How Bots, Deepfakes and AI Agents Are Forcing a New Internet Identity Layer

How do you prove somebody is human? It is a surprisingly hard problem. I think that people are going to start getting accused of being brought. We currently see less than 1% of what it will look like in probably a year or two. The idea that AGI will lead to some very fundamental shift seems obvious. AIs are really good at programming humans. Most better than humans are programming AIs. Absolutely. AIA will be able to have a GitHub account and will be able to post and also test two or five other AIs that these are in fact humans. No, they're not. Honestly, if you don't take it seriously now. Alex, welcome to the podcast. Great to have you. Thanks for having me. So, Proof of Human is having a moment right now. Why don't you first give a background for people who are unfamiliar? What is the moment that's happening and how did we get here? Yeah, and what is Proof of Human? Proof of Human, as the name suggests, is do you know if you interact with a human or something else on the internet? And actually, the kinds of questions that we're now asking is, are you interacting with a human, an agent on behalf of a human, or just an agent? I think these are roughly the three areas that we want to split apart. Well, and describe a little bit the difference between just an agent and an agent acting on the behalf of a human. How do you see that distinction? Yeah, so quickly explaining just the term Proof of Human and I think what is hard about it, and then I'll explain how it fits into an agent on behalf of a human. So, what Proof of Human really means is that, you know, every individual that interacts on a platform has only one, ideally one account, or, you know, a limited number of accounts, and stays the owner of that account. So, that's kind of the property that you're looking for. So, like, you're looking for initial verification that ideally should be, you know, something like anonymous or very extremely privacy-preserving, and then ongoing authentication that the same person remains in the control of the account. And then there's like some secondary properties that I think are good to have. But that actually tells you that the really hard thing is uniqueness. Like, what is happening on a platform like Twitter right now is that there's all these accounts, you know, all these bots that are in replies, that, you know, there's probably one human sitting somewhere and setting out 100,000 of the eyes. And there's this ketchup game where, like, you know, Twitter and X are trying to just find them and block probably millions a day of these. Which is what, like, a 100th of the bots, that's how it feels like. And then, agent on behalf of human, I think, like, how do it look like is, you know, I think all of us will have agents. It's unclear how they will look like, is it's going to be one or the multiple ones, maybe with different tasks and different, even types of characters. And I think it will then come down to, you know, I approve a certain action of my agent. I give him certain rights. So, like, act on my behalf. Okay. Post my ex account, post my Instagram. For example. But it's my Instagram and I'm a unique human that onset. That's right. You know, that X or Instagram could decide that if that's actually something they want to supply for. Right. But that's how you could do it. I make sense. And so, how do you prove somebody is human? It is, it is a surprisingly hard problem. Yeah. So, you know, it's, you know, those agents are very, very, very kind of, it's, you know, it's funny. We started this company, you know, a couple of years ago, before, before, before, all of that. But we kind of took that as an assumption that eventually we will have a eyes that, you know, both past the towing test. So, they can just claim to be a human. You will not be able to tell them anymore in the internet. And also that they would be, you know, highly agent, they can just like run around with their own thing. And so, that makes it really, really hard because back then when we started the company, they were like roughly three big ideas that people were interested in. One was this idea of a web of trust or like related ideas. So, this idea that you, you look how someone behaves on the internet or did behave in the past. So, like usually a combination of you have the certain number of accounts that you, you know, you own since a couple of years and then you post regularly or you comment regularly to get help. Like these were the kinds of things that people are using. And then let's say all three of us have them. And then I attest also that, you know, I know you in the real world. And I attest to you that I know in the real world. And that's how you would build a certain graph. And that was like a very hot idea back then for this. But we disregarded it basically because we assumed that, you know, eventually everything that is just digital and AI will be able to do as well. Yep. So, they're, they're, they're, they're exactly. So, they will be able to have a get up account and will be able to post and own an account and like also attest to five other AI's that these are in fact humans and even though they're not. So, so, you know, there was, there was area number one, area number two was to just, you know, use government IDs for everything, which we just all see me disregarded for a couple of reasons. One is the, you know, I think, you know, it's strictly better if the government would not control such an infrastructure in terms of free speech and actually breaking that apart. But then also, right, you lose anonymity. You could hypothetically set up a system that maybe preserves it, but it's very hard to do. And the second thing is also, you know, the government, I didn't even system is just not build for that. And, and what is so hard about this problem is it's going to be a global problem. And so, it doesn't really matter if, you know, one government maybe has the perfect infrastructure. For example, Singapore is like an example of a government that has, you know, perfect infrastructure all around. But that barely doesn't, doesn't matter because, you know, for example, I don't know, met as a global product with three billion users. There's a lot of other countries. Yeah, Singapore is about two million people or million people. Exactly. So, do you want to look everyone, everyone else out? So, yeah. And then there's a long list of other things. And then there's a lot of other things that are going to be a big deal. For example, what does Face ID do? Face ID checks that I'm the same person I can using my phone. And so it's a one to one authentication. So there's an embedding start on my phone. It takes a picture of my face, creates a new picture, compares to the degrees one. And if that is close enough, I can use my phone. But so that's a one to one, you know, one embedding to one new embedding. To solve the proof of human problem, you will need to distinguish one new individual from all previous individuals. You need to make sure that, you know, Ben is trying to sign up and Ben did not sign up before. Yeah. And then suddenly it goes from one to one to one to n. And n is the size of your network essentially. Right. Right. Right. Right. Right. And then you can choose the math and you can calculate how much mathematical entropy, like how much information, just information theoretically do you need to prove that? And it turns out that's a pretty high number because it's an exponential problem. Right. And so then you can choose the math and you find out that, you know, things like face or, you know, even fingerprints or something doesn't work. Like that then you would basically hit a wall after tens of millions of users. And so then you end up with, you know, something like Iris, which is the mouth of a fear of the ride. That actually has enough entry. That is unique. That is unique. That is unique enough. And how do you also then solve the, you know, one thing that biometrics have been subject to historically is just replay attacks. Where, okay. I mean, I may not have your eyeball, but I've got enough information that I can run a replay attack on you. So there's now actually, you know, again, it is important, I think, to split up the problem and verification, which is essentially in, you know, all terms. It's like you're getting your passport. Right. And then authentication, which is you showing your passport constantly for certain kinds of things. And on the, you know, on the verification piece, that's, you know, we've went down. If you know, world, you know, that we've built this thing called an OR. So, you know, it's, it's doing a lot of things to prevent these kinds of attacks. So it's, for example, it has multiple sensors in the, you know, electromagnetic spectrum to just make sure that you cannot show a display to it. And it would recognize that. So I think on that side, we've, you know, we've got it handled on the, on the consumer side, like, you know, to then reauthenticate. It turns out to be much harder because you would need to trust the phone in some sense, because what we actually do in that moment is when you verify within the OR. We not only do we check your uniqueness and fully anonymous and privacy is a language and we should talk about that. But also, we sent to your phone, assigned face image that you then can later use to reauthenticate against it. Right. And with a new iPhone, you can have meaningful amount of trust against that, but with old Android phones basically not. Yeah. Yeah. Yeah. Because you can just show a deep-take, essentially, either through a display or just directly inject it into the camera stream. So that's a problem. And so it's going to be a mix-off. If you have a new enough, let's say, iPhone or a general phone, then you can just reauthenticate against that picture that you took on verification. Otherwise, you would probably have to even go back to an orp somewhat frequently. Make a couple of times a year if you just-- All right. You can have-- Right. To reauthenticate. Yeah. That's right. Interesting. And then one of the things, one of the kind of incorrect criticisms of the approach early was, oh my god, they've got my eyeball. And now they somehow have access to my privacy, and they're going to do all these things to me. And that's my access. And then they can-- they-- WorldCoin can impersonate me and all these kinds of things. But that's not the case. So that was also like a non-trivial engineering problem. There was very much non-trivial. So actually, I think one point on iris that I think people don't appreciate enough. That's a bet we took back then, but it was essentially that iris will turn out to be super normal as a modality, just because I think we will all wear AR and VR systems to do that. Apple over here does it. Yep. Yes. And RSD and the Vision Pro. So maybe that's the general point. I think it's going to become something that we will use across many different devices and will normalize in that sense. But I think on the privacy piece, that took us a lot of time. Because when we decided back then that with our assumptions, which was six years ago, that we will need a custom hardware device for biometrics, it was actually quite scary to come to the conclusion because-- Yeah, that's an expensive conclusion. It's like very expensive. And then just having this idea that you would need to distribute them all over the world, that just assumes that you would be able to somehow bring up billions of dollars and to a massive effort to just result in the world. But then also the privacy challenge of how could you build such a system that has all the requirements that we care about. And the two main high-level ideas on how to solve it were multi-party computation and zero-nodge proofs. And so to-- again, what is different to face ID because face ID actually is, you know, can be very private just because the embedding is stored on the phone. It doesn't have to leave the phone ever just because it's just you against you in the past. But to check uniqueness, you need to check against all previous people. So something needs to leave. Yeah. Yeah. So something and be compared to someone else and that's a much harder challenge. And how we approach that is we have multi-party computation. And so that essentially means that, you know, in our case, when you verify with an org, you know, we take all these pictures, they get computed on the device, and then they actually get split up in multiple pieces. So for example, we take a picture of the iris, we calculate the iris code. Then we break that iris code in multiple pieces and send it to multiple computers such that there is no central database in some sort. So no one actually has the information about you. Right. And then you do some clever tricks of how these different parties need to come together to do a computation that still leaves the pieces apart. Right, right, right. In such a way that-- Right, nobody has the whole thing. Yeah. So no one has the whole thing. But also during the computation, no one has the whole thing. But they do some clever interactions to come to the conclusion. A little like a zero-knowledge proof kind of technique. It's very different. It's very different. But I think in terms of the properties that achieve somewhat similar, where like you-- no one knows anything about you, but you can actually together make a statement about you. And so you send it to this multi-party computation and what comes back is, yes, that individual is unique. And the second thing we do is we separate all of this from you with a zero-knowledge proof. So meaning you have to secret on your phone, but no one else has it. No server has it. We don't have it. And then you can later go back to this multi-party computation and say like, hey, I have a secret that is part of that computation. And I am in fact unique. And you can prove that to a platform. You could go to the social network and prove that you're a unique user to the social platform without us knowing anything about you or the social network knowing anything about you. And so it's this very counterintuitive property that you-- even though it uses the biometrics, you preserve anonymity and extreme levels of privacy, which I think is super cool. You know, social media is one kind of vector of things that we're annoying and are now becoming overwhelming in terms of just bots, particularly with SyOps, propaganda, all these kinds of things. What are some of the other uses of bots that are going to be kind of impossible to live with if we don't get to prove a human in the future? Yeah, actually, I think the simple model I have for it is every moment on the internet that is primarily about humans interacting with each other. Or even indirectly interacting with each other. So you can start with simple ones like dating. Yeah, that really matters. Yeah, one of the other side is in fact a person. Yeah, well, that's a bad news for listening to this. And the person who you expect it to be. Yeah, exactly. Yeah, we did talk to you before. Yeah, exactly. Yeah, so that's an always one. And some example, tenders are using it for the reason. And what's the tinder use case? So we started in Japan and as a test market, essentially exactly what we just discussed it is, if you verified with an orb, you get a little batch that signals to other people that you are in fact a human, so that has a high level of verification. And then also, I don't think that's live yet, but what will come next is that you're actually the person you claim to be. So meaning you have a world ID that is associated to the kind of profile pictures that you use. So you just run a quick check that this is all correct. And so you know, you then know you're not interacting with bot, but also you know, you interact with a fully authentic profile. Yeah. Another fun one because I think it's somewhat quantitative, but I think it will be video conferencing because you already have defects. I don't feel like going to this video conference, just put my deep fake up. And actually, you raised it to me first and that's where we started building a product for it because it actually started with very high value users. Yeah. Like for example, people like yourself that maybe manage a fund and sometimes calls actually could be very high value if it's about borrowing money or. Oh yeah, yeah, well, so somebody can be me and say, Eric, can you please wire this Nigerian Prince for $400 million? Exactly. I'll be good to know. Yeah. Yeah. Like, you know, that's still slightly hypothetical because these things are not fully real time and you can somehow. They're very close. They're very close. And so I think, you know, in a year from now, it's just going to be a full commodity and it's going to be super photorealistic and absolutely real time and you will just not know anything anymore on this really close. And so I think that's another one. I think another one then will be just think it's fun, but it's going to be gaming. You know, because, oh yeah, yeah, because gamers really care. Oh yeah, that's right. They're playing them. They're out. Oh, like, yeah, that's frustrating. Especially if we bet money. Exactly. And you lose money. You gain multiple hours of age, you get like really good at this thing and then suddenly you get, you know, you get destroyed by an AI that is just super human and I read dimension. Fun enough. I was like, I wonder what do you think about this bit because they don't have a good mental model about it. But even the whole model for video platforms, I think, is about to break because there's a couple that I mentioned that are a problem. But one, if the creation of content is becoming super scalable, like, for example, I heard about this one guy that created, I think, like, I was like on the order of 100 videos a day on YouTube and made tens of thousands of dollars a month, all of them are fully agenrated. Yeah. And people just fell for it. So now the question is, is there actually something that YouTube wants to monetize that way? Yeah. Well, it's interesting, right, they fell for it. But maybe they liked it. Yeah, I like it. That could be, but it would sure be nice to know, like, okay, this is a human video or this is an AI video. Actually, my thesis about this is like something along the lines of, I think there's categories of content that are clearly just fake. Yeah, like movies are that. You know, it's like you don't care that there's any connection to reality. It's just a fully fictional story. But now if you think about somebody like TikTok or, you know, all these kind of things, like people actually really care about them mostly because there is some connection to reality. Yeah. Well, there's reality and there's connection to human, right? That's right. You can create a pretty good pipe like you can take a scientific paper and give it to Gemini and say, "Make this into a podcast." And, you know, it'll be like a pretty entertaining podcast. Right. And it will be reality in that it came from, you know, some real thing. But you would like to know that. You would like to know that. Yeah. Yeah. I would like to know that. And then it continues as an advertising you would like to know that a human watch it, or an AI watch it. Yeah. Right. Well, right. That's the other thing is I created 100 AI videos. I had a million AI's watch it. And then I made a lot of money off YouTube. Exactly. And I actually saw that video today of a YouTube farm. Yeah. Like they just like thousands of phones that just watch videos all day for a reason. Yeah. And then like that's got zero value to the YouTube advertisers. And so that's actually a real problem for them. Right. Well, the whole sort of the creative economy platform is the last decade, you know, substacks, Spotify, and all the people who support artists or, you know, Patreon, as that are creators, YouTubers. They have a personal relationship with these people. It's not just they like the art. And so if they all of a sudden found out that they were bots that might, you know, they might not want to support them in the same way. Yeah. You might want to give them a big YouTube tip. Yeah. I think there's a certain subset of people who support, you know, want to support actual people and feel like they're having a real relationship. Yeah. And the thing that I think like people don't really get is that, you know, it should be always, but I don't think people really understand the consequence of that. I think two things. One is that what we currently experience is like a super, super tiny thing of what is about to happen, you know, just because, yeah, right. It's a glimpse. It's a glimpse. Like, you know, cost of the job, the talent is dropping almost exponentially. And the capabilities are increasing, you know, like some super linear forms. Like, yeah, would be currently sees less than 1% of what it will look like in probably a year or two. And so, and then second, these things will be actually, they will be super human in many ways. They would be like perfectly able to understand you and like talk into what, right way to you. Exactly. For example, there's this like one paper that I think you could read it after, but it was, it was the change my mind subreddit. Um, where the university was erected, the thing where they had AIs actually interact with change my mind. Yeah. And they were like super human and their ability to change it because they were going back to their profile of the people posting it and we're like, understanding their political motivation. The way they talk and like, and then they're just interacting in perfect in the perfect way. Yeah. You know, I'm just like hit all the buttons and like AIs are really good at programming humans. That's much better than humans are programming AIs. Absolutely. There's no question. And so I think that's going to get quite scary also. Yeah. But, uh, I think at least if you know you're being a victim of a siop, then or, or, or it's a very advanced one done by an AI, that would be extremely useful to understand totally. Talk a little bit more about the state of the product and the business today. Like, how many IDs are out there? Why don't you give it a little bit of an update? Maybe talk about the evolution as well. Well, first of all, it's a multi-sided problem. And I think there's like roughly three that you have to consider. One is, uh, well, you need platforms to use the technology. Then, you know, like things like Reddit or, you know, X or, you know, things like that. Um, secondly, you need a distribution of these devices. And I think the right mental model to, to half where it is, uh, how many minutes does it take a person to reach such a device on average? And, you know, currently it's, if you would take the global average, it would be terrible number. It would be like, you know, days or something because many people would need a fly. But, but, you know, how do we get the down to below 15 minutes across the US? And so that's probably roughly around 50,000 devices that you need to deploy. That's like, it's not crazy, but it's also not nothing. It's, you know, it's, it's hard to do. And then the last one is, how does all of that come together to something that a lot of people really want to use it? And that's a combination of, you know, the utility of all the sub platforms essentially. But, but all of that layers on top, like maybe you can use any Reddit account and maybe you get like, you know, certain amount of TGP subscription for free or like, so I think it's going to be a combination of things, but you need to, you need to land all three at some point at the same time, which is, uh, which is hard to do. We are now at 18 million users that are verified, 40 million in total in that, uh, but the biggest thing is because of the past administration, because we use, you know, we use crypto, we, we did not really invest in the US for a long time. And, um, that's not the main shift that we're going through. Like, yeah, for all of this, the main thing that matters is the US. And hopefully, uh, we get the clarity act past shortly. Yeah, exactly. That would be really great. So, um, to get clarity on that. Uh, yeah. So, so the big focus that we, that we now are going through right now is to kind of go all in in the US. So I think over the next year, 90% of the, of the, you know, effort of the company is just going to go about the US. And how do you get, for example, device distribution up? How do you eventually have this in every Starbucks? Um, so it becomes just, you know, super normal and people just, just use it every day. So that's kind of the, and then on the platform side, actually, we went through, uh, it's, um, it was a very interesting experience to go through personally because I think, um, like a couple of years ago, universally, people just made fun of us. You know, like it was like the universal reaction. Uh, well, minus and recent, and then a couple of other people that believed in it, but, um, yeah. Like it and the press, like, like the amount of fun making of something that just shows how short-sighted people are. That's right. It's like, you don't think the bots are coming? What did you think when we first pitched, actually, because even you must have thought, this is crazy. Well, because you had the orb, like the orb was so wild. Um, you know, okay, we're going to scan people's retinas and that's how we're going to know they're human and so forth. And this was, I mean, you pitched us six, six years ago, six years ago. Yeah, it was before COVID because you were there with the orb. Right. And you know, a, I just hadn't happened yet. And, you know, you could kind of see, but they're, you know, there, there's bots, um, but they were kind of very crude and, you know, compared to what they are now. But it, uh, it seemed inevitable. At least at the time, you know, the thing was it was so out, it was so from the future that, you know, we always worry about, okay, like what's the timing at this and this and that and the other and, and so forth. But, you know, you were impressive enough and it was going to happen eventually and it was an exciting enough idea that I think all those things kind of got us to go. Okay, we're, um, but it was, it was one of it wasn't obvious that like it was going to work in that time frame. It seemed very inobvious for a long time. And how different was that pitch from what it ended up being or talking about? It was actually pretty much exactly the same. Well, I think it's the same thing. The device changed. You know, they've made it much more economical and convenient, but that's right. It's, uh, but the initial Einstein was right. It was there. It was basically everybody's going to have to prove their, you're either going to have to have some proof that you're human on in cyberspace or like it's going to be a very bad world. I mean, the robots are going to get us. We're done. Right. And then actually the second piece that was like this was the first thing is like it's going to be a dad. It's health is going to be a big deal. But in second of all that, you know, when it's going to become a big deal, we will be able to build one of the most valuable networks as a result of that because in a world of AI, having a human network is going to be this incredibly important thing. And, uh, and so actually, yeah, two things like one, you will need to prove a few, but in second, it will have very strong network attacks. And even as the platforms, as you get into platforms, even as the platforms, largest problem has been bots. I mean, you remember Elon and you know, he backed out of buying Twitter because all the stats were based on bots. They still even knowing that it was hard for them to get all the way to the future and they're thinking and go, yeah, we need proof of human. Yeah. Kind of obvious. Yeah, because people were like, what does it even mean? You know, like what does proof of human even mean? We can just, we can just, you know, and did you have the length? The detection tools. When did you come up with the language proof of human? We had actually, we had proof of personhood for the longest time. It's even here in this on the free. Yeah. And at some point, we were like, shit, well, at some point, AIs will have personhood too. So, like, that's not going to fly. But they're not going to have retinas for a long time. That's actually all that's coming eventually. It was actually really funny. It was like some of the, some of the opening AIs people that I met, run like, man, now, like, this is going to, this is going to be so dark. Like, people will hate you for like, not giving personhood to AIs. I was like, Jesus. All right, let's, let's, let's call it proof of human then. That's funny. So that's how it changed. But then actually, so then I would say like last year, post, then there was like a big shift post, post, chat, CBT, like people were like, that was like the AI suddenly got real to people. And then actually I think, and so that's when people started talking to us, but still we're not like, you know, like it's a future problem. It's probably a couple of years out, like we don't really care about it. It'll stay in touch. Like there was like the common response and then, you know, you know, and well, but you also, you had a couple CEOs that really believed it, and we're like willing to take the long term bet to give them credit. But I think the second big shift was actually cloud bots and moldbook recently. Yeah. Just because that kind of means like the cow is way out of the barn. Yeah. And so like honestly, if you don't take it serious now, then I think you just, you should get a different job or something. Yeah, where you're not. Yeah. They're just not thinking about problems in the right way. Like it's, and so that's, that was like the moment when many, many people started reaching out. And now it feels like much more of an execution problem, not not any more market risk, like a market risk or like a thesis problem or like, just, and which is still a big fucking problem. Like how do you, how do you get 50,000 devices out there? How do you make it cheap enough? How do you make it economic? Like, you know, how do you make all these three things at the same time? It's still a very hard part. How do you normalize the behavior? It's the right. So people aren't weirded out in a Starbucks or something. Although I think that's now going to be. Poget used to it. I'm just because I think people hate the alternatives so much. And I think people are going to, by the way, take a lot more pride in being human, particularly online, because I think that people are going to start getting accused of being bots. I mean, like, it's going to get really weird. And without, like, clear delineation, it's, it's going to be a mess. Like, I don't understand how somebody can think they're going to have a social media platform that doesn't distinguish between humans and bots. Like, that seems absurd to me. It's absurd. I think we will, my guess is, over the next couple of months, we will see, we will see things like these platforms trying to use things like face, biometrics on the phone, which, you know, high note will break. So it's fine. But I think we will go through that cycle now. And yeah, so we just need to get to scale fast enough to, to meet the market to what comes after, which I think something like the Orbistie only solution. I think currently there's no real competition. I think we will also see that. I have not seen a competitor yet. Because it's so ridiculous. It's so ridiculous and it is so hard to get to in terms of building it. The fixed cost. And then there's a massive network effect, which like people are starting six years behind you on that. But yeah, I'm sure they'll come. Because it's just such an obvious problem now. What actually do you think about, like, AI continues, what in your might are the economic policies that we will need to implement or directly? I think governments do have to figure out how to send citizens money. They're good at taking money from citizens to not to reverse. I mean, well, just if you go back to COVID, the stimulus program, like, I think $400 billion was stolen. Like, that does pretty good. You would have liked to know that you were sending the money to unique humans. I mean, even if not citizens, as long as they were unique humans, that would have been good. Yeah, I mean, the Social Security System, for example, it's a mess in the US. Yeah, that's good. That's good. It's a total disaster. So we're going to have to get to some kind of way to cryptographically strong way to identify who's the citizen of what country. Like, like, that's going to be a really bad problem, I think. So otherwise, there's no way to even have a democracy. I mean, you know, like the, it's pretty crude, what they're trying to do with the SAVAC, but it's not completely insane, which is, how do you even know, like, the people are voting or actual people or living people or anything? And we really don't know now. Like we genuinely don't know. And then if you go to, I mean, the whole mail-in ballot thing, like, is built for a whole very different world, right? That's right. So like, I don't think in an AI world where you can have like very high scale impersonation that, and then with a broken social security system that like, you're going to have the will of the people anymore. Like, I think that's going to be gone pretty fast. So I think we're going to need some kind of, you know, cryptographically strong infrastructure on like, who's who. And then, you know, similarly, I think we're going to have to be able to get people money much more efficiently than through these, this crazy apparatus of social programs that we have just because like how lossy is, and fraudulent is social security or Medicare or any of these things. I mean, like the Medicare is so frustrating for people that they shot the CEO who you know, healthcare, like, and people are happy about that. Like, really happy. So like, think about how bad a system that is when, you know, and the government spends a lot of money sending you money for your health care, but they do it in a like, super any fishing way. But we have the technology to do that now. So I think that AI is going to make that problem so bad, because the ability to file fraudulent claims and create fake, you know, buy social, I mean, you can buy social security numbers on the black market. Like, for those of you don't know, that's an easy thing. That's a real thing. Like that is like everybody's social security number is for sale. And so, you know, like AI is just a way of making that kind of loose black market, underground fraud thing, just massive and extremely scalable. I agree with that. Yeah. So I think, you know, proof of human is a piece of a very important puzzle where we have to upgrade that entire infrastructure or we're not going to be a democracy anymore. I mean, that just be my guess. I agree with that. Share more. You said, okay, next year go to market. It's focused on the US. Say more about how you're thinking about that is the incentive for people to do it because they get to use a set of services. Is there some other economic incentive or how do you envision it? Basically a month ago we entered a very different phase as a project where I do believe many of the platforms that we're not integrating with will really bring a lot of users to our platform. And that changes how you think about it entirely. Like, if you have a platform of a billion users sending users to you, then it's really just all about like how do you meet that demand? Like, you know, and that's what we're now entering. And so, yeah, so I think the response is first. I think you will see and we're already working on it, but you will see a lot of the large platforms that you know integrate in the near term future. I think that will just as that expectations. I think that will be slow initially because it also should be just as you know, to get understand the product. It will be focused on certain geographies like what we did with Tinder, restart in Japan just to you know, to to test the product and also to just normalize the concept. But that will happen. And then secondly, which is now becoming like one of the main priorities for me is just how do you get this or distribution up, which is, you know, broadly speaking. There's a couple different dimensions to that. But one is, first of all, the product needs to work at scale. You know, without supervision, which is turns out to be much harder than you would think. You know, every engineering problem at scale turns out to be much more complicated than you would think because you know, fighting for one percent of improvement in quality is this cluster of. You know, all these dependencies to come together. So that's I think that's like one of the biggest engineering focuses right now. But then second, you need to find places to deploy them at and the way to think about it is. There are large scale distribution partnerships that could be something like Walmart, you know, or if you, if you're very ambitious, it could be something like Starbucks. Or it can just be you go to one of, you know, hip coffee shops and you just you just put it there or you know, and then you could go you could eventually even go to the DMV and just put it right there. So that's the problem we're currently trying to trying to puzzle together. And you know, it's going to be some some of all of that. I think there's going to be some large scale distribution partnerships, many one of coffee shops. Actually one thing that we will we will launch soon and the team is going to say that I'm saying this now, but it's going to be work on demand. So, yeah, send the bay just because actually it's such a gnarly problem to, you know, to get an orb to truly everyone. You know, it's like to get that the catbacks is insane. So it's actually it's actually much cheaper and easier to just put an orb on a motorbike and drive it to you. It's as crazy as it sounds. So like in places like the Bay Area or New York. you will just be able to say like, "Yeah, I wanna verify now." And 50 minutes later, there's an orb comes to you, you can verify. - Did you ever think about, this is probably a terrible idea, but having kind of different levels, like we know you're a unique human, or like, "Hey, this guy maybe a unique human," 'cause he's done it on his iPhone. - Great issue. - It's quite the same, but. - Yeah, yeah, we have that. So actually, we, you know, generally we just have to, you know, we have the principle of, you know, whatever it could be useful for this problem, we just build it. - And so we have something called face check that does that, so it uses face from the camera. It still uses multi-party computation, what we've built for entire systems, so you're still anonymous. - Mm-hmm. - And, you know, it of course reaches way less accuracy. So, you know, as a system, you will know something along the lines of, well, this is, you know, at least one person cannot create 100 accounts, maybe just 10 or 20. So it's like, at least it's some measure of rate limiting. And I do think just to say to this, "Clamor, I think with deep fakes and all this stuff, I think that will fundamentally break." So it's a temporary solution that I think can get us to scale. That's kind of how I think about it. We also actually use government IDs. Similarly, we use just the ones that have an NFC ID chip. And we use multi-party computation, so you remain anonymous. And platforms can choose to use that as well, but no one really did. It's just some other, they have this like, very negative stigma, which I think makes sense. - Yeah. - But yeah, basically whatever could do it. - Yeah. - By any means necessary. - That's right. Well, thanks so much for coming to the podcast. - Yeah, thank you. Thank you. (upbeat music)