B (4:06)

Oh yeah.

A (4:07)

What's your background by the born in the U.S. you, what's your.

B (4:12)

Yeah, born in spiel. Yeah, I was born in the us. I went to school at mit, was a double major in math and cs. I've always really loved math, so that's kind of how I got into zk. And yeah, I mean ZK and cryptography have a lot of fascinating math. And that was really like the draw for me. And actually before zk I was doing some AI stuff. So I was like at Google Brain doing research into like early LLM. So this is pre GPT. I was doing some stuff with Bert back then, so I'm familiar with that world. And now it's exciting to see the synthesis.

A (4:47)

Yes. I mean, the thing is, I was actually also in Machine learning prior to the deep learning era, but from the standpoint of genomics and diagnostics and whatnot. And just to digress on that for a second before we get into the ZK stuff, you know, all the stuff with hidden markup models and conditional random fields and you know, it was surprising to me that Transformers worked as well as they did to get long range context in there. It's even more surprising to me that diffusion models work and, and yet they do that. You know, you wouldn't necessarily intuit from the equations that they would work as well as they do in practice. I don't know if you have any thoughts on that. Maybe talk about that and then go to the next, next. I mean when you were working on Bert, did you think obviously there was, there were people who had the graphs of scaling and here's how it's going to go. Right. So there was some intuition that it could maybe get there. But, but it, but it worked a lot. I mean the jump between GPT2 and GPT3 and then chat GPT in terms of usability was very nonlinear I think from, you know. Right, go ahead. Maybe. Were you surprised by that?

B (6:04)

Oh, I mean absolutely. I think even the close people closest to the metal on this stuff seem surprised at how well it's going and even today the level of math problems they're solving and stuff like that. Yeah, I would say I was very surprised that this process which with deep learning there aren't really that many proofs. Like in cryptography everything we do is proven like it's deterministic. You have very concrete bounds and proofs for everything. Yeah. And LLMs is just, it's like why does deep learning work? Kind of vibes based but right now the vibes are really good. It works really well.

A (6:45)

Right. And I think it's funny because. Well actually go through your talk and then let's talk, go prove what's real.

B (6:51)

Okay, cool. So yeah, I mean I think you. One of your favorite quotes is AI makes everything fake. Crypto makes it real again.

A (7:02)

Yes.

B (7:03)

And yeah, I think like the problem is very clear now. AI makes everything fake and we're seeing that every single day. So it's like whether it's this doordash driver kind of faking the delivery, maybe that's a little bit of a trivial example that went quite viral on X to something like oh, is Jeffrey Epstein still alive? Where it's like the White House posting digitally altered pictures or it's politicians getting deep faked or you know, celebrities getting deep faked. Doing all sorts of things. Like, the problem is, I think today is like extremely clear and it's only getting to be worse and worse as these models get better and better. So like the Seed Dance release recently of like the really good video models that like, you know, can really impersonate any celebrity or any person, like is very clear that AI makes everything fake is kind of like a huge problem for the Internet. So yeah, that's like the problem statement. And then I think like one really interesting thing. So people have identified this problem statement. It's not, you know, that hard to understand why it's so problematic. I would say the state of the art right now for trying to detect AI is use AI. So people have like trained these AI detectors to train models to say, hey, is something real or is something from the model? And recently at Succinct, we did this benchmarking study to evaluate those claims and say, hey, does AI detection actually work? And we published this data set of realistic AI images and tried to benchmark all the leading commercial detectors and you can actually go to the website aidetection sysync xyz. But the resounding answer was like, yeah, the AI detection stuff is not robust and it just does not work.

A (8:53)

I'm going to slightly argue with you on this maybe, which is to say on text as opposed to images, right? So much so this reminds me a little bit of. I'm not really arguing with the results of your paper, but on the macro thing, right. And it's so with Snapchat, you know, it has a deterrent to someone taking a screenshot of, you know, like, like a disappearing message. Now, of course, you or I or someone who's a computer scientist will say, well, there's still the analog hole. You can just hold up another phone and record it. And you know, if you want to, you can just take a second phone and record it. And that doesn't have the, you know, you can defeat it with a sufficiently motivated attacker relatively easily. Right. However, most people aren't that motivated. And so the simple and dumb screenshot detection thing sets the norm and makes it relatively hard to do screenshots. Right. Similar to how, you know, you could. People could work around the Twitter 140 character limit by pasting in screenshots of 140, you know, more than 140 characters. But they didn't for a long time. Right. And my view is that there's a lot of AI text on X, for example, that at least I can trivially detect. It's not this, it's that and the M dashes and so on and so forth. Right. And there's certain people who just are clearly AI posters because of just the style. It's like this over dramatic kind of style. It's. It jumps out to you immediately when you see it, because you see it a lot. It's like seeing the same person writing over and over, you know, and pangram.com or something like that feels pretty good at detecting chat GPT type slop, which you see a lot of. And Claude and chatgpt, for whatever reason, have very similar text voice, I think. Right. On this kind of thing.

B (10:43)

Yeah. I mean, I guess they're trained on the same data to a certain extent.

A (10:47)

Yes. Whereas images, you know, maybe. I guess it depends on the class of image. I mean, obviously with hands and things like that, gymnasts, they're finally starting to get good with gymnastics with, with sea dance, because those are unusual poses. But they do like physics simulations, I guess, to train them. I don't know, maybe you have a thought on that. You understand my point, right? Like, AI detection may not work 100% of the time, but for tec text, I think it currently works well enough to get a lot of the ChatGPT type slop at a fairly high. Like you can certainly see it visually, you know, like a human can see it. Then if it's unsubtle enough for us to see. Let me pause there.

B (11:29)

Yeah, I, I do agree that the tech stuff, at least right now, there are these watermarks, almost like the M dash or the patterns. You were saying, like, oh, it's X, this is X, not Y.

A (11:41)

Right.

B (11:43)

But I still think that similar to images actually, like the study we did basically was you take an image that an AI generates. And by the way, these things are pretty good. Like I actually, I've gotten personally fooled a bunch of times.

A (11:57)

Yes, sure.

B (11:58)

So empirically it seems to be really good. And then we did this study where you basically perturb the image a bit. So you blur it or you crop it, or you add some like, indiscernible Gaussian noise to the image, and then the AI detectors all completely break. And I think even in text, that's kind of true. Right. And I mean, who's to say using AI to help you write some of your tweets, maybe that's not even a bad thing necessarily. Right. Like ultimately, like content is content and maybe you're saying something interesting with the AI's help. But like a lot of people do these tricks where they're like, get the output from ChatGPT and then they tell ChatGPT, remove all the em dashes and then, then it's not as detectable.

A (12:39)

No, that's true. I guess the thing is. So here's my view on that. It's my emerging view. So at least here's our current standard on this. We. So at ns, our. Our rule is no public undisclosed AI.

B (12:55)

Mm mm.

A (12:56)

Right. So why do I say that? Well, first is people can just go full AI. And full AI means like. Because AI is a shortcut.

B (13:05)

Yeah.

A (13:05)

And as a shortcut it's a. I think it's a good term because people can take too many shortcuts and they fake it and they don't know what they're doing and so on. The more expert you are, the more legitimate it is to take a shortcut because you know how to do it the normal way. Right. And it's like writing down a theorem without doing the full proof every time it's right. Using a function call route. There's a reason that people use shortcuts. Okay. But they can overuse them. Fine. So the alternative is no AI, which a lot of people actually are going to go to. And there's like an anti AI moment. Fine. But no public undisclosed AI. I think in four words, it captures. So you can use private AI, and that's undisclosed because you're going and editing your own stuff. Right. I mean, you're, you're or like you're editing code, who cares? You know, using it for yourself. Right. Public disclosed AI. When, whether it's a watermark at the bottom. Right. Or it's like an animation, a comic, a movie, something like that, no one can get mad because you're not trying to pull one over on somebody. Right. It's public undisclosed AI that gets people mad. And at least if I analyze my own reaction on that, when someone is sending me something that's obviously AI, I think they are either stupid or lazy. Why? They're stupid because they can't see the obvious. AI tells like they send an AI slop slide deck or they have an AI webpage that has a lot of like. It's one thing if they say, hey, this is a prototype, check it out. Okay, fine. Right. But that's disclosed AI if it's undisclosed and it's just got like a wall of AI because AI tends to, you know, in AI images, they're more full of people than normal images by default. You know, if you've noticed, unless you actually pull that back. Right. Like the outdoor scenes have too many people often. Right. And that's like one tell. Right. And similarly, AI pages and AI slide decks are not succinct.

B (15:04)

Yeah, yeah, yeah.

A (15:06)

They're just really Right. And so either they're dumb and they can't tell what's good, or they're lazy and they're hitting a few keys and then sending me a bunch of slop and I have to go through it. And fundamentally, they're taxing the other side. It's like someone leaving a voice memo for you.

B (15:22)

Yeah.

A (15:22)

You know, like I have to verify everything because they didn't verify everything. And so when they. Whenever I get an AI message from somebody, I downweight them because of that, and I. And I downweight them as a poster and so on and so forth because they. They just. They're taking shortcuts in a way that makes me question their judgment. Now, if he gets good enough that. Go ahead, Sarah, what you say.

B (15:51)

Well, I think one reason the fake images and fake text is a little different is, I mean, historically, you could just write whatever words you want. Even pre AI, you could just write a bunch of things that were not true. Like, you could lie. So I think humans are very used to critically evaluating the text they see because people can always just write whatever. I think we're much less used to being able to critically evaluate images.

A (16:17)

We see fake images.

B (16:18)

Historically, it was pretty hard. I mean, okay, you had things like Photoshop and this and that, but, like, you know, it'd be pretty hard to really fake something elaborate or, like, fake. For example, the President of the United States doing like a one minute long video saying whatever. You just could not have done that in the past. And now with the AI tools, it's very easy to do that.

A (16:42)

So. So it's really interesting you say that and. And I want to continue your presentation. My. I agree, and I'll give a partial counterargument, which is I actually think most of the images and videos people have seen are television or movies until recently. And those were actually all fictional and synthetic. And so they kind of live within a world where some significant fraction of their inbound training data is fictional, as seen by the extent to which people reference, I don't know, Star wars or the Handmaid's Tale or something like that, like the, you know, Harry Potter. That's actually more real for many people than actual history.

B (17:28)

But that's, like, disclosed.

A (17:31)

It is disclosed, but I don't think they can actually go ahead, say, say.

B (17:35)

Okay, I was just gonna say that is Disclosed and that, you know, it's not, you know, it's made up.

A (17:40)

I. I think you and I know it's made up, but I. But I think. Okay, here's my argument on this, and let's continue. But the. I call it Jurassic Ballpark. Like, you know, Jurassic park has this scene where. See, I'm actually referencing a fictional movie scene to explain fictional movie scenes. Very meta. Okay, so Jurassic park has the scene where the dinosaurs have amphibian DNA spliced in because the scientists didn't know what to make of that part, so they spliced in amphibian DNA and that leads to, you know, the dinosaurs reproducing. The point being that when we are dealing with a situation that we don't have personal experience of, like, we don't have personal data on, you implicitly rely on some movie you've seen about that area to tell you how it's. Like, for example, unless you've actually been. Unless you've worked at CIA or, you know, people at Palantir, you don't really understand what the actual CIA is, as opposed to the movie version. You think the movie version is in the ballpark. And even if it's, like, more dramatized or whatever. Right. And. And it's often just totally not. And so that's why. I mean, like, you're right, that we kind of know it's fictional, but we don't know what reality is. And so often we think that the fictional is just a jazzed up version of the real as opposed to, like, totally, totally, totally off. So anyway, so the reason. The reason I say that is I think there's a huge opportunity. One of the things I want to fund at some point is people taking actual history and then using AI to dramatize it. So now it's actually more fictional. It's fictional, but factual fictional depiction of real events, you know. Anyway, keep going. I didn't mean to digress. Keep going.

B (19:31)

There's all these examples and, like, it's actually pretty fascinating. So here.

A (19:36)

Interesting. The receipts.

B (19:37)

Yeah. I mean, even. I mean, this is like kind of maybe a mundane example, but we did all these. We had a bunch of different categories of, like, real cases where AI deep fakes could be somewhat harmful. And one is just, you know, receipts and, like, reimbursements. And we had AI generate a bunch of images that were, like, taking a real receipt and modifying the numbers to be much greater than they actually were, like, by an order of magnitude. And then we put them through these AI image detectors, and it Turns out, like, you know, they're okay, they're like, oh, this is a 36% chance it's AI. This is a 44% chance it's AI. Maybe.

A (20:12)

What's the original something.

B (20:15)

These are actually real photos.

A (20:17)

No, I mean, but did the original come up as 0%?

B (20:20)

Oh, I don't have those numbers here, but I think it was like pretty accurate. So this was.

A (20:28)

Yeah. The reason I ask is I'd love to see that data if you can pull it at some point. Because even if the detector was saying 36%, if it could, if it had variance, you could rescale the axis. You know what I mean? Like if the real photos were, were left shifted relative to the fake photos, you could recast it as, you know, like a binary classifier problem.

B (20:51)

Yeah, like basically the distribution of real, like the distribution of fake. But then the problem is if you just do simple perturbations to the AI generated stuff, like you add a simple blur or noising. And I mean, if you are looking at the video of this and not the podcast, you can see these basically look pretty identical to the human eye. They. The AI detector says 4% chance this is AI, so it's just robust.

A (21:16)

Wow, interesting.

B (21:17)

Okay, that's true across a variety of examples. And then that's even true across a variety of problems. So we did like some other examples. Okay, this one's maybe a little more higher stakes. You take a picture of a car that's not damaged, you add AI to like add dents and scratches. Maybe you're doing insurance fraud. Again, similar story. The AI says, hey, okay, like the original version, when you just do naive, like, hey, Grok, tell me like add dense. The AI detector will say, hey, it's like 44% chance or something like that. But when you add some trivial blurring and Noising, the AI detector goes down to like 2%. So, okay, there's, there's other, you know, then there. Then we took pictures of like real editorial photos so you could imagine like war zones or like, you know, other journalism or famous political leaders and like kind of similar story across all these different categories of images. And so our conclusion from this study was that AI detection is a dead end, like fundamentally. And I think if you think about how these models are trained, it kind of makes sense. Like when you're training these models, you're optimizing some sort of loss function from like the generation to like the manifold of real data. And you're literally optimizing so that the things you spit out look statistically very similar to the real data. And so it's not that difficult to imagine that it's going to be very hard to detect what's real and what's fake because the models are being trained to minimize that. And there's actually a bunch of work without going into too much detail. And also, I mean, obviously I'm no longer an AI researcher, so I'm not super in the weeds here. But there's a bunch of work done at MIT and by a bunch of other people on adversarial examples where basically they had these detectors. Back then it was these imagenet classifiers. And then they added, they did a similar study. They added like some simple noise and stuff like that. And then they found that the image classifiers were robust to these adversarial perturbations. So you could always kind of find some perturbation of an image. Like you would take an image of a panda, you would add some simple blurring, it would look the same to a human, but then the classifier would flip from panda to like dog. Right.

A (23:37)

And this is to do with basically just the fact that you would never actually see a point of that kind in the, like the manifold of where pandas live. You could perturb it out to the manifold where dogs live because there was no training data along that vector. Typically it's like very thin on that axis.

B (23:58)

Yeah. Again, I, I don't like, I wasn't in this research line. So my naive, like way I think about it is like just these are such high dimensional decision boundaries. Like you're gonna mess up at some point and like there's going to be some point in the decision boundary where you think it's a dog, but like to a human it looks like a panda. And like it's just inevitable because like the decision, you're just operating over such like a high dimensional space. That's kind of how I think about it.

A (24:26)

Yeah, there's a, there's actually a, like the petal width versus length thing. Like there's this iris data set in R. I'll bring this up here.

B (24:36)

Oh yeah, yeah, yeah. I've heard of the famous one.

A (24:39)

You know what I'm talking about? Right. And so it's like something like this is probably a 3D actually. You know what a better one is? Like Swiss roll or something like that. Right. In 3D, basically. Let me see if I can pull this up. So something like this. So Swiss roll, right, is sort of something where you have like the yellow category and the green Category aquamarine, light yellow, blue. Right. And in three space they're clearly distinct. But if, and let's say this was, you know, the panda and this is the dog or something like that, if you put a vector and you perturbed it in such a way that you had a point that was, I don't know, 60% of the way towards this blue part. And there's no normal points that existed here in image space. That's my intuition for how the perturbation works. I should look that up. But that's, that's certainly how it works with low dimensional things and probably something like that works with higher dimensional and you know, similarly to the, this petal width one over here. Anyway, I want to get into succinct because this is the probabilistic. Let's get into your deterministic. Go, go, go. So this, this also, if you had something over here that'd be outside of the training set, you could misclassify it as, you know, as a circle when it was actually a triangle or vice versa. Okay, go, go, go. It's all your.

B (26:08)

Yeah, we're, we. So we fully established that. Yeah, the AI detecting AI. So it's like not going to work. That seems bad. So it's distinct. Well, okay. AI makes everything fake. That's what you said. What's the, what's the solution? Crypto makes it real again.

A (26:23)

Yep.

B (26:24)

So we're big believers of that. It's the same doesn't apply to cryptography company. So now like, let's dive into what that actually means. So today, like, how does content actually get posted online? I mean, basically first it gets captured whether it's on like a smartphone or a camera or a microphone for audio or some other sensor. Then it goes through some editing, whether it's like Photoshop or these AI editing tools. And then it gets published. So it's like across social media, news services, news wires, traditional media, YouTube. And then it gets consumed. So you look at the content and you say like you just look at the content. So that's kind of like the current life cycle. And yeah, throughout all of this there's like no verification. So it would be impossible for you to tell if something's real or something's fake. Now how does crypto help with this? So this is like what we're building at succinct, but we think there's this notion of basically what we call the provable technology stack. So at every point in this, like capture, edit, publish, consume, lifecycle, you insert in cryptography and Provable technology to prove it's wrong. So to start when you capture something.

A (27:39)

I love this. Yeah, this is exact. This is. You must have taken some of my content and maybe.

B (27:45)

Yeah, yeah, okay, okay. A lot of it is very inspired by like a lot of your work. Yeah.

A (27:50)

Okay, well, this is great. So basically there's a crypto camera and. And then chain of custody, ledger of record, public verification. Exactly. This is exactly the stuff that I've wanted out there for, whether it's scientific experiments or something. Keep going, I'm listening. I know this, but say what you're gonna say.

B (28:06)

Yeah, yeah. I mean, and yeah, like, all credit where it's due, I think you identified that this is the solution maybe like five years ahead of its time. Five years ahead of the problem. And you're always very ahead of your time. So a lot of this stuff is like very inspired by your work. And I think there's a lot of other. Like, I think Mark Andreessen has talked about this actually, and I'll get to this later. Like, the head of Instagram is now talking about this. But yeah, okay. Just to get into what is a provable tech stack. So at capture, things are captured on hardware devices. Hardware devices can have private keys that are binded to the device. So you have a cryptographic chip with the key. That's kind of how you can think of it. And basically like, as the raw sensor data is coming into the camera, the cryptographic chip signs the content of the raw sensor data and it binds the content being captured to the specific device time and location. So that's cryptographic capture. Then as the content gets edited, you have this chain of custody and chain of edits. So there's a cryptographically signed manifest for every transformation you do, whether it's like cropping or color correction or grading or things like that. And you basically keep this append only record of what's going on to the image. And then finally you publish the piece of content and the manifest of the original signature when it got captured to the chain of edits. And you publish that to a unbiased permanent ledger, which is like this ledger record. And then when the content actually gets consumed, so it's like in some front end, whether it's YouTube or Instagram or X, the front end integrates with the ledger and it basically verifies all the signatures, verifies they're real, and displays that information to the user. And if the user wants more information, they can just click and verify all the signatures for themselves. So today on Most content platforms, we have the blue check mark for your verified identity. You can imagine in the future, maybe all content comes with a pink check mark that says, hey, this content is like actually real. And like, here's the device and here's like the series of transformations that happened to it.

A (30:18)

Mm, very cool. So, okay, keep going.

B (30:22)

So, yeah, this is the provable tech stack and this is like all the stuff we're building at Succinct. And yeah, I think to your point, you talked about this for a really long time, which is like, very cool. And I think finally, like, other people are starting to catch on because like, the problem is finally very evident. So there's this quote from Adam Mosseri,

A (30:43)

who runs Sign a Capture. Yep.

B (30:46)

Yeah, he posted at the, at the start of 2026, he posted like, hey, here's Instagram's like kind of what we're thinking, what I'm thinking about right now. And he says that basically we're going to move from assuming what we see is real by default to starting with skepticism. So he's kind of identifying this like, AI makes everything fake problem. And then he said, okay, platforms like Instagram will do good work identifying AI content, but they'll get worse at it over time. As AI gets better, it will be more practical to fingerprint real media than fake media. And then this is kind of like the thesis of prove what's real and all this cryptography stuff, camera manufacturers will cryptographically sign images that capture, creating a chain of custody. So yes, Yeah, I mean, even like people like Adam who are running Instagram are saying that crypto is going, cryptography is going to be the solution to this, like AI. The problems that AI creates for like content platforms.

A (31:45)

That's right. Now I think actually crypto, social and AI are all interlinked here because another piece of this, which is actually implicit in like the first part of what he's saying, starting with skepticism, paying attention to who is sharing something and why. I think actually AI and crypto together are going to result in, you know, like, you know, I think the future is China versus the Internet. Did we talk about that? Have you heard me say talk right about that?

B (32:12)

I've heard you say a little bit about it.

A (32:14)

So I think the future is a billion person Chinese super state or a thousand million person network states. Why? Because everybody thinks about AI improving productivity. But that was only true within a tribe where you can trust, you know, you can share information and whether you call it indexing or surveillance. Right. Because one is good and one is Consensual and one is bad and one is not. Right. So it is indexing everything and it's learning everything. And it doesn't really miss like a single remark somewhere. AI can pull out a remark from like three years ago and surface it and synthesize in a way that no human know. Or, or you'd have to have a very attentive, smart human, human. It was human limited, that level of surveillance from before. Right. So. Or that level of synthesis, you know, the. To look over every commit and find security holes from years ago. It's amazing, right? But that operates within the tribe, outside the tribe. It's spam, it's scams, it's slop. Right. And so basically the cost of production goes way down, but the cost of verification goes way up. And so this part about paying attention to who is sharing something and why, I think another big piece of this is web three of trust. So you take web of trust, like I trust you because I know you and I've known you in person. And when you cryptographically sign something on a camera, there is the human part of that as well as the machine part. Like ultimately, if I wasn't actually there with you in the room, I have to trust at some point some human assertion that this data, because I can see it on chain, that it was stamped at this time. And there's various proofs that one can put on there, like proof of location, proof of this, proof of that. But ultimately you as a human have to tell me that you didn't manipulate it before you cryptographically signed it. Like you, you. Because you could do something upstream, like the analog hole upstream, you know, the equivalent of putting something in front of the camera. Right. And we can make it hard to do that, but we can't make it impossible to do that. And unless like every single camera has one of these. And I think maybe it'll get there eventually, but there'll also be a demand for those things that don't have these kind of like burner phones, you know what I mean?

B (34:38)

Right.

A (34:39)

And so, and there's so many phones out there, there's billions of phones that do not have crypto chips in them that you, you know, like, just like you can get an old laptop, you could get a fakeable phone, you know. Right. And people will also revolt against too much tracking or what have you, you know, they want it to be free, whatever. Anyway, I think that's another piece of this is the full supply chain of custody includes the person who's sending it to you. And so who is sharing something and why is if they're within your crypto tribe, crypto thinks tribally, natively. And AI is going to make people think tribally necessarily. And so everything reduces to digital tribes where digital borders and physical borders become the same. And China is the biggest digital tribe of all because they can centrally moderate all of their chat apps and so on and so forth. Like, they just, whatever AI detection stuff they roll out in WeChat, they can force human verification and so on. And so you have just a central choke point where bas basically a billion people get onboarded into whatever AI detection, prevention, fake detection thing that they want. But the rest of the world doesn't have the same level of. I mean, Google and others can roll out certain levels of things, but they've almost opted for a more anarchic standard because of the whole freedom of speech fight. Right. Which I get. But there's, there's a undercorrection and an overcorrection on anything. And what you want is consensual moderation, I think, anyway. So it's a compliment to what you're saying. Keep going.

B (36:06)

Yeah, I think what you're. I think in the future, like, it's not. I don't imagine a future where every photo posted to Instagram is required that it's real. Because, like, I mean, some AI pictures are really cool or like, really interesting.

A (36:19)

Sure.

B (36:19)

I think it's more like, to your point of consensual moderation, it's like, yes, if you want to prove something's real, and I think a lot of people deeply care about that, well, then now you finally, using cryptography, actually have the tools to do that. And then, yeah, if you want to. And if you want to follow content creators that have those capabilities or only post real stuff, you can do that. And then, you know, social media is one thing, but obviously for things like journalism, I think Nikita Beer, who's the head of product at X, tweeted about this. There's these accounts posting totally fake pictures from the Iranian war, and it's like, pretty bad. And like, people are getting misinformed. And so obviously that's like, not okay. And I think this sort of technology, I'm hopeful will help with much higher stakes situations like that or, you know, political ads or like what the president is saying or things like that. I think it will be really important to, like, prove what's real there.

A (37:12)

Great. Okay. Cool. All right, keep going.

B (37:14)

Cool. I mean, yeah, I think the rest of the slides are just like a Little more detail about what's going on. So already today you said, you know, how many cameras actually have this cryptographic chip? Well, fun fact, every single iPhone does have a secure enclave that has this capability. And so, you know, interacting with these enclaves across all the device types is really hard. And so we've built this SDK to kind of provide a unified experience for

A (37:41)

people who want, is it free or how's it cost? How much does it cost?

B (37:45)

Yeah, yeah, the SDK. Well, it's not published yet, but we're going to publish it.

A (37:49)

Okay, I want to try. I will commission some apps on this once you publish this.

B (37:53)

Oh, okay. Yeah, yeah, that'll be cool.

A (37:56)

That is actually the foundation of a new kind of media.

B (38:00)

Yes, yeah, yeah, yes. Yeah, I think there's a lot of

A (38:04)

potential there, I think incentivizing decentralized media collection in an AI first crypto, first social, first mobile, first Internet, first way. This is like a missing piece of that where we have all of these quote, reporters from around the world and on any topic that we care about, we can incentivize first party reporting where we pay in crypto and we verify in crypto, where we pay in cryptocurrency and verify with cryptography, we essentially have like a decentralized news outlet. So this is something that I want to get going and maybe we can collaborate on this, we can talk about this right after this.

B (38:44)

Yeah, that would be very cool. And yeah, with citizen journalism like you kind of, well, especially now with the AI generation stuff, you actually do need a way to verify that it's actually real. And so I totally agree with this.

A (38:59)

And I think we would focus it on the news of the network state and startup societies and cryptocurrency and technology biotech areas that I think are not well covered but should be because they are for tech decision makers. So because the thing is, news is a huge topic, right? And rather than the news of the state, we'd focus on the news of the network and those types of things that are like, with a relatively small amount of money, you could get much more coverage of them because they're more important for technical decision makers. And that's kind of the niche that all of these tech outlets basically abdicated. And actually in part the reason they abdicated is because a full time journalist is like a professional journalist is often somebody who doesn't actually know technology because if they did, they wouldn't be a full time journalist. They'd be actually like a player on the field. Right. Building so Moreover, by being a quote, full time journalist, they're loyal to the journalist tribe as opposed to technologist tribe. And technology tribe is taking away revenue from journalist tribes. So a lot of their coverage is very hostile. So the way we solve both of those problems in my view is rather than one full time journalist making, I don't know, 50k or whatever it is, we have 50 part time journalists who earn thousand dollar bounties for writing up what they know. And because they have domain knowledge, if they write up one article a year, we're good.

B (40:34)

Right.

A (40:35)

So that's like NS news. And so maybe we can integrate.

B (40:39)

Yeah, yeah, yeah, we. Okay, yeah, we should talk about that. Yeah, you can build that with our stuff now. It's like pretty. The whole point of that is it makes it easy.

A (40:46)

Okay, great, go, keep going and let's talk more. Go.

B (40:49)

Cool. Yeah. Then there's the provable edit history part where after you get the provable capture, you do all the stuff you want to do with it. And there's actually these existing standards for it called C2PA, which kind of tracks, which is a metadata standard that kind of tracks, okay, what series of edits did you do, what production did you do? And then it appends it to a manifest. And then finally, after you've kind of compiled the proof of capture, the proof of edits, it gets published to this thing which you came up with this name, the ledger of record, which is this open, unbiased, you know, place where all this content gets published and then that's where all the content that gets displayed in front ends. So for example, Instagram or X or whatever, it can read from this ledger, which is basically just a database of like what is actually real or not. So that's kind of our vision for provable technologies and like the whole stack. And yeah, we kind of imagine that this stuff will show up one day in every single app. Every single real picture on the Internet will have a pink check mark that says it's real with all the signatures and all the cryptographic proof. And you can, anyone viewing a picture can just look at that and know what's actually real. So that's our kind of vision for how cryptography is going to solve a lot of the problems posed by AI.

A (42:19)

Amazing. Okay, so people should go to Succinct XYZ.

B (42:27)

Yeah, people can go to Succinct XYZ or follow us on Twitter CCYNCTLabs. And we will be posting, we're building this whole stack and we're going to be Releasing, like, a lot of products and related technologies in the coming months.

A (42:44)

Okay, awesome. Okay.

B (42:47)

So it'd be interesting to hear kind of your vision for how you think this is going to, like, be put into the world or, like. Yeah, you've been talking about these ideas for so long. I'm just curious to know, like, more about why, what got you excited about it, and, like, how you think this is going to, like, proliferate.

A (43:04)

Sure. So, well, what got me excited about this, you know, in the 90s, like, you know, when I was. I was a kid then some about maybe 10, 15 years old, and you. Something like that, or. I would never presume to know your age or whatever. Just saying, like, probably. Probably in that ballpark, right? In the 90s, nobody cared about politics. It was something where it was literally being interested in politics was like being interested in the train tables or the bus schedule or something like that, you know, and it was genuinely something. Why would you care about this legislative. This and that? No one cares. And you cared about music, movies, sports, video games, whatever, right. It was just a vacation from history. And so, like, for much of my life, I was essentially just an apolitical academic, and all I care about was math, computer science, bioinformatics, all that kind of stuff. And then after, you know, essentially the full political breakdown of arguably, you can. You can argue when it started. 2001-2008-2015-2020, everyone's got a different moment, right? The. You know, there's a saying which is amazing. Tweet. If the news is fake, imagine history, okay? And you actually start, you know, realizing a lot of the movies in the 90s were almost like the collective unconscious was putting out movies like the Matrix, Eternal Sunshine of the Spotless Mind, the Game, Dark City, Fight Club, 12 Monkeys, all of which were essentially about your know, memento, right. Your memory playing tricks on you. And in some sense, the world was not what it seemed, right? The Truman Show. Right, The Truman show, the Matrix. All of these are like you're living in a constructed world, right? Memento, your memory of the past isn't the same, right? And it was as if, like, almost the collective world was waking up to realize that the centralized century of the 20th century was an illusion in some ways and that there was more to the past. And they had sort of been, you know, hypnotized, zombified, or what have you. And so putting those together, you know, I started asking questions like, how do we actually know what's really true? Like, let me give an example. Maybe a Seemingly trivial example. But this is in the network state book. Let's even take F equals ma. How would. How do you actually know that's true if you track it all the way back? Ultimately, there are scatter plots, you know, when people rolling balls down inclined planes, right? Where they are taking x and y's and correlating them and then effectively doing a line fit that is then generalized into this deterministic physical law, Right. Underpinning everything that we think is true is ultimately a set of observations that you could track all the way back to Newton. Like, you know, the famous, you know, apocryphal apple falling down, right? Like what you think you know is true if you can track back all the citations all the way back to root. That's the reason that we think it's true. Why is that actually sometimes important to do? Well, I'm forgetting this is a whole complicated story. And I think it's something like there's a story about vitamin C. I believe in medicine. I'm probably getting this wrong and I'll look it up, but it's like vitamin C supplementation, but it's. Was it spinach? There was like a. There's a whole medical story. Hold on, let me find this. The iron myth, right? Spinach is a good source of iron, right? And this is one of those things where somebody tried to track it back and it was. It was either this or something else where when you tried to track the citations all the way back, it was a complicated mixture of multiple mistake and citations on top of each other. I think this is it here. So look at this. The sudden thing. I think this is it. Basically. The complex and convoluted myths is one call, for want of a less complex name, the iron decimal point error myth. And essentially it is. Decimal error knowledge gap. It's like a myth piled on top of a myth. It's like something complicated enough that I have to go and remember it. Right? You can look at this document. The point being that that is a concrete example of something where someone literally dug through every citation going all the way back, and they found that the thing that people thought was solid was actually based on nothing. Right. All kinds of social science has failed the reproducibility crisis in this way. Right? So all kinds of political science, history. Social science is something where now with LLMs, we can back solve and go all the way back. Right? Because it's much better search. Right. So you can track it all the way back to all the original citations behind a claim. Right. You can Push it pretty hard to do that deep research, whatever you want to call it. Like you know, the team of agents saying that GROK has can pull like a thousand sources or something like that much faster than any human can. And so now we can really remember that truegal thing that I was talking about. Yeah, we can really start interrogating. It's almost like the, you know, the Bertrand Russell program in math of really trying to put math on an axiomatic basis. Right. And really trying to have as few axiom as possible. And he builds the whole thing from set theory. And, and, and I think it's like on page 347 he says, and thus we've proved that one plus one equals two.

B (49:33)

Yeah, yeah.

A (49:34)

You know the thing I'm talking about, right? Yeah, it's like, it's like a famous thing in math, right. So you probably aware of it. So, so like that I, I wanted to, I realized how ignorant I was about what had actually happened in the past, about what scientific facts were actually true, about how scoped my knowledge was. And I started to ask what I know. This is a longer answer than you wanted, but this is what led me to this, right? It's like I was like, you know, as a research scientist and you're a research scientist also we're in the unusual position of being pre headline people. And what I mean by that is like this was more true on the Twitter of like five years ago. But there's a fair number of, let's call them normie, NPC type people who genuinely cannot believe something is true until it's appeared in a headline. That is to say, until NYT or the State Department or something like that, their implicit epistemology was, is a reputable source saying it. If so, then true. If not, then false. Now this was always bizarre to me because as a research scientist you're used to figuring out if something is true on your own using logic and reason. And eventually I was able to figure out this is the difference between pre headline people and post headline people. A pre headline person, you have some scientific finding and you are going to publish it and you are actually the upstream source of that finding. Like the press release will be based on your paper, right? Or conversely, you have some VC investment round and you know something is true before the world knows it's true. So you're actually upstream. It's like a miner, you're mining truth before it's being sold at the market. However, you realize that actually the guy who's a post headline person has some wisdom all his own because he implicitly. I'm not saying they're doing this explicitly. They kind of know that you can only be a pre headline person in so many areas.

B (51:34)

Right.

A (51:34)

Like you can't be an expert on Turkish and Japanese and I don't know, Brazilian iron ore and so on and so forth. Much of what you're sensing is going to be essentially on some web three of trust, which is based on some information supply chain. Right. Anyway, it was through thinking through things like this and how we build a higher standard of truth that got me to where we were. Let me pause here.

B (51:59)

Interesting. Yeah, I guess you've been thinking about this for a really long time and then I think we've been thinking about this for honestly maybe the past three to six months as we saw the AI problem get worse and worse and there's this interesting asymmetry. I mean, our team is based in sf where there's so many smart people working on accelerating all this AI stuff, which is really good. There's obviously incredible positive externalities, but I think if there's a techno, if there's a technology that's genuinely so powerful, obviously it's going to have negative externalities. And I think there's very few people focused on combating these negative externalities. And I think in this domain with the pictures and images and fake audio and you know, that poses real problems. And I do think this is an area where the combination of cryptographic hardware, cryptographic software, chain of trust, custody ledger of record provable technology, broadly as a category, can actually like help some solve those negative externalities.

A (53:08)

Amazing.

B (53:08)

Yeah, that's how I got to it. But you got to it much earlier than all of us, which is like kind of your specialty, which is. Which is very cool.

A (53:16)

Well, thank you. But I appreciate you also grinding through all the details to actually build the SDK and so on, because obviously that's non trivial. So let's talk more about that. And Uma, thank you for coming on. Never see a podcast.

B (53:31)

Thank you for having me.