Unchained - Uneasy Money: Why Erik Voorhees Calls AI's Hidden Filter 'Deceptive'

Episode Date: August 22, 2026

Venice founder Erik Voorhees says crypto's real job was never speculation. It's becoming the rails AI agents actually need. Plus, why he sold equity, not tokens. ======================================================== Thank you to our sponsors! Visit 1inch to swap tokenized securities, crypto and more. Simple. Secure. Self-custodial. Whatever asset you’re buying - swap it at http://unchainedcrypto.com/go/1inch-sn ======================================================== Stripe bought OpenRouter this month in one of the cleanest crypto-to-AI pivots yet, and Erik Voorhees says most of the industry drew the wrong lesson from it. Voorhees, founder and CEO of Venice AI, joins Kain Warwick and Taylor Monahan to argue that crypto's job was never to serve crypto people, it was to become the financial rails a decentralized AI future actually needs. He pushes back on the instinct to abandon tokens for pure AI plays, and on the assumption that America deserves to win the AI race just because it is America. They get into why Voorhees sold Venice's equity but refused to sell its VVV tokens, why he says the big labs are losing money "hand over fist" subsidizing $200-a-month plans, how DeepSeek reset the cost curve for inference, and why he calls the moderation layer sitting inside today's AI models "deceptive." His answer for who should actually win the AI race has nothing to do with flags. Hosts: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Kain Warwick⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ - Host of Uneasy Money and Founder of Infinex and Synthetix ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Taylor Monahan⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ - Co-host of Uneasy Money and Security Expert Guest: ⁠⁠⁠⁠⁠⁠⁠⁠⁠Erik Voorhees - Founder and CEO of Venice AI Timestamps 🤝 01:51 Why Stripe buying OpenRouter is one of crypto's cleanest AI pivots 🪙 04:02 Why Erik says he can't pivot out of crypto even while building an AI company ⚖️ 12:43 Crypto has principles, AI didn't: unpacking the two industries' DC playbooks 💧 27:58 1inch Aqua: See how shared liquidity keeps LPs' tokens in their wallet at https://1inch.com/aqua 💰 28:44 Why Erik sold Venice's equity but refuses to sell its VVV tokens 🧩 42:51 Inside Venice's strategy for aggregating every major AI model in one app 📉 49:15 Why Erik says labs are bleeding money on $200 plans, and how DeepSeek reset AI pricing 🌐 57:30 Why Erik says America doesn't deserve to win the AI race by default 🔓 01:02:41 Why Erik has 'zero faith' in politics and trusts decentralized tech instead 🕵️ 01:09:56 Why Erik calls the moderation layer inside AI models 'deceptive' Learn more about your ad choices. Visit megaphone.fm/adchoices

I have zero faith in the political process. Like I don't vote for presidential candidates. I think the train has left the station, like the institutional momentum and inertia of the state and not be controlled by anyone, badly. The only solution is actually technological. And this was why Bitcoin was so amazing was because you didn't have to go anywhere and vote for anything. Just use it. You didn't require anyone's permission for it.

It actually allowed you to opt out of the system just on your own. That was what was beautiful about it. Hey everyone, I'm Kangwerk and welcome to uneasy money because what happens on chain never stays on chain. Before we begin, here is a word from the sponsors that make this show awesome. This episode is brought to you by 1 Inch Aqua, the shared liquidity layer from 1 Inch, back multiple liquidity positions with one wallet balance and keep your tokens in your wallet until a swap fills. See how it works at 1inch.com slash aqua. All right, hey guys, I'm here with my co-host, Taylor Monaghan, here.

expert and we have a very special guests today Eric Voorhees, the founder and CEO of Venice. Welcome. Yeah. Hey guys. Thank you for joining us. All right. It's been an interesting week this week. We've had some Eith price action for the first time in a while. And even Bitcoin at 72K, it's been feels like we've been ranging for like six months now going nowhere. I know, went up. Yeah, yeah, exactly. Yeah. I know. I know. What are the odds? So let's jump straight in. We're very lucky to have your perspective here, Eric, on some of these topics because we kind of mix, like, Defi and AI on the show often. And one of the probably biggest things that came out this week was Stripe buying OpenRouter.

I think there's a couple of interesting things. One, OpenRouter, one of the founders of OpenC, clearly all. Also, yeah, yeah, also big fan of the word open. We'll see what he does with his next startup. But I think, you know, in terms of like crypto pivots to AI, this is definitely one of the most successful ones, right? But pretty hard pivot out of NFTs into open, open kind of API inference for these guys. Yeah, and OpenRruder, of course, wasn't crypto at all.

It was just pure. Just pure, yeah, like full pivot out of crypto, like goodbye. Full pivot out of crypto into AI inference. Yeah, it's an incredible achievement. I mean, the company is like a little over two years old, something like that. Yeah. Nailed the timing, nailed the product. Everyone loves it.

They built a marketplace with providers of inference and consumers of inference over API and just totally crushed it. Yeah. Yeah. Very impressive. Good job out. Gigi. I know, right?

Yeah. And the other one, I think, the other one is Hermes. Those guys were crypto and they kind of pivoted pretty hard, maybe a couple years ago, two and a half years ago out of crypto. But I think the, of the, let's call it, crypto plus AI projects, Venice. is probably the leading example we have right now, I would say, like incorporating crypto into an AI product and kind of mashing the two up as opposed to a hard pivot. So, you know, maybe, maybe like give us a head. Like why, why did you incorporate a token into Venice versus, you know,

just going full pivot out of, out of crypto into, into AI? Yeah, I mean, first, I can't pivot out of crypto. I've been already re. It flows through my blood. I care deeply about it because it is, it is a new way for money to move around the world. And because money is, you know, endemic to all business activity, I don't really see it as like, it shouldn't be its own ecosystem. Like, it started that way.

I'd much rather see the primitives, the best primitives from the crypto and defy world start leaving the crypto world and actually get into normal stuff. If it's all just like crypto for crypto people serving crypto products with crypto, then we've kind of missed it. So I wanted to bring some of the primitives into Venice to just like show the world that these technologies are not just for like, speculating on coins that go up and down. These are incentive mechanisms.

And there's a lot of ways to do them badly, but there are some ways to do them well. And we're trying to demonstrate that in a mass market consumer app. I mean, is it fair to say that like the existence of tokens, right, or even let's say like models generally like inference, right? Like these, you know, and I say tokens, like not crypto tokens, right? but like, you know, inference tokens, the new, the new hot time. The inclusion is really unfortunate, actually.

It is a bit unful. Although maybe we'll get like some, you know, nice tailwinds from people going, oh, I love tokens. They're really good. So is it fair to say, though, that one of the kind of interesting side effects of the fact that the world wants inference and wants these tokens so much is that you have a product, which is a digital product that needs to be paid for. And we have this like, you know, digital money that you can kind of combine the two things

in a way that like if you're trying to sell, you know, computer hardware or or, you know, some physical item, the use case or value prop of like buying it with crypto just was never, you know, I mean, we've all been there standing at a register trying to pay for something with Bitcoin and, you know, even with like the lightning network or whatever like it just never really was like a you know and we might get there eventually but like tokens inference is something that is like globally distributed it you know it's streaming is that something that that you saw as like oh this is a perfect use case for paying in in crypto um that's not really what we were excited about uh certainly crypto can be used to pay. That's kind of like the level one basic use case. And so certainly if someone wants to

buy tokens from Venice, they can be AI tokens. They can pay with crypto. Right. So that's sort of like table stakes in my opinion. You know, like crypto is a payment method like visa like, you know, stripe merchant processor, you know, like any of that. More interesting stuff I think is actually the ability to give people stake in a platform in a project. And so, for example, all the major AI labs are private.

You know, with the exception of Google. And because of the private, like normal people can't really have any kind of stake in its success whatsoever. And this is causing a lot of consternation, actually, where people see AI taking over the world. They see the tremendous amount of wealth being generated from it. And they just kind of have to sit there wondering when their jobs are

going to be stolen. Right. That's that's the impression that a lot of people have. I'm rightly wrong. And I think that's that's avoidable. And so in Venice's case, we have a token and we haven't deployed all the methods in which we will do this yet, But we want our users to be able to have a stake in the growth of Venice itself from its early days, from its founding. And we have to be careful about how we do that. But I think that's a much more equitable and interesting way to build a company. It's like if your users can be participants in it with you instead of just consumers of the thing. Right.

Yeah, you said incentive mechanisms, right? like to build, you know, tokens are, you know, a way of coordinating people. And again, like, you know, people anywhere, right, anywhere on the internet. So, so not just like as a, you know, medium exchange, but like actually getting people to, to kind of contribute to, you know, the project and the ecosystem. Yeah. And like something we haven't, we haven't announced or deployed yet. Well, we want to essentially, like, take a portion of.

user's payments to us for monthly subscription or the money they're spending on credits. And give a portion of it back to them, using it to buy VVV and giving it back. Right. And then letting them have it so long as they stay on the platform for a while. Essentially, you're like buying loyalty, which gives you the chance to teach someone the product. And if they like the product, if they stay, then they end up with a stake in the thing without having to pay it. It becomes sticky, right? It becomes stickier, right?

So this is something we're going to be experimenting with. And I like that better than like, I don't want to go to the users and be like, hey, we have this token, go buy it. You know, like that feels a little dirty. But if they're already spending money on Venice, if we can take some of what Venice is earning and give it back to the coin of the project, then I think for some portion of users, they'll find that to be a really cool rewards program.

And I mean, yeah, this is somewhat of a novel situation that you find yourself in where you have a crypto product that people actually want to buy that isn't the token itself, right? So you have all of these levers you can pull to do exciting things when you've got something like that. Yeah. And like people were, people have been experimenting with crypto so much that they didn't realize that the crypto ultimately needs to serve something else. Like in Bitcoin's case, Bitcoin is serving the purpose and the good of base money. every other cryptocurrency needs to basically be doing something else, something at a different level of a stack.

Ethereum, ETH is powering this smart contract platform. That's its purpose and its product. Each thing needs to have a product attached that it is serving. We got to the point where like the coin was becoming the product, which was never supposed to be how it works. It wasn't supposed to be the case, yeah. It's supposed to be like incentivizing and coordinating something other than itself, right?

It becomes very self-referential if it's exactly self-preferential. Yeah. Also like a race to the bottom, which is what we've seen for the last like couple of years, right? It's just the meme coins and meme coins for a meme point. Yes. You don't get any. You're not growing the pie at that point. You're just going to moving around.

If people want to do like meme coin speculating, you know, God bless them. They should have the right to do that. People have the right to go to casinos and gamble, and that's perfectly fine. But it's not accretive to society. It is a form of entertainment that is consumptive. And it's tragic that projects started just becoming meme coins, in part because it was risky from a compliance and regulation perspective. I mean, that was, yeah, exactly. You're sitting there trying to build something, and you're like, you're like,

Like, what the heck? Like these guys are just launching nonsense and not getting in trouble, right? Meanwhile. So. If you had a project that had utility to it and you had a token, it had a higher chance of being deemed a security than if you just released a meme coin. Yeah. Really tragic, actually, and deserves study, I think. I agree.

There's a reason that, like, post-ICO boom in 2017 that we ended up with several years of where mean coins, just became the thing. And actual projects with legit tokens attempting, like, D-Fi Summer was really the last time that that was. But that was a leap. Yeah. This is, I have a question for you, Eric, and it's slightly adjacent to this, to this topic.

But have you, so I've been observing sort of, let's call it like the crypto's, crypto bro approach to regulation and law versus the AI approach. And we've seen it a lot lately with specifically like Open AI and Anthropic and their policy stuff, how they're approaching DC. I would say the AI guys are much more keen to bend the knee, the government, than the crypto bros happen. I wanted to hear your take on sort of the dynamic or the dichotomy between those and anything. Any other thoughts just because I find it absolutely fascinating, right? There are two tech stacks that are moving really, really fast,

that are both sort of antagonistic, inherently antagonistic to global power, but maybe you should play ball, maybe. Yeah. Right? Yeah. Any thoughts? Lots of thoughts on that. So this is actually kind of why I started Venice, which was that, like in the crypto world,

we have principles. but we say we do. Some of the people in crypto attempt to live up to them. Many of them just talk about it, but don't really care. But at least we have some principles that we can all maintain

some degree of fidelity to. And the principles are generally shared. These tend to be things around decentralization, privacy, sovereignty of the individual, decentralization of power. These concepts are common and we debate the details of them,

but we agree generally on the direction. the AI world was, and I'm making a generalization, was completely not that. It's a same way. And so, like, again, and I'm overgeneralizing a bit, but the AI world is a much longer history and tradition than crypto. People have been studying this largely in academia for 60, 80 years.

and basically the mentality or the philosophy, the ideology within that world was very smart people in academia that believed things should be controlled and it's just what matters is who is in control and what the rules are. Very top down, very centralization of power and make sure that the person in charge is the right person kind of mentality. I mean, is it fair to say like academia is that, right? It's very, you know, you're in Harvard or something and you're tenured and you're the, you know, so like that's the ecosystem that they came out of to some existence. It's the ecosystem they came out of. It's the incentive structure of how much of that was funded over decades. And I think it's also just the dental demeanor often of people that are extremely smart like science types.

There's a certain degree of like, I am a master of my domain and competent. simply so, and thus I project out that there must be a master of every domain, including society itself. And of course, what the logical fallacy is is that they misconstrued the narrow domain, which can be understood for a complex system which cannot. So anyway, that's like sort of the milieu out of which the AI world emerged. And when I, like, you know, two and a half years ago when I started getting to this stuff, nobody in AI cared about privacy.

Nobody cared about isolation. Nobody cared about user sovereignty. In fact, it was all the opposite. It was all like this technology is extremely dangerous. Government needs to regulate it immediately. Everyone needs to be watched. Like the prompts that you send need to be censored.

The answers need to be censored. Everything needs to be tightly controlled. And our sin is that we're not controlling it tightly enough. Yeah. So that was a completely different. That was like the doomer, the doomer perspective. It's weird. It's weird.

It's, and it's, it's, it's been weird to watch. And it's, I mean, it's fascinating. Yeah. It's not weird for normal people. That's actually the more normal. We're just weirdos. That's the brawl. Yeah, right.

Fair. Fair. Fair. I've been here for too long. Yeah. We're the weirdos. So, so I looked out and I saw, you know, like, um, AI was obviously going to start taking over the world.

You had open AI and you had Anthropic with the frontier models, and they are very top-down, monolithic, control everything. We will enforce safety with our rule sets. The government will govern us, and we will do what they tell us to do, and that's how society should be ordered. And to me, that was going to lead to like a very dystopian situation, where machine intelligence itself was coming essentially through the official mouthpiece of the state.

To me, that was the dangerous outcome. And I like to half joke that Venice was set up as an AI safety company, basically prevent that kind of thing from outcome. But yeah, to your point, there's a, there's a reason that those types are much more likely to talk with the government and invite them in. I mean, I think there's like a philosophical thing to this as well, though. Like because, you know, AI, like crypto is, you know, you said this day, like it's destabilizing to power structure, right? Like, you know, it is it is kind of inherently a thing that can route around and, you know, we've seen this with defy. Like, defy versus, you know, call it like, you know, the TradFi approach to innovation, right?

You know, sandboxes and things like that that that never really went anywhere. like fintechs you know neobanks like it's all just like rappers around the same thing and defy was like uh hold my beer will reinvent finance from coach principles which has been good and bad right um so you know crypto is inherently destabilizing uh to existing power structures but AI is arguably like destabilizing to homo sapiens like possibly right and and so therefore like the stakes are even higher um you know potentially right if you get this wrong and so you know you look at like bostrum and and some of the people that you know kind of created this like uh sense of the stakes are really high here to your point though

when there's high stakes there's like a that that academia um you know solution is like well let's create some rules and regulations around it and you know that'll reduce the stakes from killing The way to frame it is like, this thing is obviously powerful and may be dangerous, thus let's centralize it. Or this thing is powerful and maybe dangerous, thus let's decentralize it. Yeah. So crypto people tend to fall in the latter category and most of the AI people fell in the form. Yeah. It's also, it's been, dare I say, some of the way that the AI companies have been navigating, especially like policy and DC.

see. I feel like perhaps Eric you and your buddy would be screaming at me on X. Like this is what we, this is what whatever. Okay. So for history, me and Eric sometimes fall on different sides of the fence with the details

on how like crypto should do certain things regarding like say hacks and money laundering. These are the argument, right? The argument is that if you if you do too much, or if you take the wrong approach, you're basically shooting yourself in the face.

And it's not necessarily a good thing because a lot of times you're just moving where the power is, where the centralization is. And that's what AI has done, essentially, right? It's a battle right now. It's not like done. But it has fully sort of, the question is not whether anyone is going to control it or not, whether it's going to serve people first or not. The question is who is going to be the person who like sort of wins that control

and what role the government is going to play, right? Yeah. And I think it's interesting that I think it's just really interesting that one, they've been so eager to do this and to just how quickly it's sort of, I don't know, I mean, they can't release models at this point, right? Like literally, open AI and anthropic art. are not releasing models. They are all releasing models.

What's not known is what is getting not released. So we see what we see. We see what's released. But you don't actually know how far ahead of that the labs are internally. Nor do you know how far they would have been if they weren't restraining themselves. My guess is that none of them are actually restraining themselves. At most, at most they're restraining what they release. and they're all in a wild competition.

I mean, I'm glad I'm not running like Open AI or Anthropic. Yeah. Seems incredibly stressful. One to two trillion dollar companies. And yet there's like these open source models that are now one or two months behind them and catching up at 10% of the cost. I mean, while, by the way, while a huge amount of leadership efforts, are dedicated to literally sitting in D.C. and holding the hands of like the Trump admin,

who's now trying to figure out how AI works and how these models work and how dangerous it is. I think it's a very delicate balance because if they go to D.C. and say, hey, this stuff's dangerous. So regulate Chinese models. Yeah. That can also mean that their own company starts getting nerfed

or caught up in the confines of it. Yeah. Yeah, the truth is like, I don't think anyone really understands how this stuff is all going to go. And everyone's just trying to make it up as they go along as humans do. And everyone's pretending more confidence in the future than they actually have. Hence decentralization, right? Like, you know, if you don't trust, you know, then.

One of the great arguments for decentralization is just the humility of it. Which is to say, like, the future is actually very complicated and we don't know. and that's okay. We shouldn't presume knowledge that we don't have. And if things are decentralized, then at least where the problems happen are localized usually instead of catastrophic and system.

That's very, very astute assessment of the situation, I think, from our perspective. And, you know, again, like, we have built a set of tools over the last 15 years, whatever, in crypto, that is like, perfectly designed to help decentralize things like this. We got a little too caught up in like doing the thing for its own sake, right?

The kind of self-referential naval gazing stuff. But now there is like a thing that people want, you know, in the form of inference. There are massive constraints in terms of how you produce it. There's all of this competition. There's this whole landscape. And, you know, this is why I think it's so interesting that Venice is one of the few projects that's actually at the intersection. Like a lot of people are like, see you later. Crypto's dead.

I'm going to AI, right? Yeah, there's a lot of fair weather crypto people. But yeah, yeah. But so like you guys are one of the few that are like, well, hang on a second. We have a set of tools that we built. There's this new thing that ever, like massive demand for this new thing inference. let's actually plant ourselves at the intersection of that and the world and, you know, create these incentive structures to make sure that we don't just centralize it.

Yeah, like I actually just think crypto is better financial tooling, period, full stop. So, I mean, yeah. Of course, anything I build is going to try to use the best financial tooling I can. It's not even a question of ideology, but of pragmatism. and I there's there's a theme I think that more people are realizing which is that crypto's always had this ux challenge with humans and every time you go to a crypto conference whether it was back in 2011 or tomorrow people talk about like how do you make this stuff usable like more usable to people and it seems like perhaps it's just much more usable for robots

and the machines and the AI and that crypto is that actually built for the machines that we didn't know it. And that the machines are now going to like start running on the stuff and utilizing it. You know, they have no trouble with public key pairs. That's easy for them. So yeah, I don't see a future where like the robots are using bank accounts at Wells Fargo. I do see a future where they're using crypto assets on decentralized rails. That I have no idea which rails or blockchains or if it is a blockchain,

But natively digital financial technology that does not have a political gatekeeper is the least friction way for value to move. And I would imagine that super intelligent beings would prefer such a thing. I mean, you know, like very obviously bank accounts are not urban omic for agents. Like you can just see, you're like, oh, this is just a dumb thing. Like an agent doesn't want any part of that, right? They're like, why have you got all these weird layers and protection? Like, I just want direct access to the thing. No, and I don't know, Kane, I don't know if it's the same in Australia, but here, if you send any amount of money via your bank, you have to literally either get on the phone, click five buttons on two different devices or walk into a physical bank if you want to wire, like literally any amount of money.

Every single time, I'm like, all right, I have to go wire money. I'm going to set outside the whole day because it's that miserable. To go and beg your money. I know, right. Yeah. Meanwhile, like last night in bed, as ours falling asleep, ours like, I don't know, like, let's long some eat. Well, like, even two seconds later, we're going to go golden, right? Like, it's night and day.

All right. Let's go to break, and then when we come back, we're going to talk tokens and equity. So we'll be back in a second. $540 million. That's how much concentrated liquidity sat idle in a given week in the first half of this year. About 30% of the Defi TVL, if you're wondering. That's according to Dune Research commissioned by, by one inch. But there's a solution. One inch Aqua is the new shared liquidity platform. It lets

LPs back multiple positions with the same token balance and keep their tokens in their wallet till a swap comes. Why does that help? Because the LPs don't have to split their tokens across positions. They can cover more market conditions and pairs with their full balance. That means more activity across deeper liquidity. See how it works at one inch.com slash aqua. Remember that providing liquidity carries risk and fees aren't guaranteed. All right. Let's talk tokens versus equity. I think over the last couple of weeks with the most recent raise that you guys did, this topic came up. And I think it's a topic we've spoken about on the show a number of times. You know, maybe I'll preface this by saying like, as a token maxi for a very long time,

I've definitely felt the, I guess, effects of like, you know, just how poorly tokens have done, like, including, you know, my, my, like, portfolio companies, how hard it has been. You know, we were talking about meme coins. Like, if you're not a meme coin, it's very hard for, for, you know, you to get any traction. But I think that, yeah, it's, it's been. It's so late. I know. It's really bad. It's really bad.

I think we'll fix it. I think we'll fix it in the next bull market. And we'll actually have some stuff that people can see, okay, like we don't really have that many good non-crypto use cases. You know, you look at something like Ave or whatever, right? And you go, okay, like Ave, there's utility there. You know, you see why the token exists. It keeps the ecosystem working.

You know, but a lot of the times it is just pseudo equity, right? And we've talked about this on the show. Pseudo equity sounds great. And there's a lot of nice things. And I think you tweeted about this. You know, you have liquidity is one of the amazing benefits and access of tokens. Anyone anywhere can buy a token. You've got a lot of liquidity.

You know, it's not gate kept in the way that equity is. But it is an equity. And it doesn't, you know, confer ownership rights. And, you know, you can get rugged. There's a lot of tail risk in being a token. token holder, you know, technical tail risk, you know, execution tail risk, et cetera. So, so I guess, like, walk us through your, your kind of thought process, because you guys launched the token first, but you obviously had an entity that had equity in it, but you hadn't raised. And

then you were presented with this opportunity where, okay, we've nailed it. People are really excited. This is working. People want exposure to what we're doing. And, you know, you know, it made more sense at the time clearly to give them equity plus tokens, including like token warrants and stuff, than pure tokens or pure equity. So maybe just walk us through, like, how, what was your reasoning there? And how do you see the trade off space? Yeah.

So these are different things. They have pros and cons, each of them. We started Venice just as a normal company. So we're a Wyoming corporation. And for a year, we built product all self-funded and didn't raise any money. Of course, we were going to bring tokenization into it. So about a year later, we released VVV and then DM. Never sold it.

And so still hadn't raised any money, but then now we had a token as well. Then another year goes by. We had grown a lot, and we were at the point where it's like, okay, we're ready to scale this thing. We've demonstrated market fit. let's go big. And we have this choice. Like, okay, we have two assets here. We have equity and we have tokens.

We have a bunch of both. Which one do we want to sell? And we decided to sell the equity. We'll sell that. We'll keep the tokens. And in some ways, it's more normal because the normal VC market understands equity better. Yeah. But really, we just like, we kind of don't want to sell these tokens.

Like, we've never sold them. There's a reason for that. This is part of what we're building toward. So we sold equity. The VCs that bought it have rights on the token. So they have an option that they can buy a token at a certain price, which then invests up for four years. And that was important for me because even though the investors don't have any

close to a controlling state. I want them to understand the direction that we're taking this, which is like, we're going to try to burn all the tokens in existence. They need to understand that. They need to be okay with that very unorthodox financial strategy, which means that they need incentive on the token side as well. So they have warrants toward it and they know the direction.

We've tried to be as consistent and communicative as we can about what we're doing. But it is a challenging thing to, kind of strike this balance, right? You know, you've got tokens and you've got equity. There is always going to be some kind of inherent conflict, right? If it was pure equity, then you return cash to equity holders through either, you know, buybacks or dividends or, you know, whatever. If it's a pure token, you move it around and probably nothing happens, which is, you know, most tokens, right? But if you have revenue, you can put it back to, you know, token buybacks or distribute, which is a bit more risky for tokens, but most likely buybacks,

right, as a way of returning value to the owners. It becomes very interesting if you have these two different ownership mechanisms and you have one stream of revenue or cash flow that you need to divvy up, right? And you've got to try and balance the, I mean, I guess you could look at it as like dual-class, you know, shareholders or something like that? Like, how do you guys think about that in balancing that? I think the main thing people are missing is that they perceive that these are two different groups that you have equity holders and that you have token holders.

And everyone involves in the company as equity and tokens. And so their interests are in both. And the company itself has more tokens than anyone. So we don't see these things as very misaligned. In other words, like every dollar that we spend burning tokens, is that harming our equity holders? No. I'm the biggest equity holder of all.

Why would I do that? No, we're doing it because we actually think it helps equity. Like this stuff was set up for this purpose. We have a token. We're going to grow the business. We're going to buy as much of the token as we can, which will send the price up. And the company has more of the tokens than anyone.

So we see these things as like aligned, certainly unorthodox, and certainly the tokens and the equity are different things with different structures. But we don't see that their interests are misaligned. But I mean, at the limit, right? If you're pure, so, you know, synthetics, we were a pure token project. Like maybe one of the most pure ones. We didn't even have entities. We shut all our entities down. And we said, like, somehow I'm not in jail.

I don't know. I don't know exactly what happened there. but the SEC just missed the memo on that one. But we literally had no protection. Like our lawyers are like, you are insane. We had a foundation. We're like, we're shutting the foundation down, nothing, right? Like pure doubt, like absolute insanity.

This is 2019. So I think it's going to be of that. I know. Well, it seemed like a good idea at the time, right? And so, you know, there was no way for misalignment to happen. That was kind of the thesis, right? That, like, you know, the owners of the token voted in the, the Dow membership, the Dow controlled every vision, et cetera.

Like there was no dual class structure at all. And I think that like, you know, at the limit, if you are that kind of structure, right, there is no even potential conflict or whatever, like all of the revenue, all of the value flows back to token holders. And so I guess the question is like, you know, what is the benefit then? Like, you guys had equity, right? But you could have said, actually, let's shut down the company and, you know, probably would have been a bad idea just from a regulatory perspective.

But, like, you know, you could have said, let's just only have tokens and be a pure Dow. And therefore, like, 100% of the revenue has to go back. And like, at that point, what's the benefit of the equity other than like? Yeah, I think maybe a lot of people don't quite recognize that building an AI company is actually quite capital intensive. like, we're spending tens of millions of dollars on GP. Yeah.

We need an entity for that kind of business operation. I mean, OpenAI had this exact problem, right? They're like, let's be a non-profit. And then like, actually, we need to raise like a trillion dollars. So we're going to go ahead and be a profit. Yeah. Yeah. I mean, us having an entity has nothing to do with regulatory protection.

because in my experience, having an entity only invites regulatory scrutiny, frankly. There's a myth, I think, that it protects you, but I've only found that it causes trouble. That's certainly not why we do it. Yeah, we did it because we started as a company. Then we grew, we released a token. And when the time came to raise money,

we're like, two assets, which one do we want to sell? Have less of? We want to keep these tokens. So let's dilute the equity and sell that. So we did. I mean, you know, reading between the lines there, right? Like, you know, you've got an asset that is infinitely inflatable, and then hopefully an asset that's a harder asset, right,

with some kind of fixed supplier, mission schedule, or whatever. As a crypto maxi, you look at that and go, I've got a money over here. I can just print some more share certificates. Like, why not, right? And if someone's willing to buy them, then great. Yeah, exactly. And like our entire strategy with VVVV is to,

take it deflationary. So the actual token supply will shrink with time. Whereas the equity, we have no such strategy. We're not trying to shrink the equity supply. Right. Right. Why would you why would you? Yeah. There's no reason to. Let's delete that. Let's delete that. We care more about the token. And let's make sure that all the people that are investing have exposure to the token on the upside. That's important. And then most important of all, let's just keep building a business that's growing very fast as people like. Yeah, it's not really much more complicated than that. I think the issue is that like these tools are all very powerful.

And so in the hands of people that are misaligned with themselves or with parts of their organization or with users or holders, they can go very badly, obviously. But I mean, I'm the second learned that's one of, you know, tokens like the, the, the, the brilliance of tokens, right, is that they are so power. Yeah. And powerful things can go wrong, right? That's right. And I want to show that they can go right, actually.

That's actually important to me. And when these people are all bickering around on Twitter, they're like calling tokens these second class citizens and stuff. I want to just yell at them and be like, bro, I am the token holder. Yeah. It's weird, though. Like, that doesn't tend to, I don't know why, but that just doesn't land on Twitter.

Right. Anytime I've ever said something where I'm like, but I'm the largest token holder, why would I do a bad thing to token holders? Like I've got the tokens. There's some there's some like brainworm where like people on Twitter are like, yeah, I don't care. Like it's like they can't get the incentive alignment there. Yeah, it's hard. So we, you know, we have to decide like how much we even want to argue on Twitter. Like most of our users are not on Twitter actually. Fair. Most of our customers are not quit with people. Must be nice. Must be nice for you. It's all transparent, right? So the flows of the of the tokens are transparent. The supply is transparent. How much we're burning is transparent. Like, we don't really need to say anything. We're just empirically demonstrating it with time.

And hopefully that will win any argument. But it is always tempting to like pop back on Twitter and try to prove the guy wrong, you know. Yeah. It never works. It never pays off it. It's on. We can help it. Yeah.

Yeah. I think I was just going to say, I think generally people, especially like the Twitter crowd, I think they've just been burned so much. They have. I get it. They're just like, you can't reason it at some point. They're traumatized. And then they go by like a meme coin and then they wonder why they're having a word by them.

Yeah. I mean, you know, it's the incentives, right? Like the incentives are structured so that you're better off buying a meme coin because people who are trying to do. real things have to pretend like their token is not even a thing. They don't, they can't talk about it. You know, they're, they're trying to hide it away instead of putting it like at the forefront of the of the thing, right? Yeah. Yeah. So hopefully we can be a good example and show that like these are interesting and powerful financial tools that when assembled correctly can be accretive to all groups. Of course, there's a lot of ways to do that badly, but there are definitely

ways to do it well. And earlier, Kane, you mentioned, like, tokens are fraught with danger. But so is equity. People just don't see it because it's opaque. Right. Right. You don't see the equity that goes to zero when it's a private company. There's no chart. Exactly. There's no charge. It's kept out. So all of the blemishes on the cryptocide are in front of everyone to see. Which is also the powerful thing, right? Like you can see. Yeah. It's a just one of its things makes it makes it tricky yeah it does it very much does um so let's talk about uh open open weight models um you know you mentioned this earlier that like uh it must be super stressful um you know as darya watching glm 5.3 start to like you know claw its way towards you or whatever

is going on there right um so so like how do you guys think about this how are you staying um you know kind of abreast of all of the different models. You mentioned before the show even started that open weights are coming for GLM 5.3 pretty soon. Like I'd love to just get like your like take on the procurement process of inference. Like in the background, you mentioned, you know, GPUs and everything. Like what's the day to day in Venice? So Venice does not train models.

And when you're using Venice, you're not using a Venice model. Basically, within Venice, you have access to all the models of the world. Every major closed source model, every major open source model is all in one place, one API key or one app, and I'll get it in one spot. So we've become an aggregator essentially of all the world's models across text, image, video, audio. Now, when we started, like, that wasn't too hard to do because new models would come out like once a month. It's important and we'd add them. Now they come out literally every other day. Every day.

Yeah, it's great. Every day. Sometimes three will drop in a single day. And my poor team is like, what do we do? And each one becomes the new hot thing. And so we have to get it live very quickly. The team does an amazing job now. Like we get models live often within 30 minutes of them being released.

And then there's nuance in terms of like when they're released, when they become open source. if they're not open source, who is actually hosting them? How much do we trust them? Do we have a zero data retention agreement with that company? All these kind of things go on in the background so that we know whether or not a model can be listed as private or not. So in Venice, the normal models, most of the models are private. They have a private label.

That means like no data retention. There's no storage of the prompts. There's no storage of the answers. There's nothing to subpoena. If you're using an anthropic or open AI model through Venice, those companies should still be assumed to be holding those things, right? So we don't mark those private. Yeah.

Yeah. And so, so, you know, okay, GLM 5.3 is about a drop. The weights drop. They're on hugging face. Yes. So like what's the day look like? BLM is the latest model from a Chinese lab. It's not open source yet, but it will be in a few days.

So right now, the only way to use it is if you go through the company, with hosting GLM, ZAI. ZAI, yeah. So if you're accessing GLM 5.3 today, you're doing it from the AI. So it is an open model, but is it private? Well, that depends on your relationship with ZAI and what they're actually doing.

Venice doesn't trust any third party company like that enough for listed as private. You can access GLM 53 through Venice, but it's not listed as private yet. When it's open source, then we will run it ourselves. Then we can ensure it is private and then we'll market us. Private. So, yeah, it's a big important model, but almost all of them are these days.

Yeah, it's very hectic and stressful. I bet. I bet. And so, so, you know, you said like the hot model, right? Like, have you guys had to, you know, like I use RunPod? Like I'll put stuff on RunPod. I've got my own local machine. it is like a punishing exercise to stand these models up and test them and you know make sure that you got like the right token throughput like how do you guys do you have to build your own infra for managing the the routing of the models and and yeah when we started we were running the models ourselves on hardware we were releasing so we controlled the full GPU and um we

We learned that we were actually quite good at running image models, but LLMs are, for some reason, much more complicated. There's, like, many more settings and configuration, all sorts of interesting caching questions. And we realized that, like, we didn't have a competency in running LLMs. So we started working with partners that we had zero data retention agreements with and that we'd roll the GPUs with them. So that's what we've been doing up until recently. And following the Series A that we did, we've started buying a bunch of GPUs ourselves. because we're going to get back to that. So we've been spending time learning how to run LLM as well,

and now we're increasing the competent at it. So yeah, it's always like a push and pull. Some models are easy. Some are extremely persnickety. And then there's like variants of models, right? Especially like uncensored variance. We have, you know, like one of our USPs is that Venice is uncensored, which means that we don't add any content moderation to the input or output of the models.

But the models themselves all have different degrees of censorship or bias. Based into, yeah. It's into how the model is trained, ranging from very censored so they're not. So when a user comes to Venice and they access a model and it refuses their question, you know, often they'll be like, yo, what the hell you guys said this is the censored? Yeah, that's Nario, man. That's not us. Yeah. It's a hard thing to communicate to a user. And then there's open, then there's like sometimes uncensored versions of the models.

but typically the uncensored versions of the models decrease the intelligence of the model. So then you have this weird tradeoff where we can add the uncensored version of it, which will make the guy happy if he asks about how to make math. That's what everyone can't tell me how to make math. Everyone wants to ask that just because it's a funny question.

It's heuristic. But once it can answer that, then it gets less intelligent on the other things. So, yeah, the customer UX question is an ongoing constant challenge that we're always trying to balance. So maybe can we talk about the economics of serving models and this for a second? Because, you know, when my team started to like really use a lot of inference, right? Like we got to the point where, you know, we had like 100, 200K monthly. bills for inference, right? And one of my, one of my engineers was like, I have a Claude Max

plan personally, right? And I'm doing a bunch of stuff personally because he's just cracked. Like he works seven days a week. Like doesn't stop. Right. And so he's like, I'm looking at the API billing here and I'm looking at this and I'm doing more stuff. I'm spending more tokens on this max plan that I'm spending on the API. How does this make any sense? How does this make any sense, right? So, so like what's what's your, as someone who, you know, is, is obviously constrained by this API billing, how do you guys think about that when the, when the labs are subsidizing these plans like so aggressively? Yeah, I mean, it's, so first of all, Anthropic is losing money hand over fist on those cloud plus $200 a month plans. I don't know how long they will tolerate it or how.

much they're using or what, but that must be the thing that is constantly being discussed and debated because every customer loves it because it's an unbelievable deal because, you know, people will spend $10,000 of tokens in a month and pay $200, $200. Yeah. Now, if that loss making center is only, is a small enough portion of anthropics entire revenue, then they can just deal with it for a while. Yeah. But yeah, that's shortly adding to their stress as well. Venice does ABI does have any unlimited plans

like that? I didn't want to try to do such a thing like we just charge per credit on the API so each model has a different price and use whatever models they want. But I think this is the most interesting thing is the economics you know, you say like there's new models

coming out, the economics shift constantly and I do feel like being in crypto for as long as we have been our tolerance for like the uncertainty of pricing and everything is. Your team must just have an inherent ability to handle this. But deep seek as an example,

came out when V4 Pro came out and was 50 times cheaper. And all of a sudden, the number of tokens that you were getting, if you're on a token basis from the max plan and how much you could actually just pay through the API was identical. Like the pricing is basically, you pay 200 bucks and you get the same amount of inference as you were getting from a Claude Max plan. But the tokens were 50 times, you know, you're getting 50 times more tokens than it would have been if you paid through the API on court. Right. Like that's an insane variance to like be dealing with in terms of pricing and how you, how you meter that.

Yeah. I mean, what's clear is just that the cost of intelligence is falling dramatically. Right. and has been now for a few years. The dollar cost to do a certain type of work is just limiting. This is an amazing thing, actually, for the whole world. It's like if the cost of food production

was falling drastically or the cost of electricity was falling drastically. These are fundamental inputs into civilization that make civilization in general thrive. But if you're the provider of the model and the tokens, it's a very stressful thing to be involved with in a product that is constantly Deflationary

Declationary and commoditized. You know because everyone's competing to run these models the margins are extremely small. Some companies are undercutting their own margins with VC money subsidize it. And there's not really like the

end state is that no one's going to make any material money on selling. Right. That's true of all all commodities and tokens are a commodity. AI tokens. I mean, It's interesting. Like, we have a counter example.

Like, you know, the only deflationary thing that I think humans have any kind of sense of in the world is tech, right? Like, you know, computers get faster, like computation, you know, Moore's laws kind of cool. And, you know, obviously even storage and, you know, a hard drive for use of store 10 megabytes now, you know, and it was a million dollars is, you know, now costs like tiny fractions of a cent, right? So we have some sense.

The only people that have really pulled off the ability to like uncommoditized themselves are Apple. Right. Like they wrap these these deflationary things and they manage to, you know, they manage to kind of put this brand wrapper around it to the point where people are willing to pay a large premium for something that would otherwise be just in that software. And there's a whole bunch of reasons why they find they find ways to add value. right? So in Apple's case, the software is so good and the hardware is so good and the symbiosis of the two so good that you're regardless of falling prices of any of the components that they've built value out of the whole experience. Yeah. But also like the app store and like the there's this there's this some things have been commoditized individually within the whole Apple ecosystem. them, but they've managed to, as a company, on the whole, sort of dodge the complete undercutting of themselves with the compound. It's very interesting.

But surely, like, for anyone selling inference or creating inference or, you know, like, that has to be an example of, like, how do you find a way to add value? Because otherwise, you know, if it is genuinely a commodity, to your point, you cannot make a profit at the limit on. Correct. So like in Venice's case, so we've essentially like two revenue liens. One is that if you're using the API or you're using premium models and paying credits for them, people are buying credits for that and that's one model. The other is our pro subscriptions. So the pro subscriptions are actually a much higher margin. Like someone's paying $18 a month and we spend or lose $6 to $10 a month on the person. So that's actually like reasonable margin. And in that case, like the value add is that they get a bunch of models for free. They get a bunch of usage for free. They don't have to worry about every time they're spending little bits of money on each request.

And so that works. That creates like a symbiosis there. But we don't expect to like make material money on the API token sale side of things. That's just a raise to zero. Sorry. You said you haven't done the anthropic like, you know, subsidized plans. but you obviously have like not the 20x max like use 10 grand and pay $200 for it. But like yeah, you do have the ability for someone to sign up for a plan and,

and you know, get some amount of inference and you're assuming some amount of usage. You just have limits. Yeah. And that's in the app, right? And the limits are all pretty reasonable so that most people don't hit them. So it feels mostly like mostly unlimited unless you're just kind of being crazy. The API is different because API is by the. nature are

mechanistic and people's usage of them can be automated so it's very dangerous to give any kind of free thing over an API, right? Because it would just be I mean, yeah, yeah, exactly. And he's doing it, so somehow. Yeah.

Yeah. Yeah. Yeah. Yeah. Yeah. Okay. Kaine has been I feel very anthropic just for Kaine and his team. And they're But how they hit them. It's absolutely insane. Eric, I want to ask you sort of on the same, I don't know, these days I feel like a lot of the conversation with AI and the different models is this geopolitical fear of China.

And it's not just, it's a bigger, different conversation than the strictly the privacy one. It is very geopolitical. And people are very, especially in the West. China is the scary thing. How do you guys think about this and approach this on us? I mean, it is the geopolitical story, right? Like this China, U.S. antagonism that is like the proxy fight is in AI now. I mean, yeah, how do we think of it?

Like I'm not a nationalist at all. I care about principles. And you not care about the United States of America as a nation. I care about the United States of America's principles, specifically the good principles related to individual freedom, privacy, these kind of things. If Chinese models better express those principles, then I care more about the Chinese models than I do about the American models.

And I don't think America deserves to win anything if it simply closes down markets so that others can't compete and or if it puts in regulations that require their ability to like watch everyone's inference feeds. You know, like America is going down a pretty dark, I would say, like a pretty dark dystopian path of trying to control everyone. Over the prior, over the past decades, the meta trend is that the, that the U.S. is controlling and monitoring its people increasingly. And the Chinese government is actually doing less, right? they started out like very authoritarian. It was a high bar at first.

They're like very authoritarian and they've become more market oriented over the last 50 years. Whereas the U.S. was extremely market oriented and I'm more authoritarian. So that frightens me and I don't know which like. Getting weird. Yeah, when people say the U.S. must win, I have to say like, what? Why? What should win actually is the principles. The principles.

right yeah at what cost does yeah the US win yeah well and a lot of times a lot of times people when they say like the US must win what they mean is like freedom must win and individual rights must win and privacy must win and you know but they don't say that it should mean that Taylor and I think they think they mean that but it's one of those things we're like over time what Americans cared about of those principles abstracted itself into symbols like the flag. The flag. Yeah. Yeah.

You know, the flag and the presidents and Mount Rushmore and like all these symbols. And people now care more about the symbols than the principles. And you can just tell that through policy, right? You can tell that just in watching the size of government get bigger and bigger and bigger and bigger, which is the most anti-American thing that could happen is that the federal government keeps the government just go.

A whole damn point. The whole damn point of the large government. That was the one. That was the one thing. It was a good run. 250 years. It was a good run.

I mean, you know. That was a great. I mean, some of the most intriguing conversations that I've seen today, especially because it is about AI, it is about China. It is about the U.S. Or the conversations where

the conversation about like potentially nationalizing the AI companies, right? or like doing it the government to taking any stake or role with our AI companies is basically the most Chinese thing you could do the most anti-inresent thing you can do it's like hey we're just going to nationalize

this cool thing that you guys made and it's bizarre and the fact that they do it like just the the mental gymnastics that it's even even unfairness right like to your point it's not even that controversial right It's not even like people are like, it should be, it should be insane that it's even like in the Overton windows. Like, yeah, like 30% nationalization of these slabs is fine.

Yeah. Yeah, it's really sad actually. And the only hope, you know, like I have zero faith in the political process. Like I don't vote for presidential candidates. I think the train has left the station like the institutional momentum. and inertia of the state and not be controlled by anyone, badly.

The only solution is actually technological, and this was why Bitcoin was so amazing was because you didn't have to go anywhere and vote for anything. You just use it. You didn't require anyone's permission for it. It actually allowed you to opt out of the system just on your own.

That was what was beautiful about it. And so people's refuge, I think, is only in technology and in particularly decentralized technology that has no central ruler. So we have crypto for that in terms of finance, which is great. But if we do not have decentralization

in the realm of machine intelligence, then we are in big trouble. We have that in parts of AI. Inference can be done fairly well in a decentralized way. The production of... Craining not yet. Bottleneck. Yeah.

Now, there are companies like making, making good progress on this. They're doing decentralized training. So there is hope, but they're not competitive yet. So we, that's TBD. Yeah, I think it is an interesting situation where even six months ago, the delta between, you know, call it like, you know, Codex 5.1 or 5.3 and like, you know, almost any open source model was just gigantic, right? And it's, and it's come so much closer. but also there is this like at least it appears recently this kind of like frontier in terms of intelligence where like if i want an agent to write a web page there like more intelligence doesn't necessarily make that much of a difference right like you like at the margins right like

it depends uh how much more intelligent and at what cost because if you can have like a model that's three or four percent less intelligent, but you can run it three times for the same price. You can put, yeah, brute force better. Exactly. You can have it validating itself and you can use more tokens in its, in the creation, even though the raw intelligence is not as high. So, yeah, it's a bit of a complicated matrices. It is.

Yeah, sorry, go. Oh, sorry. So, so like this kind of frontier, though, right? It's like, okay, if you have now, uh, GLM or, or even, I think the most recent like when data like on a MacBook Pro, which is not accessible to everyone, you know, a 128 gig MacBook Pro. It's a $10,000 device or whatever. But on a $10,000 device, like you've got a model that was better than anything you could possibly have done, you know. So yeah, I want to point out this like local model thing. The first, I like decentralization

and I'm glad that people can run models locally. That's healthy and hobbyists. should keep doing it and like that's an important thing but um you don't need a 10 000 computer to run quen you can just use like an API for venice yeah and talk but even my own book a little bit but like the point is that any person even on a 200 dollar netbook and actually access the API and pay very little there's no upfront capital costs so you can actually get this intelligence on any device um that's that's that's what matters a lot and the the payback curve you know i i I think I mentioned before we started like I bought a $20,000 max studio, right, with 512 gigs of RAM when they were still selling that much RAM. And there is no, and the payback curve was not good then, right, for like running local models versus, versus, you know, running frontier models.

And it's only gone worse. It's like the payback curve is going the wrong direction for me, right? Like there's no way to pay that back because like I just couldn't run enough inference on that machine compared. compared to if I just ran it in like run. Yeah. And like that's always going to be the case due to the economy's scale of restaurant manufacturing and servers. Like from a pure economic perspective,

it is always going to be more efficient to just get your inference from like large scale data centers. So the point of running things locally and never and should never be about saving money because you won't. It's a myth or an illusion. But what you do get is full control. You get guaranteed privacy. And, you know, what is worth a lot is actually like learning this technology.

Set these things up running it yourself. Like, that's very valuable. There is a cost to that as well, right? Like I was spending four hours trying to get a model running. And I'm like, man, I just want you to like, you know, make an edit to this doc. Yeah. And frankly, you know, Kane, I'm sure your time is worth such that the hours you spent is actually more of a cost.

That's what I'm saying. The payback curb is going the wrong direction for me, right? I'm like, I just need to hook into someone who, you know, you have a team of whatever, like, you know, 20 people that are like, I'm going to have GLM 5.3 ready for you. Like the instant that it launches, right? Like that's actually worth a lot to me to not have to think about it and not have to like, you know, go and, you know, download the weights. And this is the other, like, you download the weights and then you run it.

And it's like, oh, no, it's the end. MLX, like, the variance. It's great. You know, update something. It's just, it's important that people can do that, but it is not the save here, I think, that most people imagine. And I don't think at scale, I don't think at scale the average normal person is ever going to be running models at the edge,

because whatever you can run at the edge, you can run something 10X is intelligent from a server. What's important here is just that you can access servers that are, that maintain the sovereignty of the individual. And if you can only access servers that come through, you know, like licensed data centers that are approved by the state and will only as you ask certain things, that is the future that I want to avoid. Approved slash slightly owned by the state. Yes. Yeah.

Just 30%. Yeah. Just 30%. And that's, that's all we need. We just need 30% guys. So, so I do think it's an interesting question. I'm sure you guys are thinking about this. Like, you're talking about this frontier of intelligence, right?

And, you know, if you talk to Fable and everyone's very frustrated, all of my vibe coding channels right now, everyone's very frustrated with Fable. You mentioned this idea of like censorship. And one of the reasons why I think people are so frustrated with Fable right now is because it has been nerfed so hard. And so it's tripping over itself to try and, you know, make sure that it doesn't do anything wrong to you or the world or whatever. And it's super frustrating because you're like, man, like, you're a smart guy, but can you just like shut up, please? Yeah. Okay. So this was this was one of the things that really got me passionate to start Venice, which was that we have these machines now that are extremely intelligent.

and we have the ability for the human mind to interact with this extremely intelligent machine mind, which is so cool and actually like quite a beautiful thing. Yeah. But when you interact with it, what you don't realize is that you're interacting with the machine through like a filter of corporate committees, right? The anthropic, the opening eye corporate committees

and or any state regulator that's involved. And so your interaction isn't just between like your mind, as a human and the machine, which is a beautiful symbiosis. It's got this opaque, undefined, like, committee layer, right? And that's not okay. You know, like, that's kind of deceptive and can be abused so easily. And people don't know where the boundaries of it are. They don't know what it, they don't actually know what's coming back from the machine.

How thick is that layer. Yeah. How thick is the layer, right? That seems very deceptive to me. And that's why, like, in Venice, there is no layer. You just talk directly to the machine. Well, and this is the thing that I think people don't realize is, like, you know, the bowl of math that is the model, right, has layers and layers and layers on top of it that you think are the thing,

but are not actually the thing, right? From, like, the harness to, you know, system prompts to, you know, these committees that are like, just make it not tell you how to make math, whatever. Whatever it is that they don't want it to know how to do. And it's like, man, like you can Google how to make math. Like, why are we telling the model to not tell people how to make math? Like, if they want to make meth, they're going to do it. Just like let them do it, right?

So, so yeah, like to your point, you know, if you have the distilled inference direct from the source with like, you know, some. And this, this is a question, I guess, like, What are those guarantees that there is no layer that you're, you're kind of consuming this through with Venice, right? Like, how do you guys represent that this is the pure bowl of math with no hairs on top of it? Yeah, so people can use, if they want to be real paranoid, they want to be like proper crypto paranoid.

They can use TE models and N-D encrypted models through Venice. Right. The entire round trip of the inference can be attested by an outsider. who's mildly technical. So you can do that. For normal people, like, I think our reputation of what Venice is,

is sufficient. We say that we don't do that, and there's no reason why we would. My entire history, like anyone who knows me knows that, like, probably telling the truth in this regard. But that's why we added the, like,

attestable ones so that anyone who doesn't even want to believe us. And then presumably you can A, B, it, right? You can go, like, oh, let's go through the attestable one and I get the same response. and you can go, okay, I can see that this is the same thing. To a degree, however, one of the weird things about AI models is that they are, they always are fantastic.

Yeah, yeah, yeah. It's fun. That's why you've got to ask the same question three times and merge. Oh, I see, I see. That's life. Yeah, brute force. You just got to brute force it. Awesome.

Well, look, I think we're getting close. to time. This has been amazing. Thank you very much for joining us. It's been a great show. Yeah. Eric, where can people find you, talk to you? What do you like to talk about? Where do you hang on Twitter? He said he hangs out on Twitter. Yeah, I'm on Twitter too much at Eric Borey's on Twitter. So find me there. Please try Venice. You can use it without an account and give us any feedback on the product. I mean, ultimately we're trying to build like a mass market consumer app for for unrestricted and private artificial intelligence. So yeah, thanks for having me on. Yeah. Thank you for all your takes. This has been, this has been great. Yeah, it's been

great. All right. Thank you, everyone for joining us on this episode of uneasy money. Remember what happens on chain never stays on chain. We will be back next week. Until then, do your own research before aping in. See you later. Nothing you hear on uneasy money is financial advice. We're just three builders talking about what's happening on chain and we want you to always do your own research before aping in you can find all our disclosures at unchaincrypto.com slash uneasy money