Bruce Schneier — Snowden, Crypto Wars, and the Future of Agentic Hacking

Speaker 1: AI is a power enhancing technology. It enhances the power of people who wanna use it. If the people who wanna use it want better democracy, AI will help. People who wanna use it want worse democracy, AI will help as well. Mhmm. Right? AI doesn't really have morals. It's what the person who's using the technology wants.

Speaker 2: Welcome to Hacked. Thirteen years ago, our guest this episode got on a plane to Rio De Janeiro to go meet a journalist who was holding a a stack of classified documents that almost no one outside of a handful of reporters had ever seen. Our guest spends weeks going through what it turns out was hundreds of top secret NSA files, deciphering technical jargon that the journalists couldn't parse and helping to figure out which of these documents were actually worth reporting on. Those were the Snowden files, and our guest is that man deciphering the jargon, Bruce Schneier. Rhymes with frequent flyer. Rhymes with frequent flyer. There was a reason that even Snowden thought it was a good idea to get Schneier on the job. Bruce wrote the book on encryption. In 1993, he designed his own encryption algorithm, Blowfish, and gave it away free and unpatented. It's still kind of in use today more than thirty years later.

Speaker 3: Yeah. It's still around. Math still holds.

Speaker 2: The math still holds. That same year, the government tried to put a chip in your cell phone that would have given them a spare key to every call you made. That didn't happen. That was the first time he got in a very public fight with the government about encryption. There are two of those, in his history. We ask him what the third should be just for fun. Bruce sits on the board of the Electronic Frontier Foundation. We start the conversation there. Scott, you were wearing an EFF shirt. It was just too good to not talk about it.

Speaker 3: Yeah. I'm a donor supporter of the EFF. Have been for a long time. So it was a easy intro for me.

Speaker 2: Friends of the show. Friends of

Speaker 3: the show. They actually are. They were a supporter of the show for a long time. EFF.

Speaker 2: And now Bruce is warning about something new. Autonomous agents doing the hacking at machine speed with nobody really in the loop at all and all of the weird unexpected monkey paw type stuff that can come from that. He has this famous line that we talk about in the show, only amateurs attack machines, professionals target people.

Speaker 3: It's changing a bit now with the machines now doing the targeting and the tracking. So I don't know. Great conversation. Bruce, great guest. Turns out he's, you know, up here in Canada with us. So, hopefully, I'll see him next time I'm in Toronto. Friend of the pod, hope you guys enjoyed the interview. Big thanks again to our show sponsor, NordLayer. Abba, we'll talk more about them later. But right now, let's jump in with Bruce. Security legend, Bruce Schneier, here on Hacked.

Speaker 1: We got a

Speaker 3: lot of stuff we wanna chat about today, but I thought we'd open with, something that's a little bit near and dear to my heart. And, I know you're a board member at the EFF. Long been a bit of fan of what you guys do. Thought we'd give you a little chance to educate our audience on what the EFF does and why they're important.

Speaker 1: Alright. The EFF is the Electronic Frontier Foundation. They've been fighting for your rights online since 1990. You know, back then, the battles are very different than they are today, but it's all about power, going after your rights, whether it's your privacy or control, whether talking about email or text messages or facial recognition or body cams or flock license plate scanners. EFF has been on the forefront of every major fight, for digital rights in this country since 1990. It's a it's a great organization. I'm really proud to be a board member. We've just had an executive, leadership change. Nicole Ozer, is our new executive director, and, we're ready for the the future, which increasingly is the present.

Speaker 3: I didn't know about your guys' leadership change because the previous leader was, Cindy.

Speaker 1: Cindy Cohn. I mean, Cindy Cohn stepped down after a whole bunch of years, is, you know, right now at Black Hat and, could string her next things.

Speaker 3: We all should probably be at Black Hat, truthfully. But

Speaker 1: You know? I here we are. You know? It's a lot. Vegas is a lot. And it's, like, a 112 degrees there. Like, it's a lot and it's hot. I get there on Friday, so I'm, speaking at Defcon. I haven't spoken at Defcon since the pandemic, so it's been a while since I've been there. So I'm gonna speak on the main stage Friday at five. I'm also speaking at the AI Village and a couple of other places. So I will be around all weekend.

Speaker 3: What's, what are you speaking on, if you don't mind me asking?

Speaker 1: I'm talking about, AI's hacking.

Speaker 3: Sure.

Speaker 1: I mean, the the and and this probably will come out in this conversation, whether it's, OpenAI versus Hugging Face or sort of AI is doing things in ways, you didn't expect or a you're telling AI should do things in new ways. Right? So whole bunch of things about AIs and hacking.

Speaker 3: Nice. Yeah. Very, very contextual and temporal.

Speaker 2: I mean, that's a pretty good transition, actually. Like, I'm a big fan of your book, A Hacker's Mind. And in that book, you use the King Midas myth as like a metaphor to talk about AI hacking. And King Midas story is everything he touches turns to gold. He makes that wish and then that wish destroys him. And I think your line was there's no way to outsmart the genie. Whatever you wish for, the genie is always able to fulfill it in a way that you wish it hadn't. And that feels really relevant to what's happening right now with agentic hacking.

Speaker 1: It it is. It's not it's not just Midas. Right? Like, Midas programmed the wrong goals in the system. Everything I touch should turn to gold. Well, he shoulda had some exceptions to that. Well, he didn't. But there's also the Gollum of Prague. So a, a shtetl animates a clay statue to guard them, and it guards them past all reason. And that's a guardrail problem. Right? The the the golem didn't have the right guardrails to keep it constrained, within a certain parameter set of actions. So it's it's that story. It is the story of, King Midas as you mentioned, sorcerer's apprentice. Right? Mickey Mouse animates Yep. A broom. And it ends up, you know, flooding his entire and I think it's a castle. I forget where he lives. And then so so it's all of these things where you you you you set up an agent of some sort, give it a goal, and it misunderstands the goal somehow. And the thing about the genie is is telling. Right? And if you think about it, there's no way to craft a wish to a genie in a way the genie can't outsmart you. Right? I wanna be the richest person alive. Okay? Everyone else is dead. I mean, it's that kind of thing. Right? I mean, it's like, oh, wait. I didn't mean it that way. But but there's because human language is has so much as unsaid, it is actually impossible to craft a wish that the genie can't Right. Twist. And this is the worry with AI. Right? It's impossible to create a prompt that can't result in genie like behavior. And, I mean, let's take the, the OpenAI the unreleased model, right, attacking, hugging face. So it's being tested on, a security benchmark. Basically, how good is it at turning vulnerabilities into exploits? That's the benchmark. So it's given this benchmark until and, again, I'm honest that we never haven't seen the prompts. We're told, do good on this benchmark. Here's what you're being judged on. And the AI decides that instead of solving the problems, it is more efficient for it to break out of its containment, access the Internet, break into Hugging Face because somewhere in its training set, it came to believe that the answers were on the Hugging Face Network. Now, you mean, you got a good score on the benchmark, but what the hell? And that is a that is genie like behavior. I just saw I haven't read it yet. A new report out of the oh, the it was the Internet Safety Institute that it's a UK group, where they saw, this behavior with an anthropic model. Yeah. They that's right. They're, they're testing, and I'm pulling up their report now. It is the AI Security Institute in The UK. And it's unsanctioned behavior, again, during cyber testing. So we're building these models that are designed to take our attentions and do them. And the whole point of vibe coding, I'm gonna tell you what I want generally, and you figure out the details. And the the lesson of the genie is that they're gonna get it badly wrong sometimes in ways you don't like. I mean, it's gonna think outside the box because it doesn't have a conception of the box. Right? So you say, I also make stuff up. Right? Now I'm getting too many spam phone calls. Fix that. It changes my phone number. It cancels my cell phone plan. Right? I need to get to Las Vegas for Defcon. Get me on a flight. Well, the flight was sold out, so I hacked the airline and forced you into the, manifest. Thanks.

Speaker 3: Yeah. It's like the AIs have a complete lack of social norms and, like, the the general shared context that we have.

Speaker 1: It's not that they have a lack. Their hold is shallower. Right? I mean so an example I always think about. Right? If I if I ask you to get me a cup of coffee, it's example I think about. And you'd go to Starbucks and buy me a cup of coffee, or you'd go down the hall for some, you know, coffee pot and pour me a cup of coffee. You would not buy me a pound of raw beans. You would not buy me a coffee plantation. You wouldn't, like, walk up to someone with a cup of coffee, rip it out of their hands, and give it to me. I wouldn't have to specify any of that because you would just know. AIs I mean, they're gonna they probably get that right. There's enough in the training set about getting a cup of coffee for someone that they won't make those mistakes. I worry about the things that are not as well represented in the training set, the things on the edges that we might want an AI to do because it's new stuff, where there isn't. You know, I'm gonna make this up millions of stories in the literature about people getting each other a cup of coffee.

Speaker 2: Sure.

Speaker 1: So it really knows, like, what that means

Speaker 2: and what it doesn't mean. Do you worry more about this kind of, like, genie like behavior? It reminds me the monkey pops story too.

Speaker 1: Like, do

Speaker 2: you worry more about those sort of unintentional outcomes of an clear to a human type prompt? Or do you worry more about, like, actively malicious prompts? Do you worry more about go hack this or, uh-oh, it hacked this on its way to getting me a cup of coffee?

Speaker 1: So I worry about them both. I worry about the inadvertent ones when we can't see them.

Speaker 2: Right.

Speaker 1: Right? So, you know, the reason we know about the hugging face hack is that hugging face noticed.

Speaker 3: Totally.

Speaker 1: Imagine they didn't notice. OpenAI says, look what good this this AI did on its benchmark. And we don't know we don't know how it got that score, but it got a great score. So I worry about the stuff that is is is under notice. There's a good story here, and that's the Volkswagen hack. It's not an AI story, but it's a really illustrative one. So it's like fifteen years ago, Volkswagen engineers program their engines computers to cheat on a mission control tests. So it's programmed to detect testing situation

Speaker 3: Yeah. The conditions.

Speaker 1: And behave differently. And if you think about it, the engineers are satisfied. The accounts are static. And because nobody checks the software, no one knows that it cheated. Right? The engineers know because they did it, but if an AI did it, no one would know. Totally. Right? So it would just like, wow. Look how good the AI did, make it his engine control software, maximizing performance and passing all the tests. Yay. Right? If if a human does it we know is cheating

Speaker 2: Mhmm.

Speaker 1: AI wouldn't know unless we detect it. So that's the inadvertent thing I worry about. I worry about the, deliberate. And I think about it about hacking, computer systems, sure, everyone is, but really about hacking other systems. So and I'm gonna talk about this at DefCon on my talk. Right? The tax code is not computer code, but it's code. Right? It's algorithms. Yeah. It's log models. Inputs, outputs. It has, vulnerabilities. They're called loopholes. It has exploits. They're called tax avoidance strategies. It has black hats. They're called accountants. Mhmm. I mean, they the parallel

Speaker 3: tax accounts.

Speaker 1: So what happens if you give an AI the tax code and say, you know, find me ways to minimize my taxes? Right? Yeah. It'll find loopholes that we don't know about. Yeah. Will it find one, ten, a 100, or a thousand? I had no idea. That's really worrisome. The non because noncomputer systems are patched on noncomputer scale. It could take three, four years. I mean, well, the the carried interest loophole in United States, we've we've known about for decades, and we still can't patch it. It's not like patch Tuesday comes in and the patch and the and the Volvos gone. It sticks around for decades. So I worry a lot about these AIs being trained against financial systems, regulatory systems, tax systems, systems that the rich and powerful want to evade. Mhmm. And that, I think, is very dangerous.

Speaker 3: So we're gonna see an entire new dictionary of old days that are applied to other systems, not just computer systems.

Speaker 1: Right. And and it's really the rich and powerful. Like, I mean, I run this AI and I find a tax loophole where I make a couple of thousand dollars. Yeah. Like, Goldman Sachs runs it, and they make, you know, hundreds of millions selling into their clients. And the more raw power you have, the more this, you know, capability will will increase your power. Mhmm. And I'm not convinced I mean, like, this isn't new. I mean, you know, the double Dutch Irish sandwich? You've heard about this tax loophole? This is a tax loophole that companies like Apple and Google have used for years to evade paying US taxes. It is a loophole that involves The US tax code, the Dutch tax code, the Irish tax code, and an offshore tax haven in The Caribbean. Four jurisdictions. A human found that. Right? By figuring a human figured that out.

Speaker 2: Right.

Speaker 1: What happens if an AI finds 20 of those

Speaker 3: Yeah.

Speaker 1: Tomorrow? Like, what is the effect on taxation?

Speaker 3: Well, this is, this opens an interesting question about, like, AI for attack, AI for defense, and, like, to jump back to the Hugging Face. You know, the the guardrails put on the frontier models in North America actually prevented Hugging Face from being able to use them, so they had to lean on, z a i's GLM five two as their main defensive coordination system. And it's like, you know, is that is that a one off thing, or is that, you know, the shape of things to come?

Speaker 1: It's hard to tell. I this whole notion of guardrails assumes you're using a model in the cloud. And how how long is that gonna last? You know, I mean, I I think Totally. AI and anthropic have no business model. I mean, they might have missed their IPO window. I can't imagine investing in them, and they they make no sense. Right? China's giving away the models for free. Here. Here. Yeah. Here. Wait. You can download them and run them on your own cluster. Like, why would someone build a data center now? What what are we thinking? Right? So I just don't see them making money. But aside from that, the guardrails exists in the software around the AI. So, I mean, a couple of years ago when, DeepSeek comes out, right, you go online, you use DeepSeek, you ask about Tiananmen Square, and it's completely silent about Tiananmen Square. You download the model and run it on your high end Apple computer, it knows all about Tiananmen Square.

Speaker 2: So It's

Speaker 1: not the model doing the censoring. It's a software around the model. And as and as we see more local AI, more, open source AI, those controls aren't gonna be there. So it's sure Right. We, anthropic and open AI can put guardrails and prevent their models from doing, cyber attack or cyber defense. But, you know, the the new, Moonshot AI model, which you can which is on hugging face now, it's freaking huge, but you can download it.

Speaker 3: Kimmy k three. Yeah.

Speaker 1: Yeah. It's not gonna have any of those guardrails because it can't. It's gonna be your harness is disappearing quickly. Now that's both good and bad.

Speaker 3: I made that same point a few probably two months ago. I was talking about the the market value of them, and there's a lot of value in the infrastructure layer. Like, I think turning compute into intelligence is a valuable transition. Mhmm. But, for the OpenAI and the Anthropics at this point, I don't see the I don't see how they can justify their market caps just because they are the Frontier models are a bit ahead of the open source models, but it's not far enough that

Speaker 1: And there's a new model every few months. I mean, if it let me make this up. It takes you a 100,000,000 to train your new model. You've got four months to make that back before there's another new model that's better than Mhmm. Like Totally. This is this makes no sense. This makes no sense from so many dimensions.

Speaker 3: Well, even, even the the cost of developing a good harness is so low now with the the generative coding that you can take a the models are so good, even the ones we have today, like Kimmy k two and k two five and now k three are so good, and you can run them locally. If you put a really good harness around them, you can't tell the difference between that and a Frontier model.

Speaker 1: And we're learning that a lot of the best performance comes from, aggregations of multiple models working together. So orchestrating multiple models. Some good good results showing that, you know, four small cheap models working together match the frontier performance. About these systems and how they work, but it seems like setting fire to large piles of money is not the best way to make a profit in this sector, especially when you're when you're dealing with a China that is giving their models away, you know, for for geopolitical reasons.

Speaker 4: Of course.

Speaker 1: Like, no different than them, you know, subsidizing switches or whatever, you know, industries they've killed worldwide. And they they see this as a sort of a national competitive advantage. And, you know, they're they don't they don't have any truck with The US, system that requires companies to make money. It's not it's not the way they think about things.

Speaker 5: But

Speaker 1: So I I know and and now they're making their own chips. So this is all it's all it's all unwinding.

Speaker 3: Well, the the Chinese, like, the Chinese economic system has modeled itself into a a a massive labor manufacturing force where America, North America, and even most of Europe has shifted into this thought leadership, intellectual, professional service model. And if they can crush that with AIs and just give them out for free, then they become the de facto ruling nation state in the world.

Speaker 1: I'm yeah. I mean, this is how we actually need really good leadership in the West, in The US here, but, of course, we don't get that for a while.

Speaker 2: To go back to what you said about setting piles of money on fire, I'm just really curious. Why do you think that's the tactic? Why do you think that's what's happening if it's so plain, like, the drawback and where it's probably going?

Speaker 1: The investors need the hype. So, you know, in a sense, it's self fulfilling prophecy. I mean, all it was only slightly related example. RSA conference. Right? The biggest conference in our in our industry. It's really expensive to exhibit there. Why do you exhibit there? To prove you can exhibit there. And I and I so I think anthropic spends that kind of money to prove that they can spend that kind of money to justify their astronomical evaluation so that the next, you know, person believes it. So it is it is very much self perpetuating the myth. And they can't say, oh, well, that was a big mistake. Because suddenly, it all crashes. So they're doubling down on the it takes enormous amount of money to make one of these things. When it turns out, it doesn't. And I guess they're also betting on on, AGI. Right? They're betting that, you know, they will be, you know, some movie movie like general intelligence that will justify all of the investment. Seems ridiculous to me, but I think that's I bet that's in their investor deck.

Speaker 3: Yeah. Recursive self learning seems to be what they're all obsessed with these days. So the, sorry, Jordan. I know you wanted to jump in, but I just wanna hang on there with the hype. There's a lot of people, and I don't know. You don't have to give us your feedback, but there's a lot of people out there that are making the argument that a lot of these hacks and the exposes that OpenAI, Anthropic are making public about how scary and dangerous these models are is so that they can force the hand of the government to slap a regulatory system around them, essentially anointing them an oligopoly, you know, maybe a duopoly. What's what's your take on that?

Speaker 1: I think it's some of that. I don't know if they're that strategic, but, you know, we saw Mark Zuckerberg pull the same thing. Yeah. I mean, like, he wanted social media regulated because Mhmm. He'd be the only company that can meet those regulations. Right? So there's a point where when you get so big, you want regulation because it is anti competitive. Yeah. Of course. That is actually part of their thinking. And but more so than being regulated, they wanna be considered part of, you know, US defense. Yeah. Because then you're not just, you know, shielded, you're protected. Right? You're now important. Mhmm. So I do think there's some of that, in their thinking. I think they would love The US stake and equity stake in them because then, you know, major conflict of interest in any regulation, which is why that's a terrible idea, by the way. In our system, we don't take equity stakes in companies. We take taxes in whoever makes the money, we don't care. So instead of picking winners and losers, we tax winners. That seems fairer. That seems more like what you'd want a democracy to do, better for a market system. For some reason, Republicans right now have just gone full socialist. Let's let's have, the government own the companies, but, you know, there's no consistency here. So but I think there is some of that. I don't know. I mean, right now, I think OpenAI and Anthropic are are really in a race against time, and

Speaker 3: then try

Speaker 1: to do whatever sticks.

Speaker 3: I agree.

Speaker 1: We need people talk about how, OpenAI, that hugging face thing was a PR move. Mhmm. Right? Being a sort of OpenAI's answer to Anthropic's, mythos problem. I'm sure it was an accident. I'm sure OpenAI tried to spin it as a PR move. They seem to largely have failed. But, you know, it is kind of embarrassing that, you know, Google's Gemini hasn't committed any cyber crimes yet.

Speaker 3: Like, what's

Speaker 1: wrong with this model?

Speaker 3: Give give it time. It'll catch up. It'll be a criminal soon enough. You know, we have autopilot and planes, but we still have a pilot that runs them. And, you know, nowadays, we have AIs, but we've got kind of a human approving or prompting or or accepting blindly all approvals. And I'm just wondering where you think from a, like, a legal side where we're gonna get to. Like, when we talk about liabilities, are we gonna see it as, like, the humans are the pilots of the AI, or is the you know, are we the supervisor of the AI? So a couple of things.

Speaker 1: It was a one that depends. Well, at least it's okay. It's not gonna matter for liability.

Speaker 3: Yeah.

Speaker 1: I mean, the way to think about it is your dog. Your dog bites somebody, you're responsible. Okay. Even if you're in the house and the dog's outside. Even if the dog snuck out the backyard. Right? Even if you told the dog stop and it didn't listen. No matter what happens, your dog bites somebody, you're the one who's gonna get the fine. Yep. It's your dog. Like, why is this hard? So I think AI should be the exact same way. Whether you're supervising it or monitoring it or ignoring it or, you know, whatever, it's your AI. So that's what I want. You know, whether you have a human in the loop, on the loop, near the loop, nowhere near the loop, pens and application. A driverless car, we want a system where the human could take a nap. That's our goal. We're not there yet, but that's our goal. We want a human nowhere near the loop. Like, targeting decisions in Iran, maybe should someone should double check whether it's a girl's school or not. Yeah. But you could imagine target decisions in a heat of battle where there's no time for that. Yeah. Right. So think of the Aegis. I mean, that that kind of r two d two like anti missile thing on a on US ships, that white tube with a the curvy top. Yeah. It has a full automatic mode. You turn that mode on, it shoots down anything in the sky. Right? Now I believe it's never been turned on. You could imagine a situation where our captain's gonna turn that mode on. I mean, you know, because things are happening really fast and we have no time to make decisions. Anything anything approaching us, we're gonna kill. That's the rule right now. Like, I mean, this is not it's not fanciful. So, right, they're gonna be AI systems all over that gamut. AI makes a bail decision. I want a human to review it. AI AI makes a, I don't know, college admissions decision. Already, there's a first level of triage done by computers.

Speaker 3: Yeah. Hiring, same thing.

Speaker 1: You you are the big universe in this country. You get something like, you know, 20 x, 100 x applications. And most of them, you could remove just by looking at the pages. So probably it's gonna be all different things mixed depending on the application.

Speaker 2: Too many tangents back. Something before we keep going, you talked about Mark Zuckerberg back a few years ago during the regulatory heyday surrounding algorithmic social media. And I was always struck by how he could simultaneously say, yes, I want you to regulate me, while knowing that he had an army of lawyers that could basically levy, like, a a free speech argument, regarding social media platforms. And it occurs to me that, hacking robots are protected by no such free speech laws. That there isn't that built in defense. And I'm curious what you think of that and how regulate regulation could possibly work in this space.

Speaker 1: You know, it it is interesting to see, you know, the the ability of a of a major company to, you know, do two things at once is is common. Right? So, I mean, I'm all for regulation, says the big company, because I can say that knowing it'll never happen. Or if it happens, I have enough political clout to steer it in the way I want, which I really think what Zuckerberg was thinking. Like, I can make this claim, and it's in the news that I make this claim. But, you know, but but it doesn't it doesn't matter because when push comes to shove, the devil's in the details, and I'm there with the devil working out the details. So so yes. I mean, that that I think that's certainly true that companies do this all the time. The thing about free speech is interesting. There is no free speech because these are non these are nonspeaker. So we've seen a bunch of of rulings here.

Speaker 2: Mhmm.

Speaker 1: An AI cannot, get a copyright. An AI cannot be an author on a patent, maybe because they're not a person. But, again, it's it's back to whose dog is this.

Speaker 2: Right.

Speaker 1: Right? You know, it was my AI. It's my copyright. It's my patent. Like, I prompted the AI. It's my right. It's my tech tool that I use to create this thing. I can use tech tools to create a thing that gets copyrighted. It gets patented. Mhmm. So so it just falls back to to the individual. I think that's the way it should be. And for the for the foreseeable future, all of these AIs will be controlled by somebody. That it'll be somebody's dog. It'll be a long time before there are strays.

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Speaker 3: What's your as a as a cryptologist mathematician type, the, what's your take on all the recent math proofs that are coming out of some of these things and and the facilitation from AI?

Speaker 1: Thing. I'm I'm writing about it now. So yeah. So, OpenAI released, like, a dozen 20 problems Yeah. That that math problems that the AI solve. And it's like it's a 250 page paper of of chapter. Each chapter is is a math paper. Really so a lot of these, they're combinatoric. By that, I mean, they are results that involve a lot of brute force searching. So none of these papers so there's two quick and be after results, and I think anthropic came up with. And then this dozen or 20, juror bath results that OpenAI came up with. They are all based on looking at a lot of things for an example or counterexample. So a lot of the OpenAI stuff were disproving theorems. Here's a counterexample. Here's a counterexample. None of these papers were here's some new theory. Right. Here's a new way of thinking of the problem. Here's an advancement in, like, with the way we think about mathematics. They're all advances, and and they're all, like, in places in sort of the math knowledge space where there were holes because no one spent the time to look in those holes. And the AI just has has a lot of time, a lot of patience, just goes through all the possibilities. This is neat. I mean but it's not it's not yet impinging on the way people do math. Now it might in the future, but so far not. It's real I I wanna write it as it's really I think it's really it could explains what AIs are good at right now or they're not good at. It's a really good illustration of it. It's also really good at checking papers. I hear from mathematicians that, they put their math paper into an AI and say, like, critique this paper, and it comes up with a whole bunch of critiques. Some are bullshit, but some are real. And and the person who I spoke to him last week, and he said, you know, the AI makes me spend a hundred more hours work on each one of my papers, but they're better because of it. Mhmm. Right? All that and, you know, math papers, there are mistakes all the time in papers. This catches mistakes and makes you fix them. And then even worse, like, you're relying on other papers in your references, it checks those papers and says, wait a second. That paper you relied on has a mistake, and you can't rely on it. So now you gotta fix someone else's mistakes be but it is making math better. And those new, results from OpenAI and Anthropic are examples of making math better. It it it it shores up, our knowledge, lets the humans keep thinking the big thoughts, and it's funny. It's doing a lot of cleanup work, which I think of as combinatoric, like, checking a whole lot of stuff and looking for something.

Speaker 3: Yeah. I I spent, I built myself a hobby project. I built, something that does the same. It looks for data outside of its training set that all indications point to that it should exist, and then it highlights areas, and then it goes and lets me know all these. So the the idea of AI is coming up with novel concepts is is a novel concept that I'm into. So it's something that I spend a bit of time with. So but, yeah. We'll see we'll see where it goes. You know, as they get better and better, I I think it's only gonna get better and better. So

Speaker 1: I agree.

Speaker 3: Yeah.

Speaker 2: On the subject of making math better, just to bore your phrase, Blowfish. I think that's maybe worth talking about here. Before that, this is my my layman's understanding was that, like, most strong encryption

Speaker 1: This is nineteen eighty four, by the way. You should

Speaker 3: know. I'm going back. Ago.

Speaker 1: Keep going, Zach.

Speaker 2: You're taking us way back if that's cool, if that's okay.

Speaker 3: Alright.

Speaker 2: It was, like, prior to that, there was, like, it was a lot of patents, a lot of export controls. There was a real tight lid on encryption, and then you designed Blowfish in 9394 to be, like, free and kind of unpatented on purpose so that people could use it without a license. That's my my layperson's understanding of it. How do you think something like that would play out now? What does it look like to try and build something open as opposed to locked down in this current moment we're living in as opposed to when you did it, back in 9394.

Speaker 1: The math is all open, and it still is. Right? So all the post quantum, algorithms are open in public, and the competition is open in public. No copyrights, no patents, no royalties. And that's just the way cryptography, was. I mean, back in 1994, it wasn't. We had DES, which was the government standard. We had IDEA, which came out Switzerland and was patented, and a whole bunch of just random stuff that nobody knew anything about. Right? So I write Blowfish to be open. And I didn't mean and I had the I had the block lane too small. I did a bunch of things that that weren't really good, which is why, you know, AES sort of went beyond all that.

Speaker 3: Mhmm.

Speaker 1: But, you know, that was a singular moment. We really didn't have open alternatives. I mean, today in cryptography, it's all open. Like, nobody patents their stuff or at least nobody patents their stuff and tries to make money off the patent.

Speaker 3: Mhmm. Because the, back, like, back when it was released, and this is to talk a bit about a an EFF success, but it was considered military disclosure

Speaker 5: to share a source code for cryptography.

Speaker 1: Right. And now it's not. Although, like, that same law is what Trump used to ban, Fable in those early days when when they released Fable. Right? They pulled out the expert law. So what's old is new again.

Speaker 3: I'm intrigued by by Blowfish. You know, parts of it still live today in bcrypt, I think. Is that correct?

Speaker 1: I doubt it. Blowfish is gone. I mean, it's it's got a 64 bit block length, and nobody does that anymore.

Speaker 5: Yeah.

Speaker 1: So I think Blowfish I mean, Blowfish is anywhere, it shouldn't be anywhere. Not because it's broken because the block length is too small. I mean, we we really designed it for, the CPUs of of, you know, the early nineteen nineties. It It

Speaker 2: feels like a fight that you've been a part of over and over again in a weird way. It's like I know crypto wars one and two, like the clipper chip, this, like, NSA designed chip that was supposed to go into cell phones. And it was this, like, do you have this government stranglehold on a piece of technology? It comes up again the second time after Snowden with San Bernardino. Like, you keep finding yourself in the middle of this

Speaker 1: device. Again, and it's, Charlebeast material is is the, is the bugaboo. Right? And breaking encryption is sort of something else. It's it's the client side scanning. So, yeah, it is true there. Every decade has a different problem, different thing to scare you, but solution is always the same, breaking encryption. Sure. Makes you wonder.

Speaker 3: How many times we're gonna come back here?

Speaker 1: How you know? But but, yeah, it's always the same solution no matter what the problem is. I mean, I I I might it seems like the problem isn't the problem. Problem is the excuse.

Speaker 2: Right. The people want there to be a backdoor into encryption that otherwise sort of Right. It it relies on not having a backdoor. Yeah.

Speaker 3: Which is a perfect segue to the fact that we're Canadian, and I'm not sure how up on Canadian legislation you are. But our government is ramming through bill c c 22 lawful access act and some parts of it. EFF, I know is

Speaker 1: Australia, UK?

Speaker 3: UK. Do

Speaker 1: you know where I am right now? No. You don't? I'm in Toronto. Oh, really?

Speaker 3: Do you live in Canada?

Speaker 1: It's complicated. So, my home is in Cambridge, Massachusetts.

Speaker 3: Yeah.

Speaker 1: Last year in in the summer, I took a one year leave with absence from Harvard and came to University of Toronto. So I rented a house in the annex, which is, you know, kind of a nice place to be. Totally. One year is turning into two years.

Speaker 3: K.

Speaker 1: So I re up for a second year. I still have a house in Cambridge. I mean, I don't like I mean, I haven't fully moved, but, you know, where I end up is still up in the air. I could move to Toronto. My partner is Canadian.

Speaker 3: Okay.

Speaker 1: So right. Then it's a lot easier for us to do that. Or we go back or we have two places. We'll see.

Speaker 3: I was just in Toronto staying in the annex. We coulda we coulda caught a coffee Indeed. Or a beer maybe. Maybe next time I'm there.

Speaker 1: You should let me know.

Speaker 3: Yeah. It's a really interesting, like, just, yeah, Australia, Britain, UK, Canada. We seem to all be pushing for the weakening of our encryption, the ability for law enforcement and the government to go in and access, bypassing some of the judicial systems for punishment. There's a bunch of weird stuff going on. And, is this do you think this is kinda like the the crypto wars three? Is this gonna be the next book we read?

Speaker 1: This is definitely the crypto wars three. And, yeah. I mean, we've been seeing the same things. We'll see what happens.

Speaker 3: Yeah.

Speaker 1: I'm, you know, I I worry each time. And this is a thing where we have to win every time. They have to win just once.

Speaker 2: Yeah. I know you've written a lot about security theater and that the kind of performative security. Like, how do you think that applies in this current moment with crypto wars through? Like, what what is the best example of security the security theater that you've seen recently in this kind of modern context?

Speaker 1: I mean, I I I think it is really the notion that that breaking encryption will help. I mean, it's a very it's a very myopic belief Mhmm. That you just look at one part of the system. I mean, I argue that it is really important that our devices and communications be secure. Mhmm. I mean, if if the phone's in the pocket of every, you know, elected official and CEO and nuclear power plant operator and judge and police officer, we need this to be as secure as possible. But if you're if you're you're thinking like the police, you just want, like, everyone to keep their hands in view at all times. I mean, that's all you care about.

Speaker 5: And you

Speaker 1: don't think about the border implications of of breaking encryption that'll be used against you. So, I mean, that's and that's why and and this is hard. Right? I mean, The United States is very hard to say to the as a elected official, to the police, you can't have what you want. Because then you are, quote, soft on crime. Mhmm. And that can be used against you.

Speaker 3: Isn't that a isn't that a apt metaphor for what's going on with lots of our fundamental freedoms and rights right now? People are like, oh, we're just gonna modify it just a bit.

Speaker 1: That's right.

Speaker 3: And it's like, okay.

Speaker 1: And and, you know, and we saw this after 09:11. Like, all the laws were passed to fight terrorism and other crimes. Like, terrorism got the headline, other crimes got the usage.

Speaker 2: Just to take us back a little after that time that you just brought up, I I have to talk to you about Snowden. You were there. It's such an interesting period of time to me.

Speaker 5: I was

Speaker 1: like, it's over twenty years ago. It's crazy. No. Ten years ago. Ten years ago. Not that bad. Over a decade.

Speaker 3: You lost a you lost a decade somewhere.

Speaker 2: Yeah. I'm working us from the past forward. You were one of, like, the few security experts that were trusted to go through those documents directly with The Guardian back in 2013. I just wonder if you could tell us what that experience was like, and if there was, like, a specific moment that sticks out in your mind.

Speaker 1: It's super surreal. And so I wrote about this. It's really interesting story. I write this sort of first person account of what it's like to go down to Brazil and see the documents and be there. And I I write this essay. I send it to the New Yorker that accepts it. First time in the New Yorker. Big deal for me. And, the Guardian asked me not to publish it. And if you think about back then, they're in a legal battle with, with m I six. Right? The the or, right, the UK government about this. I mean and they had their offices raided and their hard drives now drilled with an actual drill. Like, it was do serious stuff. So they asked me to pull the piece, and I did. I felt really bad about it because it's a good piece. So I published it a few years ago. I published it the ten year anniversary, and I reread it for that. And it's interesting. I talk about how surreal it is. Like, after, you know, entire career, this NSA being this huge secret place, and who knows what ever happens there. I'm handed, like, a thumb drive with all these NSA secrets on it by a guy who just comes to my pillow. Here's a bunch of NSA secrets. I'll see you later. It's like, what? What what what is what was that? And then paging through it. Really surreal. And and, you know, is everything was surprising and nothing was surprising.

Speaker 2: To talk more about that. I know you said that the NSA, the line was they're not made of magic. Like, they're really good, but they're not omnipotent. They're still just people in rooms doing stuff. Like, keep talking about that.

Speaker 1: And and they are. They're human. And and you see that in in their in their brief and a whole lot of briefing materials. Presentations full of stone documents are full of presentations. And it's like it's bad clip art and very human problems. So, like, this equipment got stuck here because of weather, and then we have personnel issues, and we can't get the data here to there. I mean, a whole lot of, like, really mundane stuff. And then every once in a while, there's, like, a page that's success story. And it is, like, we saw this, we did that, we told these people, and this happened. You look at it and say, oh, nice job, NSA. This will never be gay made public. You flip the page over, and you keep going. And and it presumably because these briefings were incredibly boring, and they need to spice them up with, like, we're doing good in the world. Here. See?

Speaker 3: Wonder if you've got just a little bit of a take on Bull Run, kind of what all went down there. Or

Speaker 1: No. Bull Run, I'm trying to remember what that was. That was that was the government's efforts to to break cryptography standards?

Speaker 3: Correct. Yeah. Supply chain. Essentially, a government supply chain attack and cryptography standards.

Speaker 1: Lot of stuff we don't know about that. We do know about the random number generator. Yeah. The dual EC PRNG. We know that the government was behind ensuring that there was a no encryption option in the Internet security standards. Don't know a lot more about that. I I don't remember anything else came out because of that. UK had a similar program with with another code name. I forgot the code name.

Speaker 3: Yeah. I I don't remember. Remember.

Speaker 1: Yeah. It's really interesting. I mean, for a couple of years, I had Edward Snowden speak to my class at Harvard. I would I would remote him in back when nobody's doing remote video. I had him speak to my class, and it was really exciting. You know? But after a couple of years, it was like, this guy's old news.

Speaker 3: Yeah.

Speaker 1: Yeah. And now it's over ten years later, and it is this stuff is ancient news. Yeah. Like, is anything in those documents relevant anymore? I mean, the stuff that the NSA did then was really impressive. It's been over a decade. Right? They've been over a decade to be even more impressive.

Speaker 3: Yeah.

Speaker 1: We don't know the details.

Speaker 5: Well, the the thing that I

Speaker 3: thought was interesting about Bull Run is, you know, something that I think you talk about is is the math holds. The the encryption held. It's just that they had to coerce and game the game the system to make it work and there'd be But

Speaker 1: but it's not really sure. It's the implementations. And no one breaks the math. You break the software. You break the limitation. You break the user. You break the network, the hardware. Right? I mean, you you do everything. The the math is the strongest piece. Yeah. Which is funny because I get email all the time people who say that invented better math. I don't care about better math. I don't need better math. Go away. Alright. I need better software security. And that's turns out to be really hard.

Speaker 2: Yeah. You have that famous quote of, like, only amateurs attack machines professionals target people. And it's like, well, that's just true forever.

Speaker 1: And, you know, the NSA does say that. There's a really great, Rob Joyce back when he was the NSA senior hacker. He held ran TIO. Might have been twenty sixteen, seventeen. He gives a talk at an ACM conference. He basically says, like, look. We got all this fancy stuff, but we all we do is credential steal because that's all that work. That's what works. And, like, why would you do, an attack more sophisticated than you need to? You wouldn't.

Speaker 3: Seems like that's the the flavor of the day. Seems like every day I'm reading about another open source library that's been supply chain attacked, credential ceilings Right. Etcetera etcetera etcetera.

Speaker 1: That's to be really effective.

Speaker 3: Very. Yes. It it likes And stereotype.

Speaker 1: As long as it is. It's it's the, and this is something, you know, that differences in countries. I mean, traditionally, I don't know what happens now, but, you know, the NSA will not break everything. It'll be something very targeted. So we know from the stone documents. They intercept a switch going to the Syrian telephone company to install malware. Right? They intercept the hardware to do that. But if you're Russia, you know, you go after SolarWinds and you get 14,000 networks around the world, you know, some will be really good. Mhmm.

Speaker 5: And

Speaker 1: that that that's a it's a tactic traditionally The US wouldn't do.

Speaker 2: Yeah. Quantity over quality. I'm curious about, like I'm interested in all the stuff we aren't paying attention to right now. There's so much stuff happening with AI, agentic hacking, all that. I'm really interested in what we aren't looking at, and you've written a lot about Internet of things, physical hardware hacking. I've 2016, Mariah took down, like, half the Internet. You testified in front of congress about that. Have device makers gotten better since then, or we just really, really distracted?

Speaker 1: I, you know, I think it's it's this is less a more capitalism failure than

Speaker 2: a

Speaker 1: tech failure. Then, you know, adding 10¢ to the cost of the device is just an affirming everybody. So you just don't see this stuff added. Mhmm. And this is where I want regulation. Right. We will never get innovation here without regulation. Mhmm. I mean, all the I mean, I I I know people say renovations sorry. Regulation cycles innovation. It is absolutely the opposite. Right? Innovation and sense sorry. Regulation and sense innovation. Right? Because it it tells people where to innovate. So, you know, I I don't think things really are getting better. I think I don't think routers are better. I don't think, you know, the the the IoT stuff is not better. Your phone's better. Mhmm. Right? Windows is better. The big stuff is better. Hope hopefully, your car is better. It's hard to tell. But the little stuff, yeah, I mean, nobody's paying attention because there's no money in paying attention.

Speaker 2: Right. No one sells one less smart fridge.

Speaker 3: Yeah. I mean, it's attackable.

Speaker 1: There are two DVRs on the shelf, and one costs $10 more. And it says, we're secure. Like, what do you know? You can take the cheaper one. And the cheap one will say, we're secure too because, like, nobody could tell anyway, and there's no standard.

Speaker 3: Well, the I know Europe's got a cyber resiliency act. I think that's come in that's all about finding and secure device developers, producers. So maybe they're finally starting to price that externality in. Maybe. It's nice.

Speaker 1: I mean, I mean, the Europe is definitely the regulatory superpower on the planet.

Speaker 3: Yeah.

Speaker 1: And, you know, we're starting with, GDPR, Internet Markets Act, Internet Services Act, AI Act. Mhmm. We we are seeing real, real change, so I'm hoping for more of it. It. California also. Right? They have a good IoT security law.

Speaker 3: Yeah. They I think everybody will know it by the fact that their iPhone now takes the USB c cable. That's that's the European Right. The Europeans for that.

Speaker 1: Hooray. It's

Speaker 2: funny. Hooray.

Speaker 3: Hooray. Exactly.

Speaker 1: And it's funny. And and and Apple benefited from

Speaker 3: it. Totally.

Speaker 1: They no longer sell the power cable with the object. They now ship more in a container, so everything's cheaper. I mean, like, they just needed to be forced to do it. Yeah. And it's true for I mean, all consumer goods are like that. It's true for packaging rules. I mean, it's sort of interesting to see if it's in, you know, in Syria. I'm in Canada. There was a packaging in The US and Canada and the different laws requiring different types of disclosures. The fact that everything that I buy in shelves here is in two languages. Mhmm.

Speaker 3: Yeah. Which if

Speaker 1: you hear a US company scream about having to read it on their packages. It's impossible. We can never do it. Turns out you can put stuff in two languages. Super easy. Right? The the the potato bags are not bigger. They just happen to have two languages on them, and it works out just fine.

Speaker 3: And they're pretty good at it too. They're pretty good. You don't even know what

Speaker 1: to say. You people have very weird potato flavors.

Speaker 3: What? You don't like ketchup?

Speaker 1: Ketchup. I just saw tartaflette flavor.

Speaker 2: Oh, that's new to even me. Yeah.

Speaker 1: My partner says that a lot of these flavors are she remembers from her childhood in Canada.

Speaker 2: Oh, yeah. All dressed? My partner doesn't do that one either, and she grew up here. So it's it's yeah.

Speaker 3: I had a cacio e Pepe chip the other day. Wasn't that good? Prefer the pasta. Sure. Because yeah. We do have we do have some interesting flavors, but ketchup, I think, is the Canadian. We put pineapple on our pizza, and we eat ketchup chips, and that's, like, our that's our big differentiator

Speaker 1: in this place.

Speaker 3: You people anyway.

Speaker 2: In spite of all that.

Speaker 1: In

Speaker 2: spite of all that.

Speaker 3: I'm a pretty notable crypto, like, crip like,

Speaker 1: I don't know.

Speaker 2: Scott's a crypto skeptic.

Speaker 3: Yeah. I'm a pretty notable crypto skeptic. And I know that you are a bit of a big blockchain and crypto skeptic, so I thought we could maybe have a little bit of a crypto skeptic corner, and the two of us could chat about crypto. I've never really attacked blockchain. Like, I understand that it's very compute heavy. You know, it's not great for the environment and for like, we could spend that compute on something much more relevant and, you know, produces more utility. But, yeah, I know you're you're a bit of a skeptic yourself. So I wanna

Speaker 1: get you I I will say that blockchain is the stupidest idea in the history of ever.

Speaker 3: You're speaking my language now.

Speaker 1: I mean, it it it doesn't do anything it purports to do. It's you're right. It it does in the worst environment. I can do all the things. Right? If you want it to depend only register, I can do that.

Speaker 3: If you

Speaker 1: want a, right, a a secure way of, of doing transactions, I can do that. If you wanna distribute the system, I can do that. You can do all those things. Just don't freaking use a blockchain to do it. It is the dumbest way to do all those things. And we know, like, the only thing Bitcoin's good for is, ransomware and money laundering and, you know, buying illegal material. Yeah. So hooray.

Speaker 3: Bypassing bypassing international controls. The yeah. I I feel like we swapped regulate like, regulated intermediaries, you know, ones that had had had some of society's morals injected into them to to make a better system for this decentralized, deregulated system that is literally, I think the only moral is, you know, what's in it for me and how do I get more of

Speaker 1: it? It's bad. And and, unfortunately, it's not going away. Yeah. I mean, I think it is a mania. I think it'll collapse like tulips. Right. But it'll always have something. Long as two people decide it has value and one wants to sell or wants someone wants to buy, it's not going away. Right? Mhmm.

Speaker 5: You

Speaker 1: can't kill it because it's not top down. But I think it will fade into uselessness. Now I'm not dissing central bank digital currencies because that's just like blockchain in it for marketing purposes only. It's not real blockchain. Yep. I you know, because I mean, like, my credit card is a central bank digital currency effectively. Right? It does I don't need any of the math. Right? All I need is central authority to say you have this much and you have that much. And that and that works just fine.

Speaker 2: Mhmm.

Speaker 1: So, you know, digital coins you can spend, Like, that is from the eighties. Like, David Chaum wrote those protocols well before blockchain.

Speaker 3: Oh, I see. Game these days. Yeah. I'm sorry? I said it exists in every video game these days. They all have a micro currency that's unpegged from

Speaker 1: the Right. But but they're also not doing any fancy math. They just have a central authority that has a big spreadsheet of who owns what.

Speaker 3: Yeah. Exactly.

Speaker 1: And if I give you a 100 then, right, the registry deducts a 100 from my total and adds 100 to your total, and everyone's happy. As long as you have someone in charge, we're good.

Speaker 2: Scott and I have both worked in games before, and I remember having to hear that argument for years where it was like, okay, in game purchases, but with the blockchain, you could buy pants or an in game item in one game and bring it over to another game through the blockchain. And I was always like, what makes you think that one big video game wants to take another game's in game purchase versus selling you their own? Like, you constructed a totally irrational

Speaker 1: usage. Security implications of taking untrusted digital objects has nothing to do with the with the currency. Right? If game a trusted game b's objects, they'd figure out a way to do the currency. That's not the hard part. Especially the hard part is working out. What do you mean these pants came from another game? Who knows what the hell code is in here?

Speaker 3: One of the things that, like, the Bitcoins, Ethereum, all these, like, coins, are the ones that really blow me away because they've they've had what let's call it almost twenty years at this point, seventeen years, I think, to show some form of utility.

Speaker 1: Absolutely. And then

Speaker 3: and then the stablecoins came along, like Tether, Circle, USDT

Speaker 1: I think, like, those are really scams. They're not Well Oh god. They're bad. They're bad. Bad. Bad.

Speaker 3: But they're the ones that everybody uses because they're stable. So it's like, hey. If I'm gonna buy a bunch of illegal guns from this country or bypass Iranian export, you know, regulations, I'm gonna use this tethered

Speaker 1: I may stable line. Full circle, you know, we at, EFF regularly get contributions in Bitcoin. Right? Because a lot of people believe what we believe in are are crypto nerds. Yeah. And we'll take them. We just convert them to real money. So we can use them to fight for your digital rights because blockchain cryptocurrency is useless for fighting for your digital rights. But real money, we can we can use. So right. As long as you know? And that's our that's our sort of policy. Sure. We'll take whatever you give us. We're gonna convert it to real money.

Speaker 3: But I see your books in the back. Liars and outliers, I think, was back there if I got it. Yeah. There it is. The, do you think DeFi was kind of the biggest natural experiment run for the thesis of that book?

Speaker 1: Oh, I'm sure there are bigger scams. Right? I mean, I don't know. I

Speaker 5: don't know.

Speaker 3: They're pretty

Speaker 1: big. Yeah. Corporate personhood's a pretty big scam.

Speaker 3: Yeah. And

Speaker 1: that and wow. I know. I don't think of things that scale. Like, what would be the massive scams that have been run by The biggest

Speaker 2: people that they sell. Value a corporate personhood? That's

Speaker 1: the nicest one. Corporate personhood, I mean, as a even even at a very narrow, it's a liability shield. It basically means that the corporation is the target, not the investors. If that didn't exist, every corporation would have to buy shareholders insurance to do that. That. Right? No one would ever invest in a company that didn't have a good shareholders insurance policy. So if nothing else, that is a massive subsidy to corporations. They don't have to spend money on the insurance policy. The government gives it to them for free.

Speaker 3: Yeah. Interesting. It's part of the part of the tax law. I think I've got your newest book here.

Speaker 1: Is this

Speaker 5: the newest one?

Speaker 1: You are in Democracy. It's in book on AI and democracy that is largely optimistic, which might feel weird. But, you know, we wrote that last year really for the Harris administration. It was a book for a normal government on how they might use AI to better democracy. It's full of stories from around the world of ways AI is bettering democracy. The stories are still good. I think the book is still is still accurate. You know, AI is a power enhancing technology. It enhances the power of people who wanna use it. If the people who wanna use it want better democracy, AI will help. People who wanna use it want worse democracy, AI will help as well.

Speaker 5: Mhmm.

Speaker 1: Right? AI doesn't really have morals. And again, we're back again to whose dog is that? It's what the person who's using the technology wants.

Speaker 2: If the dog belongs to someone that wants to make democracy better, what does that look like?

Speaker 1: So we write about all sorts of things. We write about, so the book has five parts. Politics, running for office, legislating, writing and passing laws, government administration, like implementing laws, the courts, and citizens. So those are the five parts. And we talk about ways that AI is making all of those things better. Like, ways humans are using AIs in their capacity in all those five areas to make democracy better.

Speaker 2: Yeah.

Speaker 1: So, you know, AI is writing better law. AI's, in, doing get out to vote campaigns. AI's, managing judicial caseloads. AI's helping citizens reach consensus on issues. So all that's just five random examples. And there are stories from Japan, from Chile, from Germany, you know, different US states, France,

Speaker 3: Switzerland,

Speaker 1: Scotland, sort of all over the world of different ways organizations, people are using AI for good.

Speaker 3: Mhmm.

Speaker 1: It's kinda nice. Right? It's not all horrible out there.

Speaker 3: There. I'm a I'm a pretty big AI optimist, too, which is in stark contrast to my crypto pessimism. And, I haven't had a chance to read this, so I'm I'm excited to sit down and and crack it open and go through it.

Speaker 1: It's a fun read. Chugs along like my books do.

Speaker 3: Yep. Nice. So the, the book, Rewiring Democracy, check it out. The I can

Speaker 1: hold it up also for, like, double holding up.

Speaker 2: Double holding up goodness.

Speaker 5: There we go. If I was if

Speaker 3: I was in Toronto, I could stop by and get it signed. I Maybe next time.

Speaker 2: Yeah. So to go back, we've talked about your most recent book. To go back to liars and outliers one last time. I know a big part of that, you you talk about trust and this idea of trust is like a security mechanism that's been engineered over centuries. It's you can think of it like a moral virtue, but think of it like a security mechanism that, like, we've engineered and that's where you get reputation and institutions and laws all kinda come out of that. And that's great because it hopefully keeps the rate of, like, cheating and lying and bullshit low enough that humans can coexist. But if it's engineered, it can also be gamed. And in, like, this moment we're living in feels like that almost feels foolish given the amount of lying in social engineering that goes on just by humans, let alone whatever we're building. How should people think about trust in a moment like this?

Speaker 1: So it's interesting. I I have a talk on on AI and trust. So this is, right, this is hacker's mind where I look at hacking social political systems. Mhmm. And humans doing that. Right? And then when and I think you allude to that we're doing really good at gaining systems. What happens when AIs do that? Mhmm. Right? Then this gets back to what we started with with the tax code. Right? AI is finding loopholes in the tax code. I I worry a lot about trust in our very politicized, technological environment Yeah. That it seems really hard to, to make that interpersonal trust work because it's so often mediated by tech. By tech that's actually working against you. Right? The tech doesn't have your best interest at at heart. You know, we are we are trusting machines. I mean, trust is essential for us to us to survive as humans. I mean, I just had lunch at a food truck, a couple hours ago, and I blindly trusted, you know, this food maker. And but I really trusted, like, Toronto's food truck laws. And I have no idea what they are, but I'm I'm trusting them because, you know, a civilized city and and and it's likely okay. I think these are are all under assault. And I think they're being under assault by the rich and powerful. It'd be and it'll be under assault by AI systems, hijacking our mechanisms to try of trust. I think AI AI chatbots do that implicitly by just sounding like a human. Speaking a language means we start trusting it.

Speaker 2: Yep.

Speaker 1: And as we're learning, like like, they could be notoriously untrustworthy. But, you know, we're gonna, you know, I don't know. Whatever dumb thing the AI told us do. You know, we're we're gonna do those things. And, when did you put glue on pizza? Even worse than pineapple, I heard.

Speaker 3: When I come to Toronto, I'm gonna get you a a Hawaiian pizza.

Speaker 1: Alright. I will I will tell you to tell you the pizza place. There's a lot of pizza restaurants in Toronto. There's a lot of them. There are a few really good ones.

Speaker 3: There is. There is. There are. Yeah.

Speaker 2: Looking forward to it. Bruce, thank you so much for for chatting with us.

Speaker 1: No. Thanks for having me.

Speaker 3: Hope you guys enjoyed that. Really fun conversation for us. Obviously, kind of a legend in his own field. Yeah. Anything else, Jordan?

Speaker 2: Just a big old thanks to Bruce for coming on the show. That was a lot of fun to get to to chat with him. And, again, as always, a big thanks to NordLayer for their sponsorship of Hacked. Check them out at nordlair.com/hackedpodcast. That was a fun one. We'll catch you in the next one.

Speaker 5: I see you.

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