Preemptively, I will be running off for a flight before this panel ends. So I'm going to hand off moderator responsibilities to Hib for the end. But to get us kicked off, we had a tweet that rocketed around the world last night from Justin Drake, telling everyone not to panic.
Of course they panicked. And this is about the correctness, the validity of cryptography and math. And I do want to talk about crypto in the longer term, but now because people are panicking, I think we need to talk about math and cryptography in the short term. The fear is that AI has truly accelerated timelines on a massive scale to compress tricks, shortcuts to break classical cryptography, and then also accelerate the arrival of Q-Day as well, the day that quantum computers can break cryptography as well. So, two different paths to breaking down the entire foundation of what the blockchain industry rests upon. Hib, you've already written an article. I'm wondering if you can kind of just give me your first impressions, your first thoughts to this fear about AI threatening the entire industry.
Yeah. So, I made the analogy that this has sort of become cryptographic doomerism. It's a fear that, like, if you look at most of the cryptography that we use today, we don't actually have any proofs that this cryptography is sound. The way that we battle-test cryptography is we create new cryptographic schemes and we get a bunch of smart people, cryptographers, to look at these schemes and say, like, hey, can you come up with a way to break this? And after enough time, after enough people, after enough years, if people don't find ways to break it, then we say, well, this is probably pretty strong cryptography. That said, cryptography does degrade over time. People do find attacks, they find ways to weaken a given protocol. But we sort of measure the security of most of our cryptography not in terms of formal mathematical guarantees, which we don't have, but rather in terms of the person-hours, the sort of the mathematical intelligence that we have applied to these problems and haven't found a way to break them. Well, if that's true, then if you have OpenAI now pointing their lasers at classical cryptography, it may well be that all of the human cryptographers in the world, which is maybe around 10,000 at most, have applied this much intelligence and compute to looking at all the cryptography we have. And it could be that over the next month, we apply this much compute to all the cryptography in the world, in which case we may just be totally unprepared for how many holes exist in the foundations of classical cryptography. I think that point is correct. That said, the question is, what are you going to do about it? If the answer is what Justin Drake advocated for, which is what he calls bunker mode, and in bunker mode, the idea is don't move. Move all your coins to an address that has never signed anything. Put your head in the sand and, like, basically, you know, buy or use a blockchain.
Exactly. But, you know, buy a villa in New Zealand and, like, escape from society because you just don't know. It might be a smoking crater when you're done.
That is a bad answer. That is the equivalent of doomerism, which is like, oh my god, you know, AI is going to kill everyone. So just, like, you know, take no thought for tomorrow and, you know, abandon your family. I think this is the wrong answer to a correct observation.
So what I advocated for in this post is that every blockchain needs to basically think of creating a cryptographic recovery mode, which is a way that validators, upon seeing...
This is what we were talking about yesterday.
Exactly. Exactly. Upon seeing that there is some kind of foundational problem in cryptography, that you can fall back to, like, Cold War-level, extremely austere cryptography that makes minimal assumptions. Basically, like, you know, this kind of hash-based cryptography, which is very slow, very expensive, gigantic signatures, terrible bandwidth, right? Blockchains would run at the speed of molasses, but we know that we're safe.
Like, this is kind of like, okay, nuclear codes went off. The sirens are, we all go into the bunker and we can figure out what do we do now. What's our next move to reconstruct once we know what have been the problems that are uncovered in cryptography? What cryptographic solutions are safe? And we'll probably actually learn a lot more if AI is pointing.
AI will also help us develop methods and potentially prove the things we couldn't prove before.
You know, so go ahead. Say that. I was going to say...
No. No. So that's basically the proposal that I came up with, which is that...
So this, what we were talking about yesterday, that's very similar. It's like, if you have an existing, pre-existing social smart contract or coin holder agreement or DAO which says, under the conditions where there is some new mathematical discovery that invalidates this, then we've all pre-agreed to maybe rewind state, you know, to two weeks ago. Everybody does lose money, but they lose less money than zero. Or that this, this, and this developer have emergency authority that we can turn the keys and they can upgrade these functions or whatever. The thing I find most interesting on this whole thing, the math singularity was Matthew Green's observation, I know Dev was arguing about this, that public key cryptography may not work, you know. And by the way, the other thing that's funny about this, there's been no coverage of this in, like, the New York Times. Salzberger's guys have no concept of how to even report on this or talk about this. So, and yet it influences every country in the world in terms of whether they can secure their systems. So the network is just moving far ahead of the state. Anyway, some thoughts, maybe your thoughts.
Yeah, I mean, I think we actually, when in 2018 we were designing NEAR, one of the things we were designing for is like, hey, look, there are going to be new cryptography. We cannot bind NEAR blockchain to one specific cryptography curve. And so we designed to be able to keep adding more different methods. And so earlier this year we just added ML-DSA, which is one of the first post-quantum kind of signatures. And, you know, given where the world is moving, we're probably going to add a hash-based signature as well, so you can kind of secure your system. But I think the broader point is like, look, you cannot solidify and ossify things. You used it in your post as well, because the world is evolving really quickly. We're going to keep inventing new things. And so the kind of protocol needs to be able to continue to evolve with that, and we need tooling to evolve in unison as well. And I think it's all possible. We just need to be able to be in that mode instead of saying like, cool, we're going to build this and then nobody touches it from here on.
So there's something else we talked about yesterday, which is the token budget for the defense department of a coin. You want...
Yeah. Well, I think one of the points I want to make before we get to that part is that blockchains historically have been designed to be very resistant to change. Decentralization, by its nature, is a very slow, institutionalized governance process. It means that in order for something to change, there needs to be a lot of buy-in. There's no real way to enforce things happening quickly. And that is often considered to be a virtue of decentralized systems, is that they are very, very methodical in the way that they end up making changes over time. I sort of analogize this. This is sort of peacetime governance. If we are going into a mode of technological acceleration where the ground underneath us is going to be changing very rapidly in the midst of this kind of mathematical singularity, then that is actually the wrong kind of governance for a situation which is more like wartime.
Wartime is often when you change the sort of civilian command structure and you say, look, we have to go into more command and control mode, which means that we do have to delegate more authority to validators, to developers, for them to be able to say, hey, it just went down and we have to react on a dime to the fact that something fundamental has changed and people are no longer secure. And you have to trust us to have the good judgment to go in and change things.
So I'll give, you know, in the Network State book, I said the most unpopular but important word going forward is going to be recentralization. Why? Because centralization, decentralization, recentralization basically means decentralization is just a tactic. It's not like a destination. It's a direction. Just like you steer left and you steer right, you know, recentralization is opposed by both the centralized guys who say, wait a second, I thought you guys were, you know, why don't you keep our existing institutions? And the decentralization guy said, wait a second, I thought we were all anarchists. Aren't you sacrificing? But recentralization is just finding a new piece of digital land. It's just saying we hit the fire alarm. We got out of fiat. We got out of something and now we're forming a new group. Or recentralization is we have a network governance rather than state governance. And so then you don't have to say, oh, we're, you know, we're doing a wrong thing. We say this is a tool that we will use in these circumstances. You don't have to apologize for it.
Balaji, I want to get your perspective on as we proceed through this tumultuous state in the world of math and cryptography. There's a little bit of a difference. There's quite a big difference in the world of crypto, the network state, where the entire industry is underpinned by the math. Whereas on the state side of things, with banks, with Web2, the cryptography, the security, it's a means to an end. It's not the main arc. It's not the main thing. And so while this threat to math truly threatens all of crypto, it's urgent for us. And on the other side of this, it's an opportunity for us to win. Whereas the other side of the world, it's not really their core competency. It's not really their main thing. It's just an incumbrance for them. Do you think this is an opportunity for the network state side of the world to really shine?
Yes. And I actually also want to hear Ilia's thoughts on this. But one of the things, taking our conversations from yesterday, putting together, right now AI is an attack, right? Because you've got this math singularity, you know, we don't know how many results are going to come out and things are going to crumble. You know, things are going to break in the short run and medium run. But in the long run, you know, every coin actually, so you're the new moderator. Every coin has a very strong... Oh, see. Oh, okay. Fine. All right. Okay. Every coin has a very strong incentive to defend itself and has a budget for that. And so the token budget for auditing every line of code and also paying mathematicians to run all this stuff, that becomes the cryptographic defense department. And then you've got the attackers over here. So to your point before, you said maybe there's 10,000 cryptographers in the world or whatever that number is, you can roughly quantify sort of like with mining, you know, like hash rate, what is your, you know, token rate, right? So what's funny is this is a circular economy. The AI agents buy crypto tokens to spend and then the crypto, you know, protocols buy AI tokens so that, you know, these guys are making sure they're not hacked and these guys are proving they're... So there's a circular economy of AI agents buying crypto tokens and crypto protocols buying AI tokens. I think that actually will be a significant part of the future. I mean, one way to frame it is you need the protocol to own compute.
Yeah. Right. You effectively need to have, I mean, obviously we're coming from behind for OpenAI, Anthropic of the world, but if we know anything about blockchain, we can motivate people to bring a lot of compute up. I mean, one of the things is with AI, you can systematically enumerate exactly which papers your blockchain depends on. Like, you can literally list every single paper that ECDSA depends on, that, you know, any cryptographic scheme assumption depends on, and those are almost like library calls in the sense of a mathematical library or an educational library. And so you're attacking them. As soon as a new model is posted on OpenRouter, you ping it, you immediately are blasting tokens at it, you're trying to attack your own foundations first, much as we've sort of learned to do for our JavaScript or Python dependencies. You start thinking about math itself as a dependency that has a supply chain vulnerability.
Well, it's interesting because I think there's an asymmetry between normal cybersecurity and what we're talking about with this math singularity. With normal cybersecurity, I think many people worried that, okay, when models are going to become superhuman at cyber, that there's just going to be this apocalypse of just, I don't know, this like sort of Apocalypse Now type hellscape of things getting hacked left and right. And many people have pointed out, okay, we now have, you know, GLM 5.3, which is extremely good at cyber, it's open source, it's very easily accessible by anybody, and yet there is not this just, you know, kind of planes falling out of the sky kind of scenario that people were forecasting for quite a while. That, you know, the reason why Mythos and Daybreak and all these programs are whitelist only, not everybody can get access to these models, is because of this belief that if people had access to them openly, there would just be total pandemonium. We don't see that. Why don't we see that? Where are all the hacks? You remember last year there was the CrowdStrike attack. It was a vulnerability. It wasn't even an attack, and it just took out like half the internet. You know, airplanes weren't moving, and civil society was kind of grinding to a halt. The conjecture is that the reason why we're not seeing that is that there's a natural balance between attack and defense with AI, is that actually giving defenders access to the same power of AI that attackers get, there is not this, as some people maybe thought it might be true, there's not an advantage to attackers when defenders also can spend tokens and attackers can spend tokens. They cancel each other out, and in fact, maybe have an advantage.
Because the prompts also do matter, right? So the angle of it, it's not quite the same as hash rate because you've got angles of attack from the prompts.
I would actually argue that it's better than it was in the human world. And here's the shape of my argument, okay? In the past, before we had AIs, you would hire human attackers, right? So, let's say you're in North Korea, you would hire some hackers in North Korea, and they would go and try to attack a protocol, right? And if you were a defender as a protocol, you have to come, you have to basically try to defend against everything North Korea can come up with. And all of North Korea's hackers are all different. They're all independent minds coming up with different ways to try to attack your protocol. But in a world where North Korea is now spending tokens, the thing about AIs is that AIs are much more correlated with each other than humans are correlated with each other. Like you will see this is that if you have seven AIs all trying to attack the same problem, they will come up with very similar solutions as they are iterating and spending more and more compute on the same problem. They don't parallelize in ways that are as independent as the way that humans parallelize because they're trained from very similar distributions, even if they're different models. Whereas humans, we just act very differently. We have much higher diversity and dispersion in the way that we come up with ideas. As a result of that, if you have ten models, let's say North Korea and Russia and, you know, somebody else are all attacking you, it's very likely they will come up with the same attack ideas. And so if each of them spend a million dollars of compute and you spend two million dollars of compute, it's very likely you will come up with each of the ideas their models will come up with because they're not coordinated.
It's interesting because let's say that's true and it's possible. If you take the enumeration of the math papers as your library that underpins you in the same way the JavaScript and Python libraries do, then you could say, okay, I've got a finite attack surface now, right? I think for math you have like very different level of, like attacking math, the outcome, you're going to break everything, right? Yeah, like attacking a single library or system, you have like its supply chain, which I mean what we've seen right now, people starting to remove dependencies in general in code because actually dependency is a liability now. Like it's easier to just write all your code from scratch, from kind of as basic as possible, and then audit it with a bunch of agents. And you have formal verification now, so you can actually verify the code. And so I think actually in general the code building is becoming like completely asymmetric toward the builder because you just want to remove all dependencies and you want to formally verify kind of as much properties as possible. With math you have effectively right now the fundamental, like, okay, what is the, like, even when we use formalization, we actually have a set of assumptions, right? We say, hey, this is a hard problem. Like for example, we're verifying zero-knowledge proofs. There's an assumption in the Lean proof itself that this, you know, this problem is hard. And then from there we verify that this Zcash, for example, has no, like, double spend.
And that assumption itself is what's getting banned. And indeed, like then everybody as an attacker, it makes sense to attack that fundamental assumption.
And spend all of the kind of cost on that, and you break everything, right?
You know what's funny about this? It strikes me, you know, one of the phenomena of the last few decades has been previously obscure areas that only mathematicians or computer scientists cared about, like formal verification, then some, you know, or obviously, you know, machine learning and so on. And now pure math is about to have an AI-like bidding war, right? Where those salaries, I mean, all the mathematicians today are depressed, but in two or three years, their salaries are going to 10x because...
I don't know that that's true. I think...
Let me give you my argument.
Okay. Yeah, please.
Basically, the human-guided, I mean, because you have to prompt these things, right? It's not like anybody can just prompt their way to the Riemann hypothesis, and you have to verify it. And prompting and verifying are still skills.
I mean, if you look at the Navier-Stokes story, yeah, I mean, they made very clear we did not know anything about Navier-Stokes. We did not. So we're running right now. We're number one on LeanBench. Nobody read anything. This is like Terence Tao, Riemann hypothesis. Like this is like 120 most sophisticated papers, and we've like verified them in Lean. We like, you know, we don't even know some of the names there.
