Marc Andreessen (2:11)

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?

Alex Blania (2:19)

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. 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 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 replies, that there's probably one human sitting somewhere and sending out ten thousand or a hundred thousands 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.

Marc Andreessen (3:22)

Which is what, a 100th of the bots.

Alex Blania (3:25)

That's right. That's how it feels like. And then agent on behalf of human. I think how that 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 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.

Marc Andreessen (3:47)

Okay. Post to my ex account. Post to my Instagram for example. But it's my Instagram and I'm a unique human that owns that.

Alex Blania (3:55)

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.

Marc Andreessen (4:02)

That makes sense. And so how do you prove somebody is human?

Alex Blania (4:08)

It is a surprisingly hard problem. Yeah, so it's.

Marc Andreessen (4:11)

Those agents are very, very clever.

Alex Blania (4:13)

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, then 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 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 eventually everything that is just digital, an AI will be able to do as well.

Marc Andreessen (5:27)

Yep, we're there.

Alex Blania (5:30)

Yeah, exactly. So an AI will be able to have a GitHub account and we'll 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 there was area number one. Area number two was to just use government IDs for everything, which we just also immediately 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.

Marc Andreessen (5:58)

But then also, right, you lose anonymity instantly. Right.

Alex Blania (6:03)

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 3 billion users and for a lot of other countries.

Marc Andreessen (6:32)

Yeah, Singapore is 2 million people. A million people.

Alex Blania (6:35)

Yeah, exactly. So do you want to lock everyone else out? 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 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. Mm. 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.

Marc Andreessen (7:33)

Yeah.

Alex Blania (7:34)

And then suddenly it goes from 1 to 1 to 1 to N. And it's 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 to prove that. And it turns out that's a pretty high number because it's an exponential problem.

Marc Andreessen (7:54)

Right.

Alex Blania (7:55)

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 then you would basically hit a wall after tens of millions of users. And so then you end up with something like iris, which is the muscle of your eye that actually has enough

Marc Andreessen (8:10)

entry and that it's unique.

Alex Blania (8:12)

That is unique. That is unique enough.

Marc Andreessen (8:14)

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.

Alex Blania (8:30)

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 and then authentication, which is you showing your passport constantly for certain kinds of things. And on the verification piece we've went down, if you know world, you know that we've built this thing called an org, it's doing a lot of things to prevent these kinds of attacks. So 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 got it handled. On the consumer side 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, 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.

Marc Andreessen (9:32)

Right.

Alex Blania (9:33)

And with a new iPhone you can have meaningful amount of trust against that. But with old Android phones basically. And so. Oh yeah, yeah, yeah because 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 going to be a mix off. 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.

Marc Andreessen (10:03)

If you just re authenticate.

Alex Blania (10:05)

Yeah, that's right.

Marc Andreessen (10:06)

Interesting. And then one of the kind of incorrect criticisms of the approach early was oh my God, they've got my eyeball, you know, now they're, you know, they somehow have access to my privacy and they're going to do all these things to me and that's my access and then worldcoin can impersonate me and all these kinds of things. But that's not the case. So that was also like a non trivial engineering problem.

Alex Blania (10:40)

That was very much non trivial. So actually I think one point on Iris that I think people don't appreciate enough, that's a bet we took back then but it was essentially that Iris will turn out to be supernormal as a modality just because I think we will all wear AR and VR systems that do that. You know, Apple already does, it already has RSID and the Vision Pro. 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 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 because

Marc Andreessen (11:32)

like yeah, that's an expensive conclusion.

Alex Blania (11:35)

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 out billions of dollars and do like a massive effort to distribute all the world. 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 two main high level ideas on how to solve it were Multi party computation and zero knowledge proofs. And so again, what is different to face id? Because face ID actually can be very private. Just because the embedding is stored on the phone, it doesn't have to leave the phone ever, just because it's just you against you in the past. But to check uniqueness, you need to check against all previous people. So something needs to leave. Yeah, something needs to leave something and be compared to someone else. And that's a much harder challenge. And how we approach that is we have multi party computation. And so that essentially means that. So in our case, when you verify with an orb, 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 the information about you. 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.

Marc Andreessen (13:24)

Right, right, right. In such a way that nobody has the whole thing.

Alex Blania (13:27)

Yeah, so no one has the whole thing. And also during the computation no one has the whole thing, but they do some clever interactions to come to the conclusion.

Marc Andreessen (13:36)

A little like a zero knowledge proof kind of technique.

Alex Blania (13:40)

I mean it's very different, but I think in terms of the properties it achieves, it's somewhat similar where no one knows anything about you, but you can actually together make a statement about you. And so you send it to this multi party computation and what comes back is, yes, that individual is unique. And 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 very counterintuitive property that there is like, even though it uses biometrics, you preserve anonymity and extreme levels of privacy, which I think is super cool.

Marc Andreessen (14:48)

Social media is one kind of vector of things that were annoying and are now becoming overwhelming in terms of Just bots, particularly with Psyops propaganda, all these kinds of things. What are some of the other uses of bots that are going to be kind of impossible to live with if we don't get to proof of human in the future?

Alex Blania (15:14)

Yeah, actually, I think the simple model I have for it is every moment on the Internet that is primarily about humans interacting with each other or even indirectly interacting with each other. So you can start with simple ones like dating, you know, that really matters. The other side is in fact a person.

Marc Andreessen (15:37)

Yeah, well, they got bad news for listeners and the person who you expect it to be.

Alex Blania (15:43)

Yeah, yeah, exactly.

Ben Horowitz (15:44)

Yeah, we had this problem even before. Yeah, exactly.

Alex Blania (15:47)

Yeah. So that's an obvious one. And so, for example, Tinder is already using it for that reason, I think.

Marc Andreessen (15:55)

And what's the Tinder use case?

Alex Blania (15:59)

So we started in Japan and like, as, as a test, as a test market. And it's essentially exactly what we just discussed. It is 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 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. Um, so you just run a quick check that this is all correct. Um, and so you know, you then know you're not interacting with bot, but also you, 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 because, you know, you already have deep fakes.

Marc Andreessen (16:51)

Yeah, just, I don't feel like going to this video conference. Just put my deep fake up.

Alex Blania (16:56)

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, like, for example, people like yourself that maybe manage a fund and sometimes calls actually could be very high value if it's about borrowing money or.

Marc Andreessen (17:14)

Oh, yeah, yeah. Well, so somebody can be me and say, eric, can you please wire this Nigerian prince $400 million? Right.

Ben Horowitz (17:25)

Yeah, exactly.

Marc Andreessen (17:26)

That would be good to know.

Alex Blania (17:28)

Yeah, like, you know, 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 going to be a full commodity and it's going to be super photorealistic and absolutely real time and you will just not know anything anymore on these video calls. And so I think that's another one. I think another one then will be. Which I think is fun, but it's going to be gaming, you know, because.

Marc Andreessen (17:57)

Oh, yeah, yeah.

Alex Blania (17:57)

Because like, gamers really care.

Marc Andreessen (17:59)

Oh, yeah. That they're not playing AI. Holy cow, that's frustrating. Especially if we bet money.

Alex Blania (18:07)

Exactly. And 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 get destroyed by an AI that is just superhuman in every dimension. Funny enough, I was like, I wonder, what do you think about this? But. Because I don't have a good mental model about it. But even the whole model for video platforms, I think is about to break because there's a couple dimensions to their problem. But one, if the creation of content is becoming super scalable. Like, for example, I heard about this one guy that created, I think it was like on the order of a hundred videos a Day on YouTube and made tens of thousands of dollars a month. All of them were fully AI generated 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.

Marc Andreessen (19:02)

Yeah, well, it's interesting, right? They fell for it, but maybe they liked it. Yeah, that could be. But it would sure be nice to know, like, okay, this is a human video or this is an AI video.

Alex Blania (19:20)

Actually, my thesis about this is something along the lines of I think there's categories of content that are clearly just fictional, like movies are, that 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 all these kinds of things, people actually really care about them. Mostly because there is some connection to reality.

Marc Andreessen (19:44)

Yeah, well, there's reality and there's connection to human. 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 and it will be reality and that it came from, you know, some real thing. But you would like to know that.

Alex Blania (20:07)

You would like to know that.

Marc Andreessen (20:08)

Yeah, I would like to know that.

Alex Blania (20:09)

And then it continues. As an advertiser, you would like to know, did a human watch it or did an AI watch it?

Marc Andreessen (20:16)

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.

Alex Blania (20:27)

Exactly. And I actually saw that video today of a YouTube farm where they had these thousands of phones that just watch videos all day for a reason.

Marc Andreessen (20:35)

Yeah, yeah. And then like, that's got zero value to the YouTube advertisers. And so that's actually a real problem for them.

Alex Blania (20:42)

Right.

Ben Horowitz (20:43)

Well, the whole sort of the creator economy platforms of the last decade, you know, substack, Spotify and all the people who support artists, or you know, Patreon, et cetera, creators, YouTubers, they have a personal relationship with these people. It's not just they like the art. And so if they all of a sudden found out that they were, you know, bots that might, you know, they might not want to support them in the same way.

Marc Andreessen (21:05)

Yeah, you might not want to give them a big YouTube tipper.

Ben Horowitz (21:10)

Yeah. I think there's a certain subset of people who support, you know, want to support actual people and feel like they're

Alex Blania (21:15)

having a real relationship. Yeah. 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 consequences 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.

Marc Andreessen (21:35)

You know, just because it's a glimpse.

Alex Blania (21:37)

It's a glimpse like, you know, cost of intelligence is dropping almost exponentially. Agentic capabilities are increasing you in some super linear form. So, yeah, what we currently see is less than 1% of what it will look like in probably a year or two. And then second, these things will be actually, they will be superhuman in many ways. They will be perfectly able to understand you and talk in the right way to you. For example, there's one paper that I think it got deleted after, but 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 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 understanding their political motivation, the way they talk, and they were just interacting in the perfect way and just hit all the buttons.

Marc Andreessen (22:35)

The AIs are really good at programming humans. That's much better than humans are at programming AIs.

Alex Blania (22:41)

Absolutely. There's no question that's going to get quite scary also.

Marc Andreessen (22:44)

But I think at least if, you know, you're being a victim of a psyop then, or it's a very advanced one done by an AI that would be extremely useful to understand.

Ben Horowitz (22:57)

Totally talk a bit more about the state of the product and the business today, like how many IDs are out there? Why don't you give a little bit of an update? Maybe talk about the evolution as well.

Alex Blania (23:06)

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 have for it is how many minutes does it take a person to reach such a device on average. And you know, currently it's. If you would take the global average, it would be 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, it's not crazy, but it's also not nothing. 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 the utility of all the sub platforms essentially. But all of that layers on top. Like maybe you can use it in your Reddit account, maybe you get a certain amount of ChatGPT subscription for free. So I think it's going to be a combination of things. But you need to land all three at some point at the same time, which is hard to do. We are now at 18 million users that are verified, 40 million in total in the app. But the biggest thing is because of the past administration, because 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 for all of this. The main thing that matters is the

Marc Andreessen (24:48)

US and hopefully we get the Clarity act passed shortly.

Alex Blania (24:52)

Yeah, exactly. That would be really great. So to get clarity on that. Yeah. So the big focus that we now are going through right now is to kind of go all in on the U.S. so I think over the next year, 90% of the effort of the company is just going to go about the US and how do you get, for example, device distribution app? How do you eventually have this in every Starbucks So it becomes just super normal and people just use it every day. So that's kind of the. And then on the platform side, actually, we went through a. It was a very interesting experience to go through personally, because I think, 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 Andreessen and a couple other people that believed in it. But

Marc Andreessen (25:44)

yeah, like, and the press, like the amount of fun making of something that it just shows how shortsighted people are.

Alex Blania (25:52)

That's right.

Marc Andreessen (25:53)

It's like, you don't think the bots are coming.

Alex Blania (25:55)

What did you think when we first pitched, actually? Because even you must have thought, this is crazy.

Marc Andreessen (26:00)

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 and you know, AI just hadn't happened yet and, you know, you could kind of see. But there's bots, but they were kind of very crude and, you know, compared to what there are now. But it seemed inevitable, at least at the time. You know, the thing was, it was so out, it was so from the future that 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 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 in. But it was not. It wasn't obvious that, like, it was going to work in that timeframe.

Alex Blania (27:11)

It seemed very inobvious for a long time.

Ben Horowitz (27:14)

And how different was that pitch from what it ended up being?

Alex Blania (27:17)

Or talk a little bit. It was actually pretty much exactly the same.

Marc Andreessen (27:19)

I think it's the same thing. The device changed, you know, you know, they've. They've made it much more economical and convenient, but. That's right, it's.

Ben Horowitz (27:27)

But the initial instinct was great.

Alex Blania (27:28)

It was there.

Marc Andreessen (27:29)

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 going to be a very bad world. I mean, the robots are going to get us. We're done.

Alex Blania (27:45)

Right. And then actually the second piece that was like, this was the first thing that Itself is going to be a big deal. But then second of all, that when it's going to become a big deal, we will be able to build one of the most valuable networks as a result of that. Because in a world of AI, having a human network is going to be this incredibly important thing. And so actually, yeah, two things. One, you will need a proof of human. But then second, it will have very strong network effectively.

Marc Andreessen (28:11)

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 the, you know, he backed out of buying Twitter because all the stats were based on bots. They still even knowing that it was hard for them to get all the way to the future and they're thinking and go, yeah, we need proof of human. Yeah, like it's kind of obvious.

Alex Blania (28:37)

Yeah. Because people were like, what does it even mean? You know, like, what does proof of human even mean? We can just, we can just, you know. And did you have the length detection tools?

Ben Horowitz (28:44)

When did you come up with the language proof of human?

Alex Blania (28:47)

We had actually, we, we had proof of personhood for the longest time. It's even here in this, on this brief.

Marc Andreessen (28:51)

Yeah, yeah.

Alex Blania (28:52)

But then at some point we were like, shit, well, at some point AIs will have Persona too.

Ben Horowitz (28:57)

So.

Alex Blania (29:02)

So like that's not gonna apply. So.

Marc Andreessen (29:03)

Yeah, but they're not gonna have retinas for a long time. That's actually events.

Alex Blania (29:08)

It was actually really funny. It was like some of the OpenAI people that I met were like, man, no, this is going to be so dark. People will hate you for not giving Persona to AI. So I was like, jesus, let's call it proof of human, then that's fine. So 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 ChatGPT. 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, you know, and well, but you also, you had a couple CEOs that really believed that and were like willing to take the long term bet to give them credit. But I think the second big shift was actually cloudbots and Moatbook recently. Yeah.

Marc Andreessen (30:11)

Just because, yeah, that kind of means like the cow is way out of the barn.

Alex Blania (30:19)

Yeah. And so, like, honestly, if you don't take it serious now, then I think you just, you should get a different job or something.

Ben Horowitz (30:25)

Yeah. What are you doing?

Alex Blania (30:27)

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, now it feels like much more of an executional problem. Not, not any more market risk. Like a market risk or like a thesis problem or like, like just. 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.

Ben Horowitz (30:56)

How do you normalize the behavior, et cetera. So people. Aren't we Starbucks or something?

Alex Blania (31:00)

Although I think that's now going to be.

Ben Horowitz (31:03)

Don't you get used to it?

Alex Blania (31:05)

Just because I think people will hate the alternative so much.

Marc Andreessen (31:08)

And I think people are going to, by the way, take a lot more pride in being human, particularly online because I think that people are going to start getting accused of being bots. I mean, 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. That seems absurd to me.

Alex Blania (31:40)

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 that I

Marc Andreessen (32:12)

have not seen a competitor yet because it's so ridiculous. It's so ridiculous and it is so hard to get to in terms of building a fixed cost. And then there's a massive network effect.

Alex Blania (32:25)

Right.

Marc Andreessen (32:25)

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.

Alex Blania (32:35)

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?

Marc Andreessen (32:43)

I think governments do have to figure out how to send citizens money. They're good at taking money from citizens, but not reversed. I mean we'll just. If you go back to Covid the stimulus program, like I think $400 billion was stolen. Like that's pretty cool. 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.

Alex Blania (33:10)

Yeah. I mean the Social Security system for example is a mess and it's insane. It's a total disaster.

Marc Andreessen (33:16)

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 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?

Alex Blania (34:02)

That's right.

Marc Andreessen (34:04)

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 think that's gonna be gone pretty fast. So I think we're gonna need some kind of, you know, cryptographically strong infrastructure on like who's who. And then you know, similarly I think we're gonna have to be able to get people money much more efficiently than through these, this crazy apparatus of social programs that we have. Just because like how lossy is and fraudulent is Social Security or Medicare or any of these things. I mean like the Medicare is so frustrating for people that they shot the CEO of United Healthcare. 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 healthcare, but they do it in a like super inefficient way. But we have the technology to do that now. So I think that AI is going to make that problem so bad because the ability to file fraudulent claims and create fake, you know, buy social. I mean you can Buy Social Security numbers on the black market. Like for those of you don't know, that's an easy thing. That's a real thing. Like that is like everybody's Social Security number is for sale. And so, you know, like AI is just a way of making that kind of loose black market underground fraud thing just massive and extremely scalable.

Alex Blania (35:54)

I agree. Yeah.

Marc Andreessen (35:55)

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.

Alex Blania (36:07)

I agree.

Marc Andreessen (36:08)

Tom.

Ben Horowitz (36:09)

Share more. You said okay next year go to market is focused on the US Say more about how you're thinking about that. Is the incentive for people to do it because they get to use a set of services? Is there some other economic incentive or how do you envision it?

Alex Blania (36:22)

Basically a month ago we entered a very different phase as a project where I do believe many of the platforms that we're now integrating with will really bring a lot of users to our platform. And that changes how you think about it entirely. Like if you have a platform of a billion users sending users to you, then it's really just all about how do you meet that demand. And that's what we are now entering. I think the response is first, I think you will see, and we're already working on it, but you will see a lot of really large platforms that, you know, integrate 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 understand the product. It will be focused on certain geographies like what we did with Tinder where we started in Japan, just to test the product and also to just normalize the concept. But that will happen. And then secondly, which is now becoming one of the main priorities for me is just how do you get this orb distribution up? Which is broadly speaking, there's a couple different dimensions to that, but one is first of all, the product needs to work at scale without supervision, which turns out to be much harder than you would think. Every engineering problem at scale turns out to be much more complicated than you would think because fighting for 1% of improvement in quality is this clusterfuck of all these dependencies that come together. So I think that's one of the biggest engineering focuses right now. But then second, you need to find places to deploy them at. And the way to think about it is there are large scale distribution partnerships that could be something like Walmart or if you're very ambitious. It could be something like Starbucks or it can just be you go to one of hip coffee shops and you just put it there and then you could eventually even go to the DMV and just put it right there. So that's the problem we're currently trying to puzzle together and it's going to be 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, we will launch soon and the team is going to hate that I'm saying this now but it's going to be orb on demand. Just because actually it's such a gnarly problem to get an orb to truly everyone. It's like to get that the capex is insane. So it's actually much cheaper and easier to just put an orb on a motorbike and drive it to you. As crazy as it sounds 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. And 50 minutes later there's an orb comes to Europe and you can, you can verify and did you ever think

Marc Andreessen (39:36)

about, I don't know, this is probably a terrible idea but having kind of different levels like we know you're a unique human or like this guy may be a unique human because he's done it on his iPhone gradations quite the same.

Alex Blania (39:52)

But yeah, yeah we have that. So actually we, you know, generally we just have the, you know, we have the principle of you know, whatever could be useful for this problem we just build it and so we have something called face check that does that. So it uses face from the camera. It still uses multi party computation what we've built for the entire system. So you're still anonymous and you know, it of course reaches way less accuracy. So you know, as a system you will know something along the lines of well this is, you know at least one person cannot create 100 accounts, maybe it's just 10 or 20. So like it's like a, at least it's some measure of rate limiting. And I do think just to set a disclaimer I think with deepfakes and you know all this stuff I think that will fundamentally break.

Marc Andreessen (40:45)

So it's a temp, it's, it's, it's

Alex Blania (40:48)

a temporary solution that I think can get us to scale. That's kind of how I think about it. We also actually use government IDs similarly where like we, we use just the ones that have an NFC ID chip and we use multi party computation so you remain anonymous and platforms can choose to use that as well. But no one really did. It's just somehow they have this like very negative stigma which I think makes sense.

Marc Andreessen (41:11)

Yeah.

Alex Blania (41:12)

But yeah, basically whatever could do it.

Marc Andreessen (41:15)

Yeah, by any means necessary. That's right.

Ben Horowitz (41:19)

Well, thanks so much for coming on the podcast. It's been great.

Marc Andreessen (41:21)

Yeah, thank you, thank you.

Alex Blania (41:22)

Thanks for having me.

Podcast Host / Narrator (41:25)

Thanks for listening to this episode of the A16Z podcast. If you like this episode, be sure to like, comment, subscribe, leave us a rating, or review and share it with your friends and family. For more episodes go to YouTube, Apple Podcasts and Spotify. Follow us on x16z and subscribe to our substack@a16z.substack.com thanks again for listening and I'll see you in the next episode. As a reminder, the content here is for informational purposes only, should not be taken as legal, business, tax or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any A16Z fund. Please note that A16Z and its affiliates may also maintain investment in the companies discussed in this podcast. For more details, including a link to our investments, please see a16z.com disclosures.