[00:02] a surprisingly hard problem. I think that people are going to start getting accused of being bots. What we currently see is less than 1% of what it will look like in probably [music] a year or two. >> The idea that AGI will lead to some very
[00:15] Like >> AI's are really good at programming >> Absolutely. >> AI [snorts] will be able to have a GitHub account and will be able to post and also attest to five other AI's that
[00:27] they're not. I see if you don't take it they're not. I see if you don't take it serious now.
[00:40] So, proof of human is having a moment right now. Won't you first give a unfamiliar? What is the moment that's happening and how did we get here? Yeah, and what is proof proof of human? Proof of human, as
[00:53] the name suggests, is you know, do you know if you interact with a human or I actually think that the the kinds of questions that we're now asking is, are human, uh an agent on behalf of a human,
[01:09] or just an agent? Like I think these are like roughly the three the three areas that we want to split apart. Well, and and describe a little bit the difference between just an agent and an agent acting on behalf of a human. How
[01:22] do you see that distinction? Yeah, so um quickly explaining just the term proof of human and I think what is hard about it and then I'll I'll explain how that human. So, um
[01:35] what proof of human really means is that uh 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.
[01:49] property that you're looking for. So, like you're looking for a uh initial verification uh that ideally should be, you know, something like anonymous or very extremely privacy preserving and then ongoing authentication that the
[02:03] account. Um and then there was like some secondary properties that I think are good to have. But that actually tells you that the really hard thing is is uniqueness. Like like what what is happening on a platform like Twitter
[02:16] accounts, you know, all these all these bots that are in the replies um that, sitting somewhere and and sending out 10,000 or like 100,000 of AI's. And
[02:29] there's this catch-up game where like uh you know, Twitter and X are trying to just find them and block probably millions a day of these. Which is what millions a day of these. Which is what like uh a 100th of the of the bots. That
[02:43] that's right. That's how it feels like. Um and then agent on behalf of human, I think like how that will look like is uh you know, I as a like I think all of us will have agents.
[02:55] it's going to be one or there multiple ones, maybe with different tasks and different even even types of characters. Um and I think it will then come down to I, you know, I approve a certain action of my agent. I give him certain rights.
[03:10] So, like So, act on my behalf. Okay. The post post to my X account, post to my Instagram. For example. >> But it's my Instagram and I'm a unique human that owns that. That's right. You know, that X or Instagram could
[03:23] something they want as a platform. >> Right. Uh but that's how you could do it. That makes sense. Um and so >> [laughter] >> how do you prove somebody's human? It is
[03:37] it is a surprisingly hard problem. Yeah. So, you know, it's >> those agents are very very clever. It's uh you know, it's funny. We started this company now a couple of years ago, way before ChatGPT and before all of
[03:51] assumption that eventually we will have AI's that, you know, both pass the be a human and you will not be able to tell them anymore on the internet. And also that they would be, you know, highly agentic and just like run around
[04:06] and do their own thing. And so that makes it really really hard because company, there were like roughly Um One was this idea of
[04:21] uh web of trust or like related idea. 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 these certain number of accounts
[04:36] you know, you own since a couple of years and then you post regularly or you were the kind 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
[04:51] know you 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. immediately because we assumed that, you know, eventually
[05:06] everything that is just digital and AI will be able to do as well. Like [clears throat] AI will be able to have a GitHub account and will be able to post and own an account and like also attest to five other AI's that these are
[05:19] not. So, uh so, you know, there was there was area number one. Area number two was to just, you know, uh use government ID's for everything, which uh we just also immediately disregarded for a couple of
[05:34] reasons. One is you know, uh I think you know, it's strictly better if the government would not control such an and actually breaking that apart. But then
[05:46] also Right, you lose anonymity. Instantly, right? that maybe preserves it, but it's very also um
[05:58] system is just not built for that. Mhm. And 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 the perfect infrastructure. For example, Singapore is like an example of a of a
[06:14] of a of a government that has, you know, perfect infrastructure all around. Mhm. But that barely doesn't doesn't matter because, you know, for example, I don't billion users and there's a lot of other countries. Yeah, Singapore is what, like
[06:27] Yeah, so do you want to lock everyone uh so yeah. And and then there's a long list of other things um why we disregarded that basically immediately. And and then the
[06:39] biometrics, which actually, you know, immediately gives us this sick reaction. um and it even went further because uh mentioned in the beginning, is uniqueness. Mhm. And so just like in
[06:56] the problem is um well, first of all, for example, what does Face ID do? Face ID checks that I'm the same person again using my phone. Mhm. And uh so it's a one-to-one authentication. So, there's an embedding
[07:10] stored on my phone. It takes a picture of my face, creates a new picture, compares it to the previous one. And if that is close enough, I can use But uh so that's a one-to-one, you know, one
[07:23] embedding. To solve the proof of human problem, you individual from all previous individuals. Mhm. Uh you need to make sure that, you know, Ben is trying to sign up and Ben did not sign up before.
[07:37] Yeah. Um and then suddenly it goes from one-to-one to one-to-n. And n is the n that you're that you're trying to prove that to. Right. And then you can just do the math and you can calculate how much mathematical entropy, like how much
[07:52] information, just information theoretically, do you need to um to prove that? And it turns out that's a pretty high number because it's it's an >> Right. And so then you can just do the math and you find out that, you know,
[08:05] even fingerprints or something doesn't work. wall after tens of millions of users. And so then you end up with uh you know, something like iris, which
[08:18] actually has enough entropy. [clears throat] That it's unique. That is unique. That is unique enough. And how do you also then solve the uh you know, one thing that biometrics have
[08:32] been subject to historically is just replay attacks. Where okay, I may I may not have your eyeball, but I've got enough information that I can run a replay attack on you. Um
[08:45] know, I get it is important I think to split up the problem in verification, which is essentially in, you know, old terms, it's like you're getting your passport. Right. And then authentication, which is
[08:59] you showing your passport constantly for certain kinds of things. verification piece, um that's, you know, we we've went down >> [snorts] >> you know, it's it's doing a lot of
[09:14] 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 and it would recognize that.
[09:27] Um so, I think on that side we've, you know, we've got it handled. On the on you know, that should then reauthenticate, it turns out to be much uh you would need to trust the phone in some sense. Mhm. Uh because what we
[09:44] actually do in that moment is when you verify with an Orb, we in a fully anonymous and privacy preserving way and we should talk about that, but also we send to your phone a signed face image that you then can
[09:57] later use to re-authenticate against it. Right. Um and you know, with a new iPhone, you can have meaningful amount of trust against that, but with old Oh, yeah, yeah, yeah. Yeah, you know, because like you can just uh you can
[10:11] just show a a deep fake essentially either through a display or just So, um that's the problem. And so, it's going to be a mix of uh you know, if you have a new enough, let's say iPhone or a
[10:24] general phone, um then you can just re-authenticate against that uh picture Otherwise, you would probably have to even go back to an orb somewhat frequently. Um like let's say a couple times a year. If you just I see. Right
[10:37] Interesting. And then, you know, one of the things so one of the kind of incorrect criticisms of the approach early was, "Oh my god, they've got my eyeball.
[10:50] >> Um you know, now they you know, they they somehow have uh access to my privacy and they're going to you know, do all these things to me can they uh Worldcoin can um impersonate me
[11:06] that's not the case and um so that was also like a non-trivial >> was There was very much non-trivial. Um so actually, I think one point on iris enough. Yeah, that's a bet we took back then,
[11:21] but it was essentially that iris will turn out to be super normal as a as modality just because I think we will all wear um AR and VR systems that do that. You know, Apple already does it.
[11:34] in in the Vision Pro. So, I think it's So, maybe that's a general point. I something that we will use across many different devices and uh will normalize in that sense. But I think on the on the privacy piece,
[11:49] that took us a lot of time because like when when we when we decided back then when when we when we decided back then that you know, with our assumptions, will need a custom hardware device for biometrics, it was actually quite scary.
[12:04] conclusion because like Yeah, that's an expensive conclusion. and then just having this idea that you would need to distribute them all over that you would be able to like somehow bring up billions of dollars and
[12:17] to like a massive effort to to just resolve the world. Um but then also, the privacy challenge of like how could you build such a system that has all the all the requirements that we care about. And the the two main
[12:31] high-level, you know, ideas on how to solve it were uh multi-party computation and zero-knowledge proofs. Mhm. And so, to again, what is different to Face ID
[12:44] because Face ID actually is it you know, can be very private just because doesn't have to leave the phone ever just because it's just you against you in the past. But to check uniqueness, uh you need to
[12:59] check against all previous people. So, it So so something needs to leave. Yeah. something and be compared to someone else uh and that's and that's a much uh challenge and um how we approach that is we have
[13:14] multi-party computation. And so, that essentially means that So, in our case, when you uh verify with an orb, you know, we we take all these pictures, uh they get computed on the device,
[13:28] and uh then they actually get split up in multiple pieces. So, a picture of your iris, we calculate an iris code, um then we break that iris to multiple computers such that
[13:42] um 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
[13:57] >> in such a way that >> Right, nobody has the whole thing. >> Yeah, so no one has the whole thing and also during the computation, no one has >> Yeah. But they do some you know, some clever interactions to come to the
[14:09] >> like a zero-knowledge proof kind of technique. Uh it's It I mean, it it's the properties it achieves, it's somewhat similar. Where like you
[14:21] can actually together make a statement about you. Right. And so, you know, you send it to this multi-party computation >> [gasps] >> And then the second thing we do is we we
[14:34] >> And then the second thing we do is we we separate all of this um from you with a zero-knowledge proof. So, meaning you have the secret on your phone, but no don't have it. Um and then you can later go back to
[14:47] this multi-party uh computation and say like, "Hey, I have a secret that is part of that computation and I am in fact unique." platform. You could go to the social network and prove that you're a unique
[15:02] user to the social platform without us knowing anything about you or the social network knowing anything about you. And so, it's just like very that you there is like even though it uses
[15:14] biometrics, you you know, you preserve anonymity and and uh extreme levels of privacy, which I think is super cool. You know, social media is one kind of vector of uh you know, things that were annoying
[15:28] and are now becoming overwhelming in terms of just bots. You know, psyops, propaganda, all these kinds of things. What are some of the other um you know, uses of bots that are going to
[15:42] kind of impossible to live with if we don't get to proof of human in the Yeah, actually I think the the simple model I have for it is every moment on the internet uh that is primarily about humans
[15:56] interacting with each other. You know, or or or even indirectly uh you know, you can you can start with simple ones like dating. You know, that >> [laughter] >> Yep. It is what if the other side is in
[16:09] Um Yeah, well, they Got bad news for listeners. Well, I I I Yeah, yeah. Yeah, exactly. [laughter] Yeah, we did this problem even before the whole catfish thing. Yeah, exactly. Yeah, yeah. So, that that that's
[16:23] Um and so, and so for example, Tinder is already using it for that reason. Um I I think And what's what's the uh the Tinder use case? So, So, we started we >> [clears throat] >> like as as a test as a test market and
[16:39] discussed that is um if you verified with an orb, you get other people that you are in fact a human. So, it has a high level of verification. Um and then also, um I don't think
[16:54] 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. Um so, you just run a quick check that
[17:07] this is all correct. Um and so, you know, you then know you're not interacting with a bot, but also you you know, you interact with a fully because I think it's somewhat counterintuitive, but I think it will be
[17:20] video conferencing. Mhm. Uh because you know, you already have deep fakes. Yeah, just to I don't feel like going to this video conference with my deep fake on. Yeah, [laughter] and actually you um you
[17:32] started building a product for it because you know, it will actually start with very high-value users. You know, like for example, people you know, like yourself that maybe manage a fund and you know, sometimes calls actually could
[17:45] be very high-value if it's about borrowing money or Oh, yeah, yeah. Well, borrowing money or Oh, yeah, yeah. Well, so so so so somebody can uh
[18:06] time and you can somehow They're very close. But they're very close. And so, I 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
[18:19] not know anything anymore on these video calls. And and so, Uh I think another one then will be just I think it's fun, but it's it's going to be gaming. Uh yeah, because Oh, yeah,
[18:31] yeah. Because like gamers really care. Oh, yeah, that they're not playing an >> [laughter] >> Holy cow, that's frustrating. Yeah. exactly. And you you you lose money, you train multiple hours a day to get like
[18:44] suddenly you get, you know, you you get destroyed by an AI that is just superhuman in every dimension. Um Funny enough, uh like I wonder what because I don't have a good mental model about it, but even
[19:00] the the whole model for video platforms, I because there's a couple dimensions to the problem, but one if if if the if the creation of content is is becoming super
[19:14] scalable. Like for example, I I heard about this one guy that uh created, I think, like it was like on the order of a hundred tens of thousands of dollars a month. All of them were fully AI generated.
[19:28] Um and people just fell for it. So, now the question is, is that actually something that YouTube wants to monetize that way? Yeah, like is that Yeah, well, it's it's interesting, right? They fell for it. Um
[19:42] But maybe they liked it. Yeah, yeah. They're like like that could be, but it it would sure be nice to know like, "Okay, this is a human video or this is an AI video. Um Actually, my thesis about this is like something something
[19:55] along the lines of I think there's categories of content that are clearly that, you know? It's like you you don't care that there's any connection to story. But now, if you think about something
[20:08] TikTok or, you know, all all these kind care about them mostly because there is some connection to reality. Yeah. >> Yeah. Well, there's reality and there's connection to human, right? So,
[20:22] >> you can create a pretty good 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 and it will be reality in that it came from, you
[20:37] know, some real thing. But you would like to know that. would like to know that. >> it continues as an advertiser, you would like to know did a human watch it Yeah. >> or did an AI watch it?
[20:49] >> Yes. Right, right. Well, right. That that that's the other thing is I created that that's the other thing is I created 100 AI videos. I had a million AIs watch >> And then I made a lot of money off of YouTube. Exactly. And actually saw that
[21:03] video today of of a YouTube farm where like there's like thousands of phones that just watch videos all day for a reason. Yeah, yeah. And then like that's got zero value to the YouTube advertisers. And so that's that's
[21:15] actually a real problem for them. Right. Well, the whole sort of the creator economy platforms last decade, you know, Substack, Spotify, and all the people who support artists or you know, Patreon and other creators, YouTubers, they they
[21:28] with these people. It's not just they like the the art. And so, if they all of know, bots, that might, you know, they might not want to support them in the same way. Yeah. You [laughter] you might not want to give them a a big YouTube
[21:43] tip or Yeah, I think there's a certain subset of people who support, um, you relationship. >> Yeah. And and the thing that I think like people don't really get is that, you know, it should be always but I
[21:55] don't think people really understand the 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
[22:09] >> It's a glimpse. Like, you know, cost of intelligent intelligence is dropping almost exponentially. Agentic capabilities are increasing, you know, in like some super linear form. So, like yeah, we currently see this less than 1%
[22:23] year or two. And then so, and then second, these things will be actually They will be like perfectly able to understand you and like talk in the what right way to you. Yeah. For example, this is like one paper that I that I
[22:38] was, um, it was the Change My Mind subreddit. Mhm. Um, where the University of Zurich did this thing where they had AIs actually interact with Change My Mind. >> Mhm. Yeah. And they were like superhuman
[22:54] they they were going back to the profile of the people posting it and were like motivation, the way they talk, and like and then >> [laughter] >> You know, I just like hit all the
[23:07] >> AIs are really good at programming humans. That that that's much better than humans are at programming AIs. >> Absolutely. There's no question. And so, I think that's going to get quite scary, also. But, uh, I I think at least if you
[23:20] know you're being a victim of a side up, >> [laughter] >> then or or or it's a very advanced one done by an AI, that would be extremely little bit a bit more about the state of the product and the business today. Like
[23:34] how many IDs are are out there? Want to give a little bit of an update. Maybe talk about the evolution as well. Well, first of all, it's a multi-sided roughly three that you have to consider. One is
[23:46] One is uh, well, you need platforms to use the Uh, then, you know, things like Reddit or, you know, X or, you know, things like that. You need, uh, distribution of these
[24:01] devices. And I think the right mental model to to how many minutes does it take a person to reach such a device on average? And, take the global average, it would be a terrible number. It would be like, you
[24:16] people would need to fly. But but, you know, how do we get that 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
[24:29] nothing. It's 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
[24:44] that layers on top. Like maybe you can use a new Reddit account. Maybe you get like, you know, certain amount of ChatGPT subscription for free or like combination of things, but you need to you need to land all three at some point
[24:57] 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 the Uh, but the biggest thing is because of the past
[25:10] know, we use crypto, we we did not really invest in the US for a long time. shift that we're going through. So, Yeah. for all of this, the main thing that matters is the US. And ho- ho-
[25:23] shortly. >> Yeah, exactly. That would that would be really great. So, um, to get clarity on that. Uh-huh. Yeah. So, so the big focus that we that we now are going through right now is to
[25:36] kind of go all in on 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 how do you eventually have this
[25:50] Um, so it becomes just, you know, super normal and people just just use it every platform side, actually, we went through a It's, um, it was a very interesting experience to go through personally
[26:04] um, like a couple of years ago, universally Like just it was like the universal reaction. Uh, well, minus recent and then a couple of other people who believed in it, but, um,
[26:18] Yeah, like in the press and like like the amount of fun making of something that it just shows how short-sighted people are. That's right. coming? What did you think when when we first pitched, actually? Because even
[26:32] you must have thought this is crazy. Well, because you had the orb. Like the orb was so wild. Um, people's retinas and that's how we're going to know they're human and so
[26:45] forth. And this was, I mean, you pitched us 6 and 1/2 years ago. 6 years ago. >> Yeah, it was before COVID because you you were there with the orb. Right. Um, >> [laughter]
[26:58] see, but there, you know, there there's bots, um, but they were kind of very there are now. >> [gasps] >> Um, but it, uh, it seemed inevitable. Um, at
[27:13] least at at the time, you know, the thing was it was so out of it was so from the future that, uh, you know, we always worry about, okay, like what's the timing of this and this and that and the other and and so forth.
[27:25] but you know, you were impressive enough and 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 but it was not
[27:40] it was one of the it wasn't obvious that like it was going to work in that time frame. It seemed very in obvious for a long time. or talk a little bit about it. It was
[27:52] pitch. I think it's the same thing. The device changed. You know, you know, economical and and convenient, but That's right. It's, uh, But the initial instinct was right. It was there. It was basically
[28:05] they're 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. Yeah. I mean, the robots are going to get us.
[28:18] >> Right. And then actually the second was like this was the first thing is like to be a big deal. But then second of all that, you know, will be able to build one of the most valuable networks as a result of that.
[28:33] 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 human, but then second, it will have very strong network effects. And even as
[28:45] platforms, even as the platforms' largest problem has been bots. I mean, you remember Elon and the you know, he backed out of buying Twitter because all the stats were based on bots. They still
[28:59] even knowing that, it was hard for them to get thinking and go, yeah, we need proof of human. Yeah. Like >> people were like, what does it even
[29:13] human even mean? We can just we can just, you know, And did you have the come up with the language proof of human? personhood for the longest time. It's even here in this on this brief. Yeah.
[29:26] But then, uh, at some point we were like, well, at some point AIs will have personhood, too. So, >> [laughter] >> [clears throat] >> That's actually Oh, [laughter] that's
[29:41] actually really funny. It was like some of the some of the OpenAI people, uh, that I met were like, "Man, Alex, this is going to this is going to be so like not giving personal to AIs." And I was like, "Jesus."
[29:54] was like, "Jesus." >> [laughter] Um that's funny. So, that that's how it changed. Um but then actually, so then I would say like last year, so post then it was like a big shift post post
[30:08] ChatGPT. Like, people were then 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,
[30:20] probably a couple of years out. Like, we don't really Like, it was like the common response. And then you know, and well, but you also you had a couple CEOs that really believed that
[30:33] and were like willing to take the long-term bet um to to give them credit. But, I think the second big shift was actually Cloud Bots and Mobile Book recently. Yeah. Just because of the
[30:47] That [clears throat] kind of means like the the cow is way out of the barn. >> Yeah, and and so like honestly, if you don't take it serious now, a different job or something. They're not
[31:01] it's And so, that's that was like the moment out. And now now it feels like much more of an execution problem. Not not anymore a market risk. Like a market risk or like a thesis problem or Like like just
[31:15] And which is still a big problem. So, like how do you how do you how do you get 50,000 devices out there? How how do you make it cheap enough? How do you make it economic? Like, you know, how do you how do you make all these
[31:28] still a very hard problem. How do you normalize the behavior, etc. So, people something. Although, I I think that's now going to be think people will hate the alternative so much. Yeah. And I think people are
[31:42] by the way take a lot more pride in being human. Uh particularly online >> [gasps] >> I I think that people are going to start getting accused of being bots. Totally. It's it's going to get really
[31:57] Um and without like clear delineation, it's it's going to be a mess. Like, I don't I don't understand how somebody can think platform that doesn't distinguish between humans and bots. Like, that
[32:12] seems absurd to me. It's absurd. I think we will My guess is over the next couple months we'll see we'll see things like these platforms trying to use things like face biometrics on the phone.
[32:25] Which, you know, I know it will break, so it's fine. But, I think we'll go through that cycle now. Uh and yeah, so we just need to get to scale fast enough to to meet uh the market to what comes
[32:39] Orb is the only solution. I think currently there's no real competition. I seen a competitor yet. Because it's so because it's so ridiculous. It's so ridiculous and it is so hard to get to in terms of building
[32:53] it. And then there's a massive network effect um Right. which like people are starting 6 years behind you on that. But, yeah, I'm sure they'll come cuz it's
[33:07] it's just such an obvious problem now. What actually do you think about like AI what in your mind are the economic policies that we will need to implement or directionally? I think governments do have to figure out how to send citizens
[33:21] money. They're good at taking money from citizens, but not reverse. well, just if you go back to COVID, the stimulus program. stimulus program. Like, I think $400 billion was stolen.
[33:34] You would have liked to know that you were sending the money to unique humans. I mean, if even if not citizens. As long as they were unique humans, that would system, for example, is a mess in the US. It's a total
[33:47] it's a total disaster, yeah. 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 like that's
[34:02] going to be a really bad problem um I think. So, otherwise, there's no way to even have a democracy. It's pretty crude what they're trying to do with the Save Act, but it's not
[34:15] completely insane, which is how do you even know like the people are voting are actual people or living people or anything? And we really don't know now. Um well, like we genuinely don't know. And then if you go to
[34:30] >> the the the whole mail-in ballot thing like is built for whole very different like is built for whole very different world, right? That's right. Uh so, like I don't think in an AI world where you can have like
[34:43] 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 I think that's going to be gone pretty fast. So, I think
[34:56] we're going to need some kind of you know, cryptographically strong infrastructure on like who's who. Um and then, you know, similarly, I get people money much more efficiently than through these uh this crazy
[35:12] apparatus of social programs that we have. Uh just cuz like how lossy is and fraudulent is social security or Medicare or any of these things. I mean, like the
[35:26] Medicare is so frustrating for people that they shot the CEO of United Healthcare in mail. Like, and people are happy about that. Like, really happy. So, like think about how bad a system that is um when you know, and the
[35:38] government spends a lot of money sending you money for your health care, but they do it in a like super inefficient way. Um but we have the technology to do that now. So, I think that AI is going to make that problem so bad
[35:53] uh cuz 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. for those of you don't know, that's a easy thing. That's a real thing. Like,
[36:06] that is like everybody's social security number is for sale. um you know, like AI is just a way of kind of loose black market underground fraud
[36:22] loose black market underground fraud thing just massive and extremely scalable. I agree with that. Yeah. So, I I I I think you know, proof of human is a piece of a very important puzzle where
[36:35] infrastructure or we're not going to be a democracy anymore. I mean, that that's be my guess. I agree with that. Sure, more you said okay, next year go to market. Is focus on the on the US. Say more about how how you're thinking
[36:48] about that. Is the incentive for people to do it because they get to use a set economic incentive or how do you envision it? Basically, a month ago we project where I do believe many of the platforms that
[37:02] we're now integrating with will really, you know, bring a lot of users to our platform. And that changes, you know, how you think about it entirely. Like, if you have a if you have a platform with a a billion users
[37:14] have a platform with a a billion users um sending users to you, then it's meet that demand? It's like, you know, and that's that's that's what we're now entering. And and so, um
[37:27] Um I think you will see, and we're already really large platforms that you know integrate in the in the near term future. I think that will, just to set
[37:41] initially because it also should be. Just you know, to to get understand the product. It will be focused on certain geographies. Like, what we did with to uh
[37:54] to test the product and also to just normalize the concept. Uh but that will happen. And then, secondly, which is now becoming like one how do you get this Orb distribution up?
[38:08] which is, you know, broadly speaking, there's a couple different dimensions to that, but one is first of all, the product needs uh you know, without supervision.
[38:21] Which is turns out to be much harder than you would think. It's you know, out to be much more complicated than you would think because, you know, fighting would think because, you know, fighting for 1% of improvement in quality is this
[38:34] you know, all these dependencies that that's like one of the biggest engineering focuses right now. But then, second, um you need to find places to deploy
[38:46] it is there are large-scale distribution partnerships. That could be something like Walmart, you know, or if you something like Starbucks. Um
[38:59] or it it can just be you go to one-off, you know, hip coffee shops. And you just then it you could go you could eventually even go to the DMV and just that's the problem we're currently trying to
[39:14] Um And, you know, it's going to be some going to be some large-scale distribution partnerships. Many one-off coffee shops. Well, actually, one thing that we will
[39:26] going to hate that I'm saying this now, but uh it's going to be >> [laughter] >> Orb on Demand. Yeah. Send it send it there. Just because actually it's such a it's such a gnarly problem to
[39:40] everyone. You know, [clears throat] it's like to to get that, the cap backs is insane. Yeah. So, it's actually it's actually much cheaper and easier to just put an orb on a motorbike and drive it to you. As as as crazy
[39:54] [laughter] as as crazy as it sounds. So, like in in places like the Bay Area or New York, you will just be able to say like, "Yeah, I want to verify now." Wow. And 50 minutes later, there's an orb comes
[40:06] to you to your work and you can you can verify. And uh Did you ever think about uh I don't know, this is probably a terrible idea, but um having kind of we know you're a unique human or like hey, this guy may
[40:21] be a unique human cuz he's done it on his iPhone and it's not quite the the same, but Yeah, yeah, we have we have that. So, actually we um you know, generally we just have to you know, we have the principle of you
[40:33] know, what whatever could be useful for this problem, we just build it. and uh And so, we we have something called Face Check that that does that. So, it uses it uses face uh from the camera. It
[40:47] we've built for the entire system, so you're still anonymous. Mhm. Um and you know, it of course reaches way less accuracy. So, uh you know, as a system
[40:59] of well, this is you know, at least one person cannot create 100 accounts. Maybe it's just 10 or 20. So, like it's like a at limiting. Um
[41:13] and I do think just as a disclaimer, I think with deep fakes and you know, all fundamentally break. So, it's a Mhm. temp It's it's a it's a temporary scale. That's kind of how I think about it. Uh we also actually use government
[41:28] IDs uh similarly where like we we use a but just the ones that have an NFC ID chip. Mhm. Um and we use multi-party computation, so you remain anonymous and platforms can choose to use that as
[41:40] just some of them they have this like very negative stigma, which I think >> Yeah. Um but yeah, basically whatever could do it Yeah. I don't know. Well, thanks so much for coming on the podcast. It's been great.
[41:55] >> Yeah. Thank you. Thank you. That's Thanks for having me.