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
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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.
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.
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