Jonathan Levin of Chainalysis discusses the company's evolution from resolving the Mt. Gox bankruptcy to providing blockchain intelligence for 350 government agencies and more than a thousand institutions. Levin outlines platform use cases across exchanges, stablecoins, custody and tokenization as well as decentralized finance, platform architecture and API-driven real-time screening. They emphasize auditable artificial intelligence, AI, analyst workflows and outcome-based fraud pricing while describing capabilities for anti-money laundering, AML, threat hunting and crypto analytics.
Key takeaways highlight Chainalysis positioning itself as an intelligence provider that powers real-time transaction screening, analyst workflows and outcome-based fraud pricing. Levin explains proactive threat hunting and AI agents that engage scammers to map financial and communications infrastructure. They advise enterprises entering on-chain markets to prioritize transparent and trustworthy AI and rigorous operational security, and they underscore the importance of regulatory collaboration to accelerate institutional adoption. John Furrier of theCUBE and Gemma Allen of theCUBE Research host the conversation.
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Jonathan Levin, Chainalysis
Jonathan Levin of Chainalysis discusses the company's evolution from resolving the Mt. Gox bankruptcy to providing blockchain intelligence for 350 government agencies and more than a thousand institutions. Levin outlines platform use cases across exchanges, stablecoins, custody and tokenization as well as decentralized finance, platform architecture and API-driven real-time screening. They emphasize auditable artificial intelligence, AI, analyst workflows and outcome-based fraud pricing while describing capabilities for anti-money laundering, AML, threat hunting and crypto analytics.
Key takeaways highlight Chainalysis positioning itself as an intelligence provider that powers real-time transaction screening, analyst workflows and outcome-based fraud pricing. Levin explains proactive threat hunting and AI agents that engage scammers to map financial and communications infrastructure. They advise enterprises entering on-chain markets to prioritize transparent and trustworthy AI and rigorous operational security, and they underscore the importance of regulatory collaboration to accelerate institutional adoption. John Furrier of theCUBE and Gemma Allen of theCUBE Research host the conversation.
>> Palo Alto Studio connects with Silicon Valley and Wall Street. I'm John Furrier your here with Dave Vellante, my cohost.
Gemma Allen
>> Welcome at to theCUBE Studio here at the New York Stock Exchange. I'm Gemma Allen with NYSE Wired's Crypto Trailblazers. And joining me now for a conversation on all things the world of crypto, AI, and intelligible signals is Jonathan Levin, co-founder and CEO of Chainalysis. Welcome, Jonathan.
Jonathan Levin
>> Thanks so much for having me.
Gemma Allen
>> So Chainalysis company is 12 years old. A lot has happened in the space in 12 years. So maybe just to begin, talk me to exactly what it is that you guys do.
Jonathan Levin
>> So we started 12 years ago and we were asked to solve the sort of biggest mystery in crypto. We were asked to solve the bankruptcy case of Mt. Gox. And people asked us to follow the money in that case. The creditors wanted to know where their money had gone and we ended up solving a criminal investigation that placed us really at the heart and center of all public sector, follow the money investigations that have happened ever since. And today we work not just with one agency, but we work with 350 government agencies around the world, helping them actually oversee and protect the crypto market. What we have spread ourselves out into is not just the public sector side of things, but also how over a thousand institutions have now come onto the blockchain placing transactions, making prediction markets, tokenized equities, stablecoins, any type of use case. Chainalysis is there to protect those transactions and power real-time risk management decisions for all these different use cases that have come and gone over the last 12 years.
Gemma Allen
>> A lot to unpack in the tech and we're going to get there, but first I need to understand. So 12 years ago, what made you guys be the go-to point to help with this investigation? What were you doing at that point of your career? It seems very kind of almost MI5 to me.
Jonathan Levin
>> Yeah. The three co-founders of Chainalysis, one came from Kraken, one actually built one of the first ever Android Bitcoin wallets. And then I was just an economist who had spent time thinking about all the data that is available from the Bitcoin blockchain. And the three of us together had this ability to actually aggregate all the data that was present on the blockchain and we just wanted to package it up to whoever wanted it. And the first people who really wanted it were people who had lost money. And so they approached us and Kraken had a really close relationship with the creditors of Mt. Gox. And so Michael, my co-founder was given this project and we said, "Yeah, sure. We'll take a crack at it." And none of us had ever interacted with law enforcement or done any sort of real investigation work. And we built a methodology where someone would propose a theory and the other two would shoot it down until we got to the real answer, which was the money was stolen over a long period of time.
Gemma Allen
>> Wow. I wouldn't say just an economist either, right? An economist is a pretty impressive gig, but 12 years ago, blockchain was still relatively new, had a lot of promise, a lot of opportunity. People saw it actually as a security option, right? It's a very secure, very transparent way to operate and do business. We know though that things change, bad actors came on the scene as they do on every scene and things changed quite significantly. We're now in this AI world where the world of cryptocurrency, blockchain and the opportunity and threat from AI is somewhat of a mystery still I think to be unraveled. But talk me through the sorts of customers that you guys have helped and supported in that time aside from that, obviously, founder case, what sort of new ways are you helping institutions?
Jonathan Levin
>> Yeah. So obviously, I would say that Chainalysis is one of these companies that we help everyone come on chain. So whether you are a DeFi protocol or whether you are a global custodial institution that's been doing custody for hundreds of years, like we are there to partner with that institution, with that organization, with that team to make sure that they have all of the information at their fingertips to manage risk as they enter and put transactions on the blockchain, because that is just a new set of rails, but you have the same types of risk that you have in any type of financial transaction. You have AML risk, you have fraud risk, you have security risk, you have operational risk. And really, what they rely on us for is to have our intelligence at their fingertips whenever they're making one of these decisions, whether it's AML or fraud. And so really, the type of customers that we've had has changed a lot over the years and we definitely started with the exchanges who were the first sort of major commercial use case for blockchain technology. Then we moved into Stablecoin issuers and have been working with the likes of Tether for many, many years and making sure that they can investigate abuse of their platform and stablecoin. And then what's happened more recently is that we've had sort of an institutional move of major financial institutions, making sure that they can custody and transfer blockchain assets of all shapes and sizes. We've now making sure that the likes of Securitize can secure their tokenized products and do it securely with the right operational security mindset. And then most recently with Polymarket helping them really build the first of its kind type market manipulation detection model to make sure that you can have a decentralized prediction market with the same type of market manipulation detection that you would get in traditional settings. So we're really helping every type of use case that we see coming on chain and making sure that everyone can manage risk effectively.
Gemma Allen
>> So I'm a customer Chainalysis, let's say an AML case, right? Those cases tend to be a little bit of a slow burn. There's a lot of paperwork, a lot of points, a lot of different guardrails in place already, or I'm a bank and I'm looking at credit card applications from scam artists, et cetera. How does the UX and the way in which the customers interact with your platform vary? Is this like fluid? Is this like a dashboard? Break it down for me, like bring it to life a little bit.
Jonathan Levin
>> Yeah. We think about ourselves as an intelligence company. So we like to think about how do we serve our intelligence wherever the customer really needs us to be. And a lot of the time that is through an API. They are screening transactions against us in real time, trying to get a real time answer to know whether they should process a deposit or facilitate a withdrawal. And so a lot of our customers on the private sector side are consuming our intelligence in real time programmatically already. And then we have visualization software and alert-driven engines to make sure that they can review alerts that they need to review for filing suspicious activity reports or doing deeper dive investigations to understand a network of scam activity or fraudulent activity on the platform. And so, yeah, we definitely have solutions that are suited to humans and then we have a lot of our intelligence that ultimately is being consumed already by machines that are making decisions about risk inside these organizations.
Gemma Allen
>> And what's the business model here? How is this price? Is it by seat? Is it by usage, by outcome?
Jonathan Levin
>> Yeah. So typically, the way we are pricing, we have some outcome based pricing on the fraud side in particular where people are used to paying for outcomes to reduce fraud rates, particularly in financial services firms and I can get onto that a little bit. And then we have a sort of more traditional consumption style model for transactions that people are securing and processing. And so we sell that to the industry and then we also have a seat-based license model for an analyst seat or a seat for a public sector agency for an investigator that is using our product. But we're increasingly moving all of our business model like everyone else in the space to being much more pre-committed consumption so that people are getting the intelligence that they need from us and they feel like they're getting the right value from us at the time of delivery.
Gemma Allen
>> It's interesting that's where the entire tech landscape is headed, right?
Jonathan Levin
>> Yeah.
Gemma Allen
>> Talk about the tech. So I assume that part of your work and from a dev perspective is to say ahead of bad actors, which probably means engaging and having at least a very, very strong pulse as to what's happening in that market broadly from different labs, different orchestrators. Do you guys build proprietary? What does that look like? Like what is the typical dev cycle for you?
Jonathan Levin
>> Yeah. So we sort of split our developer lifecycle into three sort of categories. One is making sure that we can collect the intelligence that our customers really need from us. The second is making sure that that is on a platform that they can access really easily. And then the third is making sure that the access patterns match each user profile that we serve. And so our development life cycle looks like on the front end intelligence collection, we run that based on what are the biggest priorities of the day. It's the World Cup at the moment in the US. There are World Cup ticket scams. There is new types of scam activity. We make sure that we can collect that information at scale to protect consumers that are falling victim to these types of ticket scams and packaging that information up so that our customers on the FinTech side or the payment side, they can actually use that to prevent the use of their platforms for scams. And then at the front end, we're a very typical sort of enterprise software company that makes sure that we're delivering enterprise solutions that can fit all of the requirements of working with big institutions and public sector agencies. And that means making sure that if you're using AI, it's trustworthy, it's auditable, it's transparent and that we're complying with the way in which they want to be able to consume that intelligence.
Gemma Allen
>> And when you're looking for specific signals and like in the case of ticket scams, right? Ticket types are one of my big, big bugbears in life maybe yours too, but what sorts of intelligible signals are you looking for from the perspective of blockchain? Is it multiple addresses on one transaction, multiple records? How is it unique, I guess, to any other scam industry?
Jonathan Levin
>> Yeah. So what we have become really good at across all the different types of threats, including scams is we are a very much proactive threat hunting organization. So we will take a threat like scams and we will find like what are the ways in which that organization is operating and go and meet those scammers where they are. And we then now use AI agents that can interact with scammers at scale. We can have thousands of conversations a day with scammers that are trying to scam consumers and we go and collect our own intelligence about what is the financial infrastructure that they're relying on, what is the communication infrastructure that they're relying on. And then we package that intelligence up and provide it as a feed to the industry so that financial institutions can protect their customers from falling victim to scams.
Gemma Allen
>> Wow, that is fascinating. So from the financial perspective for this company 12 years in, where are you guys at? Are you raising money? What do the commercials look like and what is, I guess, the broader plan?
Jonathan Levin
>> Yeah. I'm a co-founder of the company, but I took over as CEO 18 months ago and I got very serious about making sure that we were accelerating growth with all of the entrants of all of the institutions that are coming into the space and making sure that we could also piggyback off of AI both in intelligence collection as we've spoken about, but also helping our customers really consume intelligence from Chainalysis at scale. And so we've been head down over the last 18 months sort of really building out the foundational infrastructure to launch Chainalysis agents and get our customers to consume Chainalysis intelligence at greater scale. So really where we're at in the journey of being a company is that we've got a solid balance sheet that we've been investing in the business with and we have been making sure that we can grow with the industry and with the amount of use cases that are coming on chain and we've been doing that at sort of the rate that I think is moving at the pace of the industry, which is pretty quick these days.
Gemma Allen
>> Well, it's certainly a very interesting time in crypto and it's a conversation that is growing I think day by day. Jonathan Levin, wish you guys all the best. Thanks so much for joining us on theCUBE.
Jonathan Levin
>> Thanks so much for having me.
Gemma Allen
>> I'm Gemma Allen here at theCUBE Studio at the New York Stock Exchange. This is NYSE Wired's Crypto Trailblazers. Thanks for watching.
>> Palo Alto Studio connects with Silicon Valley and Wall Street. I'm John Furrier your here with Dave Vellante, my cohost.
Gemma Allen
>> Welcome at to theCUBE Studio here at the New York Stock Exchange. I'm Gemma Allen with NYSE Wired's Crypto Trailblazers. And joining me now for a conversation on all things the world of crypto, AI, and intelligible signals is Jonathan Levin, co-founder and CEO of Chainalysis. Welcome, Jonathan.
Jonathan Levin
>> Thanks so much for having me.
Gemma Allen
>> So Chainalysis company is 12 years old. A lot has happened in the space in 12 years. So maybe just to begin, talk me to exactly what it is that you guys do.
Jonathan Levin
>> So we started 12 years ago and we were asked to solve the sort of biggest mystery in crypto. We were asked to solve the bankruptcy case of Mt. Gox. And people asked us to follow the money in that case. The creditors wanted to know where their money had gone and we ended up solving a criminal investigation that placed us really at the heart and center of all public sector, follow the money investigations that have happened ever since. And today we work not just with one agency, but we work with 350 government agencies around the world, helping them actually oversee and protect the crypto market. What we have spread ourselves out into is not just the public sector side of things, but also how over a thousand institutions have now come onto the blockchain placing transactions, making prediction markets, tokenized equities, stablecoins, any type of use case. Chainalysis is there to protect those transactions and power real-time risk management decisions for all these different use cases that have come and gone over the last 12 years.
Gemma Allen
>> A lot to unpack in the tech and we're going to get there, but first I need to understand. So 12 years ago, what made you guys be the go-to point to help with this investigation? What were you doing at that point of your career? It seems very kind of almost MI5 to me.
Jonathan Levin
>> Yeah. The three co-founders of Chainalysis, one came from Kraken, one actually built one of the first ever Android Bitcoin wallets. And then I was just an economist who had spent time thinking about all the data that is available from the Bitcoin blockchain. And the three of us together had this ability to actually aggregate all the data that was present on the blockchain and we just wanted to package it up to whoever wanted it. And the first people who really wanted it were people who had lost money. And so they approached us and Kraken had a really close relationship with the creditors of Mt. Gox. And so Michael, my co-founder was given this project and we said, "Yeah, sure. We'll take a crack at it." And none of us had ever interacted with law enforcement or done any sort of real investigation work. And we built a methodology where someone would propose a theory and the other two would shoot it down until we got to the real answer, which was the money was stolen over a long period of time.
Gemma Allen
>> Wow. I wouldn't say just an economist either, right? An economist is a pretty impressive gig, but 12 years ago, blockchain was still relatively new, had a lot of promise, a lot of opportunity. People saw it actually as a security option, right? It's a very secure, very transparent way to operate and do business. We know though that things change, bad actors came on the scene as they do on every scene and things changed quite significantly. We're now in this AI world where the world of cryptocurrency, blockchain and the opportunity and threat from AI is somewhat of a mystery still I think to be unraveled. But talk me through the sorts of customers that you guys have helped and supported in that time aside from that, obviously, founder case, what sort of new ways are you helping institutions?
Jonathan Levin
>> Yeah. So obviously, I would say that Chainalysis is one of these companies that we help everyone come on chain. So whether you are a DeFi protocol or whether you are a global custodial institution that's been doing custody for hundreds of years, like we are there to partner with that institution, with that organization, with that team to make sure that they have all of the information at their fingertips to manage risk as they enter and put transactions on the blockchain, because that is just a new set of rails, but you have the same types of risk that you have in any type of financial transaction. You have AML risk, you have fraud risk, you have security risk, you have operational risk. And really, what they rely on us for is to have our intelligence at their fingertips whenever they're making one of these decisions, whether it's AML or fraud. And so really, the type of customers that we've had has changed a lot over the years and we definitely started with the exchanges who were the first sort of major commercial use case for blockchain technology. Then we moved into Stablecoin issuers and have been working with the likes of Tether for many, many years and making sure that they can investigate abuse of their platform and stablecoin. And then what's happened more recently is that we've had sort of an institutional move of major financial institutions, making sure that they can custody and transfer blockchain assets of all shapes and sizes. We've now making sure that the likes of Securitize can secure their tokenized products and do it securely with the right operational security mindset. And then most recently with Polymarket helping them really build the first of its kind type market manipulation detection model to make sure that you can have a decentralized prediction market with the same type of market manipulation detection that you would get in traditional settings. So we're really helping every type of use case that we see coming on chain and making sure that everyone can manage risk effectively.
Gemma Allen
>> So I'm a customer Chainalysis, let's say an AML case, right? Those cases tend to be a little bit of a slow burn. There's a lot of paperwork, a lot of points, a lot of different guardrails in place already, or I'm a bank and I'm looking at credit card applications from scam artists, et cetera. How does the UX and the way in which the customers interact with your platform vary? Is this like fluid? Is this like a dashboard? Break it down for me, like bring it to life a little bit.
Jonathan Levin
>> Yeah. We think about ourselves as an intelligence company. So we like to think about how do we serve our intelligence wherever the customer really needs us to be. And a lot of the time that is through an API. They are screening transactions against us in real time, trying to get a real time answer to know whether they should process a deposit or facilitate a withdrawal. And so a lot of our customers on the private sector side are consuming our intelligence in real time programmatically already. And then we have visualization software and alert-driven engines to make sure that they can review alerts that they need to review for filing suspicious activity reports or doing deeper dive investigations to understand a network of scam activity or fraudulent activity on the platform. And so, yeah, we definitely have solutions that are suited to humans and then we have a lot of our intelligence that ultimately is being consumed already by machines that are making decisions about risk inside these organizations.
Gemma Allen
>> And what's the business model here? How is this price? Is it by seat? Is it by usage, by outcome?
Jonathan Levin
>> Yeah. So typically, the way we are pricing, we have some outcome based pricing on the fraud side in particular where people are used to paying for outcomes to reduce fraud rates, particularly in financial services firms and I can get onto that a little bit. And then we have a sort of more traditional consumption style model for transactions that people are securing and processing. And so we sell that to the industry and then we also have a seat-based license model for an analyst seat or a seat for a public sector agency for an investigator that is using our product. But we're increasingly moving all of our business model like everyone else in the space to being much more pre-committed consumption so that people are getting the intelligence that they need from us and they feel like they're getting the right value from us at the time of delivery.
Gemma Allen
>> It's interesting that's where the entire tech landscape is headed, right?
Jonathan Levin
>> Yeah.
Gemma Allen
>> Talk about the tech. So I assume that part of your work and from a dev perspective is to say ahead of bad actors, which probably means engaging and having at least a very, very strong pulse as to what's happening in that market broadly from different labs, different orchestrators. Do you guys build proprietary? What does that look like? Like what is the typical dev cycle for you?
Jonathan Levin
>> Yeah. So we sort of split our developer lifecycle into three sort of categories. One is making sure that we can collect the intelligence that our customers really need from us. The second is making sure that that is on a platform that they can access really easily. And then the third is making sure that the access patterns match each user profile that we serve. And so our development life cycle looks like on the front end intelligence collection, we run that based on what are the biggest priorities of the day. It's the World Cup at the moment in the US. There are World Cup ticket scams. There is new types of scam activity. We make sure that we can collect that information at scale to protect consumers that are falling victim to these types of ticket scams and packaging that information up so that our customers on the FinTech side or the payment side, they can actually use that to prevent the use of their platforms for scams. And then at the front end, we're a very typical sort of enterprise software company that makes sure that we're delivering enterprise solutions that can fit all of the requirements of working with big institutions and public sector agencies. And that means making sure that if you're using AI, it's trustworthy, it's auditable, it's transparent and that we're complying with the way in which they want to be able to consume that intelligence.
Gemma Allen
>> And when you're looking for specific signals and like in the case of ticket scams, right? Ticket types are one of my big, big bugbears in life maybe yours too, but what sorts of intelligible signals are you looking for from the perspective of blockchain? Is it multiple addresses on one transaction, multiple records? How is it unique, I guess, to any other scam industry?
Jonathan Levin
>> Yeah. So what we have become really good at across all the different types of threats, including scams is we are a very much proactive threat hunting organization. So we will take a threat like scams and we will find like what are the ways in which that organization is operating and go and meet those scammers where they are. And we then now use AI agents that can interact with scammers at scale. We can have thousands of conversations a day with scammers that are trying to scam consumers and we go and collect our own intelligence about what is the financial infrastructure that they're relying on, what is the communication infrastructure that they're relying on. And then we package that intelligence up and provide it as a feed to the industry so that financial institutions can protect their customers from falling victim to scams.
Gemma Allen
>> Wow, that is fascinating. So from the financial perspective for this company 12 years in, where are you guys at? Are you raising money? What do the commercials look like and what is, I guess, the broader plan?
Jonathan Levin
>> Yeah. I'm a co-founder of the company, but I took over as CEO 18 months ago and I got very serious about making sure that we were accelerating growth with all of the entrants of all of the institutions that are coming into the space and making sure that we could also piggyback off of AI both in intelligence collection as we've spoken about, but also helping our customers really consume intelligence from Chainalysis at scale. And so we've been head down over the last 18 months sort of really building out the foundational infrastructure to launch Chainalysis agents and get our customers to consume Chainalysis intelligence at greater scale. So really where we're at in the journey of being a company is that we've got a solid balance sheet that we've been investing in the business with and we have been making sure that we can grow with the industry and with the amount of use cases that are coming on chain and we've been doing that at sort of the rate that I think is moving at the pace of the industry, which is pretty quick these days.
Gemma Allen
>> Well, it's certainly a very interesting time in crypto and it's a conversation that is growing I think day by day. Jonathan Levin, wish you guys all the best. Thanks so much for joining us on theCUBE.
Jonathan Levin
>> Thanks so much for having me.
Gemma Allen
>> I'm Gemma Allen here at theCUBE Studio at the New York Stock Exchange. This is NYSE Wired's Crypto Trailblazers. Thanks for watching.
John Furrier
Gemma Allen