Trent McConaghy: Ocean Protocol – The Platform Making Waves in the Data Industry

This is epicenter episode 366 with guest Trent McConaughey. I am Sebastian culture and you're listening to epicenter the podcast, worry interview, crypto, Founders, Builders and thought leaders. On this show, we dive deep to learn how things work at a technical level, and we fly high to understand Visionary, Concepts, and long-term trends. If you like epicenter, the best way to support us is to leave a

review on Apple podcasts. And if you're on a Mac or iOS device the easiest Way to do that is to go to epicenter dot rocks /. Apple today my guest is Trent McConaughy and longtime listeners of the podcast will recognize Trent as a repeat guests. In fact, this was his fourth time on the show and today he comes to us to tell us all about ocean protocol which he and his team have been working on for some time.

Now, in fact, ocean protocol just released its P3 which builds and improves on previous versions of the platform and now lives natively on Theorem main chain ocean protocol is a platform to create data marketplaces and if you haven't noticed data is a big business. By some estimates, it is over 500 billion dollars in Europe alone. And what is obvious to everyone who is observing the space.

That there's a fundamental misalignment between those creating the data and those consuming it, it's a very one. Directional value flow, in terms of those who are providing the value, which is the data itself and those who are extracting that value and those are big Tech platforms. We typically use that data to sell signals and advertising to Brands and Merchants as Trent describes it, there is a shadow

data economy. Just as there is Shadow Banking and we need to flip this model on its head and if you haven't watched the Recent documentary by Tristana Harris. The social dilemma I would encourage you to check it out because it really kind of summarizes this problem in a very concise way. And in a way that's easy to wrap

your head around. Anyway, the ocean protocol provides an alternative to this model and alternative in which data providers users typically can sell their data on the platform to whomever is interested in it. Remember wants to buy it and that data set is represented as a token. Ian and that tokens value is a function of the usefulness of that data to whomever wants to buy it. So it's a much more Equitable Market where the value flow is more cyclical than one

directional. This was such a fun and fascinating conversation, I always enjoy speaking with Trent, gives you such a Visionary and just all around a nice guy. And I hope to see this model develop and I hope to see it really take hold because there's so much at stake here and the current model so many things that are broken with it, we're only beginning to see the impact

that that has on our society. So I think that Lots is at stake here and ocean protocol, provides an alternative if not a solution to this problem. So with that, here's my conversation with Trent McConaughey. I'm here with fellow Canadian Trent McConaughey, who, who is

repeat guests on the show. In fact, I was looking just before Trent, is actually has actually been on the show for X so he holds second place after Shawn Jones, who, of course, was our Regulatory Affairs because correspondent early in the day. And so, welcome back for a fourth time on epicenter for and great to be here. Going to be fun as usual. Yeah, the actually the last time you Iran.

You surprised me with fredersen if you guys were at some conference and you were supposed to come on to talk about IP DB and and then lo and behold you like oh I got Fred here. Let's talk about data and and machine learning and likes our data rights on the blockchain. And that was really interesting conversation and I was listening to a little bit of that in

preparation for this. But for those who perhaps a new the show or haven't heard your previous episodes, remind our listeners of Who You Are. Are and what you're doing and how you got here. Sure. So grew up in rural Canada, train us and electrical engineering computer scientist. And then I did a couple AI companies towards computer chip design as well as a PhD on that

as well. In 2013 in 2010, I actually bought some Bitcoin lost those private Keys long ago in 2013, I discovered you know, really what blockchain meant hanging out of room. 77 in Berlin, the eponymous room 77 and kind of You know, that was the beginning of really truly diving down the rabbit hole and started hacking away at ideas and stuff and it led to ascribe than between to be and IP to be then ocean.

Now, so a scribe, was basically a project about asking the question, how do you collect digital art and how do artists that create digital art, get paid and this was, you know back in 2013 2014 one of my co-founders was a professional curator had worked in the Louver all these things. So we did this and We released a beta in 2014 and worked with a lot of leading digital artists and went live in 2015 in full

production. And, you know, it was really fun, really enjoyed it. Great cause all that, but scale was an issue. We had built on top of Bitcoin blockchain if the room didn't even exist it first, right? And, you know, it was clear that the scaling Solutions weren't going to be coming any time soon. So we said, okay we don't need full smart contract, platform, Etc. Anyway.

So let's instead take an existing distributed database and And decentralize it. So at first we wrapped rethink DB and then that pivoted to mongo and we basically put the fft algorithm around it, tenement actually by the end. So that was between you me. And that was in that sort of 2015, 2016, 2017 era. And that also you know, what was a pretty useful tool. It was very appealing, especially to Enterprises looking to dip your toes in blockchain who kind of understood databases and so on.

We worked with a lot of Enterprises in that era and It was pretty neat at the same time to we saw that. We really wanted to have sort of this public blockchain database utility if you will so because bikini be software was set up in Federated way. Now we call that a POA type approach then we needed to do a lot of governance work. Sort of lining up. Many people to be nodes etc etc, etc. And around that we created a nonprofit organization germany-based one called IP.

BB interplanetary database. And so that was basically the nonprofit arm of between to be this was all tokens by the way as well. We didn't see the need for a token, the really wasn't. So we didn't have one and so basically that went along and it's still around to this day as well. Finally in 2017, we saw late, well actually even 2016 but especially 2017 we were starting to do projects and think about how do you reconcile a i with

blockchain. And at first just kind of But then people were coming to us saying, hey, you know, like Toyota said, we're doing autonomous driving, we really need to have way more data than Toyota itself has. So we think that a decentralized data exchange could be really, really useful. And you know what you wrote about about decentralized exchange is Trent. This is great. Let's you know, build a

prototype. So we built that and ship that in Spring of 2017 and just in the last show, Sebastian that's probably when I talk to you about that. We had just announced it that same week in fact. That's right. I think I think when I came on with Fred And I had just come off the stage of announcing in a consensus. Exactly. Yeah, I remember that. So, so basically from then though, and also in that episode, we had talked about data exchanges and within a month or two after that, you

know, the word ocean. We came up with the word ocean for this project that had been brewing inside inside the organization and it was really addressing.

We basically, when we were thinking, what a, I'm blockchain and all the different problems that you could solve all roads, led to data exchange, you know, there's problems related to social media where If you are a Facebook or Google, you basically have all this data of all the people, that's kind of signed it over giving you permission to do whatever you want. And it's really, really harmful to society in many ways you could go on and on or just watch

the social dilemma or whatever. But there's a lot of other issues to write if you're an AI startup and you're trying to build any, I model, you're going to be starved for data. You just don't know where to get the data from. And the thing is AI loves data with modern machine, learning based AI, deep learning, and all this sort of thing.

Thing to get more accuracy, you need more data and, you know, you want to go from, you know, 30 percent error to 10 percent to one percent, two point, one percent two point, zero, one percent, depending on the application. And for that you need, you know, 10x more data, 10x more data 10x more day to 10x more data and AI people don't have the tools for

that. You know, how do they get more data and the sort of been the state economy, but it's just like, there's been a traditional banking economy that's all Shadow, right? You know, leading to the 2008 financial crisis, and many financial crisis before that we've got kind of Same thing in the world of data, right? There's a shadow data economy and we have a data crisis. Now that has been sort of Brewing over the last 10 years.

20 years. And we haven't really noticed it because it's sort of been like a frog boiling in a pot of water right at first. It wasn't boiling, but bit by bit, now it's clearly boiling and people are asking what to do. So and just as crypto said, okay, there's the shadow banking economy. Let's use crypto to try to create an open Money economy and we have that not just with Bitcoin as a store of value and ethereum as a foundation, but also defying top and all the

things there. So, what oceans goal is to say, hey, there's also a shadow date economy and let's use crypto tools to create an open data economy, where there's Shades of Grey permissioning privacy. And so on, Yeah, thanks for that recap, and all these things, you know, that you mentioned that you worked on. So first described then Big Chain DB then. Then eye PDD to me these things were all these projects were also Visionary, like first

putting art on the blockchain. I mean, this was in like 2014. I think the first time you came on this was such an interesting concept and interesting idea, I think it was really the one of the first times that, you know, people were confronted with the idea of a non fungible good on the blockchain. And this was before etherium, this was before n ftes, And then in a big chain, DB I mean, to me like I latched onto that idea.

And of course, I pdb afterwards, I thought that it was so powerful, you know, as a developer like as a web developer, I love the idea of having a permissionless publicly available database available on the blockchain on a peer-to-peer infrastructure. I guess is better way to put it and you know, same for IPs fast like I have this sort of similar you know attachment to that to that idea. Was that concept too simplistic? No.

I think about some of the shortcuts that we sometimes take when trying to innovate with like new technologies and I wonder if the if the ipd be idea was perhaps simplistic like like let's say let's put newspapers on the internet but we'll just do that in PDF format right? Like not considering what the technology affords and the types of innovations that the

technology provides. You think that that was perhaps the case and that ocean sort of builds like a new model that really leverages Three Technologies to the fullest. So I think. Overall, first of all simple is good in general, but it's, you know, you want to make things simple but no simpler right to paraphrase, Einstein. And it's it can be hard to arrive at simple, you know, Mark Twain has this quote that I just love.

He said, I'm sorry, I had to write you a long letter because I had no time to write a short one. And I think this is really true. So you know, Simplicity is hard simple as hard. I think in the case of big chain to be, it was a very simple conceptual idea, leveraging. You know all this great infrastructure developed for the distributed systems world. Like how does Facebook actually scale? Right? So leveraging, those sorts of technologies that part was

found. I think the challenge that bikini be and IPTV, had was the sheer underestimation of how hard it is to roll out. Something that is permissioned. The technology is easier but the politics and everything else around it is much much harder. I've seen team after team after team, you know, say, hey, let's go for something that's permissioned thinking that, you know, they can use that as a stepping stone for permission has and it's just so much effort. We even did that for the first

parts of ocean and stuff, right. We actually had spent a lot of effort put on doing that the pulpit, the politics. If you will a talking to a lot of teams and organizations to get them to be nodes for IPTV and so on. I remember those conversations. Yeah, that's right. I mean, stratum was was part of that. Initial group of people we were, we were speaking. King about exactly this, you know, how do we set this up? And like exactly governance. And that was the challenging

part is like not technology. It's human interaction and governance is the most complicated. Exactly. So, you know, the government's as well as how does this relate to the law? Because once you have a POA or Federated Network, then you have to, you know, it has typically much stronger ties to Identity, and then these organizations that are running the nodes have to they have higher degrees of liability.

And then if you start to put together, Simply an agreement for sort of a note, operating agreement of, you know, what rights and responsibilities they have the responsibilities in the liability of start to away the rights quite a lot actually unless you're very careful, right? And if they're if they start to get too much reward on the other hand from just running a node, then it has the risk of becoming a cabal as well. So it's really a tough thread to

to solve and frankly. It wasn't really like we always had the plan with pitching to be to be permission this. Anyway, it was just a stepping stone. And so we saw that, okay? We could develop this more or we could go towards where we saw. There was more you know more opportunity in a sense with ocean and keep maintaining bikini be, it's not like we just shut down one and one for the other we actually you've evolved from wanted together.

Basically now you know, ocean is on a mission of substrate and even as more contracts themselves or permissionless by virtue of not having any built-in upgradability and it's it's basically it's radically simpler from a governance perspective. There is still government, right? But, you know, on the substrate side, you have to do a hard fork. And one level above you have to convince your community to switch over from one thing to the next. And I view that as a really good thing, right?

It's a high friction for, for changing decisions, that should take High friction. There are other cases where you want to have low friction, even at the substrate level and I'm super thetic of all of those approaches as well, for on chain upgrades. Great. But for oceans needs, you know, fully permissionless at the substrate level. And permissionless at the Smart contract level is what makes sense.

And that's where right now. Cool, let's come back to this idea of the, the shadow data economy. Can you describe in your view? What that means, and what that looks like and you know for for for people who perhaps are, I mean I'm sure our listeners are all very aware of, you know, the issues that exist with regards to people's data and you know you talked about the social debt on my I watch that movie just this week, describe what the problem is here and what lurks

in the shadows if you will. The big problem overall is that our personal sovereignty is at risk and by that, I mean, our ability to take action and make decisions without fear of basically oppression or, you know, negative things happening to us. And that sounds very kind of Broad and vague and it kind of is, but then you can kind of

drill into what this means. There's a quote from the World Bank at two or three years ago that says, you know, Digital economy is the data economy and that reflects this idea that as you know, with every passing your the world is becoming more and more digital and what's powering the digital world is, you know, data itself and so there's sort of this data flowing everywhere but we haven't really given it its due.

In terms of this super important thing that we have to keep tabs on and because of that, you know, the average citizen doesn't want to think about it, you know. They just want to share photos and stuff. And I get that, that makes tons of sense, but the thing is there's businesses that have emerged that real on that take advantage of this. It's an Arbitrage.

So they understand that citizens don't know about this or citizens kind of have given up on them, even if they do know about it, they can't do anything about it. So these companies say okay, well let's take, you know, group these people into a thousand or a million or a billion people, and then mine that data, and then, use that to sell stuff back at them, or basically change their decision-making Behavior somehow sometime, right? And it's at the level of the company.

It's also at the level of the nation, right? There's interplays as well. You know there's companies out there and individuals and organizations that try to sway election results, right by leveraging data and various ways. So there you know there there have been flows of data for a long time, going back to, you know, even before the early days of the PC, you know, from the birth of the computer, but it hasn't mattered for a long time

because we simply hadn't. Haven't had the scale and even on the internet, the early days, it was so small, it didn't matter. People hadn't figured this out yet. But you know, starting in the sort of early 2000s, that's one sort of AI people realize that Data was really really important. And then, you know, Google themselves published this paper in about 2007, called the unreasonable effectiveness of

data, realizing that okay. If they can actually get more data than they can have more accurate am models, which basically turns into money for them, right? And the problem is that, there they are intent of misaligned between the people and Google, there's an incentive misalignment between the people and Facebook. Why? Because of ads, right? In their case, they're trying to sell more ads, which basically means, you know, learning as much.

You, but you can so you can be as so that the ads can be as targeted as possible and that, of course, so they're trying to gather as much data as they can. The state, of course, is also flowing into prism excetera. You know, after the Snowden Revelations, it's not like prison went away, the government double down and there's a 10x 100x there.

So our decisions are getting shaped more and more and more by companies with a profit motive that is against their interests and you it's very very subtle, right? Like Sure. Does it. Facebook doesn't Google does it all this Instagram? All these. It's unfortunate this way and it affects, you know, our decision making ability, but like I said, it doesn't stop there, right? It's not just, you know, okay?

You might see these things there and might change your thought, but it actually affect the outcomes of Elections, which then leads to, you know, president's being hired who then lead to basically ignoring pandemics, right? And you have a quarter of a million, you know, you have more people killed in depend, Emma. Can you know, It's in the Vietnam War and this is one of the big levers that affected.

This is because of this interplay between data and profit motives and this misalignment of incentives. So so that's kind of like the sort of the problem we're trying to solve from the perspective of, you know, it's a misalignment of incentives and so on around data and you can view it as sort of a tear it down thing. But I also like to view things in sort of a Build It Up way. And I really prefer that this is where I see. Okay, what's the A way for people to think about this.

And it really comes down to not your keys, not your data, your keys, your data. So riffing on andreea, Central Antonopoulos on the Bitcoin, quote and this basically means sovereignty over your personal data, right? So you should be able to control your data. You should be able to choose who sees what when, you know, this also has the side effect of controlling privacy privacy, really is just about the shape of information flows, right? And that comes down to a question of access control.

So if you do that at an individual level, then you can also group it into higher and higher level groups, families. Companies, larger companies Enterprises cities and even nation's needs, right? And they're all grappling with

this. So if you can give them the tools to control and shape, the flow of information about them and about the things around them, then it can lead us towards where there is not incentive misalignment and in fact there's incentive alignment where, you know, if they share more in a healthy way and so on, then they can actually you get rewarded. And that's really the sort of Build It Up. Vision of ocean is about, you

know. Tools for control of your data at a personal level, all the way up to a national level in a way that helps to serve data sovereignty. I think that incentive misalignment is Sookie and I think a good way to sum it up, right? If you kind of go up, you go up the stack of this problem and is

kind of fresh in my memory. But like I think the social time I'm a documentary that I watched earlier this week kind of sums it up pretty well or at least it makes a good attempt at explaining it. In layman's terms, social networks platforms who that control large amounts of data are incentivized to use. While the model that was created around that data is to sell advertising.

The advertising is sold to companies that want access to people's information in order to Target those customers more effectively and at the same time the companies that hold the data are incentivized to keep their users on the longest as possible. Because that's how they maximize profit on the advertising side of that Marketplace. The way that they achieve that goal of keeping the people on the platform's, the longest well that goal was somewhat discovered by Machine learning algorithms.

There's Get out that the best way to keep people on the longest is the show them outrageous shit. That sends them into, you know, that essentially divides people that has an effect on, you know, you, we talk about sovereignty, right? You talk about people's sovereignty. Well, I mean that exists at a

conceptual level the risks. There at a conceptual level is sovereignty also in a general sense as in the sovereignty of societies and countries Etc, because people who are divided, Obviously, our are more vulnerable and at-risk and I think it's a huge problem that if you get really into the weeds of, it's like, okay, well, incentive alignments are broken here. Well, you know, is it platforms or is it simply the model? Is it capitalism? That's that's broken in this particular model.

It's kind of like a it's sort of a chicken or the egg problem here, but I wonder if as long as there is sort of unfettered capitalism and unregulated capitalism If this will ever change, and if there will be better models, that emerge, of course. I ocean protocol is one antidote to that. Yeah, I have a couple answers to that. So, I mean, one thing that capitalism recognizes that is super unhealthy for any Market

is monopolies, right? So that's why the Sherman Antitrust law was brought out and then after a couple decades, they finally gave a teeth and that's what broke up, the all the different pieces of Rockefellers Empire for example, as well as led to the breakup of AT&T Int and so on, we have companies that are now far larger and more dominant than

basically, you know, the u.s. deals of the world back in the day, yet, the The Regulators have forgotten that they can actually apply this tool to address this. And that's, that's an issue. So, this is basically not a, that's not a problem of capitalism per se. It's a problem with the enforcement of antitrust. So that's one piece of the tool. I want to mention the other one, which is to me the happier answer and it's coops. So, I grew up in rural. Out of that, where the local

grocery store was a co-op. It's literally called The Coop. We had those two. They're all closed down. Oh, wow. Yeah, yeah, yeah.

Well, and the local bank was called the credit union and that was the main bank where I grew up and, and basically, there was kind of cool apps for everything and these coops allowed, you know, a thousand, ten thousand people in the area, to each collectively owned, that grocery store for example, and then get the dividends from the grocery store and it was acting in the best. Insurance to the people and making a bit of profit on the side.

That's a very healthy thing. There's another thing in Saskatchewan, in fact, at the time, I think was to learn one of the largest co-ops in the world, if not, the largest called the Saskatchewan, we pool it. Had I believe on the order of 100,000 Farmers as members, and what it was, was collective bargaining performers. So you sell your wheat and your Burly to the Saskatchewan. Cool. Then it goes as a large entity, it ships, things by train to the port's it ships.

Things by ship to Asia and everywhere else through the ports. It does the marketing, etc, etc, etc. It was a win-win, right? The farmers basically got some dividends from the Saskatchewan, we pool, and they had a market for the green. They had distribution all of this and it served everyone. So this is actually a capitalist notion, but it's capitalist saying cooperation is a good thing, right?

And what I really love about the blockchain space is that it makes things like this much easier to implement as well as experiment with right to have, you know, Collective organization of people towards the betterment of that. Of people write in the form of dowsing. Otherwise, right. That's my kind of two answers. I don't think we need to rethink capitalism. There's been a lot of calls for that, and even if we did need to rethink, I think it'd be very, very hard the way it is.

But instead we can say, okay on one side make sure that, you know, antitrust is a respected and you know, the privacy laws of Europe Etc. And another hand double down and co-ops, right? And I think they could actually become much more common in this new age, given how much easier it is to form them. The in the world of blockchain, you know, like we're seeing for example, the first Venture Capital firms that are Dows that are actually truly working,

right? I'm a member of medical Ventures and it's an amazing organism to observe. So let's talk about ocean, protocols, switch gears a bit and so describe what what is ocean protocol and what does it try to achieve? Yeah, so I guess the goals are like, mentioned address, some of the issues around data in society, misalignment, of incentives, as well as give tools for empowerment of people at the level of individuals, families, all the way up to levels of cities and Nations,

right? Okay. And perhaps in the in the context of what we're talking earlier, in the model, how does ocean sort of flipped the the model that we were describing earlier? At the heart of it it's you know your keys your data, right. And then you can choose how to share that where but also you can turn this data into an asset if you like right.

Because, you know, the way we view it data is IP just like, you know, if you write a song and record it, then that is a piece of Ip that you, then you can monetize via music Publishers, Etc, or via Spotify, whatever you want, right? Same thing with books or podcasts or whatever, so data falls into that same, Corey and of course, data is useful, in the sense of, you know, people who build AI models need data, in order to make those models accurate, they need more data.

And once those models are accurate enough, then they can monetize it. In various ways, you know more, you know, safer, self-driving cars, more efficient traffic lights, a lot of kind of all most mundane things. But the stuff matter is showing so ocean. Basically, at the heart then is a set of tools to make this easier to do at the heart. But it's access control. So basically, it makes it easy for people to establish a data set or data service, as an asset.

And then to share share that asset or sell it transfer it. Whatever, where there's permissioning around it and the way that we do that, as of the most recent release of ocean V3, the way we do it is every single date of service is its own ear, C20 data token, and by using that we leverage the full infrastructure of ethereum. There's a lot of really cool. Applications and it also serves as you know at the heart, it's

Access Controller, right? So it's sort of like, you know, socks, you know with you know socks you can buy point one unit socks, you can buy 150 point three unit socks, but if you have 1.0 unit socks and send that those to the unit swap team, then they will mail you back in the physical mail, your pair of physical socks and so you are, you can redeem those unit socks with that pair, same thing with data tokens, you can send 1.0 data tokens to the publisher of That data asset and

in return, they will give you access to that data asset. You know, you can still speculate, you can buy point one data tokens, you can buy 100 of them whatever you want. But in terms of redeeming getting Access Control, it's that magic number of 1.0. So that's maybe a good summary. Ocean is a set of tools to enable the web three data economy which is all about open while reconciling privacy. You talked about that every data set is an ethereal nerc, 20 token. What is a data set help.

To understand what that means specifically. So there's it's every day to survive so data services can be data sets or yeah. So also with a data set though, it could be some via PDF, it could be a spreadsheet or, you know, the machine learning version, which is a CSV file. It could be a piece of music, it could be 10 gigabytes worth of worth of files, but behind a directory. So, in Ocean, we actually have, it's quite General. So you basically are simply selling a you.

Well, we have a A few different ways of defining a data service and it's flexible and it's going to expand over time to start with. There are two, one of them is a static URL, where it could be basically say a CSV file that you have sitting on Google Drive and you then sell access to that CSV file, as defined by that URL sitting on Google Drive and of course you can be a decentralized network as well.

So that's one example. The second example of a data service that we support is this is in the Privacy preserving angle is computer to data. So Rather than someone getting a URL and then downloading that CSV or whatever, instead it's saying I'm going, I'm a publisher, maybe I'm a big Enterprise. I'm going to sell my date my data, but people aren't going to download it. Rather, they can go and run an algorithm right next to my data.

And maybe just do computer simple average from a particular column or a median or maybe something, fancier building a linear model or maybe a fancy deep learning model, whatever, right? So those are the two Services right now is types of data services that ocean supports And with time, we will support more and more and more streaming data is coming down the pipe as one of the most important ones. We see that this thing can get the can be dozens or even

hundreds of these things. So that's what, that's what we mean. And ocean itself is sufficiently General. These data tokens their ear, she 20 with one extra field called blob where it's just a string. Basically, a bunch of information inside, and that basically helps to support the various types of service

provisions. So you mentioned something that I'd like to touch on, you said that the data can be stored on Google Drive when one thinks of, you know, the decentralized data Marketplace that we wish to have. I don't think that storing data on like traditional Cloud platforms is the first thing that comes to mind. I mean in terms of availability, censorship resistance privacy, Etc. You know, help us understand the

the choice. They're of storing data on these platforms and what kinds of things. Is ocean do to prevent some of the excesses that we've been talking about it since a while ago or well? I mean, I think of the day you have to be pragmatic about understanding. You know, what tools people are using right now and then providing a bridge to them to sort of you know what? Walk them bit by bit over this

bridge. It's not like you know, you snap your fingers and suddenly you're in a full permission list, decentralized world with the whole planet behind you, right? I should mention the, as you mentioned open market. So overall ocean is these tools, which is smart contracts as well as libraries python. In JavaScript libraries, and

then react on top. And then on top of that, we've shipped something that's a consumer facing web app called Ocean Market. And it is a place where people can go market ocean protocol to calm. It's a place where people can go to publish data assets and to consume them like, once you buy them and of course, you know swap back and forth trade on them and you can even stay clean them and I can get into that a bit later. So then towards your question, if you think, okay, how do we

make Ocean Market? Easy to use? Right for people. It's went through NATO. You know, you sign in basically by connecting your wallet, that's all all that. But for people that aren't running their own, you know, hosted service with datasets. How can they actually share their data? You know, maybe they don't know about file coin or three rooms warm or anything just yet or don't know how to use it. But they do know that they have a bunch of data on Google Drive

or Dropbox or something, right? So you make it easy for them to publish their data. And you might say, well that's not decentralized. The thing is, this is at the very Leaf node. Right? So it's that one single person that has that data. So the the connecting platform is decentralized on that, you know, permissionless of the room substrate as well as smart contracts on top. But that one final person at the edge node is is centralized by nature of it being supplied by

that one person, right? So even if you say okay we're going to organize this and stuff, it doesn't really help because it's still one person supplying the data or one entity, right? And that's okay, right? If they are supplying the data and people don't want it, they won't buy it if they're supplying the data and they do a bad job where they have Availability people will stop buying, right? So they're incentivized to do a good job there. And I do see though that, of

course, right? Like right now, people could store something on Google Drive and Dropbox and a few other, you know, decentralized Services wrap it all up, make those all pins with ipfs and then just give the ipfs hero, right? So and that's a nice Bridge ipfs serves as a great bridge because you can have storage on centralized and decentralized storage mediums while at the same time, providing that single

URL. So it's all about a bridge to get people across Words eventually, this sort of public utility Network infrastructure, where everything is permissionless, including you know, the cloud storage Etc for sort of all of humanity, but we have to get there one step at a time. Great. Yeah, I mean, I think that's a good approach to allowing people on board with more Simplicity, is to open it up to all the different types of storage providers, that that exists.

And if within that, we also have decentralized stores providers, like, ipfs or PSI or some of these other ones. And, you know, that at least provides Alternatives, so that we can have that censorship resistance on option if we wish to choose it. Exactly. I'm just to add thereto. Like ocean doesn't care because it's just about the URL, right? So people can provide Row, I can point to a surface oncea or and ipfs, which is wrapping some other storage service, or a Google drive, or Dropbox

whatever. So ocean doesn't care. So let's talk about the different stakeholders in Ocean. So, obviously there's like the owners of the data, those who provide that data point that URL you mentioned. Then there's also those who verify the data. There are the curators. Can you describe all of these different participants, these different stakeholders? And how are, how do they are incentives? Aligned your to create this, this platform?

Absolutely. So, at the heart of it, you know, the heart of the value creation is a publisher publishes. Is a dataset and someone else comes along and buys a data consumer. So you want to connect those data, Publishers, 432 consumers, that's the heart and the data consumer, when they consume it they're adding value to their business or otherwise, right? So you want to make sure that that Loop is a solid connection.

So, Ocean Market and the other markets that can be created and in ideally that business is providing also value to the publisher, right? It's sort of this closed loop exactly. Well, this is the thing, right? If data, consumer doesn't, you know, find Data that they buy from the publisher is garbage. Then they'll just stop buying from the publisher. So the publisher themself is incentivized to create quality data, right?

The heart of capitalism frankly right in that way and that's actually a good thing you know it aligns towards value creation. That way in between is at the lower level. Of course, the main actor is simply the the connectivity of ocean at the, you know, substrate-level if you remain at right now, this more contract level and then the marketplace is on top.

And, you know, we've shipped the First Market Place ourselves As as ocean protocol Foundation, which is it's called Ocean Market. I mentioned it, and that's basically, you know this, you can view it as a multi-sided platform, where the two most critical participants are the Publishers and the consumers, however, you want to Market to form, you want price Discovery all of this, right?

And so forth. For Price Discovery, you need the data services themselves to be sort of assets in a first-class assets and within within a theorem, of course, you have kind of two. Choices. You can make them. Non fungible are fungible. But if you think about data, right? When I'm a publisher publishing a data set, it's not just like one person is consumed. You're going to have 10 100 1000, right? So it's clearly like I'm more

fungible thing. So so basically we have these data tokens that are then there as assets and it's those that flow from the publisher to Consumer. Now you can say, okay well how does the let's say that the consumer data consumer is looking at data sets but they want to have a good feel for Like, what's a good data set? Force is not, right? This is where curation comes in. So so how do you go about? You know, leveraging crypto infrastructure for good curation.

Also, how do you go about leveraging crypto infrastructure for Price Discovery? Right? And this is a question, we would get again. And again, and again in Ocean from, you know, from the very earliest days 2016, 2017, to until basically shipping V3, like, how do you set the price? And there's lots of lots of theories about how to do this, right? You know, you can have auctions, you can Of royalties. You can have order books. You can have automated market, makers, etc. Etc.

Right, so what do you do? And we decided with all this, that sort of taking a page from the the D5 Playbook, let's put in an automated Market maker. So when someone goes to publish their data set, it becomes a data asset, its own token, and then in the same right after that, they have the option to publish a pool. And so we've got balancer

technology under the hood. So they publish a pool that is the data token and ocean token and they put in an issue of liquidity of the ocean token and now you actually have an authentic price signal between basically as a ratio of the number of data tokens to the number of ocean tokens in there. Other people can come in there and steak and also people can go

in and swap back and forth. Ocean tokens for data tokens, people can sneak in and stick to the pool and you know the cool thing about AMS automated market makers. Adding liquidity being on the credit provider is the same thing as taking. Right. This is very different than sort of the previous idea. I normally staking sort of locks up and slows down the velocity of tokens. But in a M&M's, the sort of magical. Beautiful thing happens were by

providing liquidity. It's taking that actually increases the velocity and the usefulness. So this is actually what ocean Market has another hood is these balancer pools. And that's providing this, it's basically helping to form this market around this specific data token, right? Write it in addition, if you think about it, you know you go to balancer website or the unit swap website and by default if you want to look at all the pools, it will sort by which pools have the highest

liquidity. That's a really good proxy for the quality of a given token, it's not perfect. But for sure, you know, all the garbage spam stuff is at the very very bottom with like two dollars liquidity. So it's already gone and it's a pretty good first cut, right? So that's actually also what ocean Itself. If you think about it, by default, you got Ocean Market. And it actually shows you a sort of list based on the amount of liquidity in there. So that's a signal for the

quality of a data set. It's really hard to arrive at a perfect signal for quality of data set. What you need to do is provide a bunch of Statistics authentic signals that people can use to assess whether a data set is useful or not useful to consume, but also useful to invest in, right? And this is a key thing, you know. So I'm going to mention those sort of theirs in terms of the, the stakeholders in the system.

I've talked about About the the foundational ones which is the the publisher and the consumer data consumer, there is the liquidity providers which are the stickers which are the curators. It's the same thing in Ocean and implicitly, they're also doing some soft speculation because right now there's a 70/30. Wait.

So anytime someone steaks and one of these pools, it's 70%, ocean 30% data token and that's simply because you know, to avoid price fluctuations in to align incentives or bit better. But besides that, you know, people can just purely speculative things. Right? They can invest in a data to look at and hold. If they think that the person who is published the stage token, is a high-quality person, if they know them.

If they think that, you know, maybe they try out the dataset, they see it as high quality great, they can buy it and they can hold it. So, so basically, this is also a key question in the overall ecosystem. And so at the end, you have the Publishers, the consumers, the the LPS / curator / takers and the speculators. And this is sort of the heart of it and then you've got this across many, many, many different data.

Token, / pool. Owls. And this is basically the, how the markets are forming and what's happened. You know, we released Ocean Market with three weeks ago, and it looks like a microcosm of crypto itself, right? You've got, you know, you've got people that are doing the equivalent of an IC 0, which is an ID 0, initial data. Offering the promoting on Twitter, there are actually announcing the launch of the of it, you know, 24 hours before or week before whatever.

Now and then people Pile in when the thing happens to just to invest in speculate and so on and at the same time and then you have data Schiller's. And you have rug pulls and you have fraud and all of these things that you have in the broader crypto ecosystem. But you also then, you know, in this all this messiness, you have a market for me. This is how markets are born and so, you know, 2017, yes, there was an I co bubble but this is actually how the broader crypto

Market was born before. That is pretty quiet. Right? There was a Bitcoin. There's a theorem there is maybe 10 of the coins, but 2017 after that happened, you know. Now, we have these indices and coin market cap and coin gecko that point to something quite healthy. You know, Plus coins that are really healthy. So this is what we're seeing in

in the world of data. Now, with these initial data offerings, IPOs and speculation, all this, you know, data is truly becoming an asset for the first time ever and it's fully open just like defy, right? That's really interesting. The data tokens, represent specific datasets. So there's there's a data token for each data set. Correct for a data service which is typically one dataset but it could be a local, right? But it's whatever granularity the publisher decides. Yeah, okay, I see.

And so, that's how you arrived. So, there's a little quiddity pool. Essentially like an amm for for each of these data services. That's how you arrived at Price Discovery for that specific data service and the data set or data sets that exist within it. Exactly. Exactly. And then, yeah. And I mean, it's that's the primary market, right?

So when the publisher publishes, they deploy this pool, that's the primary Market but then people, if they want, they can set up secondary markets to. So we've seen people creating, you know, a pool side by side and selling, you know, OTC did it tokens and stuff, right? Which is great. So, and as time goes on, you know, there's going to be some like large-cap data to data tokens. Right? Right now, you know, it's all relatively small. It may be traded on exchanges. Yeah, exactly. Right.

So we're in early days but already, you know, in the last three weeks, we're at about 2 million ocean staked which according to the price is right now it's about 1 million euros worth of worth of data assets. Ticked. So you know it's early days but that's quite exciting. And we're Ocean Market usage keeps growing growing growing. So The most recent numbers are about 10,000 weekly, active users. So I think that's that's the number. Yeah, things are just growing

growing growing. And right now our challenge is to adjust simply the scale issues as well as make sure that the environment is as safe as possible to mitigate the effect of rug bowls and, and fraud and so on. Yeah, well, we'll get back to the scale in one second. So, this ocean Market that you mentioned is the first I presume the only market for the moment. Do you anticipate other markets, forming, and what? What kinds of specificities could those have? What are the types of things

that would cause that to happen? Yeah absolutely. So we want to see a lot of them right you can't have any economy. I data economy with just one marketplace, right? You need to have tens hundreds. Thousands of these things, right? And so Ocean Market code itself is fully open, right?

Apache 2 license. So basically very unrestrictive very open to use and so we do encourage that and we even have We pointed to people live, how they can do that, you know, Forks of Ocean Market or create their own thing. And under the hood, of course, everything is on a to remain net, including them edited at all of that, right? So, it's very easy. Anyone, spinning up another Marketplace, can basically get have all the data assets that are on Ocean Market on their Marketplace.

So how is this happening? We view it as sort of a top-down and a bottom-up thing. So top-down we're working with a few different organizations that are interested in building on things. So, for the better part of a year, we've been working with Euler and around their own Automotive data, Exchange Market Place, still had your old Enterprise clients. I see. Yeah, I mean, you know, these are long-term relationships and whatnot, and it's kind of exciting, right?

Things have changed a lot in the last few years, where Enterprises are much more open and comfortable this so. So we're pretty happy with that and there's some other basically government organizations and went out that we're working with two that we haven't publicly announced yet but it's some of the information is already out there.

I guess in addition to that there's sort of Apps that have publicly announced that they wanted to have ocean power due to markets, such as molecule boson protocol deck straight and otherwise. And each of these has their own sort of specific vertical, right?

So decks rate for example, is in the logistics area where it's about data that for example their customers, they have about 10,000 trucking companies working with them and he's trucking company has one or a few trucks and you know what is the specific location of each of these trucks over time? What are the goods and Side each of these trucks and right now, all that data is private but it's that that information is super, super useful to in two ways, one of them is to Wall

Street, right? So, rather than having to look at satellite images, you can get much more fine-grained information, but secondly, to optimizing the scheduling of the trucking themselves, right? So you can do basically better more optimized Logistics over time and right now the average truck according to Stats, I think it's other one-third or two-thirds empty because just the optimization is so poor.

So once it starts to be a market formed around the It on this then you can actually optimize against it better better better. So those are a few there. But over time, we see that there's going to be Market places that specialize along a few Dimensions. One is verticals. Like, I've mentioned Right Automotive like Daimler or Logistics like Dex rate other dimensions are maybe you'll have a Marketplace that's totally tuned to a I right training.

AI models and and they can be variance of that like human protocol who were working with as well. You know, they do sort of the H caption which is sort of a variant of recapture that is Basically much more incentive, the line with the users basically. So there's opportunities with the H captures of the world, the human Protocols of the world and other AI plays to out there where it's just sort of a win-win and other things too, like privacy first marketplaces or a specific geographic

regions. And remember like you with a Marketplace, you have to have a terms of service. It's sort of a Last Mile face something. So certain countries might have very specific regulations that you want to serve. So maybe you focus on just that country and maybe even Geo block everyone. As you as that Marketplace. And that's okay, right?

You know, we can't control things at the level of the substrate, you know, permissionless, etherium all that, or this more contract, but that last mile, of course, things can be controlled by the the marketplace operator and that will actually help to serve specific niches. So, there's quite a lot of variety there. Oh, I guess one more important one is besides, you know, the publishing and consuming of the data assets. How do you price? Right?

So by default we have these balance your pools but you can Also, you know, make it maybe some people want to have units while pools or bank or otherwise. Maybe people want to have order book based markets, or Dutch auctions. Maybe you want to have a Marketplace, that does a better job of initial data offerings ideas, right? Just like, if you think about ico's, that's all these variants that people had, you know.

And in that case for even to write Dutch auctions, etc, etc. We can see the same thing for data sets. So there's a huge variety and we encourage people to play with all of us. Yeah, the the captcha thing is, I think is one of the things that people don't realize to what, which point their incentives are misaligned there. Where there is an incentive misalignment there.

And actually, so you mentioned H captcha and I I've seen H captcha before and I'm just on the website and I had no idea that, you know, that sort of Brendan eich was behind this and that they're built on on a theorem apparently or like The Leverage etherium. This is interesting. I'll have to look more into this. Yeah. They're, they're probably like the most used app that no one's heard of right and, you know, the people behind it. Yeah, it's an amazing team.

I believe Brendan eich isn't directly involved. He's just an investor or something like that or but also helping to support it, but he's definitely a he's involved in some way, I guess. Yeah, yeah. I was I was confronted with this recently where at least here in France to in order to use certain Public Services you're obligated to to fill in a Google recaptcha. And, you know, in French culture and probably also in Germany would probably be like similar sentiment to this.

If people knew that in order to access Public Services, they were obligated to enrich a gaffa, like what we call in French Agatha, you know, the Google Amazon Facebook, and apple Etc. I think there was there would be some outrage at least that idea and maybe we can unpack this because it's a nice example. So, you know, when you fill in a recaptcha, there's usually two in a row that you do. The first one is to basically. By whether or not you're human.

And the second one is basically Google hasn't yet classified weather, which picture has a truck and it or not which precludes a car not, right? And you're basically providing labels for it basically, with to help train, the algorithms because the algorithm needs to make this mapping of right of image 2, yes or no, there's a truck in it, right? And so you're supplying data for training and that's, you know, hugely useful to Google for its other applications.

So, you know, you think that, you know, you just need to get into the site. But Google itself, I think 99% of people don't know this. Yeah I think 90% people are totally unaware and actually I was unaware of this until recently. In fact I was unaware that the previous captures that we had before which were just a feeling these letters or write. This word was also operated by my, most of them were operated by Google and was to train their book scanning algorithms. So yeah.

So to summarize that right? So basically in doing that, second step you are giving value to Google by your human efforts, right? But what if instead of that Going to Google that value is going to the person running the website and to the holders of a token and even maybe back to you directly, right? And that's what human protocols about, right? The website now can monetize. Actually from this, they don't even need to serve ads anymore.

They just monetize based on people filling out the, you know, proving an Autobot right, which is pretty cool. And then also though, you know they're going towards having their token and stuff. And so there can be a nice alignment there for the people who believe in this and finally, you know, maybe at some point also Go back to the person that is proving the not about in the first place, right?

So to me, it's a great use case. I don't know why, but this kind of a tangent, but like I have been just bombarded with with recaptures recently, and I don't know if it's cause my IP address is flagged or something, but I'll have to do 10 or 12 attempts before. I can actually access a website and it's, it's so painstaking, I think captures, there's part of a reason for it and that is basically the AEI models have gotten better over the years, right? So they're running out of See stuff.

So, they're basically getting you to do the hard stuff because the easy stuff is already been modeled, right? I think it might have to do with the fact that I'm filling in so many capsules that maybe they think I'm some kind of a captcha feel like bought or like you know someone in some Faraway country filling in capsules for pennies. That could be it too. Yeah. So let's talk about, you know, privacy, which is so Central to

this entire topic. Well, I guess my first question is, what types of Compatibility or compliance. Does ocean have with regulations like gdpr or the the CCPA and California ocean serves these regulations very well. So in GDP are for example, there's this idea that certain sensitive data can never leave European soil. If it's generated on European soil, right?

Like Medical Data yet if I'm a medical researcher and say USA, the ideal is that I have data across You know, 10,000 hospitals, across Germany, and France, and all of Europe as well as China, and Australia, and so on, right? So how do I get access to this data with gdpr? It would be basically. Well, the traditional way was where you basically try to make deals with hospital by hospital at a time and stir it all in one, big Central database and

then build a model from that. And actually Google had something called project Nightingale doing this. And they had a huge pushback and rightly so right because like Medical Data, super sensitive and actually There's other ones who like other big organizations that we're trying similar approaches and that's really like a big No-No. Fortunately, there are better ways to approach this. And so when I think about privacy the best way is it's not

about like either. It's not a black-and-white of, I see my data, anyone else can, that's not very useful, it's more like structuring the flow of data, you know, who can see what when right. And that comes down to permission, right? Giving permissions to people over Volcom, permissions to people to see certain data. Data services. So, so that's the heart of

privacy. Now going back to this example from Health. What you can do is if you're trying to build any a model, why not do something like Federated learning, where in Federated learning you create an initial random AI model, neural, network, whatever. And then it's just random at first is super stupid, but then it goes and it sort of as this bought, it kind of walks to the

first hospital. Let's say a hospital here in Berlin, Germany, and it updates itself based on the data in the Germany. So now, instead of, you know, fifty percent error like, you know, basically random, it's got 40 percent error, right. And then it goes to another hospital, let's say in Paris and it updates itself and now it's

got three percent error. And by the way, the whole model doesn't have to go to the hospital, it's just an update of the model so you don't have an attack Vector there and then you go to another hospital in say Lyon France, you keep going going going from hospital to hospital hospital. And the error keeps going down down, down, down down.

So once If you went across 10,000 different hospitals, you've got a very accurate model, but what's cool is the data inside each Hospital, never ever left the hospital, right? You know, people have been developing these techniques for Federated learning, going back to 2015 2016 and Google started making it really popular and famous and sort of 2017 era. The thing is Google's version of it is.

Yes, the data itself can stay at the leaf nodes at the edges, but guess who gets to play the middleman Google? Right, so a problem. Once again, you've got leakage there. So what? Could actually have a middleman that is, you know, not incentive misaligned that can still help to coordinate all of the, this learning of this model. And that's really where ocean

can really help. So, basically, what you're doing is you can learn your basically, do the training weight updates, Etc. At the last mile at the edge, as well as the orchestration in between can be done using decentralized substrates, but and ocean basically can play a key role in this because it's hell, you know, these data tokens are providing the access control of, you know, who can see what data sets one, right? And so, that's kind of the dream

right now. No one has built this particular application of ocean with Federated learning. But, you know, there's some really great efforts around this in a fully open Way. Open mind. It's a project out of the UK led by Andrew Trask, an amazing person. And, you know, this is kind of where they're headed and we hope to see an integration of open-minded ocean at some point. And this talks are on that stuff

too. So I think, you know, that's Example, and you can do this in a simpler way to you don't even need to get fancy with building a model. It could be Simply Computing an average across 10, different are 10,000 different hospitals, right? So and that's very, very useful, you know, if you're a multinational Enterprise and the data can't leave any one of your offices, then you can compute an average across each or in

Canada, right? Can it actually has this problem to the trying to get Health Data from the different provinces?

But if you each province, has its own rules and so if you sort of take the intersection of the rules, From all the different provinces of Canada. You end up with an empty set is 0 the rules, don't the rules Collide so a very nice solution to this is something like this you know Federated analytics for just averages or Federated learning to get a bit fancier and that plays well with GDP are basically right. Because then the data is never ever leaves the soil.

So, how does ocean address permissioning of data as it's utilized and transformed? And I don't know if this is the right way to think about this. But I imagine, you know, data being used to train a model and and then, you know, then I kind of lose track of like how that data exists. Like it went for man exists and in the context of GDP are or other regulations like it if the you know the owners of that data still have rights too The results of the computations that

were performed on that data. You know, essentially can one retrieve the data. Once it's baked in does that even matter and is this something we should be concerned about I'd scale perhaps more than just an individual level.

Yeah, so I think there's two pieces here to unpack, this one of them is, you know, what about where they're sort of this pipeline of, you know, data being transformed, being transformed, being transformed and the other is, you know, what are the rights that would attach to that. So, you know, in a way I can Pure pipeline. You might have, you know, some initial raw training data, and

then it might get cleaned. So then now you have clean raw training data and then it might, you might train your am model and then you'll have your trained, am model and that's a piece of data as well. And then you might have some new data coming into that, you want to get it to predict on and it makes predictions and those predictions that themselves are also data. So, we've got data at various steps along the flow. Each one of those is its own

data asset, right? It can be its own data token. If you It's you don't have to it. Depends on, you know what works for you want, but with ocean ocean, doesn't care, right? If it's the super rod Training Day at the very beginning or whether it's some predictions at the very tail end or something in between, right, it really doesn't care if you do want to have it. Every step along the at every step along, the way that actually probably helps towards Providence of the data, right?

So with GDP are and all that. You actually, one of the requirements is, you need to know where the data came from, right? So that it wasn't. So this actually ocean really Oops, that way because then you can have data that is trained in a way where, you know, where the raw data came from and you can kind of vote for and stuff, right? And that's a big problem even

pre gdpr days and stuff, right? But this will actually help you address that but now you actually have the provenance of of each temple on the way to the tail. End of you know the you know you probably don't have the initial run clean data provenance of a clean training data provenance of the trained model and Providence of the the final predictions and those predictions. Probably have many sets of those over time. So that's, you know, helpful for gdpr and just as an asset in

general, right? It helps to drive the value of the asset just like, like a scribe days, right? It described, you know, we were doing digital art and the value of an artwork is only as good as its provenance, right? If I had a painting that and I claimed it to be by Leonardo da Vinci. Well, people will say, well prove it right, like show me the lineage of ownership, right? And if I was able to show that then you know I could have a fortune in my hands. But if I can't show that then

it's probably worth zero. Although there can be some Arts experts to come along and try to verify it and it's often kind of fuzzy there's you know even a fraud market around just that right. But the point is profit Providence really really matters. And you know what a scribe had done was established Providence for for digital art and that helps to establish the value of the piece in a big way. And for data it's also super

helpful, right? Because we'll have you know, much better sort of culture around provenance of data and models that are trained. And so That will actually lead make it a lot easier to comply with gdpr and so on. So that's on the sort of the one part. The second part, I'll be quick on this is the right.

So basically, we view data as IP and and specifically as copyright, if you have sweat off the brow to generate this data, then this is actually your copyrighted data as an individual, or as an organization. And so with that, then you can do whatever you want with it. But the way that In sets it up is Ocean Market specifically because you know, the lower levels, don't care.

But Ocean Market it has a terms of service that we thought through and even Drew on from our scribe days and so on of course which in turn draws on things from like second life and otherwise we in that terms of and conditions, it basically says you are claiming that you have copyright or at least the rights to this data initially and then when the next person buys it from you, they are getting rights to basically it's sort of like a license, right? His relations.

And then you have some place in some place installations are going along. But it could be where, you know, if you want you can have those of the ocean Mark distance work. That's right now. But what we envision is that you can have licenses that are more restrictive and towards re using data in various ways. And this kind of goes in the

realm of remix rights, right? Like you know when an artist say a DJ does a remix of a song created by Sarah requisition then they they have to get a license from their recommendation to get to do the remix and probably the requisition will get a cut of the royalties at the end as

well, right? So it's similar thing here, where there needs to be some sort of legal agreement, between the original creator of that work or the exclusive owner of that, that piece of Ip and the sub licensure for the remix rights. And we'll see how this forms. We see that, you know, cryptos a funny thing. There are sort of the fall of the law to the letter of the law. Eccentric Setter etcetera,

approach. And there's also the sort of more wild Westy approach that some people prefer to follow. Given that you already have a lot more protection built into the blocking itself. So how much do you need to leverage existing IP laws and we don't have a good answer to this. We don't know how it's going to play out. So we kind of support both.

Yeah, I know, I know you guys have spent a lot of time thinking about this and these conversations were happening in, even in the days of ipd be and how I pdb could be compliant with gdpr and there's been a lot of thought and so I'm like, I trust that that you guys have found suitable solutions to

these problems. One of the things we haven't really talked about very much is the fact that, so, the previous version we're now, of course, in V3 of ocean, the previous version was It as an etherium side chain and you mentioned before the show that you're now on the ethereum main net. Can you describe how that works? So we haven't really talked about the smart contract and when it does. Exactly. And then look what actually does exist on the main chain explain for our listeners.

What that looks like sure happy to. And you know the reason we initially did the POA sidechain was we saw that we would run into scale issues and the V 1 V 2 versions of ocean. We're pretty. Located. So we actually had even run tests to deploy to a theorem a net. And it was super painful. Frankly, it took you know, days for the first successful deployment.

And this is just simply because of the sheer complexity of the contract with her V3. We said, let's change the mental model where, instead of having our sort of own custom access control for contracts, that's put that into the context of your C20 and then we can leverage all the infrastructure of your C20 directly, right? Just like I mentioned before with an allergy to unisex etcetera.

So that's what the heart of the mental model for ocean is is you know, you have access to a given data asset. You have custody of this access if you have 1.0 data tokens and then you can redeem that if you send the data 1.0 ninja tokens to the publisher. And so that's the heart of it and that allowed us to simplify ocean a lot.

So it's, you know, radically simpler than before and also simpler conceptually to deal with much more interoperable and so on, right metal mask Traditionally a crypto wallet is now a data wallet. Right treasure is now a hardware data wallet, right? Balancer and Yuna swap are not only dex's they are now data exchanges. Right Aragon and Dallas Jack are now data data douse, right? So this is all possible and we can have, you know, stable coins

Based on data assets. This is all possible simply because of the ear T20 root. So that's kind of the heart of it. The mental model of data tokens as access control, and so what it looks like in the theorem, there's Basically three main groups of contracts and they're quite simple. One of them is a factory to publish your T20 data to them. And so we have a template for a new C20 data token. And I guess I mentioned before.

It's, it's actually simply the open Zeppelin you, she 20 template, plus this extra field called blob, and that allows a lot of flexibility in terms of new source of data services, and to save gas costs. Rather than like be the reason we have the template rather than just doing it from a librarian stuff, is to make Simpler to deploy and also to save gas costs were using ESP 1167 proxy contract approach such that it's just much much cheaper to deploy.

So that's the first part. The second part is the balancer pools and once again, you know, we're close with a balancer guys. I've been an advisor to them for a couple years now and we at the same time we did not use the balancer contracts that are deployed to a three-minute directly and the reason is gas costs as well.

It's okay for someone Who, you know, wants to create a pool of beef and say ocean with you know millions in the quiddity OR eith and die with tens of billions in the quiddity, whatever, right. But if you just got a data token where it's much more long tail, asset of, you know, a hundred bucks, 1,000 bucks liquidity. Then are you willing to spend the gas phase of $50, $55, whatever, the the gas prices are just to deploy that and it's much more of a stretch. So we said you know what?

Let's also do a friendly Fork of the balancer contract. So Pull and so on and the factory. So they've got a factory with plus a template and have it also follow this EIP, 1167 proxy contract pattern. So we have that as well. So that's the second part. So we've basically got a factory in template for data tokens, a factory in template for balance or pools. And the third thing is simply just for metadata. It's a very simple thing. It's a simple contract DDO dots

all. And with it you have basically, if you are the publisher of a given data token then, You have a slot in that particular smart contract where you can write the metadata and, and update it. And the metadata is things like, you know, name of the publisher description which can be pretty long and a few other fields. So so those are the basically, the three main things and they're all simple on purpose.

And from that, right? It's sort of in the stack, we have a piece of software called provider which does the handshaking for the publisher to to receive the tokens. And to give access, we also have another piece of software called Aquarius. Us, which is basically a metadata cash to make it easy for the ocean Market to sort of the data without having to retrieve it, directly from lunch

and all the time. So that's the components, simple on purpose and even as time goes on hopefully will Templar Obviously, you've been working on Ocean for some time.

And since you began this, this project like other blockchains have come along the way that perhaps offer more scalability than, you know, like the V1 etherium, have you considered building ocean like separate, instance, of ocean on other block, chains, or perhaps, at some point, porting it to like something like Solana for example, where there's like very high throughput? What that additional scalability Provide ocean that it does perhaps doesn't have today.

Yeah, absolutely. We thought about it, right? I mean I give a talk on between scalability issues in 2014 and we had such scalability issues with a scribe that we you know, we built our own blockchain just for scalability, right? Watching database. We can you be so we thought about it a long time, even theories around it right? That those sort of, this fundamental trade-off between decentralization consistency and scale, right?

Like you want, ideally, you're fully permissionless, decentralized and you are consistent as in. You solved it Friend and you are scalable. As in can handle, sort of planetary needs, right? And when I wrote that post in 2016, I think it was, it was, you know, that was kind of a revelation and it was very useful as a model, right? And so I profess, for example, is decentralized, permissionless, and scalable, but it doesn't solve the double spending problem, right?

So, it's not really a blockchain per se, but it actually has a really cool data management with your duties and stuff. If the room in Bitcoin they are Alice and decentralized as well as consistent. You know, they self double spend, but they haven't been scalable traditionally and then big Cheney be went. The other route where it said, okay we need scale and we need consistency.

So I'll double spend. So we're going to loosen off a little bit on the decentralized by starting out being Federated, when a p08 poh, right? And that was the decision at the time and others have discovered that since right now, you know, about a year later vitalik, discovered it are. So it may be happier and now it's more, commonly known as the Ability. Trilemma, right? I called it. The DC has triangle. It's quite quite a well-known thing.

And the cool thing is, from the time that I wrote it, I was hopeful. I said this is just an engineering problem, it's going to get solved. People are going to find ways and lo and behold they have right, which is great, right? And the usual trick of a lot of these is to leverage random numbers, you know, Monte Carlo, algorithms are an amazing trick for scalability across the board. You know, use them a lot of places and you know how III to does it help polka dot.

Does it. How Elgar on does it add more is where you, you know, have this list of 1,000 or 10,000 Canada, validators. And then you randomly select 100 of them are so, right? And then those become your validators for the next, you know, hour or 24 hours or whatever. And that's a very nice approach because it kind of addresses issues and there's other approaches as well, right Solana for example.

Yeah. They focused on solving the bandwidth issue drawing on their days of Qualcomm Engineers right and I have great respect for Cole come Engineers that worked with them a lot in the past. So I think there's a lot of great approaches out there that, you know, Essentially DCS triangles / scalability. Trilemma. You know at first there was just theoretical Solutions and then people started building the real solutions and you know, they're coming live, which is great for ocean.

We've been, you know, tracking all of these and we said you know to start with we're going to deploy in aetherium. But after that you know we envision that ocean to be truly ubiquitous and that is part of the goal of ocean you know to have a true dat economy for the globe, we need to be on all the substrates. Basically any substrate that has any usage at all. We should be on it. And so it's a journal write to get their bit by bit by bit.

So start with a theorem and then search deploying to others either ourselves as the court, ocean team or encouraging other people out there in the ecosystem to deploy, but it's not just deploying the ocean contracts. You need to have Bridges to connect to the ocean Community, because we need long-term sustainability and we can get into that as well. So that's actually sort of the constraint that holds us back from, you know, deploying a

bunch of them right now. And you know, there's other examples of the South are already, right? Like u.s. DT is on many chains, more recently chain link. Sort of done, sort of a blitzscale to lots and lots of chains as well. And I think that's great, right? So we envision the same, we think there's a lot of great teams with great technology out there and Cosmos and polkadot have done some extra cool stuff to make this easier for a lot of things too.

So you know, we have relationships with a lot of these teams and we're hopeful and it's definitely a part of oceans future towards ubiquity cool, it's good to hear.

So, as we wrap up, I I wanted to talk a little bit about the future, and, and the ways in which you Envision ocean will evolve and what are the things that came to mind is this idea of data markets as a data stream and currently in the ocean market, like, as I understand it, one uploads, the data a data set and that's sort of like a fixed asset and one can purchase it. And you know, that data set might have different versions or it might evolve over time, but

it's very much like a sort of fixed type of asset. But, of course, you know, data Flows In Permanence and and one, you know might have used for like a constant stream of data and I think this would be useful for building applications like social networks Etc. How do you see ocean? Evolving towards a more fluid marketplace where data flows can be supported. Yep, so this is yet another type

of data service. So just like right now ocean has static URLs and computer to data as two types of services, definitely. Streaming data is a type of service that is a priority for us to support. There's actually many variants of that, right? Like, graphql actually has built-in streaming support, and there's other, you know, sort of web two-ish technologies that also help to support streaming. You do so. So we look forward to having support for that.

And then, as time goes on, you know, we see that these are going to get become more and more refined in terms of the support, so more specialized. So maybe there's going to be ten different variants, assuming Dina, you know there's some great projects out there that do streaming. Probably streamer comes to mind as one of them and you know we're collaborating them great team and that will be a very nice feed of data into ocean ecosystem and Ocean Market

itself, right? So so that's I think a good example, there's a nice stepping stone piece and that is because the the URLs themselves are static under the hood. People can keep updating the data set. So we see people in Ocean Market where they post a data set for sale but then they promised you updated every four hours or every 24 hours and that's happening, right?

So that's sort of a way to kind of get They're a good example of that is swash where it's this data, Union of thousands of members where they're selling collectively bargaining, collectively selling their browser data their browser history data as this data Union and then they're selling that actually on Ocean. So and that's also related to the human project to. So we're quite excited for many, many data services over time and there's other, you know,

decentralized data services that we think are going to be very, very useful feeding into ocean as well. You know, the ones that are More pure data ish such as numerous signals chain-link, feeds and more. And then also the storage Services themselves, right? They're starting to accumulate more and more data to the file coins and see us and the theorem swarms of the world and so on, right? So all these things I think are going to be you know better and better supported over time in

more direct, less friction. Anyways, The note to end on, I'd like to ask you what types of things do you hope people will build on Ocean, what would be for you? Likes of a sign that ocean has achieved its gold overall. I mean, generally, right. This isn't really it's a vague go post but ubiquity right, like where it's kind of just part of Internet infrastructure in a way that everyone kind of, accepts it, right? Just the way that TCP IP is that like that, and the wet with the web on top.

And so on. And to me, that would be great to get to that point, but it's going to take, you know, probably decades, right? That's okay, right? What does that look like in specific measurements? Maybe just, you know, knowing that ocean tools are used by

these higher level. But by different, like all the different organizations as just part of their overall toolbox but also critical to that, you know, because this is not just a one or two-year Journey but you know, decades-long sort of thing and also it shouldn't Dependent on just myself and the correlation team. We need to actually have a plan for long-term sustainability to help. Make sure this is well funded. Right?

And on that, you know, I won't go into too much detail but we did design System Dynamics around ocean for exactly that where there can be funding over time 51% of the token Supply follows Bitcoin style emission curve and this goes into basically funding for the community to keep building and developing projects on top. Whether it's well if you things core infrastructure apps and in regressions on top Outreach and unlocking specific data assets and so on.

So those four things are things that can get funded over time by this year marks, apply lotion, the majority of ocean supplies earmarked for this. And and then how do you curate that? And the answer to that is via Dow technology, right? That's basically technology for collective decision making over time. So we are in the process of rolling out ocean Dow which is sort of the final piece of the puzzle of the overall ocean

system. And it's going to be a humble sort of We're going to start slowly start with small budget but then over time Bit by Bit by Bit grow, the amount that is finding and then at some point flip the switch and it'll be funding from this 51% Supply as well. So that's kind of, you know, critical towards oceans goal of ubiquity. And from there, you know, there's going to be other goal posts and stuff that are really going to be interesting on the way.

But I guess another one is where, you know, when you stop hearing about people complaining about how bad Facebook is and so on, right? Like when that's no longer Were part of the conversation, that'll be a pretty good goal post, right? Or when people talk about how, you know, they're making half of their income from data that they're selling from the personal data or from other things on the side. That's a good goal post.

So so things like that, right? Towards this overall goal of ubiquity as sort of infrastructure for civilization. That's a great note to end on Trent. Thanks so much for coming on once again and hopefully we'll have you on a fifth time maybe in some in some time and hopefully we'll get to see each other in person very soon. For sure. Thank you very much. Thank you for joining us on this week's episode. We release new episodes every week.

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