Inside Nansen's AI Trading Agent Platform

Transcript

Welcome to epicenter the show which talks about the technologies project and people driving decentralization and the blockchain revolution. I'm Fria Ans and today I'm speaking with Alex Vanri who is the CEO of Nansen. >> Most people who are active on a chain are active because they're trading or investing. What is your trading strategy? What is your investment strategy? You can tell that to the agent and then of course the first thing you would want the agent to do is to give you like feedback on it. Would this actually have worked if you deployed the strategy 3 months ago or 1 month ago? Would you have made money? That capability for a retail investor is like a superpower. Do we want to be kind of a horizontal data company or analytics company that can serve many different

use cases or do we actually go vertical on the primary user segment that we know we can create the most value for and then do more than just data analytics. Welcome to epicenter the show which talks about the technologies project and people driving decentralization and the blockchain revolution. I'm Fria Ans and today I'm speaking with Alex Swanik who is the CEO of Nanen. So Nan started off as an onchain analytics company and has recently moved into an AI trading outfit and I am here to learn all about this today. Hey Alex, it's super nice to have you on again.

>> Yeah, good to be back. Good to see you again. >> Cool. So maybe for everyone who isn't familiar with Nanzen, can you give us the Nanzin backstory in a minute? >> Yes. So um my name is Alex. I'm the co-founder CEO. Uh my background is in AI initially. My two co-founders are data engineers. So we started out trying to label the entire blockchain more or less. Uh because blockchains are pseudonmous or anonymous some people think of them as. You don't know who's behind the address. Uh and so we tried to solve that and so we've built um a data platform that has more than 500 million addresses labeled and a whole analytics infrastructure that allows you

to see the flows on chain in real time. That's kind of the backstory and we are mostly used by investors and traders in the space although other people also find our product useful whether it's for uh journalism or reporting analysis even compliance and so on. But the primary focus we have is on onchain investing and trading. And for that reason, which we'll I'm sure we'll get into, we've started basically turning Nunson into a full stack trading onchain agentic trading product. So yeah, it's a very exciting moment in our history and and super super excited to talk more about agentic trading.

>> Yeah, absolutely. It's fascinating how kind of um lots of people who have some AI background kind of found their way into the blockchain space um and now kind of it's all coming full circle. It's um uh it's exciting. Um maybe before we dive into the AI aspect um tell us about the um technical goings on for onchain analytics. So kind of how do you go from raw blockchain transactions to kind of meaningful insights?

>> Yeah. So in a nutshell, the most basic thing you have to solve is to get the raw onchain data into a more convenient uh storage and compute layer. And so um you can't really run fast queries on just a normal blockchain node. So you pull the data out of that into for example click house which is what we use which allows for extremely large kind of queries um um across tons of data to be you know run in less than a second. Uh so that's kind of the first part. Um the second part uh is that you need to kind of harmonize all that data uh maybe across different chains to make sure that you can look at things like dex

volume uh for different chains that might have different data schemas and so on and so forth. And then the third part which we I would say probably are the best in the world at is the attribution layer where you label the addresses. And so, you know, as nice as it is to have like very fast queries on raw onchain data or even like harmonized onchain data, it really starts becoming useful for investors and traders once you can kind of know what these flows actually are, right? If you just see $100 million going from address ABC to 1 to3, like you don't necessarily learn very much from that other than, oh, that was a large tether transaction. But if you see that, oh, this is from FTX going out to one of their, you know, fund uh clients,

for example, that's kind of an interesting uh thing to know about, especially if FTX, you know, says that they have closed off their withdrawals, which literally happened. They they did that or they said that and then we could see on chain that flows were actually coming out of FTX. So yeah, so the attribution layer, which we could in theory spend the whole podcast talking about, is very multifaceted. We've kind of evolved our approach to labeling addresses over the years. Right now it's very agentic and so we have basically agent teams that can go out and figure out who's behind addresses. We also do a lot of algorithmic work that is not agentic uh but is still super important.

And then we have human attribution specialists internally who also um make sure that the quality is super high of our labeling that we are prioritizing the right domains that need to be labeled and so on and so forth. This episode's brought to you by Near AI Cloud. OpenClaw is one of the biggest stories in AI right now. Rapidly, it gained over 200,000 GitHub stars with adoption from Silicon Valley to Beijing. Why? Because OpenClaw makes it easy to create an AI agent that actually does stuff like manage your emails, browse the web, schedule appointments, and remember contacts across weeks of interaction. But where do you securely store and run an always on agent that needs persistent access to your most sensitive data? Local deployment means you have to manage expensive hardware at

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H >> how do you go from raw blockchain transactions to meaningful insights? >> Yeah, so there's uh there's a few different layers to it. The first part is you need to get the raw entrata into something that is more convenient for running analytical queries. So typically a database uh in our case we use something called click house uh and we index all the data inhouse well we do run some nodes inhouse but a lot of the node provisioning is actually outsourced because it is very commoditized so we use you know providers like quick node alchemy and so on and then we index the data ourselves the second layer is you have to kind of harmonize the data across chains think of dex trades that's

like a conceptual thing but actually it's different on every blockchain and on every DEX. So you need to make sure you you have harmonized data that you can aggregate. And the third layer is attribution which is labeling addresses which is what we are I think the best in the world at doing uh and that's a very multiaceted area. We run aentic teams to label addresses uh which is pretty exciting. We've been doing that for years now. Um and we're looking to scale that up um right now actually. We're also doing a lot of algorithmic work that is non-aggentic. You know, think of kind of huristics that you can work out on the structure of addresses of centralized exchanges or dexes or what have you. So, those are probably the three layers I

would uh I would think about. There's the raw entry data, there's the harmonized uh and cleaned up data, and then there's the enriched attribution uh powered data at the top. So I think I kind of understand how you go from kind of like the basic to the enriched. Um but kind of how do you even come by the information that you need for the basic? So say Binance spins up a new wallet, they don't have an API that kind of you can pull that information from, right? So kind of like how do you uh kind of glean these insights from the blockchain?

>> Yeah. So you have to study the behaviors uh of different entities. Some behaviors are deterministic because they're smart contract driven. So think of like a unis swap pool that's deterministic. You can literally just look at the pair created events from you know one of the deployer contracts and then you know hey if this smart contract generated with this event uh then we can label the outgoing new smart contract as a unis swap pool for Binance because the logic is technically offchain you have to infer it so you have to look at okay this is a you know Binance main wallet you know it could could either be like widely reported to

be that or you can literally send some money to Binance and see where the flows go and then you could say hey this main wallet is where the funds ended up this is my deposit wallet let's look at any characteristics uh of that that I could find and sometimes you have hybrid approaches some um centralized exchanges will also use smart contracts they used to use something called like forwarder uh contracts I think bitco uh maybe created those smart contracts back in the day back in the day. Um but you have to do kind of rely on a combination of uh inference and deterministic approaches.

>> Okay. Um do you have an error reporting um path? So kind of like if if for instance I I feel something's been mislabeled because kind of like some of it some of it is inference. It's it's and I'm not just saying this to brag but it's very rare that we get uh reports of errors because you do a lot of quality assurance inhouse and for every label that is created in nunen we have the actual evidence compiled for it literally every single label has evidence and because of that because of that hard requirement like you cannot add a label to the database unless you've compiled the evidence for it whether you're like an agent or a human.

That means it's much harder to screw things up. You can screw screw things up of course, but of course we also have human attribution specialists internally who review both individual submissions, not all 500 million admittedly. That's that wouldn't work. But they do take like the harder cases for example and they also of course review the processes and the huristics and the agents. We also do a lot of eval. This is kind of moving a bit into the AI area, but people might be familiar with like benchmarks. If a new model launches, you look at model evals or benchmarks to say, hey, you know, Kimmy K 2.5 is better than, you know, miniax 2.1 or

whatever. Uh these are often like model evals, but you also have task evals that uh you can run for specific tasks. So we have our own attribution evals internally that we run for our agents and this is kind of following best practices for how you run benchmarks and eval agents. Um so we use you know LLM as a judge where other agents are judging the work of the main agents. Uh and then the humans judge the LLM as a judge. So it's a it's kind of like a meta thing where you have to like judge the judges. But yeah, so without going too much into the weeds, uh we do focus a lot on quality assurance because we've always kind of strongly believed that

it's very it's it takes a long time to gain trust, but it's very easy to lose trust. So you have to make sure that the precision is extremely high. And you know, I'm I'm proud to say that based on some of other vendors I've seen out there, uh we tend to have very few errors reported. And I'm I'm uh I'm happy about that. Yeah, your tools are so powerful that it allows um kind of labeling addresses as kind of individual people if even if they're not particularly notable, right?

So, where do you draw the line? So, kind of like having Vitilix main wallet kind of labeled. this is something that probably probably fine but kind of um I have wallets that I don't want um attri attributable to to me. Where do you kind of draw the line there? >> Yeah, I think there are two kind of core principles that are worth thinking about for this. The first one is that we rely on public information, right? Information that's in the public domain. So if we think about a specific example, let's say that you are active as a governance participant. Uh which is maybe not totally unlikely, right?

>> It's the same for myself. I might write on a forum, hey, you know, I'm voting for this and here's my address and so on. And that's information in the public domain and we take that as kind of a signal that well, it's in the public domain. So presumably it's fine to label that entity whether it's an individual or a company because it's in the public domain. Now the other principle is that individuals are different from corporations and projects and so if you come to us and you say hey I actually want that label removed as an individual you can do that and we will remove the label. There are a few edge cases though which maybe are uncomfortable for people but it's worth knowing about. We have

had examples of people who maybe have an ENS uh name >> that they registered not thinking about the fact that now you're basically potentially exposing the identity of the address. And so there was one example where a user reached out because they had bought an ENS domain for their wife and it was the wife's name. And so they wanted to remove the ENS name. But then we had to educate them and say look this is actually on the blockchain like this is not it's like immutable even if we remove it from our database the ENS name is always going to be etched into that address that's just life I'm sorry so I think that's kind of maybe a third principle that like look blockchains are

public and transparent by default and so don't hate the player hate the chain at the end of the day uh if you are not happy with with with that but it's like you know some people learn this the hard way some people um maybe think a bit about it before they do stuff but yeah so those are kind of some of the basic principles we follow >> yeah um makes complete sense um maybe let's talk about the trading aspect more so kind of if you kind of look at blockchain data I mean you guys are very good at it but it is all public do you think in some way it's kind of become commoditized or is there real alpha still in it >> I I think raw onchain data definitely

the data itself is commoditized. There's no doubt about that. Um I think what is not commoditized is the enrichment um that you do on top of it. And so we constantly get you know requests from other analytics or data providers to get access to our labels for example because they know they're very valuable and it's hard to recreate. So if you know that like I said before if you kind of know that this is actually you know a fund investing into a protocol's treasury that's like a very interesting sign and like it's you might even there are tons of examples where you could have seen that onchain before it's even announced

right and as an investor I think most people want access to that but it does depend on like the attribution and the labeling of addresses so I think that part is not uh yet commoditized. I don't think it'll be commoditized in a while. Although I would say that you know we are trying to find ways to rapidly scale this up so that the unit economics might be so favorable that we could share more of our data actually. But that's that's a work in progress. Um I think the other part is it's also kind of non-trivial to do a lot of the um infrastructure around onchain data especially as chains have

more and more throughput right it's important to understand that if a chain brags about having a very low latency or very high throughput that means they're going to have a lot of data right if people use the chain which if we're being honest is not always the case with with many blockchains But if you think about a chain like Solana, it has tons of data, right? Because the throughput is so high and people actually use it. And so you need to have really good infrastructure where yeah, you could potentially get all that data onto your like hobby, you know, computer setup at home or something like that. But it's actually pretty hard to make sure that it stays in sync and has super good performance in terms of the

query latency. So some of those things are a bit harder to to do as a hobbyist or you know if you're not super serious about it. So I would say probably if I would kind of highlight two things that make it difficult would be number one the the enrichment and the attribution which fundamentally is offchain data that you're adding on top of the onchain data. And then the second part would be the infrastructure that's required. But but to your point, you know, we we have kind of never wanted to be only a data company, right? And this kind of we we've we've seen that over the years, the primary user segment that gets the most value out of our product uh is the trader and the investor, right? And this

is like I think we should know, we should not pretend otherwise. This is the most common persona in crypto, right? Uh there's been a lot of talk about kind of web 3 gaming which I was very excited about as many others and and other use cases for blockchains. I'm not saying that those will never pan out, but the reality is that most people who are active on a chain are active because they're trading or investing, right? And that's not a bad thing, but it's just something we should acknowledge. So we kind of had a choice when it when it comes to the strategy of the company. Do we want to be kind of a horizontal data company or analytics company that can serve many different use cases or do we actually go vertical on the primary user segment that we know

we can create the most value for and then do more than just data analytics >> and we chose that second path. >> Yeah. >> So kind of you saw your users >> discover signals on Nansen and then leave the platform to execute trades elsewhere, right? So kind of is is that kind of the >> Okay, I understand the rationale. Um tell me what it kind of entails to kind of build up um this uh almost like a trading operating system, right? >> Yeah. And I I want to be very clear, right? The way I explained it now, it sounds like it's all coming from the company's perspective like what is best for the company. But I think anyone who has traded on chain will acknowledge that the user experience is pretty

right? If you think about it, it's it's it's horrendous. And like one reason why centralized exchanges have been dominant versus decentralized exchanges is because the user experience is so much better. It is faster. You know, it has historically had lower fees and it's all integrated into one product. And so the realization we had was that actually we think the infrastructure now for onchain is good enough to create a user experience that is as good or better than a centralized exchange. And I want to be very clear in our ambition to create the best onchain trading products in the universe there's no chance we can do that without relying on incredibly

good infrastructure partners. And so we use Privy for example for embedded wallets, self-custodial embedded wallets. We use multiple different deck aggregators under the hood. You know, we use LiFi, we use OKX DEX API, um we use Jupiter on Salana. We'll probably integrate more and then of course all the different blockchains, right, that you that you can use and layer 2. And so um I think the exciting thing from a user point of view is that if you download our mobile app nonsen AI and you go in and you literally start talking with the agent, you now have an agentic way to explore what's happening

on chain, but also to say, "Hey, put $20 into this token and it finds the best route for you." let me say the best route that we can find among our aggregators and of course we aspire for that to be the best in the universe and then you just execute uh in the same product. So, so instead of this kind of go to, you know, an information product like Nunen, then you go to say cow swap or you go to Jupiter, then you have to like connect with a wallet. It's like three different products that you have to touch just to do that one thing.

We've tried to just kind of bundle it together in one unified uh integrated user experience. So, so I think like that's important. It's not just that it's good for us to get the trading onto our product. It's fundamentally just a better user experience at the end of the day. >> That makes a ton of sense. Um, how do you um seed the trust in that product, right? Because people are kind of like they're set in their ways, right? Kind of like even if you look at kind of like how many people still use UniS swap despite the fact that it probably gives you worse prices than than cow swap just because they the UI is sticky, right? kind of like it's kind of like what you're used to and how how

do you unline that behavior? >> Yeah. I mean, one benefit we have is that the user is already on nonsense because they're already looking for the information. And so, this is kind of one of the realizations where like I do think we have an advantage because we already >> I'm not going to say we own the user, that sounds kind of bad. Uh but we we own the direct relationship with the user because we are already part of their journey, their investor journey. They come to Nelson to discover tokens. They come to Nelson to do diligence them. They come to N to see what the smart money is doing. And so we're already in their journey. And so the thinking is that if we can just have

like a very simple one-click button that allows them to execute the trade there too, then it's actually very convenient. And the the the key thing is that it also has to be, you know, as good prices as possible to get as well as low fees, right? So you you shouldn't feel that okay cool has this but like the fees are outrageous you know and I'm not going to call out certain wallet providers uh who charge a lot of fees uh to place the swap but yeah the the idea is that we're kind of already in the in the investor journey and I think a lot of them trust us because they know we have the best data and so we kind of have a bit of a starting point there and then I think we

should also just be transparent on like what's happening under the hood which infrastructure uh providers do we work with? What the hell is this wallet? You know, who we actually show in the UI as well like which aggregator is going to fulfill the the transaction, where are we getting the quote from, right? So, we one of our values at at nun as you can imagine is transparency and so we also try to lean on that a lot. I think if you're transparent, you can build trust faster. >> Yeah. Tell me about this uh nan AI agent. So kind of I open the app what can I ask what what can I ask them?

>> You can ask it anything about onchain. So the way I would think about it if you have used the nonsense product you might have found it like overwhelming which section do I go to which dashboard do I look at and so now you can bypass all of that and you can just ask it what you want like hey who's buying this token right or here's the transaction hash just help me understand what the hell happened in this transaction or you know uh what tokens are smart money buying right now what are the top holdings of defiance capital uh which token tokens are winter mute market making the most like anything that you can imagine finding on a blockchain. Uh it it will most likely have the best answer for that. We've also gone a little bit

beyond onchain. So we have integrations with X because you you want to get social media feeds. We're actually and we're also integrating with prediction markets now. That's that's a bit of an alpha leak that's I think was rolled out yesterday into the the agent so people can like try that out. You can ask about pers. We have like probably the best data on hyperlquid for example. Um so you can kind of anything that's onchain related I would say you can ask it about and it has all of the data in real time. It's not like we train the model and then there's some historical snapshot.

It has access to literally data from seconds ago. So you can make a transaction. That's like one fun thing. You can make a transaction, copy the hash, dump it into the chat, and just see, hey, does it understand what this transaction was? So, yeah, you can do a lot of stuff. I think people should just like download it and try it out, then play around with it. >> Yeah, super interesting. Have you ever thought about kind of using this in a forward looking way rather than kind of a backward looking way? So kind of I mean there are companies that specifically kind of screen transactions for you and kind of tell you what they do and uh flag if something puts you at risk. So with all the data in the background seems like you would be ideally suited to kind of offer this.

>> Yeah. I guess you're talking about like chain analysis and RM labs those kinds of compliance or AML focused companies, right? >> Yeah. Yeah. >> Yeah. So about once every two weeks someone asks me like when are you going to launch an AML or compliance product? Uh you have all the data so why not do it? And the short answer is that it's very different from our mission. Um our mission is to surface the signal and create winners and we are very focused on the crypto natives. These companies are I think great companies at what they do but I think it would be a distraction for us. I think we want to make sure that we can focus on the core kind of onchain investors and and traders. But on your point on forward looking to have basically spin on

>> it basically. Yeah. So kind of the security aspect right. So kind of like the kind of like what what hyper native does for instance. So kind of like uh where kind of they screen your transaction before you click send. >> Yeah that's a that's that's a great clarification. So there's there's different ways to look at it. I think that makes sense to do like to have some kind of risk score if you're like hey this wallet you know has done certain nefarious things in the past. Again, I think it's a little bit of a side quest for us and we want to be laser focused on the main quest. Some of these things I've often thought maybe we should just like do a joint venture with someone or someone else could do that and focus on it and use our data. Um but there is

like a third. So there's the compliance and ML, there's the security one that you you mentioned. And like a third way to think about forward looking um at the address level which I'm very bullish on and excited about is can you actually predict which addresses are going to make money in the future. And so that's something we are working on right now and we have some promising research results internally and I think ultimately this is going to be released as what we refer to as smart money 2.0.

So two you can think of 1.0 I know as being like backwards looking which addresses have had the highest P&L in the past in the last 30 days 90 days 180 days but actually what you care about as an investor if you want to follow what the smart money is doing is I want to look at the addresses that are likely to make money in the future right and in some ways people might think this is like impossible and of course it is impossible to do 100% accurate but if you can get like you know depending on how you measure it like 60 65% accuracy you know as opposed to like a random coin toss 50% accuracy and then you aggregate it then I think it starts getting pretty interesting right so if you can with like a two to

3x uplift on precision identify the addresses that end up in the top 1% of trading next week that's pretty exciting so so that is something we're looking at literally right now and we'll hopefully release in a matter of weeks not months as smart money 2.0. >> Yeah, super interesting. Uh looking forward to that for sure. What are the um challenge areas of trying to apply LLMs to blockchain data? I mean they have shortcomings, right? Like they sometimes they hallucinate and what breaks or what what kind of do you need to pay special attention to that it doesn't break?

>> It's a great question. So let's consider taking a vanilla LLM and getting it to do onchain related tasks. Uh where does it break? Well, firstly of the most obvious place it breaks is that it doesn't natively have the data, right? So if you ask it about, you know, flows, tokens, addresses, transactions, it just doesn't have it and it would have to go like on the web and maybe if you're lucky, it would like find something about it. but it's likely going to be stale. It's definitely not going to be real time. So, that's the first part where it breaks. There are a few other places it breaks, too. Um, if you wanted to sign a transaction or like make an

actual transaction, it doesn't natively have that. So, you have to add that capability to it. And then you have to make sure that it can do that reliably. Right? If I say, "Hey, buy $20 of Pangu," it needs to know that like Pangu is on Salana. Here's the token address, you know, here's the right um amount of tokens to buy to match $20 and then to execute it. And those things an LLM cannot do natively. You have to do, you know, tool usage somehow, whether it's through an MCP or a CLI or or anything like that. It also breaks down in some other ways like weirdly it often doesn't have a good understanding of

significance. So by significance I mean you know if you say hey like go and find some alpha for me on chain look at the onchain flows what whatever it might come back and say like wow you know this smart money address is buying like $300 of this token and you're like $300 is not that much money you know that's not necessarily super interesting but it doesn't have like the common sense or the domain understanding to to get that which sounds like a like a dumb naive or trivial thing, but if you just use it out of the box, it's going to make silly mistakes like that, which makes it like less useful. And then there's other things that are more generic, like formatting in the responses. Do you want

it to be concise? Do you want it to be very verbose and elaborate? Mostly investors and traders want concise output. So, you have to like get that right. And then another thing is that when you invest and you trade and you make decisions, most likely you're not going to be comfortable only looking at text. And if you think about a lot of use cases for like other software or apps, you often want to see something visual before you make a decision. Like if you book uh a taxi or like a you do ride hailing through like Grab here in Southeast Asia or Uber, um you often want to like see the map and like get comfort that it's like going to the

right place and and all these different things before you click the button. And so as humans, I think we're very visual when it comes to decision-m and LLMs are not natively very good or or sometimes they they are not able to at all visualize the information. So we have like the concept of nonsen artifacts which you can see in our mobile app occasionally which are like summoned at the right time. Um, if you ask about like, hey, tell me about, you know, Lido tokconomics or something, it'll like spawn a little token card at the top with the basic information about it, which just makes it feel a bit more delightful and also gives you more comfort in your decision- making. And

later this year, we're going to release the next version of Nunen, which is called Nunson 3. And in three, we've kind of taken the concept of artifacts to the next level where it's kind of like a science fiction like user experience where you talk to the agent and the agent just in like subsecond spins up a visualization that is like exactly what you want for that question. And a lot of the magic there is that the artifacts are curated, right? Uh they're not like coded in React on the fly. we have a library of the right artifacts and then we make sure that we can fine-tune the LLM to pick the right artifacts for the right questions. And so anyway, so there's there's a bunch of different things, but I I'd say like

those are some of the the ways that LLM's like a vanilla LLM falls short and you have to add a lot of capabilities on top of it. >> Yeah, I hear that. seeing that kind of like you have to put guard rails on your agents. If there's kind of literally thousands, tens of thousands of agents trading based on the same signals, what does that do to the market structure? >> Yeah. So, firstly, I I think it's going to be like millions, if not billions of agents that are trading in the next couple of years. And I firmly believe that in like 2028, maybe perhaps earlier, the default way that you invest is going to be through agents. You're

not going to be picking individual tokens anymore. >> Yeah. >> Uh and this is very analogous to like you're not coding lines of code anymore. If you're an engineer, you orchestrate agentic uh engineering and you create the build loops and the quality gates to make sure that you can create really great software, but you're not actually writing the code. And so I think it's totally analogous to what's going to happen with trading and investing. But to your point, like you can't just say, okay, start trading, that's it. I think you need some kind of strategic intent initially, right? And so we talk about the concept of a trust ladder where let's take self-driving self self

driving cars as an example or an analogy. Um most people are not would not be comfortable just jumping right into a car that they don't know the brand or anything like that and just sit in the back seat and drive. Although admittedly I I did that in Shenzhen, China quite recently. Um but most people would not be very comfortable doing that. And so you might sit in like the driver's seat first. you might have your your hand near the the wheel and then like eventually you may maybe make it to the back seat and you just let the car like figure it out. And so I think it's similar where you will have a trust ladder. You have to earn the user's trust by keeping them involved in the beginning. So I think in the beginning it's going to be vibe trading where

you're talking to the agent but you actually pull the trigger for every trade. That's what you do in the mobile app today that we have. you say, "Hey, you know, buy this token." And then it spawns the the sort of modal that allows you to execute. And then you have to tap execute and do a biometric scan for MFA to execute it. But over time, people are going to be like, "Okay, I just like auto tap on execute because I know the agent's going to be so good. It doesn't hallucinate. It finds the right quote. It finds the right token." So at that point, you might say, "Why don't I just like let it auto execute, you know, for me?" And then after that, you might say, "Hey, actually, like I don't feel like I need to micromanage which tokens it's looking at. I should just tell it my

overall strategy." Like I want to buy, I don't know, I want to buy like tokenized commodities, you know, under these conditions or I want to buy meme coins that have launched in the last 15 minutes or whatever your strategy is. And then you just let the agent execute the strategy. So to go back to your question, I think people will have different strategies just like they have take different actions when they look at data, right? And so and they will have different models and they will use different tools and they will have access to different elements of data.

So, and and crypto is so large in terms of like the different corners of that market and it's just going to get larger as we get all assets on chain. Commodities, real world assets, stocks, fixed income, all this stuff is coming on chain. So, the investment universe is just going to grow on chain. So I think the the probability that like two agents will do like exactly the same thing in a way is actually pretty low because there are so many parameters and variables that you can play with to get different outcomes. And like even LLMs are unpredictable. They have like the notion of temperature, right? If you have high temperature, they can do like crazier things. If you have lower temperature,

they're more predictable. Um so that's kind of generally my take. I think people are going to have different ways to use them. And because of all these different input factors and the fact that LLMs are not fully predictable, I think actually agents are going to do a bunch of different things uh on chain. And so that's also how we have to build our product. Like we don't build the product such that like you and I have the exact same user experience because you have a different portfolio than I have and you have a different intent, you have a different strategy, you have a different risk tolerance, right? And all of these different things. So yeah, I I think it's actually like extremely unlikely that these agents are going to be doing the same thing.

>> At least if you give it a varied enough input, right? Kind of like if I just go correct um go in and say um hi agent, nice to meet you. Make me the most money. Uh then obviously that's very kind of undifferentiated. >> I Yeah. And I think like another very exciting thing is the concept of like co cocreation of strategies, right? like what is your trading strategy? What is your investment strategy? And you can tell that to the agent. And then of course the first thing you would want the agent to do is to give you like feedback on it. Not not like qualitative feedback, you know, like oh that sounds nice, but like would this actually have worked if you deployed the strategy 3 months ago or one month ago? Would you

have made money? And so we had this concept that we called nonsense gym. Sounds a little bit cheesy, but nun nun gym is where the agents go to train uh the trading agents. So the trading agents go to the nun gym and they are given a simulated world. It's actually not simulated. It's like a replay of all the onchain history and the agent is unaware that it is a replay. >> So the agent thinks this is happening right now. How stressful for the agent.

This is like you can live them through all the cryptos west >> west. Oh, FTX, you know, every every day kind of uh the new >> Oh my god. Oh man, that's like that's like a groundhog torture. FTX like Yeah, that's torture. That's the what? That's like a It's like a Nian concept, isn't it? Uh the eternal recurrence. >> Um yeah, that's that's a funny like Yeah, that sounds pretty dark actually when you think about it. But uh we will try to not make sure it has to live through FDX like a million times. But anyway like you you have it goes back to kind of the the infrastructure and the data that you have to create an environment where you can sort of we we call it time travel, right? Where you can time travel on chain and then replay

a certain uh section of history and then place the agent in there and then like validate the agent but do it really fast, right? You don't want to sit and like wait for three months obviously. you speed it up and so you have to speed up the whole process of the agent like living in this world. Um, and then you have you seen this Black Mirror episode where they do this like dating um it turns out I'm spoiling the episode but uh it's like a it's like a dating program and it turns out that the whole thing is just simulations that are run to match two people.

>> It's actually a fantastic episode. It's it's an amazing Black Mirror episode. Uh it's my probably probably probably my favorite one of my favorite. Uh and so it's it's like a little bit similar to that where you just do it over and over again and you try to like optimize it and see that it actually works, right? And so that capability for a retail investor is like a superpower because historically it's only been like Renaissance and you know like these amazing hedge funds that had these kind of capabilities whereas the rest of us retail investors are like yeah this token has a cool name I'll buy it right and so I think what AI potentially could do that I'm very bullish on you know there's a lot of AI negativity in the

world but like I'm really bullish on how AI can level the playing field and empower individuals a lot more. And you're kind of seeing that with OpenClaw if you think about it. I think there are very strong parallels between open-source AI and crypto. Um but without, you know, going off on a complete tangent, um I think the the point here is that you have to have a really strong environment where you can very quickly test these strategies through trading agents. And I think that's going to make you a better retail investor at the end of the day. Uh, and so that's very exciting for me. Like I I have to admit, you know, there's a lot of like institutional focus in crypto right now and like oh yes, the trady funds are coming and like hooray. Which to me it it feels like that moment in

the matrix where you know one of the guys is like backstabbing the rest of the crew because he wants to go back to the Matrix. That's kind of what the Trady wave feels like to me. Like I want to stay true to the cipher punk open- source um version of crypto. I think the opportunity we have is actually to empower individuals instead of like making like institutions do better. I'm not super excited about that if I'm being totally honest. I think it's much more fun to empower individuals and and make sure that they're making better investment decisions.

>> How do you ensure that your product remains open to retail? Right? Because kind of like an institutional can just outspend retail every time. So kind of and if there's only so much alpha to be gleaned from onchain data wouldn't kind of be the logical business decision to kind of just make access more expensive so it's more exclusive and you earn more and basically your retail users will then have become kind of like the second level nonsen gym for your agents. >> Yeah we've actually done the exact opposite. You know we used to be not going to say ridiculed but some people were like hey nonsense is just too expensive. you know, it's like $2,000 a month. It was super expensive. I mean,

like it depends how you think about it. It was like expensive for a retail user for sure. But we actually done the opposite. Like we've reduced pricing to a flat $49 a month for the subscription tier. That's the subscription tier number one for everyone. There are no like plans above that. It's just the $49 plan. And you get everything you used to get in the $2,000 plan by the way and more. Uh that's number one. And number two, we've actually made the product more open even for people who don't pay anything. And so you can use nansen. You can actually, for example, Solana is fully free on nunen. Like you can go on nonsen and see all of the labels we have for Salana. You could trade with Salana.

You can even stake soul. Uh and it's fully free. And it's basically just subsidized through staking revenues from Salana and uh through trading fees like as people use our product. So I think the business model here is actually pretty straightforward which is that if you have a lot of people trading well you can get trading fees just like any other trading venue, right? And so in a way I think that move makes the whole product more open actually. Whereas if we just gatekept it, which arguably has been like was the strategy in the past, then I think you're right. You have to keep like raising the price and you have to keep making it more exclusive. But now I think you know the approach we're

taking is actually to open up everything more and more and actually our command line interface which everyone should try. If you have an open claw, if you have an AI agent that you're managing, you can give the nonsense CLI to that the CLI is open source like you can even make contributions to the to the GitHub repository. So we've actually kind of done the opposite and gone more and more open whether it's open- source CLI, you know, more accessible free version of the product and actually lower pricing. So I'm I'm actually pretty happy about that. >> The AI doomerism has kind of died down >> more more I mean the I mean I think kind of like it had it had its moment and now it's kind of become very quiet. Um, I hope it's not because the AI killed

those guys first, but um, kind of are there any worries that you have with respect to AI and uh, survival of human civilization? >> I mean, the short answer is yes, I do have some concerns. Uh, I have many concerns. Um at the same time I do feel like the concerns get enough air time you know and and like humans have a very strong cognitive bias which is called loss aversion >> and so if you are averse to losses you will tend to focus on that more and so the the classic example is jobs right literally like loss aversion expressed through understanding AI is like fear of

jobs going away. But at the same time, yeah, and I don't mean to like criticize humanity. I think it's just a fact that like generally we have like poor imagination. Like let's let's say everyone is equipped with strong loss aversion. Very few people are equipped with very great imagination. And so the ability to imagine the jobs we're going to have is much harder than to think about which jobs we think we're going to lose, right? So I think like everyone should generally think about that. The other thing is like the economic value of an employee or a team member who uses AI and is super productive is much higher than it was before AI. So like the

economically rational thing to do is to hire more people. Um and so I see you know I see kind of companies talking about AI as a justification for cutting people but I think then they weren't very wellrun in the first place or they didn't have like the right people. But like on a more existential level, I mean I think definitely the the risk of like a total human wipeout is real. Uh I'm not sure how I would rank the probability. Uh I think our best case uh scenario is to merge with machines. uh at one point uh AI you know I think of as children of our minds and that is a beautiful and poetic thing to me but I'm

biased I'm I literally have a degree in AI and that is you know what I'm excited about so I think most people would be better off thinking about what are the opportunities that come with AI and like how should I lock in right now to skills max and learn as much as I And because you're going to have a huge advantage, I think as like a builder, as a team member, you know, as a human and that's what I would focus on. >> I hear that. So kind of AI has made me so much more productive. It's insane.

But um kind of pushing back on the children metaphor. So kind of AI are they are like children but kind of children that are not necessarily like you or kind of kind in >> yeah I mean your kids are also not necessarily aligned with you right kind of like >> but kind of they are kind of in terms of most people have kind of uh between one and probably like five kids or so um you can have millions of AI agents right um that's one um and secondly kind of just from a biological evul evolution perspective um kind of your kids are mostly like you right kind of

there are no step changes whereas in AIs this is possible and I also find it curious um how LLMs in particular kind of spark this fear right kind of like no one has a fear of alpha fold taking over the world right so it's it's always gear at LLM. So I think it's kind of like this uncanny valley thing where kind of like they are a lot like us but kind of not quite and not containable and uh yeah >> I'm afraid Dave right it's the hell 9000 feeling that you get >> um yeah so I think the other way to think about this though is like game

theory and prisoners dilemma >> and I would say that in the west you see a lot of these calls to like data center moratorum Um, and like we need to regulate AI. >> Yeah. I mean, we don't want that. It's kind of like otherwise kind of like only the bad guys kind of have kind of the good AI, right? Kind of like this is and this and this is like it's a shame that people don't like recognize that logic. It's so obvious. It shouldn't even be like a political thing really. It should just be like look that's in that's inevitable. It's bound to happen. Even if you don't you do AI, someone else will do AI. So I think like the responsible thing to do in a way is to like maximize

our own research and focus in this area to get ahead of any potential like bad actors that have it because why shouldn't AI also be able to be protectors of humanity like if we build them well and we have the right alignment right so then it becomes a bit more of like an arms race where Maybe uh you can kind of keep things in check, but certainly, you know, overregulating or like moratoriums, that kind of stuff is not only um going to help us, it's probably going to make things a lot worse, right? Um so, so yeah, it is a very interesting it is a very interesting

area. I I generally think like we have no choice but to accelerate. Like that is that is the out like the whatever way you kind of look at it, you just have to accelerate and and then try to make the best possible future um based on that and that's what I would advise people to do and like I think everyone should actually like tinker and dabble with open source AI. I think that's been extremely exciting to see how like open- source AI has gotten this massive upswing with open claw and you're seeing a lot of fine-tuning of models is being done distillation which maybe is a little bit um in the gray

area but there's a lot of cool stuff happening in open source AI and there's a lot of interesting people to follow it feels a little bit like early DeFi. I see a lot of the same people I used to engage with during DeFi summer who are now like deep into the weeds on OpenClaw or you know PI agent or Hermes and so on and so forth. So I think like my call to action here to everyone listening would just be to like don't be afraid. You have to do this anyway like just lean into it um and and ride the wave. >> Yeah. Um, speaking of riding the wave, um, tell us what, um, you're looking forward to with respect to Nansen over the next year or two.

>> Yeah, so first of all, I want to see Nunson in every agentic stack, every agentic trading stack. So people who are hobbyists, tinkerers, using open claw, hermis, etc. I want to see people use nunen because it's going to make their agent so much better. We have a lot of stuff I I mentioned like time traveling capabilities. Um full feature parity with the core product in terms of what you can do with it. Um more chains to support even better liquidity through more aggregators. Then so that's for like the DIY folks and the tinkerers. And then I'm also very excited about nonsense 3 which is going to be more focused on

like mass market and kind of consumers who aren't necessarily deep down the rabbit hole on openclaw. Based on the early versions we had internally I think it's no doubt it'll be the best product we've ever created at nunen. Maybe even the best product ever created in crypto. Although maybe that sounds arrogant, >> but I think it's just going to be incredibly cool. Like the design of it, the user experience, the futuristic feel, uh, but also the simplicity and beauty of the product. So, that's like another thing I'm extremely excited about. And yeah, I could go on, but agentic trading is overall the main

thing we're focused on. And whether it's for the DIY folks, the tinkerers, or for the more consumers, mass market folks who don't want to tinker so much, they just want a product that's great and works. Uh, we will have really good offerings for for both coming out this year. >> Fantastic. Where do we send people to find out more about Nansen? >> Go to nansen.ai, our website. You can also install our CLI by typing in npm install nonsen- CLI. uh for the technical folks uh and you can find us on x at nansson_ai.

>> Perfect. Thank you so much for coming in again Alex. >> Thanks for having me.