The Only Defense Against Ai With Uma Roy

TITLE: The Only Defense Against AI with Uma Roy CHANNEL: Network State Podcast DATE: 2026-04-23 ---TRANSCRIPT--- Uma, welcome to NAE podcast.

Thanks for having me. Awesome. You want to introduce yourself briefly? Yeah. So, I’m co-founder CEO of Sisuin. Um, Sisin’s applied cryptography company. Uh, we probably best known for making uh the fastest zero knowledge virtual machine, ZKVM for short, in the world, known as SP1. Uh for those of you who who aren’t aware, um ZK is this really powerful cryptography technique where it lets you prove to someone else something is true without revealing all the details. Uh kind of the canonical real world example is that if I I can prove to you that you know I’m over 21 without revealing my birthday or my home address or anything like that. So instead of showing a driver’s license at a bar, you can just show them a proof of, you know, that you’re of age. um we built this ZKVM which um how can I describe it? I would say it’s somewhat like a foundation model for cryptography. So if you want to prove really complex statements in ZK such as a roll up state transition function or like very complex predicates, uh the thing we built makes it super easy. You just write normal code, you stick it into SP1 and out comes a proof. Um and then yeah, we made it super fast and really easy to use which is awesome. And I would say right now succinct although historically our ZKVM has been used mostly for proving blockchains and proving proving rollup state transition functions and things like this and like Ethereum and other chains. Right now we’re really excited about the potential of cryptography to solve a lot of the problems that AI poses. I think Bology has been an extensive tweeter about this topic for many years. You’re very ahead of your time honestly. Um and uh so yeah, I think honestly that’s probably one of the most important things cryptography can do right now. Um and there’s like a finally a clear catalyst. Every model release where the image stuff or video stuff gets better and better. It’s like we need cryptography as a defense. So I think it’s time for cryptography to be on like a societal stage right now as like a solution to all the AI stuff. Um so I’m very excited about that. Awesome. So yeah, actually actually many years ago um partly actually because of AI but also with social media when people were talking about misinformation, disinformation and so on and so forth years ago I remember I tweeted something and I was like oh so you want to ban lies on the internet okay give me a function that says whether the remand hypothesis is true right and so you know that’s a reduction at absurdum where we uh we don’t know whether it’s true and it could be true and it’s plausible that it’s true but there’s many things in math which have really arcane counter examples that you know you get up to n equals whatever and it it’s actually not true um and uh so but as I thought about that I was like well how would you code trugal t r u g- l e you know if you were actually you know going to do it right how would you do it and the thing is LLMs get you some of the way towards that, right? Because they will take a statement and they’ll do at least a probabistic search of the literature and pull things up and so on and so forth, right? And the thing is though, of course, then those assertions themselves need to be underpinned, the citations and then that’s how you get to onchain everything. And so my view is like with LMS actually you can you can kind of show a version of this today. If you ask any um LLM to summarize some major crypto hack it will show you probably some link that shows some onchain block explorer record among other things. And so that’s currently only used to document financial things like the onchain transaction you know during let’s say FTX had a hack or whatever during that period. M right but as more and more things get logged onchain then more and more references from LMS will point to onchain events and we get what I call ledger of record and I think succinct could be maybe a big part of that so you had some slides so maybe you want to go through your slides oh yeah what’s your background by the way you’re born in the US you um what’s your what’s your um yes 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. Um, and yeah, I mean ZK and cryptography have a lot of fascinating math and that was really like the draw for me. Um, 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 preGPT. I was doing some stuff with BERT back then. Uh, so I’m familiar with that world and now it’s like exciting to see the synthesis. This Yes. I mean, you know, the thing is I uh you know, I was actually also in machine learning prior to the deep learning era, but from the standpoint of genomics and diagnostics and and whatnot. And you know just to digress on that for a second before we get into the ZK stuff like you know all the stuff with hidden markup models and conditional random fields and you know um 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 it 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 we talk about that and then go to the 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? Oh, I mean absolutely. Uh I think even the close people closest to the metal on this stuff seem surprised at like how well it’s going and even today like the level of math problems they’re solving and stuff like that. Um yeah, I I would say I I was very surprised that this process which you know 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. Um, yeah, in LMS it’s just it’s like why does deep learning work? Kind of vibes based. Um, but right now the vibes are really good. It works really well, right? And I think it’s funny because well actually go through your talk and then let’s talk. Go prove what’s real. Okay, cool. Um, so yeah, I mean I think you one of your favorite quotes um is AI makes everything fake. Crypto makes it real again. Yes. Um 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 Door Dash driver, you know, 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? Or it’s like the White House posting digitally altered pictures or it’s politicians getting deep faked. Um, 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. Uh so like the seed dance released recently of like the really good video models that like you know can really impersonate any celebrity or any person like it’s 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. Mhm. Um 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. Um 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 uh to train models to say hey is something real or is like something from the model. And recently at succinct we did this you know benchmarking study to you know evaluate those claims and say hey does AI detection actually work and we published you know this data set of realistic AI images and um tried to benchmark all the leading commercial detectors and uh you can actually go to the website AI detection.sync.xyz XYZ, but the resounding answer was like, yeah, the AI detection stuff is not robust and it just does not work. Um, I’m going to slightly argue with you on this maybe, which is to say on text as opposed to images, right? Um, so much so this reminds me a little bit of uh I’m not I’m not really arguing with the results of your paper, but on the macro thing, right? And and it’s so um 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 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 overdramatic 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 panggram.com or something like that feels pretty good at detecting chatgpt type slop which you see a lot of and claude and chat for whatever reason have very similar text voice I think right on this kind of thing. Yeah. I mean I guess they’re trained on the same data to a certain extent. Yes. Whereas uh images, you know, maybe uh I 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 uh with sea dance because those are unusual poses, but they do like physics simulations, I guess, to to train them. I don’t know, maybe you have a thought on that. You understand my point, right? AI detection may not work 100% of the time, but for text, I think it currently works well enough to get a lot of the chatbt type slop at a fairly high like you can certainly see it visually, you know, like a human can see it then if if it’s unsuttle enough for us to see. Let me pause there. Yeah, I I do agree that the text 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 right um but I 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. Yes. Yes. Sure. 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 in incernible gausian 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 Chad GPT and then they tell GP remove all the M dashes and then then it’s not as detectable. No, that’s true. I 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 uh so at NS um our our rule is no public undisclosed AI. Mhm. 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. Yeah. And as a shortcut, it’s 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? Um, and it’s like writing down a theorem without doing the full proof every time. It’s right using a function call out. There’s a reason that people use shortcuts, okay? But they can overuse them. Fine. So the alternative is no AI, which is a lot of people actually are going to go to and there’s like an anti-AII 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’re 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 on somebody, right? It’s public undisclosed AI that gets people mad. And at least if I analyze my own reaction on that, I don’t 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 a AI web page that has a lot of um 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 not unless you actually pull that back, right? Like uh their outdoor scenes have too many people often, right? Um and that’s like one tell, right? Uh and similarly, AI pages and AI slide decks are not succinct, right? Yeah. Yeah, they 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. Yeah. 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. M and I and I down with 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. If he gets good enough that Go ahead, Sarah. What you say? 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. It even prei 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 like people can always just write whatever. Um, I think we’re much less used to being able to critically evaluate images we see. 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. Uh, you just could not have done that in the past and now with the AI tools, it’s very easy to do that. So, so it’s really interesting you say that and and I want you continue your presentation. my I agree and I’ll give a partial counter-argument which is I actually think most of the images and videos people have seen are television or movies until recently and uh 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. But that’s like disclosed. It is disclosed, but I don’t think they can actually Go ahead. Say, say, say, say. Yeah. Yeah. I was just going to say that is disclosed in that, you know, it’s not, you know, it’s made up. 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 the scene where uh see I’m actually referencing a fictional movie scene to explain fictional movie scenes. It’s very meta. Okay, so Jurassic Park has this 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 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 if unless you’ve worked at CIA or you know people at Palanteer 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 uh 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. Mhm. 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. There’s all these examples and like it’s actually pretty fascinating. So here recedes. 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 at by an order of magnitude and then we put them through these AI image detectors and it it’s 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 the original something. Uh, these are actually real photos. No, I mean, but what did the original come up as 0%. Oh. Um, I don’t have those numbers here, but I think it was like pretty accurate. Um, so because the reason, yeah, the reason I ask is I’d love to see that data if you could 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 if the real photos were left shifted relative to the fake photos, you could recast it as, you know, like a binary classifier problem. Yeah. Yeah. Like basically the distribution of real and like the distribution of fake. 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. The AI detector says 4% chance this is AI. So it’s just not robust. Wow. Interesting. Okay. Um 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, Grock, tell me like add dense.” Um, 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%. Um, so 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. um 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 cuz the models are being trained to minimize that. Um, and there’s actually like 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 like 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 imageet classifiers and then they added they did a similar study uh they added like some simple noise and stuff like that and then they found that the image classifiers weren’t 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? And this is 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. Yeah, 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. 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 highdimensional decision boundaries. like you’re going to 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. Um yeah, there’s a there’s actually a like a the pedal width versus length thing. The I like there’s this Irish data set in um in R. I’ll bring this up here. Oh yeah. Yes. the famous. Yeah, I’ve heard of the famous one. You know the one I’m talking about, right? And so it’s like um like something like this is uh probably a 3D Actually, you know what a better one is like Swiss roll or something like that, right? Um in 3D basically, let’s see if I can pull this up. So something like this. So Swiss roll, right, is sort of something where um you have like the yellow category and the green category. aquamarine, light yellow, blue, right? And in three space, they’re clearly distinct. Um, but if and let’s say this was, you know, uh, 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 lowdimensional things and probably something like that works with higher dimensional. Um, and you know, similarly to the uh this uh the pedal width width one over here. Anyway, I want to get into succinct because this is the probabistic. Let’s get into your deterministic. Go. So this this also if you had something over here that’d be outside the training set, you could mclassify it as you know uh as a circle when it was actually a triangle or vice versa. Okay, go go go. It’s all all your Yeah, we’re we uh so we fully established that yeah the AI detecting ass is like not going to work. Um that seems bad. Um so it’s succinct. Well, okay, AI makes everything fake. That’s what you said. What’s the what’s the solution? Crypto makes it real again. Yep. So, um we’re big believers of that as succinct doesn’t apply to cryptography company. So, now like let’s dive into what that actually means. Um 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 uh whether it’s like Photoshop or these AI editing tools and then it gets published. So it’s like across social media, news services, newswires, traditional media, YouTube, and then it gets consumed. So you look at the content and you say like you 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? Um so this is like what we’re building at succinct but um 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 life cycle you insert in cryptography and provable technology to prove it’s real. So to start um when you capture something Yeah. This is exact this is I you must have taken some of my content and and maybe Oh yeah. Yeah. Okay. Okay. A lot of it is very inspired by like a lot of your work. Yeah. Okay. Well, this is great. So basically there’s a crypto camera 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. Okay. Keep going. I’m listening. I I know this. But say say what you’re going to say. 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. Um like and you’re you’re always very ahead of your time. So, a lot of this stuff is like very inspired by your work and I I think there’s a lot of other like I think Mark Andrees has talked about this actually and I’ll get to this later like the head of Instagram is now talking about this. Um but yeah, okay, just to get into what is approvable tech stack. So at capture um things are captured on hardware devices. Uh hardware devices can have private keys that are binded to the device. So you have a cryptographic chip with a 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 like the content of the raw sensor data and it binds like the content being captured to the specific device time and location. So that’s cryptographic capture. Then as the content gets edited um you have this like 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 appendon 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 um 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 you know, if the user wants more information, they can just click and like verify all the signatures for themselves. So today on most content platforms, we have the blue check mark for like 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.” Very cool. So, okay, keep going. So, yeah, this is the provable tech stack and this is like all the stuff we’re building at succinct. Um, and yeah, I I think to your point, um, 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 cuz like the problem is finally very evident. So, there’s this um quote from Adam Maseri who runs Instagram sign. Yep. 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 even like people like Adam who are running Instagram are saying that crypto crypto is going cryptography is going to be the solution to this like AI the problems that AI creates for like content platforms. That’s right. Now I think actually crypto social and AI are all interlin 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. Um I think actually AI and crypto together um are going to result in you know like you know I think the future is China verse the internet. Did we talk about that? Have you heard me say talk right about that? I’ve heard you say a little bit about it. So I think the future is a billion person Chinese superstate 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 you know or or you’d have to have a very attentive, smart human, 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? Mhm. 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 um there’s various proofs that one can put on there like proof of location, proof of this, proof of that. But ultimately at like 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? Um and we can make it hard to do that, but we can’t make it impossible to do that. And uh 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, right? 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? 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 say have just a central choke point where 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. They’ve almost opted for a more anarctic 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. Um, yeah. I I think what you’re I think in the future like it’s not I don’t imagine a future where every photo posted Instagram is required that it’s real cuz like I mean some AI pictures are really cool or like really interesting. Sure. I think it’s more like to your point about consensual moderation. It’s like 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. Um, 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 uh 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 it’s like pretty bad and like people are getting misinformed and so obviously that’s like not okay. Um, and I think this sort of technology I’m I’m hopeful will help with much higher stake situations like that or, you know, political ads or like what the president is saying or things like that I think will be really important to like prove what’s real there. Great. Okay, cool. All right, keep going. Cool. I mean, yeah, I think the rest of the slides are just like a little more detail about what’s going on. Um, 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. Um, 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 people who want Is it free? How or how’s how’s it cost? How much does it cost? Yeah. Yeah. The SDK Well, it’s not published yet, but we’re going to publish it. Okay. I want to try I will commission some apps on this once you publish this. Oh, okay. Yeah. Yeah, that’ll be cool. Um, that that is actually the foundation of a new kind of media. Yes. Yeah. Yeah. Yes. Yeah. I think there’s a lot of potential there. I think uh 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. Yeah, that would be very cool. Um, 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 I totally agree with this 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 uh 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 tribe. 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 bounties for writing up what they know. And because they have domain knowledge, if they write up one article a year, we’re good, right? So that’s like NS news. And so maybe we can integrate. Yeah. Yeah. Yeah. We Okay. Yeah, we should talk about that. Yeah, you you can build that with our stuff now. It’s like pretty the whole point of is it makes it easy. Okay, great. Go, go, keep going and let’s talk more. Go. Cool. Um, 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. Um 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 who 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 um it gets published to this thing which you came up with this name the ledger of record which uh is this like 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 um of like what is actually real or not. Um so that’s kind of our vision for provable technologies and like the whole stack. Um, 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. Amazing. Okay. So, um what should people should go to succinct.xyz. Yeah, people can go to succinct.xyz um or follow us on Twitter at succinctlabs and we will be posting we’re building this whole stack and we’re going to be releasing like a lot of products and um related technologies um you know in in the coming months. Okay. Awesome. Okay. So maybe 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 got what got you excited about it and like how you think this is going to like proliferate. Sure. So um well what got me excited about this? um you know in the ‘9s like you know when I was I was a kid then so about maybe 10 15 years older than you something like that uh or I I would never presume to know your age or whatever just saying like probably probably in that ballpark right in the 90s um 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 a-political academic and uh all I care about was math, computer science, bionformatics, all that kind of stuff. And then um after uh 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? Um the, you know, there’s a saying, uh, which is amazing tweet, if the news is fake, imagine history. Mhm. Okay. And you actually start, you know, realizing a lot of the movies in the ‘9s were almost like the collective unconscious was putting out movies like The Matrix, uh, Eternal Sunshine of the Spotless Mind, The Game, Dark City, um, Fight Club, 12 Monkeys, all of which were essentially about your, you know, momento, 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? Momento, 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 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. Uh maybe a seemingly trivial example, but this is in the network state book. Let’s even take F equals MA. How would how would you actually know that’s true? If you track it all the way back, ultimately there are scatter plots uh you know when people rolling balls down incined 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 uh I’m I’m forgetting this is a whole complicated story and I 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 it’s like vitamin C supplementation, but it’s uh was it spinach? There’s like a there’s a whole medical story. Um hold on, let me find this. The iron myth, right? Spinach is a good source of iron, right? Mhm. And this is one of those things where somebody tried to track it back and it was um 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 mistaken citations on top of each other. Uh I think this is it here. Look at this. The sudden thing. I think this is it. Um, 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 uh decimal error knowledge gap. It’s 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 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 backsolve and go all the way back, right? Because it’s 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 that deep research, whatever you want to call it. like uh you know the team of agents thing that gro has can pull like a thousand sources or something like that much faster than a human can and so now we can really remember that trugal thing that I was talking about we can really start interrogating it’s almost like the um 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 aims as possible he builds the whole thing from set theory and and I think it’s like on page 347 he says And thus we’ve proved that 1 plus 1 equals two. Yeah. Yeah. 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 uh 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 5 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. M that is to say until NT 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 minor. 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, right? 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. Interesting. Um 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 3 to 6 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 techn 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 th 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. Um so amazing. 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. Well, thank you. And but I but I appreciate you also grinding through all the details to actually build the SDK and so on because obviously that’s non-trivial. Um so let’s talk more about that. Uh and um Uma, thank you for coming on Never Say podcast. Thank you Robin.