$VVV Community Call | September

Venice‘s Head of Strategy Jon, joins David for the September $VVV community call to unpack Venice reaching 250B daily tokens, the latest $VVV emissions cuts and $DIEM supply changes, accelerating burns, rising demand for private AI, Venice Minds, the upcoming data center rollout, and what comes next. --- 📣SPOTIFY PREMIUM RSS FEED | USE CODE: SPOTIFY24 https://bankless.cc/spotify-premium --- [TIMESTAMPS] 0:00 Intro 0:21 September AI Update 2:12 Token Growth Surge 8:36 Emissions and Diem Supply 16:10 Burn Mechanics Explained 23:40 OpenAI Privacy Controversy 32:00 Data Retention Warnings 35:52 Anthropic's Retention Shift 39:24 Privacy Boosts Venice 43:03 Minds and Model Combinations 52:48 Staking and Burn Questions 55:47 Data Center and Near 59:37 Lumara Film Festival --- Not financial or tax advice. See our investment disclosures here: https://www.bankless.com/disclosures

• 0:04Speaker 00

  Hey, Bankless Nation. We've got some bonus content for you here on the weekend. Every month, Bankless and Venice partner together to do a VVV community call. That's a one-hour-long episode that I host live on X with John, the head of strategy at Venice. And this call, this episode, is really meant for VVV holders. We talk about VVV economics, Venice product updates, and cover the news in the AI industry as it relates to privacy and dignity in the world of AI. And, as you might guess, there's always a lot to talk about. While typically we don't place content with our partners on the RSS feed, we've got enough requests from the community that we do so, since that is where they consume their content. So we're adding the monthly VVV call to the RSS feed on the weekend after it's recorded. If you liked the episode and you want to join the VVV Telegram,

• 0:49Speaker 00

  where we talk, learn, and educate about VVV, you can hit me up in the Bankless Discord or in my Twitter account, and I'll throw you the link. So here is the September VVV call with John from Venice. Welcome to the September VVV call. Every month, John, the head of strategy at Venice, and I go through the developments, the news, the events in the Venice and private AI space, keeping you up to speed with all things VVV and everything related to user privacy and sovereignty in the AI age. John, happy September. How are things over at Venice?

• 1:21Speaker 01

  Yeah, happy September, David. Things are good. It's crazy that it's been a month already, but yeah, things happen fast.

• 1:30Speaker 00

  I think this week, or this month specifically, has been when the social zeitgeist around AI has kind of hit new highs with attention on Anthropic, attention on OpenAI, Dario's having a meeting with Donald Trump. The big AI labs keep on... The market conversation around the big AI labs is looking at their IPO coming down the pike at the end of this year or early next year. And I think as a societal conversation, I think we're having the AI conversation kind of like more than ever, which kind of feels crazy to say, but because we've been having this AI conversation for years now. But it seems to be nonstop in terms of just like how much,

• 2:12Speaker 00

  how ready AI is just a conversation on like the Daily Podcast and the New York Times. Like, everyone is talking about AI everywhere.

• 2:21Speaker 01

  Yeah, I mean, I think as these labs push forward, as the models continue to get better, I don't see how that doesn't continue to be the case. Like, the thing just becomes more and more relevant as they become more and more capable and more people actually wake up to the capability. I'd actually argue that only a very small slice of the population has even woken up to what they can do today, much less what they're going to be able to do, you know, three months from now. So I don't see that train slowing down.

• 2:50Speaker 00

  In these VVV calls, we're going to go through just updates in the Venice ecosystem, updates to the VVV and DM tokenomics, updates to the Venice products. But we're also going to talk about some AI current events, where I want to pick John's brain on some of the things happening around the Venice space in some adjacent companies, just to kind of keep us up with the news. But I want to start with this tweet from Eric here. This came in pretty early this month, I think. September 16th was when this was tweeted, so two weeks ago. Venice now at 250 billion daily tokens. This is Eric Ford. He's one of the founders of Venice. He's retweeting himself from a tweet a while ago, when he tweeted out that Venice is at 100 billion daily tokens gone through the Venice platform. Now, Venice was processing 100 billion daily tokens on June 25th,

• 3:34Speaker 00

  250 billion daily tokens in September 16th. And if you look at this curve, I see a skateboard ramp. I see a curve going upwards. So it's not just growth, but the growth itself is growing. John, talk to me about what this chart means for you as a strategy and for Venice as a company. Why is this chart so significant?

• 3:58Speaker 01

  Yeah, so I mean, at a high level, I mean, every time Eric posts this chart, it's always one of my favorites. And I always like to sort of, you know, half facetiously, but not really, like to say it's the best chart in crypto, because this is just a chart of the fundamental business at the very base level. There's obviously a lot more that goes into Venice's business than just how many tokens are we doing a day. But at a high level, directionally, like, this is the growth. This is where everything is. And I think also, when you look at comps, when you look at the open routers, et cetera, they have, you know, similar charts. AI usage is growing across the industry. We are certainly experiencing that at Venice. I think this chart is one of the best examples of just fundamentally,

• 4:42Speaker 01

  we're just simply doing more tokens every day. And this chart, I believe, is specifically LLM tokens, too. So like, you're not even talking about image models or video models as part of this. But at a base level, it just means more users on the app, more users on the API, more overall usage. The models themselves get better or cheaper, and so that can also push a bit more token usage. But yeah, I mean, this story has been pretty consistent, certainly since the beginning of this year.

• 5:13Speaker 00

  When you say that this is the best chart in crypto, your favorite chart, is it accurate to say that like, if there was one chart to distill down the health of Venice, it would be this one? Would you say that's accurate?

• 5:25Speaker 01

  Yeah, I think at a high level, yeah, that is accurate. There's obviously things that go into this of like, you know, how much revenue is Venice actually doing? What do the burns actually look like in those derivatives? But at a high level, this drives everything. You know, if this is growing, it's very likely everything else in the business is going to continue growing.

• 5:45Speaker 00

  I didn't realize that this is only LLM models, and image and video models are actually not contained in these tokens, because those have to be pretty consumptive in terms of inference, of total inference. Do you have any sort of ballpark metric about what this chart encapsulates?

• 6:02Speaker 01

  Yeah, so I mean, what I can tell you is image and video have been growing in similar-looking charts. In terms of total amount of tokens, just because of the way those models work, they're actually, it's not going to be as many tokens. LLMs consume basically the most tokens, because especially agentic LLMs tend to be doing multiple terms, and they might go do a bunch of things and pull out a bunch of context, and then they might even work themselves, you know, on an image model or a video. Like, the LLM may actually interact with those things. So the actual amount of tokens on the video and the image are not going to be as much, but they're obviously, they're much more spendy, too. You know, you spend far more on a turn on a video model than you do on an LLM generally.

• 6:45Speaker 00

  Yeah. I want to focus on the fact that this chart is bending upwards. If you kind of linearly extrapolate the growth on daily token spend, you get to about 1 trillion tokens a day by towards the end of January. But if you lean into the fact that this chart is curving upwards, so the growth is growing, you get to 1 trillion tokens by December. So I guess the question to you, John, that I have is, when will Venice hit 1 trillion tokens a day, before or after 2027?

• 7:16Speaker 01

  Yeah, it's a good question. It's hard. You know, that would still be, you know, like a nearly 4X in that time from this point. So I don't want to be overly aggressive, and I'll say probably this happens after December. But I, myself and many of us internally at Venice, have been very wrong about the growth rates a number of times this year, with it accelerating far past what we expected. So I don't think a trillion by the end of December would be at all crazy. But if I had to guess, I think it's probably more likely like something like January or February of next year, just based on the current growth rates. But yeah, I mean, the growth rate in general has just been nuts. And a lot of our models and forecasting, we, at the beginning of this year, we assumed something like, you know, if things were going amazing,

• 7:59Speaker 01

  that we'd be doing something like 15% to 20% month-over-month growth. And in the actuality of this year, I think it's been closer to like 35% or 40%. So we just tend to underestimate our own growth.

• 8:11Speaker 00

  Yeah, yeah. One thing that is nice that we have in the crypto ecosystem, and I'm speaking as a crypto investor here, is that like, AI is very exciting. Anthropic, OpenAI is very exciting. People really want exposure to those things. It's nice to be able to see the growth of AI demand in a chart. This is like what that chart would look like, you know, slightly up and to the right with a slight crescendo of acceleration on growth. And it's also nice just to be able to have an instrument that has exposure to the economics of those tokens. I like, maybe there's other tokens out there in the crypto ecosystem that I'm not familiar with, like Tau comes to mind. But like, having something, having an instrument that has strong correlation to just AI inference demand,

• 8:55Speaker 00

  it's nice that we have that option in the crypto sphere.

• 8:59Speaker 01

  Yeah, yeah, I mean, I agree. That's definitely one of the reasons, you know, we have done the things we have done, and we think it's important. And, you know, even as you started this conversation, talking about these IPOs coming up from Anthropic and OpenAI and all these things, you know, we think it's important that people have access to something like this so that they can hopefully help participate in all of this enormous wealth that AI is creating, that, you know, many people, if they try to buy into those frontier lab IPOs, they're not going to get any exposure to until it's, you know, well north of a trillion dollars, most likely.

• 9:33Speaker 00

  Yeah, yeah. Let's talk about VVV emissions. We've talked about this for the last two VVV calls, but as we all knew, VVV emissions has gone down from 3 million a year down to 2.5 million a year. This is the first of two emission reductions. VVV issuance will be going down to 2 million a year again later in October, in a few days, actually. So on Thursday, yeah, so it's Monday on Thursday, it'll be down to 2 million a year. What do we know about the final equilibrium of VVV issuance? Do we like the 2 million number? How do we determine that that was correct? What would change your mind about that needing to either like increase or decrease further? What are the inputs here?

• 10:12Speaker 01

  Yeah, I mean, I mean, we look at a number of things as as these emissions come down. So obviously that affects basically staking yield the most.

• 10:23Speaker 01

  How, what the yield is, is both determinant of what is the emissions, but also how much VVV is actually staked. So those things kind of balance each other a little bit. So we'll be certainly watching closely what happens after this next emission reduction, how this all plays into things. I would say in terms of where we want to end up, we're still, my guess is even at 2 million a year, we're still going to probably be higher than than we need to be. You know, the goal is still a deflationary asset and that's likely going to require more emissions reduction. So this is our, you know, this is what we have announced and planned right now, but there will likely be more communication from that on us. Maybe as soon as, you know, mid October or something like that.

• 11:05Speaker 00

  Okay, so not, not official comms from Venice, but John's hunch is that 2 million might be on the high side. So the near term plan for you and Venice is to get down to the 2 million yearly issuance number on Thursday? And then see how the market reacts, see if people stake more, see if people unstake, see what people do. Let the dust settle for a little bit, collect some data and then proceed from there.

• 11:27Speaker 01

  Yep, I would say that's, that's pretty much accurate. No, no decisions have been made past the current ones that have been announced, but yeah, we're always watching this closely and you know, we, we really mean it when we say we want this asset to become deflationary and that's going to mean being very careful with emissions and the supply in general.

• 11:45Speaker 00

  Speaking of the supply, the DM target has been going up, it's now up to 40,000 is the new DM supply target. So same kind of question, what are the inputs? What's the data that you're looking at? Has anything, because the DM supply target has been going up for, I think, six weeks in a row now. What data have you got? What have you observed to kind of inform your decision making about whether this equilibrium of 40,000 is correct or not?

• 12:10Speaker 01

  Yeah, so same kind of thing. We, we observe the changes in real time. We see what impact this actually has on people staking, locking their stake to mint. Whether they burn some of their DM to get their stake back and just where, how much VVV is actually locked. And what we had been seeing is that there was definitely an acceleration on the locking as this had happened, which was kind of what we hoped to and expected to see. But that has definitely come back down a little bit in the last week, mostly driven, I think, by price action and some people wanting to unstake their VVV understandably, which required them to burn some DM. So this one actually, it's going to be, it really, really need a little more time.

• 12:50Speaker 01

  So we only just had the last target increase take effect, what, like a week or two ago? And really this one is, is a more sensitive one in some ways. I want to see, you know, some months of data of how the market actually responds to this thing long term. Where the VVV supply actually ends up now that we have this new target? And have made it easier for people to mint, mint some DM. But that's not, you know, it's not in a vacuum. You have all these other market effects going on. So I think over time, this will be less noisy and the short term it's a little bit noisy.

• 13:27Speaker 00

  Right, right, right, yeah. It was something that you said, people will want to unstake their VVV when the VVV price goes up. September has been an incredible month for VVV price performance. And so does, did you, does that imply that when VVV goes up in price, there is a DM demand? Because people who want to get liquidity at that higher VVV price have to go buy back DM off the market so they can unlock their VVV. And so that can kind of like throw a little curve ball, I think, in the stability of the VVV DM relationship when there's a bunch of VVV volatility to the upside.

• 14:01Speaker 01

  Yeah, this is, this is very much what I would call a, a feature, not a bug of the tokenomics. In that, you know, if anybody, if anybody mints and sells VVV, what they're effectively doing is betting that VVV, that the DM price at least will be lower at some point. But they'll be able to buy that DM back at a lower price and go from there. When, when, when you have things like this, when the price is going up, it basically creates an incentive for those to want to buy back their DM. Which naturally means it brings some more buy, buy demand to DM. Because generally, unless they're still holding that DM, they're going to have to buy it off the market somewhere. If they ever want to unlock their VVV.

• 14:41Speaker 01

  So yes, it creates kind of this natural, natural pendulum swing where when people want to unlock, they have to buy DM. When VVV price and DM are accelerating, it creates the, the opposite natural incentive for people to want to mint and potentially sell that into the market. And so the idea is that those kind of always that pendulum swings back and forth and you get a relative equilibrium. And I think it's interesting to point out, obviously these things are correlated, but you know, I, I, when we first announced that we were doing this DM supply, there was at least some feedback from a few community members who were concerned that we were quote unquote inflating DM. I don't, I still don't think that's really the right way to look at this. And I think the market kind of bared that out, like DM price is actually up and to the right, even as supply increased.

• 15:26Speaker 00

  Yeah, yeah, in the last like six months to a year or so, since the VVV price went from like a dollar or two up to where it is now at like $30. This has been the first real substantive like price appreciation that has also, the tail kind of has wagged the dog with impacting the VVV and DM economics. Now with any future price appreciation, at least we have like some hindsight to look back on, we have some data to look back on. But nonetheless, there's always just like a, some inherent instability in the smoothness of everything when there is like rapid price appreciation. And also the DM economy is so young as well. That like, this is really the first data set that you guys are really able to work with to make sort of like informed decisions. Is that right?

• 16:12Speaker 01

  Yeah, I mean, yeah, the DM target supply in particular had stayed the same for, you know, a year since launch. And then this was our, our first actual change to the target. And for us to see a chance of how that actually impacts the economics, how that actually impacts how many people are minting, what people are willing to pay for a DM, all these things that the market gives us good feedback on. But yeah, I do think it'll take a little time, just like the initial DM tokenomics took, you know, I would say some months to kind of settle out into an equilibrium. I think we're still going to probably need a few months of time as people adjust to all these various changing variables to settle out what, what, what I think the long term impact of this change actually is.

• 16:55Speaker 01

  So today, like I said earlier, I think it's a little bit noisy. There's so much going on that it's hard to draw too many conclusions from any of this right now. But I think within a couple of months, we'll have a better view.

• 17:06Speaker 00

  Now, on previous VVV calls, we've talked plenty about the different mechanisms for how VVV gets burned. There's an automatic burn triggered by any time a new subscription comes, somebody signs up for Venice, they, they buy a subscription. An amount of VVV is burned. There's also a 5% of API credits that people spend. So if somebody buys a hundred dollars of API credits, 5% of that goes to buy and burn VVV. One thing we haven't covered, one mechanism we haven't covered is the discretionary burn. Discretionary can be, mean a lot of things. It's a pretty, a pretty big term. What does it mean for the burn to be discretionary? Can we discretionarily burn more? Can we, can we discretionary burn less? What are the variables that go into this discretionary word?

• 17:51Speaker 01

  Yeah, so the, the variables, so it is, it's right, kind of right there in the word in that this component of the burn today is, is ultimately up to the team and the team's discretion. That generally, generally this has tracked more or less, you know, loosely. You know, not exactly, but loosely revenue growth. And this was, this was the first burn bucket that was introduced into VVV's tokenomics before we added any of the programmatic burns. So we have continued to use this lever to, as a way to effectively, you know, kind of capture some of the, some of the revenue appreciation and buy and burn that we, we have not directly put into anything programmatic yet.

• 18:36Speaker 01

  What's interesting is I think this might've been, this was either the first or second month where the programmatic burns, I think eclipsed the discretionary burn. Which is great to see, even though the discretionary burn has continued to grow. So that's honestly a great sign. Exactly what goes into it is it's, it's, it's ultimately the team looking at what we think makes sense for, based on high level revenue numbers, high level growth numbers, what we think we can put into this and how the programmatic is scaling. I think in the long run, you know, over the long term, it might make sense. Right now, we want to keep growing discretionary, I think. Over the long term, it might make sense for discretionary to kind of take a backseat to programmatic as the, as the business really continues to grow and take off, hopefully.

• 19:21Speaker 01

  If that's the case, then I think we want more and more of that to be on the programmatic side and less up to us or anything like that. But for now, it's, it's a nice lever for us to basically add on to the burn every month. And we're going to continue doing that at least for a bit.

• 19:35Speaker 00

  Is the idea here that like on a month-to-month basis or on a quarterly basis, the team opens up the Venice P and L, the Venice team has an amount of burn associated with it. You guys have to pay salaries, you guys have costs, you have data center costs that you have to pay for, but then you also have like the revenue and you also have already the programmatic burns. And then when you net everything together, there's some money left over. And out of that money left over, you discretionarily decide to use some of that money, some arbitrary amount to buy and burn VDV as you guys so choose. Is that kind of how it works?

• 20:09Speaker 01

  Yeah, at a high level, I think that's a good way to put it is it's it's some, you know, some portion of our free cash flow, after all those various things you mentioned, all the various expenses, all the inference we need to buy, the salaries we need to pay of, in addition to our programmatic burns, what else can we put back here into the burn? And it's not like we decide to be clear, like on one number and then on one day this happens. These buys, these discretionary burns, just like programmatic burns are actually happening all the time. Effectively, there's just a constant T WAP going in addition to the programmatic burns. That's just buying all the time and that adds up over the course of a month. And then then you end up with a discretionary burn number based on that.

• 20:50Speaker 00

  Right, right. So there is, it is like the excess cash, it's on like a buy, like a market smash, the buy button on VDV is a T WAP, but none, but the idea is like there's excess cash that Venice has rightly created a buffer with and ultimately at the end of a month or I don't know how long, a quarter, you guys have some like slush, slush fun built up and you're like, okay, let's T WAP that into VDV and burn it. And that's a discretionary burn.

• 21:15Speaker 01

  Yep, I think at a high level, that's, that's the right way to think about it.

• 21:19Speaker 00

  I like my things being like 90% accurate. If I'm like 90% directionally correct, I think we're doing good. One other change that I saw that happened this week is the increase of the programmatic VDV burns from pro subscriptions now burns $2. If anyone signs up for a pro sub, it now burns $2 in VDV, which is doubling from a $1 VDV. Same kind of question, what is the input into what gives you guys the room, the tolerance, the flexibility to feel that it's safe to like double the amount of VDV burn in the economics of Venice?

• 21:51Speaker 01

  Yeah, so this isn't, this is back actually in April, it looks like.

• 21:55Speaker 00

  Oh, this is in April?

• 21:56Speaker 01

  Yeah.

• 21:57Speaker 00

  Oh, this is old. Okay. Well, this is a similar kind of question though, because like this, this does kind of show that there's going to be changing VDV economics with a programmatic burns. Maybe you guys rate it up, rate it down. How do you guys make those decisions?

• 22:09Speaker 01

  Yeah. So I mean, we're, we're still, you know, this is all kind of a work in progress, but we, we try to right-size these based on it. So when we first released these programmatic burns, it was just a, a basic $1 for whether it was pro, pro plus, max. And then shortly after that, like this was probably no more than a month after we originally released these programmatic burns. We, we made this change so that we wanted to stratify it. So that there's a bit more burned per, per sub and that it's actually more in accordance with the level of sub that people are buying. Part of this too was to just, this also allows the on-chain data to be more readable and knowable by something like a Venice stats. We know when it was all a dollar, you couldn't tell what was pro, what was pro plus, what

• 22:53Speaker 01

  was max. With the different amounts, that's much easier for that on-chain data analysis to happen. And then obviously, you know, for us, it was just like, we wanted to have kind of a roughly similar proportion of the sub payment that goes to the burn. So the higher ones burn more. That was really it. This is something we will, you know, periodically reevaluate if something makes sense. And certainly if we did something like change the subscription pricing, this would probably change with it. But right now it's kind of just based, you know, based off the cost of those subs and what we think makes sense.

• 23:27Speaker 00

  When you guys make decisions like this, is one of the inputs into your guys' decision making, if we make it this number, that puts more data on chain so that the VDV community, mainly Gecko on Venice stats, but the VDV community gets more data to work with. And so it's like a selective disclosure of the internals of Venice and you guys actually like do think about when and when not to put data on chain? Is that something, a conversation that you guys have?

• 23:54Speaker 01

  Yeah, I would say that's definitely a conversation we have about what is the right data to put on chain? What's the right way to do it and how to differentiate it? So like, yeah, actually, you know, like there was actually a problem when we, when we added the, the credit purchases as burns, because they had the same $5 number as pro plus. So you'll actually, if you look on chain, you'll notice there's actually a slight difference between those. It's like a five or six cent different, but it's enough of a difference that those who are doing on-chain analytics can tell the difference between those things. So it is definitely something that we think about.

• 24:27Speaker 00

  Nice. I like the idea of this Venice intentionally leaving very explicit breadcrumbs for those who look for it can have the data that they want to find. Let's turn into some of the market commentary that happened this week. On September 8th, I believe, VDV broke through all-time highs at the same day as OpenAI had this Navier-Stokes Millennium Prize problem controversy. This was a big day. I think if anyone was holding VDV, they remember where they were when they were reading the news this morning, because they, maybe they did what I do and I looked at the charts and I'm like, oh, wow, wait a second, that's a very different price. Or they were looking at crypto Twitter and crypto Twitter was blowing up all about this anyways. But I'll kind of go through the story here. VDV started

• 25:09Speaker 00

  the day at $18 and peaked at $30, while a couple of mathematicians accused OpenAI of spying and stealing their work that they had prompted into OpenAI codex. And so the TLDR of the controversy was that OpenAI claimed they found a solution to the $1 million Millennium Prize math problem. There's a, there's a handful of these math problems and this is one of them. And this, this particular math problem involves like fluid dynamics that had like stumped mathematicians for something like 200 years. Roughly 12 hours before OpenAI claimed credit, two mathematicians made a public statement, said that they had been closing in and on solving the same problem using AI tools, including OpenAI's codex.

• 25:53Speaker 00

  Tristan, one of the mathematicians, insinuated that OpenAI may have secretly accessed his notes that he had deposited into codex because OpenAI's solution to the problem basically had the same logic processed so that Tristan was using with. And so the claim is that OpenAI just used Tristan's work as like a jumping off problem after spying on his work, using his work that he had submitted into OpenAI. OpenAI didn't explicitly deny that this was a possibility. And overall, this, I think, awoken a lot of people outside of crypto Twitter, outside of people around Venice, to the idea that the big AI labs are kind of looking over your shoulder at your homework to see what value that they

• 26:36Speaker 00

  can get from, from whatever you submit into OpenAI, Anthropic or whatever. John, give me a peek into the Venice Slack when this happened. What did you guys talk about? What did you think? What was your reactions? What was being said?

• 26:50Speaker 01

  Yeah, I mean, I think this, this was another one of those events. I mean, I feel like almost every month we end up talking about like, this one was particularly big, obviously. And there was a big reaction online about it, you know, obviously outside of just crypto Twitter, but across, you know, all AI, people were very interested in this subject. And I think it's just another one of those reminders to people that like, yeah, actually, keeping your thoughts private might actually matter, especially if you're working on new, novel, interesting things like, you know, novel mathematics solutions. Everything you put into a frontier model generally is kind of just getting hoovered up by default. And they don't always even know themselves what, like that was kind of part of the, of the conversation here is OpenAI was like, well, we didn't specifically do anything here,

• 27:35Speaker 01

  but we can't rule out that that information basically had been taken and been put into the training data of our newer models. And that's kind of always the case. Like they don't, they don't have like a whole list where they understand every single piece of information that goes into the training data. And these models, they want, you know, at a core level, they want novel information. Like that's kind of the best thing to put into the training of these things is any sort of novel, novel ideas, novel theories, like novel mathematics solutions. So it's really hard to divorce the fact that these guys had been working in codex and like likely supplying a lot of these. And then OpenAI just happens to announce that they've solved this longstanding mathematics problem. I

• 28:19Speaker 01

  just, I think it was another reminder to folks about the importance of privacy in AI. Obviously, we were very, you know, interested and excited about this from a Venice standpoint. But for us, it's kind of like another day, I know, another day at the office, another example of exactly why Venice exists, exactly to solve problems like this so that people don't have to worry that everything they're doing, the company's looking over their shoulder, that every thought they have is just getting taken and put into AI training data. And like, even when someone, you know, checks the thing on the frontier labs, it says, don't train on this. It's just like, what level of verification, if any, do they have that that's actually what's happening and that something doesn't get scrubbed and anonymized

• 29:03Speaker 01

  and just brought into the training data because it's useful? Like, we just don't know. And so you have to trust the labs at their word. And even at their word, they don't really know. So I think it's just a good reminder to everyone about the fact that when you talk to these things, that they are likely taking everything you give it.

• 29:21Speaker 00

  Yeah, I think the conversation downstream of how actually the data got into hands of OpenAI was the most interesting because OpenAI distanced themselves from like, hey, we didn't quite know that this exactly was happening, but nonetheless, we can't rule it out that our models weren't trained on the data. And when they have to distance themselves from how they can't rule it out, to me, that shows how structural it is, as in this is built into the way that the company works, as in they must steal your data in order to have an edge over their competition, who, by the way, they are in an arms race with. And so it's kind of this like toxic arms race that forces the frontier AI labs to need to be able to access as much

• 30:05Speaker 00

  data as possible. And I think this is why I think societies like heckles are being raised at some of the AI labs, because they've seen this pattern before, right? Like Zuckerberg with Facebook, you know, if you're, if you're, the product is free, maybe the product is you actually. And so people are kind of pattern recognizing with the frontier AI labs. And I think there's the frontier AI labs are losing a lot of trust in society because of stories like this.

• 30:32Speaker 01

  Yeah, I think exactly. I think it's just, it reminds so many people use these products and don't think about these problems until they get stories like this. And they either are materially affected or in a similar field or something where they actually have to worry about this, and then they think harder about it, or something else comes along, you know? Sometimes this story is a subpoena because people realize that the company has all their information and it comes up in like some various lawsuit where that information is discoverable. Or sometimes it's a story like this where, you know, some novel solution to a mathematics problem is seemingly taken from the people who had actually been working on it. And the big labs gain, you know, credit for it. And I think the labs themselves are

• 31:14Speaker 01

  even reconsidering how they put out information about solutions like this because of this exact problem and worrying about how people respond to that. But that's not necessarily a good thing either. Like, you know, to the degree that we are getting things like novel solutions in mathematics, that's something we want widely shared. That's good for society. Those are, who knows what things could be built on top of things like that. And we don't want the labs to be in an incentive where they like, don't even want to share that such a thing has occurred or not share the proof. Like, we absolutely want that. But we also want them to, you know, respect those who are actually working on these things and have, you know, novel views in their field and not just getting hoovered up. And

• 31:55Speaker 01

  people just, it's so easy to forget that, exactly like you said, like, these are built into how these companies work. This is not like a one off or a, they forgot to click the button or whatever it is.

• 32:06Speaker 00

  This wasn't a boopsies.

• 32:08Speaker 01

  No, these are, these, these are products and companies that are built off of these massive, you know, or hugely expansive data collection, because that's how you train the best model. And they have such competition with each other, with China, across the world. And these things are really not slowing down. Obviously, there were some various discussion that happened, you know, around potential slowdowns and among like Dario and Sam and some of the big labs. But the, the reality is they just, they cannot slow down. They're, they're just, everybody, everyone in the world, in my opinion, is, is caught in this like prisoner's dilemma, where if you slow down, you know, the, the result seemingly is worse. And so they're all trying to find a way to balance this. And it generally means

• 32:51Speaker 01

  they're going to use every edge they can, including every piece of data they can get their hands on.

• 32:55Speaker 00

  Yeah. You use the illustration of what if a lawsuit happened to one of these AI labs and you, as a user, your data gets caught up in that lawsuit. Well, John, one of the news this week was that OpenAI must turn over 20 million chat GPT logs, according to a judge. So this is not just an illustration, not just an anecdote. This is real, that OpenAI Inc. will have to turn over 20, 20 million anonymized, anonymized chat GPT logs. So there's something there. And a consolidated AI copyright case after it failed to convince a federal judge to throw out the judge's order about insufficient privacy concerns. And so this is happening in reality. And this was earlier this year in January, but

• 33:40Speaker 00

  the, I think the testament, the evidence is here nonetheless.

• 33:44Speaker 01

  Yeah, I mean, I, I'm sure you could do a, you know, a search or SKI about it and find 10 or 20 other such examples just like this. Like, I feel like I hear one every other week. Like, there's always something going on. Legal cases are kind of canaries in the coal mine because they will push for the, whatever data a company actually has. You know, this is one of our barometers at Venice is like, how secure actually is the data? How effectively subpoena proof is it? Like, we can only turn over what we have, you know? We get a legal request to Venice, we're going to turn over what we have. But the key is that we just simply don't have anything. There's nothing to turn over if you don't have it. But all these companies, they very much have it because it's their, their whole business model is built off having and using that data.

• 34:30Speaker 00

  I think two calls ago, John, we talked about the luxury that Venice has of not having to be in the arms race of model development. You guys don't develop models. That's not what you guys do. It's not the business that you guys have. You guys allow the rest of the world to develop models and then you just plug it into the Venice platform. And I think that, that removes you from the AI arms race in a way that must feel kind of nice because A, you don't have to have the burden of training the models. That's not a cost that you guys have to bear. And as a result of not being in the arms race, you don't have to feel compelled to retain users' data or, you know, do anything you can to get your hands on more data. So I feel like it gives you guys a much more relaxed position

• 35:14Speaker 00

  in the market where you don't have to worry about these sorts of things. That's my intuition. Does it feel that way?

• 35:20Speaker 01

  Yeah, I mean, this has very much been a strategic decision on Venice's part to not wade into this field of model training and everything that comes with it. And I think so far, it's very much been the right decision for Venice, for the product we want to offer. This is not necessarily our expertise or our skill set. Doesn't mean we could never look at it or never lean into such a thing. But generally, you know, there's just so many, so many labs spending so much money on all these models that it's better for us to just see them come out. And I, you know, I'm, I'm very happy personally that we don't have the stress of training these models because, you know, these companies, they spend so much time and effort on training these models. And at this point, it's like, if they're lucky, they get, they get to basically, you know, their model gets to shine in the sunlight

• 36:04Speaker 01

  for maybe two weeks before it's surpassed by something else. So it's just so much stress to get that thing out there as fast as possible so that you can maximize that time that you get in the sunlight. Where for us, it's just like, you know, Eric, Eric has this example that, or this analogy that I like to use, where he basically says it's for us, it's like manna raining from heaven of all of these models that just get dropped and plugged into Venice and useful for our users and great for us. And we don't care which one is the hot one right now. That's not, it's just not really our game. So we, the fact that we've kind of opted out of that, I think is a big structural advantage for, for us and how our company works compared to a lot of other AI projects.

• 36:44Speaker 00

  Manna raining from heaven is very poetic from Eric, as always. There's one more in the trad AI space, one more news event I want to talk about. This is actually from Anthropic's own privacy.clored.com website. This was updated, I think on September 8th. Anthropic released a new data retention policy. And so this is a quote from the page that I have pulled up. "To ensure we are responsibly deploying covered models, we are requiring limited data retention and review as part of our safety work. Prompts submitted to and outputs generated by covered models are retained for 30 days to support our safety work on every platform where these models are offered." So if you are on a consumer plan from Anthropic, that's like Claude, Free, Pro or Max, you don't have to worry

• 37:29Speaker 00

  because your data was already being collected and retained permanently by Anthropic. But according to Anthropic, this is for enterprises and organizations that have already set up workspaces with Anthropic with a zero data retention policy. So if you have that zero data retention agreement with Anthropic, it is now actually a 30 day data retention agreement. And Anthropic is citing safety concerns as the motivation behind this. Now, to put a negative spin on this, when I see Anthropic citing safety concerns, I kind of equate it to like the government citing like state security, or this is because of terrorism, or, you know, to protect the children, or something, something, something. But

• 38:12Speaker 00

  it's really something that the government wants. And I see that same sort of structure where like Anthropic gets to retain your data for 30 days because of safety concerns. But then if it gets to retain your data, it gets the value of that data. That's, that's my like insipid way of looking at this, but I'm sure it's probably somewhat accurate too.

• 38:30Speaker 01

  Yeah, unfortunately, I don't think that's an overly cynical view. I think it's relatively accurate. And yeah, you always have to wonder, you know, certainly with Anthropic and OpenAI of just like, what do they mean when they say these words, safety? You know, it's everything they talk about lately, safety, alignment, what does it actually mean? Safety for who? Alignment for who? Who, who, what does that mean? So it's this very vague term that they use to justify something like this. Like, we can't even, you know, they can't even ascribe to their own ZDR policies anymore because now they have to do 30 days of retention for safety. And, you know, and there may very well be like a few legitimate reasons why they have to do that. But they then turn it into this

• 39:13Speaker 01

  broader catchall that seemingly catches everybody in it and all of their data. And just so happens that they also need that data to train on. So, you know, it's, it's hard to not be slightly cynical in terms of their positioning and what they're actually doing and what they actually mean. And how concerned are they actually about the safety components of these things, especially when they are already building so many guardrails into their models to begin with about what they will actually respond to, how they will respond to things, even on things that are often innocuous. Like, again, this remains one of the leading kind of funnels for Venice is people using OpenAI, using Anthropic and getting a refusal over something that they really don't think they should be refused

• 39:55Speaker 01

  on, tends to lead them to search for alternatives and things like Venice. So for us, yeah, it's hard to not be a little skeptical what they mean. Ultimately, they're their own private company. They're going to do what they, what they want and what they feel they need to do. And I think that they are, I think that they just don't want to give up that that training data feels like more of the root of this than actually, you know, operating as safely as possible, in my opinion.

• 40:20Speaker 00

  So John, I think this is our fourth call together, the VVV call we do every single month. I think this month is definitely the hottest in terms of the rest of the AI world's conversations around privacy and concerns around like user alignment, user alignment with like the AI platform. Has that turned into any sort of change in signups or conversations with enterprises? How is that manifesting on the Venice side of the fence?

• 40:47Speaker 01

  Yeah, I would say it's seemingly just keeps adding to the growth that we are seeing, which has just really not slowed down this year. I think even internally, we keep waiting to see if we're going to hit some sort of growth plateau or you know, things are going to go sideways for a little bit. And it just really hasn't happened, to be honest. And I think, I think part of that is these conversations and people waking up to the importance of privacy in AI. And it's still, you know, you know, we live in a bubble, especially those of us who live on Twitter, et cetera. So we're seeing, you know, these loud conversations that are probably, you know, only representative of a very small percentage of the population, even being aware of them or involved in them, but they're at least becoming louder. And I think that does wake up more people. And then you just, you get another story like this every week or two and people realize that.

• 41:32Speaker 01

  So for us, we just kind of are seeing continually steady, you know, adjustments to the growth rate that seemingly keeps going up into the right. And so we're not complaining about that, but also AI itself just continues to grow and just becomes so much more capable. And some of the things you can do with these models compared to what you could do even three months ago is just so wildly different. You know, some of my own tests that I run on some of these internal models, it's just wild how much better they are, they are getting at things and the types of things they could do that you couldn't even imagine them doing three or four months ago. And then a lot of those things tend to drive a lot more revenue and token usage itself. So it's like, the better they get, the better they also are at burning tokens.

• 42:16Speaker 01

  So you see just a lot more of that. It goes back to the chart we showed earlier, like all these things coalesce and then you just have growth of these things. And I just, it's again, it's hard for me to imagine that train slowing down anytime soon because these things are just becoming so much more capable and so many more people are becoming aware of that capability. But at some point, we're going to hit like an inflection point where like a large, a real large, you know, percentage of at least the American population, but really the world's population becomes more understanding of what these things could do. Because, you know, we all feel like we understand it, but we live in this bubble where, you know, we're using things like harnesses, you know, like the Claude codes and codexes and open clause and Hermes agents and all these things that really, you know, are Grok bots

• 42:59Speaker 01

  are now views and like all these things that make these things far more accessible and useful, but it's such a small percentage of people that are actually, actually using any of those tools. So like my, my best example is always that there's like, there's such a wide swath of the population that don't realize AI is anything more than souped up Google. Like that's how they use AI mostly. They ask it questions like they would ask Google. They use chat GPT's free product. They get an answer back. They're like, wow, this is really cool. I can have a conversation with it, but it's really just question, answer, question, answer. But the gap that's been forming is those who use AI in that way, which is basically really where AI was, you know, nearly 18 months ago.

• 43:40Speaker 01

  And then the people who, especially in December of last year, January of this year, realized that there was this leap where AI stopped answering questions and started doing things. And I just think so few people are actually turned on to that yet. It's still, it's growing dramatically, but it's still such a small slice.

• 43:59Speaker 00

  I want to have a discussion with you actually about the way that the utility of all these models, where it gets expressed in like the tech stack. I think people will like the, the most normal thing for the unexposed to AI person, they'll go to OpenAI or Anthropic, just like one of the two AI apps is likely their first stop. And if they want to learn how useful an LLM is, how useful an AI agent is, that's what they would go and they would tinker around there. But Venice has this product called Minds that allows for LLMs to get combined together. And that kind of opens up the conversation of like, is the most useful place for AI at the like, the layer of an AI agent with an AI lab? Or is there a layer?

• 44:43Speaker 00

  I kind of think Venice is an abstraction layer around above all the models. Is that, is that, you guys are placing a bet on that's actually where a lot of the value of AI gets expressed because that's where agents get to talk to each other. That's, that's where LLMs get to combine in weird ways. Do you think that's a more creative layer than just seemingly something on top of the AI labs that's super powerful?

• 45:06Speaker 01

  Yeah, I think there's kind of a, there's kind of a couple of schools of thought on this question. So there's, there's kind of the view of the way the frontier labs have viewed it. And they kind of have to view things this way because of the way their companies and their products work. Their general view is that these kind of specialized, you know, agents or outputs or even things that use multiple models, that they think there's that where everything's going is it's just like one big fat, super smart model and that that's where everything will be. But that is not our view and it's not really the view that I think long-term is going to be correct. So something like Minds, which is a product that we have in beta right now, we're going

• 45:51Speaker 01

  to be pushing that a lot more as we get some, some additional features shipped here over the coming weeks. But the idea of Minds is that a creator can assemble a model or various models together in a way with an agent that produces a far better output for a specific task than the kind of quote unquote naked model would by itself.

• 46:16Speaker 01

  I personally think that's more where things are going and that being able to direct models in the way, like effectively orchestrate or like give the agent instructions about how to orchestrate in specific ways for specific tasks will generally get the average kind of call it normie user, a much better output for the thing that they're looking for. And that's kind of what the bet of something like Minds is, is that there are really smart people out there who understand these models really well. And then there's a lot of people who just want the output and they, they have no idea how to get it. They don't know how to prompt well. They don't realize how much their prompt affects things. The, the models in general are getting better, even with very bad prompts, but the, the, you can, you, there can be a wide gap still in turn.

• 46:59Speaker 01

  And I think this will persist of the outputs that are possible for those who know how to assemble things in a specific way and those who just are asking a naked model and really don't know what they're asking for. So the idea of Minds is to make that far easier, let creators kind of ship these AI experiences, these types of AI products that are assembled in specific ways to help you get a specific output. And I do think in many cases, that's going to be better for the average user, you know, they don't, and part of it is it demonstrates what these things can do. A lot of people, they just, they get to that chat GPT screen or whatever, and they don't, they just don't even know what to do. And that's why they end up using it like Google, like they don't know what to do other than ask it a question.

• 47:40Speaker 01

  They haven't, they just aren't in that world where these models can do things. So something like Minds will more demonstrate, hey, you want something that does X, you know, you want trading, a trading, an agent that's going to help you with your trading, or you want an agent that's going to help you with your accounting or spreadsheets or, you know, a PDF creator or whatever it is. And someone has assembled it in such a way to make that thing super, you know, dead easy for you, where all you have to do is put the input in and get exactly the output you want. That's, that's kind of the whole thrust and idea of Minds. And I do think that that is, there is something very powerful there that is a very different place than the, than the labs will go themselves because all of their resources are dedicated

• 48:24Speaker 01

  into just making their one model so smart. And I think we saw an example of this even in the industry over the last few weeks because we had this whole thing with like this new class of models come out from Jev, a former co-creator of ChatGPT, who effectively created rather than a LLM text-based model, effectively a classifier or decision-based model, which is something that had existed before, but not exactly in this way. And now people are combining these things with the LLMs and realizing that these constituent parts, like an LLM plus using Jev in the right places can be a lot smarter, a lot more efficient, a lot cheaper than just using the LLM on a bunch of things where it might get confused non-deterministically.

• 49:05Speaker 01

  So I do think you're going to see more and more of this type of thing where the, the real power comes from combining various types of models and kind of system instructions in various ways to get a specific output.

• 49:20Speaker 00

  The word biodiversity comes to mind. I think you, when you started talking, you said there's like two schools of thought. Maybe the way to define the schools of thought that I got from you speaking just now is you have the frontier AI labs, which they're trying to produce the one model to rule them all. They're trying to produce God, basically.

• 49:39Speaker 01

  They want digital God.

• 49:40Speaker 00

  They want, they're trying to make digital God, and it's just one model. And then there's the OpenRouter Venice side of things. Like, I remember Alex Atala from OpenRouter. He was pretty early to this. He was like, no, no, no, there's actually going to be thousands of models. There's going to be more models than we can count. And what you're talking about with Minds is like, the combinatorial value of unique, specialized models, when you sum all the parts together, it's going to be greater than the whole, and that's going to be greater than God. And these are the two races going on. Both can be true, like, they're not mutually exclusive. But like, maybe one is more valuable than the other. And I think what you're saying is like, with unique, specialized models, the combination is the valuable thing.

• 50:23Speaker 00

  And that's what we are, that's what Venice is trying to plant a flag and make a bet on with Minds.

• 50:28Speaker 01

  Yeah, I think that's a really good way to put it. It's the combination and the power of those who understand these and their ability to help deliver that output to someone who doesn't have any understanding of these things. You know, most people are not going to be good at prompting. Most people are not going to, you know, this is, this has been a longstanding problem even at Venice and how we're evolving the product is like, you know, it's great that you can come to Venice and choose from all these models, but the average user gets in and they don't know the difference between, you know, these hundreds of different models and what to choose. So we have to be smart about how we recommend them the right model, how we get them the right actual functionality they want. The average user doesn't care what model they're using. What they care about is, are they getting the intelligence that they want? And are they getting the outputs that they want?

• 51:11Speaker 01

  So the idea is like, yes, the digital God thesis that a lot of these labs are going for, I think there's obviously something important and powerful about that. But, you know, for specific use cases, these combinations could be a lot better for a lot of specific use cases. They're not going to be generalized better. They're not going to be better at everything, but they can be better in very specific areas where the user really cares about. You know, the user really cares about, do I get the output I want for the thing I'm looking for? Not, is the model capable of doing everything under the sun? That's, you know, most users are going to use only such a small fraction of what a model is capable of anyway. What they really care about is, can I get the thing I want? And can I get it relatively fast and easy?

• 51:56Speaker 01

  That's where I think something like Minds could be very powerful. That's what we are exploring.

• 52:02Speaker 00

  Is there any other company out there who kind of has a similar vision of combining models together to create combinatorial outputs? Or is Venice really the pioneer on this frontier?

• 52:14Speaker 01

  I definitely think we are in the phase of pioneering on this. I wouldn't say that there's nobody else. I mean, there are other, there are other companies that are doing things like, you know, that are effectively councils of models or ways to get models to route to each, you know, in, in specific ways so that you use this model on this specific task and a smarter model on this specific task. So all the model routing is a combination of this. But I don't think anyone's combining these things in the way that we are in like a consumer-focused package, even though there are ways.

• 52:44Speaker 00

  An intentional direction towards the combination idea.

• 52:47Speaker 01

  Yeah, I think that's definitely something hopefully we are leading on and that we are very excited about and that we think is really interesting. I would say the, the harnesses kind of steps into this world. You know, the various harnesses do this a little bit, but even those are more generally about, you know, very broad use cases, very broad generalized usage. We're interested in these very specific use cases that you otherwise cannot necessarily get as good of an output for unless you really know what you're doing.

• 53:15Speaker 00

  That's fascinating. I think that's for any VVV holder, I think that's something that to absolutely keep an eye on to see the progress of this. If you accept the spectrum between the God models from the AI labs or the biodiversity like realm of the spectrum, I think that is something that a VVV holder would be very interested in seeing is like this, the biodiverse models coming up and coming together and like making some sort of weird outputs, kind of like life. I always like the biodiversity metaphors. We need to get into the investor Q and A. John, we got a few questions from you. The first two from GW Jack and the VVV Telegram. How can Venice create additional demand to stake VVV?

• 53:55Speaker 00

  So as Venice improves the way, the ways holders can use staked VVV, how does the team think about designing these features so that they can create incremental demand to own and stake VVV?

• 54:08Speaker 01

  Yeah, thinking about this in a number of different ways. You know, obviously, over time, VVV stake has kind of been up until the right. There's a, you know, sometimes with various price action, this will go up and down. But over any long timeframe, if you zoom out enough, this has kind of been one directional since the genesis of Venice. It's definitely something I spend a lot of time thinking about. What are, what is additional utility that might make sense for the staking sphere? You know, obviously, one of the big things even right now is just the fact that you can get effectively lifetime pro access if you have 100 stake VVV. That's something a lot of people like, that functionality. And then, of course, the primary ability to mint DM and get that functionality out is something that we expect to drive over time.

• 54:49Speaker 01

  I think that in some ways, this will actually be, this question of like, what drives VVV stake will be kind of downstream of DM utility. Like, the more useful DM is, the more useful things you can do with it, the more places you can effectively resell that when you're not using it or get yield on it in various ways, the more demand there is to mint Venice, or mint DM rather, from VVV, the more likely you're going to see staking continue to rise and people locking up to do those things. There's certainly a number of things, nothing I'm necessarily ready to talk about today, but a number of things that we're considering on that front that, you know, could get added over time. And it's something we pay a lot of attention to.

• 55:31Speaker 00

  This next question comes from Malcolm from the VVV Telegram. Excuse me, no, sorry. This one comes from Iguana Arcist and also Gecko, Gecko.eth, Gecko, the creator of Venestats. Could reoccurring subscriptions become the next expansion of VVV burn? So, so I think people are asking about if reoccurring subscriptions is where that is on the docket in the conversation of VVV burn?

• 55:55Speaker 01

  Yeah, it's definitely something that, you know, obviously, we have talked about, including even on these calls before. It's definitely something we're looking at. I don't know if it will be the next one necessarily. There's actually something interesting going on. One of our focuses that I think is gonna, I think people are gonna love. We're not necessarily ready to talk too much about these details yet, but Eric has hinted at this kind of incentive and reward program that we've talked about. And I think that that will likely be one of the next big drivers of effectively additional VVV buys for that reason. So something to stay tuned for future calls. But yeah, some really cool things coming on that front. And then, you know, certainly things like renewals are something we're considering and

• 56:38Speaker 01

  could be on the docket sooner than later, but it may not be the next one.

• 56:43Speaker 00

  Another one from GW Jack, who wants to know about any timeline updates on the data center build out and any details that you can give about the Venice data center.

• 56:53Speaker 01

  Yeah, so I mean, this has been ongoing. Our, you know, we've, we've kind of, since we announced the raise in, back in July, this has been one of our, you know, big capital expenditures is building out this initial data center and then looking even at multiple potential for, for additional data centers after that. I'd have to follow up with my team on the exact timeline, but I believe that the first data center is supposed to come online, hopefully before the end of October. So maybe like late October, maybe if, if there's delays, as there can be with a lot of these things, maybe that leaks into November. But it's pretty soon. It's coming up that we're going to be able to turn one of these online. We won't necessarily know how that affects things like margins and then how that affects things like VVV burns downstream right away.

• 57:36Speaker 01

  It'll take some time of actually getting these things online, optimizing them, seeing which models we can run most efficiently and experimentation on that front. But you'll definitely be hearing more from us as that gets built out. And I think, you know, over the next couple of months.

• 57:52Speaker 00

  For somebody who wants to pay attention to that topic of conversation with Venice, the increased margins from the data center, because you guys own your own data center, you're vertically integrating, why are you doing that? Because you get better margins that way. You can like absorb some of your own costs. The, is that the part of the VVV burn where that would be expressed is the discretionary VVV burns? Is that right?

• 58:13Speaker 01

  It might be in the discretionary or it might, there might be a world where we decide that, you know, our margins are good enough that we can increase the amount on, for something like the credit purchase or the subs, like we talked earlier. So it could, it could come in, in that way. And then, yeah, probably initially it might come via discretionary if we feel it's creating enough additional free cash flow that we can route that back into VVV.

• 58:38Speaker 00

  Last question from Iguanarkist and also Cameron in the VDV chat. What role does NEAR play in Venice's infrastructure? And basically, I think that's a question I would love to know, too. It's like, I know Venice uses NEAR for accessing some very much of the top-tier privacy AI products. How do you guys use NEAR? How does it work into the Venice stack?

• 59:01Speaker 01

  Yeah, so NEAR is used right now as kind of at our top privacy kind of levels of TEE and end-to-end encrypted inference. NEAR is one of our providers for those types of models. So if you go into the Venice app and you try to look for things like TEE and end-to-end encrypted models, you'll see some of these models and some of those are offered by NEAR. So that's kind of built into our stack. This is definitely a, what I would say is a smaller part of Venice's overall inference. These are not necessarily things that everybody cares about or wants, but it's really important and critical to our offering that it is available and that those who really care about kind of that cryptographic proof

• 59:46Speaker 01

  of the privacy that they're looking for, that they have those options. So NEAR has been a great partner in allowing us to offer such a thing.

• 59:54Speaker 00

  That's like for the parts of Venice that touches the NEAR stack, that's for like the nuclear levels of privacy, right? Like the most hardcore, probably also the most expensive, because now we have to start to talk to distributed systems?

• 1:00:06Speaker 01

  Yeah, it's the highest level of privacy. Some ways, the functionality is a little more limited because of that, because there's certain things you can't get out of the context or that has to all be delivered at once. And yes, sometimes that means those are a little more expensive as a result, but it depends on the model. I think NEAR has generally done a good job of being able to deliver those at relatively competitive pricing. So sometimes they're just good models to use if you care about privacy and you want that cryptographic proof.

• 1:00:33Speaker 00

  Well, John, this has been the September VDV call. I have learned a lot. There's just so much data to talk about with Venice and also just the wider world of AI. There's one last topic to talk about before we go. I think you and I are going to hang out here in about a month in New York because of the Lumara Film Festival. Talk to me about the Lumara Film Festival. What is it? Why are you excited about it? And what are you hoping to see there?

• 1:00:57Speaker 01

  Yeah, so this is a film festival that Venice has collaborated with Moonpay to put on. It was something internally that like our team really cared about doing something like this because, you know, we talked to a lot of creators every day. Creators, especially since the advent of, you know, the better video models, they've really loved creating and creating via Venice because we don't block things that they get blocked on a lot of other platforms. And so their ability to effectively express themselves artistically is a lot less limited through Venice. And we wanted to give, you know, a demonstration of how, you know, AI video has come along and how artistic some of these things are actually getting, that this is real, you know, we're getting to the point where we can get real short films

• 1:01:40Speaker 01

  and soon enough, like real feature-length films produced with AI that people are going to be really impressed of. Obviously, a far fraction of the cost of traditional film development and film production. So yeah, I think we've gotten something crazy, like over 700 submissions to this thing, last I heard. And there's basically, you know, a process going down now of the judges of whittling those down and figuring out what's actually going to be aired at the film festival. But it's really cool to see this happening. It's really cool to see how excited people are about it. And it's going to be really fun to see that. And, you know, we've come a long way from, you know, Will Smith eating spaghetti in various terrible ways to things that actually look like they could have been produced by like, you know, a high-tier film production.

• 1:02:26Speaker 00

  Would you say the creators creating on Venice are at the absolute frontier of what it means to be film creators with AI?

• 1:02:35Speaker 01

  I think, I think yes. I mean, obviously, there's varying levels of skill and professionalism, depending on who you're talking to. But some of these high-tier creators, absolutely. You know, some of the initial previews of things I've seen, and I've only seen a small fraction of what's actually been submitted, but some of them are like incredibly impressive and aesthetics and, you know, continuity in a way that you just would not necessarily expect or not necessarily call out that it was AI unless you already knew it was. And I just think it's going to kind of democratize the ability for artists, you know, singular artists to create full films that they're really proud of and that really speak to what they're trying to express where, you know, basically, the production costs go from, you know, millions or tens

• 1:03:18Speaker 01

  or hundreds of millions of dollars to, you know, a few thousand dollars to produce something of similar quality. And I think, you know, like we always say in AI, this is the worst it will ever be. It's only going to get better from here.

• 1:03:31Speaker 00

  Yeah, well, if you are in New York in 28 days, it's the third week of October. This is when the Lumara Film Festival is. So come check it out. Get to see what the frontier of film development with AI is. And then one last CTA for the audience. You've heard me talk about the VDV Telegram. That is a Telegram that we host to just talk about VDV AI inference DM, tokenized economics. If you want an invite into that Telegram, shoot me a DM on Twitter and I'll throw you in there. John, spend another month, another exciting month with Venice. Thank you for doing what you do. It's very valuable to have Venice here on the earth. And I can't wait to see everything coming down the pike with Mines and the data center and everything else. And we will talk about all that stuff in a month.

• 1:04:14Speaker 01

  Yep, thank you, David. Talk to you more soon.

• 1:04:17Speaker 00

  Hi, everyone.