WEBVTT - AI Safety and Tech Earnings

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<v Speaker 1>From Marhard where Innovation, money and power Collie in Silicon Valley, NBN.

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<v Speaker 1>This is Bloomberg Technology with Caroline Hyde and Ed Ludlow.

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<v Speaker 2>I'm Caroline Hyder a Bloomberg's World headquarters in New York, and.

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<v Speaker 3>I'm Ed Ludlow in San Francisco. This is Bloomberg Technology.

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<v Speaker 2>Coming up ed full earnings coverage ahead was rake down

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<v Speaker 2>the results from Qualcom. From Etsy, we push her head

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<v Speaker 2>to the all important Apple out after the bell.

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<v Speaker 3>Plus we dive deep into the world of artificial intelligence

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<v Speaker 3>and talk AI safety, as well as investing with Soundbenture's

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<v Speaker 3>general partner Ashton Kutcher.

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<v Speaker 2>Plus Ali Baba weighing a USIPO for its online commerce business.

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<v Speaker 2>Could that be valued at thirty nine billion dollars? Will

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<v Speaker 2>bring you the latest details this hour, but of course

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<v Speaker 2>we've got so much to discuss, particularly with the world

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<v Speaker 2>of executives coming up right now. And we welcome bron

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<v Speaker 2>Chesky of Airbnb and I'm very peace to say Blue Megs,

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<v Speaker 2>Emily Chang. Emily do take away the conversation.

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<v Speaker 4>Hey there, Brian, thank you so much for joining us.

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<v Speaker 4>Obviously you've got your huge summer release out now, lots

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<v Speaker 4>of different new features and categories. The one I want

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<v Speaker 4>to focus on is rooms, which, as I know, the

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<v Speaker 4>headline here feels a little like Groundhog Day because rooms

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<v Speaker 4>were where you started.

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<v Speaker 5>What do you want?

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<v Speaker 4>What's the real pain point you're trying to solve with

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<v Speaker 4>these new features.

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<v Speaker 6>Well, the primary pain point is that people, once again,

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<v Speaker 6>just like when we started Airbnb in two thousand and eight,

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<v Speaker 6>want an affordable way to travel, and the most one

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<v Speaker 6>of the most affordable ways to travel is to stay

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<v Speaker 6>in a room in someone's house. That's how airbanbe started.

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<v Speaker 6>The average room on Airbnb is only sixty seven dollars

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<v Speaker 6>a night, but we talked to a lot of people

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<v Speaker 6>and a lot of people said they weren't comfortable staying

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<v Speaker 6>in a room in someone's house. Who's the other person

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<v Speaker 6>I'm going to be staying with. So that's why we

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<v Speaker 6>created this new thing called the host Passport. The host

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<v Speaker 6>Passport is basically this really robust profile We verify the

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<v Speaker 6>identity of every host and get you can get to

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<v Speaker 6>know the host before you book. We also have really

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<v Speaker 6>cool privacy features, like we show if the bathroom is

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<v Speaker 6>share a private, and if there's going to be a

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<v Speaker 6>lock in the bedroom door, So this is I think

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<v Speaker 6>what we're trying to do. Additionally, we think this is

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<v Speaker 6>a really good way to experience a local culture. If

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<v Speaker 6>you want to go to a new city for the

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<v Speaker 6>first time, this is a great way because you get

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<v Speaker 6>to step in the shoes as someone who lives there.

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<v Speaker 4>You of course invented Airbnb in the middle of the

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<v Speaker 4>last recession, and here we are on the verge of

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<v Speaker 4>another one, which you know, obviously not good for consumers,

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<v Speaker 4>potentially not good for travel. How much is the economic

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<v Speaker 4>climate impacting.

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<v Speaker 2>This new release.

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<v Speaker 6>It's absolutely impacting what people care about. A year ago,

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<v Speaker 6>what people cared about primarily was flexibility, and so we

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<v Speaker 6>launch air and B categories which allow you to inspire

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<v Speaker 6>you to be able to travel beyond any place you

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<v Speaker 6>can think. Now, I think the equations now people really

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<v Speaker 6>really are focused on affordability more than the even we're

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<v Speaker 6>a year ago.

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<v Speaker 5>That's our roots.

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<v Speaker 6>I mean, our roots are started in affordable travel, being

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<v Speaker 6>on the share space, So this is what we're really

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<v Speaker 6>focused on. We're also just really trying to focus on

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<v Speaker 6>providing a great service. So we've been really obsessed over

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<v Speaker 6>the feedback we're getting from customers. We've been pouring over

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<v Speaker 6>millions of pieces of feedback from customer service. We've been

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<v Speaker 6>talking to guests and hosts, and based on that, we've

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<v Speaker 6>made over fifty core improvements to the service based on

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<v Speaker 6>feedback directly from our community. And so hopefully if people

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<v Speaker 6>use earing the summer, they'll find it's more affordable and

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<v Speaker 6>even better level service.

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<v Speaker 4>You're seeing that with simpler, more affordable monthly stays, you've

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<v Speaker 4>got priority customer service, you've got more transparent and upfront pricing.

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<v Speaker 4>I know you're using AI and machine learning to make

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<v Speaker 4>a lot of this work on the back end. How

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<v Speaker 4>are you thinking about the AI hype cycle and is

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<v Speaker 4>it going to be as big as everyone says it's

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<v Speaker 4>going to be, and what that means for Airbnb.

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<v Speaker 6>It's probably going to be a lot bigger than everyone

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<v Speaker 6>and says it's going to be. In every few years,

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<v Speaker 6>there's new AI hypes, there's new technology hype cycle. But

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<v Speaker 6>the reason why is because there have been technologies that

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<v Speaker 6>have changed the world and change how we all live,

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<v Speaker 6>the Internet, the personal computer revolution, Mobile AI will certainly

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<v Speaker 6>be as big as all of them, maybe bigger than

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<v Speaker 6>all of them combined. And I think this is just

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<v Speaker 6>the very beginning. It's going to play out over a

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<v Speaker 6>long period of time. It's happening quickly. It's going to

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<v Speaker 6>change I think how people travel, it's going to change

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<v Speaker 6>how we learn, It's going to change how we get

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<v Speaker 6>care for. It's going to change a lot of things

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<v Speaker 6>in society. What we're trying to do right now is

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<v Speaker 6>number One, we want to use the tools to make

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<v Speaker 6>everyone to air and be more productive. I don't think

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<v Speaker 6>this will replace people initially as much as will augment

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<v Speaker 6>people and make them more productive. Two, I think it

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<v Speaker 6>can help customer service. We have a very complated customer

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<v Speaker 6>service problem. We have millions of people living together every

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<v Speaker 6>night from different countries. This will help augment our agents

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<v Speaker 6>provide better service faster. And finally, AI can be like

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<v Speaker 6>the ultimate matchmaker. Imagine if your app being almost like

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<v Speaker 6>the ultimate concierge and they can match you whatever you're

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<v Speaker 6>looking for. These are the near term things. Beyond that,

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<v Speaker 6>it's really up to everyone's imagination. What you could do,

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<v Speaker 6>kind of like the computer forty years ago.

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<v Speaker 2>Call just about matching your talent for this new AI

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<v Speaker 2>door and you say you want to be at the vanguard.

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<v Speaker 2>Have you got the right people within the business to

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<v Speaker 2>be building it for you.

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<v Speaker 6>I absolutely think so. I mean, of course we're still hiring.

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<v Speaker 6>If people are really great at this area, we certainly

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<v Speaker 6>want to talk to them, But we have one of

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<v Speaker 6>the we have a great team. I mean, we have

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<v Speaker 6>people that have been working in this area for quite

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<v Speaker 6>a long period of time. And the other thing I

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<v Speaker 6>just want to say is there's many different layers the

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<v Speaker 6>stack of AI. You have Open Ai or Google that

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<v Speaker 6>are working on the base models. And we're not going

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<v Speaker 6>to be a research lab building base models. That's like

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<v Speaker 6>building bridges infrastructure. We'll build the cars on the bridge,

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<v Speaker 6>so to speak, the applications. So what we're going to

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<v Speaker 6>be really good at, I hope is building personalized layers

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<v Speaker 6>on top of AI. We can tune the models and

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<v Speaker 6>ultimately I want airbe to feel like the kind of

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<v Speaker 6>company that it knows you, it understands you, it learns

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<v Speaker 6>about your preferences, and I think that's what we can

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<v Speaker 6>use AI for.

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<v Speaker 2>What's interesting, We've got one of early back is coming on.

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<v Speaker 2>Who's all about AI today? Ashton Kutch Show.

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<v Speaker 6>Oh yeah.

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<v Speaker 2>Also, we're thinking about how it can lead to more productivity,

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<v Speaker 2>more profitability. Go back to your announcements that you're making

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<v Speaker 2>in this economic environment, we are thinking about how you

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<v Speaker 2>can be more profitable. You're saying how you can be

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<v Speaker 2>more affordable, reducing fees. Does that ultimately impact your bottom line?

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<v Speaker 6>In the longer term, I think the AI is going

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<v Speaker 6>to affect everything. AI is going to make people significantly

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<v Speaker 6>more productive. AI is going to be able to also

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<v Speaker 6>allow things to happen that you could never have done

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<v Speaker 6>without that technology. Again, it's a little hard to know

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<v Speaker 6>exactly what it's going to affect. I think it's going

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<v Speaker 6>to reduce the cost of our service. I think it's

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<v Speaker 6>going to make the product significantly more efficient. It's going

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<v Speaker 6>to allow engineers to be at least thirty percent more productive,

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<v Speaker 6>eventually twice as productive. So that is the equivalent of

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<v Speaker 6>reduction at fists costs or a lot more throughput. So

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<v Speaker 6>those are the near term things we'll have. But beyond that,

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<v Speaker 6>I think we're just getting started. I think we're at

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<v Speaker 6>the beginning of something that we're probably many years from now,

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<v Speaker 6>going to look back in this period of time and

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<v Speaker 6>remember alive at this period and being in a seminal

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<v Speaker 6>moment in history. That's what I think we're going to

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<v Speaker 6>think about this period in Ai.

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<v Speaker 4>Brian, You've been hosting guests in your own home for

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<v Speaker 4>the first time and act, yeah, so excited. I got

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<v Speaker 4>to take a peek. You're going to see that on

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<v Speaker 4>my new show. I don't want to give too much away,

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<v Speaker 4>but there were some Cheskey's chips involved. What are you

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<v Speaker 4>learning from, you know, your own experience hosting, and how

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<v Speaker 4>you want to integrate that back into Airbnb. You know,

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<v Speaker 4>given you know what you've been hearing from hosts and guests,

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<v Speaker 4>whether it comes to fees or affordability or privacy.

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<v Speaker 6>I mean, you know, I always feel like companies that

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<v Speaker 6>create great products create them for themselves because then you

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<v Speaker 6>have total empathy. You understand what the data means. And

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<v Speaker 6>I wanted to be a host again now. I was

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<v Speaker 6>one of the first hosts of my roommate fifteen years

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<v Speaker 6>ago in Airbnb, and as I started hosting again, I

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<v Speaker 6>have people my house with me. They stay in the

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<v Speaker 6>guest room, I'm there with them, and it really helped

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<v Speaker 6>me understand Number one, trust and safety is paramount. You know,

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<v Speaker 6>now verify the identity of every guest booking around the

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<v Speaker 6>world on Airbnb. But it also helped me understand that

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<v Speaker 6>we need to make hosting easy, and as we make

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<v Speaker 6>the product easier for hosts, it will become a better

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<v Speaker 6>experience for guests. I'll give you one example. A lot

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<v Speaker 6>of people have complained about fees on Airbnb and host

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<v Speaker 6>charging cleaning fees.

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<v Speaker 5>Well, as I started hosting.

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<v Speaker 6>I noticed that it was kind of complicated to figure

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<v Speaker 6>out how much to charge, and we weren't doing a

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<v Speaker 6>great job coaching hosts about how to provide a great

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<v Speaker 6>value to guest. And as you use the product, you

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<v Speaker 6>start to see these things and posting this actually helped

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<v Speaker 6>lead some of the innovations that we announced yesterday.

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<v Speaker 2>Brad Chesky, we thank you of Airbnb and of course

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<v Speaker 2>our own Emily Chang.

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<v Speaker 3>Let's go to AI. Earlier this week at Milky and

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<v Speaker 3>actor and entrepreneur Ashton Kutcher said that companies not investing

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<v Speaker 3>in AI will go out of business. But does that

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<v Speaker 3>mean that all Way I startups are equally good investments.

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<v Speaker 3>Here's what Cosla Ventures found of in our Cosla told

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<v Speaker 3>us yesterday.

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<v Speaker 5>Have a listen.

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<v Speaker 7>I would say there's many more bad AI startups than

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<v Speaker 7>good startups, and it's very hard to differentiate if you're

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<v Speaker 7>not experienced with AI, so I do think lots of

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<v Speaker 7>bad investments will be made, but overall more money will

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<v Speaker 7>be made than lost, even if ninety percent of the

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<v Speaker 7>startups fail, which they.

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<v Speaker 3>Will sound ventures Ashton Kutscher joins us now for more

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<v Speaker 3>and on his firm recently closing a day I fund

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<v Speaker 3>oversubscribed about two hundred and forty million dollars. It's interesting

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<v Speaker 3>action because you're really focusing on foundational models. That's what

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<v Speaker 3>jumped out at me from the announcement. That's where a

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<v Speaker 3>lot of people are putting their activity. Why so focused there?

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<v Speaker 5>So we have multiple vehicles.

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<v Speaker 8>Our core vehicle will be focusing on the application layer,

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<v Speaker 8>and that's where there's going to be an absolute boom

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<v Speaker 8>of innovation from business models that are being unleashed that

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<v Speaker 8>were never possible before.

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<v Speaker 5>But our core.

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<v Speaker 8>AI fund that we basically we saw in November when

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<v Speaker 8>GPT was launched. We've been investing in AI for the

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<v Speaker 8>last seven years, several companies that are narrow AI companies

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<v Speaker 8>that are application based. But when we saw GBT be launched,

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<v Speaker 8>we realized that this was an absolute breakthrough and these

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<v Speaker 8>foundational models are going to be the underpinning of the

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<v Speaker 8>next absolute transformation for technology. When we first started investing

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<v Speaker 8>in two thousand and eight two thousand and nine, it

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<v Speaker 8>was right around the mobile revolution, and companies that didn't

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<v Speaker 8>didn't embrace that in a forward basis really struggled. But

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<v Speaker 8>it also unleashed extraordinary new business models that never could

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<v Speaker 8>have existed before. When you had a GPS in your pocket,

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<v Speaker 8>when you had a camera in your pocket, when you

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<v Speaker 8>had a radio in your pocket, when you had all

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<v Speaker 8>of that capability in your pocket, unleashed these business models

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<v Speaker 8>that were never possible before. And what's about to happen

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<v Speaker 8>because of these large transform of models that have so

0:11:09.760 --> 0:11:13.920
<v Speaker 8>such extraordinary output, We're going to see innovation of business

0:11:13.920 --> 0:11:17.040
<v Speaker 8>models that were never capable before that are that are

0:11:17.040 --> 0:11:21.520
<v Speaker 8>about to be launched. Yes, and and and and that's

0:11:21.800 --> 0:11:24.680
<v Speaker 8>that that is just going to be an absolute And

0:11:24.800 --> 0:11:27.440
<v Speaker 8>what I said at Milken was that if you were

0:11:27.440 --> 0:11:30.600
<v Speaker 8>a commerce company ten years ago and you didn't embrace

0:11:30.640 --> 0:11:33.680
<v Speaker 8>e commerce, you're probably not in business anymore.

0:11:34.200 --> 0:11:35.400
<v Speaker 5>And I firmly.

0:11:35.080 --> 0:11:37.599
<v Speaker 8>Believe that if you're a company today and you're not

0:11:37.880 --> 0:11:41.520
<v Speaker 8>embracing the changes that are taking place with AI, you're

0:11:41.559 --> 0:11:43.520
<v Speaker 8>going to be behind and you're going to have a

0:11:43.559 --> 0:11:45.520
<v Speaker 8>hard time catching up Ashton.

0:11:45.520 --> 0:11:47.520
<v Speaker 3>I'm really interested in how quickly you moved on this

0:11:47.559 --> 0:11:50.839
<v Speaker 3>thematic fund as well. Who are the LPs and how

0:11:50.880 --> 0:11:52.080
<v Speaker 3>quickly did it come together?

0:11:53.720 --> 0:11:53.840
<v Speaker 2>Uh?

0:11:54.200 --> 0:11:57.120
<v Speaker 8>We we pulled the fund together in about five weeks.

0:11:58.080 --> 0:11:58.360
<v Speaker 5>Wow.

0:11:59.120 --> 0:12:02.560
<v Speaker 8>And we have an extraordinary We have a base of

0:12:02.679 --> 0:12:05.679
<v Speaker 8>LPs that have been with us for years on end.

0:12:06.080 --> 0:12:08.080
<v Speaker 5>So we have about a billion dollars.

0:12:07.800 --> 0:12:11.000
<v Speaker 8>Under management at Sound Ventures and have been working with

0:12:11.120 --> 0:12:13.880
<v Speaker 8>some really extraordinary LPs over the years, and so we

0:12:13.920 --> 0:12:16.720
<v Speaker 8>reached out to them and said, hey, this is happening now.

0:12:17.640 --> 0:12:19.600
<v Speaker 8>These large transformer models a lot of them. You look

0:12:19.640 --> 0:12:22.760
<v Speaker 8>at open Ai, you look at Anthropic, you look at Stability.

0:12:23.040 --> 0:12:25.520
<v Speaker 8>They didn't they didn't start yesterday. They've been around for

0:12:25.520 --> 0:12:27.160
<v Speaker 8>a while. They've been building this. You look at what

0:12:27.240 --> 0:12:30.360
<v Speaker 8>Google has been doing with deep Mind and Barred and

0:12:30.880 --> 0:12:33.520
<v Speaker 8>those those companies have been in development for a while.

0:12:34.000 --> 0:12:36.760
<v Speaker 8>What we've just seen is an extraordinary breakthrough where you

0:12:36.800 --> 0:12:39.920
<v Speaker 8>can take a massive corpus of data and be able

0:12:39.920 --> 0:12:44.280
<v Speaker 8>to index through it and again create statistical, highly likely

0:12:44.320 --> 0:12:49.240
<v Speaker 8>outputs that are beneficial to humans. And these utilities are

0:12:49.320 --> 0:12:54.560
<v Speaker 8>really really valuable. It's it is going to exponentially change.

0:12:54.600 --> 0:12:58.720
<v Speaker 8>I agree with everything that Brian saying before I came on.

0:12:59.240 --> 0:13:02.480
<v Speaker 8>This is going to change business forever, and we need

0:13:02.520 --> 0:13:04.720
<v Speaker 8>to embrace it because right now this AI can be

0:13:04.880 --> 0:13:07.440
<v Speaker 8>used to improve humanity.

0:13:08.880 --> 0:13:09.679
<v Speaker 5>I actually look at.

0:13:09.640 --> 0:13:12.520
<v Speaker 8>It as an equity and an inclusion play, where you know,

0:13:13.120 --> 0:13:18.080
<v Speaker 8>as we commoditize things like legal advice, in medical advice

0:13:18.160 --> 0:13:22.880
<v Speaker 8>and education and personalize those things down to the individual consumer,

0:13:24.120 --> 0:13:26.320
<v Speaker 8>all of a sudden, people that can't get a doctor

0:13:26.360 --> 0:13:28.040
<v Speaker 8>on the phone, or can't get a lawyer on the phone,

0:13:28.080 --> 0:13:30.679
<v Speaker 8>or can't get a pediatrician on the phone, they're going

0:13:30.720 --> 0:13:34.120
<v Speaker 8>to have access to these services and access to these

0:13:34.120 --> 0:13:37.199
<v Speaker 8>services at an affordable price. Yeah, and I think that's

0:13:37.240 --> 0:13:40.400
<v Speaker 8>extraordinary for humanity and extraordinary for all of us.

0:13:40.520 --> 0:13:43.880
<v Speaker 2>It augments you mentioned education there, and I think about

0:13:43.960 --> 0:13:48.679
<v Speaker 2>your exited portfolio company, cheg, which lost about half of

0:13:48.720 --> 0:13:51.040
<v Speaker 2>its market value over the course of the week because

0:13:51.320 --> 0:13:54.280
<v Speaker 2>already chat GPT is upending its own business model. What

0:13:54.320 --> 0:13:57.240
<v Speaker 2>are you doing with your current portfolio, Ashton, to see

0:13:57.280 --> 0:14:00.000
<v Speaker 2>around this corner to ensure that they are augmented rather

0:14:00.120 --> 0:14:01.559
<v Speaker 2>then completely disrupted.

0:14:02.360 --> 0:14:04.360
<v Speaker 8>So that was the very first thing that we did

0:14:04.400 --> 0:14:07.480
<v Speaker 8>before we even thought about putting a vehicle together to

0:14:07.559 --> 0:14:10.079
<v Speaker 8>invest in these things. Is we reached out to every

0:14:10.120 --> 0:14:12.240
<v Speaker 8>one of our portfolio companies and said, how are you

0:14:12.280 --> 0:14:13.520
<v Speaker 8>embracing this technology?

0:14:13.800 --> 0:14:15.760
<v Speaker 5>How are you implementing this technology?

0:14:16.120 --> 0:14:20.680
<v Speaker 8>And I think, I think maybe from my perspective, the

0:14:21.760 --> 0:14:24.800
<v Speaker 8>what happened with CHEG the other day is somewhat short sighted.

0:14:25.440 --> 0:14:28.960
<v Speaker 8>CHEG is implementing GPT four into their product they announced

0:14:29.040 --> 0:14:31.920
<v Speaker 8>check Mate, and and I think a lot of the

0:14:32.040 --> 0:14:34.640
<v Speaker 8>value is going to fall to the incumbents. So if

0:14:34.640 --> 0:14:37.840
<v Speaker 8>you have an extraordinary product, you have consumers and you're

0:14:37.880 --> 0:14:40.880
<v Speaker 8>implementing this, I think there, I think value is going

0:14:40.920 --> 0:14:45.560
<v Speaker 8>to fall to some of those incumbents, and as it should.

0:14:46.200 --> 0:14:49.160
<v Speaker 8>And don't dismiss the fact that a lot of these

0:14:49.160 --> 0:14:53.200
<v Speaker 8>companies have extraordinary data sets that are their own proprietary

0:14:53.280 --> 0:14:56.840
<v Speaker 8>data sets, and they'll tune their own models on top

0:14:56.960 --> 0:15:01.280
<v Speaker 8>of these large transformer models, which will will create unique

0:15:01.800 --> 0:15:03.400
<v Speaker 8>value propositions for consumers.

0:15:03.880 --> 0:15:06.280
<v Speaker 2>Actually, the word that keeps coming up Timing time again

0:15:06.640 --> 0:15:09.920
<v Speaker 2>is hype cycle and being able to see wood for

0:15:09.960 --> 0:15:12.720
<v Speaker 2>the trees, but also being able to understand that this

0:15:12.760 --> 0:15:16.440
<v Speaker 2>isn't kind of like crypto, where yes, there's still ultimate value.

0:15:16.440 --> 0:15:18.080
<v Speaker 2>We see it with the OG's at Bitcoin but we

0:15:18.120 --> 0:15:20.760
<v Speaker 2>have seen in up ending in valuations of NFTs an

0:15:20.800 --> 0:15:23.120
<v Speaker 2>area that you were playing heavily in. How do you

0:15:23.240 --> 0:15:25.640
<v Speaker 2>worry about some of the similarities there and how do

0:15:25.680 --> 0:15:28.440
<v Speaker 2>you ensure that the same mistakes aren't remade.

0:15:29.800 --> 0:15:32.040
<v Speaker 8>Well, I think the biggest mistake that was made in

0:15:32.160 --> 0:15:36.520
<v Speaker 8>crypto was is and still is, just the absolute lack

0:15:37.200 --> 0:15:41.080
<v Speaker 8>of clear cut regulations so people know what the rules

0:15:41.080 --> 0:15:43.560
<v Speaker 8>are that they should be playing by. I think that

0:15:43.640 --> 0:15:47.560
<v Speaker 8>there were a lot of companies that were assuming that

0:15:47.640 --> 0:15:50.960
<v Speaker 8>blockchain technology could be used for a lot of different

0:15:51.000 --> 0:15:53.600
<v Speaker 8>things that it really shouldn't have been used for. Look,

0:15:53.800 --> 0:15:58.320
<v Speaker 8>it's a public ledger database, so it's really that blockchain

0:15:58.400 --> 0:16:01.840
<v Speaker 8>is super valuable for any of transaction between two parties

0:16:01.840 --> 0:16:04.360
<v Speaker 8>that don't trust one another. So if two parties don't

0:16:04.360 --> 0:16:06.320
<v Speaker 8>trust one another, they want to transact, they want to

0:16:06.360 --> 0:16:09.560
<v Speaker 8>transact out in the open, so everybody could see what

0:16:09.600 --> 0:16:13.360
<v Speaker 8>that transaction is and have a consistent, constant, historic ledger

0:16:13.440 --> 0:16:17.400
<v Speaker 8>of those transactions. It's really valuable for that. I actually

0:16:17.440 --> 0:16:21.080
<v Speaker 8>think for artists that want to have enduring art out

0:16:21.120 --> 0:16:24.400
<v Speaker 8>in the market and be able to timestamp this new

0:16:24.480 --> 0:16:27.080
<v Speaker 8>piece of art as hey, I made this at this

0:16:27.160 --> 0:16:30.080
<v Speaker 8>date and everything else that comes after that is derivative

0:16:30.120 --> 0:16:34.600
<v Speaker 8>of that's it's extraordinarily valuable to have a public ledger

0:16:34.640 --> 0:16:38.440
<v Speaker 8>for that. I think some of the tokenization that was

0:16:38.440 --> 0:16:43.600
<v Speaker 8>taking place was really short sighted and pretty manipulative. But

0:16:43.680 --> 0:16:46.720
<v Speaker 8>the biggest problem was there was there's been no clear

0:16:46.760 --> 0:16:47.720
<v Speaker 8>regulation and there's.

0:16:47.560 --> 0:16:50.080
<v Speaker 2>No clear regulation for AI either. Is that needed?

0:16:51.160 --> 0:16:54.840
<v Speaker 8>Yes, it is needed, And I think that the most

0:16:54.840 --> 0:16:57.600
<v Speaker 8>promising thing because by the way, it's not just needed,

0:16:57.640 --> 0:16:59.880
<v Speaker 8>it's needed badly. And I think that we've seen some

0:17:00.400 --> 0:17:03.800
<v Speaker 8>extraordinarily intelligent people that have come out and it said

0:17:03.920 --> 0:17:06.800
<v Speaker 8>it's needed and we need to really focus on this.

0:17:07.200 --> 0:17:10.719
<v Speaker 8>I think the companies that are building this recognize that

0:17:11.040 --> 0:17:12.159
<v Speaker 8>regulation is needed.

0:17:12.520 --> 0:17:14.119
<v Speaker 5>And I think, and what.

0:17:14.000 --> 0:17:17.400
<v Speaker 8>I'm seeing is really promising is the founders of these companies,

0:17:17.440 --> 0:17:20.359
<v Speaker 8>the founders of Stability, the founders of Entropic, the founders

0:17:20.400 --> 0:17:25.040
<v Speaker 8>of open Ai, Google and otherwise are really forward thinking

0:17:25.200 --> 0:17:29.000
<v Speaker 8>about what regulation should be because I don't think anybody

0:17:29.000 --> 0:17:32.120
<v Speaker 8>wants this to be an unfettered market. And I think

0:17:32.119 --> 0:17:35.359
<v Speaker 8>there are data privacy implications. I think there are data

0:17:35.480 --> 0:17:39.120
<v Speaker 8>there's what is the value of the data? I think

0:17:39.160 --> 0:17:43.880
<v Speaker 8>there's misinformation issues. I work really heavily in eliminating child

0:17:43.920 --> 0:17:47.600
<v Speaker 8>sexual abuse material from the Internet. I think that's an

0:17:47.640 --> 0:17:51.360
<v Speaker 8>issue that needs to be considered. I think biases need

0:17:51.400 --> 0:17:54.960
<v Speaker 8>to be considered. I think that the traceability of this

0:17:55.080 --> 0:17:57.040
<v Speaker 8>data needs to be considered. All of these things need

0:17:57.080 --> 0:17:59.480
<v Speaker 8>to be considered. The good news is is that the

0:17:59.520 --> 0:18:03.159
<v Speaker 8>founders these companies and the people that are building this technology,

0:18:03.160 --> 0:18:07.199
<v Speaker 8>they're cognizant of it, they're proactive about it. They're considering

0:18:07.280 --> 0:18:10.160
<v Speaker 8>what needs to be done and what good regulation would

0:18:10.200 --> 0:18:14.040
<v Speaker 8>look like, because it's really hard if you don't understand

0:18:14.119 --> 0:18:17.359
<v Speaker 8>how this technology works to understand how to regulate it.

0:18:17.440 --> 0:18:18.879
<v Speaker 5>And the truth, there's a lot.

0:18:18.720 --> 0:18:23.439
<v Speaker 8>Of people that are fear mongering on general artificial intelligence,

0:18:23.480 --> 0:18:27.000
<v Speaker 8>and we're not there. We do not have general artificial

0:18:27.000 --> 0:18:33.639
<v Speaker 8>intelligence yet. It's closer, and we need to be really

0:18:33.680 --> 0:18:37.200
<v Speaker 8>considering what the implications of that are. But the benefits

0:18:37.240 --> 0:18:41.199
<v Speaker 8>to society for the utilization of this technology are going

0:18:41.240 --> 0:18:43.520
<v Speaker 8>to be extraordinary. And what we don't want to do

0:18:44.000 --> 0:18:45.720
<v Speaker 8>is have it regulated in a way that puts it

0:18:45.720 --> 0:18:49.040
<v Speaker 8>inside of a box in box and doesn't allow us

0:18:49.080 --> 0:18:53.720
<v Speaker 8>to continue to innovate, because this isn't just happening domestically.

0:18:54.560 --> 0:18:58.919
<v Speaker 8>This is happening all around the world, and the push

0:18:59.080 --> 0:19:03.600
<v Speaker 8>towards general order official intelligence is going to happen. If

0:19:03.640 --> 0:19:05.520
<v Speaker 8>not here, it's going to happen in China, it's going

0:19:05.520 --> 0:19:07.920
<v Speaker 8>to happen all over the world, and the implications of

0:19:07.960 --> 0:19:08.399
<v Speaker 8>it are.

0:19:08.240 --> 0:19:09.200
<v Speaker 5>Going to be massive.

0:19:09.760 --> 0:19:12.800
<v Speaker 8>So we don't amstring ourselves in a way where we

0:19:12.880 --> 0:19:17.160
<v Speaker 8>are now behind and we're underneath the rock of somebody

0:19:17.160 --> 0:19:20.880
<v Speaker 8>else's general AI. And so it needs to be done

0:19:20.920 --> 0:19:25.359
<v Speaker 8>in an intelligent, thoughtful, proactive way. And the good news

0:19:25.480 --> 0:19:28.240
<v Speaker 8>is that the founders of these companies recognize that, realize that,

0:19:28.359 --> 0:19:29.640
<v Speaker 8>and are being proactive about it.

0:19:29.720 --> 0:19:33.240
<v Speaker 2>Ashton Koscher, thanks for being proactive on the subject with us,

0:19:33.280 --> 0:19:36.359
<v Speaker 2>Sam Venture's co founder general partner. And we're going to

0:19:36.400 --> 0:19:38.919
<v Speaker 2>stick so much more with what's happening in the private markets,

0:19:38.920 --> 0:19:41.320
<v Speaker 2>also what's happening in the public market. It's Etsy earnings

0:19:41.359 --> 0:19:44.320
<v Speaker 2>out last night, first calling on revenue beating analyst estimates.

0:19:44.400 --> 0:19:47.959
<v Speaker 2>Joining us now ever Core II analyst Shwedder Kajuria she

0:19:48.000 --> 0:19:50.520
<v Speaker 2>has an outperform rating on the Etsy stop with one

0:19:50.560 --> 0:19:53.840
<v Speaker 2>hundred and twenty dollars price target. But ultimately we're just

0:19:53.840 --> 0:19:56.640
<v Speaker 2>talking about how AI is going to OpenD everything, and

0:19:56.720 --> 0:19:58.840
<v Speaker 2>if you weren't an e commerce player back in the

0:19:58.920 --> 0:20:01.359
<v Speaker 2>day that managed to pivot. How are you seeing Etsy

0:20:01.480 --> 0:20:04.040
<v Speaker 2>navigate these current macro conditions?

0:20:04.440 --> 0:20:07.399
<v Speaker 9>A Hi, thanks for having me well in terms of

0:20:07.440 --> 0:20:10.920
<v Speaker 9>the In terms of Etsy platform, I first of all,

0:20:11.160 --> 0:20:14.040
<v Speaker 9>just lay the land. We do have an outperform rating,

0:20:14.080 --> 0:20:16.840
<v Speaker 9>but we also had a tactical underperform rating into the

0:20:16.880 --> 0:20:19.840
<v Speaker 9>print into the year, which we don't have right now.

0:20:19.720 --> 0:20:22.480
<v Speaker 10>Because we remain near from cautious on et Sea.

0:20:22.800 --> 0:20:25.520
<v Speaker 9>And the biggest reason really is that the macro environment

0:20:25.760 --> 0:20:28.639
<v Speaker 9>is very unclear. There is a lot of volatility and

0:20:28.760 --> 0:20:33.680
<v Speaker 9>there is a lot of choppy waters that creates cloudiness

0:20:33.680 --> 0:20:35.320
<v Speaker 9>in terms of lack of visibility of.

0:20:35.280 --> 0:20:37.040
<v Speaker 10>What's to come and so ETS.

0:20:37.240 --> 0:20:41.600
<v Speaker 9>Actually the management team did comment on discretionary spend being pressured.

0:20:42.080 --> 0:20:45.560
<v Speaker 9>There's a mix shift of spend away from goods into services,

0:20:45.840 --> 0:20:50.240
<v Speaker 9>and the lower household income folks are actually watching their

0:20:50.240 --> 0:20:53.120
<v Speaker 9>spend even more closely and so they're feeling the pressure.

0:20:53.200 --> 0:20:55.960
<v Speaker 9>And some two of their biggest categories are moment living

0:20:56.000 --> 0:20:59.760
<v Speaker 9>in crafts and those are seeing pricing pressures as people

0:20:59.760 --> 0:21:03.080
<v Speaker 9>are shifting away from higher priced items to low price items.

0:21:03.200 --> 0:21:04.640
<v Speaker 10>All this put together and you.

0:21:04.640 --> 0:21:07.240
<v Speaker 9>See pressure on ETS stop line growth and we did

0:21:07.280 --> 0:21:09.680
<v Speaker 9>see that. So in the first quarter they did beat

0:21:09.720 --> 0:21:13.520
<v Speaker 9>all the estimates. But the guidance for the second quarter suggests.

0:21:13.280 --> 0:21:14.520
<v Speaker 10>Fairly muted trends.

0:21:14.600 --> 0:21:17.400
<v Speaker 9>And most importantly, it is unclear what the back half

0:21:17.440 --> 0:21:19.719
<v Speaker 9>of this year will bring, especially if we head into

0:21:19.840 --> 0:21:22.560
<v Speaker 9>a deeper recession. So near term I remain cautious, but

0:21:22.720 --> 0:21:24.359
<v Speaker 9>long term I'm bullish.

0:21:24.400 --> 0:21:27.199
<v Speaker 3>On the platform, Sweer, good morning to you. I mean,

0:21:27.240 --> 0:21:31.280
<v Speaker 3>a lot of your cell side colleagues complemented ETS on

0:21:31.359 --> 0:21:34.960
<v Speaker 3>its execution in that tough macro environment. They complemented ETS

0:21:35.359 --> 0:21:39.120
<v Speaker 3>on how they communicated guidance. But the stocks down significantly.

0:21:39.160 --> 0:21:40.679
<v Speaker 3>Why are they not being rewarded for that?

0:21:41.200 --> 0:21:44.520
<v Speaker 10>Well, because the execution has been good. This is a

0:21:44.560 --> 0:21:45.760
<v Speaker 10>stellar management team.

0:21:45.800 --> 0:21:49.000
<v Speaker 9>They've done a fantastic job in terms of turning around

0:21:49.000 --> 0:21:50.800
<v Speaker 9>at Sea over the past several years.

0:21:51.200 --> 0:21:51.560
<v Speaker 4>Now.

0:21:51.720 --> 0:21:55.560
<v Speaker 9>The challenge is that there's not a ton of clarity

0:21:55.640 --> 0:21:56.360
<v Speaker 9>on the back half.

0:21:56.359 --> 0:21:58.960
<v Speaker 10>If we just take what the guidance calls for in

0:21:58.960 --> 0:22:01.399
<v Speaker 10>the second quarter, it is calling for a.

0:22:01.440 --> 0:22:06.000
<v Speaker 9>Europear decline, meaningful decline at the low end of their guide,

0:22:06.040 --> 0:22:08.480
<v Speaker 9>and then some some growth at the high end of

0:22:08.520 --> 0:22:09.000
<v Speaker 9>their guide.

0:22:09.040 --> 0:22:11.280
<v Speaker 10>And so what that tells us is that the back

0:22:11.400 --> 0:22:14.440
<v Speaker 10>half we could potentially see low single digit growth rate

0:22:14.520 --> 0:22:16.840
<v Speaker 10>in GMS grow and then what does that tell us

0:22:16.840 --> 0:22:19.920
<v Speaker 10>about next year? And I think that's that's what's creating

0:22:20.560 --> 0:22:22.280
<v Speaker 10>uncertainty among among investors.

0:22:22.320 --> 0:22:25.600
<v Speaker 9>And then just one more point, there are definitely great

0:22:25.640 --> 0:22:27.760
<v Speaker 9>things to call out, and that's what probably some of

0:22:27.800 --> 0:22:30.000
<v Speaker 9>the other other folks were talking about. His new buyer

0:22:30.040 --> 0:22:33.760
<v Speaker 9>growth came in strong, they reactivated a lot of new

0:22:34.359 --> 0:22:35.280
<v Speaker 9>other buyers as well.

0:22:35.400 --> 0:22:37.800
<v Speaker 10>Those were those were pretty strong metrics.

0:22:37.920 --> 0:22:40.320
<v Speaker 9>But at the same time, habitual buyers, which down for

0:22:40.400 --> 0:22:43.040
<v Speaker 9>about forty three percent of their GMS, came in lighter

0:22:43.040 --> 0:22:45.320
<v Speaker 9>than we thought. And that's that's something not to miss

0:22:45.440 --> 0:22:45.800
<v Speaker 9>as well.

0:22:45.960 --> 0:22:50.280
<v Speaker 3>Schwideck, cojerior of Evical Things, Etsy, thank you so much. Now,

0:22:50.359 --> 0:22:53.280
<v Speaker 3>coming out the Biden administration meeting with CEOs from the

0:22:53.320 --> 0:22:57.280
<v Speaker 3>top AI names pressuring them to implement safeguards for the

0:22:57.280 --> 0:22:59.320
<v Speaker 3>emerging technology, We're going to talk to Adam wensch Or,

0:22:59.400 --> 0:23:03.399
<v Speaker 3>co founder CEO of Arthur about the tools necessary to

0:23:03.520 --> 0:23:07.440
<v Speaker 3>navigate the risks in artificial intelligence. From San Francisco and

0:23:07.520 --> 0:23:12.520
<v Speaker 3>from New York. This is Bloomberg.

0:23:19.400 --> 0:23:21.840
<v Speaker 2>Welcome back to Bloomberg Technology. I'm Caroline hired in New.

0:23:21.760 --> 0:23:24.960
<v Speaker 3>York and I'm ed Ludlow in San Francisco. Let's turn

0:23:25.000 --> 0:23:28.160
<v Speaker 3>to Qualcom learnings, where the largest maker of smartphone process

0:23:28.280 --> 0:23:32.000
<v Speaker 3>is offered a disappointing outlook on demand for mobile phones.

0:23:32.080 --> 0:23:35.840
<v Speaker 3>Joining us here in San Francisco, Cunjan Sabani of Bloomberg Intelligence.

0:23:35.880 --> 0:23:38.119
<v Speaker 3>I look at the forecast eight point one billion dollars

0:23:38.440 --> 0:23:41.000
<v Speaker 3>to eight point nine billion dollars for the fiscal third

0:23:41.520 --> 0:23:44.520
<v Speaker 3>What does that tell us about the recovery for qua

0:23:44.600 --> 0:23:45.560
<v Speaker 3>COM's end markets?

0:23:45.840 --> 0:23:47.600
<v Speaker 11>And my mentor al is used to tell me, the

0:23:47.600 --> 0:23:49.919
<v Speaker 11>longer the party, the bigger the hangover. And that's what

0:23:50.080 --> 0:23:53.240
<v Speaker 11>exactly is happening here. Smartphone makers are not drawing down

0:23:53.280 --> 0:23:56.280
<v Speaker 11>in inventry as fast as the company expected, and it's

0:23:56.359 --> 0:23:59.200
<v Speaker 11>driven by lack of visible signs of demand coming back.

0:23:59.359 --> 0:24:01.840
<v Speaker 11>So you have this combination of high inventories and low

0:24:01.920 --> 0:24:03.680
<v Speaker 11>demand that's causing really the pain.

0:24:04.440 --> 0:24:07.720
<v Speaker 5>Longer the party, bigger the hangover. Remember that one.

0:24:08.000 --> 0:24:10.320
<v Speaker 3>I guess that our expectations at the beginning of this

0:24:10.400 --> 0:24:14.040
<v Speaker 3>year were the inventories would clear up in the second quarter.

0:24:14.760 --> 0:24:16.439
<v Speaker 3>What do we now think is going to happen in

0:24:16.480 --> 0:24:19.040
<v Speaker 3>the rest of the year for the smartphone market, but

0:24:19.119 --> 0:24:21.239
<v Speaker 3>also Quowcomm in particular.

0:24:21.640 --> 0:24:24.679
<v Speaker 11>So we expected the first half to be the bottom,

0:24:24.720 --> 0:24:27.240
<v Speaker 11>which now looks to be kind of pushed out, and

0:24:27.720 --> 0:24:30.919
<v Speaker 11>we don't think the recovery will come until the fourth quarter.

0:24:31.400 --> 0:24:34.160
<v Speaker 11>There is no current trigger for the demand. The next

0:24:34.160 --> 0:24:37.320
<v Speaker 11>trigger will be in September when Apple launches his flagship

0:24:37.359 --> 0:24:40.040
<v Speaker 11>device and then going into December in the holiday season

0:24:40.200 --> 0:24:42.560
<v Speaker 11>that generally spurs up demand. So until then, there is

0:24:42.600 --> 0:24:44.400
<v Speaker 11>no clear trigger for demand to come back.

0:24:44.600 --> 0:24:47.760
<v Speaker 3>The CFO mentioned their modem only customer, which is code

0:24:47.800 --> 0:24:49.879
<v Speaker 3>for like Apple wink wink, and I think the idea

0:24:49.920 --> 0:24:52.320
<v Speaker 3>is they've done a big order earlier in the year.

0:24:52.480 --> 0:24:56.239
<v Speaker 3>Bloomberg Intelligence, you also spotted something about China. What are

0:24:56.240 --> 0:24:57.800
<v Speaker 3>we learning about quowcom in China?

0:24:58.359 --> 0:25:00.760
<v Speaker 11>So China is a big market for them. It's over

0:25:00.840 --> 0:25:04.520
<v Speaker 11>fifty percent exposure for them. So like China had was

0:25:04.560 --> 0:25:06.919
<v Speaker 11>the last to go in lockdowns, and that's where the

0:25:06.960 --> 0:25:09.920
<v Speaker 11>demand has not come back yet. So when we look

0:25:09.920 --> 0:25:13.919
<v Speaker 11>at China data, we were expecting that coming second quarter calendar,

0:25:13.960 --> 0:25:16.240
<v Speaker 11>second quarter demand will start picking up, but we're not

0:25:16.280 --> 0:25:18.560
<v Speaker 11>seeing the signals for it to come back. And again

0:25:18.600 --> 0:25:21.720
<v Speaker 11>it's that we are no longer in the supply constraint environment,

0:25:21.800 --> 0:25:26.280
<v Speaker 11>so smartphone manufacturers don't have the incentive to overorder ahead

0:25:26.320 --> 0:25:27.919
<v Speaker 11>in anticipating demand ahead.

0:25:28.000 --> 0:25:31.640
<v Speaker 3>Let's go back to Apple really quick that that commentary

0:25:31.640 --> 0:25:35.000
<v Speaker 3>around modemon only ordering earlier. How did that impact the

0:25:35.040 --> 0:25:36.280
<v Speaker 3>guide going forward?

0:25:37.200 --> 0:25:40.359
<v Speaker 11>So typically they would start ordering up for the September launch,

0:25:40.640 --> 0:25:43.840
<v Speaker 11>but because they had ordered over ordered ahead, they have

0:25:43.960 --> 0:25:47.119
<v Speaker 11>enough inventory and they're trying to order in time looking

0:25:47.160 --> 0:25:49.240
<v Speaker 11>at near term demand instead of overordering.

0:25:49.760 --> 0:25:54.040
<v Speaker 3>Conjen Sabani new to the team here at Bloomberg Intelligence, Caroline.

0:25:53.880 --> 0:25:57.600
<v Speaker 2>Great conversation, and let's well go back to artificial intelligence again,

0:25:57.640 --> 0:26:00.399
<v Speaker 2>shall we, because look, it's important on Capital Health President

0:26:00.440 --> 0:26:03.280
<v Speaker 2>Kamala Harris of course, well meet with leaders of major

0:26:03.320 --> 0:26:06.280
<v Speaker 2>AI companies including Google, Microsoft, Open Ai to address the

0:26:06.400 --> 0:26:10.440
<v Speaker 2>risks that can mitigate from their systems. QAI company Arthur

0:26:10.680 --> 0:26:13.640
<v Speaker 2>launching its new service called Arthur Shield Look. It acts

0:26:13.680 --> 0:26:16.760
<v Speaker 2>as a firewall to protect firms against the risks posed

0:26:16.760 --> 0:26:21.280
<v Speaker 2>from large language models like chatchipt afur CEO Anam Winschel

0:26:21.440 --> 0:26:24.119
<v Speaker 2>joins us now to discuss in the news day in,

0:26:24.280 --> 0:26:28.520
<v Speaker 2>day out, is the opportunity around artificial intelligence, but the

0:26:28.600 --> 0:26:31.400
<v Speaker 2>nerves around it too is Kamala Harris is currently discussing

0:26:32.119 --> 0:26:34.720
<v Speaker 2>what worries are you seeing with your clients. You serve

0:26:34.760 --> 0:26:36.320
<v Speaker 2>some of the biggest banks, you serve some of the

0:26:36.359 --> 0:26:39.480
<v Speaker 2>key companies department defense. How is chat ChiPT becoming a

0:26:39.560 --> 0:26:40.120
<v Speaker 2>risk for them?

0:26:40.520 --> 0:26:41.119
<v Speaker 5>Absolutely?

0:26:41.200 --> 0:26:42.600
<v Speaker 12>So, I think you know, the first thing is they

0:26:42.640 --> 0:26:45.040
<v Speaker 12>see the opportunity. Right there's you know, studies that have

0:26:45.080 --> 0:26:47.159
<v Speaker 12>shown that in certain job families they can get up

0:26:47.200 --> 0:26:49.560
<v Speaker 12>to a fifty percent increase in productivity, which is just

0:26:49.800 --> 0:26:51.960
<v Speaker 12>a game changer. But in the rush to kind of

0:26:51.960 --> 0:26:55.320
<v Speaker 12>adopt it and take advantage of those those benefits, they

0:26:55.400 --> 0:26:57.639
<v Speaker 12>run into a lot of walls. And those things include

0:26:57.720 --> 0:26:59.400
<v Speaker 12>the fact that it makes things up with some people

0:26:59.520 --> 0:27:01.640
<v Speaker 12>term hallution and nations, the fact that it can leak

0:27:01.720 --> 0:27:04.320
<v Speaker 12>back sensitive data that it was trained on or that

0:27:04.359 --> 0:27:06.919
<v Speaker 12>it was asked about. The fact that sometimes it can

0:27:07.000 --> 0:27:11.840
<v Speaker 12>return toxic or responses that aren't value aligned with your organization.

0:27:11.920 --> 0:27:14.560
<v Speaker 12>And so, you know, we're helping our customers solve these

0:27:14.560 --> 0:27:16.360
<v Speaker 12>problems so that they can take advantage of this game

0:27:16.440 --> 0:27:17.400
<v Speaker 12>changing technology.

0:27:17.640 --> 0:27:21.359
<v Speaker 2>Describe how that happens, because there are rivals to open Aiyes,

0:27:21.400 --> 0:27:23.480
<v Speaker 2>chat GPT looking at at least showing you where the

0:27:23.480 --> 0:27:26.359
<v Speaker 2>information has come from. How do you ensure that when

0:27:26.760 --> 0:27:30.639
<v Speaker 2>we get incorrect information or indeed we're worried about data leaks,

0:27:30.680 --> 0:27:31.320
<v Speaker 2>we're flagged.

0:27:31.800 --> 0:27:34.440
<v Speaker 12>Yeah, absolutely so, I mean these models fundamentally, they don't

0:27:34.440 --> 0:27:36.800
<v Speaker 12>contain all the data they were trained on. They contain

0:27:36.880 --> 0:27:40.040
<v Speaker 12>sort of some like really efficient summaries of it, right,

0:27:40.119 --> 0:27:42.240
<v Speaker 12>and so they don't really know what's true and what's

0:27:42.280 --> 0:27:42.720
<v Speaker 12>not true.

0:27:42.960 --> 0:27:44.120
<v Speaker 5>What they know is what is.

0:27:44.119 --> 0:27:47.480
<v Speaker 12>Probably could be likely to be true. And as they're

0:27:47.520 --> 0:27:50.800
<v Speaker 12>making these determinations, there are certain flags and signals which

0:27:50.840 --> 0:27:53.720
<v Speaker 12>you can key off of to Jenet to decide like

0:27:53.760 --> 0:27:56.040
<v Speaker 12>what are the how confident are we that this model

0:27:56.200 --> 0:27:59.399
<v Speaker 12>knows what it's talking about. And that's what our research

0:27:59.440 --> 0:28:02.040
<v Speaker 12>team has developed is the way to to really kind

0:28:02.080 --> 0:28:03.879
<v Speaker 12>of set threshold and block things that you know you

0:28:03.920 --> 0:28:06.840
<v Speaker 12>really shouldn't be confident are correct. Because these are getting

0:28:06.840 --> 0:28:10.640
<v Speaker 12>deployed into legal and medical contexts and you know, business

0:28:10.640 --> 0:28:13.040
<v Speaker 12>people and uh, you know when you're when you're deploying

0:28:13.080 --> 0:28:14.480
<v Speaker 12>them in this way and people are actioning off them,

0:28:14.480 --> 0:28:17.240
<v Speaker 12>there's real consequences to having misinformation out there.

0:28:17.359 --> 0:28:19.440
<v Speaker 2>How many of your clients just want to now add

0:28:19.440 --> 0:28:23.440
<v Speaker 2>out bad of open ais chat GIPT within the four

0:28:23.480 --> 0:28:26.040
<v Speaker 2>walls of their businesses or virtual four walls, because we

0:28:26.080 --> 0:28:27.320
<v Speaker 2>just saw it was Samsung for example.

0:28:27.359 --> 0:28:29.800
<v Speaker 12>Yeah, the majority of them absolutely there. You can't use

0:28:29.800 --> 0:28:32.720
<v Speaker 12>the public open AI. They're starting to be able to

0:28:32.720 --> 0:28:35.600
<v Speaker 12>stand it up with inside of Microsoft Azure Security Zone.

0:28:35.600 --> 0:28:37.600
<v Speaker 12>But even that, there's there's a number of problems that

0:28:37.640 --> 0:28:39.800
<v Speaker 12>come with that because ultimately, what these firms want to

0:28:39.840 --> 0:28:42.080
<v Speaker 12>do is be able to take something like a chat GPT,

0:28:42.240 --> 0:28:44.680
<v Speaker 12>but then train it on augment it with training it

0:28:44.720 --> 0:28:46.720
<v Speaker 12>on their own data so that they can ask it

0:28:46.840 --> 0:28:49.520
<v Speaker 12>questions that are you know, more informed by their their

0:28:49.560 --> 0:28:52.800
<v Speaker 12>business knowledge, and that's one of the areas where you know,

0:28:52.880 --> 0:28:54.920
<v Speaker 12>leaking that data back in the wrong context can be

0:28:54.960 --> 0:28:56.360
<v Speaker 12>really harmful and really damaging.

0:28:57.200 --> 0:29:00.239
<v Speaker 2>What ED seems to be in every part of our

0:29:00.280 --> 0:29:02.880
<v Speaker 2>conversation is the pros and the cons here we just

0:29:02.880 --> 0:29:05.200
<v Speaker 2>heard it from Ashton Kutsher. We're now talking about really

0:29:05.280 --> 0:29:07.880
<v Speaker 2>cyber can be helped and hindered by.

0:29:07.760 --> 0:29:10.640
<v Speaker 3>This, right, Yeah, Adam, I'm interesting for your take on this.

0:29:10.680 --> 0:29:13.440
<v Speaker 3>So as at RSA conference last week and everyone's talking

0:29:13.440 --> 0:29:17.320
<v Speaker 3>about generative AI balance between how useful it's going to

0:29:17.320 --> 0:29:22.000
<v Speaker 3>be in making data secure and tracking attacks versus how

0:29:22.160 --> 0:29:25.400
<v Speaker 3>dangerous it is in the hands of bad actors, author

0:29:25.440 --> 0:29:26.800
<v Speaker 3>sits at the kind of the intersection of that.

0:29:26.960 --> 0:29:30.360
<v Speaker 12>What's your view, Yeah, absolutely, I think Look, the risks

0:29:30.360 --> 0:29:32.680
<v Speaker 12>around it are just beginning to be understood as it's

0:29:32.760 --> 0:29:35.240
<v Speaker 12>rolled out in some of these large applications, and so

0:29:35.280 --> 0:29:37.480
<v Speaker 12>I think we're sort of in the early days of

0:29:37.920 --> 0:29:41.000
<v Speaker 12>really figuring out how to effectively operationalize lms and the

0:29:41.040 --> 0:29:44.040
<v Speaker 12>benefits they bring. But even in the early days, we've

0:29:44.040 --> 0:29:47.239
<v Speaker 12>seen lots of examples of all sorts of problems that,

0:29:47.480 --> 0:29:49.600
<v Speaker 12>you know, just just make it prevent people from putting

0:29:49.600 --> 0:29:51.760
<v Speaker 12>it into production without the appropriate safeguards.

0:29:53.200 --> 0:29:57.040
<v Speaker 3>So Vice President Harris is welcoming all these great names

0:29:57.040 --> 0:30:01.760
<v Speaker 3>across Open AI Microsoft today to have a conversation about

0:30:01.960 --> 0:30:06.520
<v Speaker 3>artificial intelligence. When industry participants like you see something like that,

0:30:06.680 --> 0:30:08.600
<v Speaker 3>what do you actually expect to come out of it?

0:30:09.440 --> 0:30:12.080
<v Speaker 12>Yeah, Look, I think it's a it's a great first step,

0:30:12.080 --> 0:30:14.800
<v Speaker 12>and there's a lot of value in convening that and

0:30:14.800 --> 0:30:17.000
<v Speaker 12>certainly there's a really important role for the government to

0:30:17.040 --> 0:30:19.320
<v Speaker 12>play in this, and so I'm glad the conversation is

0:30:19.360 --> 0:30:21.920
<v Speaker 12>getting started. My hope is that they also, you know,

0:30:21.960 --> 0:30:25.320
<v Speaker 12>solicit the opinions of not just the people making the technology,

0:30:25.320 --> 0:30:26.080
<v Speaker 12>but those who.

0:30:25.920 --> 0:30:27.560
<v Speaker 5>Are part of the process.

0:30:27.240 --> 0:30:29.360
<v Speaker 12>Of actually deploying it in the world and making sure

0:30:29.360 --> 0:30:31.920
<v Speaker 12>that it works properly and that it and that it

0:30:32.000 --> 0:30:36.040
<v Speaker 12>you know, serves its purpose in a credible and unharmful way.

0:30:37.280 --> 0:30:39.800
<v Speaker 3>All right, thanks to Adam Wench or Arthur co founder,

0:30:40.160 --> 0:30:52.160
<v Speaker 3>and see.

0:30:48.560 --> 0:30:48.600
<v Speaker 9>That.

0:30:48.720 --> 0:30:50.800
<v Speaker 2>We keep talking about the macro, We keep talking about

0:30:50.840 --> 0:30:53.560
<v Speaker 2>the banking fallout as it continues to be a crisis,

0:30:53.600 --> 0:30:57.280
<v Speaker 2>how that's impacting startups general to AI apparently seems to

0:30:57.280 --> 0:30:59.440
<v Speaker 2>be some sort of green shoot for the venture capitalists

0:30:59.480 --> 0:31:01.520
<v Speaker 2>out there and in terms of the industry, but there's

0:31:01.560 --> 0:31:05.680
<v Speaker 2>perhaps a broader area higher growth potential the female economy,

0:31:05.800 --> 0:31:07.920
<v Speaker 2>from women's health, tech to e commerce and much more.

0:31:07.960 --> 0:31:11.520
<v Speaker 2>That's the topic of Monique Woodard's white paper out today

0:31:11.560 --> 0:31:14.440
<v Speaker 2>and it's called Finding Alpha the Trillion Dollar Female Economy.

0:31:14.640 --> 0:31:17.720
<v Speaker 2>Kate Lynch's founding partner and managing director Money wood joins

0:31:17.800 --> 0:31:20.400
<v Speaker 2>us right now and great to welcome you back. Many

0:31:20.560 --> 0:31:23.280
<v Speaker 2>talk to us about sort of the focus you have,

0:31:23.400 --> 0:31:26.080
<v Speaker 2>What problems do you want to solve for women that

0:31:26.160 --> 0:31:30.040
<v Speaker 2>you think is so highly advantageous for investment too.

0:31:32.120 --> 0:31:36.640
<v Speaker 13>Absolutely so, investors are always looking for these growth markets,

0:31:36.720 --> 0:31:39.880
<v Speaker 13>and I think that women are an emerging consumer that's

0:31:39.960 --> 0:31:44.120
<v Speaker 13>emerged for quite some time. Women are now now control

0:31:44.200 --> 0:31:49.440
<v Speaker 13>eighty five percent of consumer spending, they control a significant

0:31:49.440 --> 0:31:51.920
<v Speaker 13>amount of assets, and that number is expected to triple

0:31:51.960 --> 0:31:55.160
<v Speaker 13>over the next decade. And that points to a few

0:31:55.160 --> 0:31:59.480
<v Speaker 13>different things. It points to opportunities in consumer retail and

0:31:59.520 --> 0:32:02.200
<v Speaker 13>e commerce, of course, but also things like women help,

0:32:02.240 --> 0:32:06.800
<v Speaker 13>women's health, semtech, and the care economy. We have to

0:32:06.800 --> 0:32:10.440
<v Speaker 13>figure out how both care and work get done as

0:32:10.520 --> 0:32:14.000
<v Speaker 13>more women enter the workforce and start to move up

0:32:14.000 --> 0:32:14.480
<v Speaker 13>the ladder.

0:32:15.000 --> 0:32:17.840
<v Speaker 2>You name check some companies Maven Health of course, which

0:32:17.880 --> 0:32:21.800
<v Speaker 2>has been proven, and Unicorn and Resilient in this headwind

0:32:22.120 --> 0:32:24.680
<v Speaker 2>macro headwind kind of environment. You mentioned him in hers

0:32:24.760 --> 0:32:29.280
<v Speaker 2>carot fertility. I'm interested, though, like why write this white paper?

0:32:29.880 --> 0:32:32.800
<v Speaker 2>We talk a lot about well, the need to invest

0:32:32.840 --> 0:32:35.040
<v Speaker 2>in women, the need for female investors, people who look

0:32:35.040 --> 0:32:38.120
<v Speaker 2>like you managing the money, and yet ultimately things don't

0:32:38.120 --> 0:32:40.400
<v Speaker 2>really change. What did you need to spell out here

0:32:40.400 --> 0:32:41.080
<v Speaker 2>that was different?

0:32:42.520 --> 0:32:44.920
<v Speaker 13>So the problem is twofold. You do have the problem

0:32:44.960 --> 0:32:48.840
<v Speaker 13>of not enough investors investing in companies led by women

0:32:48.960 --> 0:32:52.320
<v Speaker 13>founders and CEOs. But I also think there is an

0:32:52.400 --> 0:32:55.560
<v Speaker 13>opportunity to show people that, look, there is a massive

0:32:55.600 --> 0:33:00.280
<v Speaker 13>consumer market here that is being underinvested in, and we

0:33:00.320 --> 0:33:04.480
<v Speaker 13>are under investing in companies and products that solve the

0:33:04.520 --> 0:33:07.560
<v Speaker 13>problems and meet the needs of female consumers. And I

0:33:07.560 --> 0:33:10.400
<v Speaker 13>think that is a really interesting opportunity to try to solve.

0:33:11.200 --> 0:33:16.840
<v Speaker 13>People don't, especially investors, don't change their behavior based on

0:33:17.320 --> 0:33:20.360
<v Speaker 13>things that are nice to do. They change their behavior

0:33:20.440 --> 0:33:23.800
<v Speaker 13>based on the movement of capital markets and discovery of

0:33:23.840 --> 0:33:25.000
<v Speaker 13>new growth markets.

0:33:26.560 --> 0:33:29.360
<v Speaker 3>On that note, Manie, how actively have you discussed your

0:33:29.400 --> 0:33:35.040
<v Speaker 3>thesis with your LPs, with other vench capital piers to

0:33:35.160 --> 0:33:39.280
<v Speaker 3>try and make this into something more substantive in terms

0:33:39.280 --> 0:33:42.680
<v Speaker 3>of deploying capital extremely active.

0:33:42.720 --> 0:33:45.400
<v Speaker 13>It's the second layer of the cake ventures thesis, So

0:33:46.360 --> 0:33:50.080
<v Speaker 13>cake ventures investing companies that touch areas of demographic change

0:33:50.080 --> 0:33:52.920
<v Speaker 13>that are changing technology. The second layer of the cake

0:33:53.040 --> 0:33:57.600
<v Speaker 13>is the increased spending power of women, and I have

0:33:57.760 --> 0:34:01.240
<v Speaker 13>been very open about that with both my pis the

0:34:01.240 --> 0:34:03.520
<v Speaker 13>companies that I invest in, and I think that's why

0:34:03.600 --> 0:34:06.000
<v Speaker 13>founders want to work with me. They know that I

0:34:06.120 --> 0:34:09.080
<v Speaker 13>understand that this is not just a niche. It is

0:34:09.840 --> 0:34:13.280
<v Speaker 13>moving women are moving into you know, a majority position

0:34:13.360 --> 0:34:15.080
<v Speaker 13>in a lot of in a lot of ways, and

0:34:15.080 --> 0:34:19.480
<v Speaker 13>that presents a really compelling opportunity for both my investors

0:34:19.480 --> 0:34:22.280
<v Speaker 13>in the fund and founders who are building businesses.

0:34:23.320 --> 0:34:29.080
<v Speaker 3>Monique, how is the ongoing regional banks crisis impacting you

0:34:29.080 --> 0:34:31.200
<v Speaker 3>your firm but also your portfolio companies?

0:34:33.440 --> 0:34:38.200
<v Speaker 13>Yeah, it's it's you know, candidly, First Republic is my bank.

0:34:38.640 --> 0:34:43.200
<v Speaker 13>I also have portfolio companies who banked with Silicon Valley Bank.

0:34:44.040 --> 0:34:46.799
<v Speaker 13>But looking broadly at the challenges that we've seen with

0:34:46.920 --> 0:34:50.600
<v Speaker 13>Silicon Valley Bank, First Republic and now potentially path West,

0:34:51.760 --> 0:34:54.759
<v Speaker 13>you know, I think the public has has started to

0:34:54.760 --> 0:34:58.359
<v Speaker 13>see these as very high dollar venture capital problems. When

0:34:58.880 --> 0:35:02.000
<v Speaker 13>regional banks are are, you know, providers to small and

0:35:02.080 --> 0:35:08.120
<v Speaker 13>medium sized businesses all over America and these are their customers.

0:35:09.560 --> 0:35:13.239
<v Speaker 13>I think it's become a challenging position that startups and

0:35:13.320 --> 0:35:17.000
<v Speaker 13>small businesses and venture capitalists are all in and I

0:35:17.040 --> 0:35:18.760
<v Speaker 13>think that we need regional banks.

0:35:18.800 --> 0:35:19.560
<v Speaker 10>We need We.

0:35:19.520 --> 0:35:22.640
<v Speaker 13>Don't need more consolidation. But I am very excited that

0:35:22.760 --> 0:35:26.880
<v Speaker 13>JP Morgan is now taking over the First Republic. But

0:35:26.920 --> 0:35:30.319
<v Speaker 13>we do need those regional banks to exist. You also

0:35:30.560 --> 0:35:34.680
<v Speaker 13>worry about carry on, Sorry, I also worry about you know,

0:35:34.680 --> 0:35:38.799
<v Speaker 13>how it will affect underrepresented founders, women founders, women who

0:35:39.000 --> 0:35:40.960
<v Speaker 13>lead venture capital funds and the like.

0:35:41.600 --> 0:35:46.080
<v Speaker 2>Yeah, access equality basically, and the worry that we revert

0:35:46.520 --> 0:35:50.439
<v Speaker 2>to pass behavior. I'm interested in how you're finding those

0:35:50.440 --> 0:35:54.920
<v Speaker 2>portfolio companies that you have back that are led by minorities.

0:35:55.239 --> 0:35:57.920
<v Speaker 2>Are they getting follow on checks or indeed are they

0:35:57.960 --> 0:36:00.800
<v Speaker 2>having to trim more than most? Are they well savvy

0:36:00.840 --> 0:36:03.160
<v Speaker 2>with cash in this current environment to be able to

0:36:03.239 --> 0:36:04.799
<v Speaker 2>weather this macro headwindstorm.

0:36:07.080 --> 0:36:12.000
<v Speaker 13>I find women led companies and companies led by underrepresented

0:36:12.000 --> 0:36:15.400
<v Speaker 13>founders to be really resilient in times of crisis, and

0:36:15.480 --> 0:36:17.600
<v Speaker 13>I think that we are in a time of crisis,

0:36:17.600 --> 0:36:20.520
<v Speaker 13>and I think those companies are proving themselves to be

0:36:20.560 --> 0:36:25.560
<v Speaker 13>incredibly resilient. Unfortunately, many of them never had the kind

0:36:25.560 --> 0:36:28.200
<v Speaker 13>of access to capital that their peers did and so

0:36:28.800 --> 0:36:30.960
<v Speaker 13>learn to do more with left and so I think

0:36:31.000 --> 0:36:35.440
<v Speaker 13>that is created extremely strong companies that will weather the storm.

0:36:35.960 --> 0:36:39.840
<v Speaker 13>But you know, they are still firmed out here like

0:36:39.880 --> 0:36:43.359
<v Speaker 13>myself and like Cake Ventures, that are actively investing in

0:36:43.440 --> 0:36:47.000
<v Speaker 13>women and founders entrepreneurs of all types.

0:36:48.080 --> 0:36:51.040
<v Speaker 2>Mynie, what I could always talk longer for you. Thank you.

0:36:51.120 --> 0:36:54.160
<v Speaker 2>Kate bench, A's founding partner and managing director, such a

0:36:54.160 --> 0:36:56.759
<v Speaker 2>great conversation. Mean while coming up ed so much more

0:36:56.840 --> 0:36:59.160
<v Speaker 2>to do with you from Weimo says it's ready to

0:36:59.200 --> 0:37:03.000
<v Speaker 2>expand right giving riders in San Francisco and Phoenix area

0:37:03.120 --> 0:37:05.680
<v Speaker 2>what options to get around town. We're going to talk

0:37:05.680 --> 0:37:08.279
<v Speaker 2>a bit about dating too with a Waymochief Product.

0:37:08.000 --> 0:37:12.480
<v Speaker 3>Officer ed, Yeah, that's saswap panigrah. We're going to talk

0:37:12.480 --> 0:37:14.719
<v Speaker 3>about that. Keeping our eyes though right now in Ali

0:37:14.800 --> 0:37:18.359
<v Speaker 3>barber Is, its international online shopping unit, is looking at

0:37:18.360 --> 0:37:21.680
<v Speaker 3>a US initial public offering is it weighs its options

0:37:21.719 --> 0:37:24.680
<v Speaker 3>to spur growth for that business, and that includes major

0:37:24.680 --> 0:37:27.320
<v Speaker 3>e commerce brands LAZARDA and Ali Express, the firms in

0:37:27.360 --> 0:37:31.520
<v Speaker 3>the early stages of consideration around the IPO size, which

0:37:31.600 --> 0:37:33.239
<v Speaker 3>is yet to be determined.

0:37:34.080 --> 0:37:47.239
<v Speaker 2>This is Bloomberg, the biggest US IPO since twenty twenty one,

0:37:47.680 --> 0:37:50.680
<v Speaker 2>and shares are popping as they start trading. Health company

0:37:50.760 --> 0:37:54.920
<v Speaker 2>ken View has begun its trading as a independent company

0:37:54.960 --> 0:37:57.400
<v Speaker 2>from J and Jay and shares run the Rise tailan

0:37:57.480 --> 0:38:00.520
<v Speaker 2>old band aid all the sexy stuff ed current up

0:38:00.520 --> 0:38:02.720
<v Speaker 2>for grabs and it looks as though there's some interest

0:38:02.719 --> 0:38:05.839
<v Speaker 2>in this particular stock after what has been kind of

0:38:05.880 --> 0:38:08.840
<v Speaker 2>a drought in the overall world of initial public offerings.

0:38:08.880 --> 0:38:09.560
<v Speaker 2>It was upsized.

0:38:10.600 --> 0:38:10.799
<v Speaker 5>Yeah.

0:38:10.840 --> 0:38:13.160
<v Speaker 3>Look, the shares opened at twenty five dollars fifty three

0:38:13.239 --> 0:38:16.440
<v Speaker 3>cents to share. They're kind of floating around that level.

0:38:16.560 --> 0:38:19.360
<v Speaker 3>Three point eight billion dollars raised from the IPO, biggest

0:38:19.400 --> 0:38:21.719
<v Speaker 3>since twenty twenty one. As you say, care, remember this

0:38:21.800 --> 0:38:25.360
<v Speaker 3>was Johnson and Johnson's consumer health business. They're essentially spinning

0:38:25.400 --> 0:38:27.440
<v Speaker 3>it off. But this is one that we've been waiting

0:38:27.480 --> 0:38:31.120
<v Speaker 3>for a little bit of activity in the IPO market.

0:38:31.239 --> 0:38:33.520
<v Speaker 2>Yeah, gold Mas, Sachs, JP, Morgan Bank of America are

0:38:33.600 --> 0:38:36.280
<v Speaker 2>leading this particular charge on the IPO front. I remember

0:38:36.280 --> 0:38:39.160
<v Speaker 2>this is a company that still perhaps has some overhanging concerns,

0:38:39.280 --> 0:38:42.080
<v Speaker 2>legal concerns. Ken You already has been sued over TALC

0:38:42.160 --> 0:38:44.440
<v Speaker 2>injury claims. In caution, it might be subject to claims

0:38:44.480 --> 0:38:47.040
<v Speaker 2>arising outside the US and Canada. But I got to

0:38:47.040 --> 0:38:49.560
<v Speaker 2>say it, I unfortunately was dosing up my kid with

0:38:49.600 --> 0:38:52.480
<v Speaker 2>taalanol last night. I was usering listery in this morning.

0:38:52.480 --> 0:38:56.200
<v Speaker 2>These things are pretty pretty resilient in the face of

0:38:56.239 --> 0:38:57.080
<v Speaker 2>economic headwinds.

0:38:57.120 --> 0:38:58.239
<v Speaker 5>Yeah.

0:38:58.400 --> 0:39:00.920
<v Speaker 3>Well, it will continue to monitor Chem shares throughout the

0:39:00.960 --> 0:39:04.719
<v Speaker 3>hour as they begin trading here on Bloomberg Television now

0:39:04.760 --> 0:39:08.040
<v Speaker 3>Alphabet's waymos as it plans to expand its self driving

0:39:08.080 --> 0:39:11.040
<v Speaker 3>taxi service in its two main markets. Customers in the

0:39:11.080 --> 0:39:14.480
<v Speaker 3>Greater Phoenix area of Scottsdale and other neighboring cities will

0:39:14.480 --> 0:39:17.240
<v Speaker 3>be able to hail Rise from waymost driverless cars. Service

0:39:17.280 --> 0:39:21.280
<v Speaker 3>will also offer trips in new neighborhoods here in San Francisco.

0:39:21.400 --> 0:39:24.720
<v Speaker 3>Joining us now with the details is Saswat Panagrahi, Weaymo's

0:39:24.760 --> 0:39:29.760
<v Speaker 3>chief product officer. Welcome to the program, Saswat. That's the expansion. Okay,

0:39:30.280 --> 0:39:33.200
<v Speaker 3>so how do we now start measuring progress for WEIMO?

0:39:34.320 --> 0:39:36.000
<v Speaker 5>Yeah, hey, Ed, great to be with you.

0:39:36.600 --> 0:39:37.799
<v Speaker 14>Yeah, it's an exciting time.

0:39:38.600 --> 0:39:38.839
<v Speaker 3>You know.

0:39:38.920 --> 0:39:41.800
<v Speaker 14>We have been the first company to open up in

0:39:41.920 --> 0:39:44.920
<v Speaker 14>the first right healing service to the public, now expanding

0:39:44.920 --> 0:39:47.800
<v Speaker 14>it to the largest contiguous service area on the planet

0:39:47.920 --> 0:39:51.080
<v Speaker 14>with av access, the first to serve airports and we're

0:39:51.080 --> 0:39:54.120
<v Speaker 14>expanding that and in San Francisco, as you did, we're

0:39:54.120 --> 0:39:56.080
<v Speaker 14>expanding that as well. So what you can expect from

0:39:56.120 --> 0:39:59.960
<v Speaker 14>us is we cross ten thousand trips are fully outumum

0:40:00.080 --> 0:40:02.440
<v Speaker 14>strips to riders and we were going to tenext that

0:40:02.600 --> 0:40:05.319
<v Speaker 14>over the course of the next year, roughly by about

0:40:05.400 --> 0:40:10.120
<v Speaker 14>next summer, so more cities, more occasions, more riders the

0:40:10.160 --> 0:40:11.040
<v Speaker 14>same way MO Rider.

0:40:12.120 --> 0:40:15.080
<v Speaker 3>Have you set a date or a target for full

0:40:15.120 --> 0:40:17.720
<v Speaker 3>commercial deployment in any of these markets?

0:40:19.239 --> 0:40:20.880
<v Speaker 14>Ed, I would say in Phoenix we are in a

0:40:20.880 --> 0:40:23.640
<v Speaker 14>full commercial deployment. You just download the app and ride.

0:40:23.719 --> 0:40:27.680
<v Speaker 14>There's no wait lists, there's no NDA, there's no approvals,

0:40:27.719 --> 0:40:29.640
<v Speaker 14>you just download the app and ride. So to give

0:40:29.680 --> 0:40:31.759
<v Speaker 14>you an idea, during Super Bowl, we work with the

0:40:31.760 --> 0:40:35.720
<v Speaker 14>host committee to deal with all the riders and visitors

0:40:35.719 --> 0:40:38.560
<v Speaker 14>to Super Bowl. So that's pretty massive scale already. And

0:40:38.600 --> 0:40:40.719
<v Speaker 14>now that if you look at it, we're covering most

0:40:40.760 --> 0:40:43.520
<v Speaker 14>of major metro Phoenix and anybody can download an app

0:40:43.520 --> 0:40:46.600
<v Speaker 14>and rider, and we're seeing pretty healthy ridership as well

0:40:46.640 --> 0:40:48.880
<v Speaker 14>as engagement as well as retention. So I would say

0:40:48.880 --> 0:40:52.880
<v Speaker 14>it's pretty commercial in Phoenix already full commercial deployment.

0:40:55.520 --> 0:40:56.960
<v Speaker 5>No sorry, SATISFI continue.

0:40:57.520 --> 0:40:57.719
<v Speaker 8>Yeah.

0:40:57.719 --> 0:41:00.680
<v Speaker 14>And in San Francisco we have done of thousands of

0:41:00.760 --> 0:41:03.240
<v Speaker 14>riders in our waitlist. We're waiting for a paid permit

0:41:03.280 --> 0:41:05.640
<v Speaker 14>to be able to begin charging them, but we're still

0:41:05.680 --> 0:41:08.480
<v Speaker 14>offering almost the entirety of the city and today have

0:41:08.600 --> 0:41:12.040
<v Speaker 14>opened up North Beach as well as fishermen's work some

0:41:12.080 --> 0:41:15.480
<v Speaker 14>of the busiest locations of San Francisco to trust testers

0:41:15.520 --> 0:41:15.879
<v Speaker 14>as well.

0:41:17.160 --> 0:41:19.200
<v Speaker 3>There's a lot of discussion at the moment around ours

0:41:19.239 --> 0:41:22.359
<v Speaker 3>fisheral intelligence. My first exposure to that field was talking

0:41:22.360 --> 0:41:26.600
<v Speaker 3>about machine learning in the context of training the perception

0:41:26.880 --> 0:41:30.040
<v Speaker 3>side and the compute side of self driving. So what

0:41:30.080 --> 0:41:31.960
<v Speaker 3>are you doing in the field of AI. How are

0:41:32.000 --> 0:41:35.960
<v Speaker 3>you ramping up? Are you using lllms to improve your

0:41:36.000 --> 0:41:37.040
<v Speaker 3>existing technology?

0:41:38.280 --> 0:41:41.400
<v Speaker 14>Yeah, Ed, I mean we have been focusing on machine

0:41:41.480 --> 0:41:44.040
<v Speaker 14>learning for a very very long time and it's in

0:41:44.600 --> 0:41:47.080
<v Speaker 14>every part of our stack. Everything from perception, like you

0:41:47.120 --> 0:41:49.920
<v Speaker 14>mentioned on how we perceive the world, how we predict

0:41:49.920 --> 0:41:53.040
<v Speaker 14>other people's behavior, being able to distinguish when a pedestrians

0:41:53.080 --> 0:41:56.200
<v Speaker 14>is standing by a curb but not intending to cross

0:41:56.360 --> 0:41:59.680
<v Speaker 14>versus crossing. Also in how we plan how we drive

0:41:59.760 --> 0:42:02.520
<v Speaker 14>through in every part of it that are deep learned

0:42:02.560 --> 0:42:05.560
<v Speaker 14>models all across our stack. We're also in simulation as

0:42:05.560 --> 0:42:09.160
<v Speaker 14>well as in validation, for example simulating rain, fog and

0:42:09.200 --> 0:42:11.920
<v Speaker 14>those things. There's a tremendous amount of machine learning and

0:42:11.960 --> 0:42:13.960
<v Speaker 14>we're staying at the cutting edge of it with our

0:42:14.000 --> 0:42:15.960
<v Speaker 14>research and have published some of it as well.

0:42:16.920 --> 0:42:19.960
<v Speaker 3>All right, thank you, disessat panagra he Weimo's chief product office.

0:42:20.080 --> 0:42:22.120
<v Speaker 3>We'll get both of us in a Weymo car very

0:42:22.160 --> 0:42:23.400
<v Speaker 3>seeing Caroline.

0:42:23.080 --> 0:42:25.760
<v Speaker 2>Excited for it. Meanwhile, while we always talk about cars

0:42:25.760 --> 0:42:27.360
<v Speaker 2>with Apple, but then it seems to be quite on

0:42:27.360 --> 0:42:29.160
<v Speaker 2>the horizon, let's talk about the here and the now.

0:42:29.200 --> 0:42:31.400
<v Speaker 2>The earnings are out after the ball for a preview,

0:42:31.680 --> 0:42:33.719
<v Speaker 2>let's go to mark Gum and then we're likely to

0:42:33.719 --> 0:42:34.680
<v Speaker 2>see sales drop again.

0:42:35.680 --> 0:42:38.680
<v Speaker 15>Yeah, there will be no Apple car announced today during

0:42:38.719 --> 0:42:43.040
<v Speaker 15>earnings Like you said, in terms of Apple earnings today, yeah,

0:42:43.080 --> 0:42:47.040
<v Speaker 15>we're likely looking at another five percent sales decline. Don't

0:42:47.080 --> 0:42:49.640
<v Speaker 15>take my word for it. That's what Apple said would

0:42:49.680 --> 0:42:53.680
<v Speaker 15>be the case when it provided its color on the

0:42:53.719 --> 0:42:55.680
<v Speaker 15>current quarter during its last earnings call.

0:42:55.760 --> 0:42:55.960
<v Speaker 9>Right.

0:42:56.160 --> 0:42:58.319
<v Speaker 15>Traditionally they like to go a little bit under to

0:42:58.360 --> 0:43:01.319
<v Speaker 15>show more of a beat. But it does seem likely

0:43:01.360 --> 0:43:04.120
<v Speaker 15>based on everything that Wall Street is saying, combined with

0:43:04.120 --> 0:43:06.239
<v Speaker 15>what Apple is saying, that we are likely in for

0:43:06.320 --> 0:43:09.800
<v Speaker 15>revenue around ninety two billion, which would be a decline

0:43:09.800 --> 0:43:12.719
<v Speaker 15>from around ninety seven point three billion reported in the

0:43:12.760 --> 0:43:15.759
<v Speaker 15>year ago quarter. This will be the second quarter in

0:43:15.760 --> 0:43:18.279
<v Speaker 15>a rower Apple's going to show an annual decline. So

0:43:18.400 --> 0:43:22.560
<v Speaker 15>that's something that's obviously concerning. Last quarter around they had

0:43:22.719 --> 0:43:25.840
<v Speaker 15>a bit of an explanation, right, the iPhone fourteen pro

0:43:26.200 --> 0:43:29.440
<v Speaker 15>had major supply chain constraints. They were just not able

0:43:29.480 --> 0:43:32.560
<v Speaker 15>to produce produce them because of the COVID zero policies

0:43:32.600 --> 0:43:35.160
<v Speaker 15>at the time in China. Now I'm curious to see

0:43:35.160 --> 0:43:37.040
<v Speaker 15>what they're going to say the reason for this decline

0:43:37.080 --> 0:43:40.520
<v Speaker 15>is whether that's the economy. Maybe they're supply chain issues

0:43:40.560 --> 0:43:42.759
<v Speaker 15>we don't know about. So we'll be interested to see

0:43:42.800 --> 0:43:43.840
<v Speaker 15>how that goes later today.

0:43:44.040 --> 0:43:45.880
<v Speaker 2>I'll wait to see you on the blog, Mark German,

0:43:46.000 --> 0:43:48.480
<v Speaker 2>We thank you. Meanwhile, well we've got some action in

0:43:48.520 --> 0:43:51.160
<v Speaker 2>the IPO market, don't we add finally the popping of Kenvy.

0:43:52.080 --> 0:43:52.239
<v Speaker 1>Yeah.

0:43:52.320 --> 0:43:54.840
<v Speaker 3>Look, we're still trading around that twenty five dollars fifty

0:43:54.880 --> 0:43:58.920
<v Speaker 3>cents mark, but eagerly anticipates we finally got a big ipo,

0:43:59.000 --> 0:44:01.520
<v Speaker 3>the biggest since twenty twenty one.

0:44:01.560 --> 0:44:03.759
<v Speaker 2>That does it for this edition of Bloomberg Technology Ed.

0:44:04.760 --> 0:44:08.080
<v Speaker 3>Yep, don't forget recap the podcast. Wherever you get yours.

0:44:08.320 --> 0:44:09.200
<v Speaker 3>This is Bloomberg