Jamie Siminoff (5:06)

Thanks for having me.

Neil Cybart (5:08)

I'm excited to talk to you. I've interviewed you before. When we were secretly piloting Decoder on the vergecast feed in 2018, you and I did a great interview. That was right after you'd sold the company, Amazon. Since then, you left Amazon, you've come back as chief inventor of Ring. That's a big deal. There's a lot there that I want to unpack. But let's just start with the basics. Why'd you go? Why'd you come back?

Jamie Siminoff (5:32)

So I actually did stay for five years. So it was like, a fairly long time. I didn't just sell and leave. I built it literally from my garage to. When I sold it to Amazon, we had gone from like 3 million, 30 million, 174, 80. So, I mean, the. It was crazy. Then we got to Amazon and we almost 10x the revenue there got it profitable. So, like, we. I just was flat out for so many years that I did finally get to a point where I just. I could feel myself not being the best leader of the overall business. Like, I could just kind of feel that. Like, just the. I just was burning out. And so in 2022, 2023, I talked to leadership at Amazon and they were. They were like, awesome. They were like, do something else here. Do this. And I was like, I think, guys, I just need to, like, step back and reset. And of course, as soon as I did that and got out, I realized that I only like doing one thing, which is Ring. I love Ring. I love the mission we have. I love, like, what we do. And, you know, fortunately it worked out that I was able to come back. And so I was able to take, like, a little bit over a year off, like, almost two years off, do some other stuff that I realized. I just, you know, it was cool, but I just didn't get the same satisfaction. I didn't. When I wake up in the morning as the chief inventor of Ring, like, I pop out of bed, like, I'm ready to go. Like, I want to get to the office. Like, I'm actually like, I just want to get here and do stuff. And it's. And that's true. And so it's been fun.

Neil Cybart (6:54)

I want to dig into all that. The book is about that grind, right? Starting the company, going on Shark Tank. There's a great section about how you felt about Mark Cuban and whether he thought he was going to invest and he didn't. That's an interesting story. There's a lot of bumps on that road, right? There was a lawsuit from ADT that you thought was going to kill the company. There's a great scene where you've accidentally written the number of weeks of payroll you had left on the whiteboard, and your team saw it and got freaked out. That grind is a lot. Right. But Ring is a very different company now. It's the market leader. It's. It's a brand. It's up there in the hall of fame of tech brands. It means things to a lot of people. What's your perspective on that now? To go from you had this idea to now everyone's expectations of what Ring are. Are kind of outside of your control.

Jamie Siminoff (7:44)

I mean, to unpack it. I think it is. Part of why I left was like, it got so, I'd say, in a way, overwhelming. Like, it was kind of crazy. I mean, I literally did start this in my garage. Like, I. I had an idea and I started at my garage. It's like the true, like, American Dream got on Shark Tank. And then I say, you know, you're, like, at Amazon and you're this thing still building, and it's becomes a verb and it's the thing. And so I. I didn't understand how impactful it truly was until I left. That's from, you know, when you're in the business, you're trying to figure something out, you're trying to fix something. You're talking to, you know, we call our customers neighbors. You're talking to a neighbors about a neighbor, about a problem you have. And so you don't feel the sort of impact of it. And as soon as I stepped out of it fully, where I didn't have any of those other signals, I did have this just sort of holy cow moment of wow, like this. This is really something. Like, the impact is truly there in every single level. And so going back, I came back with, I'd say, a newfound respect for that, a newfound understanding of that, and also, I think, like, a very clear mission for myself of what to do here, not just for the. Not just the mission of the company, but even for myself of what I can do to have a greater impact at Ring for our neighbors.

Neil Cybart (9:01)

I want to dig into that because the notion that we should have cameras everywhere and that will connect directly to safety, which is what you have been talking about the whole time. I went back and looked at our interview in 2018. You were talking about that back then as well. But that has always been your thesis, right? If we can put enough of these products everywhere, we can dramatically increase safety. I know you just spoke with our reporter Gentouhe and you said we can bring crime down to zero if we get it right with AI. There's a lot there. But the reason I want to start with why go and come back and connect it to. What do you think Ring is now? Amazon itself has changed dramatically in just the short time that you left, right. You left under. Andy Jassy was just the new CEO. Dave Limp was still the head of products and services. Dave left, he went to join Blue Origin. Jeff Bezos space company Panas Panay, who I know very well, he is the new head of devices and services. He's got a big vision for how to bring that ecosystem together, make it tighter. Is Amazon differently situated for you now to achieve your goals? Was that part of it?

Jamie Siminoff (10:04)

Ring changed every year for me. So, like, you know when you would go from a $3 million company to a 30 million to a 1. So I think I'm very comfortable in that. Every year something is different that it's like there's. It's like coming back would be different. So. But certainly, yeah, there's been a ton of changes at Amazon. I think they've been. Know if you look on, I think any metric, they've been positive. I mean, Dave Limp is still a great friend of mine. We, like, I travel with him. My son became very close friends with his son. So it's like, you know, there's, there's. He's still kind of like within the family. And so I, I still stay in touch with him. Panos has been very awesome to work with and we've had a lot of fun and we've been building stuff. We, we already launched some stuff at our launch event in the fall, so, like these Alexa greetings and some of these familiar faces and some of this stuff. So we're. I do think the idea of bringing the brands together, it's very smart. Let's leverage what we have as Amazon together, especially with AI, and see how we can get the most out of it for everyone.

Neil Cybart (11:05)

Take me inside the process here. You leave, then in short order, Dave leaves, Panos shows up. It's like 18 months later, we're hearing rumors you're coming back. And then you come back. Did Panos call you and say, hey, you got to come back and run this thing? Did you show up and say, hey, you got a new vision, I'm here for it?

Jamie Siminoff (11:22)

Yeah, it wasn't. I mean, it's not as Hollywood as you'd hope for, maybe. But, you know, it was. We started kind of just chatting a little bit. I was giving some ideas of what I would. The reality is I left before AI. I'll say. Like, you know, there was, sure, there's neural networks and there was computer vision, but there was really what we see in AI today was not there when I was, you know, kind of burning myself out in 22 and 23. It was, there's pieces of it, but not, not anything like what we have today. And so I started seeing things like our, our search party for dogs. I was thinking, like, how could we not as Ring, be looking for pets that are lost in neighborhoods using AI? This is amazing. We could do this now. So I started to sort of talk to them about some of these ideas. I think they liked the ideas. And then things kind of came together where it made sense. And I told them, I said, I think I made a mistake to leave, or I shouldn't say I made a mistake. I left for the right reason of being burnt out. I think I realized that I wish I had done more of a sabbatical, which who knows if that would even worked, but that I do love Ring and I want to be sort of here to especially take it through this next generation of AI and what we can do for the impact to neighbors with Ring and really sort of fully see out my vision of what we started with in the garage so long ago.

Neil Cybart (12:36)

That vision really implies there are going to be a lot of cameras, right? Like Amazon obviously has that scale. You know, I'm curious, like, as you look at Ring now, again, it's a verb. It's a household name.

Jamie Siminoff (12:48)

It.

Neil Cybart (12:48)

You could do more if you had even more cameras, right? If you could connect other competitor cameras to Ring, if there are other.

Jamie Siminoff (12:56)

It's been rumored by me that there's over 100 million cameras that we have. So, like out in the field. So.

Neil Cybart (13:01)

But you could, you could, you know, you could make that number a lot bigger. I'm just wondering if you think that the centerpiece of the ecosystem is your cameras or if it's the network.

Jamie Siminoff (13:10)

The centerpiece of Ring is the mission to make neighborhoods safer. I really think you have to go back to that. And so far it's been, you know, I do believe by selling our own sort of first party cameras, it's been very good. We're able to tie into them in a way that's, you know, makes it easy if it became that, you know, to make neighborhoods safer, tying into third party Cameras was the right way to go. And that's faster.

Neil Cybart (13:32)

Sure.

Jamie Siminoff (13:33)

I think, you know, definitely doing partnerships. How we could do that, I really do believe whatever we can do to get there faster. That said, I do think there has been value and other companies have seen this, of having vertically integrated software to hardware. That does help. And a lot of times when it's not like that, it creates a lot of issues with customers and sort of experience.

Neil Cybart (13:53)

I asked that because I was reading your book and there's a paragraph in here that just made me start laughing. I'm just going to read this paragraph. There was risk in agreeing not to be bought by Amazon. They could swoop in and buy one of our competitors, like Blink, based in Boston, smaller than us, but growing fast and impressively creative. Amazon did buy Blink, and they did. Last time I spoke to you in 2018, I said, when are you going to integrate Ring and Blink? Like, I have Ring cameras, I have Blink cameras. It's crazy to me. These are not the same platform. And you're like, we'll work on it. And then I interviewed Dave Limp and asked him the same question. He's like, we're working on it. And I interviewed Panos recently and asked the same question. And we're working on it. And it's not even third party cameras. It's inside Amazon's own ecosystem. There isn't these integrations to make the network.

Jamie Siminoff (14:36)

So I mean like one integration. I mean, so. So I think by, I think having Blink. Blink has been a great brand that, you know, just like you have Blink, it's like it delivers a different experience for customers. I think that's good. I don't think it's bad to have different experiences for customers that not everything has to be integrated. I think that's actually like, fine. That said, we're working on it.

Neil Cybart (15:01)

I knew it was.

Jamie Siminoff (15:01)

I got it. I got to give you that. But no, but like Search Party for Dogs. This thing that we' doing, like we're, you know, we're making sure that that works with Blink cameras. So I think there are ways to start to tie more of those pieces to like, again to the making neighborhoods safer. Tie those pieces together. You know, the hard part is Blink was truly a. It was a startup. Like it was a separate company. Ring was a separate company. Amazon bought both. And you know, it is hard to grow. They both grew very fast when they got here. And it's actually, it's really hard to integrate when you're growing fast. Like, it's kind of like in Some ways you sort of like. It's like you get one or the other. It's like, do you want to grow fast or integrate? And so it actually been hard. Part of it has been hard because both brands and Blink has been extremely successful, have just grown really fast.

Neil Cybart (15:47)

The reason I asked that question in that way and in that sequence is, you know, Dave left. Dave's strategy and I talked about this at length was we should get Alexa everywhere, right. We're going to have this platform for ambient computing and we need to put microphones and speakers everywhere and get the intelligence as far out in as many places on the edge as we can. We'll see what works and then that will become the basis of the ecosystem. And that led to, like, Alexa and microwaves. Right. There was just a lot of ideas and Dave's product launch events, we used to clock how fast products could get introduced. Yep, that's Dave. I know. I've known Panos for years. Panos is not that personality. Right. He's like, I'm going to make one diamond and then we're all going to look at this diamond. I'm going to tell you how shiny it is. And it's. It is very effective. And he is very charming at doing that. And he has really pulled everything together. And when I've talked to him about what the Amazon sort of consumer ecosystem should be in the AI moment, it really is pulling things together. Ring is a startup, you're the founder, you've come back. How much push and pull is there between Ring as an ecosystem unto itself, a household name, and it's part of the larger Amazon ecosystem that I know. The strategy is to pull it together and make it more integrated.

Jamie Siminoff (16:56)

Yeah. And I think we're trying to figure that out. I mean, Alexa for sure is the centerpiece. It's like the center of the universe. It's where gravity comes from. And so everyone's kind of. We're kind of all floating out there around it in its solar system. So I think it is trying to figure out one is where naturally to bring it in and try to make it a better experience for customers as we do that. But at the same time, Ring also has, as you said, it's been pretty successful on its own, on itself. And so you sort of want to make sure you also just don't smash things together for no reason and figure that out so that it doesn't hurt customers. Because there's also been, historically, I think people have gone that way too, where they've just taken two things and just kind of like pushed them together so hard that for customers, it actually doesn't work. I think we're doing a good job of it. I mean, I think panels and I are working well together. There's also Daniel at Alexa. I mean, there's all like a. There's also fire TV in that. So it's with Aidan. I mean, we have a whole team that's like, I think, coalescing and coming together and figuring out Nick with Eero. So there's actually like a lot there that we can bring together naturally and I think does create a great experience in the home and I am looking forward to, like, what we can do with that. Alexa certainly will be the centerpiece as the, like a genic AI to it.

Neil Cybart (18:15)

These are the decoder questions I always ask everybody how their teams are structured. You left, you came back. Did you restructure Ring at all? Did you make any changes to how Ring was operating? How were you structured? How are you structured now?

Jamie Siminoff (18:26)

I did. I'm certainly not like enough of like a student of business to even tell you, like, what type of structure it is. I built it like again from my garage to, you know, sort of five years in at Amazon. There are things that when I left, I realized I was doing wrong, that I had set up wrong. And a lot of the problems at Ring I'd say that I came back to. Most of them, I'll even say were things that I had set up that I just had allowed to fester and become wrong. And so when I came back, I did have a lot of clarity around how to fix that. And so I did come in pretty quickly and we fixed a lot of things. You saw the results of that with the fall event. We got a lot of product out. We're getting a lot of product out. We have a lot of new invention happening. Even the search party for dogs like all this stuff. I mean, I've only been back for, I don't know, seven, eight months now, now. And we've literally launched from start to finish hardware products, which I'd say I don't think we ever did in the history of Ring that fast. And a lot of that is reorienting how teams are AI just pushing things, understanding where to push. And so a newfound energy. So it has been fun to come back. And again, for being able to see it from the 30,000 foot clear level, no noise, I got to really understand what it should do. And then coming back, I feel like I had this clarity, like this sniper focus on it.

Neil Cybart (19:45)

Give me an example, I mean this is a weedsy show about structure. More often than not, what are the actual changes you made?

Jamie Siminoff (19:52)

I mean, I'll go into the asking why? So over time, processes, you start building product and it's like it takes X months and you sort of just the PDP process and they always have like every, you know, it's like three letter words for everything. And then people even forget what the three letter words are, but you still have the process. So it's like all these different. So we're trying to get something out. It was a product we came up with when I came back in. So it's like we wanted to launch it, which by the way it is shipping now. So this is called seven months. So from like zero to seven months, like that's crazy. And the team said like, we can't do this. And so instead of like before, I would have been like, okay, like okay, let's look at it. And they would have shown me like the PDP process or some three letter word. And it's like 90 days. And I'm like, oh, well, I guess you can't do IT. You have 90 days for the PDP process. How can we. So I would have just kind of let that go. So this time like, no, why, why, why? And we drove down and drove down and drove down and drove down. And then you realize like that process could be four hours if, if everything goes okay, but they give it 90 days because if something goes wrong, you need the time to fix it. And I'm like, well the problem is, of course you're gonna need the time to fix it. And of course it's gonna go wrong if you give yourself the time. So let's just say we only have four hours for it. Let's give it one day. And of course we're not going to ship a broken product. So don't worry if it's broken, we're not going to ship it. It'll just push it out. But if we don't pull that in, we're also not telling the factory to start cutting the steel on this. It's like everything cascades from that. And so we took this product, we sort of broke everything down from that. And instead of it taking probably 18 months, which would have been the regular, it shipped in six months.

Neil Cybart (21:27)

All right, now I gotta know what's the product?

Jamie Siminoff (21:29)

I'm not gonna tell you.

Neil Cybart (21:31)

Well, I have to know.

Jamie Siminoff (21:32)

You're not gonna get that out of me. It's one of the products we shipped. Just look through the nine cameras I.

Neil Cybart (21:38)

Shipped and one of them had the accelerated approval process.

Jamie Siminoff (21:42)

No, nine cameras. I shipped nine new cameras. Just look through those. Just look through those.

Neil Cybart (21:48)

I'll see. I'll see which one is the most obviously accelerated PDP products. You can see that as consumers, right? Did you change anyone on your team? Did you change how your reporting lines work inside of am you change where. I'm curious, like, you have this outside view. How did you think about making those changes?

Jamie Siminoff (22:04)

Yeah, we changed a bunch. Changed a bunch of stuff. Like changed a bunch of the reporting stuff. I mean, I've never been a big reporting person, you know, at Amazon, you do listen, a big company does need, like, you need to have a structure. Like, it's like even I'll admit it, Like, I hate to admit it, but like, you do need to have some sort of like a decent structure. I mean, you have, you know, 1.5 million people, 1.7. I don't even know what Amazon is, but it's a lot. So I guess you have to have some structure. But I did, yeah, I did change who goes where. I brought some people that I'd say people probably thought were more junior that maybe wouldn't normally report to me and I had them report to me. I broke up how I made them single individual contributors more. So instead of having to have to have reporting people and try to make these triangles, I said, you're just going to now run this thing and you're going to report to me and let's see how that works. And then, you know, and I'd say also more willing to break stuff this time. Like a little bit more willing to, like, should try to like break things. And then obviously, you know, within reason that you break things and then fix them up if you have to. And you know, we're not always right. So we do try some stuff and change it. But certainly, yeah, no, change some people around. Change some stuff around. And so far I think it's like, I think we've really gotten sort of fast and it's been team seems excited. I mean there's always, you know, there's always gonna be some people that are less excited when you have change. But overall I feel like, I do feel like it's actually like the energy here is. I'll call it exciting.

Neil Cybart (23:34)

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Neil Cybart (28:01)

Foreign's Jamie Simonoff about Amazon selling his company to Amazon, leaving Amazon, coming back to Amazon and managing his team and his product. Within Amazon's fairly legendary corporate environment, one of the more interesting themes that we experience making decoder is everybody who comes into contact with Amazon leaves just speaking Amazon, right? Talking about one way doors and two way doors and two pizza teams and single threaded owners. And it's very rare that Amazon's culture does not turn into everyone's culture. You had the opportunity to leave and come back. What parts of Amazon's culture were valuable out in the world and what parts of Amazon's culture did you think, oh this actually isn't working. When I go back I want to actually reorient myself and tweak that.

Jamie Siminoff (28:56)

I think so I do think the, the doc like doing docs, it literally.

Neil Cybart (29:03)

I got out the writing the memos and all that stuff.

Jamie Siminoff (29:05)

So I get out in the real world and I'm in like a real meeting with like people and they're doing a PowerPoint and I literally lose my mind. Like I'm like I'm losing my. I can't sit here, I can't do this. I can't get information in this way anymore. Like I've been so trained. The thing about a doc that's so amazing is you get to like teach yourself the information. So you get to go at your speed. You get to think about things and sort of like you get to process it yourself. When, when it's on a PowerPoint, someone's literally like they're teaching you at the speed of the entire room. It's all dumbed down. And so I realized like that to me the doc is one of these just like uber, uber powerful things. The one way door and two way door thing. I do think it's a great concept. I think it's been weaponized too much that it's too easy to say something's a one way door. And so what I've decided is I came back that there are one way doors, but you better not be able to break them down with a hammer. So like to me it's like, don't tell me it's a one way door unless it's really a one way door. Because a lot of the things that we sort of decide are and for anyone listening doesn't understand like the two way door decisions are like, it doesn't matter. You could like make the decision, you just go back on it. Like it's like a very sort of easy decision. One way door decisions are supposed to be something that if you make that decision basically you in essence can't change it. It's so impactful that you can't change it. And I think over time people have leaned too far into one way doors as like this is a one way door decision and we have to meet on it versus no, it's actually not. It's a little bit painful. But so what? And so yeah, my mental model is if you can break down the one way door, it's a two way door.

Neil Cybart (30:44)

Give me an example of your decision making framework then. I mean, this is the other question I ask everybody on decoder. Again, the joke is whenever I ask anybody who's come within a hundred miles of Amazon headquarters, I say how do you make decisions? And I hear about one way doors and two way doors. You've easily rift on that. Right? But it is your framework.

Jamie Siminoff (30:59)

But it's a good mental model. So it does work because you should be allowing people at scale to make decisions that don't that could Be changed. It makes sense, right? It's fast. If you want to make a decision that can be changed and is not that impactful, make the decision. If it's a super impactful decision, we should talk about it. But it's just, but then it's like the bar on that. How do you change the gauge to how sensitive you are to what is a one way door? That to me is what we have to do. And when I came back, that's where we have to figure that out. And I do think I'm very good. My superpower is I'm good at making these decisions. I was just in a meeting, we were branding a bunch of features and I'm like, do this, this and this. And they're like, it's like, do you, do we need to have like seven meetings? And I'm like, no, no, just do these. They're like, oh, you know, it's a kind of. Everyone's like shocked. And I'm like, guys, it's. If it's a bad brand, we can just change it. Like it's a feature. Like it's like, but you don't want to do that. Like you don't want to call. It's like a new AI feature for us for one of our kind of motion alerts things. You wouldn't want to rebrand it. Like it's not great. But it's also like customers, like our neighbors aren't going to care if we go from calling it this to that. Like it's not going to, like it's not going to ruin their experience. What's going to hurt them is us not launching something or taking more time. That's worse. And so to me, I'd much rather make quick decisions than to sort of stew on these things. So I do think the one way door, two way door still is a good framework. I think it's just trying to figure out what is a real one way door.

Neil Cybart (32:29)

Do you think that that ability to move fast is the luxury of being the sort of the market leader ensconced in Amazon?

Jamie Siminoff (32:35)

I think it's the luxury of also being a founder. I do appreciate the fact that the difference of being able to be the founder and having that, like, even though I am, listen, I'm an employee of Amazon, I'm not trying to act as if I'm not. But there is something that you get from being the founder overall that I'd say you'd be harder to have if you just came in as a VP of whatever and you were Recruited in.

Neil Cybart (33:01)

Yeah, that piece, the sort of founder mode dynamic inside of a big company. Amazon has preserved it, I think more than anyone. Right. Like you mentioned Nick Weaver. I've known Nick forever. I knew Nick when he was selling Eero to Amazon. He retained the title of CEO. You had the title of CEO. You left, you came back. You told me right before we started our career.

Jamie Siminoff (33:21)

I actually did not. So I always had chief inventor. I actually never. I never took CEO. Even when I was at Ring, I never had CEO. If you go back in my oldest emails, it says chief inventor and founder.

Neil Cybart (33:35)

Well, you get to do that when you're the founder of a standalone company. You can pick whatever title you want, but inside of Amazon structure, purposefully not taking that title, is there a reason for that? Inside of Amazon, do you have all the, like, authority of a CEO inside of that Amazon structure and you just don't have the title?

Jamie Siminoff (33:50)

I would say I probably do. I probably have, like, what would be considered like this internally? A CEO. I think it's a CEOs. It's a little bit of a misnomer, though, inside of a big company, because the reality is, like, there's a CEO, there's Andy, and then I definitely have the feeling, though, of autonomy to lead and make decisions for the area that I'm responsible for. So, like, whatever that's called, I do feel like I have that. I think Nick feels like he has that with Eero, and I think that's why people listen. It is not a thing. It is why people say, because you give them, whether it's a CEO title, but they certainly have the power to make the decisions. And, you know, in the end, what we like to do, what Nick likes to do, what, you know, founders like to do, is build stuff and make stuff happen. And so that's the thing I missed most about being outside of Amazon and Ring is like, I can make things happen at scale here, you know, which in a way, that's just unbelievable. I mean, it's just like I'm going to. We're launching this dog search party thing, which is like one of the most. I'm so excited about this thing because we're going to find hopefully all these dogs. There's over a million dogs are entered into our neighbor's app every year. So it's like, it's. The problem is so crazy. It's like. Like, when we went, I said, like, how many dogs are actually entered into our neighbor's app? I'm thinking they're gonna come back with, like, if they said 50,000, I'd be like, that's a lot. Wow, 50,000, that's crazy. They're like, there's over a million pet interactions a year on the neighbor's app. And my mind exploded. That's crazy. And so, you know, to be able to like touch something like that and build something like that and think of something like that, you know, at that scale and get it out there and impact people, that's I don't know who, like, especially for an inventor or you know, founder, that's the coolest thing ever.

Neil Cybart (35:29)

I want to actually talk about search party and how that interacts with like sort of Amazon's platforms and Amazon scale. I'm curious, right? There's founders, there's moving fast. I want to put some of the one way door, two way door dynamic into practice here.

Jamie Siminoff (35:42)

Okay.

Neil Cybart (35:43)

I look across sort of the Amazon's portfolio which is coming together. I look at Eero. Eero has a big bet on thread radios, right? Like my Eero routers. There's one sit right over there has thread radio in it.

Jamie Siminoff (35:53)

Yep.

Neil Cybart (35:54)

One day that's going to connect to, you know, Apple's thread network and for some reason the iPhone has a thread radio in it now. And this is like the smart home standard that a bunch of big companies, including to some extent Amazon, are pushing forward for sure. You made a bet a long time ago when Ring was started on a different protocol on Z Wave, which I would describe as like the security system protocol. Right? That's the one that all security systems run on. That feels like a one way door. Like you made that decision. There's no coming back from it now to the big standard that. Right. Your stable mate at Amazon has bet on Blink operates on like a totally different, random, inexpensive RF protocol. It's, there's, there's a whole thing over there about why those cameras are cheap and can run on AA batteries forever. That's just like an Amazon, right? A big company has three divisions. They've all made three different technology bets. Someone, Andy Jassy could say, what are we doing here? Make it all one platform.

Jamie Siminoff (36:45)

You didn't even make it as bad as it actually is. I also came out with, I also came out with Sidewalk, which is another protocol. So I just, it's even worse than you're saying.

Neil Cybart (36:52)

So, so put that into practice. Like how does that work? Like is there a meeting where someone's like, yo, like we could get a ton more value if we undo what felt like a one way door decision and we all center ourselves on one platform, one protocol for sure.

Jamie Siminoff (37:10)

Especially like those protocols, like they get close to one way door or for sure. The product that ships is the definition of a one way door. You ship a Z Wave product, like that is a Z Wave product, like that's it. It's like a Z Wave product is a Z Wave product. So that is a one way door decision. The two way door part though is if all of a sudden you see something in thread that's really happening, the replacement cycle on these products call it is you know, even, even on the long side, maybe three to five years, maybe six, seven, eight years. So if you look like if all of a sudden you saw that everything was really going to go thread and it was like that's it. And if you were not on thread, you were like thread or dead. Like if that was like the what we decided we could flip to that. We have enough of our toe in the water to figure that out. And so maybe you hurt yourself for a tiny short term thing, but you've also tried to figure out other things in the long term. So I think that one as much as it's one way door on the actual product, I think it's still two way door to figure that out. And I do think like Sidewalk, I think we're going to see a lot of interesting stuff with Sidewalk next year, which is a sort of a IoT product, sort of protocol, but more really a replacement of Internet for the trillion devices that are going to need to come online that have very low data. So very low low data. Like you know, kind of in and around the home devices.

Neil Cybart (38:33)

The connection I make there is, you know, you look at the first ring cameras, you look at the Doorbot. You had to invent a lot of stuff, right? There's chapters of your book that are how am I going to get a camera to run on this power draw in this WI FI enclosure? Whoops. I made the thing out of aluminum and I shouldn't have done that. There's a lot of that for the people who are interested in that, right? We are way on the other side of the smartphone commodity supply chain. You can just take a bunch of sensors and WI FI chips and camera modules off the shelf and make a door. And you have lots of competitors who are effectively doing that. The innovation is going to come from, okay, we have AWS exists, right? There's an entire AI platform for us to build on. Alexa exists, there's an entire AI platform, there's Echo devices, there's whatever. And I'm just wondering how you think about the balance of the things you want Ring to do versus the potential benefits of taking advantage of Amazon's ecosystem scale. Because that feels like the core tension of sort of the entire Amazon device ecosystem.

Jamie Siminoff (39:36)

For us. We're lucky that most of our products and we have a lot of different products, but if you look at the core products, they go to the cloud. And by going to the cloud again, if you want to look at the two way door, one way door thing, the cloud's a two way door. The cloud, you know, a new Nvidia chip comes out, it'll be available in the cloud. And so we don't like we're not that constrained because we're not again, most of our products, especially our core products, they're not sort of stuck in the home where we can't upgrade them. And that's where we've been. You know, the older ring doorbells are doing smart video descriptions. Like they're not doing it because we planned so far ahead, you know, eight years ago I wasn't smart enough to put an AI chip in it. It's because now there's AI chips in the cloud and they're already up there and that's what we use to do it. So I think it is for specifically for around us. Like integrating with Alexa is easy because it doesn't have to be done locally. Like that is the problem is when you get into these locally things, did you plan ahead enough? And that's the planning ahead is years ahead. Like you know, because if I think of a product now the reality is on a, and on Most I made 16 months. But you know, it used to be it would take you like almost two years from when you thought of a product till it came out in the market. Then it's another year to get it to scale. So it's basically three years from when you think of a product till it's at a point where it has enough in the field to matter. And so whatever chip you chose three years earlier better have aged well because.

Neil Cybart (40:59)

It'S out there now moving everything to the cloud, particularly video footage of people's cameras from their homes.

Jamie Siminoff (41:05)

Right.

Neil Cybart (41:06)

That's where the privacy concerns come into play. That's where the hey, are we building an accidental surveillance network comes into play. You know, I'm just looking at the headlines in my prep talk. It's like you left Amazon said we're going to stop working with police. You came back, boy Ring is going to work with police again. Right? You have a Partnership with Axon, which makes the taser that allows law enforcement to get access to ring footage. Did that feel like a two way door? Like you just made the, they had made the wrong decision in your absence and you came back and said we're going to do this again.

Jamie Siminoff (41:35)

I don't know if it's wrong or right. I think different leadership does different things. I spent a lot of time going on ride alongs. I spent a lot of times in areas that I'd say are not safe for those people. And I've seen a lot of things where I think we can impact it in a positive way. And so we don't work with police in the way of like it's, you know, I just want to be careful is like we're not, you know, what we do allow is that we allow agencies to ask for footage when something happens and we allow our neighbors, which I'll say in this point, customers, just to be clear, we allow our customers to anonymously decide whether or not they want to partake in that. So if they decide they don't want to be part of this sort of network and don't want to help this public service agency that asks them, they just say no. If they decide that they do want to, which by the way, a lot of people do want to increase the security of their neighborhoods. A lot of people do want their kids to grow up in safer neighborhoods. A lot of people want to have the tools to do that and are in places that are dangerous. We give them the ability to say yes and make that more efficient for them to communicate with those public service agencies. And also do it in a very auditable, you know, a digital audit. Like that's the other side is that today, without these tools, if you wanted to have, you know, if a police officer wanted to go and get footage from something, they'd have to go and knock on the door and ask you. And that's not comfortable for anyone. It's also, there's no digital audit trail of it. And with this they can do it efficiently. But there's also an audit trail. It's very clear and it's anonymous. If you say no, you never have to say like that officer's at your door. You have to say like no. Which you could, I guess, say it's very strange. It'd be like a weird situation if you say no on this. They don't even know that they asked you. I'm curious.

Neil Cybart (43:22)

You mentioned the audit trail. I know you're really excited about the prospect of AI to analyze huge amounts of video and think about, you know, sensor, like all the sensors in your home coming together with AI to bring people more insight. That's interesting. And certainly that's how you get to build searchparty. There's the other side of it that we cover at the Verge all the time now, which is, boy, people are using Sora to generate footage that looks like ring video doorbell footage. And ring being a verb, the angle of the doorbell, all that stuff creates a feeling of authenticity. Even though the footage is totally synthetic, it's fake. Are you thinking about that? Hey, we need to put in content credentials to ring doorbells before law enforcement gets them so we can verify this is real and not AI generated.

Jamie Siminoff (44:10)

Yeah, I mean, we're certainly thinking about that. We've been thinking about that and I do think it's where we have to. In the end, the source of truth is going to have to come from like a secure server because I do believe these AIs will be able to generate. You can see it with Sora. It's like there's some videos where I just like, I have to watch it 50 times to understand if it's fake or real. And at some point I think like, if that's where it is today, in five years it's going to be better. And so the only source of truth will be from these servers where it's like where it was captured. So, you know, 100% that you're getting like the, the, the video, like you're streaming it directly from there and there's no chain of custody issue. So I do think, yeah, I think about it a lot and I think we have to, we're going to all have to go into a world where you got. The information is going to matter because there's no way for a human to determine. Even watermarks, I think are going to get. It's going to be very hard to out watermark an AI at some point.

Neil Cybart (45:12)

Yeah, I'm curious. I mean, there's the whoops. We relate to thread standards decision. Right. By the way, I think you're late to thread. I don't know if it's coming through. I think you should make ring ring cameras.

Jamie Siminoff (45:22)

Right, I'm listening.

Neil Cybart (45:25)

Let me know if that's going to happen. That's one kind of decision with one set of stakes. Whoops. We relate to C2PA, which is the Content Authenticity Initiative standard, or we need to make a better standard and there's going to be a format war. Or actually we think standards are not the way to go. Because I could maybe fake the metadata too. And we're going to build an entire evidentiary system that requires the cops to come to our secure server that we will authenticate. The stakes of that are radically different. Right. In a world of AI deepfakes where everyone presumes video evidence is the gold standard and it's coming from your network.

Jamie Siminoff (45:59)

Yeah.

Neil Cybart (46:00)

How are you thinking about structuring that decision? How are you thinking about privacy in that mix? Right. That means you have to store a lot of video from a lot of people for a long time. And then how are you thinking about how quickly you might need to implement it in a world where AI is changing that so fast?

Jamie Siminoff (46:13)

Yeah. So if you go back. So the privacy thing is that our, our customers control their video. That's it. Like, so I think you always have it that like actually.

Neil Cybart (46:21)

But let me just complicate that. Like just presuming we have to have an authenticated server. Right. There's a crime in my neighborhood and I'm opted in. And we're going to say the cops can only get the video from the ring server because that's where we know it's true. I might not be as in control of my video anymore.

Jamie Siminoff (46:35)

No. Because not how it's built and not while I'm here. Because the way it works is that you will decide if you want to or not want to share that video, which is your property, with someone. Now once you share it, then it is up to us to figure out to your point of how do we share it, how do we make sure that the digital fingerprint goes all the way through or how does that chain of custody work of this video to make sure there's no fake in the process of it. I think this is why it is important to build these systems. It's going to be important though. I mean, this is like where also government is going to have to step in. We're going to have to deal with this across the board. Because we also have video coming off of cell phones. We have all these. So we do need to figure out how to build. And there's going to be companies. You know, Axon would probably be one of the companies. I don't want to speak for them, but you know, would they have evidence.com so to build these evidentiary systems to take in, because there's. There's ring is one. One part of taking in sort of data around. Call it a crime scene. You know, cell phone video is maybe even more still today than that. So how do you take that in? How do you make sure that it actually was captured on the iPhone directly and not, you know, tampered with? Between the two things, we're going to. We're going to have to figure it all out. I think we do have to work together on it. And it is, you know, the AI stuff is pushing us to sort of have to do it. I am proud with Ring is that we have built it so that you can, you know, take it directly, you keep it on the server so you can understand where it was, where it's from, where it was created. And we have that. That digital fingerprint on it and the audit trail of it.

Neil Cybart (48:07)

Are you having meetings about this problem? And that sort of cadence and with the urgency is we got to get through PDP faster?

Jamie Siminoff (48:14)

Yes. I mean, we are definitely trying to figure out how to make sure that our videos are always a source of truth. And right now, I mean, it's how we do our sharing. I mean, you go to a link and you take it from Ring. And so, I mean, I think. I think you're going to have to do that more and more as this. This world is changing. Like, you're just not going to be able to see. Just because someone sends you a video doesn't mean it's true.

Neil Cybart (48:35)

Yeah, I mean, that to me, right. Just because someone sends you a video doesn't mean it's true is as deep a flip of our expectations of photography and video as has ever existed in, like, the history of photography.

Jamie Siminoff (48:49)

Because it's. Up until now, you would have had to spend $100,000 to make that fake video or something or some, you know, some crazy amount to. If you wanted to make what you're talking about, you, like, actually produced it. And now it's just you type in make me a video that looks like this and does this. It's crazy. And it has. It has really flipped this on the head. So I would say to anyone, like, do. Like this is where you do your research. Like, use even AI to research what something's coming from. I mean, you have to sort of use multiple. Multiple things to figure out what is truth and what is not. Trut. But we have to teach. Like, we have to teach our kids that too. I mean, it's just. That's the world they're going to be in. And we need to just like, you need to understand that.

Neil Cybart (49:31)

We have to take another quick break. We'll be back in just a minute. Support for this show comes from agency. These days, when we talk about AI. We're not talking about one isolated agent.

Jamie Siminoff (49:50)

Or system working alone.

Neil Cybart (49:51)

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Neil Cybart (52:19)

When you need it. Learn more@schwab.com avoiding your unfinished Home projects because you're not sure where to start. Thumbtack knows homes, so you don't have to. Don't know the difference between matte paint, finish and satin or what that clunking sound from your dryer is. With Thumbtack, you don't have to be a home pro, you just have to hire one. You can hire top rated pros, see price estimates and read reviews all on the app download today. Welcome back. I'm talking with Jamie Siminoff from Ring. Right before the break, we were talking both about how Ring uses AI and how so much AI slot being loose in the world changes the way people feel about ring footage. Then I had to know, how does he see all these AI tools actually improving safety or our lives? I'm asking all this because, you know, again, you told my colleague Jen Tuohy that with Ring cameras we can get very close to zero crime. That's the combination of Ring and AI and that you can get much closer to the mission than I ever thought. Zeroing out crime with ring cameras. There's a lot of steps there. Explain what you mean by we can.

Jamie Siminoff (53:30)

Get and also like to Astro stat. If you go to the overall, what I saw, I said in certain situations. So like around neighborhoods, I said, if you have all of our different products in a neighborhood, I do think with AI that we can finally see a path where before I would say with the mission, you know, we want to reduce crime in neighborhoods. Great. We want to make neighborhoods safer. Okay, sure. But it was like a forever mission. Like, I couldn't see with technology how to get to a point that you were, you were, I could see where you were impactful, but like, you could never like sort of get. But when you put AI into it now, all of a sudden you have this like human element that AI gives you. I think with our products in neighborhoods. And again, this is like, you have to be a little bit specific to it. I do see a path to get where we can actually start to get to where like, yeah, we take down crime in a neighborhood to call it close to zero. And I even said there are some crimes that you can't stop, of course. So it's not, it's, you know, it's a little bit, it's always crazy to say something like zero out crime. But it is, it's a good goal to have. And I think that's, that's what we're trying to do is take the goal of like, how, how far can we go in affecting this now with AI?

Neil Cybart (54:38)

Yeah, I read that quote and I was like, oh, that's Jamie. I know that Guy Severian character.

Jamie Siminoff (54:44)

I mean you gotta have, you know.

Neil Cybart (54:46)

Think big but mechanically walk people through. What you mean you're, you put enough ring products in a neighborhood and then AI does what to them? That helps you get closer to the mission of zeroing out crime.

Jamie Siminoff (54:57)

The mental model, or how I look at it, is that AI allows us to have. If you had a security, if, if you had a neighborhood where you had called unlimited resources. So every, every house had security guards. And those security guards were people that worked the same house for 10 years or 20 years. And I mean that from a knowledge perspective. So the knowledge they had of that house was extreme. Like they knew everything about you and that sort of residence and your family, how you lived, the people that come in and out. And then if that neighborhood had an HOA with call it private security and those private security were also sort of around and knew everything. Like what would happen? Like when a dog gets lost, you know, you'd be like, oh my gosh, my dog is lost. Well, they would call each other and one of them would find the dog very quickly. So like that's kind of like. So how do we sort of change that and bring that into the digital world?

Neil Cybart (55:46)

Can I just ask you a question about that neighborhood specifically? Do you ever stop and consider that that neighborhood might suck? Right. Like, I've just like the idea that every house on my street would have all knowing private security guards and I would have an HOA and that HOA would have a private security force. There's. You can easily paint that to dystopia. Right? Like everyone's so afraid that we have private cops in every corner and I'm paying HOA fees, which is just a nightmare of its.

Jamie Siminoff (56:14)

I would assume you live in a safe neighborhood.

Neil Cybart (56:17)

I hope so. Yeah.

Jamie Siminoff (56:18)

No, I mean today I assume, I mean, so like I go to, I mean if you want, I'll take you to a place where people live that they have to, when they get home from school, lock their doors and you know, stay in their house and they can't go out and.

Neil Cybart (56:30)

But I'm just saying the model, like the, the model is everybody is so afraid that they have private cops is.

Jamie Siminoff (56:36)

I think the model is that doing crime in a neighborhood like that is not profitable. And I think that that is that you want people to move into another job. Like, I don't think that crime is a good thing. And so I think, I don't know. But like, listen It's. It's a. It's a. It certainly is an argument to have. I. I do believe that. I think safer neighborhoods allow for kids to grow up in a better environment, and I think that that allows them to be able to focus on the things that matter. And so that's kind of what we're going for. I just wanted.

Neil Cybart (57:03)

I just wanted to challenge the premise that, like.

Jamie Siminoff (57:05)

No, I think it's a.

Neil Cybart (57:06)

The model is. There's cops everywhere, right? Like, that level of privacy.

Jamie Siminoff (57:10)

Yeah, it's not cops. I think it's more that you have the ability to understand what's happening. It's not. It's not like. But I mean, yeah, I think, listen, it's a fair statement, I guess. I think, you know, our. I want to live in a safe place.

Neil Cybart (57:24)

So the model is there's a lot of intelligence. Right? That's what. I know that's what you're trying to say.

Jamie Siminoff (57:29)

Yeah, there's a ton of intelligence.

Neil Cybart (57:29)

There's a lot of intelligence in your neighborhood. Maybe it's private security, maybe it's not. What does the AI do? Does it just make the camera smarter? It lets you do more intelligent assessment of what the cameras are seeing?

Jamie Siminoff (57:39)

Yeah, I think the basis is that the AI allows you to. Like, right now we just say, like, motion detection. Motion detection. Motion. I mean, it's funny, like, when I started Ring It Go Back. Like, the book was fun because I get to go back and, like, actually, you know, go through this whole, like, story of, like, how this thing came is like, motion detection was, like, an amazing invention. Like, you're in the airport and there's a motion at your front door, and you look at it like, wow, this is crazy. Now it's like, with AI, we shouldn't be telling you about motion detection. We should be telling you what's there. Like, when should you look at it? Like, when does it matter? And we shouldn't be bothering you all the time. That's what I mean about this, like, idea of these sort of like a security guard at your house, security guard in the neighborhood is like, there should be this intelligence in your neighborhood that can tell you when you should be trying to be part of something but not always telling you. So it's not just like, car, car, dog, you know, person, person. It's like, hey, look at this. Like, you. You want to pay attention to this right now.

Neil Cybart (58:35)

There's a piece of that intelligence. I mean, again, we have ring cameras around our house. Boy, am I often told that there's a package at our doorstep, we just had a baby. There's a lot of packages in our doorstep lately. There's a turn where you can connect that to Amazon's database, right? And the package that arrives at my doorstep, you know, it's in it. There's a turn where you can connect it to, I don't know, a facial recognition database and you can tell me who is at my door. Once you start connecting these databases, the privacy implications start to sort of fractally explode. Right now we know a lot, and maybe we know too much. And that's where, you know, when we hear from privacy advocates, when we do our coverage, it's the connecting of all the data sources that actually reduces the sense of privacy that people might feel in their home. And I hear you that if you look at my neighbor's feed, it's a bunch of lost pets. And then constantly people asking if other people have heard an explosion, everyone wants to know, what was that sound? But when you connect a bunch of those databases, when you connect particularly to facial recognition, there's a turn in the privacy conversation where the stakes ratchet up really high, where maybe it's gone forever. How are you thinking about that kind of decision making? Okay, we have a lot of intelligence in the AI. It's trivial for the AI to go connect to another store of information. Right? That's a thing you can do with AI, especially at a big company like Amazon, where you have lots of other stores of information. There's a line. What's the line for you?

Jamie Siminoff (59:58)

I mean, there is a responsibility, obviously, just to build safe products. So let's just start with that. Yeah, we did announce facial, like we call Familiar Faces, but that's not connected. That's just for your. Like, that's. You know how your. I mean, your iPhone today, if you search your iPhone, it's crazy. Like, search for someone's name in your photos and they're like, their pictures come up. And so I do think there's a. There's a balance between not allowing technology to exist that should exist, that helps people and gives them more efficiency, gives them safer homes. And then also, obviously, not creating to where you're going to, like this sort of dystopian place. That is that. And so I think that is, like, that's the responsibility. But, like, what we're doing with Familiar faces is we're just giving you the ability to say, like, when my wife comes home, don't, don't, don't. You know, because it is like, silly, like, why do I get an alert when my wife comes home, I don't want it. Like, I don't need it.

Neil Cybart (60:54)

I'm asking this for a lot of reasons, but, you know, I look at sort of broadly what's happening with surveillance footage out in the world world. And I'm not saying RING is participating in this. I'm just giving you the example. ICE has facial recognition systems, right. And they are arguing that a positive match in their facial recognition system is a definitive determination of someone's immigration status. That's way out there. I don't think you're doing that, but you can get to, okay, we have facial recognition, we have a bunch of evidence coming off of RING cameras to make it really safe. You want to go from passive surveillance to active surveillance, right? That's what the studies show. Now we can just. The camera will literally identify the criminal by face and tell the cops, this person tried to steal a car from this driveway. And that's the thing that would get you to actually zero out crime. There's a lot of risk in those steps. But, you know, if I draw the thread from what you're saying, it's all the way to the criminals won't come here because the cameras will know who they are and tell the cops. Are you willing to go that far?

Jamie Siminoff (61:53)

It's not about the. I think it's also that the cameras will alert people in a way that, you know, if, you know, part of what made Ring and what made neighbors safer with like Ring 1.0, and I think we are in like Ring 2.0, is that there was no presence at the home. Like, what. How did people break into homes? It's like they go literally, knock, knock, burglars. They go like, knock, knock, like no one was home. And then like, it was at 3 o' clock in the afternoon, they'd go to the homes next door, find a place that was empty, and they go in the home. Ring allowed you. To. Now all of a sudden, like when someone comes up to the door, like, you're like, oh, I got emotional, or hi, like, what's going on? And so it gave presence to the home. So. So I don't think you have to go as far as that real time stuff to get to where we're talking about. I think it's more of like the anomaly detection and allowing people to make it so that if, you know, if someone comes in that you're aware of what's happening around the neighborhood. Because right now there's like no awareness of what's going on around and So I don't think it's as dystopian as where you're going. It's certainly, it's not what we're building. And I do think we can impact things to a really high level in neighborhoods, which again to the Gentooie thing in neighborhoods is where we were talking about that with AI and what we're doing with a bunch of rings together. And I think even dog search party is a good way to look at it, which is like, how do these cameras come together for good in the neighborhood?

Neil Cybart (63:10)

How do you think about hallucinations in this context? Your competitor Google just launched Gemini. They can do a bunch of recognition of various NEST cameras. The immediate reports were this thing is saying a guy named Michael is in my house. There's like no one in my house named Michael. There's a deer in my living room. That's obviously not happening. You know, Google's models are cutting edge. They're as good as anyone's. They're still hallucinating, right?

Jamie Siminoff (63:32)

Well, I mean, all I'll say is like we've had smart video descriptions out for a while and I would say ours are pretty good. So I won't talk about others, but I think ours are pretty good. The idea is that these things drive to a human decision though it's not that they're autonomously creating some sort of decision, it's that they're telling you what to do. So hallucination that there's a deer in your living room. It might be annoying because you're going to now check to see the deer in your living room and you realize that it's not a deer in your living room. But I do think it's driving. That's the idea is that these things drive to a human, that they're not creating some sort of autonomous decision making cycle.

Neil Cybart (64:11)

Do you think the models are good enough to do all the things you want to do?

Jamie Siminoff (64:15)

I've certainly no one I think in our. I don't think anyone's ever seen something like how fast AI is moving. From a technology side, I've never seen anything where every few weeks or every month it's like literally something comes out that has surprised us that's that much better than the next thing.

Neil Cybart (64:32)

Well, let me flip that question. Is there something you want to build where the models aren't good enough yet?

Jamie Siminoff (64:37)

Probably not. Probably everything is there from the models being good enough. The question is from a processing like the cost of processing might be off. So it might be that the cost to do that thing is still so expensive that it's not rational to do so again. The dog search party I go back to is like five years ago. Could you have built something like that? Sure. It just would have cost you so much money in resources, time to develop it. Everything else it literally would next to impossible to execute on. Whereas today I'd say it was reasonable. It still cost us money, but it's reasonable to do that kind of a product.

Neil Cybart (65:18)

Do you think when you talk about 0outryme in a neighborhood, the idea that everyone in a neighborhood has one of those illuminated ring signs in their front yard, is that enough? Is that just enough of a deterrent? The bad guys will know my face is going to capture on video and that will be analyzed by an AI and something will happen. Do you have to do more outbound deterrence?

Jamie Siminoff (65:40)

I think that's a. I think that's a. That's a part of it. I mean awareness is a big part of it. I think there's ways with lights also like lighting to do stuff. That's a big part of it. I think having just, you know, if all of a sudden someone comes outside because something's an anomaly, that's a big part of it. Like it doesn't have to be. It doesn't have to be some sort of like crazy thing. Like I think a lot of these. That's what I was saying is it's a lot of these little things add up to make that work.

Neil Cybart (66:05)

So when you think about, okay, we can bring it down, we can bring crime down a neighborhood to close to zero in a neighborhood. What are the sort of ratcheting steps? Right? Is it everyone just gets the ring camera and your platform does all the work? Is it someone gets caught and they tell all their friends in jail that they got caught. Don't go to like what are the steps?

Jamie Siminoff (66:25)

I think it's really about bringing neighbors together for this particular thing. So it's about how you individually and we've always thought about how like each house is its own node controlled by the neighbor, so controlled by the person. And that's like we. That. That we kill. I'll keep going back to that which is 100%. Is that your video is your control. Everything you're doing is in your control. Whether you sort of want to take part in anything is in your control. Like that has to be the first layer of all of it. But then when something happens, do you want to take part in it? So if, if it gets, you get an alert that like this dog Looks like this dog that's in front of your house. Can you contact your neighbor, you can decide not to take part in it, and then no one will ever know. And it's fine. It's just like basically deleted, or you can take part in it. And so I think that's how we can do things that can make a neighborhood into this node where individual neighbors are all sort of on their own, but when things happen, they can work together as they want to.

Neil Cybart (67:18)

And you think that AI will just sort of accelerate the process of working.

Jamie Siminoff (67:21)

I think AI, it is like a copilot. It's their assistant. It's helping them to figure this out because again, if you're just getting every motion alert and if you have eight cameras and you're just getting motion alerts all day, you can't, no human being can parse all this data. And so that's kind of what I was talking to Jen about, is that I do think I see now a way to use AI to help feed better data to us, which allows us to make better decisions and work together better.

Neil Cybart (67:49)

That's really interesting because that's a vision of AI, that it has a beginning and end, right? There's a huge amount of data. AI can parse that data better. The LLM technology we have today can make better inferences out of that data. And I understand that processing chain. Let me ask you about the other side of it, which is you're part of a big company that has a smart home stack that has lots of microphones and speakers in lots of people's houses. I went to the Alexa launch event and it was so funny. Pano specifically gave the demo of Show Me when the Dog Came Home. And then it showed footage for a Ring camera. And I went, oh, that's not Alexa. Like, Ring already had that, right? You're just asking Ring to show the thing from its platform that it already had. I see all of that. I look at the big companies, Amazon, Google, Apple. No one has actually managed to add the LLM to the assistant in a way that works great. We've all shipped it. Everyone's shipping, except for Apple, which apparently had to start over. But even the ones that are shipping Gemini with Google, Alexa, their first steps and the idea that you can orchestrate between what's happening. I just want to talk to you about. My kid just wants to ask you questions about space, which is all she wants to do with any of these tools. And I need you to turn on the light. And that is a very deterministic process that you shouldn't get wrong. That orchestration is very complicated and no one has really quite nailed it yet. When you look at that and how Rings should interact with that, does that feel like the AI tools can do the things we need them to do? Because when I say there's a product I want that I don't think the models can achieve, that's the first one that comes to mind, the LLM powered assistant that does everything that we thought Alexa should do when it came out in 2014.

Jamie Siminoff (69:35)

I would say I'm very hopeful that Alexa is there in a lot of ways today. And obviously the team is working on lots of new features as well. I do think the vision that they have there for it, both what's out and in the market, as well as what's coming, is like, I think it's that. I mean, I do think it's that. And it's also where, as you said, like, Ring, you know, it's great you bought it maybe for security and you had it for this, but, like, it also tell you if you fed the dog or not and remind you because it actually, like, there's a intelligence behind Alexa. And in this case, I'll say is like, almost like a house manager, where it's taking this intelligence, this digital data, saying, like, I think you didn't feed that. It's like 8 o'. Clock. Like, you didn't feed the dog. Like, do you want to feed the dog? And it's like, no, I did. I did it over here. Like, you didn't see it or, oh, you're right, I forgot to feed the dog. And so I do think. I think that's what we want is, listen, I think we all want assistance. And so, like, what I'm talking about with Ring is obviously, like, I'm more focused on this idea of like, security assistance for your home in your neighborhood. And what you're talking about then is like, how do you get assistance in your home inside the home, inside the four walls?

Neil Cybart (70:45)

Well, no, I think it's much more like, I understand we are generating vastly more video footage than ever before, and we have vastly more sensors. And LLM seems like a capable, if not always appropriate tool to manage all that data. Right. And to get something out of it that's human readable. Like, literally human readable. I understand that and I see that argument. I see why there's so much interest there. It's the next turn, which is there's just an always on intelligence in my house that can understand all of these different systems and connect them all together and actually sometimes do very predictably dumb things. I need the lights to turn on and sometimes go out in the world and book a concert ticket. All the promises that we're making. I'm asking you because I think you have more insight into it. I'm not actually sure that LLM technology as it's presently constituted can make that leap all the way.

Jamie Siminoff (71:38)

I'd say I believe that we have all the technology pieces together today. So like the individual, like if it was a construction site, like all the things are on the site, like the wood, the concrete, like all the pieces are now there. I think to your point of like building the building, like there might be some pieces like where it's like we have to figure out how to exactly put the concrete together with the wood to make it, like to make it exactly work. But I would say we now have crossed over to where I do believe that all of the actual technology pieces are there. I think some of it might be even on the processing side, like it might just be too expensive. Like to, to do what you're trying to do might be so expensive. You might need an Nvidia N100 in your house. And like that's just like. But that Nvidia N100, in a year or two, you know, it could be like 50 bucks. And so if that's the case, then you probably will have that. So I do I. But I, I look at the roadmap for Alexa and so what I can see and I do think we're going to have like that in essence always on intelligence that goes from turning on the lights to a turkey timer or whatever, but also to doing very complex things like understanding that it's garbage day, the garbage cans are now. Because seeing it on this, I've been at home, it kind of does more than it does multiple turns of intelligence, like a human would do to say, can you look at this? You should do this as if it was like a full time. That's kind of what I say with the. When I say like a security guard at your house and in the neighborhood. What I meant about it is like this idea of this intelligence that really knows you. And the only way to get that historically has been like not only a full time person, but someone who's been there for a long time because they have to know everything you're doing. And I do think like Alexa in the home will be that, if not already is on Alexa in a lot of ways today.

Neil Cybart (73:24)

Well, Jamie, we're going to have to have you back when that all comes to fruition and when you announce the inevitable thread powered Ring cameras. That's I believe that promise was made on this on the show.

Jamie Siminoff (73:34)

We're going to call them NP cameras. I'll give you the ugly one.

Neil Cybart (73:39)

I was promised a seven month development cycle on these cameras.

Jamie Siminoff (73:42)

I'll do four months. I got to get faster.

Neil Cybart (73:44)

The book is called Ding Dong How Ring Went From Shark Tank Reject to Everyone's Front Door. It is a very dishy read if you are the sort of person who loves reading about how hardware is built and how companies are started. I can't recommend it enough. It is I believe available on Amazon.

Jamie Siminoff (73:58)

It is. I self published it through Amazon. I mean it's like, I mean it's like, it's like what else could you do? I mean I had to. I mean it's just. I had to. But I like it was a great. That was fun too.

Neil Cybart (74:07)

So yeah, what's next for Aang? What should people be looking out for?

Jamie Siminoff (74:10)

I mean we have our. We just launched all our new 4K cameras that are awesome. Search party for dogs is going live very soon. Lots more fun stuff coming and can't wait to show people what we can do.

Neil Cybart (74:23)

All right man, we'll have to have you back soon. Thank you so much for being on Decoder.

Jamie Siminoff (74:25)

Thank you very much.

Neil Cybart (74:29)

I'd like to thank Jamie Sinoff for joining Decoder today and thank you for listening. I hope you enjoyed it. If you'd like to let us know what you thought about this episode or really anything else, drop us a line. You can email us atdecoder the verge.com we really do read all the emails. Or you can hit me up directly on threads or bluesky. Now's a great time to ask us any questions that's on your mind too. Since our end of the year special is only a few weeks away, Decoder is on YouTube now. You can watch full episodes at DecoderPod and you can also watch clips on TikTok and Instagram. They're Decoder Pod too. It's a lot of fun. If you like Decoder, please share it with your friends and subscribe wherever you get your podcasts. Decoder is a production of the Verge and part of the Vox Media Podcast network. The show is produced by Kate Cox and Nick Statt and edited by Ursa Wright. Our editorial director is Kevin McShane. The decoder of music is by Breakmaster Cylinder. We'll see you next time. Adobe Acrobat Studio so brand new show me all the things PDFs can do. Do your work with ease and speed.

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