# Proof of Human: Navigating the AI-Bot Era

**Podcast:** web3 with a16z crypto
**Published:** 2026-04-17

## Transcript

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 bots.
What 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.
AIs are really good at programming humans.
Much better than humans are at programming AIs.
Absolutely.
An AI will be able to have a GitHub account and will be able to post and also attest to five other AIs that these are in fact humans even though 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, you know, 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 known?
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.
Well, 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 that fits into an agent on behalf of a human.
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.
Like that's kind of the property that you're looking for.
It's 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.
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, you know, all these bots that are in replies that, you know, there's probably one human sitting somewhere and sending out 10,000 or like 100,000 of AIs.
And there's this catch-up 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 right.
That's how it feels like.
And then agent on behalf of human, I think like, how it will look like is, you know, I think all of us will have agents.
It's unclear how they will look like.
Is this going to be one or are there 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 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 decide 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, how do you prove somebody is human?
It is a surprisingly hard problem.
Yeah.
So, you know, it's...
You know, those agents are very, very clever.
It's, you know, it's funny.
We started this company, you know, 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, you know, both pass the Turing test.
So they can just claim to be a human.
You will not be able to tell that anymore on the internet.
And also that they would be, you know, highly agentic and just like run around to 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, you know, you own since a couple of years and then you post regularly or you comment regularly to GitHub.
Like these were the kinds of things that people were 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 immediately because we assumed that, you know, eventually everything that is just digital an AI will be able to do as well.
We're there.
Yeah, 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, you know, that was area number one.
Area number two was to just, you know, use government IDs.
For everything, which we just all seem to be disregarded for a couple of reasons.
One is that, 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 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, you know, the government ID 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, 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 matter because, you know, for example, I don't know, Meta is a global product with 3 billion users and with a lot of other countries.
Yeah, Singapore is like 2 million people or a million people.
Yeah, exactly.
So do you want to lock everyone else out?
So, yeah.
And then there's a long list of other things why we disregarded that basically immediately.
And then the last one is biometrics, which actually, you know, immediately gives us this ick reaction.
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.
But so that's a one-to-one, you know, one embedding to one new embedding.
Mm-hmm.
To solve the proof-of-human problem, you will need to distinguish one new individual from all previous individuals.
Hmm.
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 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.
Yeah.
To prove that.
And it turns out that's a pretty high number.
Yeah.
Because it's an exponential problem.
Right.
And so then you can just do the math and you find out that, you know, things like face or, you know, even fingerprints or something doesn't work.
Yeah.
Like then you would basically hit a wall after tens of millions of users.
Yeah.
And so then you end up with, you know, something like iris, which is the muscle of your eye that actually has enough entropy.
And that it's unique.
That is unique.
That is unique enough.
Yeah.
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 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, it is important, I think, to split up the problem and verification, which is essentially in, you know, old terms it's like you're getting your passport.
Right.
And then authentication, which is you showing your passport.
Right.
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 have built this thing called an orb.
An orb, yeah.
You know, 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, you know, 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 consumer side, like, you know, to 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, we, 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, you know, with a new iPhone, you can have a meaningful amount of trust against that, but with old Android phones, basically not.
And so, you know, because like you can just, you can just show a deepfake essentially either through a display or just directly inject it in the camera stream.
So that's a problem.
And so it's going to be a mix of, you know, if you have a new iPhone, you know, if you have a new enough, let's say iPhone or 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, like let's say a couple of times a year if you just...
I see.
Right, to re-authenticate.
Yeah, that's right.
Interesting.
And then, you know, one of the things, one of the kind of incorrect criticisms of the approach early was, oh my God, they've got my eyeball.
You know?
You know, now they're, you know, they somehow have access to my privacy and they're going to, you know, 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.
And so that was also like a non-trivial engineering problem.
That was very much non-trivial.
So actually, I think one point on Iris that I think people don't appreciate enough, and 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 that do that.
You know, Apple already does it.
Yep.
Already has Iris ID in the Vision Pro.
So I think it's, so maybe that's a 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 like 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, you know, to come to that conclusion.
Yeah, that's an expensive conclusion.
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 just assumes that you would be able to like somehow bring up billions of dollars and to like a massive effort.
Massive effort to just resolve the world.
But then also the privacy challenge of like how could you build such a system that has all the requirements that we care about.
And the two main high-level, you know, ideas on how to solve it were multi-party computation and zero-knowledge proofs.
And so to, again, what is different to Face ID, because Face ID actually is, you know, can be very private just because, you know, 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.
You know, something needs to leave something and be compared to someone else.
And that's a much harder challenge.
And how we approach that is with a multi-party computation.
And so that essentially means that, you know, in our case, when you verify with an orb, 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 an 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, you know, 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...
Where 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.
I mean, it's very different, but I think in terms of the properties, yeah.
In terms of the properties it achieves, it's somewhat similar.
Yeah.
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 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.
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 like very counterintuitive property that you...
There is like, even though it uses biometrics, you, you know, you preserve anonymity and 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 just bots, you know, particularly with psyops, propaganda, all these kinds of things.
What are some of the other, you know, uses of bots that are going to be kind of impossible to live with if we don't get to proof of 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, you know, or even indirectly interacting with each other.
So, you know, you can start with simple ones like dating, you know.
Dating really matters.
The other side is in fact a person.
Yeah.
Got bad news for listeners.
Well, and the person who you expect it to be.
Yeah.
Yeah, exactly.
Yeah, we had these problems even before.
The whole catfish thing.
Yeah, exactly.
Yeah.
So, that's an obvious one.
And so, for example, Tinder is already using it for that reason.
Yeah.
I think… And what's the Tinder use case?
So… So, we started in Japan and, like, as a test market.
And it's essentially exactly what we just discussed.
It is, if you verified with an orb, you get a little badge that, you know, signals to other people that you are, in fact, a human.
So, it 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.
Yeah.
And so, you know, you then know you're not interacting with bots, but also, you're, you know, you interact with a fully authentic profile.
Yeah.
Another fun one, because I think it's somewhat constitutive, but I think it will be video conferencing.
Hmm.
Because, you know, you already have deepfakes.
Yeah, just, I don't feel like going to this video conference.
Just put my deepfake up.
Yeah.
And actually, you, you raised it to 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.
Like, for example, 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, so, so, so somebody can be me and say, Eric, can you please wire this Nigerian prince?
Exactly.
$400 million.
Right.
Yeah, exactly.
That would be good to know.
Yeah.
Yeah.
Like, you know, that, that's still slightly hypothetical, because these, these things are not fully real time, and you can somewhat.
You know, in a year from now, it's just going to be a full commodity.
Right.
And it's going to be super photorealistic and absolutely real time, and you will just not know anything anymore on these protocols.
Yeah.
And, and so, I think that's another one.
I think another one then will be, which I think is fun, but it's, it's going to be gaming.
Yeah.
You know, because.
Oh, yeah, yeah.
Because, like, gamers really care.
Oh, yeah, that they are playing AI.
Yeah.
Exactly.
And 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.
Yeah.
You, you get destroyed by an AI that is just superhuman in every dimension.
Funny enough, I was, like, I wonder what you think about this, but, 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 that are a problem.
But one, is the creation of content is, is becoming super scalable.
Yeah.
Like, for example, I heard about this one guy that created, I think, like, it 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 AI generated.
Yeah.
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.
I mean, I would like to do that for it.
But maybe they liked it.
Maybe they disliked it.
Yeah, they didn't 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, something along the lines of, I think there's categories of content that are clearly just fictional.
Yeah.
Like, movies are that.
Yeah.
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 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?
That's right.
So, 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.
Right.
And it will be reality in that it came from, you know, 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'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.
And I actually saw that video today of a YouTube farm.
Yeah.
They had these, 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.
Right.
And so that's, that's actually a real problem for them.
Right.
Well, the whole, sort of, the creator economy platforms of the last decade, you know, Substack, 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, you know, you might not want to give them a, a big YouTube tip or.
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, 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.
Yeah, right.
It's a glimpse.
It's a glimpse.
Like, you know, cost of intelligence is dropping almost exponentially.
Agentic capabilities are increasing.
Yeah.
You know, in, like, some super linear form.
So, like, yeah, what we currently see is 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 superhuman in many ways.
They would be, like, I don't know if I said it after, but it was, it was the Change My Mind subreddit where the University of Zurich did this thing where they had AIs actually interact with Change My Mind.
Yeah.
And they were, like, superhuman in their ability to change it because they were going back to their profile of the people posting it and were, like, understanding their political motivation, the way they talk and, like, AI's are really good at programming humans.
Totally.
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.
Yeah.
But, I think at least if you know you're being a victim of a PSYOP, then, 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, 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, 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.
Secondly, you need distribution of these devices and I think the right mental model to, like, how many minutes does it take a person to reach such a device on average?
And, you know, currently, if you would take the global average, it would be a terrible number.
It would be, like, you know, days or something because many people would need to fly.
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, 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 all of that layers on top.
Like, maybe you can use it on your Reddit account.
Maybe you get, like, you know, a certain amount of TouchGPD 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, and you need to get a million total in the app.
But the biggest thing is because of the past administration, because we use, you know, we use crypto, we did not really invest in the US for a long time.
And 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, we get the Clarity Act passed shortly.
Yeah, exactly.
That would be really great.
So, to get clarity on that.
Yeah.
So, so the big focus that we, 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 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?
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, it was a very interesting experience to go through because, like, a couple of years ago, universally, people just made fun of us.
You know, like, just, it was like the universal reaction.
Well, minus in recent and a couple other people that believed in it, but, Yeah, like, in 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?
Well, because you had the orb.
Like, the orb was so wild.
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 years ago?
Six years ago?
Yeah, it was before COVID because you were there with the orb.
Right.
And, you know, AI just hadn't happened yet.
That's true.
And, you know, but they were kind of very crude and, you know, compared to what there are now.
But, it, 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 of this and this and that and the other and so forth.
But, you know, you were impressive enough and it was essentially, and it was an exciting enough idea that I think all those things kind of got us to go, okay, we're in.
But, but it was not, it was one of the, it wasn't obvious that, like, it was going to work in that timeframe.
It seemed very inobvious for a long time.
And how different was that pitch from what it ended up being?
It was actually pretty much exactly the same pitch.
I think it's the same thing.
The device changed.
You know, they've made it much more economical and convenient, but the initial instinct 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 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, like, it's going to be, that itself was going to be a big deal.
But then second of all that, we had 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, and so actually, yeah, two things, like, one, you will need a proof of 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 platforms' largest problem has been bots, I mean, you remember Elon and, you know, he backed out of buying Twitter because all the stats and all that.
It was hard for them to get all the way to the future in their thinking and go, yeah, we need proof of human.
Yeah.
Like, it's 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 language, when did you come up with the language proof of human?
We had, actually, we had proof of personhood for the longest time.
Yeah.
We had proof of personhood too.
Yeah.
So, so, like, that's not going to fly.
But they're not going to have retinas for a long time.
Although that's coming eventually.
It was actually really funny.
It was like some of the open AI people that I met were like, man, Alex, this is going to be so dark, like, people will hate you for like, not giving personhood to AI.
So I was like, Jesus.
Let's call it 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 there was like a big shift post chat gbt 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 it 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 give them credit but i think the second big shift was actually clodbots and moldbook recently yeah just because yeah that kind of means like the the cow is way out of the barn yeah yeah and so like honestly if you don't take it serious now yeah then i think you just you should get a different job or something yeah what are you doing yeah they're just like 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 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 do you make it cheap enough how do you make it economic like you know but but like how do you how do you make it how do you make all these three things at Mean mein lehmann time is still a very hard problem how do you normalize the behavior that's right so people aren't weirded out in a starbucks or something although i i think that's now going to be until she gets used to it just because i think people will hate the alternative so much yeah and i think people are going to by the way take a lot more pride in being human uh particularly online because i i think that people are going to start getting accused of being bought i mean like it it's going to get weird out of the way and uh other harder laws get in effect later I mean, it's going to get really weird.
And without, like, clear delineation, 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 seems absurd.
I think we will, my guess is over the next couple months, 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.
And, yeah, so we just need to get to scale fast enough to meet 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'll also see.
I have not seen a competitor yet.
Because it's so ridiculous.
It's so ridiculous, and it's so hard to get to in terms of building it.
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 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, well, just if you go back to COVID, the stimulus program, like, I think $400 billion was stolen.
That's pretty crazy.
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.
The Social Security system, for example, is a mess.
Yeah, it's good.
It's insane.
It's a total disaster.
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, that's going to be a really bad problem, I think.
Otherwise, there's no way to even have a democracy.
I mean, you know, like, 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.
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.
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 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 shut the CEO of UnitedHealthcare.
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 inefficient.
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 who 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 the entire infrastructure or we're not going to be a democracy anymore.
I mean, that'd just be my guess.
I agree with that.
Sharon Moore, you said, okay, next year.
Go-to-market is focused on the U.S.
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 of the project where I do believe many of the platforms that we're 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 platform of 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 restart 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 of 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 1% 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, 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 of, 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 DMV 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 going to be some, some of all of that.
I think there's going to 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 going to hate that I'm saying this now, but, uh, it's going to be.
Orb on demand.
So just because actually it's such a, it's such a gnarly problem to, you know, to get an orb to truly everyone, you know, it's like.
Together.
Together.
With 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 as, as, as crazy, 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 to New York 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 different levels, like we know you're a unique, unique human or like, Hey, this guy may be a unique human because he's done it on his iPhone.
It's not quite the same, but.
Yeah.
Yeah.
We, we have that.
So actually we, um, you know, generally we just have to, you know, we have the principle of, you know, whatever could be useful for this problem.
We just build it.
And, and, uh, and so we, we have something called face check that does that.
Yeah.
Yeah.
Yeah.
Yeah.
Yeah.
Yeah.
Yeah.
Yeah.
Yeah.
Yeah.
Yeah.
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 10 or 20.
So 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, you know, 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.
Uh, we also actually use government IDs, uh, similarly where like we, we use 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.
Right.
Which I think makes sense.
Yeah.
Um, but yeah, basically whatever could do it.
Yeah.
By any means necessary.
That's right.
Well, thanks so much for coming to the podcast.
It's been great.
Yeah.
Thank you.
Thank you.
Thanks for having me.