Doug Colkitt：AI会攻破加密货币吗？“堡垒模式”与Y2K类比
- Doug Colkitt认为，AI在数学上的跃迁意味着不确定性进入新阶段，而不是证明加密货币会在数月内被攻破。 模型已经从 SAT 和奥数式题目推进到 Erdős 问题、据称解决 Navier–Stokes，以及 OpenAI 一次性发布跨多个领域的 400 道开放问题。关键担忧是，在没人知道这些长期以来被认为困难的问题会以多快速度失守时，“所有下注都不再作数”。
- Justin Drake提出的“堡垒模式”针对的是一种真实的密码学不对称，但Doug认为以今天的情况看，这一方案过于激进。 椭圆曲线和格密码系统可能藏有可利用结构，而哈希相对更像“暴力穷举、没什么花样”。因此，Drake提议把公钥藏在哈希后面，并在每笔交易后把资产转到全新地址。
- 签名层面的突破威胁远不止代币余额。 Doug表示，伪造签名可能让攻击者冒充网站和消息发送者、扰乱银行App，还能伪造验证者身份，让权益证明链随意重组。他的严厉判断是：“我们熟知的互联网，某种程度上已经坏了。”
- Doug的基准情景是渐进式迁移，更接近Y2K漏洞修复，而不是一夜之间的灭绝事件。 哈希签名已经提供了一条退路，但使用麻烦，尺寸甚至可能大到一笔Solana交易都装不下。决定性变量是速度：几年足以协调升级，几周可能不行。
- 他不太相信OpenAI已经秘密破解离散对数或椭圆曲线，因为AI在数学上的标志性成果仍然狭窄且只是渐进式推进。 模型“非常非常擅长完成清单上的一项”，往往只是略微越过理论门槛，短期没有实际用途。他认为突然出现一个能在现实中运作的密码学突破并非不可能，但“相当不太可能”。
- AI在削弱密码学之前，可能先增强加密货币安全性。 形式化验证可以把程序要求转化为定理，并证明代码符合要求，直击Doug认为当下最大风险的智能合约漏洞。他认为这可能带来所谓的“完美安全”，并提到Zcash中的形式化验证工作以及他自己基于Lean的EVM AMM。
- 真正可执行的防御是常规操作安全，而不是不断迁移钱包。 Doug建议将大部分资金放在2-of-3多重签名中——Solana上用Squads，Ethereum上用Gnosis Safe——并搭配两只采用不同技术栈的硬件钱包和一个可信的移动钱包。与其认为今天AI会破解你的资产，不如说你更可能在尝试堡垒模式时操作失误，把币弄丢。
1. AI从竞赛数学走向开放研究
Doug梳理了AI从SAT题、AMC和国际数学奥林匹克竞赛题，逐步推进到具有挑战性的Erdős问题的过程。他说这些题确实难，但未必特别重要。
在他看来，真正“鸣枪示警”的是AI解决了Navier–Stokes，这是7道100万美元千禧年大奖难题之一，此前7道中只有1道被解决。他强调，尽管构造范围狭窄、没有即时实际应用，这项成果在技术上仍意义重大。
随后，OpenAI一口气抛出400道开放数学问题。Doug认为，比数量更重要的是广度：模型此前在不同子领域表现参差不齐，如今却突然“好像什么都会了”。
他认为，这一转变主要来自思维链推理，以及在可验证奖励上进行强化学习，而不是某个神奇发现。基础模型仍然只是“自动补全……超级加强版”，但现在能写出推理过程、检查答案，并在答案可验证的问题上通过反复试错逐步优化：“数据加规模等于智能”（data plus scale equals intelligence）。
2. 密码学依赖的硬度假设突然被重新定价
现代密码学依赖被认为很难的运算，尽管这种难度并不总是得到证明。过去数学进展的速度让人觉得，这类难题短期内不太可能被攻破；离散对数就是一例。AI则扩大了未来1-2年内可能结果的波动区间。
Doug认为Justin Drake把“几个月，而不是几年”（months, not years）说得太满，但认可这一警告背后的逻辑。椭圆曲线和格签名背后的数学复杂性，可能藏着某种“反转它的诡异方法”；哈希的复杂度较低，更接近暴力破解。
Drake提出的“堡垒模式”利用了这一差异：Ethereum地址会对公钥做哈希，但一旦花费资产，公钥就会暴露。方案是把所有资产转入未使用地址，此后每笔交易重复操作；Doug称其“相当极端”，并怀疑今天有必要这么做。
3. 签名失守将演变成互联网级危机
Doug将影响范围扩大到加密货币之外：Zoom对话、经过认证的消息、网站和银行App，都依赖签名确认身份。如果攻击者能伪造签名，“任何人都能冒充别人”，在系统完成迁移前，人们可能只能回到线下银行办理业务。
即便单个余额仍然安全，PoS链也会遭遇二阶故障。伪造验证者签名可以任意重组链，意味着“链根本跑不起来”。Doug说Bitcoin是例外——它会继续运行的那条链——因为PoW依赖哈希而不是签名。
哈希签名提供了一条退路，Doug认为许多军方系统已经转向这类方案，NIST也在推动。缺点在运营层面：签名“真的很大”，也“麻烦得要命”——大到一份签名甚至放不进一笔Solana交易。
4. Y2K类比下，时间成为决定性变量
Doug记得，当年有85%-90%的人认为Y2K会平稳度过，约10%的人则囤积食物。结果平静并不意味着威胁是虚构的：“人们花了很多钱修复那些Y2K漏洞。”
他对加密货币的对应判断取决于突破来得快还是慢。若突破在几年内逐步展开，网络有时间采用哈希签名；若在可被利用前1个月才公布，几乎没有准备窗口。一周级别的跃迁看起来“非常不可能”，几个月仍然存疑，几年则足以提前准备。
秘密的OpenAI突破无法完全排除，但Doug看不到支持这一判断的模式。现有成果通常只是“非常非常小地”跨过一道边界；能带来即时实际后果的椭圆曲线破解，则代表完全不同的能力层级。
早期预警可能来自安全性较低的“金丝雀”：例如用96位而不是256位的构造锁住1万美元。攻击者如果能力逐步提升，应该会先收割这些低垂果实，再去碰更强的系统——这会是让Doug意识到“糟了，我得进入堡垒模式”的信号。
5. AI近期回报在于更好的代码与实用数学
在写过涉及数百万美元资金的代码后，Doug真正睡不着觉的风险仍然是智能合约错误。形式化验证让开发者把所需行为编码为定理，并证明实现符合这些定理，从而把此前对人类来说过于枯燥、耗时、冗长的工作变得可行。
他提到Zcash的新隐私池，以及自己完全用Lean编写的EVM AMM，作为早期案例。启用隐私池的Zcash在末日场景下也可能相对更具防御性，因为观察者看不到暴露在外的签名；但Doug也承认，基于Merkle树的保护仍可能无法阻止共识机制崩溃。
在人们嘲笑OpenAI对整数乘法的改进“在宇宙尺度上都不实用”后，Doug用这个问题测试AI的研究价值。他称OpenAI的形式大约为
2^1.82m − 1.82，自己改写后为2^1.34m——缩减幅度巨大，但结果仍然是天文数字。转录中还单独出现了“1 over 2^82”这一表述，因此规范化后的数字并不明确；据称其他人做到了2^16，但他也说自己需要核实。在这些“金丝雀”都没有被攻破之前，他的威胁模型仍然很普通：朝鲜黑客攻击、计算机零日漏洞和钱包失陷。他自己的2-of-3多重签名配置分散了硬件和软件故障风险；而他的早期项目Baranos AI正在探索由AI裁决预测市场结果。
完整逐字稿
Boom. Mr. Doug, how are you, man?
Hey, Trent. How’s it going?
I’m good, dude. It’s good to see you, man.
Yeah, good to see you. Thanks for having me on. I’m excited.
Dude, I appreciate you coming on. I think this is basically our 1-year anniversary of the last time you came on, which I think was 10/11/2025.
Yeah. Yeah.
So—
Every year around this time, something crazy happens in crypto.
Every year around this time—
Why are coins selling off?
I know. I wish it could be on a more bullish day, but the somber days are just as important. Do you want to give a really quick intro into who you are and why you’re a math wizard, by the way? I wasn’t aware of this background.
Yeah, yeah. My name’s Doug. I’ve been in crypto for a while now, working on a perp exchange, Superluminal, that’s not out. Now I’m working on a crypto AI project as well, Verinos AI, which is kind of on-chain LLMs and inference.
Math-wise, I don’t know, I’ve always just been a math hobbyist. I’ve always wished I was a mathematician. Maybe not now, though, because none of them seem to have jobs.
Yeah.
So—
Sick. Well, thank you for being here with us. I guess to start, can you lay out— I mean, you can get niche and complicated if you want, but maybe at a high level as well—what exactly did OpenAI release, and what have they been releasing on the math side of things, and why is it so significant?
1. OpenAI's Mathematical Breakthrough
Yeah. No, that’s a great way to get started. What happened is, obviously, we’ve had these LLMs, these language models, and their capabilities have been increasing over time. Starting this year—or I guess for a little bit—you’ve kind of seen them improve over time.
Even 2 years ago, it was like, “Okay, can they do SAT-type math problems?” Then they started doing these AMC, these kinds of math contests that smart high school kids would do, like IMO, the International Mathematical Olympiad. Those aren’t new problems, but they’re definitely problems that only the brightest kids could get, and they started doing those.
Then, beginning this year, they started solving what are called Erdős problems. They’re named after this mathematician, Erdős, who basically came up with, I don’t know, something crazy, like 1,000 problems or something. These aren’t really that important, but they’re challenging, and any time you solve one, it’s kind of hard to do. So they started solving those this year, and it wasn’t just OpenAI. People were using ChatGPT Pro and solving them.
The real shot across the bow this year was when they solved Navier–Stokes, which is a Millennium Prize problem. Basically, there are these 7 problems that had a prize of $1 million attached to them. At the beginning of the century, they said, “What are the hardest, biggest problems in math?” Seven of them. Here they are. One had been solved up to that point, so the fact that they solved one was huge.
It has to do with fluid and fluid dynamics. It’s complicated, but the idea is that this is a really big accomplishment. Great mathematicians have been working on this for a long time. Obviously, this would be huge if a mathematician solved it.
Then, just—what was it?—2 days ago, they released 400 open problems in math, and these were all pretty significant. I don’t even know if any were on the level of a Millennium Prize problem, but the fact that they just dumped 400 at once was like, holy shit.
The other small thing is that, so far, AI in math has been good at certain areas and not great at other areas. Even if it’s combinatorics or certain subfields, it doesn’t really matter. It has been good at certain things and maybe not so good at other things.
I think with this problem dump, they seem to be good at everything now. So now it’s like, “Okay, this is the moment.” OpenAI—or AI in general—is really just knocking out math problem after math problem, and pretty significant things across the field.
Yeah. And so what changes now, knowing that these LLMs have the capability to solve problems at this level?
Yeah. I mean, obviously, the thing people are talking about in crypto is that there are a lot of things in cryptography, which is obviously the root of cryptocurrency, where we don’t have a great sense of how hard something is. There are certain things we don’t know how hard they are to do, and a lot of cryptography relies on the idea that we think doing one operation is easy and doing another operation is really hard.
But we’re not 100% sure how hard it is. People have been trying for a while to solve things like a discrete logarithm in an easy way, but we don’t know 100% for sure. I think what’s happening now is that we don’t know what the pace of math is going to look like for the next year or 2.
We’ve had a sense of, “Okay, math advances roughly this much per year, so this is a really hard problem. It’s pretty unlikely this is going to get broken anytime soon.” But I think what people are worried about now is that all bets are off the table, and we don’t know how fast things are going to go.
A lot of assumptions that we could make about certain things are now like, “Okay, I’m not really sure.” In the next 6 months, there’s a chance that a lot of things in this recent jump could be things people didn’t expect to be there—very counterintuitive results. So I think people are now thinking about the risk that these long-held assumptions could get overturned very quickly.
Was there any pinpoint advancement in these LLMs that allowed them to all of a sudden be incredible at math?
2. How LLMs Learned To Reason
Yeah. I don’t know if there’s any one thing. The biggest thing was probably chain-of-thought, which is basically the LLM thinking about something and writing it out. It writes out its thinking steps and keeps going back and forth.
That was the big one for reasoning. Before, LLMs were more vibe-ish, and that’s often why you got hallucinations. They’d be like, “I kind of feel like this is right.” Now they actually have to write it out, check their work, and review it.
That, and maybe what they call RLVR, which is reinforcement learning with verifiable rewards. Basically, they take these base-trained models, and the way the base-trained model works is that you just dump a whole bunch of data from the internet into it and run it. So it’s literally autocomplete.
When you think, it’s the same kind of autocomplete you had on your phone 20 years ago, but super, super souped up. Those are the base models. Then they took those and started plugging in real math problems to get results, and they could kind of hill-climb those.
So it’s not one thing, but the data has gotten a lot more accurate, and the labs have invested a lot more in these types of problems, I guess. I think the story of LLMs is just: data plus scale equals intelligence.
And so then Justin Drake makes this post, which is like a bomb going off on crypto Twitter. Could you maybe break down what he is suggesting could happen—not fear-mongering, but what he’s suggesting—and what your personal thoughts are on the Justin post?
3. Cryptography Faces New Uncertainty
Yeah. I think it’s probably oversold, what he’s saying, but there is definitely something worth breaking down and engaging with there. Justin’s point is—and going back to what I said before—a lot of things in cryptography are based on things we don’t know for sure, but we’re pretty sure they’re hard. We don’t know if they’re hard.
Also, like I was saying before, we don’t know the pace of advancement right now. Things that we think are hard may actually turn out not to be hard, and we might discover that pretty soon. I think Justin’s post was saying months, not years. I think that’s probably a hard thing to say, but the point is that there’s a wider variance now.
The other point, which I think is a little more subtle, is that certain things in cryptography are more complex than other things. Pretty much all crypto chains today rely on elliptic-curve signatures. Long story short, that just means that we have these curves, and you have to find 2 points on the curve, and those are hard to do.
And then people are also looking at lattice signatures. Again, these are complex things. Justin's point was that the more complex things are probably more likely to have some weird way to reverse them.
Got it.
Versus hash-based signatures, like hashing. That's the other thing: hashing is just brute force and dumb, so there's probably not a weird backdoor into it. Justin's point specifically was that you should not have your public keys exposed anywhere. You should only have the hash of the public key, because reversing a hash is probably a lot harder than going from a public key to a private key.
With Ethereum, your address is actually a hash of your public key, but when you send a transaction, you have to send your public key so someone can see it. His point was that you should go into bunker mode and move all your assets into an address that's never been used before. Every time you use it, move everything to a virgin address and just keep doing it. That seems pretty extreme. I don't know if we have to worry about that today, and if we did have to worry about it, I think enough things would be fucked that your ETH probably wouldn't be worth anything—
So this is a tough question, right? It's like people are, you know, if crypto— We have bigger problems than your coins going to zero. I'm like, personally, that is my biggest problem. I don't know about you all, but could you touch on that a little bit—cryptography broadly? What is he saying could happen, and if it were to happen, what is it connected to? What are the implications beyond just Bitcoin and crypto?
reserved anyway.
4. Signature Failure Breaks Everything
Yeah, everything relies on it. Even the fact that we're talking over Zoom and people can't eavesdrop on our conversation, or they can't fake a conversation, or if you send me a message, I know it's from you and not from North Korea—those all rely on pretty much elliptic curve signatures, these standard signatures.
If anyone can fake a signature, the internet as we know it is kind of broken, because anyone can fake being someone else and anyone can fake a website—your banking website. So maybe the whole internet's broken and you just have to show up at your bank for a little bit. You can't use your banking app. That happens.
The other thing is, if signatures are broken, pretty much every proof-of-stake blockchain is affected. Even if your coins are safe, Ethereum relies on signatures from the validators. So if somebody can lie about the signatures of the validators, anyone can reorganize the chain at any time, and the chain's not going to run. The only chain that would actually be running is Bitcoin, because it's proof of work and relies on hashing, not signatures.
How would this play out from this scenario where it's like, okay, if anyone could fake a signature, then the entire internet as we know it ceases to exist? Obviously, there are massive, massive, massive implications to that. There are bank accounts. There's every message that's ever been written everywhere online. How do you even—playing what if—how do you see this playing out from here? This is government intervention stepping in. How does this—
The good thing is that we do have these things called hash-based signatures, which are probably not—again, anything could be breakable, but these are just dumber, so there are fewer hooks into them to break them. We do have these things called hash-based signatures. What we would probably have to do is move everything to hash-based signatures.
The problem with hash-based signatures is that they're really big and annoying to work with. You could even have a blockchain with hash-based signatures, but they're so big that you couldn't even fit one signature into a Solana transaction. That's why nobody uses them. They're just a pain in the ass. But they exist, so you could use them. You would eventually start moving more and more things over.
What uses them right now?
I think a lot of military stuff has moved to them. I know NIST is pushing them pretty hard. Blockchains don't use them because we care about performance. But maybe we should start thinking about it.
Interesting. It feels like a crazy generational fearmonger, because it's like this thing could hypothetically happen in the future at a rate of acceleration that we can't measure, and there's no way to disprove that it can't happen.
There's no precedent for it, is the problem. I guess I'm tempted—I'm old enough that I remember the Y2K stuff.
Okay.
Yeah.
People are saying, "I wasn't even born during Y2K." But were people really—
Yeah. Zoomers don't get that. Y2K was kind of like this, right? Most people—85% or 90% of people—were like, "Yeah, Y2K's probably fine." Ten percent of people were fucking nuts, like, "Okay, I'm hoarding food and stuff. I don't know what's going to happen."
Obviously, it didn't happen. But on the other hand, people spent a lot of money to fix all those Y2K bugs. It's not like everyone was just like, "Oh, the worries were unfounded." They were there, and you had to do a lot.
I guess I'd say with this AI stuff, there's a big difference between this being super fast and this happening over months. If the AI goes crazy fast, which I think is very unlikely—as someone who's using AI to try to solve math problems, I don't think it's going to solve anything in a week. I doubt it's going to solve any of these big things even in months. Maybe in years, but if you have years, you have time to switch stuff over.
If it's like, "Oh, shit, we're publishing something and it's going to be out in a month," that goes faster. So it's fast versus slow. I think so far, though, what we've seen from the results is that AI is very, very good at checking a box. Some of these results with OpenAI technically broke some barrier, but they broke it by a very, very small amount.
I think AI is very good at that, and that ends up not being that practical. Then you have to go back and forth
Got it.
to get anything practical. That's why I'd be a little bit more optimistic, at least in this scenario. These things are smart, but they do incremental things more than I think people realize.
What do you think about this idea that there might be some secret breakthroughs that OpenAI has already solved internally? How likely is that?
You can't rule anything out. I think that's pretty unlikely.
Really?
Well, again, these would be pretty big breakthroughs. If they had a breakthrough on discrete log, elliptic curves, or anything like that, it would be bigger than anything we've seen. Even now, Navier–Stokes is another example. They technically checked the box for the Millennium Prize, but there are no practical applications for it yet because the way they did it is very, very specific.
So far, we haven't really seen any major mathematical result from AI that has immediate practical applications. If they broke cryptography this way, it would be very different. I think it would imply even more capabilities than what we've seen from AI.
Okay. I like the framing of that. What about on the positive side? Are there any positive implications from AI's newfound capabilities with math?
5. AI Could Deliver Perfect Security
Yeah, I think enormous ones. The story here is that, especially for someone who's written smart contracts and has had millions of dollars in smart contracts I've written, that's fucking terrifying. Going to bed and thinking, "Oh, shit, I really hope I didn't make any mistakes in the code I wrote"—that's definitely one of the most stressful things about being a crypto developer.
One cool thing we can do now is what's called formal verification, where we can write programs, structure them as theorems, and have proof systems prove that this code follows this. You have to get your conditions right, but the whole idea is formal verification. They're doing this with Zcash now, with the new shielded pools.
That's just huge from a security standpoint. So far in crypto, the security risk isn't cryptography breaking; it's smart-contract hacks. I'm super excited that AI can do formal verification. Writing formal verification before was just so tedious and so long that humans couldn't do it.
We're already doing stuff now where I actually put a little AMM contract out on EVM, which is purely in Lean, so formally verified—all of that stuff. From a security perspective, I think it is possible we get quote-unquote “perfect security” because of AI.
Perfect security because of AI? That's insane. Is Zcash uniquely positioned for some of these doom scenarios?
I think Zcash is actually, for a doom scenario, maybe better situated, especially if you have shielded Zcash. That's probably one of your better situations, because shielding gives you two layers. One is your signature, but the other is that technically nobody knows your signature, and it's this Merkle tree.
Yeah.
If you have to go into bunker mode, like Justin Drake says, I'm going to say Zcash is guaranteed. I mean, consensus would break too, but Zcash is probably one of the better places you can go to bunker mode.
Okay, I'll take that. What are you working on right now on the math side of things? Your timeline has been electric the last 72 hours.
6. AI Finds Practical Math Uses
As soon as OpenAI dumped those, I did these little stupid explainers where I say, “Okay, try to take a thing and put it into a little comic or whatever,” and people kind of like them. So I did that, and then I thought, “Okay, this is kind of fun.”
Then I asked it, “Okay, out of any of these problems, if I just wanted to make an improvement on what OpenAI published, which one do you think would be fun?” It was this one on integer multiplication, which is basically: You have 2 really, really big integers, and how hard is it to multiply them together? That's basic asymptotic complexity, which just means, as they get bigger, how much harder does it get?
OpenAI's result was really cool because it found that what we thought was the limit wasn't quite. We could go below the limit, but only very, very slightly below the limit. Everyone was clowning on it, saying, “What's the point of this? What's the point of this, Doug? It doesn't even matter. There's more atoms in the universe.”
I also thought it would be kind of cool to show that you have to be more optimistic about math. Things will advance. So I took this problem and put it into Astra. I was going back and forth. I like to think I kind of contributed, but it was a lot of Codex.
Basically, the rate of improvement was 2^1.82m − 1.82, so 1 over 2^82. We took it down to 2^1.34m—so a quadrillion, quadrillion, quadrillion. A huge reduction. It started astronomical. It's still pretty big, but not as astronomical.
Then people took off with it, and now people are doing it. Somebody has it down to 2^16, I think. I have to go back and check everything now. So it's actually getting to practical applications, which was kind of my hope. I want people to be less cynical about this stuff.
Whoa.
If we could actually get it to something practical, where people are clowning on it as, “What is even the point of this?” I think it would be good for society.
How many tokens to get there?
I burned through my Codex subscription, and then I made my wife give me her Codex tokens. I might have to do some resets.
That's not too bad.
Yeah, not too bad.
That was optimistic. I like that. I guess one more on the original doomsday: Are there any sort of flare-gun signals to look for—“Oh, shit, we maybe have a bigger problem than we thought”?
7. Cryptographic Canaries Signal Danger
I think there are canaries. One is, how many bits of an address? Because what is Ethereum—160 bits? I forget how many. In all these signature schemes, you can put more bits into them. Maybe it's 256 bits. Basically every signature scheme uses a lot of bits, and those numbers of bits are hard to break.
I think there are canary amounts out there where it's like, okay, a certain amount—$10,000—in a smart contract that's 96 bits instead of 256 bits.
Yeah.
Those are kind of interesting, right? Because if someone's ramping up and getting good at it, they're going to hit the low-hanging fruit first.
Yeah.
If you see someone break those types of canaries—and again, I don't know the specifics off the top of my head; I should—but there are canaries out there that should get hit before everything else. Then you're like, “Oh, shit, I gotta go into bunker mode.”
Okay. This is basically my last question for you on the doom stuff. You said what Justin proposes is a little bit extreme. Bunker mode even sounds a little bit crazy. Is there any precaution you think people should take right now that's more practical or reasonable?
I think doing—
Are you doing anything?
Yeah. I think doing bunker mode is pretty extreme. You're more likely to fuck up and lose your coins if you try to move them every single time than actually have the AI hack them, especially today.
I would say the biggest risk still isn't cryptography; it's North Korea. North Korea is getting way better at hacking, and you don't know what kind of zero-days are on your computer. For the money you have, use a multisig: Squads on Solana, Gnosis Safe on Ethereum.
Put most of your money in a multisig, 2 out of 3. I would use 2 different hardware wallets, so different stacks, and maybe 1 mobile wallet on your phone that's pretty well trusted. If 1 stack gets hacked, you're okay. Then you have this recoverability thing.
That's what I personally do, so I think that's kind of the best option right now.
I feel like we're due for a security stream at some point. That was good. That was really good.
Doug, you're a legend, man. Honestly, I appreciate you coming on here optimistic. I figured you would be, but I wasn't sure what the vibe was going to be.
Do you have any, like, sort of, one, sign-off that you want to shill, and then two, anything you think people should read, pay attention to, anything, like, you should want to direct people to? Um, yeah.
Uh, I guess I'll, I guess I'll shill, like, the AI stuff we're doing right now, so it's Baranos AI, B-A-R-A-N-O-S AI. Um, we're early. We're just doing early stuff. But I think, like, we have some cool stuff coming there with, like, prediction markets and using, like, AI to, like, judge, like, prediction market results and stuff. So that's, uh, that's my shill.
Doug, you're awesome, man. Look, it's our annual stream. Hopefully next one will be—
Yeah. Hopefully next one will be in bunker mode.
ATHs.
Next year, 2027.
Yeah, not bunker mode, but, dude, I appreciate you taking some time out to come on. Much love, man.
Awesome.
Good to talk to you.
Thanks, man. Appreciate it.
Have a good one. Peace.