Nansen: AI & Blockchain Analytics - Alex Svanevik. Ep. 560

Transcript

so we' sort of industrialized the whole evm chain uh ecosystem and we can onboard evm chains actually quite fast now we have 3 to 400 million addes labeled for every one of those labels we have evidence and documentation and of course a lot of that documentation is uh 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 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 the stuff welcome to epicenter the show which talks about the Technologies projects and people driving decentralization and the blockchain revolution I'm Fred an and today I'm speaking with Alex vanck who is the co-founder and CEO of nanen which is um a blockchain analytics company we'll discuss them um in a lot of detail in just a bit um before we do that let me tell you about our sponsors this week this episode is proudly brought to you by nosis a Visionary Collective

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one it's a pleasure to have you on Alex before we get uh started with nans and properly tell tell us about yourself what's your background and H how did you end up where you're now yeah oh great to be here so um depends how far back we want to go I guess uh my background initially is an AI uh that's my my degree uh from University uh in Edinburgh UK uh so I was in AI before a was cool is what I like to say so I spent a few years working with data science in machine learning uh also a few years in management consulting and in 2017 I discovered ethereum during lunch

uh at work that some Engineers 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 picked my interest um this was uh 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 uh I felt I was very

late at the time joining crypto now I feel like I'm kind of a veteran almost which which I guess uh speaks to how young the industry is but basically after working a few years in crypto both with a startup that unfortunately ran out of money pretty quickly I spent some time with the decentralized exchange protocol ZX uh helping them with with analytics and understanding slippage across Texas and things like that in 2019 worked a little bit with Aragon as well the Dow platform and um as mostly as a consultant helped them out with data and analytics and then I co-founded nonen R company um late 2019 at least 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 um 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 um a blockchain analytics company um you also I mean you also have a background in a in Ai and I assume General uh it stuff so was it was it kind of the the fact that you felt like you had already done this before or 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 uh the main motiv to kind of go into this you know full time I think it's a good question I think there were 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 a van 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 W became like sort of enamored with um the crypto industry because when I was interacting with people on Twitter and Telegram and 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 like antagonistic or adversarial because it's very technical and all that 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 and maybe even more specific to to Europe you know which is uh where I was based at at the time which just a gdpr like privacy regulations were kicking into full force and um I think I kind of became a bit frustrated as a data scientist working with data and there were so many regulations you had to navigate that it 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 so 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 uh 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 onchain analytics uh which was chain analysis I mean you you had other products like I and and I don't want to

belittle them but like most people knew about chain analysis for um AML uh purposes but I felt that you know people who are just um in crypto who are trading who are investing who are using blockchains they're not necessarily law enforce 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 could 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 blockchains or l2s now these days right so so yeah there's kind of

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 um a lot particularly for one use case for which um it just beats all the Alternatives out there and that is kind of um you're very good at labeling wallets um and kind of saying who you think uh they belong to what kind of person or kind of uh it is and uh I I'm super interested in um how decentralized our token holdershome 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 um yeah but I I yeah so this is this this is how I this is how I first learned about um nanson 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 uh 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 uh and you know that is one of the core things that we do very well right and and we have at this point like three three 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 nanson for I check whether I have doxed myself so obviously kind of I have assets on different addresses and I try not to to I try to kind of keep them

apart so kind of if if if you guys don't if they're not labeled on N I think I'm probably okay yes yeah that is uh that that is true I mean maybe it you know we should we should also just call out that you know if individuals have their name label announc and they can contact us and we will and if you want to remove it we will remove it there are uh there's a bit of nuance to that because uh sometimes people inadvertently docks themselves on chain so they might like buy a eth name or something like like a and enss uh and we can't do anything about that because that's immutable and like etched into the history of the blockchain but yeah so so you know there there's uh that this is like a kind of a

blessing and a curse of blockchains that they are transparent they're immutable Etc so um something that I've always wanted to ask you is nanen actually named after fridge of nansen um of passport Fame okay fantastic how maybe maybe tell us about nanen and kind of why you why said it on that name yeah I mean um I you know I I think a lot about culture in the context of a company or a project and I felt that nson is kind of an embodiment of the values we have in our company and so values like courage um curiosity you know nons for those who are not aware is most famous

for um actually 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 uh another polar Explorer reached the South Pole first of any human being um but he was also a scientist and interestingly it sounds like you know him for his work on um creating passports for refugees uh which which he did for 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 um 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 uh so that they could become independent for Sweden in uh 1905 but um but yeah so he's an embodiment of a lot of the values that we uh live by at nonon uh 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 uh you know uh what you need um 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 um 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 uh 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 we named the company and was the the URL nansen doai from the GetGo yes it was and uh but I will say that the aai was aspirational in the beginning in the sense that you know my so I said that in the beginning my my degree is in 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 is part of our product uh 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 transaction history Etc 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 um we use AI for personalizing signals in the product so I think you know Nelson you know we we did have some foresight and 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 AI even more now than that we were originally um and so it's not like there's one AI angle with nson it's more like it sort of powers the whole product in many different ways uh 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 in great ways in the product and in the organization cool um let's maybe dive into um the the core of your product so kind of like you started out with and that's still very much your Coe offering is kind of um analytics for

onchain data um most people who don't work on actual blocks themselves um they don't appreciate how much engineering effort actually goes into kind of creating um like a state and a 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 note it's um a terabyte or whatever kind of the the current depends on kind of what what you're running but um 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 uh we make use of um you know RPC Json end points from the nodes and then we

pull out specific data from from the nodes and so uh you pull out the blocks and they have transactions and you uh you if you want to go one level deeper you parse out the events from Smart contract uh interactions like if you have the ABI uh of a smart contract then you would use that to be able to parse out the data that's uh 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 uh so one of my co-founders is the creator of an open source project called ethereum ETL which um which basically does this in an open source manner for ethereum and so you can actually like index uh all this data you know if you have an endpoint uh or you run your own node and you have that endpoint you can index all this into like CSV files or into you know a database and so um so he he built that and that was kind of that was one of the building blocks that we used to get started uh over the years though we have basically uh moved over to a a different

Paradigm of loading the data uh 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 um the uh Json RPC um uh endpoint uh 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 this schema harmonization of all the data and to to

a later point and then we've also changed the the database that we use um we used to be based on big query um which is a Google Cloud um sort proprietary analytical data warehouse technology now we use something called pleck house which is uh 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 mostly using nson but nson 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 in like you know 300 milliseconds or something like that so so we we made

we've actually kind of evolved our TCH 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 Etc so which chains do you currently support uh so we H we actually have kind of a suite of different product so for example if you look at nson 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 uh long pale of evm chains um and uh for dson query which is kind of the Enterprise product where you can

write SQL queries and people make dashboards we support about I think it's 20 plus chains so a little bit fewer but still more than 20 and then in nson 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 um I think it's now just over 12 chains but we're adding a lot of chains every quarter actually to it um and so yeah so so it sort of depends a bit on which which products you're actually using but uh the the ambition is to be adding like roughly one chain per month or more going forward because you do have like

the the world is very multi-chain uh at the moment and so you want to make sure that you're supporting all the chains that people care about and that they use uh and you know we've sort of invested a lot in our Tech in our Tech to to make it both faster and and frankly cheaper for us to integrate new chains so we sort of industrialized the whole evm chain uh ecosystem and we can onboard evm chains actually quite fast now um yeah so so so that's kind of how we how we look at it interestingly there are a lot of nonm chains that want to inter integrate with us which on the one hand is great because you you want to

support them but on the other hand it's so quite technically challenging cuz you have to sort of build up a Spoke solution for every chain almost but uh but yeah EV the evm chain use case we've sort of industrialized inh 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 uh is about the onchain 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 uh data quality checks on that I think the harder part though is on the attribution the labeling 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 than to 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 addresses labeled for every one of those labels we have evidence and documentation and of course a lot of that documentation is uh 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 neighbors 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 they 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 label an address dquan 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 um and you might have similar thing with like uh suzu or or three Aros and and things like that but um 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 label I mean h h and how do you come up with them I'm sure you kind of add stuff all the time right yeah so so um It's a combination of man and machine

right so uh the heuristics would be some of them are deterministic and quite simple right so think of you want to label every unisa pool then you can literally just look at the unisoft 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 uniso 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 onchain

data you need to do some inference and you need to understand like how these entities manage uh private keys and manage addresses and so there you typically have kind of a Baseline um juristic 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 binance main wallet then you can 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 uh uh let's say htx or you

know gate.io might get a new main wallet you need to make sure that you're on top of that and you need to have sort of alerting in inhouse if you see lots of funs move because maybe they move to a new like cold wallet or new hot wallet and so on and so forth so um and and so you you can State these heuristics programmatically and you can label up L addresses in this way so it's kind of like a an inventory uh of many different heuristics and sometimes the heuristics can build off of each other but you know living up to our name we've started um using more AI for the labeling recently but um The Challenge there is like you might end up with

probabilistic labels and like I was saying before you you want to make sure 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 setup 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% that the human does can be very valuable and it can also be used by the machine to Lael all this stuff and so yeah so it's interesting right because the the AI approaches we started making use of now they kind of like almost sit in between the sort of the man in machine

like the human in the machine um but we've we've seen and then 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 labor or even like the cloud cost of the heuristics and then you start looking at like optimizing that and saying actually you know theistic is very cheap so you want to make sure that the heuristics can label as much as they can and you want 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 so 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 address 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 um factors here 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 um 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 um how fast do you um label these addresses and big movements I'm asking because obviously this um kind of if you're 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 um chances are they're going to sell them um possibly on on an exchange so you you might kind of want to front run them in the traditional sense um not in the blockchain sense so

um how um how fast do you do this and um do people explicitly use it um 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 uh 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 it 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 um yeah maybe minutes um at most hours you know You' let loose the Army of AI labelers on these hot contracts and because we will have tune and and quality assured the Precision um 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 who 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 um are are probably goingon to see like almost like a night and day uh you know change in that view uh 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 a VC 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 um so it really depends on like what kind of uh what kind of address or what kind of entity we're talking about so for the for the newly deplo deployed smart contracts um do you also do you also speculate about kind of what

it's going to do I mean is it kind of uh does it is there a label that says we think this is going this is this is a newly launched Pap exchange or something yeah so so the so the idea the idea is like um with labels right there are few different ways to think about labels one is just give it a name right so like you know um gnosis chain something you know Bridge or something like that right um 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 d pool it's a

yield Farm like there's sort of a taxonomy of different things it could be and the idea is it um what we're aiming to do is both give it a name a specific name and also give it a category uh and and in fact like the category also it doesn't always needly fall into one one category so you might want to give it like multiple different labels right like multiple different indicators right so this is both a stake and contract and it's a token like think of staked eth with Lio for example that's kind of it doesn't neatly fall into one category um so the idea is do both like give it a name and give it a category so I've not tried this yet so um if um if you were to kind of put a newly deployed

um smart contract or any smart contract into uh into uh chat GPT or any of its competitors will it be able to tell you what what it does no um if if it were that easy then we would have just done that uh we have we have tried no but you have to um you 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 chat GPT it's a good idea to use chap GPT but you have to sort of guide it with the right prompts and make it use the right sources for it okay so kind of you you want you want chat 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 again 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 want it 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 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 could think of this as like um you have llms 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 find to in 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 chbt doesn't have our existing 300 million address labels right and so that's where you get kind of an edge too because our own version of this can also tap into the existing labels we have and because of that compounding effect I

said earlier where like if you know the neighbors it can help you figure out what the label of this one is you get this kind of sort of a mo that's built around kind of being able to label stuff with high precision and very fast yeah absolutely um you guys also use AI on the other side so kind of if I I'm a user I search for Stuff um you you have Smart search and um similar similar things how do 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 um you know uh Pepe token has you know this amount of million dollars 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 um 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 uh in the next few months uh we're bringing together our portfolio tracker with the analytics product so if you have your portfolio tractor with us we can

personalize the signals that you see in your feet based on your your your own portfolio and your history the history of trading and so on and so forth so um it's actually a toggle in the product where you can switch on and off personalization so either you could 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 in the platform what you search for what youve say Saed 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 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 catalog 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 so that actually puts you in a super powerful position because not only do you have um 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 codex of kind of like how you treat the user dat 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 crossr reference things behind the scenes yeah I mean there are yeah

it's a great question right uh 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 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 nonon are you going to use what I search 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 uh company obviously or a project um 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 that want 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 and you know you get access to the product so in a way I kind of like that business model because it's the most transparent business model

you yeah it's very honest 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 uh you you pay or do you do you want us to kind of monetize 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 2 and I think that the default business model of web 3 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 nson and when you find tokens at token god mode you click buy and you execute the trade through Cal Swap and maybe Cal Swap and nonen share any fees that are involved in that right so you know that to me seems like a more futureproof

business business model um 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 um H 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 um how do you think that's going to change yeah I mean um I think of this as you can't have both uh at the same time to the maximum extent and so it becomes

a trade-off as with many things and Technology you can't have full Tri privacy and you can't have full transparency at the same time and so um you know our product makes the most sense obviously when there's room to have trans transparency and so I think the reality is that many people value the transparency of blockchains because it gives them a sense of comfort that if you know that hey the funds that are sitting in a I can actually like see all of the transactions that have ever happened with a 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 know they can do it make gives them 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 blockchains 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 uh where you do want

to have more privacy and I think it makes sense that you'll probably get some uh some world where either protocols or even chains or l2s you know have full privacy but maybe there are some guard rails 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 um somehow but you could not make transactions over some certain amount you know size right again I'm not saying this is necessarily the world I want but I could see that 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 uh uh potentially transact hundreds of millions of dollars in in volumes so I think I think you can look at look at it from sort of an ethical SL moral perspective and then you can also look at it just from a sort of a pragmatic perspective of 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 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 will ever get to a point where everything you do on chain is totally private and I think that also defies the the um the object to a certain extent right so I mean what what ideally you you kind of want is kind of transparency for the man and uh and kind of like privacy for the little guy right so kind of you you you want you want you you want transparency to kind of hold power

accountable but you you you don't so you want to know what your government spends its money on you don't need to know what your neighbor spends and Mone the crazy thing is is 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 um some sort of back channel into your web two products you know whether that's Google or Twitter or whatever uh 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 um 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 be amazing like kind of I would absolutely love that um so you you alluded to this in the very beginning so um you label well-known people on chain um so obviously there's ethical considerations

kind of that come with it so say I'm um dope one and I feel like I don't I mean you have rightly labeled me um but I don't want this to be no nonchain can I can I kind of send you an email and you will delete the the label or what what's your policy yeah you can I mean that's the short answer as you can um but there's always there's always been a nuance and may it might be helpful just to explain like how you get there in the first place like why someone an individual might get their name uh on an address right and that's typically uh 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 uh proposed s 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 uh in our database in the first 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 you don't look over people's shoulders 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 exact but if we wanted to sort of go on Twitter and hunt down every time someone like is a little bit silly in 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 docks as 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 what we do can be seen as a form of journalism right so then you have to kind of ask yourself like okay if the talic has a wallet that has like you know a billion dollars in it yeah that's news forth the people should probably know about that if the founder of a project you know has a lot of money in that token that's news for the people should know about that if some person has a 200 bucks you 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 um so tell us about um what what's coming for nansen what what kind of so you already talked about um the the hot contracts update that uh that is what else do you have in store yeah so the hot contract 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 um and that's going to happen I think faster than we initially anticipated actually um the second thing that I also mentioned is portfolio is going to get integrated into nonon 2 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 onchain investor and so we are very much onchain 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 what what you might be interested in investing in that's a really potent com combination and so bringing portfolio inance to I think is going to be a game Cher 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 um hopefully within two months um 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 um and in addition to that I think you know we're going to strengthen some of the things that we're 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 p&l tracking of Traders finding wallets that you might want to monitor because they're really good at

trading um that's something that we're leveling up and improving making the overall product easier to use because it can be 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 um so those are some of the things but I I think like you know overall we've put nonon one behind us in fact we're switching off nons one literally tomorrow so we've kind of firmly made the transition to Nelson 2 and that means we can sort of put the we can travel lightly we can put behind us a lot of the 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 announcing new wonderful um so where can we send listeners to kind of check out nansen yeah you can go to nansen doai that's uh the best place to start and you can also follow nonon AI on Twitter perfect thank you so much for coming on Alex it's been a pleasure thanks for having me