Speaker 1: I think what's really powerful about crypto is its ability
Speaker 1: to like mobilize a massive amount of resources. Right, Like,
Speaker 1: if you look at just the number of GPUs that
Speaker 1: were in the proof of workdays of ethereum set up
Speaker 1: to mine ethereum, it's like fifty times as much compute
Speaker 1: power as what was used to train chatchipt. Right, So
Speaker 1: you have like a huge amount of resources. And AI
Speaker 1: model required huge amount of resources to be trained, right,
Speaker 1: it can cost like ten million dollars of train a model.
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Speaker 2: bitgo dot com link in a description. Welcome into the
Speaker 2: Thinking Crypto Podcast. I'm your host, Tony Edward, and with
Speaker 2: me is Anna Keslawskis, who's the co founder and CEO
Speaker 2: of VANA and a great to have you on.
Speaker 1: Yeah, great to be here.
Speaker 2: Anna. You're doing some great things and very interesting things
Speaker 2: at VANNA with the convergence of AI and blockchain, and
Speaker 2: I'm really excited to speak with you because I've been
Speaker 2: noodling and thinking a lot about these things and data
Speaker 2: and how you can monetize your data and own your
Speaker 2: data in the web three world and token i should
Speaker 2: say token iss world. We're headed to. So lots of questions,
Speaker 2: but let's start with your background. Where are you from
Speaker 2: and where'd you grow up? Yeah?
Speaker 1: Yeah, so I was born in Montreal, lived there for
Speaker 1: the first five years, moved to Sweden, and then moved
Speaker 1: to Minnesota, so mostly grew up there. I yeah, half Lithuanian,
Speaker 1: hence the last name, half Filipino, and then I ended up.
Speaker 2: Moving to.
Speaker 1: Boston for school at MIT, and then yeah, left school,
Speaker 1: moved to San Francisco and have pretty much been here since.
Speaker 2: And before you started Vana, what was your professional background.
Speaker 2: I noticed you were at crypto company Celo. If I'm
Speaker 2: not mistaken, tell us a bit about that. Yeah.
Speaker 1: Yeah, So I got into crypto through, like I guess,
Speaker 1: an interest in traditional currency. So I had worked at
Speaker 1: the FED and high school. I actually had a picture
Speaker 1: of Janet Yellen, chair of a FED and in my
Speaker 1: high school bedroom, which is like totally like who does that, right,
Speaker 1: And then got to MIT and learned about these decentralized currencies.
Speaker 1: This was back in twenty fifteen, and then it was
Speaker 1: like the five person MIT Bitcoin Club. I started mining
Speaker 1: ethereum from my dorm room and just got really fascinated
Speaker 1: in kind of the world of decentralization. And I also
Speaker 1: sort of came across the world of AI through MIT
Speaker 1: as well. I was doing some research at c Sale.
Speaker 1: Basically this was when the attention is all you need paper,
Speaker 1: which is kind of the foundations of chat. GPT had
Speaker 1: come out, but people hadn't figured out how to use
Speaker 1: it to generate new texts. They had just figured out
Speaker 1: how to use it to kind of model some text
Speaker 1: and so I use some of that to automate my
Speaker 1: job sorting documents at the World Bank, and then ended
Speaker 1: up leaving school, going through I Combinator, discovering this world
Speaker 1: of like Silicon Valley, my life's purpose is not to
Speaker 1: sort the world's documents, And so I actually ended up
Speaker 1: joining Cello as an early engineer there focused on kind
Speaker 1: of building a mobile first cryptocurrency and kind of having
Speaker 1: this stable store of value that you can use from
Speaker 1: any mobile phone around the world.
Speaker 2: Now, and you're at the forefront of two major emerging
Speaker 2: tech sectors AI blockchain and crypto. What do you love
Speaker 2: the best or do you love them both equally?
Speaker 1: Yeah, I would say ideologically crypto like, and I think
Speaker 1: because with crypto, it's really it's a very foundational shift
Speaker 1: in terms of how power structures in the world could work.
Speaker 1: And it is a sort of like I would say
Speaker 1: most crypto outcomes, it's sort of the sort of it's
Speaker 1: the thing where if it works, it really really works
Speaker 1: in this massive way like bitcoiner ethereum. And I think
Speaker 1: that that is just a really interesting problem space to
Speaker 1: be in. And it feels like the frontier right of
Speaker 1: like this is really still the very beginnings of the
Speaker 1: crypto kind of decentralized industry. But I guess there's some
Speaker 1: really cool math with a so like maybe, yeah, there's
Speaker 1: like definitely some fun math of AI, and yeah, I
Speaker 1: mean today a lot of the AI work is like
Speaker 1: training AI models, so it's almost like DevOps side stuff.
Speaker 1: It depends, so yeah, hard choice. I think that you
Speaker 1: can apply a lot of the principles of crypto, which
Speaker 1: are kind of like censorship resistance, true like self sovereignty
Speaker 1: towards AI, both in kind of an open source AI
Speaker 1: context and a decentralized AI context. And yeah, then that's
Speaker 1: a lot of fun. So sort of depends if I
Speaker 1: had to choose one. Yeah, crypto's ideology and like AI's
Speaker 1: core technology. But yeah, I mean I kind of like
Speaker 1: a little bit of both, so for sure.
Speaker 2: So what do you think about the I guess what
Speaker 2: you would call symbiotic relationship with crypto, excuse me, blockchain
Speaker 2: and AI, because AI we're seeing is being used to
Speaker 2: enhance certain blockchain attributes and crypto attributes, while blockchain is
Speaker 2: being used to leave AI a bit with deep fakes
Speaker 2: and things like that, and there's different technologies being built.
Speaker 2: So do you see them working together as the future
Speaker 2: as things progress.
Speaker 1: Yeah, yeah, I think that's a good way to break
Speaker 1: it down, right, you sort of have like AI for crypto,
Speaker 1: so that means like how do you make models available
Speaker 1: on chain use them for trading and stuff like that,
Speaker 1: And then you have crypto for AI where you're using
Speaker 1: the tools of blockchain and for crypto to really advance
Speaker 1: AI progress. And that's where Vona sits, and so that's
Speaker 1: where I spend most of my time kind of thinking
Speaker 1: about the intersection. I think what's really powerful about crypto
Speaker 1: is its ability to like mobilize a massive amount of resources. Right,
Speaker 1: Like if you look at just the number of GPUs
Speaker 1: that were in the proof of workdays of ethereum set
Speaker 1: up to mine ethereum, it's like fifty times as much
Speaker 1: compute power as what was used to train CHATCHYBT. Right,
Speaker 1: So you have like a huge amount of resources, and
Speaker 1: AI models requires huge amount of resources to be trained, right.
Speaker 1: It can cost like ten million dollars of train a model.
Speaker 1: And that's just thinking about the compute like the GPU cost,
Speaker 1: which I has been talked about a lot, and not
Speaker 1: even yet thinking about the data costs. So I think
Speaker 1: that kind of using crypto incentives to allow for very
Speaker 1: large scale projects to be funded in more kind of
Speaker 1: community oriented, open source type ways is like what's most
Speaker 1: interesting to me. And what I would say too, is
Speaker 1: that's distinct from just sort of like the naive approach
Speaker 1: is basically Okay, let's put AI on the blockchain, right,
Speaker 1: but I think it's important to take a step back
Speaker 1: and be like, why do we even have a blockchain? Okay,
Speaker 1: it's because we value like censorship, resistance, like true self sovereignty.
Speaker 1: And then how do we create a system that uses
Speaker 1: some of that distributed consensus that's worked really well in blockchains,
Speaker 1: but applies it towards this AI native world that we're
Speaker 1: moving towards over the next five to ten years.
Speaker 2: So when did the idea come about to start VANA?
Speaker 2: And what's your mission?
Speaker 1: Yeah? Yeah, So VANA was first started actually back at
Speaker 1: MIT as like a research project super early on. This
Speaker 1: is like twenty eighteen of like, basically, what I had
Speaker 1: seen is the only thing that matters for AI models
Speaker 1: in the long run is data. Like data is really
Speaker 1: the new oil. It is just this immensely valuable thing.
Speaker 1: And so the question I was asking along with my
Speaker 1: co founder, who I met he was doing his godduate
Speaker 1: degree at Harvard when I was doing my undergrad at MIT,
Speaker 1: and so we had taken a class together at the
Speaker 1: MIT Media Lab. This question was like, how can you
Speaker 1: leverage the power of like many people rather than just
Speaker 1: having like centralized institutions training a single model to all
Speaker 1: collectively contribute. At the time, we were doing like data labeling,
Speaker 1: where we would have people label data from their phone
Speaker 1: super early on and earn based on that. And then
Speaker 1: I started working on Vona full time a little over
Speaker 1: like three and a half years ago. That was still
Speaker 1: quite early in the crypto AI space. In twenty twenty two,
Speaker 1: we got our patent for kind of non custodial data,
Speaker 1: which was kind of a way for you to be
Speaker 1: able to manage your data in a similar way to
Speaker 1: how you can manage your funds with a crypto wallet,
Speaker 1: so you can use your meta mask to manage your data.
Speaker 1: That's on Vana, and yeah, our mission is to kind
Speaker 1: of like shift the power of data and AI towards
Speaker 1: many people rather than just one centralized institution. The economic
Speaker 1: framing I have it is to ensure that users own
Speaker 1: their data and the value that it creates. But that's
Speaker 1: a little bit abstract, so it's really just about how
Speaker 1: do you make it so that things that get created
Speaker 1: from your personal data that you've created you kind of
Speaker 1: are attributed to you and you earn from an and
Speaker 1: also allowing AI progress to be pushed forward through that m.
Speaker 2: You know, for years, I've been thinking a lot about
Speaker 2: this and data going on the blockchain and where I
Speaker 2: can lend it out to advertisers or I could do
Speaker 2: whatever I want and monetize it. And it sounds like
Speaker 2: you guys are building that solution. And I think in
Speaker 2: the world we're headed towards where with digital identity and
Speaker 2: updates to different advertising laws, GDPR and privacy and all
Speaker 2: these things. Do you see that eventually all of us
Speaker 2: will have wallets, it'll have our digital identity, our data
Speaker 2: and so forth on a smart contract, locked and whatever
Speaker 2: it may be, and then we choose how who we
Speaker 2: want to give it to or lend it out to
Speaker 2: and so forth.
Speaker 1: Exactly. Yeah, you're just pitching VANA right back. So there
Speaker 1: you go.
Speaker 2: But let's talk a bit about the economics of that.
Speaker 2: So would it be like let's say, Apple compute, right,
Speaker 2: I don't know, and I'm spitballing here because I don't
Speaker 2: fully understand all the concepts of this yet. Would it
Speaker 2: be I give them my data and they pay me
Speaker 2: a monthly fee or a one time fee, Like, how
Speaker 2: do you think that might work?
Speaker 1: Yeah, that's a good question. And I think that it's
Speaker 1: sort of been this shift as people have become aware
Speaker 1: of the immense value that AI creates. Right, Like, if
Speaker 1: you think sort of five years back, the value proposition
Speaker 1: of hey, we're going to give you twenty dollars for
Speaker 1: your data, it's honestly not that interesting. Like to some
Speaker 1: people it might make sense, but like to the average person,
Speaker 1: it's just like not that interesting, of like why would
Speaker 1: I want that? Whereas if the value prop is like, hey,
Speaker 1: contribute your data in exchange for ownership in an AI model,
Speaker 1: right like own the part of the next CHATCHYBT, and
Speaker 1: it acts much more like equity, like a stake in
Speaker 1: an AI model, rather than a one time payment. I
Speaker 1: think that that is much more compelling for users from
Speaker 1: what we've seen at Vana, and I think that it
Speaker 1: also points towards like this economic shift that's happening where
Speaker 1: a huge amount of economic value is created through AI,
Speaker 1: right like chat GBT or open ai. They hit three
Speaker 1: point four billion dollars in revenue this summer, Right, that's
Speaker 1: just an immense amount of money that's primarily coming from
Speaker 1: intelligence that is trained on everyone's knowledge, right, everyone who's
Speaker 1: created data has created that knowledge which has helped teach
Speaker 1: that AI. And our view is that if you have
Speaker 1: the right incentive system, you can build much better AI
Speaker 1: and also align incentive. So yeah, kind of moving towards like, Okay,
Speaker 1: what does it look like tactically today, Like the Reddit
Speaker 1: data daw which is one of the projects that's built
Speaker 1: on VONAM. They aggregated like one hundred and forty thousand
Speaker 1: users data they trained an AI model from that. It's
Speaker 1: really good at sounding like a snarky redditor. I've heard
Speaker 1: some people joke it's sort of like the leading shit
Speaker 1: posting model, right, So like in terms of it's not
Speaker 1: yet feeding CHATGBT, but like at some point there will
Speaker 1: be enough data across all these different data sources that
Speaker 1: it will and every time that model is used, all
Speaker 1: of the users actually get paid out, right, And so
Speaker 1: you have kind of this nice flow where, yeah, in
Speaker 1: the future, it's like every time someone's interacting with an
Speaker 1: AI model like chat GBT or claude, the people who
Speaker 1: have contributed towards teaching it the knowledge that it has
Speaker 1: are the ones that are rewarded, and so you have
Speaker 1: this really strong kind of data flywheel in place.
Speaker 2: That's so fascinating because you know, you mentioned about the
Speaker 2: shit posting and so forth. So it made me think
Speaker 2: of you have these niche AI models, so to speak,
Speaker 2: backed by communities. They have their own personality, their own
Speaker 2: take on things. That's so fascinating to me. So is
Speaker 2: that the world we're we're headed to where it's different AI?
Speaker 2: Am I artickling it well? Is in models or bots?
Speaker 2: I don't know what to call it.
Speaker 1: Yeah, yeah, no models or boss however you want to
Speaker 1: say it. That's both of those are perfectly correct. I
Speaker 1: think that, yeah, the world we're headed towards. Yeah. I
Speaker 1: think there's sort of this debate right now of like
Speaker 1: what is truth right because we're trying to all build
Speaker 1: a single AI model and be like the AI model
Speaker 1: knows this and this is true. But that's a really
Speaker 1: hard question, right, Like, actually we have not solved that
Speaker 1: as human beings aside from an AI context. Like that
Speaker 1: is a massive debate that happened in the social media
Speaker 1: days and now we're kind of having the same thing
Speaker 1: and in the AI days and our view is just
Speaker 1: having this healthy ecosystem. I think it aligns actually a
Speaker 1: lot with the open source work that Meta is doing,
Speaker 1: just putting AI models out there and allowing many people
Speaker 1: to sort of modify them, customize them, align them towards
Speaker 1: a particular set of preferences or views depending on what
Speaker 1: they'd like. Right, so you have this balanced, healthy ecosystem
Speaker 1: rather than one single massive AI model that is controlled
Speaker 1: by just one or two companies.
Speaker 2: So and give me some rope here. Let's say with
Speaker 2: the Thinking Crypto podcast, I have a community of people.
Speaker 2: Let's say it's a total is like over two hundred
Speaker 2: thousand people that listened to me? Right? Can I build
Speaker 2: an AI model to teach newbies about crypto and my
Speaker 2: community sources that info to that that model, and then
Speaker 2: people can pay to have that model teach him crypto.
Speaker 2: Would that be the economics or business models?
Speaker 1: Totally?
Speaker 2: Hmm. That is fascinating. I have to talk to you
Speaker 2: after this, and I.
Speaker 1: Think the way to think about that is like, so,
Speaker 1: think about what AI models can't do today, and then
Speaker 1: think about the knowledge that you uniquely have or your
Speaker 1: community uniquely has beyond what's included in AI models today,
Speaker 1: and that's really where the sort of like alpha is,
Speaker 1: right of like where what information is contained in the
Speaker 1: data that most models don't have access to. So generally
Speaker 1: the Internet you can publicly scrape. That means just like
Speaker 1: all the sites you can visit without logging into them.
Speaker 1: So that's sort of already in a lot of these
Speaker 1: AI models. But there's a huge amount of information that,
Speaker 1: like you know and your community knows that not very
Speaker 1: many other people know, right, So having the ability for
Speaker 1: you to train an all powerful AI model based on
Speaker 1: that unique knowledge and have that truly belong to your community.
Speaker 2: And I guess, like you said, aside from what's scraped
Speaker 2: on the web and what's already out there, the nuance
Speaker 2: of hey, I'm from this part of the United States
Speaker 2: or this part of the world, I'm thirty something years old,
Speaker 2: I'm twenty something years old. Here's what my investment story
Speaker 2: and journey was here, here's how it impacted my life.
Speaker 2: That's something the AI can pull from it anywhere except
Speaker 2: someone sharing that story, right totally.
Speaker 1: Yeah, that's fascinating I think too. So often the data
Speaker 1: that's really valuable is this like contextual data on a person, right,
Speaker 1: So it's like, hey, like here's maybe how I got
Speaker 1: into crypto, and then also here's kind of my here
Speaker 1: are my demographics, here's my perspective on the world. Kind
Speaker 1: of being able to share like both your tweets, but
Speaker 1: also your financial history, which could include like your trades,
Speaker 1: your credit card history, and maybe even your journal entries.
Speaker 1: And there's really no single tech company that has access
Speaker 1: to all of that, right, they might have access to
Speaker 1: a small slice of it, but if you want to
Speaker 1: get all of those different data sources across a given user,
Speaker 1: you really need to work with the user directly. And
Speaker 1: so that's kind of the power of using decentralization. We
Speaker 1: can actually leverage data regulation like GDPR which you had
Speaker 1: mentioned CCPA in California to ensure that a user can
Speaker 1: get that data out of where whichever platform they've kind
Speaker 1: of posted it, and then actually contribute it to these
Speaker 1: powerful at models.
Speaker 2: Now let's go back to the redded data DOWT question.
Speaker 2: I guess first part is why DOOS did decentralize a
Speaker 2: toon of autonomous organizations? And is that the only way
Speaker 2: to do this where you can have the crowd sourcing
Speaker 2: and to have it in a decentralized way.
Speaker 1: Yeah, that's a good question, and I think like doos
Speaker 1: have a pretty mixed reputation in crypto, right, Like, if
Speaker 1: we look over the past maybe five years of dows,
Speaker 1: I think there are a few breakout success stories, but
Speaker 1: as a whole, like, it's actually very hard to get
Speaker 1: a decentralized group of people to govern something, and so
Speaker 1: it can be a challenge. What we've seen with data
Speaker 1: dows is that often you have a large number of
Speaker 1: people come in initially and connect their data, and then
Speaker 1: a smaller percent of them stay and help manage that
Speaker 1: data and sort of like decide, hey, this company wants
Speaker 1: to buy access to it, or this machine learning engineer
Speaker 1: wants to train an AI model on it. Should we
Speaker 1: grant them access? What are fair economics, et cetera. And
Speaker 1: so by default all that is decided by the TAO,
Speaker 1: because it's very hard to price data, right like, how
Speaker 1: should you decide whether? So we basically we see the
Speaker 1: doos as making that decision ultimately. But in the future
Speaker 1: there are ways too where you don't need to create
Speaker 1: a DOO and there can just be kind of stable
Speaker 1: coin based payments for a particular data pool. And this
Speaker 1: is a little bit technical, but it's kind of the
Speaker 1: distinction between what we call a data liquidity pool, so
Speaker 1: a DLP on vana. Most of them are data DAOs,
Speaker 1: but they actually don't have to be a DOO if
Speaker 1: they don't want a token affiliated with their particular data set.
Speaker 1: So yeah, we have this slightly more general framing, which
Speaker 1: is a data liquidity pool, which is just about kind
Speaker 1: of building up that data liquidity, getting many people's data
Speaker 1: all pooled together, because your data is only valuable once
Speaker 1: you combine it with others. Right, you can't train an
Speaker 1: AI model on just one person's data. You really want
Speaker 1: a lot of data. And so that's that's a good question,
Speaker 1: and there is some nuance there.
Speaker 2: So the Reddit data dow, how are they monetizing that data,
Speaker 2: what are they selling it for? And what's the currency
Speaker 2: being used?
Speaker 1: Yeah, yeah, so they created their own currency which is
Speaker 1: used as a governance. It's basically the DOW kind of
Speaker 1: token that's used to govern the Reddit data set, and
Speaker 1: its primary purpose is governance, basically voting on hey, should
Speaker 1: we let new people come in and contribute their data
Speaker 1: and reward them, and also should we let someone access
Speaker 1: our data? So one of the folks they granted access
Speaker 1: to the data is actually like ex Google Brain machine
Speaker 1: learning engineer who trained an AI model that like ship
Speaker 1: posting model that I had mentioned, so that it's in
Speaker 1: the phase of like research preview right now and then
Speaker 1: they're rolling it out and to interact with that, you'll
Speaker 1: have to actually sort of like hold a minimum amount
Speaker 1: of that Reddit data that governance token. They're also sort
Speaker 1: of finalizing a data sale in the works right now,
Speaker 1: and so the way I'm actually not sure exactly how
Speaker 1: they structured those economics. It could be that they're having
Speaker 1: the data buyer burn the governance token, or it could
Speaker 1: be that they're distributing revenue to the governance tokenholders. So
Speaker 1: it could be either of those two, and it depends
Speaker 1: on what the two decided.
Speaker 2: A couple questions of follow questions on that. Do you
Speaker 2: have to create your own token or can you use
Speaker 2: an existing token or a stable coin as you mentioned before, Yeah,
Speaker 2: because if you get into the creation of a token,
Speaker 2: you know, could you have securities issues, security laws issues,
Speaker 2: I should say.
Speaker 1: Yeah, yeah, you can use a stable coin, you can
Speaker 1: use a pre existing token, and so you can kind
Speaker 1: of collectivize however you'd like. And I'll mention too the
Speaker 1: Reddit data now actually only intended for their token to
Speaker 1: be used for kind of like governance and voting, and
Speaker 1: so that was their intention from the beginning. I think
Speaker 1: there was some trading activity that came up on it,
Speaker 1: but it's primarily used for voting, so I think in
Speaker 1: the long run we'll see sort of some activities like that.
Speaker 1: And yeah, depending on kind of your risk profile and
Speaker 1: how you structure it, right, each daya now has their
Speaker 1: own preferences in terms of what they're looking for from
Speaker 1: a governance perspective, from a revenue distribution perspective, and so
Speaker 1: they can structure their own token. It's similar to like
Speaker 1: an ERC twenty token, right, so you can kind of
Speaker 1: modify it however you'd like.
Speaker 2: So, are you primarily using eth to build these dows
Speaker 2: or are you using all the block chains as well?
Speaker 1: Yeah, that's a good question. So we actually started as
Speaker 1: basically we were like, okay, let's start and let's deploy
Speaker 1: this too like ETH or an L two and sort
Speaker 1: of see what happens. What we realize is that like
Speaker 1: a lot of the optimizations that people have made for
Speaker 1: primarily financial transactions don't work that well in a vana context,
Speaker 1: which are data transactions, right, So on VONA, basically all
Speaker 1: the transactions are like permissioning out data or interacting with
Speaker 1: a data token, or interacting with an AI model, and
Speaker 1: that's a very different set of constraints. You care a
Speaker 1: lot about low fees, you care a lot about pretty
Speaker 1: fast block times, and then you also have data regulation constraints.
Speaker 1: So actually, if we were to run our own kind
Speaker 1: of like sequencer as an L two, that would be
Speaker 1: regulated as a data processor. And so we realize like,
Speaker 1: actually we need a standalone set of VANA nodes that
Speaker 1: are basically operating this kind of like data transaction network.
Speaker 1: And so yeah, VONA is it is a layer one
Speaker 1: blockchain that's designed for private data. It's fully em compatible,
Speaker 1: and I see a world in the future where everything
Speaker 1: is kind of cross chain, right, so you have all
Speaker 1: these different bridges and it's really easy to kind of
Speaker 1: like be interoperable across different chains. But yeah, for data
Speaker 1: regulation reasons, we actually had to land on a standalone
Speaker 1: layer one for private data. And the tokenomics look pretty
Speaker 1: similar to ethereum, where essentially there is kind of like
Speaker 1: a native gas fee that's paid for the different data transactions,
Speaker 1: and then the top sixteen data dows actually earn a
Speaker 1: share of block rewards because they're kind of onboarding data
Speaker 1: into the network, and so similar to I'm not sure
Speaker 1: how familiar you are with bit tensors kind of model.
Speaker 1: They emit rewards to the top thirty two. I think
Speaker 1: maybe now they're up to like forty six or something. Subnets. Yeah,
Speaker 1: the Vona protocol admits rewards to the top sixteen data dows,
Speaker 1: and that's also kind of governable as more top data
Speaker 1: dows emerge.
Speaker 2: That's fascinating. So let's say I create that now for
Speaker 2: my podcast? Right? Can I use the Vona token as
Speaker 2: as as the currency sort of thing.
Speaker 1: Yeah, Yeah, you can choose to use, yeah, the Vana
Speaker 1: native token, which is similarly just sort of like the
Speaker 1: gas token on main net, which isn't yet launched that
Speaker 1: will launch later this year. You also could use like
Speaker 1: a stable coin, or you can use, yeah, an e
Speaker 1: ARC twenty like token, depending on what you're looking for.
Speaker 2: M You and I have a lot to talk about offline,
Speaker 2: but the steam engine is going here. Question why would
Speaker 2: someone want and I know this is a question people
Speaker 2: listening and watching, what would have Why would someone want
Speaker 2: to buy that AI model from Reddit? And and I
Speaker 2: understand certain use cases that you know, certain companies may
Speaker 2: create AI models to do different things, but why would
Speaker 2: they want to buy that Reddit AI model?
Speaker 1: Yeah, so the Reddit one, I believe that the Reddit
Speaker 1: Data NOWS model is being used. Okay, it's a company
Speaker 1: that's kind of building like crypto Reddit, And what they're
Speaker 1: doing is they're allowing you to simulate, Hey, if you
Speaker 1: post this comment or you post this post, what are
Speaker 1: how are people going to respond?
Speaker 2: Right?
Speaker 1: What are people going to say? And so they're actually
Speaker 1: using the AI model to simulate that, so that before
Speaker 1: you post something, you're like, hey, this is probably how
Speaker 1: people are going to react to it. And so yeah,
Speaker 1: for that particular case, that's how one of the models
Speaker 1: is used. There's another model that I think they're creating,
Speaker 1: which is predicting how many up votes the posts would get, right,
Speaker 1: and then you can kind of optimize for that. So
Speaker 1: I think there are many different applications beyond just sort
Speaker 1: of your standard chatbot. Like it it's not that useful
Speaker 1: to chat with just a chatbot. That's good as you're posting, right, Like,
Speaker 1: I think we're on the same page there, and so
Speaker 1: sometimes it's these like industry specific niches where people are
Speaker 1: finding a lot of value for the AI model.
Speaker 2: I would love to test that out because I want
Speaker 2: to drive more engagement. I want my tweets, my posts,
Speaker 2: whatever to drive the I want to get more of vote.
Speaker 2: It's on Reddit, right, So that makes sense? Okay?
Speaker 1: Yeah? Yeah, I mean I think too, Like if you
Speaker 1: look if you use like chatubt and claude and you're like, hey,
Speaker 1: like modify this tweet or whatever, and it adds hashtags,
Speaker 1: and it just sounds so AI generated, right, and you're like, no,
Speaker 1: like that's not what it should be.
Speaker 2: Right.
Speaker 1: So you need a trained from kind of higher quality data, right,
Speaker 1: like actual real data, like the organic good stuff that
Speaker 1: you can get directly from people, and then you can
Speaker 1: create better AI models that can serve that purpose.
Speaker 2: So are you working with any let's put it this way,
Speaker 2: real world companies like the Apples or McDonald's or whoever, right,
Speaker 2: that are trying to build these models.
Speaker 1: Yeah. So there's a Fortune five hundred fashion company that
Speaker 1: we've worked with really closely basically doing kind of like
Speaker 1: consumer insights type prediction. So their model is focused on
Speaker 1: how do you predict what's going to be in style
Speaker 1: in three to six months using like cultural data about
Speaker 1: fashion and music, And so I think those sorts of
Speaker 1: use cases are really powerful. The ones that I'm most
Speaker 1: excited about that I think are going to grow really
Speaker 1: exponentially are these new kinds of entrepreneurs Like the guy
Speaker 1: who started the LinkedIn Data Out. He actually just raised
Speaker 1: money for the LinkedIn Data Out and he used to
Speaker 1: sell data at LinkedIn and really deeply understands the value
Speaker 1: of that data set. He's selling it to sort of
Speaker 1: like Web three native LinkedIn type competitor. And so there
Speaker 1: are all these different use cases of like, hey, what
Speaker 1: if as a business you could just like access all
Speaker 1: the data that today is stuck in these web two
Speaker 1: wallt gardens. But there are also kind of more traditional
Speaker 1: companies like the Fortune five hundred fashion company I mentioned,
Speaker 1: who are basically just trying to get data that they
Speaker 1: can't get access to otherwise and train AI models on that.
Speaker 2: What are the laws around that if any or is
Speaker 2: this just a business model you can create because I
Speaker 2: know that you know you have the data privacy stuff,
Speaker 2: but then it's going through the AI. So how do
Speaker 2: you once I guess sorry collecting my thoughts first, how
Speaker 2: do you secure the data that's being put into the
Speaker 2: AI model. And two, are there any laws that govern
Speaker 2: these things?
Speaker 1: Yeah, so there are a lot of laws that cover
Speaker 1: and data privacy, which generally I think are actually very
Speaker 1: strong laws. I think a lot of people don't realize
Speaker 1: just how powerful the laws are and actually how much
Speaker 1: ownership they have over their data. Right, So when you
Speaker 1: use a platform, it's sort of like when you park
Speaker 1: your car in a parking lot, Like the parking lot
Speaker 1: doesn't own your car. It's still definitely your car, and
Speaker 1: they can't just like randomly take parts out of your
Speaker 1: car like it is yours, you get it back, you
Speaker 1: can do what you want with it. And so data
Speaker 1: regulation like GDPR and CCPA and just basically the laws
Speaker 1: view on data is that you fully own your data.
Speaker 1: When you use a platform and you check that like
Speaker 1: terms of service box, you're granting a very permissive license
Speaker 1: to them to be able to use your data for
Speaker 1: different things. They generally cannot train AI models on your
Speaker 1: d anonymized data. They can train it on your anonymized data,
Speaker 1: so they can like, yeah, remove personal identifiers, but that
Speaker 1: ends up being kind of not as good of a
Speaker 1: data set to train on In Avana context because we
Speaker 1: work with what we call non custodial data, which is
Speaker 1: where like a user has full control over their data,
Speaker 1: they can grant whatever legal rights they would want for
Speaker 1: someone to do something to their data. You're actually able
Speaker 1: to give ai researchers access to de anonymized data. But
Speaker 1: then you have the question of, like, well, how do
Speaker 1: you ensure privacy, right, because you want to make sure
Speaker 1: that if you're contributing data. And it's both from the
Speaker 1: perspective of like I, as ANNA, don't want my data
Speaker 1: to leak, and it's also from the perspective of with economics,
Speaker 1: like okay, if something is public, it's really hard to
Speaker 1: charge someone money for it, right, So it's both about
Speaker 1: protecting one's privacy but also about protecting one's economics. Right.
Speaker 1: So if I in the future have like ai ana,
Speaker 1: who can kind of autonomously act on my behalf or revenue,
Speaker 1: et cetera, like, I want to fully own that. I
Speaker 1: don't want it to just be like open source and
Speaker 1: randomly out there, like I want the full rights to it.
Speaker 1: So that's why that kind of privacy piece is important.
Speaker 1: We have the distinction between renting data and selling data.
Speaker 1: So by default, you are just renting your data where
Speaker 1: you're allowing an AI model to be trained in a
Speaker 1: privacy preserving way on your data, but you're never letting
Speaker 1: the data leave the system, so your data sort of
Speaker 1: stays in this secure compute environment. And that's actually one
Speaker 1: of my old professors who's been really helpful and kind
Speaker 1: of the monitoring invented like privacy preserving machine learning ten
Speaker 1: years ago at MIT, and so a lot of his
Speaker 1: kind of work had he was really pioneering a lot
Speaker 1: of this. But now you're able to rely on like
Speaker 1: a secure computing environment to train these models and protect
Speaker 1: the model ways, so that you're protecting the economics and
Speaker 1: the privacy of the data.
Speaker 2: It's fascinating. I feel like I can talk about this
Speaker 2: for hours, but I know we got to talk about
Speaker 2: some other stuff. You guys also got some funding. I
Speaker 2: believe Paradigm is one of your investors. Tell us a
Speaker 2: bit about that.
Speaker 1: Yeah, yeah, So poly Chain let our initial round and
Speaker 1: then Paradigm led our most recent rounds. So we've raised
Speaker 1: about twenty million dollars a day. I think that standing
Speaker 1: up like a new network for user own private data
Speaker 1: is like is quite an undertaking, like we've been building
Speaker 1: for years, and I think that often, Yeah, you need
Speaker 1: to basically be willing to make a pretty big investment
Speaker 1: from a technical perspective in order to deliver like an
Speaker 1: end user product that's actually usable, right, Like, Okay, one
Speaker 1: of our guide and goals is how do you get
Speaker 1: one hundred million people to all contribute their data and
Speaker 1: train like a user owned AI foundation model that actually
Speaker 1: beats something like GPT six So a future version of
Speaker 1: chat GPT in performance. And to get one hundred million
Speaker 1: people to contribute their data is like quite an underdate.
Speaker 1: We're like a little over one percent of the way there, right,
Speaker 1: But it takes time, right, and it also takes like
Speaker 1: empowering like builders to create really good experiences that bring
Speaker 1: users on board. But yeah, we chose to work with
Speaker 1: Paradigm because honestly, they're really close to the MIT folks,
Speaker 1: like the original MIT Bitcoin Club. I think they've invested
Speaker 1: in like sixty percent of our companies and so yeah,
Speaker 1: the just sort of like a lot of MIT overlap,
Speaker 1: and I think they really see the long term potential
Speaker 1: of what crypto can be, right, Like they're thinking on
Speaker 1: a ten twenty year time horizon, which I think is
Speaker 1: so important when building in this industry that just has
Speaker 1: these massive cycles that kind of go up and down,
Speaker 1: Like you really need to keep that sort of long
Speaker 1: term view of like, hey, like what should the Internet
Speaker 1: look like in ten years? Like what should data look
Speaker 1: like in ten years? And what role will kind of
Speaker 1: on a play in that in order to build like
Speaker 1: a long lasting protocol that truly shifts the way that
Speaker 1: people think about their data.
Speaker 2: Now a quick follow a question on that, what do
Speaker 2: you guys charge for companies and people to build these things?
Speaker 2: And on the flip side, what are you seeing people
Speaker 2: selling their AI model data for?
Speaker 1: Yeah? Yeah, So I'll give one example of like the
Speaker 1: Fortune five hundred fashion company that I mentioned, So they're
Speaker 1: paying users and so you can structure the economics however
Speaker 1: you like, right, So you could have it be like
Speaker 1: equity and a stake in an AI model, which I
Speaker 1: think in the long run is going to be the
Speaker 1: majority of how people interact. Some people choose to use
Speaker 1: more of like a stable coin payment today. So five
Speaker 1: hundred fashion company, I mean they don't touch tokens like
Speaker 1: that's you know, like they have a hat a log
Speaker 1: of dogin for this, so they have, Yeah, they pay
Speaker 1: people twenty five dollars for their fashion data, their Spotify data,
Speaker 1: and their demographic data. And that's pretty high, right, Like,
Speaker 1: if you were just selling your Spotify data, it might
Speaker 1: be worth like ten cents. But because you're combining the
Speaker 1: data from across different sources, they're able to use that
Speaker 1: for forecasting of like, hey, based on the music you
Speaker 1: listen to, what's going to be in style in like
Speaker 1: three to six months, and that's super super powerful. So
Speaker 1: that's kind of one example from just like a pure
Speaker 1: data yeah sales view. In terms of Vana's economics, so
Speaker 1: we charge a small like the protocol charges a small
Speaker 1: data transaction fee. It's like less than a cent on
Speaker 1: test net right now. It's similar to Ethereum, where basically
Speaker 1: as volume goes up, like fees come up, but we've
Speaker 1: architected it to try to keep fees really as low
Speaker 1: as possible. And then by default, Vana is the currency
Speaker 1: for data sales, right, So if you are actually buying
Speaker 1: data on if you're buying a quarter million dollars a
Speaker 1: million dollars of data on the platform, you are buying
Speaker 1: the von a native token and then distributing that to
Speaker 1: the particular data daut that you want to offer data to,
Speaker 1: whether that's in the form of buying their token burning it.
Speaker 1: And the reason why there are all these different tokens
Speaker 1: in the system is that you really need quite flexible
Speaker 1: economics to work with data, right Like, if I were
Speaker 1: to ask you, like, hey, how much is your Amazon
Speaker 1: purchase data worth relative to your Spotify data? Like that,
Speaker 1: that's possibly an unsolvable math question, right like, And so
Speaker 1: we've basically pushed it to Hey, you have these data
Speaker 1: set specific tokens which allow for both data DAWs and
Speaker 1: flexible economics, right of, like, Hey, each data dou can
Speaker 1: choose how much they want to charge for their day,
Speaker 1: how much they want to yeah, what they would say
Speaker 1: yes to, what they would say no to, And so
Speaker 1: you get a lot of that flexibility.
Speaker 2: And I apologize, I'm thinking a lot about the things
Speaker 2: you're saying and how I can leverage it. So I
Speaker 2: guess I'm thinking about let's say I set up that model.
Speaker 2: I can pay my viewers and listeners to contribute to
Speaker 2: the data set, and they can also be stakeholders. So
Speaker 2: the revenue that comes in when we sell the or
Speaker 2: lend the model out, whatever it is they get a
Speaker 2: cut of the.
Speaker 1: Revenue exactly, Yeah.
Speaker 2: And how are the payments? Is it done automatically like
Speaker 2: if you use stable coins.
Speaker 1: Yeah, yeah, it's sort of all in smart contracts. So
Speaker 1: anything you can express in solidity. We've seen some people
Speaker 1: use viper, which is just like the Python way to
Speaker 1: write smart contracts, because some of them are more like
Speaker 1: AI people. And so yeah, anything you can express in solidity,
Speaker 1: you can kind of used to distribute payments and kind
Speaker 1: of reward the contributors who kind of put their knowledge
Speaker 1: towards making the AI model more capable. And then one
Speaker 1: thing I'll mention too is one important concept within VANA
Speaker 1: is proof of contribution, just basically measuring, Hey, for a
Speaker 1: given piece of knowledge or data that comes in to
Speaker 1: help an AI model, how much is that actually helping, right,
Speaker 1: And so you can basically measure, hey, how much new
Speaker 1: information new knowledge is contained in this data point and
Speaker 1: then reward them proportionally to that. And that is implemented
Speaker 1: at the data set and AI model level. So there's
Speaker 1: a different one for Reddit, for LinkedIn, for your AI model.
Speaker 1: But yeah, I mean I think also the best way
Speaker 1: to understand a system is yet to think about how
Speaker 1: you would fit into it. So I think these are
Speaker 1: great questions to be asking.
Speaker 2: And what does the interface look like for people to
Speaker 2: submit that data? Is it like a gouge interface app?
Speaker 2: What does that look like?
Speaker 1: Yeah, so Vona as a network is like as a
Speaker 1: it's a period of peer protocol similar to ethereum. Right,
Speaker 1: So like many like you can interface with a network
Speaker 1: through like a command line terminal that's like the raw
Speaker 1: way to interface, so that most people don't do that, right,
Speaker 1: Most people use like an interface. So you can add
Speaker 1: it to MetaMask and then you can basically see your
Speaker 1: kind of like data financial profile in MetaMask, and then
Speaker 1: there are a couple of different UIs that people have
Speaker 1: built out, so like the Reddit data down they built
Speaker 1: their own UI so you can contribute your Reddit data.
Speaker 1: There's another project that has this automatic data scraper where
Speaker 1: they sort of go and get your data automatically for you,
Speaker 1: and then they're working on kind of adding away so
Speaker 1: that you can contribute that back to the Vona network.
Speaker 1: So yeah, you can use like a web It just
Speaker 1: looks like a website like an app, which is sort
Speaker 1: of like traditional DApp, or you can kind of like
Speaker 1: open things up and get into it if you want
Speaker 1: to touch the code. But yeah, the core network is
Speaker 1: a peer to peer network, so there are lots of
Speaker 1: different UIs on top of it.
Speaker 2: Interesting, what's on your twenty twenty four rown map or
Speaker 2: the remainder of twenty twenty four.
Speaker 1: Yeah, so a lot of our focus right now is
Speaker 1: really supporting the dataaps that are kind of being built
Speaker 1: on Vana and then getting ready for main net launch
Speaker 1: and so kind of going through the audit process, going
Speaker 1: through kind of like load testing and all sorts of
Speaker 1: other kind of like preparations from that front, and then yeah,
Speaker 1: so main net will be stood up by a set
Speaker 1: of independent validators who are all kind of like choosing
Speaker 1: to run the Vona network from scratch, so stewarding that
Speaker 1: launch process. It is. Yeah, it's sort of basically standing
Speaker 1: up the new blockchain from scratch and supporting these like
Speaker 1: data now applications on top of it.
Speaker 2: Well, you and are going to talk offline about what
Speaker 2: I want to do, but I want to get your
Speaker 2: take on with the limited time we have on the
Speaker 2: aggress of the crypto market. Obviously, you started out in
Speaker 2: blockchaining crypto working at Celo. What are your thoughts on
Speaker 2: how the market is matured. We got ETFs, now crypto
Speaker 2: regulations seemed like they're on the cusps, we're getting past.
Speaker 2: You got politicians you know, supporting crypto. What are your
Speaker 2: thoughts on the growth?
Speaker 1: Yeah, I mean I think that broadly, we've seen this
Speaker 1: shift where bitcoin is pretty much like mainstream accepted as
Speaker 1: like a store of value, right, And if we look
Speaker 1: back nine or ten years, like that's kind of crazy
Speaker 1: that people are now just accepting like, yeah, it's bitcoin,
Speaker 1: Like it's like a reasonable store of value that has
Speaker 1: like predictable kind of like inflation and other properties people
Speaker 1: value in terms of like having a store of value.
Speaker 1: So I think that that is an oudible shift. It's
Speaker 1: really easy to forget, right when you go through these
Speaker 1: kind of like bull and barcycles, you're like, oh, like
Speaker 1: bitcoin is it? But it's like okay, Like if we
Speaker 1: look over a long time horizon, like this is doing amazing.
Speaker 1: I think it's also led to some really cool shifts
Speaker 1: around how people think about kind of like true ownership
Speaker 1: and control. I think crypto has yet to deliver on
Speaker 1: this promise of like owning your data, right. That is,
Speaker 1: like I think the promise that actually got a lot
Speaker 1: of people into crypto super early on, but we're not
Speaker 1: there yet, Like Vana is working to make sure that
Speaker 1: we get there over a several year time horizon, but
Speaker 1: I think we're getting closer and closer to mainstream adoption.
Speaker 1: I think that there are a lot of kind of
Speaker 1: emergent use cases that we're seeing related to yeah, kind
Speaker 1: of moving beyond just like stable coins or like stable
Speaker 1: means of payment, which are more like decentralized applications, right,
Speaker 1: Like I think Farcaster is an example of like they're
Speaker 1: basically using sovereignty like that that's the core proposition they have,
Speaker 1: it's not financial, and I think those are quite cool.
Speaker 1: So yeah, generally super optimistic, like we're still so early
Speaker 1: in building so much of this out. And I think
Speaker 1: another good sort of Yeah, something I like to track
Speaker 1: is just like where are smart people spending their time?
Speaker 1: And I think a lot of my like smart friends
Speaker 1: are spending time in decentralized AI, and that makes me think, like, Okay,
Speaker 1: there's a lot of growth here. There are a lot
Speaker 1: of like there's a lot of talent basically going towards
Speaker 1: solving some of these key problems. And so that's another
Speaker 1: kind of internal metric at track and there's some cool
Speaker 1: stuff happening in like DSi as well. I think more
Speaker 1: smart people are starting to spend time there figuring out Yeah,
Speaker 1: some of the decentralized science questions, do you.
Speaker 2: Think AI can help defind to be more secure and
Speaker 2: we avoid some of these exploits and things we've had
Speaker 2: over the years. And I know one version one point
Speaker 2: oh of anything is going to have flaws, right, But
Speaker 2: I haven't really touched a lot of DeFi because of
Speaker 2: all these hacks and all these exploits. But do you
Speaker 2: think AI could help solve some of those problems?
Speaker 1: Yeah, I think that there's a lot Like if you
Speaker 1: think about AI is almost giving you knowledge on demand.
Speaker 1: There's so many different ways you can use that, right,
Speaker 1: like having knowledge on demand to like do engineering work
Speaker 1: or do code auditing work. It seems plausible to say, yes,
Speaker 1: that could help reduce exploits, Like I would say, I
Speaker 1: haven't spent enough time thinking about it because there are
Speaker 1: a couple of different variables, right. You also could have
Speaker 1: more bad actors on the AI side, so I like
Speaker 1: would have to kind of like reason through that a
Speaker 1: little bit more, and I haven't thought about it in depth.
Speaker 1: But yeah, generally, when you have like knowledge on demand,
Speaker 1: like it really changes the economics of a system and
Speaker 1: kind of what's possible where value accruise. We think it
Speaker 1: accrues in data and how markets play out.
Speaker 2: Yeah, yeah, as you're saying now, I'm thinking of AI
Speaker 2: wars AI models fighting each other, trying to one's trying
Speaker 2: to police, one trying to attack, and you know, hack things.
Speaker 1: Yeah, it's a crazy world out there, you know, Like
Speaker 1: I think a lot of that's already going on and
Speaker 1: we don't even we might not even realize it. But yeah,
Speaker 1: I want like an AI and a agent who kind
Speaker 1: of like defends my interests and make sure but all.
Speaker 2: As well for sure, And I know I kept you
Speaker 2: a bit over time, so I got some wrap up
Speaker 2: questions there for you. First, If you could create your
Speaker 2: own metaverse, what would the theme be?
Speaker 1: Oh, I see myself as kind of already living in
Speaker 1: this like digital world primarily, so it's it's themed around
Speaker 1: data sovereignty and data ownership and kind of like some
Speaker 1: research related topics.
Speaker 2: Yeah, yeah, that makes sense. I figured it was going
Speaker 2: to be like something related to what you're already doing.
Speaker 2: Rapid fire questions. Favorite food.
Speaker 1: Probably green tea. I guess that's a drink, but it's
Speaker 1: just consistent.
Speaker 2: Yeah, favorite musician or band.
Speaker 1: I actually didn't start listening to music until a few
Speaker 1: years ago, so I probably like like a major laser
Speaker 1: workout playlist that I just work out to.
Speaker 2: Yeah, favorite movie, let's.
Speaker 1: See, you know what. I actually I only watch movies
Speaker 1: on airplanes, so you know, I actually, okay, I'm actually
Speaker 1: a huge fan of mister Beast. I think he's a
Speaker 1: really interesting entrepreneur, So he's in the video category. I
Speaker 1: would say, like mister Beast and sort of the arc
Speaker 1: that he's pulled off from an attention perspective.
Speaker 2: Yeah, and favorite book.
Speaker 1: Favorite book, Actually, I love reading. I think David Sedaris
Speaker 1: is this amazing writer. He's like a humor comedy typewriter,
Speaker 1: but he's a really good storyteller and he has this
Speaker 1: like arc of stories that always like comes back in
Speaker 1: just the right way. And so yeah, he's like one
Speaker 1: of my favorite writers.
Speaker 2: When you're not working at Vanna, what are you doing
Speaker 2: for fun?
Speaker 1: Probably like biking or skiing, maybe like some I don't
Speaker 1: get to write that much code anymore in Havana context,
Speaker 1: and so I still try to just like explore, especially
Speaker 1: some of the new AI stuff when I have some
Speaker 1: time on the weekends.
Speaker 2: And a pleasure chatting with you. I'm really really fascinated
Speaker 2: and excited for the work Van is doing, and we're
Speaker 2: going to talk more. But thank you so much for
Speaker 2: joining me.
Speaker 1: Yeah, thanks so much for the questions.