A (2:24)

I.

B (2:24)

Think for reasons that we just have to make sure our current customer base, you never want to abandon Your current customer base, you just can't. And while it will work on 97% of devices out there right now in people's homes, we still want to do it in a way where when we switch people over because they choose to, that they feel great about it. So it's right now because early access is, hey, ask for it. We'll give it to you. It's that simple. End of October, though, just so you know where it's at, we're going to be rolling it out to everybody if you buy a new Echo device. We launched four new Echo devices yesterday. We're pretty pumped about it, or I'm pretty pumped about it maybe, but all those folks will get it right out of the box.

A (3:06)

Okay, so end of October, this show will air right around then everybody will get Alexa. If your device is compatible or if you buy a new device.

B (3:16)

Yeah, you'll opt in if you want it. Like, I'm not going to. We're not going to just give it to anyone because, you know, people love their classic Alexa and we want to just serve our customers like the way they want to be served. But I think everybody should get it.

A (3:29)

So I think it's good that everyone's going to get it.

B (3:32)

Yeah.

A (3:32)

Or have the option to. I want to ask about this sort of gap in time because we watched the live demo on stage in March and then the notion was it was going to come out the next month. And I read that at least as everybody was going to get it in a month, not a small section of people. And was there a difficulty in getting the technology to work that led to that delay, or was it you saying we just need to test a little bit more. What happened?

B (4:07)

Well, the way what we had done and we had asked for if you wanted to get it, you know, email me when ready, type of thing, like create a list and we're going to give access to customers as they go. And then as the product continued to develop and finish, it was then rolling it out slowly to make sure. Because you have to learn that all environments are so different. And so the whole concept and construct was learn as we release. And so we had basically tranches of customers that you released to that were on this list. And we got it to everybody who's asked for it at that point. And then that list continued to grow and then we continued to release it. But it really was about, yeah, making sure the product was refined to a point that when we did put it in people's homes that they were both excited about it. But then can use it for everything that they're already using it for. There's some nuance on how you would use it and you're kind of learning to use the new Alexa. Not complicated, just nuanced. And so it was just a little bit more methodical. I would say. We rolled it out as we intended, but also it did feel a little bit slower than I think what people expected. So maybe it was about how to balance the expectation versus what we delivered.

A (5:20)

I was listening back to our conversation for March and a topic came up and I was like, wow, I can't believe I didn't ask a follow up on that.

B (5:27)

Oh, no.

A (5:27)

And you've already brought it up now.

B (5:29)

Oh, no.

A (5:29)

Which is that you have to think about the.

B (5:31)

And then I came back for more that I came back from work.

A (5:35)

I have to say, no.

B (5:36)

It's great that you're here sometimes some.

A (5:38)

People would probably think, don't do it, don't do it. No. But we should say it's great that you're here. For many years, we didn't hear from Amazon executives in the way that you're having the conversation.

B (5:47)

Oh, got it.

A (5:47)

So I'm thrilled that you're here and I'm thrilled we're able to talk about it. Cool. This is not. This is not.

B (5:53)

And you've been doing this five years.

A (5:55)

Five years. Doing this. Five years. We just celebrated our five year anniversary, which is rad.

B (5:59)

I mean, and I think that's cool. Five years in. I'm sorry I couldn't make it last night, but that's a very good.

A (6:03)

We had a nice party. But I was also, I mean, I was covering Amazon long before that as a reporter and wrote this book called Always Day One. And this is why I was upset that I didn't follow up. And it's not a gotcha. It's actually. I want to learn a little bit about your product philosophy.

B (6:18)

Okay.

A (6:20)

Always. Day one is something Jeff Bezos popularized within Amazon. To me, it always meant you build as if you're a startup. And the nice thing about being a startup is you don't have a flagship product to maintain. And I think the message from Bezos, if I read it right, was a lot of big companies spend their entire existence trying to maintain their flagship product. And they don't build for what the market needs at that moment. And that's the nice thing about being a company on day one. You just build what the market needs.

B (6:51)

Yeah. You need to start from zero.

A (6:52)

Of course you can start from zero.

B (6:53)

Yeah.

A (6:54)

And so that sort of brings us into this conversation of how you approach the Alexa customer. You said in our last conversation, and just now, you have to be careful about the way that the current customers are using the product when you're introducing something new. And there's that leadership principle within Amazon, customer obsession. You obsessed about what the customer wants.

B (7:18)

And that's where that comes from.

A (7:19)

And then there's that balance between.

B (7:20)

And believe me, by the way, if you don't. If you don't do that, you just. You lose trust. Right, but then lose trust with the customer. You lose it overnight, Alex, like, at a level that they don't come back, you know, they didn't ask for it, and even if they did and you didn't give them what they wanted, you lose trust. And that's such a big deal. You're going to be obsessed. But go ahead. This. There's a dichotomy here. I got it.

A (7:48)

I think you see where I'm going, because this is like a core question about Amazon in the age of AI, is can you hold that customer obsession? People who love your current flagship product and that always, day one, value together and have them work in sync.

B (8:04)

You can. You absolutely can. But, you know, one of the most incredible parts of Amazon is there's a level of patience to construct. Like, where are we headed? And a lot of time. Invention takes time. It doesn't mean you're not being relentless. It doesn't mean you're not acting as if it's day one. Like with Alexa, we are being relentless. If you just look at the breadth of what it can do, with the amount of APIs it can call, with the amount of experts that are embedded, the fact that you can be full conversational assistant and call them, this takes time. And when you have hundreds of millions of customers, the things that they do, the things that they connect with. I think we talked about this last time. I don't necessarily want to get into that, but I think you can still be relentless and you can still act as if it's day one. And never, never dismiss or not put your customer first. It's very real. And I think that's what we're doing right now. And we don't act. So when we're building, when we're creating, when we're focused on the product that we're just getting after, it is relentless. Like, we are going as if we're not. Like, year three, slow down. Where is the year five or year ten? Where's the customer at? Let's just look at the data. None of that is happening right now. It's full speed. What does the customer need? Are we sure we know what the vision is? Are we locked in? And then the team pushes like we're really clear where we need to go. Then there's the commitment to the current customer. But by the way, you're also relentless about that. And this is one where, you know, I see the dichotomy, like, well, you have to serve your customer. That doesn't mean it's not day one. It just means you. Every day you wake up worried, thinking, understanding, learning about the customer, but then acting, acting with the same passion and relentless focus. You need to move the ball. I see that happening on Alexa right now.

A (9:56)

Okay, so let's go one level deeper. And I could be wrong about this, but I'll give you my read of what Bezos was saying with OSK1. And since you're in the company, you can help me get to the truth.

B (10:07)

I don't know. I mean, you know, this will be tricky. I'm not sure I can get in the minds of everybody, but I'm so.

A (10:13)

To me, the message. So I came in, when I came into my reporting, I wrote a book. Book title always day one. I came in thinking that day one mentality meant that you just kind of work 247 and you have that relentlessness. And after spending time reporting on the culture of the company, my perspective shifted a little bit. And it was, to me, it really meant it was a message to the company. Don't worry about what got you here at this point. Flip when you need to. And One example is Amazon.com, which was this first party marketplace flipped effectively to a majority third party marketplace, which caused real disruption with the first party vendors who were used to working with a certain system and now found internal competition. Now for the customer. On the customer side, you never really realized, saw that disruption because it was all taking place effectively on the back end. But with AI, it's different because if you have a system where you're like, all right, it doesn't matter what got us here, let's build what the market needs. The customer can feel that disruption.

B (11:16)

Yeah, I think they can for sure. And that's where the patience comes in. But I still, even as you say it, I go, but the mentality remains this relentless day one, you know, for the first think about it, with activating the first 10 million customers, it's a switch. It's not. We literally build, go, take it, move. And that engagement happens. We Learn as fast as we can, and then we're continuing to iterate. So I feel like that the spirit of it remains, but I don't think you ever abandon your customer. I don't think that would ever happen. If you're acting in a kind of day one mindset, you're serving the customer, and so the change is. Okay, your example, I wasn't there for the transition, but I will say let's just use Alexa as an example. Right now, we are building on the spirit of when they're ready, go, and they, meaning the customer, when they want it. Day one is about building, creating, knowing where you're headed, starting from zero, making it great. And then, you know, part of that is bring your customer along. It has to be. You can't. You just never leave them behind. But to your point, I think your example might be perfect. I'm sure there's a team that was going full speed. I don't know the details, so I can't. I just got to be careful. I never want to be presumptuous. But now you end up in this place where it serves the customer, then serve them, and I think that's what happened. Again, I wasn't that close to it, but when you think about Alexa, it's. The same thing's happening right now. I'm not even trying to obfuscate. I mean, as you say it, I'm like, I think that's exactly where we are. And I'll say that humbly because I always worry, like, oh, I'm overstating it. Or I'm like, you know, I take so much, like, Alexa should be this much better, and it should have been done. You know, you should have known this. And you're like, okay, we have a team that's pretty passionate. They know what they're getting after. But I'll never leave. We just won't leave the customer behind. I just won't do it.

A (13:19)

Yeah, I think it's one of these things that's happening with a variety of big tech companies today, which see the promise of generative AI, see the bumps of generative AI, and are deciding how quickly to bring it forward in their products without alienating those who love their flagship products. Like, we had the people who were running search at Google in here a couple weeks ago, a couple months ago, and it's that balance of, like, you have this great traditional search product people use. A lot of people use it every day.

B (13:50)

Yeah.

A (13:50)

And then when do you decide to flip it and make it AI Mode? And I Think it's very interesting to see all these companies going through this moment of, when is it time to flip that switch to generative AI? Because like you mentioned. You mentioned when we spoke last, a startup can do it quicker because they don't have to worry about their current customers.

B (14:09)

Yeah, and I think you can. It doesn't mean they build it quicker. Just they don't. You know, you start from zero and you build up, you know, versus starting from hundreds of millions and handing it over. I think that's just reality. But it doesn't mean we're not fast or not. That's where I take exception to, like, when I hear it. Like, to hear, you know, what's taking you so long? Like, wait, hold on. There's no other agent out there or kind of assistant that's using tens and tens, seventy, hundred. I won't give you the amount of experts, but those experts also, all of them looking across, calling hundreds and hundreds of APIs and being able to serve. There's no other expert out there that I can send. My daughter's, you know, she's 15, man, she's amazing. And she's trying. She's learning. She's playing on the golf team at school, which is kind of a weird thing. She's just. And I just wanted to see, like, I want to get to a match. And so she sent me a picture of her schedule. It was a picture. And she's like, can you come? I'm like, yep. So I take the picture. I just share it with Alexa. All right. In that moment, Alexa sends me a notification. I open the app. The app says, do you want me to download everything here, or do you want me to add everything to your calendar? Or do you just want some of these things? And in the future. So very smart. It's just. It's agentic. And it's like, do you want me, in the future, do you want me to add them all, or do you want me to ask you again? I said, just in the future, add them all. Thank you. Add everything. My calendar populates with the location, the name of the school they're playing against, a link to directions, and basically the calendar entry. And then comes back and says, here's a few conflicts, just so you're aware. Maybe you want to resolve them. And here's what I suggest. Okay, what just happened? That seems simple, right?

A (16:06)

Not to me.

B (16:06)

You added a calendar event. No, I gave it a picture. And now, of course, it can read the picture, but in most cases, it just spits out. Here's the summary. That's not what it does. Here's the summary. And then went and took action and then updated the calendar and then Mary and my wife got the updates on her calendar. And so now all of a sudden I think for the first time in our kids high school career, we actually have. We actually have the kids sports calendar on our personal calendars. Because it's always so hard. It's like over here, over here, over there. You have to manually enter it. Nobody's gonna do that. Yeah, it's like doesn't happen. And then there's just an argument. It's Monday. Like are you going to cosis this on Thursday? I'm like, I didn't even know it existed. Why didn't you tell me? Well, don't you remember the mail? You're in this place. But now it's all solved like that. But what happened? There was an expert, the calendar expert, there was a communications expert, there was a understanding the document expert. Then there was APIs called where you're turning on calendar entries, then checking against old calendar entries and then updating other. And when you start seeing that all happen, it's incredible work. But I look at it and go, it's just not happening anywhere else. Not yet. It's not a simple piece, but it's so delightful when it does happen. And back to your original point, that is, that's a great feat of engineering at speed and just kind of product making that we're pretty proud of. And then you start seeing it and when we say like being day one, like there's nothing that holds that back.

A (17:36)

Right.

B (17:36)

There's just nothing. You're starting from zero to create that and then that's when you're serving a customer and that's an added value to something they didn't have before.

A (17:47)

You shared some.

B (17:47)

Can't get anywhere else. Right.

A (17:48)

You shared some stats about those that do have Alexa are using it much more often.

B (17:54)

Yeah.

A (17:55)

Three. What is it?

B (17:56)

Can you talk about how engagement.

A (17:58)

I have it written down somewhere.

B (17:59)

Yeah.

A (18:00)

You can get it three times as usage shopping, three and a half times, five times more recipes.

B (18:05)

You do have it written down. Yeah. Ton more music Actually what's happening is they're using the products and right when you start realizing its agentic capabilities go far beyond just answering questions. People engage it once, you engage it once or twice, the usage spikes. And that's one of the challenges. This is ambient AI. It's working there. It's in the background. It's right there. How do you address it? When do you address it? How do you get something out of it? How do you learn what it's capable of? Those are all challenges, but once customer engages it, it's pretty awesome to see it just simplifies life and that we're starting to see that happen in the product.

A (18:46)

You have this belief that with a product you really have to have this central thing, this one thing within a product that you go after.

B (18:52)

Yeah, that's true. How do you know that? Did we talk about that last time? You're right. You got your sources. I call it the one thing.

A (19:01)

One thing?

B (19:01)

Yeah.

A (19:02)

Is that AI does everything. So what is the one thing with AI?

B (19:05)

Well, every expert has their one thing. It has to do great.

A (19:08)

But then you have a product that does.

B (19:10)

Yeah, it's a tricky one. Right. I don't know if it's a catch all, but it is. It's supposed to be the world's best personal assistant. Like that's the one thing it does at all times. Like it, is it going to do that? And if, and if the expert that's delivering against it has to do its part, then you can, you can step back and look at it. It's like anything. I've built plenty of products that were platform level products in my past and it's never as you know, it's what I try and do with teams philosophically in product making is let's focus on the one thing that your team, these are big teams and big, you know, different levels within a product. But you can break that down and compartmentalize and say, all right, what's the one thing? What's the one thing? As long as the vision is clear on where you're headed and that one vision is clear, then teams can really rally on what they want to go accomplish for the customer. And that does keep you in the day one mentality because it's super clear, like, here's what we're going to deliver. By the way, that might mean there's trade offs in other places. And by the way, it may not be perfect for everybody. That's okay. That's always okay. Like if you, if everybody was, if every single person was happy with what you built, I promise you, you didn't push it far enough. I promise you, you just did not. But if everybody's pissed at you, you definitely pushed it too far, you know, and so you're, and so you have to find a little bit of that balance. But the truth is, if you're going to push it to the right level of innovation, and invention. And if that one thing, you just have to get that right and then majority of your customers, they'll just be delighted. And then if you push it a little bit further, that's okay. Maybe not everybody comes on board, but teams tend to find a balance.

A (20:43)

I want to go back to this catch all nature of AI and the difficulty in building it. So I'll just talk to you about the perspective from the outside and would be great to hear your perspective from the inside. From the outside. Those of us who watch this space see great potential. I think. Yeah, we've seen visions from Google, from, from Apple, from Amazon about this catch all or I don't know, this always there, contextually aware, helpful personal assistant. The thing that I've heard is difficult when you want to build something like that is when you're working with a large language model. They can only handle a certain amount of context before they just kind of lose their minds. And when you're trying to integrate calendar and photos and I don't know, maybe ring camera data, there's so much I can't tell you.

B (21:37)

These are just simple, high level, but there's so much.

A (21:39)

Is there a technical wall that you eventually hit when you try to integrate that within an LLM?

B (21:45)

Well, there's not a. I don't. Is there a technical wall? The way to think about it is if you're wrong, it's devastating.

A (21:53)

Right.

B (21:55)

You know, and that's why. So your original question is why isn't it going faster? Well, for sure what I'm not going to do is when you decide to turn off a light, lock your door.

A (22:07)

That would be bad.

B (22:08)

Right. And so that's a dog out. That's a microcosm of what you just asked.

A (22:13)

Right.

B (22:14)

I don't think there's a technical wall, but is there enough training and learning that you have to do in understanding does your architecture has to be set up in a way where it does know and rechecks and triple checks that it's calling the API but still hold the latency that customers come to expect because nobody's sitting there going, I think this is a hard problem. It's going to take a while.

A (22:32)

Yeah, 30 seconds to get my light on.

B (22:33)

It's not going to happen. Once it's easier to go touch the light switch, you just go touch the light switch. It's inherently true of product making. We always, as our customers, you always have to think they're going to take the easiest path to get to their solution. That's it. Because we are Efficient in our nature. Other people call it lazy, but you could pick. But we want to get to the solution as fast as we possibly can. As people on almost anything you do, of course. Or maybe not fast. How about easiest? And then you start realizing that time is part of easy. Like if you need it quick, you want it quick, even though it might be easier to say it versus touch it. And so we look for that path at all times and so on our products. I don't think there's a. There's not a limitation at all, as a matter of fact. And I'm not going to talk about the architecture or the construct of it. That's what I won't do. Because I think what we're doing is spectacularly unique and it is forward thinking for sure to be able to do it all. But I will say you have to be right. And that's where the limitations start. Because if you deliver the wrong outcome for the customer, that's when you start losing trust. And so like during early access, like rolling it out is a lot about that. Like, okay, you know, if the customer's asking for it, then they're coming on board with us to be like, yeah, okay, maybe it takes a millisecond too long. Or your deep fryer doesn't work the way you thought it would with Alexa, the way it did six months ago. And now you can tell us, like, my air fryer, it's not turning on when I ask it to. And we're like, ah, you have an air fryer. You don't think that is something agentic? Which it's not. But at the same time, because you have this LLM doing the work, because you have this orchestrator that's trying to call the right APIs and still kind of figure it out, maybe your air fryer wasn't part of the product in the redesign. And so now we know and we just make sure we get it right. So there's not a limitation, but it takes time and you have to get it right. And so we, you know, that's been part of the methodical process. And I don't. Yeah.

A (24:40)

Am I right in thinking that that's the most difficult part in building this? Just incorporating that?

B (24:45)

Yeah, because. Right. I mean, there's a couple of things that are challenging, but I think it's one of the hardest parts is first understanding the customer's context and what they're going for and then executing on it properly. And they generally, once you understand, you can execute. But if you're wrong about the understanding and the LLM, you know, relative to. And our Agentix system, if you will, is the way I'll put it. We have a pretty sophisticated way of handling it. But remember this, you have to do it at speed. You know, customers have the. It's pretty unique. I'm going to give you an example. When you speak to something, you expect a certain speed. When you type something, you expect a different speed in a response. It's pretty fascinating, as a matter of fact. I call it, you know, being our product has to be contextually aware. Like if you use the Alexa app right now and you, and you speak to it, it's going to give you a shorter answer with less information.

A (25:35)

I think that's a good choice.

B (25:36)

Right. But if you take that same question and type it into the app, it's going to give you that full, familiar AI response that you would want with the bars and the graphs and the context and the link. But you have to understand the contextual moment for your customer. It's just a very different level in getting into your question. It's like, so there's another layer. One is you're speaking. Got it. You're in the home. Got it. You're asking, you want a point and shoot answer. Like you want the time. You don't want to talk about the time, you just want the time. But sometimes you want to talk about the time. We got to know the difference. And you might say, alexa, play. I want you to play this. And that same thing could have been a movie or a song. We got to know. And then when you get down to. That's contextual awareness. And then when you get down to the phone, if you're speaking to it, we don't think you want us to recite, have a conversation with two paragraphs back to you. So the verbosity has to be checked. But if you type it, we're like, okay, you have a little bit more patience. And so now all this, just understanding the customer's mindset is really important. If you're looking for something deep and you're searching for it, then we'll just take more time and get it for you and give it back to you. But that's also being contextually aware and the system is adapting for it real time.

A (27:00)

So just to sort of wrap this segment up, I think a lot of people are confused about timelines here.

B (27:07)

Okay. Yeah.

A (27:08)

And it probably when we see. I'll just pick on Apple for a moment. When we see the Apple Intelligence WWDC where you see cool videos of all this working seamlessly, you might say to yourself, this technology is ready. It's coming tomorrow, coming in a couple months. I just read a Bloomberg story that you participated in and you had a colleague participate in. And the last line was like, in 12 months we'll have a good concept of where we're going to be. And even then we're not finished. So this idea that our interactions with computers are going to change overnight seems at this point, I think we can safely say that's not going to happen. It's going to take years to actually see this promise bear out.

B (27:56)

I don't know that I can agree. So let me just see if I can balance it because why don't we. Let's do our best to remove timelines. If you're a tech forward thinker, you should be using Alexa right now, period. By the end of October, you should be full speed getting in. If you want to just be at the front end of it, like right now it's working. I mean, it is in my daily flow. Is it still getting a few kinks out and bugs? Sure. But I think there's every product at this size on the planet. I mean, it was getting bugs out on the classic sense as well at some point. And so there's always more. And so when you think about, but where will you be in 12 months? I mean, it's going to evolve so fast. But this is the day one mentality, like, get in and it's gonna evolve tomorrow. It'll just keep getting better. Like literally just keep getting better. And I think so. Yeah. If you said, where are you gonna be in 12 months? Like the product's still gonna be evolving, I think that's a fair assessment. But it doesn't mean the product's not ready for you to get after it now. It just can do, it'll be able to do more in 12 months. Like, I get super pumped about this because, like, if you can get in, not like defensive, but like excited. If you want to use Alexa, like you get an Echo show or an Echo Studio or an Echo Max, an Echo Max this holiday, you get it right away out of the box, you're going to be delighted. You're going to have a blast. It's going to help transform literally how you work with AI around you. I mean, I mean that, like, I mean, I believe it. Right. But is it going to keep getting better as well? Like, this is where we get confused about a few of these things. Absolutely. If you were using the product in April when it came out and you're using it now. It's an evolved product. It's a different product. So it's, it's that much better. I think you will see a lot of evolution over the next several months and years. It's not going to stop. That's not Alexa comment. But it's just in general, like it's just moving fast. You know that. I mean, you know it better than anyone. You, I bet you the majority of a few of the conversations I've listened to you have kind of. They don't. Implicit. You know, it's implicit, it's implied, but it's not necessarily, it's not easy to say that.

A (30:14)

Right.

B (30:15)

Because you know. So your product's not ready, like. No, I think it is. I think you got to get after it.

A (30:19)

Okay.

B (30:19)

You know. And is it still in early access? Yeah, it does hint at. Hey, come in here. There's a little bit of risk to the program. It's called an LLM as a matter of fact. Like you, you take that risk every day if you're using any AI product. And it's not a risk like you know, some weird worst case scenario, but you just got to make sure the information you're getting is right. I think with Alexa we're far ahead of it right now and I'm pumped about it, but you know, the end of October is really where it starts to come to life. That's when the devices, the new devices come to market at the end of October. And right at that point, I think, you know, you're gonna, you will see another surge of energy around the product.

A (30:59)

Okay, so you are head of devices and services at Amazon. We've talked about services, really in this first half you love doing plus.

B (31:07)

You do that to me every time so far.

A (31:09)

Now we'll flip it, we'll talk about devices, we'll do it after the break. Shape the future of enterprise AI with Agency AGNT CY now an open source Linux foundation project, Agency is leading the way in establishing trusted identity and access management for the Internet of Agents. A collaboration layer that ensures AI agents can securely discover, connect and work across any framework. With Agency, your organization gains open standardized tools and seamless integration including robust identity management to be able to identify, authenticate and interact across any platform, empowering you to deploy multi agent systems with confidence. Join industry leaders like Cisco, Dell Technologies, Google Cloud, Oracle, red hat and 75 plus supporting companies to set the standard for secure scalable AI infrastructure. Is your enterprise ready for the Future of Magentic AI? Visit agency.org to explore use cases. Now that's agntcy.org did you know your credit card points and miles can lose value to inflation Credit card companies often reduce the redemption value of your points and miles. Now imagine a credit card with rewards that can grow in value. With the Gemini credit card, you can earn Bitcoin or one of over 50 other cryptos instantly with no annual fee. Every swipe at the store or gas pump earns you instant rewards deposited straight to your account. Plus sign up now for a $200 Bitcoin bonus. To kickstart your rewards, visit gemini.com card today. Check out the link in the description for more information on rates. Again, if you're looking to invest in Bitcoin but don't know where to start, the Gemini Credit card makes it easy. The Gemini Credit card is issued by Web Bank. In order to Qualify for the $200 crypto intro bonus, you must spend $3,000 in your first 90 days. Some exclusions apply to instant rewards in which rewards are deposited when the transaction posts this content is not investment advice and trading. Crypto involves risk. The Gemini credit card cannot be used to make gambling related purchases. We're back here with Panos Panay, the head of devices and services at Amazon. First half we talked all about services. Alexa?

B (33:28)

I think so, a little bit.

A (33:29)

I always do it to you. So now let's shift, let's change it up and talk about devices.

B (33:34)

Let's do it.

A (33:35)

And I want to start kind of theoretical. Maybe it's actually reality for you. Because as we end up getting what you call ambient AI people have called ambient computing. Right? An assistant. You started your presentation at this press event in New York talking about how you could be at the kitchen table without your phone. That was interesting to me. As you as you get to this.

B (33:58)

Point where it was interesting or it resonated?

A (34:01)

Both. Okay, it resonated because I would like that. But it was interesting to me because it showed the direction of where devices could go.

B (34:10)

Gotcha.

A (34:12)

If we get this contextually aware AI assistant that's with us, it's possible that we will need a new set of devices. And the primacy of the phone, which you not so subtly kind of hinted at in this opening scene, could end up decreasing as we have things like maybe smart glasses as we have things like contextually or maybe even smart headphones. Even we have AirPods with Siri in them today. But we have there's echo earbuds. So what does the future of the device look like to you when we get past these early days of the AI assistant and into where you really want to see the vision play out.

B (34:58)

First, you focus on the devices that are available to you now. For me, because what we're building and releasing right now are probably, I think, the right and best devices for kind of ambient AI or AI around your house. Just that contextual awareness that's possible, whether it's the Ring cameras or the Echo devices. I think, you know, this here's my plug. You know, we just launched our Ring 4K line, which I'm very pumped about, and we. And we launched four new Echo devices, some new Fire TV devices, Kindle Blink. But the energy, I think you start there, like great devices in the home, I think are critical to the vision for if you're going to be connected to your assistant. And I think that's the promise in what we're delivering this holiday, for sure. I think the way to answer your question different outside of now because you never want to talk about, here's what the future of the device is when you're really. When I'm handing you and hoping that you love what we're presenting right now to buy.

A (36:03)

But it's such a relevant question, though. It's a good question.

B (36:05)

Yeah.

A (36:06)

You've acquired this startup. This is from the Bloomberg story, the startup called Bee, which has a wristband that can record your day and send a summary to a phone app. There's also new reports that you have smart glasses under development, earbuds under development.

B (36:21)

Right.

A (36:21)

So I mean, having a chance to speak with you is a chance for myself and our audience to see, to get a chance to dream a little bit about where this could end up.

B (36:32)

If you look, I think over time, what's going to happen is jobs are going to move to different devices for sure. And because right at this point, the smarter your assistant is about you, the better, better it can work for you, the more personalized it is. And by the way, the way we think about that is customer's choice. You choose how much information you want to give to your assistant. The more it has, the better off it is. And you can then see a world where jobs move to different types of devices. I love the story of years ago. I remember I was building a laptop.

A (37:12)

To Microsoft.

B (37:14)

Yeah. And I remember at that time I had a couple colleagues that were like, what are you thinking? The laptop's dead. It was 15 years ago. Look at what you're using. I don't know if they can see it on your camera, but look what you're Using, by the way, 15 years ago, it was dead. And it was like, what do you mean it's over? The phone has taken over. The phone has replaced the laptop. And when laptops came out, desktops were dead. And when desktops came out, mainframes were dead. None of that's actually what happens. What happens is the job moves, the appropriate job moves from device to device to device. And again, as users and customers think of it as the desktop got stronger at what it does, let's just call it full rendering cad, you know, if you will code like just the things that you need the desktop for. It just got stronger. Just got better, actually. And the laptop turns out, thank goodness for phones because the laptop got better. It didn't go away. It got better for the jobs it had to do. And the jobs that were better on the phone, they moved to the phone. I think if you. And then there were tablets and I can tell you that story, it would take us another whole podcast. But if you just look at that funnel, I do think to your question is jobs are moving off the phone, but it doesn't mean the phone isn't critical. Just like your laptop today is still critical. And those jobs, they might look like wearables, they might look like earbuds, they might look like other devices because those points of input, if you have a great assistant, you want your assistant with you. You want to be able to give it those inputs. Your example of B is a good one. But outside of pointing at products that I don't talk about, because I'm not pulling a product out of my lab and putting it on this table right now and talking about it, but I will. We have to think about it that way if we want your assistant to be with you and to be great and to understand you. Take that calendar example, it needs more information, it needs all of it. And the more you give it, the stronger that kind of foundational outcome of having a great assistant becomes. And because Alexa is that because we believe it's the world's best personal assistant. I think it is right now and I sure it will be in three years. But that contextual awareness is going to matter and so the device itself will be. I believe there is a world where there are devices that are going to be doing specific jobs for you that I will tell you.

A (39:52)

So what jobs do.

B (39:52)

And there will be different form factors to do.

A (39:55)

So what jobs do the wearables do.

B (39:58)

I teed you up for that one. So I brought that onto myself. You know, I think important like listening at the right time, watching at the right time, or looking or taking pictures, maybe taking senses off your body. Like, yesterday, we announced an integration with Aura for Wellness, and you're wearing the wearable. You get back home, you ask Alexa, you know, how much. How many steps did I have today? Can you just help me think about it? Or do you think I'm on pace and you can tell Alexa, this is a good example. I could just stay in the present where. Let's use Aura. There's a wearable on my finger. I get home. Aura understands my bedtime rhythms and lets Alexa know. And Alexa basically says, hey, P, it's time for bed. Or don't forget. I'm going to set a reminder for you every night when you're 30 minutes away from your bedtime. And I'll just announce it for you so you're aware. I think that's a job that gets simpler. You don't need a phone with you. You don't need anything. You have your AI assistant tell you at the right time. I think it's an example of what is it that you want to share. So then your product can come together, and then ultimately your assistant can deliver the information so you can be informed and make the right decision.

A (41:11)

Are you independent?

B (41:15)

Am I independent? Oh, my God. You're not going to let me go. All right. I don't know.

A (41:21)

I don't know either. That's my answer. Yeah.

B (41:22)

Yeah. I don't know. Like, you know, like, I think it's. I think you'd be wrong not to look at all the potential form factors if you were sitting in my seat.

A (41:31)

Right.

B (41:32)

And studying them. I will tell you. We won't. Here's what I won't do. I just won't create something to see if it works.

A (41:40)

Okay, so then let me ask you a question about that, because I'm curious.

B (41:44)

Is that a fair answer? Because I don't. It's a great answer. Come on, man. I'm not going to tell you. Like, here are the seven things I might be building. But at the same time, we're in this place of, like, we still have to learn, like, this agentic, kind of the agented opportunity with devices, which, by the way, I mean, we can give me four hours. Let's just talk about product.

A (42:02)

But we could do that.

B (42:04)

Yeah. I mean, it's what I geek out over. I just. In this case, it'd be wrong of me to say what I'm building or thinking about, but also tell you that I'm not. Right.

A (42:14)

Okay.

B (42:14)

There's a lot to learn and you have to take your time on this stuff. You have to act with urgency. You have to be relentless. You have to act on these. Let's use your day one example right now. Every device in the lab, we're day one on it. There's teams building, there are team thinking, what is it? But none of them are potentially candidates to put in market. That's not the goal.

A (42:36)

This is a great follow on to that because I did want to ask you what Amazon's device release strategy is. Now I'll tell you. This is the conception that I think the public had for a while.

B (42:47)

I don't know that I can comment on the past, but I will try.

A (42:51)

So we thought. I'll talk about myself.

B (42:53)

Okay, good.

A (42:54)

Apple would release a product when it was perfect and the thought was Amazon in some ways would throw spaghetti at the wall that there was an Alexa microwave and an Alexa alarm clock. Everything with Alexa in it. But I'm curious, is that the right way to think about it? Like, when do you say it's time to bring product to market?

B (43:18)

No, when it's. Well, here's how we talk as a team. Yep, how about that?

A (43:29)

Great. I'd love to hear it.

B (43:33)

One, you have to know what the one thing you're delivering to your customers is and you have to be perfect about it. Like perfect. We have to think about these products in a way that every single detail matters and is thought through for the customer. Not just for the sake of design, not just for the sake of beauty or cost, but for the customer. If you can pull those two things together and it ties to the greater vision of serving the customer to go deliver against, in this case, Alexa for the AI Assistant, like, that product moves, but it is purposeful. It is not guesswork. It's tied to a vision. The way we talk to the team is very simple. You make a great product for the customer. Great. I mean, great. Think it through and back to those principles I just gave you. What's the one thing? What are the details? What is the speed? When should it be in market? And of course, there's a whole business case around each one of them. But is it first? Is it great for the customer? And it needs to stand alone as a great product and then you have to look across and these are both true. Great on its own, but magical when connected to the other ones. End of story. It's very, very simple to say it's like it teeters on, impossible to deliver, which is how it should feel. Like if you're pushing the boundary and making a great product to the point of maybe you're not going to make everybody happy, but you got to know damn well what you're doing to make the customer you're targeting happy. That is not random. It's not. What was it? Spaghetti.

A (45:12)

Spaghetti at the wall.

B (45:13)

Spaghetti at the wall. It's not. Maybe it is. Be purposeful. There are plenty of devices that get towards the end that don't make it to the shipping floor.

A (45:22)

Okay.

B (45:23)

And you have to think about it. Of. And plenty's a relative term in my world, but it is. You do have to think about it as customer first. What do they need? What are you delivering? Make a great product for them and then make sure. And Alexa is that tissue that connects it all. Like make sure it connects across. So when you're buying a device from Amazon, you know you're going to have a great product. A great product. And by the way, it's likely affordable. It's likely a pretty dang good price. It's likely what you want to pay for something to still covet it, but also that it's going to connect with the other devices in a beautiful, seamless way.

A (46:02)

Okay, so last time you were here, I asked with. I ended with a question of whether I should get an Alexa with an echo with a screen check. Now I have another concluding question for you. How do I recycle my old Alexa?

B (46:16)

Send them back to me. Send them back to me. I'll give you a massive discount and I recycle them for you.

A (46:21)

But is that the standard way? Like a standard customer who has. Because I have first gen echoes in my house, I'm going to go out and buy this new studio. Actually, in the March event, I think you showed the last generation of the studios and the sound was so, so good. I told myself I need that. Yeah. So I'm gonna go out and get.

B (46:36)

We sold out of those, by the way. We sold out. I've been looking@Amazon.com sorry, that was my fault. I don't know if it's my fault, but the team. That's a great product and so.

A (46:44)

Right.

B (46:45)

I can't get any for myself, by the way.

A (46:47)

Okay. So there's a new. A new studio, which is the big premium speaker that.

B (46:51)

Yeah, it's not big introduced. It fits beautifully, man. It's really, it's. I mean it is literally designed. It's the larger one. It's the larger one.

A (46:57)

Larger one, yeah. Premium sound.

B (46:59)

Yeah.

A (46:59)

Sounds really good.

B (47:00)

Did you get into the spatial Audio booth.

A (47:03)

No, you got a lot. There were a lot of reporters at this event, so.

B (47:06)

Come on, man. It was so good. It sounded. It was magical. All right, I'm bummed. I'm super bummed.

A (47:16)

I'm gonna get in trouble if I end up buying like six Echoes.

B (47:19)

You will get in, but I think.

A (47:20)

It'S gonna be okay. But for a standard customer, right? You want to recycle your current devices. The worry that I have is I have my. I've logged in with all my personal information. Do I go to just go to the Alexa app and erase it and then toss it or what's the standard process to recycle one of those?

B (47:42)

There's a couple. We have a trade in program. I'm not even kidding. We have a trading program. Trade it in, get a discount for it. We're going to make sure we do. We're very aggressive with it because we like, environment is like, it's one of our. The climate pledge is a massive deal for us and like making sure the recyclability is there. We do build our products to make them as recyclable as possible, of course, but there's there, you know, consumer electronics is tricky. You can take it back to any Amazon store, I believe, like, or what do you call the shipping center?

A (48:11)

Drop point.

B (48:12)

Yeah, the drop points. And we can take care of it there for you as well.

A (48:17)

All right, well, like I said at the beginning of the show, I don't take it for granted to get a chance to sit down with you and get a real conversation about where this is heading. It's obviously very enjoyable for me, but for our listeners, extremely illuminated.

B (48:33)

Did you enjoy the event yesterday? Yeah, I did. Just to see how it went for you.

A (48:38)

I think there's an art in being able to speak to a crowd, but also feel like you're having a conversation with them and I think you have it down.

B (48:48)

Oh, thanks, Matt. But did you get to meet. I'm really curious. Did you get time to meet with the team and hang out a bit and spend your time there?

A (48:55)

I had a big interview to prepare for.

B (48:57)

You did? What does that look like? Is it you talking about this one?

A (48:59)

You're in it.

B (49:01)

I appreciate the time with you, definitely.

A (49:03)

So thank you and please come back.

B (49:04)

Will do.

A (49:05)

All right, everybody, thank you so much for watching and we'll see you next time on Big Technology Podcast. It.