speaker 1: news in the world of Elon Musk because sources say

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Speaker 1: Tesla is close to a prelim deal to set up

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Speaker 1: a factory in Indonesia, a nation where reserves of key

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Speaker 1: battery metals. Multiple facilities in the country could be serving

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Speaker 1: different functions like production across the supply chain. That's according

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Speaker 1: to one source, and the plant could produce as many

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Speaker 1: as one million cars a year in him with Tessa's

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Speaker 1: ambitions for all of its factories globally to eventually reach

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Speaker 1: that capacity. But the deal is not signed it could

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Speaker 1: still fall through, according to sources. Meanwhile, Tesla remains of

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Speaker 1: technology leader in EV's and three top pick in the

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Speaker 1: auto industry for Goldman Sacks, with the firm emphasizing that

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Speaker 1: the Inflation Reduction Act will have a positive impact on

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Speaker 1: the EV maker. The brokerage maintains Tesla at a bye,

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Speaker 1: though it cut its price target for the second time

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Speaker 1: in less than a month last week to two five

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Speaker 1: dollars per share. That's down from a target of four

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Speaker 1: hundred dollars a share a year ago, when the firm

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Speaker 1: also called Tesla a top pick and to boot Caroline

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Speaker 1: Elon Musk is the subject of Wednesday's Big Take, and

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Speaker 1: what a big take? Was? The big take all about

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Speaker 1: money or Musk Casa CEO. And he called himself, of

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Speaker 1: course chief twit. What else has he called himself across

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Speaker 1: the years? But he might never reclaim perhaps the title

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Speaker 1: as the world's richest person overall aid This entire take

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Speaker 1: from Bloomberg is about the way which he paid a

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Speaker 1: self basically, and the way he's being taken to task

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Speaker 1: in the courts an example, the way in which he

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Speaker 1: was compensated. And the whole point was he was compensated

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Speaker 1: by options and certain ways in which he had to

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Speaker 1: meet certain targets. Huge growth in the share price. But

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Speaker 1: for many a shareholder, they're basically saying, look, the idea

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Speaker 1: is to lock you in and keep your attention on Tesla,

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Speaker 1: but his attention isn't on Tesla. So the Moonshot awards

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Speaker 1: so much money, lots of options, but he borrowed against

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Speaker 1: it margin loans to invest in other things. When Tesla

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Speaker 1: stopped was buoyant. Well it's not now, So now we're saying, well,

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Speaker 1: what about margin calls? Will he ever get back to

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Speaker 1: top spot? A really fascinating read from our team at

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Speaker 1: Bloomberg News. I mean, I think the only person who

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Speaker 1: got to the wealthy heights that he did and then

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Speaker 1: the only person who have lost it. What is it?

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Speaker 1: Two hundred two hundred billion dollars? Welcome back to bloembog Technology.

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Speaker 1: I'm Caroline Hyde in New York and I'm Ed Ludlow.

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Speaker 1: Also in you special tree this week only for one

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Speaker 1: week only though. Meanwhile, well the more updates aid at

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Speaker 1: the moment on FTX is bankruptcy case advisors. Well they

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Speaker 1: found some money, indeed, about five billion dollars worth in

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Speaker 1: casual crypto assets that may help repay creditors RESUS saving

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Speaker 1: emotion anti us is here to explain, Well, how far

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Speaker 1: this is actually going to go? Yeah, exactly five billion

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Speaker 1: dollars is probably not enough for the nine million accounts

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Speaker 1: that are owed money. At the end of the day,

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Speaker 1: and the bankruptcy advisors did advise as much. Caroline. Now

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Speaker 1: remember that number, that sheer number of customers that they're

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Speaker 1: dealing with here as they go through this bankruptcy process

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Speaker 1: is important because of course there are of all different sizes.

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Speaker 1: We know that they have asked earlier on to keep

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Speaker 1: the names silent of especially the fifty largest which have

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Speaker 1: much more at stake. But at the end of the day,

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Speaker 1: once you're spreading the love around the money back to

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Speaker 1: the clients, it's not as much as you would get

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Speaker 1: back as if you're going to be paid and whole.

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Speaker 1: Remember also, they are also saying that above and beyond

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Speaker 1: that five billion dollars, there are a lot of illiquid

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Speaker 1: sets here that they're dealing with. So in addition to

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Speaker 1: the assets that they have said about five billion dollars

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Speaker 1: or so, the question is how much is in crypto,

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Speaker 1: how much is in cash, and how much of that

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Speaker 1: is still worth five billion dollars at the end of

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Speaker 1: the day. O pationally one of the more obscure headlines

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Speaker 1: across the Bloomberg terminal. Salana based bunk in neu n

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Speaker 1: f T surge tenfold after mint, but listing attracts criticism.

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Speaker 1: Very simple question, what is the controversy around bunk? A

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Speaker 1: simple question without a very simple answer, because at the

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Speaker 1: end of the day, Bonk, what was interesting about it

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Speaker 1: is really when it made this n f T mint,

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Speaker 1: if you will, And a lot of it happened on

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Speaker 1: Magic Eden, which remember has been closely tied to Salana.

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Speaker 1: They had recently raised money here. A lot of it

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Speaker 1: comes back to the purpose of the bonk itself. Of course,

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Speaker 1: you have this idea here that the meme stock or

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Speaker 1: the mean crypto crazes back in some ways the idea

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Speaker 1: of having fun in the industry again, which even Mark

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Speaker 1: Cuban wanted. But what happened at the end of the

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Speaker 1: day is after the listing here of the n f

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Speaker 1: T s in particular and Magic Eden, a question then

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Speaker 1: became very very straightforward and simple, that question of royalties

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Speaker 1: and where it goes, and what controlled the exchange has

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Speaker 1: over those royalties, as well as governance of the token

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Speaker 1: itself that is uh perennially linked here in some fashion,

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Speaker 1: but doesn't govern the n f T community itself or

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Speaker 1: the broader kind of what do you call it? There

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Speaker 1: are many communities around here to think about but the

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Speaker 1: governance of the of the n f T community and

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Speaker 1: its relationship to Bonk has gotten very confusing after the

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Speaker 1: listing itself has pursued. Not complex at I tried, but

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Speaker 1: here it's only been a day. It is the reality.

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Speaker 1: It was these it was fifteen n f T s

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Speaker 1: here and again I want to reiterate here the purpose

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Speaker 1: was largely without utility. The purpose was largely for art,

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Speaker 1: and so the governance and the royalties that are associated

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Speaker 1: with them becoming as complicated as they are and therefore

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Speaker 1: leading up to the decline and value of the bonk

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Speaker 1: is important. Shanali, thank you for breaking that all down.

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Speaker 1: Let's get back though to Salana based Bonk can actually

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Speaker 1: here to discuss that story. Is Salana's head of strategy,

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Speaker 1: Austin Federer. Thank you for joining us, Thank you for

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Speaker 1: being here with us in New York. Straightforward question to start,

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Speaker 1: I guess what is Salana's response to all of this

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Speaker 1: with the shale is just outlined for us. You know,

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Speaker 1: the last half of this year has been a tough

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Speaker 1: one for the global crypto community, uh and for some

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Speaker 1: users on Salana as well. I think when you're looking

431
00:22:37,680 --> 00:22:40,320
Speaker 1: at Bonk you're looking at people having fun with blockchain. Again,

432
00:22:40,640 --> 00:22:42,960
Speaker 1: it's a it's a meme coin that got air dropped

433
00:22:43,000 --> 00:22:47,080
Speaker 1: to people two thousands and thousands of wallets on the ecosystem,

434
00:22:47,119 --> 00:22:49,280
Speaker 1: and it's something that the community is kind of galvanized

435
00:22:49,320 --> 00:22:51,639
Speaker 1: behind and been able to sort of dig into and

436
00:22:51,680 --> 00:22:53,480
Speaker 1: really find a lot of fun in it. You see

437
00:22:53,560 --> 00:22:55,359
Speaker 1: volumes pick up on the back of it. Is certainly

438
00:22:55,400 --> 00:23:00,680
Speaker 1: helping breathe excitement around Salana again. But I'm interested when yes,

439
00:23:00,720 --> 00:23:03,040
Speaker 1: it's Joe, Yes it might be a feel good But

440
00:23:03,080 --> 00:23:05,920
Speaker 1: when something has no utility and everyone at the moment

441
00:23:05,960 --> 00:23:08,720
Speaker 1: is saying, prove to me the use case of crypto,

442
00:23:08,920 --> 00:23:11,520
Speaker 1: Why hasn't just eroded wealth but in fact it's being

443
00:23:11,560 --> 00:23:14,000
Speaker 1: built for a force of good. What are your response then,

444
00:23:14,000 --> 00:23:17,720
Speaker 1: when the next thing we're talking about is a meme token? Yeah,

445
00:23:17,800 --> 00:23:21,159
Speaker 1: so memes are fun um, but memes are also a

446
00:23:21,200 --> 00:23:24,720
Speaker 1: proxy for community, and one of the utilities of crypto

447
00:23:24,760 --> 00:23:26,760
Speaker 1: that's often overlooked is that it is a system for

448
00:23:26,840 --> 00:23:30,520
Speaker 1: galfanizing community. And so the excitement around Bonk is on

449
00:23:30,680 --> 00:23:33,560
Speaker 1: one level, Yes, it's a meme. Yes it doesn't actually

450
00:23:33,720 --> 00:23:36,600
Speaker 1: specifically do something, but it is a it is a

451
00:23:36,600 --> 00:23:39,920
Speaker 1: token of community, and especially after an ecosystem that's been

452
00:23:39,960 --> 00:23:43,360
Speaker 1: through you know, a rough few months, Yes, to say

453
00:23:43,400 --> 00:23:46,359
Speaker 1: the least, and let's go there because we thank you

454
00:23:46,400 --> 00:23:48,600
Speaker 1: for coming on to talk about what is a rough

455
00:23:48,640 --> 00:23:53,160
Speaker 1: few months. Because Solana became very intertwined with the downfall

456
00:23:53,160 --> 00:23:55,920
Speaker 1: of FTX with Samamma Reed had very much been sort

457
00:23:55,920 --> 00:23:57,920
Speaker 1: of a driving force in some ways the Salana become

458
00:23:58,119 --> 00:23:59,800
Speaker 1: so popular, but they held a lot of it and

459
00:23:59,800 --> 00:24:03,280
Speaker 1: there's a worry that your price would fall on the

460
00:24:03,320 --> 00:24:06,720
Speaker 1: back of force selling. What's your experience how do you

461
00:24:06,720 --> 00:24:09,359
Speaker 1: move past the fallout of ft X. Yeah, if you

462
00:24:09,359 --> 00:24:11,399
Speaker 1: look at the beginning of November, a lot of the

463
00:24:11,400 --> 00:24:14,080
Speaker 1: headlines were doom and gloom for the Salanta network, and

464
00:24:14,080 --> 00:24:16,280
Speaker 1: what we've really seen since then, we're about what two

465
00:24:16,280 --> 00:24:19,880
Speaker 1: months out from from that initial news is active addresses

466
00:24:19,880 --> 00:24:22,280
Speaker 1: are up, more people are using the network than we're before.

467
00:24:22,560 --> 00:24:26,280
Speaker 1: There's actually more validators on the network than before FTX collapsed.

468
00:24:26,520 --> 00:24:28,840
Speaker 1: So we've seen is the community and developers all around

469
00:24:28,840 --> 00:24:31,280
Speaker 1: the world really come together and replace the parts of

470
00:24:31,320 --> 00:24:34,920
Speaker 1: the ecosystem that had ft X involvement and then expand

471
00:24:34,960 --> 00:24:37,840
Speaker 1: from there. So if you look at active addresses, each day,

472
00:24:38,119 --> 00:24:40,520
Speaker 1: Salana is higher than all other box chains. At this point,

473
00:24:40,600 --> 00:24:42,760
Speaker 1: that kind of goes somewhere into answering my next question,

474
00:24:42,800 --> 00:24:44,879
Speaker 1: which is, you know, the Salana network, there was the

475
00:24:44,960 --> 00:24:51,119
Speaker 1: market volatility, the kind of lack of faith in crypto assets, cryptocurrencies,

476
00:24:51,320 --> 00:24:54,000
Speaker 1: and then this is the debate about the underlying technology, right,

477
00:24:54,000 --> 00:24:56,040
Speaker 1: and when it comes to the Salan network, I think

478
00:24:56,040 --> 00:25:00,880
Speaker 1: the idea is security and resistance against sent to ship.

479
00:25:00,960 --> 00:25:05,280
Speaker 1: How do you kind of restore faith in the technology

480
00:25:05,359 --> 00:25:08,440
Speaker 1: part of that story? Forget the markets for a second. Yeah, Well,

481
00:25:08,600 --> 00:25:11,639
Speaker 1: the interesting part is the technology actually wasn't part of

482
00:25:11,680 --> 00:25:14,280
Speaker 1: what happened at all, Right, from a technology standpoint, what

483
00:25:14,359 --> 00:25:16,520
Speaker 1: was kind of you know, I guess perception is to

484
00:25:16,600 --> 00:25:20,000
Speaker 1: the outside of perception, right, Yeah, But the fundamentals of

485
00:25:20,000 --> 00:25:22,439
Speaker 1: the network are actually just the same as they were before,

486
00:25:22,520 --> 00:25:25,360
Speaker 1: which is that Salona's vision is to be the fastest

487
00:25:25,400 --> 00:25:28,800
Speaker 1: settlement layer for the global ecosystem, right, And whether that

488
00:25:28,880 --> 00:25:32,240
Speaker 1: means you're talking about fast finality of transactions tens of

489
00:25:32,280 --> 00:25:34,600
Speaker 1: thousands of transactions per second being able to go through

490
00:25:34,600 --> 00:25:38,840
Speaker 1: the network, what we're really getting at is a technology

491
00:25:38,880 --> 00:25:42,800
Speaker 1: platform that can handle loads at other places, can't I

492
00:25:42,840 --> 00:25:46,200
Speaker 1: have to ask you about Sam Bankman Freed the relationship

493
00:25:46,359 --> 00:25:49,959
Speaker 1: or historic relationship between SPF and Salana. What are you

494
00:25:50,119 --> 00:25:53,199
Speaker 1: doing to move past that relationship? How what is the

495
00:25:53,200 --> 00:25:56,719
Speaker 1: relationship in present day? Sure? So you know in about

496
00:25:56,800 --> 00:25:59,920
Speaker 1: in summer of that was when Sam and ft F

497
00:26:00,040 --> 00:26:02,280
Speaker 1: Scott involved in the Salanta network and they were building

498
00:26:02,280 --> 00:26:04,639
Speaker 1: real infrastructure on the network that couldn't be built on

499
00:26:04,680 --> 00:26:07,400
Speaker 1: any other blockchain network. Serum, which was a central limit

500
00:26:07,480 --> 00:26:09,600
Speaker 1: order book built on chain. It was the first central

501
00:26:09,600 --> 00:26:12,880
Speaker 1: limit order book deployed on a blockchain. That code has

502
00:26:12,920 --> 00:26:16,160
Speaker 1: been taken over by the community and relaunched a something

503
00:26:16,200 --> 00:26:18,439
Speaker 1: called open Book. And so that's kind of what I

504
00:26:18,440 --> 00:26:20,520
Speaker 1: was talking about about the community healing parts of the

505
00:26:20,560 --> 00:26:23,800
Speaker 1: ecosystem that f t X and and Sam were involved in.

506
00:26:24,040 --> 00:26:26,679
Speaker 1: I think moving past it though is really it's on

507
00:26:26,840 --> 00:26:29,680
Speaker 1: chain metrics, right, It's that users are still here, developers

508
00:26:29,680 --> 00:26:31,840
Speaker 1: are still building, and there's a lot of excitement around

509
00:26:31,840 --> 00:26:33,680
Speaker 1: what's getting built on the network. And as well said that,

510
00:26:33,720 --> 00:26:35,960
Speaker 1: you say building because in and of itself, the blockchain

511
00:26:36,040 --> 00:26:39,400
Speaker 1: isn't finished. It's not a final product. You're still iterating,

512
00:26:39,480 --> 00:26:42,080
Speaker 1: still improving Salana. Just talk to us about Therefore, your

513
00:26:42,119 --> 00:26:46,359
Speaker 1: own network stability outages that have occurred, which you know

514
00:26:46,520 --> 00:26:48,359
Speaker 1: this happens when one bill is just look at the

515
00:26:48,440 --> 00:26:51,240
Speaker 1: f a A today, talk to us about what you're

516
00:26:51,240 --> 00:26:53,480
Speaker 1: doing to ensure that you continue to its rate, and

517
00:26:53,480 --> 00:26:55,560
Speaker 1: then it becomes a stronger product moral. So one of

518
00:26:55,560 --> 00:26:58,520
Speaker 1: those biggest investments is a second validator client, which is

519
00:26:58,560 --> 00:27:01,720
Speaker 1: really a second copy of the system that runs the network,

520
00:27:01,960 --> 00:27:04,000
Speaker 1: and so that means if one system goes down, there's

521
00:27:04,000 --> 00:27:06,800
Speaker 1: a second system that can can step in. That client

522
00:27:06,880 --> 00:27:08,720
Speaker 1: is being built by Jump, which is a high frequency

523
00:27:08,720 --> 00:27:12,080
Speaker 1: trading firm. So there's all sorts of additional benefits that

524
00:27:12,119 --> 00:27:15,560
Speaker 1: come from that, such as high performance. So they're they're

525
00:27:15,600 --> 00:27:18,239
Speaker 1: testing system right now. It's been benchmarked at about one

526
00:27:18,240 --> 00:27:20,880
Speaker 1: point two million transactions per second. I don't think we'll

527
00:27:20,880 --> 00:27:23,600
Speaker 1: see something like that in actual production environments when it

528
00:27:23,640 --> 00:27:26,280
Speaker 1: actually gets onto the network, but we're talking about a

529
00:27:26,280 --> 00:27:29,360
Speaker 1: lot of performance optimizations that allow people to build new

530
00:27:29,440 --> 00:27:32,200
Speaker 1: kinds of products and services built on blockchain that are

531
00:27:32,240 --> 00:27:34,880
Speaker 1: just as performant as the web. Two counterparts. Let's talk

532
00:27:34,920 --> 00:27:37,719
Speaker 1: about products and services because that's what everyone's waiting for.

533
00:27:38,040 --> 00:27:41,080
Speaker 1: The kill adapt the finally that we start to use

534
00:27:41,880 --> 00:27:45,080
Speaker 1: crypto not just as an asset or a means of exchange,

535
00:27:45,080 --> 00:27:49,560
Speaker 1: but something more with utility. Yeah, exactly what is what

536
00:27:49,800 --> 00:27:51,720
Speaker 1: is the that you almost excited about? What are the

537
00:27:51,760 --> 00:27:53,359
Speaker 1: areas that are being built on and one of the

538
00:27:53,359 --> 00:27:56,560
Speaker 1: aren't means. So if you look at two in general,

539
00:27:56,680 --> 00:27:58,480
Speaker 1: apart from the price, it's very hard to not look

540
00:27:58,520 --> 00:28:00,520
Speaker 1: at the price, but the technology is really why most

541
00:28:00,560 --> 00:28:02,960
Speaker 1: of us are here. It was a breakout year for

542
00:28:03,000 --> 00:28:06,000
Speaker 1: blockchain globally. We saw web two companies for the first

543
00:28:06,040 --> 00:28:09,479
Speaker 1: time build real user facing products. They've all had some

544
00:28:09,520 --> 00:28:12,840
Speaker 1: back end experimentation for a while, but you know, from

545
00:28:13,000 --> 00:28:17,120
Speaker 1: Google to Amazon, to Meta to Starbucks, they're all launching

546
00:28:17,160 --> 00:28:21,000
Speaker 1: products built on blockchain at this point. And so the

547
00:28:21,040 --> 00:28:23,640
Speaker 1: types of products and services are starting to become much

548
00:28:23,680 --> 00:28:26,080
Speaker 1: easier for people to engage with, and they're starting to

549
00:28:26,080 --> 00:28:29,840
Speaker 1: be things that change the relationship from I have to

550
00:28:30,040 --> 00:28:33,000
Speaker 1: understand exactly how a blockchain network works to use this

551
00:28:33,440 --> 00:28:36,399
Speaker 1: to something that's that's getting closer to the experience of

552
00:28:36,400 --> 00:28:39,360
Speaker 1: downloading and app and installing it. Austin, great test in

553
00:28:39,400 --> 00:28:42,360
Speaker 1: time with you. Thank you Austin for the here's a Salana,

554
00:28:42,600 --> 00:28:45,520
Speaker 1: head of strategy. We thank him. Meanwhile, coming up the

555
00:28:45,680 --> 00:28:49,560
Speaker 1: rise of women's healthcaps with the Camel, co founder of Peppy,

556
00:28:49,760 --> 00:28:52,800
Speaker 1: just raised forty five million dollars. Even in this environment,

557
00:28:53,480 --> 00:29:06,920
Speaker 1: this is Blomberg. I think, you know, actually the jury

558
00:29:06,960 --> 00:29:09,560
Speaker 1: is out a little bit on exactly what telemedicine has

559
00:29:09,560 --> 00:29:11,920
Speaker 1: given us. I think it's solved a couple of key gaps,

560
00:29:11,960 --> 00:29:14,600
Speaker 1: but a lot of the telemedicine swing in particular has

561
00:29:15,160 --> 00:29:17,720
Speaker 1: swung back to in person and a preference for in person,

562
00:29:17,760 --> 00:29:20,720
Speaker 1: a recognition that a lot of patients need in person

563
00:29:20,840 --> 00:29:25,360
Speaker 1: procedures and care that requires physical um, the physical presence

564
00:29:25,400 --> 00:29:28,640
Speaker 1: and a physical infrastructure. That being said, I think especially

565
00:29:28,720 --> 00:29:32,800
Speaker 1: in pockets of of our ecosystem, including the way in

566
00:29:32,800 --> 00:29:37,080
Speaker 1: which we run clinical trials, the availability of virtual care,

567
00:29:37,120 --> 00:29:40,360
Speaker 1: and the ability for patients, UM and providers to connect,

568
00:29:40,520 --> 00:29:46,480
Speaker 1: make decisions, take steps, try new therapies and monitor UM

569
00:29:46,480 --> 00:29:51,680
Speaker 1: you know, patient performance on those therapies telemeds. In that

570
00:29:52,040 --> 00:29:56,720
Speaker 1: discussion point with Andrews and Harrow, it's general partner Finita Aguala. Meanwhile, well,

571
00:29:56,720 --> 00:29:59,960
Speaker 1: the healthcare platform Peppy just announced a forty five million

572
00:30:00,040 --> 00:30:03,000
Speaker 1: donor Series BE funding round is led by albim VC

573
00:30:03,200 --> 00:30:06,160
Speaker 1: and plenty of others at that tier to discuss. It's

574
00:30:06,160 --> 00:30:10,240
Speaker 1: the co founder, co CEO and PhD Mao Della Paray.

575
00:30:10,600 --> 00:30:13,440
Speaker 1: Thank you so much for joining us at the moment, Madela,

576
00:30:13,520 --> 00:30:17,560
Speaker 1: and talk to us about your vision for Peppi. You're

577
00:30:17,600 --> 00:30:20,200
Speaker 1: using forty million, it's going to be investing. You're already

578
00:30:20,200 --> 00:30:23,160
Speaker 1: pretty forceful there in Europe. You're coming to the United States.

579
00:30:23,480 --> 00:30:26,040
Speaker 1: What sort of offerings are you providing at the moment,

580
00:30:26,120 --> 00:30:30,720
Speaker 1: High Caroline. So, what Peppi does is we provide support

581
00:30:30,880 --> 00:30:34,400
Speaker 1: two employees during health stages in their life which are

582
00:30:34,600 --> 00:30:37,320
Speaker 1: very disruptive and can be disruptive to them both at

583
00:30:37,360 --> 00:30:41,040
Speaker 1: home and at work. So you talked about menopause. That's

584
00:30:41,040 --> 00:30:42,880
Speaker 1: certainly a big product of ours and what we will

585
00:30:42,880 --> 00:30:45,800
Speaker 1: be launching with in the US. But we've offered we've

586
00:30:45,840 --> 00:30:49,840
Speaker 1: also launched other services around episodes that we've seen in

587
00:30:49,880 --> 00:30:53,280
Speaker 1: the market, things like going through a fertility journey, becoming

588
00:30:53,280 --> 00:30:56,960
Speaker 1: a parent, and then we've also launched our mental health service,

589
00:30:57,080 --> 00:30:59,640
Speaker 1: the first of its kind as an employee benefit and

590
00:30:59,680 --> 00:31:02,840
Speaker 1: also women's health service, and we'll be bringing some of

591
00:31:02,880 --> 00:31:05,320
Speaker 1: that to the US as well. So you already got

592
00:31:05,360 --> 00:31:08,680
Speaker 1: clients Accenture, Adobe, Canada, Life, Disney to name but a few.

593
00:31:08,680 --> 00:31:10,960
Speaker 1: What's the growth are you seeing, particularly as we worry

594
00:31:10,960 --> 00:31:13,719
Speaker 1: about a downturn in the economy, we think about perhaps

595
00:31:13,760 --> 00:31:16,720
Speaker 1: companies putting back on benefits. What do you say to

596
00:31:16,800 --> 00:31:19,360
Speaker 1: that sort of narrative. So we've seen as a business,

597
00:31:19,360 --> 00:31:21,840
Speaker 1: like you said in in Europe, we've grown almost four

598
00:31:22,080 --> 00:31:24,600
Speaker 1: x UM this year, and what we are really seeing

599
00:31:24,680 --> 00:31:31,240
Speaker 1: is companies taking much more seriously the reasons that keep people, um,

600
00:31:31,280 --> 00:31:34,280
Speaker 1: maybe out of the workplace, absent from in the workplace,

601
00:31:34,440 --> 00:31:38,400
Speaker 1: or less you know, causing present heer is um as well. UM. Really,

602
00:31:38,440 --> 00:31:42,440
Speaker 1: when you look at these factors such as fertility, becoming parents,

603
00:31:42,560 --> 00:31:45,400
Speaker 1: menopause and so on, or maybe living with a long

604
00:31:45,520 --> 00:31:49,440
Speaker 1: term gynecological condition, the business case kind of speaks to itself.

605
00:31:49,520 --> 00:31:52,400
Speaker 1: None of these are niche things. So, for example, in

606
00:31:52,440 --> 00:31:56,520
Speaker 1: the US of the u S workforce are women aged

607
00:31:56,560 --> 00:31:59,280
Speaker 1: over forty five, typically the age of which you would

608
00:31:59,320 --> 00:32:03,840
Speaker 1: expect to see menopauseal or perimenopause or symptoms appear with them.

609
00:32:03,880 --> 00:32:06,000
Speaker 1: So none of these are niche. They're all hiding in

610
00:32:06,120 --> 00:32:09,720
Speaker 1: plain sight in any organization of any size, and that

611
00:32:09,800 --> 00:32:13,200
Speaker 1: makes the business case for itself. Talk to us just basically,

612
00:32:13,240 --> 00:32:16,440
Speaker 1: how your tech company in and of itself then, because

613
00:32:16,880 --> 00:32:19,800
Speaker 1: you know, we think about treatment. We were just hearing

614
00:32:19,840 --> 00:32:22,280
Speaker 1: from A sixty and Z talking about how people perhaps

615
00:32:22,280 --> 00:32:25,239
Speaker 1: are returning to wanting to have face to face in

616
00:32:25,400 --> 00:32:28,800
Speaker 1: person meetings with some of their clinicians. How are you

617
00:32:28,840 --> 00:32:31,880
Speaker 1: seeing that you're solving a problem that wasn't solved before.

618
00:32:32,360 --> 00:32:35,840
Speaker 1: So what we realized with Peppie is that, yes, traditional

619
00:32:35,840 --> 00:32:39,600
Speaker 1: healthcare does a phenomenal job at the point at which

620
00:32:39,680 --> 00:32:43,040
Speaker 1: you are walking into a doctor's office, be that virtual

621
00:32:43,320 --> 00:32:45,280
Speaker 1: or physical. And as many that were saying, you know,

622
00:32:45,280 --> 00:32:48,160
Speaker 1: people are wanting to return back to physical and that's

623
00:32:48,200 --> 00:32:52,560
Speaker 1: necessary in so many cases, whether that's the examinations, diagnostics, etcetera.

624
00:32:53,200 --> 00:32:56,400
Speaker 1: But upstream of that, there is a huge gap, and

625
00:32:56,480 --> 00:32:59,000
Speaker 1: that is a universal gap, regardless of what health care

626
00:32:59,040 --> 00:33:01,880
Speaker 1: system you're in. So think about you know, when you

627
00:33:02,000 --> 00:33:06,240
Speaker 1: first out have concerns, questions, niggles about your health, there's

628
00:33:06,280 --> 00:33:09,520
Speaker 1: often weeks, months, sometimes even years before you think that's

629
00:33:09,600 --> 00:33:13,640
Speaker 1: concern is big enough that it warrants the effort, the time,

630
00:33:13,720 --> 00:33:16,800
Speaker 1: the inconvenience of going to see your doctor about it.

631
00:33:17,080 --> 00:33:19,400
Speaker 1: That's really the gap that PEPPI is filling. So what

632
00:33:19,440 --> 00:33:24,040
Speaker 1: we do is we provide access to expert support. So

633
00:33:24,120 --> 00:33:27,720
Speaker 1: typically nurse lead, nurse practitioner lead, but they are experts

634
00:33:27,760 --> 00:33:32,840
Speaker 1: in these areas. So whether that's menopause, their women's health specialists,

635
00:33:32,880 --> 00:33:36,760
Speaker 1: whether that's fertility nurses, whether that's midwives for baby. What

636
00:33:36,800 --> 00:33:39,360
Speaker 1: we can do with Peppy and what the tech enables

637
00:33:39,480 --> 00:33:42,200
Speaker 1: is for you to communicate with those specialists in the

638
00:33:42,240 --> 00:33:45,800
Speaker 1: way that suits you best. And that could be a

639
00:33:45,880 --> 00:33:48,520
Speaker 1: video console, but there are so many other options. You

640
00:33:48,560 --> 00:33:51,480
Speaker 1: can chat to them, you can join virtual events. We

641
00:33:51,600 --> 00:33:53,920
Speaker 1: have of our own content team that works with our

642
00:33:53,920 --> 00:33:57,160
Speaker 1: clinicians to put out written video audio content. So you

643
00:33:57,160 --> 00:33:59,960
Speaker 1: can imagine the barrier to actually accessing help with Pepper's

644
00:34:00,080 --> 00:34:02,400
Speaker 1: really low, and that's what the tech is enabling. It

645
00:34:02,400 --> 00:34:04,520
Speaker 1: can be a simple message sent off when you're waiting

646
00:34:04,600 --> 00:34:06,479
Speaker 1: at the bus stop, or you know, just because when

647
00:34:06,520 --> 00:34:09,200
Speaker 1: you think of something at BED you browsed through some content,

648
00:34:09,880 --> 00:34:13,880
Speaker 1: but always with access to real health care professionals on

649
00:34:13,920 --> 00:34:17,560
Speaker 1: the other end. How hard is it moving from European

650
00:34:17,640 --> 00:34:23,160
Speaker 1: healthcare provision to US healthcare provision navigating a different regulatory landscape?

651
00:34:23,280 --> 00:34:26,000
Speaker 1: How do you envisage that? Yeah, so the regulatory roundscape

652
00:34:26,080 --> 00:34:29,000
Speaker 1: is a you know it's certainly something that needs navigating.

653
00:34:29,040 --> 00:34:31,800
Speaker 1: We are now able to offer the Peppy service in

654
00:34:31,840 --> 00:34:35,280
Speaker 1: all fifties states, which we're really proud of for our clients,

655
00:34:35,280 --> 00:34:37,239
Speaker 1: and that's important for our clients. As you mentioned that

656
00:34:37,360 --> 00:34:42,120
Speaker 1: some of the leading enterprise as globally UM. That is

657
00:34:42,120 --> 00:34:44,319
Speaker 1: one difference, but like I said, the really the need

658
00:34:44,400 --> 00:34:48,279
Speaker 1: that Peppy's addressing sort of independent of what the health

659
00:34:48,280 --> 00:34:51,560
Speaker 1: care system is. Recognizing that the US health care system

660
00:34:51,560 --> 00:34:53,880
Speaker 1: and I lived in the States for a while, UM

661
00:34:54,120 --> 00:34:57,000
Speaker 1: is very different to what a lot of what we've

662
00:34:57,040 --> 00:35:00,480
Speaker 1: seen in the UK be that NHS private medical insurance.

663
00:35:00,920 --> 00:35:06,080
Speaker 1: Like I said, Peppy's designed to complement and supplement traditional healthcare.

664
00:35:06,400 --> 00:35:09,040
Speaker 1: We don't overlap, we don't replicate the services that you

665
00:35:09,040 --> 00:35:11,960
Speaker 1: would get from your doctor, from your specialist or a

666
00:35:12,000 --> 00:35:15,880
Speaker 1: primary care physician. Really, what we're about is helping you

667
00:35:15,920 --> 00:35:18,799
Speaker 1: in that time where you maybe don't need a doctor yet,

668
00:35:19,560 --> 00:35:21,799
Speaker 1: or nudging you to go and seek that care if

669
00:35:21,800 --> 00:35:24,520
Speaker 1: you're may be hesitant or not sure or you're you know,

670
00:35:24,920 --> 00:35:27,480
Speaker 1: our practitioners think that there's something to be concerned about,

671
00:35:27,680 --> 00:35:29,480
Speaker 1: and also being there for you when you come out

672
00:35:29,520 --> 00:35:32,600
Speaker 1: of that medical care, be surprised you're able to raise

673
00:35:32,640 --> 00:35:35,200
Speaker 1: this money in this environment or as health care fantech

674
00:35:35,320 --> 00:35:37,960
Speaker 1: just in its own niche At the moment, I'm delighted,

675
00:35:38,200 --> 00:35:41,600
Speaker 1: I mean, especially to have the you know, continued support

676
00:35:41,640 --> 00:35:43,480
Speaker 1: of our investors, some of whom have been with us

677
00:35:43,480 --> 00:35:46,000
Speaker 1: for a number of years now and Albion who've known

678
00:35:46,040 --> 00:35:47,800
Speaker 1: us for a long time, as well as some of

679
00:35:47,840 --> 00:35:51,000
Speaker 1: the others that you mentioned. Look, I mean we raised

680
00:35:51,000 --> 00:35:53,440
Speaker 1: this round in a context where the business was growing

681
00:35:53,560 --> 00:35:57,880
Speaker 1: incredibly well. We grew a tex one, we grew almost

682
00:35:57,880 --> 00:36:01,840
Speaker 1: four x in twenty two. Yes, the market is more difficult.

683
00:36:02,280 --> 00:36:05,360
Speaker 1: I think there is much greater scrutiny of our financial

684
00:36:06,080 --> 00:36:10,440
Speaker 1: financial performance you're and so on, But underlying, you know,

685
00:36:10,480 --> 00:36:13,239
Speaker 1: the numbers really did speak for themselves. Great has some

686
00:36:13,320 --> 00:36:16,200
Speaker 1: time with the peppy co founder of course, doctor or

687
00:36:16,200 --> 00:36:19,359
Speaker 1: PhD as we called Chicoli Jolla poor a stay well

688
00:36:19,360 --> 00:36:21,799
Speaker 1: thanks to staying up late over in the UK as well.

689
00:36:21,840 --> 00:36:33,759
Speaker 1: We appreciate it going viral. It is glass doors Analysts

690
00:36:33,760 --> 00:36:36,480
Speaker 1: are the best places to work. This year, forty one

691
00:36:36,880 --> 00:36:41,160
Speaker 1: of the top one companies were still surprise, but some

692
00:36:41,280 --> 00:36:45,319
Speaker 1: big names matter. Apple Zilo not in the top one

693
00:36:45,360 --> 00:36:47,600
Speaker 1: hundred anymore. You have to go back to two thousand nine,

694
00:36:48,239 --> 00:36:51,000
Speaker 1: the last time when records began for Apple to not

695
00:36:51,040 --> 00:36:55,319
Speaker 1: be in the top one meta two thousand eleven, layoffs,

696
00:36:55,600 --> 00:37:00,360
Speaker 1: changing times, changing names, Mark Zuckerberg, under pressure. It's a

697
00:37:00,400 --> 00:37:02,680
Speaker 1: different it's a different companies the one you and I've

698
00:37:02,719 --> 00:37:04,839
Speaker 1: been covering over the last decade or so. And what's

699
00:37:04,880 --> 00:37:07,040
Speaker 1: interesting the meta though, is that they haven't changed their

700
00:37:07,120 --> 00:37:09,680
Speaker 1: work from home policy, and some would say, particularly the

701
00:37:09,760 --> 00:37:12,239
Speaker 1: CEO of glass Door, was calling out that actually, note

702
00:37:12,280 --> 00:37:15,600
Speaker 1: of the winners are the ones that are more flexible

703
00:37:15,640 --> 00:37:18,200
Speaker 1: in their working approach, still giving the good benefits. So

704
00:37:18,239 --> 00:37:21,200
Speaker 1: it's not that that's hitting metal. Although Apple did change

705
00:37:21,200 --> 00:37:23,319
Speaker 1: its work from home approach, they're wanting people to come

706
00:37:23,320 --> 00:37:25,440
Speaker 1: back three days in a week. I wonder if that

707
00:37:25,520 --> 00:37:28,080
Speaker 1: sort of in some way affecting. And then I wonder

708
00:37:28,120 --> 00:37:30,319
Speaker 1: what game site is really of what is it? What

709
00:37:30,360 --> 00:37:33,320
Speaker 1: are they doing? The making groundbreaking? And it's interesting in video.

710
00:37:33,640 --> 00:37:35,920
Speaker 1: And then this is cat shout out my California people

711
00:37:36,280 --> 00:37:39,840
Speaker 1: in an out Burger in the top ten places to work.

712
00:37:39,920 --> 00:37:42,640
Speaker 1: Not a tech company, but interesting to see nonetheless, and

713
00:37:42,719 --> 00:37:44,919
Speaker 1: Google a store work, I guess, and I would say

714
00:37:44,920 --> 00:37:48,080
Speaker 1: in an out burger important because female CEO and actually,

715
00:37:48,120 --> 00:37:50,400
Speaker 1: when you go to the list of the top CEOs

716
00:37:50,400 --> 00:37:53,560
Speaker 1: on class door, you have to get till number twenty

717
00:37:53,920 --> 00:37:56,759
Speaker 1: do you actually get the CEO is deemed one of

718
00:37:56,760 --> 00:37:59,680
Speaker 1: the best and a female. So that's interesting that some

719
00:37:59,719 --> 00:38:02,439
Speaker 1: of these companies are still coming out top but they're

720
00:38:02,480 --> 00:38:04,440
Speaker 1: female leadership is singing. There's a long way to go

721
00:38:04,480 --> 00:38:07,120
Speaker 1: in that respect. You great chat, but that does it

722
00:38:07,160 --> 00:38:09,080
Speaker 1: for this addition of bloom Meg Technology, You're going to

723
00:38:09,120 --> 00:38:12,000
Speaker 1: stay tuned for Tomorrow Thursday. We have Kate Ventures Managing

724
00:38:12,000 --> 00:38:16,320
Speaker 1: director Monique Woodard. Don't forget check out your podcast apples, Spotify,

725
00:38:16,400 --> 00:38:19,920
Speaker 1: our Heart, wherever you get your podcast has been fantastic.

726
00:38:19,960 --> 00:38:21,880
Speaker 1: A few days in New York. This is Boomberg