So we sort of industrialized the whole EDM chain ecosystem and we
can onboard EDM chains actually quite fast now.
So we have 3 to 400 million adders is labeled For every one
of those labels, we have evidence and documentation.
And of course a lot of that documentation is algorithmically
generated. It can get out of hand really
quickly and you can get like a negative spiral if you start
getting the wrong labels. You know, living up to our name,
we started using more AI for the labeling.
The challenge there is like you might end up with probabilistic
labels and like I was saying before you, you want to make
sure that the precision is as high as it can possibly be.
The machine is going to be doing like 99.95% of the work in terms
of the just the quantity of addresses, but the .05% of the
human does can be very valuable and it can also be used by the
machine to label all this stuff. This episode is proudly brought
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.1. Welcome to epicentre, the show
which talks about the technologies, projects and
people driving decentralization and the blockchain revolution.
I'm Fredrika Anne and today I'm speaking with Alex Vanwick, who
is the Co founder and CEO of Nansen, which is a blockchain
analytics company. We'll discuss them in a lot of
detail in just a bit. It's a pleasure to have you on,
Alex. Before we get started with
Nansen properly, tell tell us about yourself and what's your
background and how did you end up where you're now?
Yeah. No great to be here.
So depends how far back we want to go I guess.
My background initially is an AI.
That's my my degree from university in Edinburgh, UK.
So I was an AI before AI was cool is what I like to say.
So I spent a few years working with data science and machine
learning. Also a few years in management
consulting and in 2017 I discovered Ethereum during lunch
at work, some engineers who were very excited about it at the
company where I was working. And then I fell down the rabbit
hole very quickly because I think several people started
talking about Ethereum at the same time, and so it sort of
piqued my interest. This was the summer of 2017, and
so a few months later, I decided to leave my job as a data
science manager at the time and I moved to Hong Kong to join a
startup in the crypto space. So that was how I basically got
into crypto uh, sort of, I would say I, I was not definitely not
one of the earliest in crypto. I felt I was very late at the
time joining crypto. Now I feel like I'm kind of
veteran almost, which which I guess speaks to how young the
industry is. But basically after working a
few years in crypto, both with the startup that unfortunately
ran out of money pretty quickly. I spent some time with the
decentralized exchange protocol 0X helping them with with
analytics and understanding slippage across Texas and things
like that. In 2019 were to be little bit
with Aragon as well the Dow platform and as mostly as a
consultant helping them out with data and analytics.
And then I Co founded Nonsen, our company late 2019 early.
That's when we started working on it and we went to market
April 2020, about one month after COVID started when
everyone was gambling on governance tokens and and yield
farming during COVID. So that's kind of how I ended up
where I am now. So with your background and kind
of makes sense that you would found a blockchain analytics
company. You also, I mean, you also have
a background in a in AI and I assume general IT stuff.
So was it, was it kind of the, the fact that you felt like you
had already done this before? Did you kind of, was there a
larger mission to kind of ordering this mess that kind of
actually is kind of like if you run an archive note, you'll,
you'll learn nothing unless you kind of do proper analytics on
it, right? So what was what was, what was
the main motivation to kind of go into this, you know, full
time? It, I think it's a good
question. I think there are a few
different things happening at once.
I, I will maybe say that firstly from like a career perspective,
I thought of it as like a Venn diagram of two competencies, one
being data and the other one being blockchain.
I figured that if you are very good at both of those, you
probably end up in a intersection that's pretty
small. So from a career perspective, I
figured that it's a good idea to learn about these two things
because not that many people in the world are going to know
about those two things. That was probably from a career
perspective. And then I also like very
rapidly what became like sort of enamoured with the crypto
industry because when I was interacting with people on
Twitter and Telegram, things like that, I found that people
were very open minded and they were very inviting in a way that
was almost a bit surprising to me.
I kind of thought of crypto as being a little bit kind of, you
know, almost like antagonistic or adversarial because it's very
technical and a lot. But I found that people were
very open minded and sort of intellectually quite
interesting. So I think that also appealed to
me from like almost like a culture perspective.
And then there was another thing that was I think more specific
to data, maybe even more specific to to Europe, you know,
which is where I was based at the at the time, which is a
GDPR. Like privacy regulations were
kicking into full force. And I think I kind of became a
bit frustrated as a data scientist working with data.
And there were so many regulations you have to navigate
that it became really hard to do your job, frankly, like it was
because everything you had to, you know, check all the boxes
and all this stuff. And so I was, I kind of wanted
to work with data that was not like customer or user data.
I wanted to just work with data sets where you don't need to
like fill out a form to be able to use them.
And so blockchains were interesting because some so much
of it is from public blockchains, right?
The information is just there and the data is there.
And so you don't need to ask permission for someone to dive
into like all this exciting activity that's happening on
chain. So, so you know, those were some
of my personal reasons. And then I think if I look at it
from like the market opportunity side, you know, you didn't have
great analytics tools or products at that time.
I think there was really only one game in town for on chain
analytics, which was chain analysis.
I mean, you had other products like, and, and I don't want to
belittle them, but like most people knew about chain analysis
for AML purposes, but I felt that, you know, people who are
just in crypto who are trading or investing or using block
chains, they're not necessarily law enforcement or tax
authorities. They should have great analytics
products too. And so I felt like there was an
opportunity there to just provide them with a better
product so that they can understand what's happening on
chain and they could make better decisions investing.
They could, you know, make better decisions building
products and protocols and building block chains or L2's
now these days, right? So, so yeah, there's kind of
meant there were many different factors that sort of led me, led
me here. I can chime in here and say that
as a blockchain founder myself, I have used your tool a lot,
particularly for one use case for which it just beats all the
alternatives out there. And that is kind of you're very
good at labelling wallets and kind of saying who you think
they belong to, what kind of person or kind of it is.
And I, I'm super interested in how decentralized our token
holdership is, right? So kind of I would go to, I
would go to kind of like the noses token and kind of just
listed by kind of like by, by which address holds how many.
And I mean, in the beginning, I knew who a lot of the people at
the top of the list were, right? I mean, kind of that's the kind
of how, how, how projects start out.
But kind of kind of how, how, how far can I go down the list
until I find the first person who I honestly don't know who
that is. To me, that's been, that's
always been very comforting to know that kind of like there's
lots of people out there who are involved in the pro project in
some way and I have absolutely no idea who they are.
And I mean, that's, I mean, that's only increased over the
years. So, yeah.
But I, I, yeah. So this is this, this is how I
this is how I first learned about Nansen, I think when it
came out in 2020 or so. Yeah, you, you bring up that
that's like a very common use case, right?
Especially among builders who want to just understand their
investor base or like who's holding the token.
And, and I think you're right that this is also one of the
opportunities that we saw that, you know, in a way it wasn't
that interesting to just get the blockchain data because in
theory, anyone could, could do that.
The hard part is to figure out like what's the entity that's
associated with the address? And we saw an opportunity there
to, to sort of help people get more transparency on that front.
And, and you know, that is one of the core things that we do
very well, right? And and we have at this point
like 3-3, maybe even 400 million addresses labeled at this point.
I can speak to that as well because this is also one of the
use cases I use Nansen for. I check whether I have docs
myself. So obviously kind of I have on,
on different addresses and I try not to, to, I try to kind of
keep them apart. So kind of if, if you guys
don't, if they're not labeled or Nansen, I think I'm probably OK.
Yes, yeah, that is, that is true.
I mean, maybe you know, we should, we should also just call
out that, you know, if individuals have their name
label announced and they can contact us and we will and if
you want to remove it, we will remove it.
There are there's a bit of nuance to that because sometimes
people inadvertently ducks themselves on chain.
So they might like buy a dot ETH name or something like an ENS
and we can't do anything about that because that's immutable
and like etched into the the history of the blockchain.
But yeah, so, so you know, there, there's that this is like
a kind of a blessing and a curse of blockchains that they are
transparent, they're immutable, etcetera.
So what something that I've always wanted to ask you is
Nansen actually named after fridge of Nansen of passport
fame. OK, fantastic.
How that's right. Maybe, maybe tell us about
Nansen and kind of why you why you said it on that name?
Yeah, I mean, I, you know, I, I think a lot about culture in the
context of a company or a project.
And I felt that nonsen is kind of an embodiment of the values
we have in our company. And so values like courage,
curiosity, you know, nonsen, for those who are not aware, it is
most famous for actually, I've been a polar explorer.
He crossed Greenland on skis as the first person.
He went as far north on the globe as anyone had ever done.
And the same ship that he used, another polar explorer, reached
the South Pole first of any human being.
But he was also a scientist. And it was interestingly, it
sounds like you know him for his work on creating passports for
refugees. Yes, which which he did for, you
know, almost half a million people.
For stateless people, right? Yes, for stateless refugees, I
think mostly around Armenia. So he was, he was a, you know,
kind of a renaissance person, an explorer, scientist, a
humanitarian even had played a big role in the creation of
modern Kingdom of Norway. He convinced the Prince of
Denmark to become the king of Norway so that they could become
independent for Sweden in 19 O 5 But but yeah, so he's an
embodiment of a lot of the values that we live by at
nonsense curiosity, courage, transparency, speed, which is
important when you're doing an expedition.
You want to make sure you get there in time before you starve
or run out of you know what you need.
So, so yeah, so he's kind of an an icon.
And I think like it's I, I also kind of like the idea that bit
similar to Tesla, right, where like there's a you've named the
company after someone who's not the founder, but it's like an
inspiring person. And and then it's two syllables.
It's easy to pronounce in any language, which is nice.
So yeah, that's those are some of the reasons why we where we
named the company else. And was the the URL nansen dot
AI from the get go. Yes, it was.
And but I will say that the dot AI was aspirational in the
beginning in the sense that you know, my, so I said that in the
beginning my, my degree as an AI and I always knew that we would
be making use of AI for what we do things like labeling
addresses. Now we use AI for, you know,
estimating the price of an NFT that's fully machine learning
powered and it's part of our product, which is actually kind
of non trivial, right? If you have a specific NFT, how
much is this one valued based on its traits and for section
history, etcetera. We use AI to, you know, weed out
spam tokens, which there's a lot of, especially on chains that
have lower gas fees and things like that.
We use AI for personalizing signals in the product.
So I think, you know, Nelson, you know, we, we did have some
foresight in that we knew that AI was going to become, you
know, a big part of the world. It happened admittedly a bit
sort of faster or more suddenly than I personally expected.
But we're leading into the AI even more now than than we were
originally. And so it's not like there's one
AI angle with nonsen. It's more like it sort of powers
the whole product in many different ways.
So, yeah, that's, that's always been the been been the, the
ambition to make sure that we are an AI trailblazer and we're
making use of AI and great ways in the product and in the
organization. Cooler, let's maybe dive into
the the core of your product. So kind of like you started out
with, and that's very much your core offering is kind of
analytics for on chain data. Most people who don't work on
actual blocks themselves, they don't appreciate how much
engineering effort actually goes into kind of creating like a
state and the database and so on.
Can, can you maybe talk us through that kind of what, what,
what kind of say, say I have an Ethereum archive node, it's a TB
or whatever kind of the, the current site depends on kind of
what, what you're running, but and how, how do I get from there
to kind of something that I can actually query?
Yeah. So the way we do it mostly is we
make use of, you know, RPC Jason endpoints from the notes and
then we pull out specific data from from the notes.
And so you pull out the blocks and they have transactions and
you, you, if you want to go one level deeper, you parse out the
events from smart contract interactions.
Like if you have the ABI of a smart contract, then you would
use that to be able to parse out the data that's that's included
in the transactions. So, so that's kind of the, I
mean that's sort of like at a very high level, you know how
you do it. And you know, we, we actually
started out with a pretty different tech stack and
architecture and we've changed that recently.
So we we used to use, so one of my Co founders is the creator of
an open source project called Etherium ETL, which which
basically does this in an open source manner for Etherium.
And so you can actually like index all this data, you know,
if you have an endpoint or you run it on node and you have that
endpoint, you can index all this into like CSV files or into, you
know, a database. And so, so he, he built that and
that was kind of, that was one of the building blocks that we
used to get started. Over the years though, we have
basically moved over to a different paradigm of loading
the data. Initially it was, you know,
Ethereum ETL, so extract, transform, load, which many data
engineers and so on will be familiar with.
Now we do basically ELT extract, load, transform.
So one of the reasons we do it this way now is because we
integrate with lots of different chains and different chains
might have slightly different schemas.
So the idea is if you first can just extract the data from the
the Jason RPC endpoint and you can just load the raw data in,
then you can transform it later. So it's sort of you delay the
transformation and the schema harmonisation of all the data
and to to a later point. And then we've also changed the
the database that we use, We used to be based on Bigquery,
which is a Google Cloud sort of proprietary analytical data
warehouse technology. Now we use something called pick
house, which is also an, an analytical database, but it's
more performant for the type of use that we have.
So in the past we might have a dashboard that I don't know when
you were most using nonsense, but nonsense version one was
actually pretty slow and it some of the dashboards would load in
like 30 seconds, which is kind of hilarious if we look back at
it now. But with with click house, you
know, the same dashboard might load and like, you know, 300
milliseconds or something like that.
So, so we, we may, we've actually kind of evolved our
tech stack and replaced the whole thing, both the data
pipelines to sort of extraction of the data and also how we
store the data and how we could query it etcetera.
So which chains do you currently support?
So we we actually have kind of a suite of different products.
So for example, if you look at Nonsense portfolio, which is our
portfolio tracker, we support more than 50 chains.
And so it, it's kind of all of the usual suspects, you know,
Bitcoin, Ethereum, even Solana and then a long tail of EDM
chains. And for Bunsen query, which is
kind of the enterprise product where you can write sequel
queries and people make dashboards, we support about I
think it's 20 plus chains. So they'll be fewer, but still
120 and then announcing 2, which is the product that actually
most people know, which is kind of the the product that you've
used and where you see your your holders for the token, token God
Mode profiler, we support, I think it's now just over 12
chains, but we're adding a lot of chains every quarter actually
to it. And so, yeah, so, so sort of
depends a bit on which, which products you're actually using.
But the, the ambition is to be adding like roughly 1 chain per
month or more going forward because you do have like the,
the world is very multi chain at the moment.
And so you want to make sure that you're supporting all the
chains that people care about them that they use.
And you know, we've sort of invested a lot in our tech, in
our tech to make it both faster and and frankly cheaper for us
to integrate new chains. So we sort of industrialized the
whole EDM chain ecosystem and we can onboard EDM Chase actually
quite fast now. Yeah.
So, so, so that's kind of how we how we look at it.
Interestingly, there are a lot of non EDM chains that want to
integrate with us, which on the one hand is great because you
you want to support them, but on the other hand it's also quite
technically challenging because you have to sort of build a
bespoke solution for every chain almost.
But but yeah, EV, the EVM chain use case, we've sort of
industrialised in house. I am sure you kind of you have
protocols for that, but how do you ensure kind of data accuracy
and the reliability of the analysis?
Yeah, you can. You can sort of talk about data
accuracy or data quality in a few different ways in our
product. So the most basic data accuracy
is about the onshare data itself, right?
So you want to make sure that you're not missing data that you
know, you have tests where you see, you know, the number of
transactions, is it in line with today, with what you saw
yesterday and that kind of stuff.
So you can have basic sort of almost like unit tests, data
quality checks on that. I think the harder part though,
is on the attribution, the labelling of addresses, right?
That and that's where there's potentially room for error.
And so, you know, our philosophy is that we would rather not have
a label that have a wrong label. And so that means we have very
strict requirements on precision.
And so as an example, we for, you know, I mentioned we have 3
to 400 million adders labeled. For every one of those labels,
we have evidence and documentation.
And of course, a lot of that documentation is algorithmically
generated, but you can always look up, you know, if this
address has this label, why does it have the label?
So there's, you always have the documentation for it.
And I think this is something that we, we take pride in that
we, we actually take that stuff really seriously because it can
get out of hand really quickly. And you can get like a negative
spiral if you start getting the wrong labels.
Because typically what happens is if you're looking at a new
address and you want to label it, you start looking at what
are the labels of the addresses, the, the neighbours of that
address. And so if you have a wrong
label, it can propagate very quickly and it goes out of
control. And of course, you know, you get
more wrong labels. That's the first thing.
But secondly, more importantly, it can impact the user
experience if people see a wrong label and it's lose trust in
your product. So this is something we take
very seriously. And, and, and we, of course you
will always, you will always have some errors like that's,
it's just not possible to have literally 100% precision.
But it's actually very rare that we have incorrect labels.
And even if you do arguably have incorrect labels, very often
there's a very logical explanation for it.
So at some point I remember we were called out for having
labeled the address Doquan. And you know, we were told that
that was incorrect, but it turned out that it was basically
Terra Labs or you know, the company related to it.
So so you know, is that an error?
Like maybe it is in a strict sense, but of course, you know,
it's a very related entity and you might have similar thing
with like some Suzu or or three arrows and and things like that.
But you know, we take pride in having the best precision on the
labeling that we do. And this is something that's
very important to us. Which specific heuristics do you
actually use to kind of generate the labels, I mean, and how do
you come up with them? I'm sure you kind of add stuff
all the time, right? Yeah.
So it's a combination of man and machine, right?
So the heuristics would be, some of them are deterministic and
quite simple, right? So think of you want to label
every Unisop pool, then you can literally just look at the
Unisop factory. And like we were talking about
earlier, you can, you know, look at the events that are emitted
and the events contain all the information that
deterministically say here are all the UNICER pools.
This is like the easy case. And in theory, anyone who can
like read blockchain data and have a system for this could,
could do this. Then there are other things that
are more complex like exchanges, centralized exchanges, because
they're technically the information is not deterministic
from just the on chain data. You need to do some inference
and you need to understand like how these entities manage
private keys and manage addresses.
And so there you typically have kind of the baseline heuristic
that is sort of somewhat universal for any exchange.
So you might say, actually, if you send funds to an address and
the address automatically forwards it to what we call a
main wallet, like a finance main wallet, then you could be pretty
sure that that wallet is a deposit wallet for the exchange,
right? And so this is going to be, you
know, correct in most cases, but you may have to tweak it and you
need to curate the main wallets because those can update, right?
Let's say HTX or, you know, gate IO might get a new main wallet.
Do you need to make sure that you're on top of that and you
need to have sort of alerting in in house if you see lots of
funds move because maybe they move to a new like cold wallet
or a new hot wallet and so on and so forth.
So, and and so you, you can state these heuristics
programmatically and you can label upload addresses in this
way. So it's kind of like an
inventory of many different heuristics.
And sometimes the heuristics can build off of each other.
But you know, living up to our name, we started using more AI
for the labeling recently. But the challenge there is like
you might end up with probabilistic labels and like I
was saying before you, you want to make sure that the precision
is as high as it can possibly be.
And so I kind of. But yeah, it comes back to the
same point where you have this man and machine set up where the
machine is going to be doing like 99.95% of the work in terms
of the just the quantity of addresses.
But the .05% of the human does can be very valuable and it can
also be used by the machine to label all the stuff.
And so, yeah, so it's interesting, right?
Because the, the AI approach we started making use of now they
kind of like almost sit in between the sort of the man and
machine, like the human and the machine.
But we've, we've seen anything you can also, by the way, you
can look at, I mean depends like how far down the rabbit hole you
want to go. But you can also look at the
economics of it, like how much does it cost us to label an
address, right? Like if you if you just think of
the human labour or even like the cloud cost of the
heuristics, and then you start looking at like optimizing that
and saying actually, you know, the heuristics very cheap.
So you want to make sure that the heuristics can label as much
as they can. And you want to be very
selective of what you use human labor power for because that can
be like $10 per label maybe depending on, you know, many
different factors. So, so yeah, we're, this is kind
of an interesting optimization problem over time that you you
have to like balance out different things.
You want to make sure precision is very high.
You want to make sure that also the the recall or the coverage
is very high. You want to label as many
addresses as you can. You also want to make sure that
you can do it in a timely manner so that you can label addresses
very fast. And you want to make sure that
the economics are permissible so you don't break the bank.
If it costs us like $20 million to label 20 million addresses,
like, yeah, that's probably not going to work, right?
So, yeah, so, so there are many interesting back to Sarah and
like this in a way this is kind of the most unique thing we do
at the company, right, If you think about it and it's it's
exciting because it's one of the areas that probably can be, you
know, enhanced the most with AI in my opinion.
Yeah, absolutely. And how fast do you label these
addresses and big movements? I'm asking you because obviously
this kind of, if you're a trader, this can actually give
you a lot of alpha, right? So, so if someone kind of moves
funds from a cold from a known cold wallet to a hot wallet,
chances are they're going to sell them possibly on, on an
exchange. So you, you might kind of want
to front run them in the traditional sense, not in the
blockchain sense. So how, how fast do you do this?
And do people explicitly use it for this sort of use case?
Yeah. So we have a feature called hot
Contracts. And what hot contracts does is
it looks at newly deployed smart contracts that have a lot of
funds going into them. And hot contracts now actually
very soon, like probably in a matter of weeks, is going to be
enhanced with AI labeling. And so that means the idea is
like probably, I don't know if they will be minutes because you
kind of need to accumulate a bit of data on the address in terms
of transactional patterns and stuff like that.
But yeah, maybe minutes at most hours, you know, you'd let loose
the army of AI labelers on these hot contracts.
And because we will have tuned and, and quality assured the
position, you'll be able to get pretty descriptive labels of
what these contracts actually are, right?
The the, it's interesting, right?
Because in a way, like some of our users are very sort of power
users and advanced users, they sort of see the alpha in us not
having labeled an address because they know that that's
like a new address. And because it's not labeled
yet, if they figure out what this address is, they might be
sort of one step ahead of the game.
And so if you look at hot, the hot contracts table, ironically,
a lot of the addresses are not labeled.
But I think that's going to change literally like in a
matter of weeks when we roll this out.
And so I think some of the people who are using that
feature, hot contracts are, are probably going to see like it
almost like a night and day, you know, change in that view.
For other types of addresses, it depends like a fund, typically
it takes a while to actually figure out what the fund is.
So if they move like AVC fund or like a liquid venture fund or
something, if they move funds from one address to another,
that's maybe one of the more clear cut cases.
But if you have a totally new address that is like providing
funds in a seed round or something like that, it can be
quite tricky. You need to have multiple data
points to figure it out. And so those cases you can't
talk about like minutes or hours, that's like, you know,
days or weeks or maybe months. So it really depends on like
what kind of what kind of address or what kind of entity
we're talking about. So for the for the newly deep
people like smart contracts, do you also, do you also speculate
about kind of what it's going to do?
I mean, is it kind of does it, Is there a label that says we
think this is this is this is a newly launched PAP exchange or
something? Yeah, so, so the so the idea,
the idea is like with labels, right?
There are a few different ways to think about labels.
One is just give it a name, right.
So like, you know, Gnosis chain something, you know, prognosis
and bridge or something like that, right?
But then there's a category to which I guess is what you're
getting at. So you have a category
description. So this is like a staking
contract. It's a bridge, it's a deck, it's
a, it's a defy pool, it's a yield farm.
Like there's sort of a taxonomy of different things it could be.
And the idea is it what we're aiming to do is both give it a
name, a specific name and also give it a category.
And, and in fact, like the category also, it doesn't always
need to fall into one category. So you might want to give it
like multiple different labels from like multiple different
indicators, right? So this is both the staking
contract and it's a token. Like think of staked Eat with
Lido, for example. That's kind of, it doesn't need
to fall into one category. So the idea is to do both, like
give it a name and give it a category.
So I've not tried this yet. So if if you were to kind of put
a newly deployed smart contract or any smart contract into into
ChatGPT or any of its competitors, will it be able to
tell you what what it does? No, if, if it were that easy,
then we would have just done that.
We have, we have tried. No, but you have to.
You don't want to, I guess like give away too much of the secret
sauce. But but you have to sort of, you
do have to use ChatGPT as a good idea to use ChatGPT, but you
have to sort of guide it with the right prompts and make it
use the right sources for it. OK.
So kind of you, you want, you want ChatGPT to kind of figure
out what's the business logic behind this smart contract?
Yeah, kind of. And you have to sort of chain it
like do multiple steps, right. So it's like, yeah, yeah, I
don't want to give away too much, but but the idea is like
roughly you want to try to make it understand you wanted to make
sure, you want to make sure that it has all the information A and
then B, that it can synthesize all of the information and then,
you know, put it into like a meaningful category or give it a
meaningful name. And then, you know, so you can,
you can think of this as like you have LLNS and then, you
know, you could fine tune LLMS, but it's actually in practice,
you end up making better use of the context than you do to fine
tune the LLM. And then you also do it
iteratively. So you kind of ask it to solve
multiple different problems iteratively or like in a
sequence. And then at the end you kind of
get something that is useful, but it's more so it's, you
could, you know, the very sort of short form way of putting it.
It's like it's a form of prompt engineering, but it's, it's
actually like pretty involved prompt engineering.
Yeah, I can imagine. Sorry, just one more thing on
that, right? Because it's not enough like
ChatGPT doesn't have our existing 300 million address
labels, right? And so that's where you get kind
of an edge to because our own version of this can also tap
into the existing labels we have.
And because of that compounding effect, I said earlier, we're
like, if you know the neighbours, it can help you
figure out what the label of this one is.
You get this kind of sort of a Moat that's built around kind of
being able to label stuff with high precision and very fast.
Yeah, absolutely. You guys also use AI on the
other side. So kind of if I I'm a user, I
search for stuff. You you have smart search and
similar similar things. How does?
What does it allow me to do and
how does it work? Yeah.
So maybe on this end, probably the best example is signals,
which is a feed and almost looks like a Twitter feed or
something. And you have sort of these cards
and each card is a signal that we've observed on chain.
And so this could be, you know, Pepe token has, you know, this
amount of $1,000,000 going into centralized exchanges.
That's 20 times more than an average day.
Like that's an example of a signal.
And these signals are personalized based on, you know,
what you have done in our platform.
So if you have saved certain tokens to your watch list, if
you have maybe added certain addresses to your watch list or
NFTS, and then soon, this is something we're rolling out in
the next few months. We're bringing together our
portfolio tracker with the analytics product.
So if you have your portfolio tracked with us, we can
personalized signals that you see in your feed based on your,
your, your own portfolio and your history, the history of
trading and so on and so forth. So it's actually a toggle and
the product where you can switch on and off personalization.
So either you can just get the kind of vanilla feed that
everyone gets, or you can get a personalized feed based on, you
know, what you have indicated to us that you're interested in
through your behavior and the platform, what you search for,
what you've saved, and so on and so forth.
So this is kind of, this isn't new, right?
This is just, you know, what Amazon has been doing since
almost the 90s or at least early 2000s in like people who are
interested in this are also interested in that like
recommender systems. So this part isn't necessarily
that new, but interestingly, you haven't seen a lot of
personalization in Web 3 yet, which is something that's a
little bit puzzling. I think it's maybe because
firstly, it's a very young space, but secondly, we didn't
have like enough data that it was needed.
People could still just go on coin Gecko and like find the
coin. But I think we've, we've entered
the era now where you have literally millions of assets.
And so it's no longer feasible to just search for the asset you
care about. You actually need to get stuff
recommended to you because the inventory has become so large
that it's not, you can't just look through it in a catalogue
or like on a ranking. And so that's why I think now
personalization is probably going to play a bigger role in
crypto and that's what we're trying to lead into.
And obviously you make use of machine learning and AI to make
that happen at scale. So that actually puts you in a
super powerful position because not only do you have really well
organized repertory of all the data that's on chain, you also
have kind of like the private user data that they share with
you. Do you, is there kind of some
sort of ethics codecs of kind of like how you treat the user data
that's kind of shared with you? Do you kind of do you monetize
that? Do you kind of use that to kind
of cross reference things behind the scenes?
Yeah, I mean, there are, yeah, it's a great question, right?
Naturally, you have to first of all respect just general privacy
regulations, right, GDPR and so on and so forth.
And so, so and that's, you know, has its own sort of set of rules
and things to to make sure that you're not violating, right, and
that there's consent and so on and so forth.
Secondly, you know, we have a Chinese wall.
This is because this is a concern I think that many people
have, and it's a valid concern. It's like if I use nonsen, are
you going to use what I searched for to label like my addresses,
for example? If I search for my own address,
you're going to use that. And the answer is no, because
there's literally a Chinese wall between the department that has
access to any user data and the department that has access to
labeling wallets. And it's kind of hard for us to
prove this because we're not like an open source company,
obviously our project, but that that's the reality.
And so that's in our privacy policy.
And it's also how the company is structured.
And literally people don't have access to both of those two
things at once. We don't monetize that data.
I don't think it, I don't think we, we, we, we don't need to
like we don't have an ads based business model.
Like our business model is very straightforward.
You just pay for the subscription and you know, you
get access to the product. So in a way, I kind of like the
business model because it's the most transparent.
Business model. You yeah, it's very honest.
This many people don't like it because they're so used to
getting stuff for free, but on the back end their data is being
sold to. Yeah, exactly.
So in a way, I feel like we're like the this the the dumb
honest people like we're just charging you to use the product
and like, that's it. We don't need to have some like
nefarious way to exploit their data on the back end.
You could give people the choice.
You could say, do you, do you, do you, do you want to be
private and you, you pay or do you, do you want us to kind of
monetise your data in some other way and you get to use it for
free. Yeah, maybe maybe that's an
idea. Yeah, I mean, I, I will like on
the topic of business models, right, I think I sort of think
of ads as the the default business model of Web two.
And I think that the default business model of Web three is
going to be transactional. So you know, it's not unlikely
that our subscription model at some point will get displaced by
a more transactional business model.
So does that mean maybe you have cow swap integrated into nonsen
and when you find tokens, a token God Mode, you click buy
and you execute the trade through cow swap and maybe cow
swap and nonsen share any fees that are involved in that,
right. So, you know, that to me seems
like a more future proof business model and I'm not super
excited about the ads business model, but yeah, that's a that's
a big kind of strategic topic on its own.
Kind of thinking forward, how do you see the rise of privacy?
I mean, kind of a lot of your business model kind of hinges on
the fact that things on chain are inherently transparent,
right? So with the rise of privacy
preserving technologies on chain, how do you think that's
going to change? Yeah.
I mean, I think of this as you can't have both at the same time
to the maximum extent. And so it becomes a trade off as
with many things in technology, you can't have full privacy and
you can't have full transparency at the same time.
And so, you know, our product makes the most sense obviously
when there's room to have transparency.
And so I think the reality is that many people value the
transparency of block chains because it gives them a sense of
comfort that if you know that, hey, the funds that are sitting
in Avenue, I can actually like see all of the transactions that
have ever happened with Ave. and I can see all the funds sitting
in the smart contract and so on and so forth.
That gives people a sense of comfort.
They might not actually do it, but the fact that they know they
can do it may give some sort of more trust.
And if you contrast that to say, a bank or an FTX, then you kind
of quickly realize that the lack of transparency can become an
issue. So I think our product naturally
works best when you have chains that are public and transparent.
It seems obvious to me as like a consumer that block chains in
their current form don't really work really well for payments,
for example, and things you might want to do in your daily
life where you do want to have more privacy.
And I think it makes sense that you'll probably get some, some
world where either protocols or even chains or L twos, you know,
have full privacy, but maybe there are some guardrails or
like some rules around it. So I'm not saying I this is what
I want, but I think like one way, one way you might imagine
this is what if you had an L2 that basically had privacy
somehow, but you could not make transactions over some certain
amount, you know, size, right? Again, I'm not saying this is
necessarily the word I want, but I could see that's being
something that regulators might be more comfortable with than
one where there's like no limit. And you know, Lazarus from North
Korea can, you know, potentially transact hundreds of millions of
dollars in volumes. So I think I think you can look
at look at it from sort of an ethical slash moral perspective.
And then you can also look at it just from a sort of a pragmatic
perspective, like what what are regulators going to always
allow? And and then finally, you can
look at it just from a trade off perspective, like if you if you
interact with something that has full privacy, what are you
giving up in terms of transparency?
And then there's like interesting solutions around
zero knowledge proofs and so on, which in some cases can give you
sort of the trust you want and like some form of transparency
without revealing everything that's going on.
But I think it's it's a really interesting space.
I don't think you'll ever get to a point where everything you do
on chain is totally private. And I think that also defies
the, the, the object to a certain extent, right?
So I mean, what, what ideally you, you kind of want this kind
of transparency for the man and, and kind of like privacy for the
little guy, right? So kind of you, you, you want
part, you want, you want transparency to kind of hold
power accountable. But you, you, you don't.
So you, you want to know what your government spends its money
on. You don't need to know what your
neighbour spends and the. And the crazy thing is it's like
the inverse in, you know, the world, in, in many countries,
it's the inverse, right? Where like governments can see
everything you do in in practice, like they could just
reach out to a bank, get all your data or whatnot, or they
could sort of have a, you know, some sort of back channel into
your web two products, you know, whether that's Google or Twitter
or whatever. But then, you know, there's
really no transparency on like how they're like where where did
all of that money that was spent on initiative X by the
government go and so on and so forth.
Yeah, totally. So I think you do want
transparency for the people you elect for sure, right.
That's in a way it's kind of crazy that you don't have that
in like every democracy, literally down to every
transaction that they make with taxpayers money, right.
So, so exactly like that, you know, could there be like a
nonsense for all government spending?
Yeah, that'd be amazing. Like I would love that.
Kind of. Yeah, I would absolutely love
that. So you you alluded to this in
the very beginning. So you will label well known
people on chain. So obviously there's ethical
considerations kind of that come with it.
So say I'm dope one and I feel like I don't, I mean, you have
rightly labeled me, but I don't want this to be no nonchain.
Can I can I kind of send you an e-mail and you will delete the
the label or what? What's your policy?
Yeah, you can. I mean, that's the short answer.
Yes, you can, but there's always, there's always been a
new answer. And maybe it might be helpful
just to explain like how you get there in the 1st place, like why
someone, an individual might get their name on an address, right?
And that's typically because there's information in the
public domain that we can point to.
So for example, someone says on a governance forum, hey, this is
my address I'm voting on, you know, initiative X or proposed
Lex, and they are basically declaring that they own this
address, right? And, and of course, there are
caveats to that, like someone could just be pretending to be
them and so on and so forth. But if that is credible, we
would label that and then you could point to that in your
evidence, right? If they then choose that,
actually, I don't want that to be labeled.
I want it to be deleted. Yeah, then we will do that.
But at least that's the explanation of like how the
information ended up in our database in the 1st place,
right? So it's not like we go around
trying to sniff out, you know, normal people's sort of
individual names and like label our.
People showed us when they kind of do transactions at ECC.
No, absolutely not. And in fact, like, you know,
you, if we, if we wanted to do stuff like that, I mean, maybe
not that, that thing exactly, but if we wanted to sort of go
on Twitter and hunt down every time someone like is a little
bit silly and declaring something, for example,
responding to tweets about post your address and you'll get an
AirDrop or, you know, here is my new NFT that I bought, which
again, uniquely, you know, basically doxes your wallet.
If you wanted to do that kind of stuff systematically, like we
could, but we just don't think that's the right thing to do
firstly. And secondly, I don't think it's
like newsworthy in the sense like people, the what we do can
be seen as a form of journalism, right?
So then you have to kind of ask yourself like, OK, if Vitalik
has a wallet that has like, you know, a billion dollars in it,
yeah, that's newsworthy. People should probably know
about that. If the founder of a project, you
know, has a lot of money in that token, that's newsworthy.
People should know about that. If some person has a 200 bucks,
you know, and bought some NFT on base and then they told someone
about that on Twitter, we don't systematically track down that
information and put it into our system.
We could in theory, because it's public information, but we don't
really do it. Sure, absolutely.
So tell us about what, what's coming for Nansen, what what
kind of so you already talked about the the hot contracts
update that that is what, what else do you have in store?
Yeah. So the hot contracts update is a
kind of a smaller example of how the labels are just going to get
a lot better because we are investing a lot in AI driven
attribution and that's going to happen I think faster than we
initially anticipated. Actually the second thing that I
also mentioned is portfolio is going to get integrated into
nonsense two and this is actually kind of a big deal
because it allows us to personalize the product even
more. And I think you know the
ambition there is to be the preferred portfolio tracker of
any on chin investor. And so we are very much on chain
oriented, right. So we believe that if you have
the best coverage of chains and assets and protocols and we can
give you signal on white what you might be interested in
investing in, that's a really potent combination.
And so bringing portfolio antonyms to I think is going to
be a game changer frankly and it's going to happen in the next
few months. The third thing is we are
integrating lots of new chains. The one chain that we have been
asked about the most is Solana. And so we're going to launch
that hopefully within two months and I think that's going to be
pretty big and we have some some exciting ideas to try out with
Solana because it's kind of a its own little ecosystem and
pocket. So we're going to try out some
more experimental ideas with Solana.
And in addition to that I think you know, we're going to
strengthen some of the things that were already known for like
smart money tracking is getting better.
We have a now we have a new squad internally that's just
focused on taking that to the next level.
So things like really good PNL, tracking of traders, finding
wallets that you might want to monitor because they're really
good at trading. That's something that we're
levelling up and improving, making the overall product
easier to use because it can be a bit overwhelming for people,
but it's I think constantly getting simpler to use in a way.
Like we're trying to strip away stuff like eliminating stuff
that's not absolutely necessary in the user experience.
So those are some of the things. But I, I think like, you know,
overall, we've put nonsan one behind us.
In fact, we're switching off Nonsan one literally tomorrow.
So we've kind of firmly made the transition to Nelson two and
that means we can sort of put that we can travel lightly, we
can put behind us another tech debt that we had from the first
version of the product and then we can really just focus on the
the innovations for the new version of the product.
So yeah, this actually, literally in the next three
months, there's a lot to look forward to if you're a nonsense.
Wonderful. So where can we send listeners
to kind of check out nonsense? Yeah, you can go to Nonsense dot
AI, That's the best place to start.
And you can also follow Nonsense under Score AI on Twitter.
Perfect. Thank you so much for coming on,
Alex. It's been a pleasure.
Thanks for having me. Thank you for joining us on this
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