# Proof of Human: Securing Digital Trust in the AI Era

**Podcast:** a16z Podcast
**Published:** 2026-04-02

## Transcript

How do you prove somebody is human?
It is a surprisingly hard problem.
I think that people are gonna start getting accused of being bots.
But we currently see is 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.
Much better than humans are at programming AIs.
Absolutely.
How do you prove you're real?
In 1950, Alan Turing proposed a test.
If a machine could fool a human into thinking it was also human, it had achieved intelligence.
For decades that remained theoretical.
Today, AI agents run thousands of social media accounts at once, outperform humans in controlled persuasion tests, and generate hundreds of videos a day that audiences believe are real.
The Turing test didn't just get passed, it got commoditized.
Every platform built on the assumption that its users are human now faces a problem no one has solved.
Facial recognition fails at scale.
Government IDs weren't designed for a global internet.
A proof of human layer for the AI era, alongside A16Z co-founder and general partner, Ben Horowitz.
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 like something else on the internet?
And I actually think 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 like roughly the three areas that we want to split apart.
And describe a little bit the difference between just an agent and an agent acting on 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 will explain how it fits into an agent on behalf of a human.
So what proof of human really means is that every individual that interacts on a platform has only one, ideally one account or a limited number of accounts, and stays the owner of that account.
Like that's kind of the property that you're looking for.
So like you're looking for uh initial verification that ideally should be something like anonymous or very extremely privacy preserving, and then on ongoing authentication, that the same person remains in 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, all these bots that are in the replies, that there's probably one human sitting somewhere and sending out 10,000 or 100,000 of AIs.
And there's this catch-up game where Twitter and X are trying to just find them and block probably millions a day of these.
Which is what a one hundredth of the bots.
That's right.
That's how it feels like.
And then agent on behalf of human, I think how did it will look like is I think all of us will have agents.
It's unclear how that will look is it's going to be one or there are multiple ones, maybe with different tasks and different even even types of characters.
And I think it will then come down to I approve a certain action of my agent.
I give him certain rights.
So act on my behalf.
Okay.
Post to my X account, post to my Instagram for example.
But it's my Instagram and I'm a unique human that onset.
That's right.
You know and then X or Instagram could decide if that if that's actually something they want as a platform.
Right.
But that's how you could do it.
That makes sense.
And so how do you prove somebody is human?
It is a surprisingly hard problem.
Yeah.
So it's those agents are very very clever.
Yeah.
It's funny we started this company now a couple of years ago before ChatGPT and before all of that.
But we kind of took that as an assumption that eventually we will have AIs that pass the Turing test.
So they can just claim to be a human you will not be able to tell them anymore on the internet.
And also that they would be highly agentic and just run around do their own thing.
And so that makes it really, really hard because back then when we started the company, there were like roughly three big ideas that people were interested in.
One was this idea of web of trust or like related ideas.
So this idea that 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 that you own since a couple of years, and then you post regularly or you comment regularly to GitHub.
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 I know you in the real world, and I attest to you that I know in your 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 immediately because we assumed that eventually everything that is just digital and AI will be able to do it as well.
Yep.
So we're there.
Exactly.
So an 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 AIs that these are in fact humans and even though they're not.
So that was area number one.
Area number two was to just use government IDs for everything, which we just all seem to be disregarded for a couple of reasons.
One is that 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 instantly, right?
You could hypothetically set up a system that maybe preserves it, but it's very hard to do.
And then the second thing is also the government identity system is just not built for that.
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 one government maybe has the perfect infrastructure.
For example, Singapore is like an example of a government that has perfect infrastructure all around.
But that barely doesn't matter because, for example, I don't know, Meta is a global product with three billion users and a lot of other countries.
Singapore is two million people or a million people.
Yeah, exactly.
So do you want to lock everyone else out?
So and then there's a long list of other things why we disregard that basically immediately.
And then the last one is biometrics, which actually immediately gives us this Ig reaction.
It's it's like and it even went further because what is so hard about this problem, as I mentioned in the beginning, is uniqueness.
And so just like in very simple words, how you can describe the problem is well, first of all, for example, what does face ID do?
Face ID checks that I'm the same person again 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 previous one.
And if that is close enough, I can use my phone.
So that's a one-to-one.
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 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 it's the size of your network essentially.
Right 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 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 just do the math and you find out that things like a face or even fingerprints or something doesn't work.
Like that then you would basically hit a wall after tens of millions of users.
Yeah.
And so then you end up with something like Iris, which is the mouth of your eye that actually has enough entropy.
And that it's unique.
That is unique.
That is unique enough.
And how do you also then solve the one thing that biometrics have been subject to historically is just replay attacks.
Where, okay.
I may not have your eyeball, but I've got enough information that I can run a replay attack on you.
So again, it is important, I think, to split up the problem and verification, which is essentially in old terms, it's like you 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, we've went down, if you know world, you know that we've built this thing called an org.
So it's doing a lot of things to prevent these kinds of attacks.
So it's, for example, it has multiple sensors in the 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 we've got it handled.
On the consumer side, you know, that should then re-authenticate, 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 with an orb, not only do we check your uniqueness 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 later use to re-authenticate against it.
Right.
And with a new iPhone you can have meaningful amount of trust against that, but with old Android phones basically and so like you can just you can just show a deep fake essentially either through a display or just directly inject it in the camera stream.
So that's a problem.
And so it's gonna be a mix of you know if you have a new enough, let's say iPhone or a general phone, then you can just re-authenticate against that picture that you took on verification.
Otherwise you would probably have to even go back to an orb somewhat frequently, let's say a couple of times a year if you just interesting.
And then one of the kind of incorrect criticisms of the approach early was oh my God, they've got my eyeball um you know now they you know they they somehow have uh access to my privacy and they're gonna you know do all these things to me and and that's my access and then they can they uh WorldCon can um impersonate me and all these kinds of things, but that's not the case.
And um so that was also like a non-trivial engineering problem.
It was that was very much non-trivial.
Um so actually I think one point on Iris that I think people don't appreciate enough, uh that's a bet we took back then, but it was essentially that iris will turn out to be super normal as a as a modality, just because I think we will all wear um AR and BR systems that do that.
You know, Apple already does it.
Uh already has RSID in in the Vision Pro.
So I think it's so maybe that's a general point.
I think it's going it's going to be something become something that we will use across many different devices and uh we'll normalize in that sense.
But I think on the on the privacy piece, um that took us a lot of time because like when when when we s when we decided back then that, you know, with our assumptions, you know, which was six years ago that we will need a custom hardware device for biometrics, it was actually quite scary.
Um, you know, to come to that conclusion because like yeah, that's an expensive conclusion.
It's it's like it's like very expensive, and then just having this idea that you would need to distribute them all over the world, like that that just assumes that you would be able to like somehow bring up billions of dollars and to like a massive effort to just produce all over 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 high-level, you know, ideas on how to solve it were uh multi-party computation and zero knowledge proofs.
And so um to again, what is different to face ID, because face ID actually is it 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, uh you need to check against all previous people.
So it's so something needs to leave.
Yeah.
Uh you know, something needs to see leave something and be compared to someone else.
Uh and that's uh and that's a much harder uh challenge.
And um how we approach that is we have multi-party computation.
And so that essentially means that 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, and uh then they actually get split up in multiple pieces.
So we for example, we take a picture of the iris, we calculate an iris code, um, then we break that iris code in multiple pieces and sell set send it to multiple computers such that 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 the pieces apart.
Right, right, right.
In such a way that nobody has the whole thing.
Yeah.
So no one has the whole thing.
And also during the computation, no one has the whole thing.
Yeah.
But they do some, you know, some clever interactions to come to the conclusion.
A little like a zero-knowledge proof kind of technique.
Uh it's it I mean, it it's very different, but I think in terms of the properties it achieves, it's somewhat similar.
Where like you no one knows anything about you, but you can actually together make a statement about you.
Right.
And so, you know, you send it to this multi-party computation, and what comes back is yes, that individual is unique.
And then the second thing we do is we we separate all of this from you with a zero-knowledge proof.
So meaning you have that secret on your phone, but no one else has it, no server has it, we don't have it.
Um and then you can later go back to 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.
Uh 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 like very counterintuitive property that you there is like even though it uses 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 you know, things that were annoying and are now becoming overwhelming in terms of uh just bots, you know, particularly with psyops, propaganda, all these kinds of things.
What are some of the other um you know, uses of bots that are gonna be kind of impossible to live with if we don't get to proof of human in the future?
Yeah, actually I think the the simple model I have for it is every moment on the internet uh that is primarily about humans interacting with each other, you know, or or or even indirectly interacting with each other.
So uh, you know, you can you can start with simple ones like dating, you know.
Yeah.
That really matters.
What is the other side is in fact a person?
Yeah, well they've got bad news for listeners.
Well, I uh and the person who you expect it to be.
Yeah, yeah.
Yeah, exactly.
Yeah, we had this problems even before it.
Yeah, exactly.
Yeah, yeah.
So that that I that's that's an obvious one.
Um and so for example, Tinder's already using it for that reason.
Um I I think And what's what's the uh the Tinder use case?
So we started we we started in Japan, uh and like as as a test as a test market, and it's it's essentially exactly what we just discussed.
It is um if you verify it with an orb, you get a little badge that you know signals to other people that you are in fact a human, so it high has a high level of verification.
Um and then also um 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.
Um so you just run a quick check that uh this is all correct.
Um and so you know, you then know you're not interacting with bot, but also you're you know, you interact with a fully authentic profile.
Yeah.
Another fun one because I think it's somewhat counterintuitive, but I think it will be video conferencing.
Uh because you know, you already have deep fakes.
Yeah, just suggest I don't feel like going to this video conferences, just put my deep fake up.
Yeah.
And actually you um you raised the two me first, and that's why we started building a product for it.
Because you know, it will actually start with very high value users.
Yeah, yeah.
Like, for example, people, you know, like yourself that maybe manage a fund.
And you know, sometimes calls actually could be very high value if it's about borrowing money or Oh, yeah, yeah.
Well said, so so somebody can uh be me and say, Eric, can you please wire this Nigerian prince 400 million dollars?
Right.
Yeah, exactly.
Um good to know, yeah.
Yeah.
Yeah, like you know, that that's still slightly hypothetical because these these things are not fully real time and you can somehow we're very close.
But we're very close.
And so I think, you know, in a year from now it's just gonna be a full commodity and it's gonna be super photorealistic and absolutely real time, and you will just not know anything anymore on this video calls.
And and so I think that's another one.
Uh I think another one then will be, which I think is fun, but it's it's gonna be gaming.
Uh you know, because like gamers really care.
Oh yeah, that's playing a AI.
Oh my god, that's frustrating.
Yeah, especially if we bet money.
Yeah, exactly.
And you you you lose money, you train multiple hours a day to get like really good at this thing, and then suddenly you get, you know, you you get destroyed by an AI.
That's just superhuman in every dimension.
Um funny enough, I was like, I wonder what do you think about this bit because I don't have a good mental model about it, but even the the whole model for video platforms, I think is about to break because there's a couple dimensions to their problem.
But one if if if the if the creation of content is is becoming super scalable.
Like for example, I I heard about this one guy that uh created, I think like I was like on the order of uh a hundred videos a day on YouTube and made tens of thousands of dollars a month.
All of them are fully AI generated.
Yeah.
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 i it it's interesting, right?
They fell for it.
Um but maybe they liked it.
Yeah, yeah, 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 along the lines of I think there's categories of content that are clearly just fictional.
Yeah.
Like movies are that.
You know, it's like you you don't care that there's any connection to reality.
It's just a fully fictional story.
But no, if you think about something like TikTok or, you know, all these kinds of things, like people actually really 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 you can create a pretty good podcast.
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 and that it came from, you know, some real thing.
But you would like to know that.
You would like to know that.
Yeah.
I would like to know that.
And then it continues as an advertiser you would like to know did a human watch it.
Yeah.
Or did an AI watch it.
Yes.
Right, right.
Well right.
That that that's the other thing is I created a hundred AI videos.
I had a million AIs watch it.
And then I made a lot of money off of YouTube.
Exactly.
I actually saw that video day of of a YouTube farm.
Yeah like they just 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 a that's actually a real problem for them.
Right.
Well the whole sort of the creator economy platforms of the last decade you know Substacks, Spotify, you know, and all the people who support artists or you know Patreon, et cetera creators, YouTubers, they they have a personal relationship with with with with these people.
It's not just they like the the art and so if they all of a sudden found out that they were, you know, bots that might, you know, they might not want to support them in the same way.
Yeah you you you might want to want to give them a a big YouTube tip or yeah, I think there's a certain subset of people who support um, you know, want to support actual people and feel like they're having a real relationship.
Yeah.
And and the thing that I think like people don't really get is that, you know, it should be obvious, but I 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 know, just because it's a glimpse.
Like, you know, cost of intelligence is dropping uh almost exponentially, agentic capabilities are increasing, you know, in like some super linear form.
So like, yeah, what we currently see is less than one percent of what it will look like in probably a year or two.
And that's so and then second, these things will be actually they will be superhuman in many ways.
Uh that they would be like perfectly able to understand you and like talk in the right way to you.
For example, there's this like one paper that I that I think you could delete it after, but it was um it was uh the Change My Mind subreddit um where the University of Zurich did this thing where they had AIs actually interact with Change My Mind.
Yeah.
Yeah, they were like superhuman in their ability to change it because they 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.
You know, and just like hit all the buttons and uh like AIs are really good at programming humans.
That's much better than humans are at programming AIs.
Absolutely.
There's no question.
And so I think that's gonna get quite scary also.
But uh I I think at least if you know you're being a victim of a psy-op then or or or it's a very advanced one done by uh uh 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 are out there, w why don't you give it a little bit of an update?
Maybe you can 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.
Uh then, you know, like things like Reddit or, you know, X or you know, things like that.
Um secondly, you need uh distribution of these devices.
And I think the right mental model to have for 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 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 to fly.
But but you know, how do we get that down to below 15 minutes across C 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 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 it in your Reddit account.
Maybe you get like, you know, certain amount of chat GPT subscription for free.
Or like so I think it's gonna be a combination of things, but you need to you know you need to land all three at some point at that same time, which is uh which is hard to do.
We are now at 18 million users that are verified, f 40 million in total in the app.
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.
It's like for all of this, the main thing that matters is the US.
And hopefully uh we get the Clarity Act passed shortly.
That would be a good idea.
Yeah, exactly.
That would 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 on the US.
So I think over the next year, 90% of the of the you know effort of the company is just gonna go about the US.
And how do you get, for example, device distribution app?
How 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 a 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.
Yeah.
Like just 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 it 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 gonna scan people's retinas and that's how we're gonna know they're human and so forth.
And this was, I mean, you pitched us six years ago, six years ago.
Yeah, it was before COVID because you you were there with the orb.
Right.
Um and you know, a AI just hadn't happened yet.
And and you know, you could kind of see, but they're you know, there's bots, um, but they were kind of very crude in, you know, compared to what there are now.
Um but it uh it seemed inevitable.
Um at least at the time, you know, the thing was it was so out of it was so from the future that uh, you know, and we always worry about okay, like what's the timing of this and this and that and the other and and so forth.
Um but you know, you were impressive enough and it was gonna 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 i it was one of the it wasn't obvious that like it was gonna work in that time frame.
It seemed very inobvious for a long time.
And uh how different was that pitch from what it ended up being?
Or talking about that.
It was actually pretty much exactly the same.
I think it's the same thing.
The device changed.
You know, you know, they've they've made it much more economical and and convenient, but that's right.
It's uh but the initial instincts was great, it was there.
It was basically everybody's gonna have to prove their you're either gonna have to have some proof that you're human on in cyberspace, or like it's gonna be a very bad world.
I mean the robots are gonna get us.
We're done.
Right.
And then actually the second piece that was like this was the first thing is like it's gonna be that itself is gonna be a big deal.
But then second of all that, you know, when it's gonna 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 human, but then second, it will have very strong network effects.
And even as the platforms, as you get into the platforms, even as the platform's largest problem has been bots.
I mean, you remember Elon and uh, 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 in their thinking and go, Oh, yeah, we need proof of human.
Yeah.
Like it's kind of obvious.
Yeah, because people are 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 like detection tools?
When did you come up with the language proof of human?
We had actually we had we had proof of personhood for the longest time.
It's even here in this on this breathe.
Yeah.
But then uh at some point we were like, shit, well, at some point AIs will have person of two.
So like that's not gonna apply.
Yeah but they're not gonna have retinas for a long time.
Yeah.
Oh that's coming eventually.
It was actually really funny.
It was like some of the some of the open AI people that I met were like, man Alex this is gonna this is gonna be so dark.
Like people will hate you for like not giving personhood to AIs and I was like Jesus.
Let's call it proof of human then.
That's funny.
So that that's how it changed.
But then actually so then I would say like last year, so post then there was like a big shift post Chat GPT.
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.
Let's stay in touch.
Like there was like the common response and then uh you know and well but you also you had a couple CEOs that really believed it 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 Claude bots and and moatbook recently.
Yeah.
Just because that kind of means like the the cow is way out of the barn.
Yeah.
Yeah and and so like honestly, if you don't take it serious now, then I think you just you you you should get a different job or something.
Yeah, where you're not.
Yeah.
Just not thinking about realms 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 now it feels like much more of an executional problem.
Not not any more uh market risk.
Like a market risk or like a thesis problem or like like just uh and which is still a big fucking problem.
It's 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 meet all these three things at the same time is still a very hard point?
How do you normalize the behavior, et cetera?
So people aren't weirded out in a Starbucks or something.
Although I I think that's now gonna be used to it.
I just because I think people will hate the alternative so much.
And I think people are gonna by the way, take a lot more pride in being human, uh, particularly online because I I think that people are gonna start getting accused of being bots.
I mean, like it's it's gonna get really weird.
Yeah.
Um and without like clear delineation, it's it's gonna be a mess.
Like, I don't I don't understand how somebody can think they're gonna have a social media platform that doesn't distinguish between humans and bots.
Like that seems absurd to me.
Sounds absurd.
I think we will my guess is over the next couple months we will see we will see things like these platforms trying to use things like face biometrics on the phone, 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 meet uh the market to what comes after, which I think something like the orb is the 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 it's so hard to get to in terms of building a fixed cost.
And then there's a massive network effect.
Um, which like people are starting six years behind you on that.
But yeah, I'm sure they'll come because it's it's just such an obvious problem now.
What actually do you think about like a AI continues?
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 money.
They're good at taking money from citizens, but not the reverse.
I mean, uh the well, just if you go back to COVID, the stimulus program, like I think 400 billion dollars was stolen.
And like that that that's pretty crazy.
You would have liked to know that you were sending the money to unique humans.
I mean, if even if not citizens, right.
As long as they were unique humans, that would have been good.
Yeah, I mean, a social security system, for example, is a mess in the US.
Yeah, it's insane.
It's it's a total disaster.
So we're gonna 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 gonna be a really bad problem.
Um I think.
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 Save Act, but it's not 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 like we genuinely don't know.
And then if you go to uh I mean uh the the the whole mail in ballot thing, like is uh built for a whole very different world, right?
That's right.
Uh 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 gonna have the will of the people anymore.
Like I I think that's gonna be gone pretty fast.
So I think we're gonna need some kind of you know, s cryptographically strong infrastructure on like who's who.
Um and then, you know, similarly, I think we're gonna have to be able to get people money much more efficiently than through these uh this crazy apparatus of social programs that we have.
Uh 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 of the United Healthcare.
Like, and people are happy about that.
Like, like really happy.
So, like, think about how bad a system that is.
Um 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 inefficient way.
Um, but we have the technology to do that now.
So I think that AI is gonna make that problem so bad, uh, 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.
Uh and so um, 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, Todd.
Yeah.
So I I I I think y you know, proof of human is a piece of a very important puzzle where we have to upgrade the entire infrastructure or we're not gonna be a democracy anymore.
I mean, that that just be my guess.
I agree, Tom.
Share more.
You said, okay, next year go to market is focused on the on the US.
Say more about how 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 of the project where I do believe many of the platforms that 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 of a a billion users um sending users to you, then it's really just all about like how do you meet that demand.
It's like, you know, and that's that's that's what we're now entering.
And so um yeah, so I think the response is first.
Um I think you will see, and we're already working on it, but you will see a lot of really large platforms that you know integrate uh in the in the near term future.
I think that will just to set expectations.
I think that will be slow initially because it also should be, just to, you know, to to get understand the product.
It will be focused on certain geographies, like what we did with Tinder Ravesort in Japan, just to, you know, to uh 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 of the main priorities for me, is just how do you get this orb distribution up, which is 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, uh, 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 clusterfuck 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, um, 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.
Um or it it can just be you go to one-off, you know, hip coffee shops and you just you just put it there.
Or, you know, and then it you could go you could eventually even go to the DMB and just put it right there.
So that's the problem we're currently trying to trying to puzzle together.
Um and you know, it's gonna be some some of all of that.
I think there's gonna be some large-scale distribution partnerships, many one-off coffee shops.
Oh, actually, one thing that we will uh we will launch soon, and the team is gonna hate that I'm saying this now, but uh it's gonna be orb on demand.
So yeah.
So in the bay.
Just because actually it's such it's such a gnarly problem to, you know, to get an orb to truly everyone.
You know, it's like to to get that, the capex 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.
And 50 minutes later there's as crazy as as crazy as it sounds.
So like in in places like the Bay Area or New York, uh, you you will just be able to say, like, yeah, I want to your orbit you can you can verify now.
And uh did you ever think about uh I don't know th this is probably a terrible idea, but um having kind of different levels like we know you're a ne unique human or like this guy may be unique human because he's done it on his iPhone and it's not quite the the same.
But yeah, yeah, we we have that.
So actually we um you know j generally we just have the you know, we have the principle of you know, what whatever could be useful for this problem, we just build it.
And uh 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 still uses multi-party computation, what we've built for the entire system, so you're still anonymous.
Um and you know, it of course reaches way less accuracy.
So uh, you know, as a system, you will know something along the lines of, well, this is you know, at least one person cannot create a hundred accounts.
Maybe it's just ten or twenty.
So like it's like a at least it's some measure of rate limiting.
Um and I do think just to set a disclaimer, I think with deep fakes and and you know, all this stuff, I think that will fundamentally break.
So it's a temp.
It's it's uh it's a temporary solution that I think can get us to scale.
That's kind of how I think about it.
Uh we also actually use government IDs.
Uh similarly, where like we we use uh but just the ones that have an NFC ID chip.
Um and we use multi-party computation, so you remain anonymous and platforms can choose to use that as well.
Uh but no one really did.
It's just somehow they have this like very negative stigma, which I think makes sense.
Yeah.
Um, but yeah, basically whatever could do it.
Yeah.
By any means necessary.
That's right.
Uh well thanks so much for coming to the podcast.
It's been great.
Yeah, thank you.
Thank you.
Thanks for having me.
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