X Space #34 — Why Web3 Needs Intention Analytics, Not Descriptive Token Data. Listen to the full recording on X

X Space #34 tackles the analytics problem at the root of Web3’s growth crisis. Co-founders Martin and Tarmo open with a framework observation that most Web3 founders have never heard articulated clearly: every new technology paradigm requires two distinct innovations, not one. The first is business process innovation — building the product, the protocol, the smart contract logic. The second is customer acquisition innovation — developing the tools to find the right users, understand them, and convert them at sustainable cost. Web3 has invested enormously in the first and almost nothing in the second. The result is a DeFi customer acquisition cost of $1,000 or more per transacting user — a figure that makes every business model structurally unviable and drives founders toward token-based exit strategies instead of sustainable growth. The session explains why current Web3 analytics tools make this problem worse (by providing descriptive token data that looks like insight but enables no action), what intention analytics actually is and why blockchain data makes it more powerful than anything in Web2, and how any Web3 founder can get started with two lines of code in Google Tag Manager — free, today.

Two Innovations Every Technology Needs — Web3 Has Only One

Martin opens X Space #34 with a structural observation that reframes the entire Web3 growth debate. Every successful technology paradigm, he argues, requires two independent innovations to achieve mainstream adoption. Neither one alone is sufficient, and building only the first while ignoring the second will eventually kill even the most technically superior product.

The first innovation is business process innovation — the core technical contribution that the new paradigm enables. For Web3, this means smart contracts, decentralised protocols, non-custodial finance, trustless settlement, and all the genuine architectural improvements over legacy financial infrastructure. Web3 has invested billions in this dimension and produced real, valuable innovation: automated market makers, lending protocols, yield optimisation, decentralised governance, and more. The second innovation is customer acquisition innovation — developing the tools, methods, and infrastructure to find the right users, communicate with them effectively, and convert them to active participants at sustainable unit cost. Web3 has barely begun this second innovation. As Martin states: “Every new technological paradigm will need as well innovation of customer acquisition. You need always two innovations. There is innovation on the business process and there is innovation of customer acquisition. In Web3 there has been massive innovation with full heart in the business process innovation. But there has to be as well innovation in customer acquisition.”

Why Both Innovations Are Non-Negotiable

The reason both innovations are necessary is straightforward: a better product that nobody can find or afford to acquire is not a better business. Web3’s technical innovations are real, but they exist largely inside an ecosystem of 50 million technical enthusiasts. Reaching the remaining billions of potential users requires the second innovation — customer acquisition tools that make it economically viable to identify, target, and convert mainstream users. Without that second innovation, even genuinely superior products will remain trapped serving the early-adopter segment. For more on the growth dynamics, see our Web3 growth restoration guide.

Web3 Today Is Web2 in 2000: The Same Crisis, The Same Playbook

Martin and Tarmo anchor the entire session in a historical parallel that makes the current Web3 situation both less alarming and more solvable than it appears. Web3 in 2025 is not experiencing a unique crisis — it is experiencing the same crisis that Web2 experienced at the beginning of the 2000s internet era, with the same root causes and the same available solutions.

In the early 2000s, Web2 faced two specific barriers to mainstream adoption. First, fraud was rampant: credit card fraud was so prevalent that many consumers refused to enter payment details online, stifling e-commerce growth entirely. Second, customer acquisition costs were catastrophic: dot-com companies spent enormous sums on billboard advertising, TV spots, and mass media campaigns (the famous “pets.com” highway billboards became a symbol of the era’s marketing waste) with customer acquisition costs in the thousands of dollars — and no way to measure which half of the spend was working. As Martin recalls: “People were afraid to transfer their credit card as a payment means over Internet because the fraud was so high. And e-commerce companies, half of the developer power went into fraud detection. Acquisition costs of users were enormous.” Both problems were eventually solved: fraud through better detection systems, and CAC through Google’s AdTech innovations. Web3 faces identical structural challenges and has access to the same solution blueprint. For more on the fraud detection parallel, see our Web3 fraud and growth guide.

The Secret Everyone Knows But Nobody Admits

Martin makes a pointed observation about why the Web3 CAC crisis receives so little public discussion despite being universally known among founders. Admitting a $1,000+ customer acquisition cost to a venture capital investor essentially ends the conversation — it signals that the business model cannot become cash-flow positive regardless of how good the product is. Consequently, founders avoid discussing it publicly while silently dealing with the consequences: burning treasury on ineffective mass marketing, failing to hit growth targets, and eventually pivoting toward token-based revenue extraction rather than genuine product growth. As Martin puts it: “It’s a secret everyone knows but no one is speaking about this. No one wants to admit it — no one wants to say it loud — how difficult it is to acquire users in Web3.”

Descriptive Analytics vs Predictive Analytics: The Fundamental Difference

The core technical argument in X Space #34 is the distinction between descriptive analytics and predictive analytics — and the specific reason why Web3 analytics tools have remained stuck in the descriptive category while Web2 moved to predictive analytics over 15-20 years ago.

Descriptive analytics documents what happened. It tells you which tokens users held last month, which protocols they interacted with historically, and how transaction volumes changed over time. This data is backward-looking by definition. Crucially, it cannot tell you what a user will do next — which is the only information that matters for targeted acquisition and conversion campaigns. Predictive analytics uses behavioral pattern data to calculate forward-looking probabilities: what is the likelihood that this specific wallet will borrow in the next 30 days? Will this user stake, trade, or exit? Is this address behaviorally aligned with a high-leverage product or a conservative yield strategy? As Tarmo explains: “Today the most analytics in Web3 is descriptive — it just describes what happened in the past. The difficulty is past actions don’t predict what is going to happen. What is the user going to do in future?” For the full framework, see our behavioral analytics guide.

Why Web2 Made the Jump and Web3 Has Not

Web2 completed the transition from descriptive to predictive analytics in the early 2000s, driven by Google’s development of intention-based advertising technology. Google’s core insight was that search and browsing history, despite being lower-quality than financial transaction data, contained enough behavioral signal to calculate user intentions with sufficient accuracy for targeted advertising. The result was a dramatic reduction in customer acquisition costs: Web2 businesses that adopted Google’s AdTech moved from spending thousands of dollars per customer with no idea whether it was working, to spending $10-30 per transacting customer with measurable ROI at every step. Web3 has access to behavioral data that is qualitatively superior to anything Google uses — and has still not made the transition. That gap is precisely what ChainAware’s analytics tools address.

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Why Token Holder Data Is Not Actionable

Martin introduces a specific critique of the most common form of “analytics” offered by current Web3 data platforms — token holder overlap analysis — and explains precisely why this data type, despite appearing informative, cannot drive any marketing or growth action.

Token holder analytics tells a protocol that, for example, 10% of their users also hold a specific token from another protocol, or that a percentage of their wallet addresses have previously interacted with a competing platform. This type of data describes the current composition of a user base at a superficial level. However, it answers none of the questions that matter for acquisition and conversion: What does this user intend to do next? Are they a borrower or a trader? Do they have the experience level to use this product? Are they likely to convert, or are they purely exploratory? As Martin challenges: “Let’s imagine you’re a founder and now you see this data — 10% of the people who hold your token have as well Uniswap. What do you do? How does it help you to get more users to your platform?” The honest answer is: it does not. Token holder data describes a static snapshot with no forward-looking signal. For more on what actionable data looks like, see our intention-based marketing guide.

Protocol Usage Data vs Token Holding Data

ChainAware deliberately focuses on protocol interaction patterns rather than token holdings. Protocol interactions reveal behavioral intentions: a wallet that has repeatedly used lending protocols is a behaviorally confirmed borrower or lender. A wallet that consistently interacts with high-leverage trading products has a demonstrated risk appetite. A wallet whose protocol history shows only simple swaps and staking is likely in an early lifecycle stage. These behavioral protocol patterns, combined with transaction frequency, timing, and counterparty analysis, produce the intention profiles that make targeting possible. Token holding tells you what someone owns. Protocol behavior tells you what someone does — and what they are likely to do next.

Why Blockchain Data Produces Better Predictions Than Web2’s Behavioral Data

Tarmo returns to the proof-of-work data quality argument that distinguishes blockchain behavioral data from the social media and browsing data that Web2’s AdTech systems rely on. The argument is foundational: Web3’s predictive analytics advantage is not just equivalent to Web2’s — it is structurally superior because the data quality is higher.

Web2’s behavioral data — search queries, page views, app usage — is generated at zero cost per interaction. A user can search for “DeFi borrowing” once because a friend mentioned it, then never engage with the topic again. That single search creates a behavioral signal that Google’s algorithms will interpret as a genuine interest, serving DeFi-related advertisements for weeks. The signal is noisy because the cost of generating it is zero. Blockchain transactions, by contrast, require real money (gas fees) and deliberate action. Nobody accidentally executes a DeFi lending transaction. Every transaction represents a considered, intentional financial commitment that reveals genuine behavioral priorities. As Tarmo explains: “When you have to pay cash for every transaction, you don’t just fool around. You think twice before you do your transactions. Financial transactions have very high prediction power because users think twice or three times before they submit.” For how this applies to prediction accuracy, see our predictive AI guide.

The User-Product Mismatch: Your Real Users Are Not Your Marketing Persona

One of X Space #34’s most practically useful arguments addresses a problem that many Web3 founders privately suspect but have no way to confirm: the users actually connecting to their platform may be fundamentally different from the users their marketing was designed to attract. This user-product mismatch is, according to Martin and Tarmo, one of the most common root causes of poor conversion rates — more common than actual product quality problems.

Every marketing team creates user personas — fictional representative characters who embody the ideal target customer. “Our persona is a DeFi-experienced borrower with 50+ on-chain transactions, comfortable with 150% collateralisation, seeking fixed-rate lending for predictable financial planning.” This persona guides all acquisition spend: the content, the channels, the messaging, the influencer selection. The problem is that there is currently no way to verify whether the marketing is actually attracting this persona or an entirely different audience. Without intention analytics, a protocol might spend $30,000 per month attracting traders who have no interest in borrowing, or attracting complete DeFi newcomers to a product designed for experienced users. As Martin explains: “Every founder is saying like oh I have 20,000 clicks a month. Cool. From which users? What is their profile? What are their intentions? And usually you don’t know it until now.” For the complete targeting methodology, see our AI marketing for Web3 guide.

The Reality Check: Persona R vs Persona P

Martin frames the user-product mismatch with a memorable shorthand. Founders design their product and marketing for “Persona R” — the imagined ideal user who perfectly matches the product’s value proposition. Analytics reveals that “Persona P” is actually arriving — a different behavioral profile with different intentions, different experience levels, and different risk tolerance. Neither outcome is necessarily catastrophic: sometimes Persona P represents a genuinely valuable market that the founder had not considered. However, it is impossible to respond to the mismatch — either by adjusting the product, refining the marketing, or deliberately targeting Persona R instead of Persona P — without first knowing it exists. Intention analytics creates this feedback loop, replacing the founder’s assumptions with market reality.

Risk Willingness: The Credit Suisse Model Applied to Web3 Audiences

Tarmo introduces the risk willingness dimension — a concept central to private banking client profiling at Credit Suisse and other major institutions — and explains why it is equally essential for Web3 platform design and user acquisition.

Risk willingness describes the level of potential loss a user is psychologically and financially comfortable absorbing. The spectrum is wide: some investors will sleep soundly through a 50% portfolio decline overnight, treating it as a normal fluctuation in a volatile asset class. Others cannot function effectively when facing even a 5% potential loss — the anxiety impairs their decision-making and leads to panic selling or avoidance behavior. Neither profile is wrong; they simply require different products, different communication styles, and different interface designs. As Tarmo explains: “In Credit Suisse, everything is based on the willingness to take a risk. Some people tolerate 50% loss overnight — they even don’t care. Other people cannot sleep if they have 5% possibility of loss.”

Matching Product Risk Profile to User Risk Willingness

The practical implication for Web3 protocols is direct: if a platform offers high-leverage products but its user base consists primarily of risk-averse wallets, the mismatch will produce poor conversion, high churn, and negative user experiences. Risk-averse users who encounter high-leverage products either avoid them entirely (reducing conversion) or engage inappropriately and suffer losses (damaging trust and creating churn). ChainAware’s analytics calculates risk willingness from transaction history — a wallet that has consistently taken large leveraged positions in volatile markets has a demonstrated high risk tolerance; a wallet that holds stable assets and rarely trades has a demonstrated risk-averse profile. Matching acquisition and interface design to these calculated risk profiles dramatically improves both conversion rates and long-term retention. For more on wallet behavioral profiling, see our wallet audit guide.

Mass Marketing in Web3: The 50/50 Problem Nobody Admits

Martin draws on a famous quote from the dot-com era that describes Web3’s marketing situation with uncomfortable precision: “We spend 50% of our marketing budget, but we don’t know which half is working.” This observation — originally attributed to department store magnate John Wanamaker in a pre-internet era — re-emerged as a central frustration of Web2’s early marketing phase, and it perfectly describes Web3’s current state.

Web3 marketing today consists primarily of KOL (Key Opinion Leader) campaigns, crypto media placements, loyalty programs, Discord community management, and airdrop campaigns. These channels all share one characteristic: they reach broad, undifferentiated audiences with identical messages and provide no meaningful feedback on whether the right users were reached. A protocol spending $30,000 per month on 20,000 clicks at $1.50 per click does not know whether those clicks came from wallets that will ever transact, wallets that are exclusively airdrop hunters, wallets that are completely misaligned with the product, or wallets that are genuine prospects. Without intention analytics providing the feedback loop, every optimization decision is guesswork. As Martin states: “At the moment, the Web3 marketing is something in the style — you spend 50%, but you don’t know which part worked.” For more on the mass marketing critique, see our Web3 KOL marketing guide.

How Web2’s $180 Billion AdTech Industry Solved the Same Problem

Martin and Tarmo contextualise the Web3 analytics opportunity by quantifying the industry that Web2 built to solve the identical user acquisition problem. Global AdTech — the technology infrastructure that enables targeted digital advertising based on user behavioral data — represents approximately $180 billion in annual revenue worldwide, with approximately $30 billion in Europe alone. This industry did not exist before Google’s AdWords innovation. It emerged specifically because the combination of user intention data and programmatic targeting reduced customer acquisition costs from thousands of dollars to tens of dollars, making digital business models viable at scale.

The mechanism was straightforward: by calculating user intentions from search and browsing behavior, Google could match advertisements to users whose behavior indicated genuine interest in the product being advertised. The result was dramatically higher conversion rates (users saw ads relevant to their actual intentions), lower cost per click needed for conversion, and measurable ROI that replaced the old 50/50 guesswork. Web3 has not yet built this infrastructure — but the data necessary to build it is available free of charge on every major blockchain. As Martin argues: “The first step, understand who your clients are. Not what you think, who they are, but who they really are. This is not possible without calculating user intentions and aggregating them.” For the complete AdTech framework, see our Web3 AdTech guide.

From Analytics to Automated Targeting

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Intention Analytics: The First Step Toward Sustainable Web3 Growth

Having established both the problem and its historical parallel, Martin and Tarmo turn to the specific solution that ChainAware provides. The solution architecture has two sequential steps — and X Space #34 focuses deliberately on Step 1, because attempting Step 2 without Step 1 is precisely the mistake that most Web3 marketing efforts currently make.

Step 1 is intention analytics: understanding who your users actually are, what they intend to do, and whether they match the profile your product is designed to serve. This step requires no immediate change to marketing strategy, creative, or spend. It requires only adding ChainAware’s tracking pixel to the platform and observing the aggregated intention data that emerges from actual wallet connections. Step 2 — which ChainAware also enables through its Marketing Agents product — is acting on that data: targeting acquisition campaigns at the right behavioral audiences, personalising on-site messaging to match individual wallet profiles, and converting matched users through intention-aligned calls-to-action. Step 2 is impossible to execute correctly without Step 1’s data. As Tarmo concludes: “What ChainAware offers is the key technology — a no-code environment to get a summary of your users of your Web3 applications. It’s free. It doesn’t cost anything. You get this feedback and with this feedback you can start doing actions, real actions which lead to user conversions.” For the complete analytics implementation, see our Web3 analytics guide.

Two Lines of Code: How to Get Started with ChainAware Analytics

Martin emphasises the implementation simplicity of ChainAware’s analytics pixel repeatedly throughout X Space #34, because the perceived complexity of analytics integration is one of the primary barriers preventing Web3 founders from adopting intention-based approaches. The actual integration requires no engineering resources and no changes to the protocol’s existing codebase.

The integration process uses Google Tag Manager — a standard no-code tag management platform that virtually every Web3 project already uses for analytics, tracking pixels, and conversion tools. Adding ChainAware requires two lines of code inserted as a new tag in the existing Google Tag Manager workspace. No application code changes. No engineering deployment. No smart contract modifications. No user-facing changes of any kind. Within 24-48 hours of adding the tag, ChainAware’s dashboard begins populating with aggregated intention profiles of the wallets connecting to the platform: experience levels, risk willingness scores, behavioral intention categories (borrower, trader, staker, gamer, NFT collector), protocol usage history, and predicted next actions. As Martin explains: “From the day after, you see the users, you see the weekly users, you see the monthly users. Two lines of code. If you don’t like it, delete them. You don’t have to change your application.” For the setup guide, visit chainaware.ai/subscribe/starter.

Free for Founders Who Build Real Products

ChainAware’s analytics tier is free. Martin clarifies the offering directly: founders who join before end of May 2025 receive the analytics product free permanently. After that date, ChainAware will revisit pricing — the infrastructure cost of running the intention calculations at scale requires eventual monetisation. However, the current offer represents a genuine opportunity for any Web3 founder to access enterprise-grade intention analytics at zero cost simply by integrating two lines of code. Martin is specific about the target user: founders who are building real products, want real users, and intend to generate real revenue — not founders whose primary goal is token price manipulation or exit strategies. For the complete pricing overview, see chainaware.ai/pricing.

The Feedback Loop: From Imaginary Persona to Real User Profile

Martin introduces a powerful framing for what intention analytics actually delivers to a founder who has been operating on assumed user personas. The moment a founder connects ChainAware’s analytics to their platform and sees real intention data for the first time, they experience what Martin calls a “moment of reality” — the point at which the imaginary persona the marketing team invented is replaced by the actual behavioral profiles of real users.

This reality check is often uncomfortable. Martin acknowledges this directly: “Oh, I designed this Persona R. But here I see totally a Persona P is using my application. And this is like a reality check. It’s very hard probably for all founders to see who really are the users.” However, this discomfort is enormously valuable. A founder who knows their actual user base can make rational decisions: adjust the product to serve the actual audience better, refine acquisition targeting to attract the intended audience instead, or recognise that a product-market fit exists in an unexpected segment worth pursuing. Without this data, every product decision and every marketing investment is based on untested assumptions. Intention analytics replaces those assumptions with market feedback — the most valuable input any product team can receive. For more on the analytics-to-action workflow, see our Web3 growth guide.

From Analytics to Action: Fully Automated Web3 AdTech

X Space #34 deliberately focuses on analytics as Step 1, but Martin briefly introduces the Step 2 product — ChainAware’s Marketing Agents — to give founders a view of the complete growth infrastructure available after establishing the analytics foundation.

ChainAware’s Marketing Agents take the intention profiles calculated from on-chain behavioral data and automate the entire content creation and targeting pipeline. The system analyses each connecting wallet’s behavioral profile, calculates their specific intentions, generates content that resonates with those specific intentions, creates appropriate calls-to-action matched to the user’s likely next action, and delivers the personalised experience automatically — without human intervention for each individual user interaction. The input required from the founder is minimal: a set of URLs describing the platform’s products and value propositions. The output is a fully automated, intention-matched marketing layer that converts identified prospects more effectively than any mass-marketing alternative. As Martin explains: “It is 100% automated. It analyzes users, it calculates their predictions, it creates the content which resonates with user intentions, it creates call to actions. The result is much higher user conversion, user acquisition. The dream of every Web3 founder.” For the complete marketing agent documentation, see our AI marketing guide.

The Role of Marketing Agencies Is Changing

Martin notes a parallel between Web3’s current marketing agency culture and Web2’s pre-AdTech marketing agency culture. In the dot-com era, marketing agencies controlled enormous budgets with no accountability infrastructure — the 50/50 waste was industry standard, and agencies benefited from the opacity. Google’s AdTech innovation changed that permanently: agencies that mastered the new tools thrived, while those who resisted were replaced by programmatic platforms. Web3 is at the equivalent inflection point. Founders who adopt intention analytics will gain the data needed to hold their marketing partners accountable, replace ineffective mass campaigns with targeted intention-based programs, and reduce CAC from the current $1,000+ to the $20-30 range that makes Web3 businesses viable. For more on this transition, see our high conversion without KOLs guide.

Comparison Tables

Descriptive vs Predictive Web3 Analytics: Full Comparison

Web3 Marketing Today vs Intention-Based Approach

Frequently Asked Questions

What is the difference between descriptive and predictive Web3 analytics?

Descriptive analytics documents what happened: which tokens users held, which protocols they used in the past, how transaction volumes changed over time. This data is backward-looking and cannot predict future user behavior. Predictive analytics uses behavioral pattern data from on-chain transaction history to calculate forward-looking probabilities: what is this wallet likely to do next? Are they a probable borrower, trader, or staker? Do they have the experience level and risk tolerance for this product? Predictive analytics is actionable — it directly informs acquisition targeting, on-site personalisation, and conversion strategy. Descriptive analytics, while informative, cannot drive any specific marketing or growth action.

Why is token holder overlap data not useful for marketing?

Token holder data tells you what users own, not what they intend to do. Knowing that 10% of your users also hold a competitor’s token does not tell you whether those users are active traders, passive holders, or protocol explorers. It does not tell you whether they are likely to borrow, stake, or trade. It provides no basis for targeting specific messages, creating personalised interfaces, or allocating acquisition budget to the right channels. Actionable marketing data requires intention data — what will this user do next, and what message or offer is most likely to convert them to a transacting customer? Protocol usage behavioral patterns produce this intention data; token holdings do not.

How does ChainAware’s analytics pixel integrate with a Web3 platform?

Integration requires two lines of code added to Google Tag Manager — a no-code tag management platform already used by virtually every Web3 project. No changes to the application’s codebase, smart contracts, or production deployment are necessary. After adding the tag, ChainAware begins calculating intention profiles for every wallet that connects to the platform. Within 24-48 hours, the ChainAware dashboard shows aggregated data: how many high-probability borrowers connected, how many traders, what the experience level distribution looks like, what the risk willingness profile of the user base is, and what intentions the majority of connecting wallets have signalled. To get started, visit chainaware.ai, navigate to Pricing, select the Starter tier (zero cost), and follow the five-step setup workflow.

Why is Web3 customer acquisition cost so much higher than Web2?

Web3 CAC is high for the same reasons Web2 CAC was high in the early 2000s: mass marketing to undifferentiated audiences with no feedback loop. When every marketing message reaches the same broad population regardless of intention alignment, the vast majority of contacts are not genuine prospects — meaning the cost is spread across mostly irrelevant interactions. Web2 solved this with Google’s micro-segmentation and intention-based AdTech, reducing CAC from thousands of dollars to $10-30 by reaching only users whose behavioral data indicated genuine interest in the product. Web3 has access to behavioral data that is qualitatively superior to Google’s (because blockchain transactions carry higher proof-of-work signal than search queries) but has not yet built the analytics and targeting infrastructure to exploit it. ChainAware’s analytics pixel is the first step in building that infrastructure.

What is risk willingness and why does it matter for Web3 user acquisition?

Risk willingness describes the psychological and financial tolerance for potential losses that a specific user has demonstrated through their transaction history. Users who have consistently made large leveraged positions in volatile markets have demonstrated high risk tolerance; users who hold primarily stable assets and rarely trade have demonstrated risk aversion. This dimension matters for Web3 acquisition because serving high-leverage products to risk-averse users — or conservative products to risk-tolerant users looking for high returns — creates fundamental product-user mismatches that prevent conversion and cause churn. Credit Suisse and other major banks have used risk willingness profiling for decades to match clients to appropriate products. ChainAware calculates equivalent profiles from on-chain behavioral history, making this private-banking-grade insight available to any Web3 protocol through the analytics pixel.

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This article is based on X Space #34 hosted by ChainAware.ai co-founders Martin and Tarmo. Listen to the full recording on X . For questions or integration support, visit chainaware.ai.

The post Why Web3 Needs Intention Analytics, Not Descriptive Token Data first appeared on ChainAware.ai.

Based on X Space #33 — ChainAware co-founders Martin and Tarmo. Last Updated: March 2025.

Listen to the full X Space recording: https://x.com/ChainAware/status/1905979016835703227

Every week in Web3, protocols are exploited, funds are laundered through DApp interfaces, and founders discover — too late — that they had no idea who was connecting wallets to their platform. Learning how to integrate AI-based AML and transaction monitoring into DApps is now a regulatory and commercial priority — yet most DApp teams either don’t know where to start, assume compliance is only for centralized exchanges, or believe that integration requires months of engineering work.

In X Space #33, ChainAware co-founders Martin and Tarmo — both veterans of Credit Suisse with over 25 combined years in banking technology — spent an hour breaking down exactly what AML and transaction monitoring mean for Web3 in 2025, why existing solutions miss the core problem, and how ChainAware has reduced a compliance integration that once required enterprise contracts and engineering sprints to a single Google Tag Manager pixel that takes under 12 minutes to deploy.

This article expands that conversation into a complete resource: the regulatory foundation, the technical architecture, the difference between AML and transaction monitoring (they are not the same thing), the specific failure modes of current Web3 compliance tools, and the step-by-step integration approach that makes continuous monitoring accessible to every DApp regardless of team size.

Web3 Is Where Web2 Was 25 Years Ago

To understand why AML and transaction monitoring are existential priorities for Web3 in 2025 — not just regulatory checkbox items — it helps to understand the historical parallel that Tarmo and Martin return to repeatedly in their X Spaces.

Twenty-five years ago, Web2 faced two problems that were threatening to kill the entire ecosystem before it could grow. The first was rampant credit card fraud. Online transactions were being intercepted at scale — network sniffers capturing HTTP traffic and harvesting card data. The fear this created among consumers directly suppressed transaction volumes and revenue for legitimate online businesses. Investors looked at the numbers and saw risk. Growth stalled.

The second problem was catastrophically inefficient user acquisition. Without behavioral targeting or ad tech infrastructure, online businesses resorted to billboard advertising for websites. The conversion economics were so poor that scaling a legitimate online business was nearly impossible. Customer acquisition cost was prohibitive.

Two technologies solved both problems: AI-powered transaction monitoring (which crushed credit card fraud and restored consumer confidence), and Google AdWords (which made targeted user acquisition affordable for the first time). These two technologies — fraud prevention and ad tech — formed the foundation for the exponential growth of Web2.

Web3 in 2025 is at the identical inflection point. According to TRM Labs’ 2025 Crypto Crime Report, illicit crypto volume exceeded $158 billion in 2025. Fraud is scaring away both users and legitimate capital. And user acquisition for Web3 projects remains brutally expensive because behavioral targeting for wallet-connected users barely exists. ChainAware was built specifically to solve both problems — the fraud infrastructure and the behavioral ad tech — and transaction monitoring is the compliance layer that makes the whole system trustworthy. As detailed in our complete guide to AML vs transaction monitoring, these are distinct tools that serve complementary purposes.

AML vs Transaction Monitoring: Two Different Disciplines

One of the most important clarifications in X Space #33 is the distinction between AML and transaction monitoring. In common usage these terms are often conflated, but they are architecturally different systems with different purposes, different regulatory bases, and different technical approaches. Both are required. Neither substitutes for the other.

Anti-Money Laundering (AML)

AML is the tracking of the flow of bad funds. Martin’s analogy in the X Space: imagine mixing red wine with water and tracking exactly where the red wine goes, which paths it takes, and where it ends up. AML answers the question: has this wallet interacted with funds that have been flagged as bad?

“Bad” in AML context includes addresses associated with Tornado Cash or other mixing services, OFAC sanctioned wallets, addresses involved in law enforcement investigations, wallets used in known phishing or scam operations, addresses that have used fake KYC, and any address appearing on major sanction or watchlist databases.

Critically, AML is rules-based. The algorithms are codified in law — regulators specify exactly what must be checked and what constitutes a bad actor. This means AML is entirely transparent: the rules are public, and sophisticated fraudsters know them and can work around them. A wallet that deliberately avoids contact with flagged addresses can pass AML checks cleanly even while engaging in fraudulent behavior through entirely “clean” funds.

Transaction Monitoring

Transaction monitoring is pattern recognition applied to behavioral data to predict future fraud. Where AML asks “has this wallet touched bad funds?”, transaction monitoring asks “does this wallet’s behavioral pattern match the signature of a wallet that is about to commit fraud?”

Fraud is rarely spontaneous. There are preparation phases — specific behavioral patterns that appear on-chain in the weeks and months before a fraudulent event. Transaction monitoring identifies these patterns and flags wallets showing pre-fraud behavioral signatures, even if those wallets have never touched a single flagged address and would pass every AML check cleanly.

Because these patterns are behavioral and complex — not simple rules that can be published and circumvented — transaction monitoring must be AI-based. It is pattern matching across thousands of behavioral signals, retrained continuously on new on-chain data. As Tarmo explained: “To avoid behavior-based transaction monitoring is very hard. And this is why regulators in traditional finance make it mandatory.”

For a deeper technical comparison of these two disciplines, see our dedicated article on crypto AML versus transaction monitoring and the complete KYT and AML compliance guide for DeFi 2026.

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The Irreversibility Problem — Why Web3 Compliance Is Harder

The most fundamental reason Web3 compliance is structurally different from traditional finance compliance is transaction irreversibility. In traditional banking, if a fraudulent transaction is detected — even after it has been processed — it can be reversed. The bank can claw back funds, freeze accounts, and restore the victim’s balance. This reversibility is what makes post-transaction analysis viable as a compliance strategy in Web2 and fiat finance.

In blockchain, transactions are permanent. Once a transaction is submitted and confirmed, it cannot be reversed. The only exception in the history of Ethereum was the 2016 hard fork that created Ethereum Classic — a one-time event that split the chain and was deeply controversial precisely because it violated blockchain’s core immutability guarantee. Nobody is doing daily Ethereum Classics to reverse fraudulent transactions.

The implications are significant. Any compliance approach that analyzes transactions after they have occurred — even in real time — is too late for Web3. By the time the analysis is complete and a flag is raised, the funds are already on-chain and immutable. The blockchain equivalent of “reverse the charge” does not exist.

This creates a single, unavoidable requirement: Web3 compliance must be preventive, not reactive. The compliance decision must be made before the transaction is submitted. The only viable intervention point is the wallet connection moment — the instant a user connects their wallet to your DApp, before they have the ability to submit any transaction at all.

As Martin explained in the X Space: “You want to stop them already before. If they start a transaction already, what can you do? There is not much you can do.” This architectural reality is what makes the ChainAware approach — screening at connect wallet, not at transaction level — the only approach that is both technically viable and regulatory compliant for DApps.

Why Existing Tools Fail DApps: The CEX Problem

The existing market of crypto AML and transaction monitoring tools was built almost entirely for centralized exchanges (CEXs) — Binance, Coinbase, Kraken, and their peers. Understanding why CEX tools don’t work for DApps requires understanding the fundamental difference in how CEXs operate.

Centralized exchanges don’t deal in real crypto assets most of the time. When you deposit ETH to Binance, that ETH goes into a cold wallet. What you receive in your exchange account is an IOU — a balance entry in Binance’s internal database that says you are owed that much ETH. All trading on the exchange is trading IOUs. Your ETH never moves. This is why CEX withdrawals can take hours: the moment you try to exit the IOU system and receive real on-chain assets, compliance checks happen at that gateway.

This architecture makes compliance straightforward for CEXs. There are defined entry points (deposits) and exit points (withdrawals) where real assets move. All the AML and transaction monitoring checks happen at these gateways. In between, users are trading IOUs and the exchange has complete control — it can reverse, freeze, or adjust balances at any time because nothing is actually on-chain.

DApps are structurally the opposite. There are no IOUs. Every interaction is a real on-chain transaction. There are no defined entry/exit gateways to perform compliance checks at. Transactions are instant. And — critically — they are irreversible from the moment they’re confirmed.

CEX compliance tools are designed for a world with controlled gateways, reversible transactions, and dedicated compliance departments that can review flagged transactions before processing them. This design makes them completely inappropriate for DApps where there are no gateways, transactions are irreversible, and the team may be three people without a compliance officer.

The second failure mode of existing tools is the forensic analysis model — tools like those popularized by on-chain investigators who publish detailed post-mortems of hacks with beautiful transaction flow diagrams. These tools are valuable for understanding what happened after a fraud event. They are useless for preventing fraud from happening in the first place. As Martin put it: “It’s like the train is going against the wall and then everyone is now analyzing after the event how did it happen and documenting it in slow motion. But that’s not the point.”

The Regulatory Mandate: MiCA, FATF, and What’s Required Now

The regulatory mandate for Web3 AML and transaction monitoring is no longer a future concern — it is a present obligation for any platform operating in or serving the European Union.

Under MiCA (Markets in Crypto-Assets Regulation), which entered full enforcement in 2025, crypto asset service providers operating in the EU are subject to comprehensive AML obligations equivalent to those applied to traditional financial institutions. This includes transaction monitoring requirements, not just static AML screening. The regulation explicitly requires continuous monitoring of transactions for suspicious patterns — not just one-time checks at onboarding.

According to FATF’s updated guidance on virtual assets, the “control or sufficient influence” test means that even DeFi protocols with meaningful administrative control over their interfaces may qualify as Virtual Asset Service Providers (VASPs) subject to AML obligations. The frontier between “pure DeFi” and regulated activity is narrowing rapidly.

The practical test, as Martin stated plainly: “If you are based in Europe or if they are serving European clients, there’s no defense. Under European regulation the company based in Europe pursuing European plans — this is then required.”

Beyond MiCA, OFAC sanctions compliance applies globally to any platform with US users or US infrastructure. The consequences of facilitating transactions for sanctioned entities have become severe — enforcement actions in 2024 and 2025 have demonstrated that regulators are actively pursuing DeFi platforms that fail to implement adequate screening.

The analogy Tarmo used in the X Space captures the regulatory logic well: if you’re walking down the street, one set of rules applies. The moment you get behind the wheel of a car, a completely different regulatory framework applies — driving license, traffic laws, insurance. The same applies to code. You can publish a smart contract as free speech. The moment that contract executes financial transactions, financial regulation applies. The freedom-of-speech argument does not exempt financial activity from financial regulation.

The Connect Wallet Moment: Where Compliance Must Happen

Given transaction irreversibility and the regulatory mandate for preventive monitoring, the logical conclusion is unavoidable: the compliance decision must happen at the wallet connection event.

This is the moment — the specific milliseconds between a user clicking “Connect Wallet” and the DApp receiving the wallet address — where every piece of intelligence that exists about that wallet must be evaluated and a decision made: allow this wallet to proceed, or deny/flag/limit access.

This single insight restructures the entire compliance architecture for Web3. The question is no longer “how do we analyze transactions after they occur?” — it’s “what do we know about this wallet address before we allow it to submit any transaction at all?”

This is what ChainAware’s system is built around. At the connect wallet event, ChainAware evaluates:

• AML status — has this wallet touched Tornado Cash, sanctioned addresses, mixing services, or other flagged fund sources?
• Fraud probability — what is the ML-predicted probability that this wallet will engage in fraudulent behavior? (98% accuracy, trained on 14M+ wallets)
• Behavioral profile — what are this wallet’s intentions, experience level, and risk tolerance based on its full on-chain history?
• Reputation score — what is the composite 0–4000 reputation score that combines experience, risk profile, and fraud probability?

All of this happens in real time, before the user has any ability to submit a transaction. The DApp then has the information it needs to make a compliance decision — automatically, continuously, and without any manual review process. For a detailed breakdown of what behavioral signals ChainAware analyzes at wallet connection, see the <a href="/blog/chainaware-transaction-monitoring-guide/

The post AML and Transaction Monitoring for DApps: The Guide first appeared on ChainAware.ai.

Based on X Space #32 — ChainAware co-founders Martin and Tarmo. Last Updated: March 2026. Watch the full recording on YouTube Listen X-Space #32 on X

Every Web3 founder is being told their project needs AI. The question nobody is answering clearly is: which AI, integrated how, doing what exactly? The difference between a Web3 project that uses AI and one that has genuinely integrated AI is the difference between a team member who occasionally opens ChatGPT to write a tweet and a platform that runs fraud detection, behavioral targeting, and credit scoring continuously on every wallet connection — automatically, via API, with measurable accuracy.

In X Space #32, ChainAware co-founders Martin and Tarmo — both veterans of Credit Suisse’s private banking division, with backgrounds in architecture, quantitative finance, and machine learning — spent an hour building a framework for distinguishing real, integrable AI use cases from the hype. The result is one of the most practically useful taxonomies of Web3 AI we’ve produced: a clear map of what is genuinely AI, what is rules-based optimization with AI branding, what is a one-time tool versus a continuous API integration, and — crucially — which of the five real AI use cases every Web3 project should be integrating right now.

Web3 Means 100% Digitalization — Not 80% + Human Employees

The foundational point in X Space #32 — the one that underlies every subsequent analysis — is a precise definition of what Web3 actually means in operational terms.

Web3 means 100% digitalization of business processes. It does not mean a blockchain-based product where your compliance officer manually reviews flagged wallets, your marketing team generates tweets with ChatGPT every two weeks, or your analytics pipeline requires a human to export data, run an analysis, and update a dashboard. That is Web2 infrastructure with a Web3 logo.

As Tarmo stated plainly in the X Space: “Web3 means full digitalization. If you are in Web3 you are 100% digitalized. And as soon as you start putting pieces of AI prompts with manual interaction in between, you can call it Web3, but it’s not anymore fully digitalized.”

This definition has an immediate practical implication: the only AI that counts as genuinely integrated in a Web3 context is AI that runs automatically, continuously, via API, as part of an end-to-end automated business process. Everything else — however sophisticated the tool — is a human using software, which is Web2.

This is not a semantic distinction. It directly determines which AI use cases are worth investing in for a Web3 project. If the AI requires a human to invoke it, review the output, and decide what to do next — even occasionally — it is not a Web3 AI integration. It is a productivity tool for your team. Valuable, but categorically different from the AI infrastructure that powers genuine competitive advantage in 2026.

The Two Types of AI: Generative vs Predictive

Before analyzing specific use cases, Martin and Tarmo establish the most important technical distinction in the entire AI conversation: generative AI vs predictive AI. These are not two flavors of the same technology. They have fundamentally different properties, different accuracy profiles, different use cases, and different integration models.

Generative AI (LLMs)

Generative AI — ChatGPT, Claude, Gemini, Grok, and all large language model derivatives — generates content based on statistical patterns in training data. It creates text, images, code, and other outputs on demand. It is powerful for certain tasks and genuinely useful as a productivity tool.

But it has a fundamental limitation that makes it unsuitable for continuous autonomous operation in financial and security contexts: you cannot measure its accuracy. Generative AI produces outputs that may be correct, may be hallucinated, or may be somewhere in between — and there is no reliable way to know which without human review. As Tarmo explained: “In generative AI, what is the accuracy of generation? You just generate something. Is it correct? Is it not correct? Is it a hallucination? You can’t prove it.”

This makes generative AI inherently a human-in-the-loop tool. You generate, you review, you deploy. It is not suitable for autonomous real-time decision-making in a financial protocol where the decisions have immediate, irreversible consequences.

Predictive AI (Machine Learning)

Predictive AI — machine learning models trained on historical data to predict future outcomes — has the opposite property: measurable, backtested accuracy. When ChainAware says its fraud detection model achieves 98% accuracy, that number means something specific: on held-out data the model had never seen during training, 98% of wallets flagged as fraudulent actually exhibited fraudulent behavior. The accuracy is verifiable, reproducible, and improvable through continuous retraining.

This measurability is what makes predictive AI suitable for autonomous continuous operation. You know exactly what you’re getting. You can set thresholds, automate responses, and build business processes around the output — because the output is reliable enough to act on without human review for every individual prediction.

As Tarmo noted, a well-trained ML fraud detection model at 98% accuracy already exceeds the performance of experienced human bank compliance officers, who typically operate at approximately 97% accuracy — and it does so in milliseconds rather than hours, at any scale, 24/7, without fatigue, bias, or vacation days.

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Tool vs Continuous Integration: The Framework

With the generative/predictive distinction established, Martin and Tarmo introduce the second axis of their framework: tool vs continuous integration.

A tool is something a human invokes to accomplish a specific task, then doesn’t use again until the next time that task needs doing. Content generation tools, NFT generators, smart contract audit tools, governance proposal review systems — all of these are invoked occasionally by a human operator, produce an output, and are then set aside. The human makes the decision about what to do with the output. The AI is an assistant, not an autonomous actor in the business process.

A continuous integration is an AI system that runs automatically as part of an ongoing business process, without human initiation for each instance. Every wallet connection triggers a fraud check. Every new liquidity pool is evaluated for rug pull risk. Every user session generates personalized marketing content based on behavioral profiling. The AI is a participant in the process, not a tool invoked by a participant.

The practical test is simple: “Is this something you will need continuously, or is it a once-per-week action?” If it’s once-per-week, a human employee performs the task using an AI tool — and however powerful the tool, the business process is not AI-integrated. It’s human-operated with AI assistance. If it’s continuous — every transaction, every connection, every user interaction — then true API integration is both possible and necessary.

This distinction filters the vast majority of “AI in Web3” claims down to a much smaller set of genuinely integrable use cases. For the full technical architecture of how continuous AI integration works at the wallet connection level, see our Transaction Monitoring Agent complete guide and the Prediction MCP developer guide.

Generative AI Use Cases: What They Actually Are

Running through the most common “AI in Web3” use cases through the tool/continuous filter reveals that almost all of the generative AI applications are tools, not integrations. This is not a criticism — tools are valuable. But it’s an important clarification for founders who believe they have “integrated AI” because their marketing team uses ChatGPT.

Chatbots

Web3 chatbots sound continuous — they’re always on the website, always responding. But as Martin observed, they suffer from a fundamental UX problem: “When users understand that it is a chatbot, they say don’t waste my time and switch over.” The moment users recognize they’re talking to an AI, engagement drops sharply. Chatbots have their place in FAQ deflection and simple support tasks, but they are not a primary AI integration for a Web3 protocol in 2026.

Content Generation for Marketing

This is the most common AI use case across all of Web3: a marketing employee opens ChatGPT, generates blog content, social media posts, or ad copy, reviews it, edits it, and publishes it. It’s a tool. The human performs the task with AI assistance. It happens sporadically — “you generate content, you come back in two weeks.” Beyond the frequency issue, there’s a quality problem: search engines have developed detection systems for AI-generated content, and undifferentiated AI content provides no SEO value and diminishing user engagement.

NFT Generation

AI-generated NFTs had a moment. The moment has largely passed — the NFT market is oversaturated and AI-generated art is now a commodity. More fundamentally, NFT generation is a one-time batch process. You generate a collection, you mint it, you sell it. The AI is invoked once (or a few times), produces an output, and is not used again for that collection. Classic tool usage.

Smart Contract Generation

Generating smart contract code with AI tools like GitHub Copilot or ChatGPT is useful for developers and genuinely accelerates development. But it’s a one-time activity per contract — “you generated it and then you release it in four years and generate again.” It’s not a continuous integration. And as Martin noted, these are “more hello world cases” — simple contracts that don’t require AI, or where the AI-generated code requires extensive human review before deployment.

Twitter/Social Bots

Social media automation in Web3 is widespread — Twitter bots, Discord auto-responders, Telegram notification bots. These are mostly rules-based systems with a thin generative AI layer for content variation. They are not AI integrations in the meaningful sense — they are automated content distribution with predefined rules determining what gets sent and when. The “AI” component is often minimal or absent entirely.

The Rules-Based Problem: DeFi AI That Isn’t AI

Beyond generative AI, there’s a second category of false AI claims that Martin and Tarmo spend considerable time examining: rules-based optimization systems that are marketed as AI. This is arguably a more significant source of confusion than generative AI in Web3, because these systems genuinely do complex computation — they just don’t do AI.

Trade Routing

Trade routing — finding the optimal path through liquidity pools to execute a trade at the best price — is described by Tarmo with precision: it’s a “traveling salesman problem,” solved by the A* algorithm or similar optimization methods. The rules are manually extracted by humans who understand the problem, encoded into an algorithm, and executed deterministically. There are no unknown patterns being discovered, no model being trained, no accuracy being measured. It’s optimization, not AI. Many DeFi protocols call their trade router “AI-powered.” It isn’t.

Yield Farming Optimization

Yield farming optimization follows the same pattern: find the highest-yielding pools given risk parameters. Again, optimization problem. Again, A* or similar. Again, rules-based. “You can add some AI components,” Martin concedes — but the core logic is deterministic rule execution, not machine learning. The AI label is applied to what is fundamentally a mathematical optimization routine.

Portfolio Management

This is where Tarmo brings the strongest professional credentials to the discussion: “Portfolio management systems have to be auditable and 100% auditable. How did you make this decision? If you go now over to AI models you will not have machine learning models 100% accuracy. And then comes your audit and all surprise — why did you do this decision? I don’t know.” Portfolio management in regulated contexts is not just technically rules-based, it is legally required to be rules-based and fully explainable. If you’re telling clients your portfolio management uses AI and they lose money, you’ll need to explain the AI’s reasoning to a regulator. Good luck with that.

Risk Management

The same applies to quantitative risk management. Value at Risk (VaR), stress testing, position limits, exposure calculations — these are all regulatory mandates with explicit calculation methodologies. They are rules defined by regulators and implemented as code. Adding an “AI layer” on top doesn’t change the underlying calculation, and in many cases would actually create regulatory exposure by making the risk calculation less explainable.

Smart Contract Audits

AI-powered smart contract audit tools scan contracts for known vulnerability patterns. Tarmo makes a subtle but important point: “Real-time systems depend a lot about external inputs and there is no way to predict in which sequence external inputs will come to a contract. You can run huge simulations but you will not get 100% accuracy.” The most significant exploits in DeFi history — flash loan attacks, reentrancy exploits, oracle manipulation — exploit the interaction between the contract and unpredictable external conditions, not static code vulnerabilities that pattern-matching can reliably detect. Getting 15 contract audits doesn’t make a protocol secure if the vulnerability emerges from runtime behavior.

Predictive Rug Pull Detection — Not Rules-Based

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Unlike rules-based scanners that check for known vulnerability patterns, ChainAware’s Rug Pull Detector predicts whether a contract will execute a rug pull in the future — based on behavioral ML models trained on confirmed rug pull cases. Covers ETH, BNB, BASE, HAQQ. Free to check.

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The 5 Real AI Use Cases Every Web3 Project Can Integrate

After filtering out generative AI tools and rules-based optimization systems, the framework converges on a specific set of use cases where genuine ML-based predictive AI is both technically appropriate and practically integrable via API by any Web3 project. These are the use cases where unknown patterns exist, where accuracy is measurable, where the process is continuous, and where the business value justifies the integration effort.

Martin and Tarmo identify five: fraud detection, rug pull detection, Web3 ad tech (behavioral targeting), credit scoring, and AML/transaction monitoring. ChainAware offers all five via its Prediction MCP server and 31 open-source agent definitions on GitHub.

1. Predictive Fraud Detection

Fraud detection is the clearest example of where predictive AI genuinely outperforms both human judgment and rules-based systems. The problem is precisely the kind where ML excels: there are patterns in behavioral data that predict fraudulent activity, those patterns are too complex and numerous to encode as rules, and the patterns evolve continuously as fraudsters adapt — requiring ongoing model retraining.

ChainAware’s fraud detection model achieves 98% accuracy on held-out test data — meaning it correctly predicts fraudulent behavior for 98% of wallets it flags, before any fraud has occurred. The key word is “predicts.” This is not forensic analysis — not examining what a wallet has already done wrong, not checking against a list of known bad actors. It is forward-looking behavioral prediction: given this wallet’s complete on-chain history, what is the probability it will exhibit fraudulent behavior in the future?

This distinction matters enormously for practical effectiveness. A fraudster who funds a wallet through entirely legitimate channels — fiat on-ramp, clean exchanges, no interaction with flagged addresses — passes every AML check cleanly. But their behavioral pattern may still match the profile of a pre-fraud wallet with high probability. Predictive AI catches this; rules-based AML does not.

For DApps, this integrates at the wallet connection event: before the user can submit any transaction, ChainAware scores their wallet address and returns a fraud probability score (0.00–1.00). The DApp can then decide whether to allow full access, apply tiered restrictions, or block the connection entirely. The entire pipeline runs in under 100ms — invisible to legitimate users, protective for the platform.

As Martin summarized the broader vision: “The more platforms would integrate predictive fraud detection, the more we can exclude the bad addresses from the ecosystem. Not just on platform one or platform two, but on everyone.” This is the Web3 equivalent of the AI-powered transaction monitoring that eliminated credit card fraud in Web2 — a rising tide of fraud protection that makes the entire ecosystem safer and more trusted. For a full technical breakdown, see our complete Fraud Detector guide and the comparison of forensic vs AI-powered blockchain analysis.

2. Predictive Rug Pull Detection

Rug pull detection extends the fraud detection model from wallet addresses to smart contracts. Where fraud detection asks “will this wallet address commit fraud?”, rug pull detection asks “will this contract execute a rug pull — draining its liquidity pool completely?”

The numbers from Pump.fun and PancakeSwap are stark: the overwhelming majority of new token launches are designed to extract value from investors rather than build genuine projects. Most retail investors have no way to distinguish legitimate launches from rug pulls before the event occurs. This is where predictive AI creates concrete, immediate value — telling users, before they invest, whether a contract matches the behavioral profile of confirmed rug pull cases.

ChainAware’s rug pull detector analyzes the contract itself, the liquidity pool, the developer wallet’s behavioral history, and trading patterns — combining them into a prediction of whether the contract will execute a rug pull. A rug pull is defined precisely: not a 2-3% loss, not a gradual decline — a complete drainage of the pool, typically executed in a single transaction, leaving all holders with worthless tokens.

For platforms that list new tokens, run launchpads, or provide DeFi protocol access, integrating rug pull detection into the listing or connection workflow protects users and the platform’s reputation simultaneously. For individual investors, the free Rug Pull Detector provides the same intelligence on demand. For developers building automated screening systems, the predictive_rug_pull MCP tool is accessible via the Prediction MCP server. The full integration workflow is documented in our guide to identifying fake crypto tokens and rug pulls.

3. Web3 Ad Tech — 1:1 Behavioral Targeting

This is ChainAware’s most commercially distinctive use case and the one that requires the most explanation, because it combines predictive AI and generative AI in a specific way that solves the most expensive problem in Web3 growth: converting wallet connections into transacting users.

The current state of Web3 marketing, as Martin describes it: “Everyone is getting the same message. Everyone independently of your age, location, technology, standard parameters, now we’re not speaking of intentions — independently of descriptive parameters. So the conversion rates are so low. The engagements are going down.”

The problem is not just that messages are generic. It’s that Web3 has access to the richest behavioral dataset in marketing history — every wallet’s complete transaction record — and almost nobody is using it for targeting. Web2 marketers would kill for this data. Web3 teams ignore it because they don’t have the ML infrastructure to turn it into behavioral profiles and targeting signals.

ChainAware’s approach is a two-step process. Step one: use predictive ML to calculate each wallet’s behavioral intentions — what is this wallet likely to do next? Will they trade, stake, borrow, provide liquidity, buy NFTs? What is their experience level, risk tolerance, and protocol preference history? Step two: use generative AI to create personalized marketing messages that directly address those intentions — messages that resonate because they speak to what the user actually wants, not what a generic campaign assumes they might want.

Tarmo describes the user experience: “It’s like somebody knows you very well and talks with you. Exactly. So both have rapport. You both understand each other very well.” When a DeFi lending protocol sends a borrower-intent wallet a message about their lending product, and a yield-farming-intent wallet a message about their highest-yield pools, and a new-to-DeFi wallet a message about how the platform works — each message is the right message for that user. The result is higher engagement, longer session duration, and dramatically higher conversion rates.

This is the Web3 equivalent of what Google AdWords did for Web2: reduce customer acquisition cost by targeting users who are predisposed to convert, rather than buying mass traffic and hoping some percentage is relevant. For a detailed breakdown of how this works in practice, see our guides on why personalization is the next big thing for AI agents and Web3 behavioral user analytics. For a real case study with measured results, see the SmartCredit.io case study: 8x engagement, 2x conversions.

4. On-Chain Credit Scoring

Credit scoring is the original AI application that gave rise to ChainAware — the model was first built for SmartCredit.io’s DeFi lending platform, and has been running in production for nearly five years. It is one of the most mature and well-validated use cases in the portfolio.

Traditional credit scores (FICO, FICO-equivalent) are the backbone of the fiat lending economy. They determine who gets loans, at what interest rates, with what collateral requirements. Without credit scoring, all lending must be overcollateralized — the borrower puts up more than they’re borrowing, which defeats much of the purpose of credit. DeFi today is almost entirely overcollateralized for exactly this reason: there’s no credit infrastructure to support anything else.

ChainAware’s on-chain credit score changes this. Based on a wallet’s complete on-chain transaction history — cash flow patterns, repayment history in DeFi lending protocols, asset management behavior, risk profile — the ML model calculates a credit score that predicts lending risk. This enables DeFi protocols to offer reduced collateral requirements, better rates, and access to capital for wallets with strong on-chain financial histories — without requiring any KYC, without collecting any personal data, operating entirely on public blockchain data.

The integration model is straightforward: when a user initiates a borrowing position, the DApp calls ChainAware’s credit scoring API with the wallet address and receives a score and risk classification. The DApp then applies the corresponding collateral ratio, interest rate, or borrowing limit. Fully automated, real-time, no human review required. For more detail, see the complete Web3 credit scoring guide and the Credit Scoring Agent guide.

5. AML and Transaction Monitoring

Martin makes a precise technical distinction in X Space #32 that is worth stating clearly: AML is rules-based; transaction monitoring is AI-based. These are often treated as synonyms but they are different things requiring different technology.

AML (Anti-Money Laundering) checks are codified in law. The rules are explicit, public, and static: check if this wallet has interacted with Tornado Cash, sanctioned addresses, known exchange hacks, mixer services. These are deterministic lookups against maintained databases. Rules-based. Necessary for compliance. Not AI.

Transaction monitoring is different: it identifies unknown patterns in behavioral data that predict future suspicious activity. Fraudsters are sophisticated. They know the AML rules. They deliberately avoid triggering AML flags while building toward a fraud event. Transaction monitoring catches the behavioral signatures of this preparation — patterns that no human could enumerate as rules because they emerge from the data, not from regulatory text. This is where AI is not just useful but necessary.

According to FATF’s guidance on virtual assets, both AML screening and transaction monitoring are now expected for any platform qualifying as a Virtual Asset Service Provider. Under MiCA, EU-based crypto platforms are explicitly required to implement both. The combination of AML screening (rules-based) and transaction monitoring (AI-based) is the complete compliance stack — neither alone is sufficient. For a full treatment of this topic, see our dedicated article on crypto AML versus transaction monitoring and our complete KYT and AML guide for DeFi 2026.

Integrate All 5 Use Cases via MCP

31 Open-Source AI Agent Definitions — Fraud, Rug Pull, Ad Tech, Credit, AML

ChainAware’s Prediction MCP server exposes all five integrable AI use cases as callable tools. Any MCP-compatible AI agent — Claude, GPT, custom LLMs — can call fraud detection, rug pull detection, behavioral targeting, credit scoring, and AML scoring in real time. 31 MIT-licensed agent definitions on GitHub. API key required.

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AI Agents: Where They Work and Where They Don’t

The X Space #32 framework culminates in a nuanced analysis of AI agents — one of the most hyped concepts in 2025-2026 Web3. Martin and Tarmo’s conclusion is both specific and somewhat contrarian: the space where genuine AI agents are viable in Web3 is actually quite narrow.

The defining characteristic of a genuine AI agent is not just that it runs autonomously — it’s that it learns and improves over time, eventually reaching superhuman performance. An automated script that executes rules without learning is not an agent. A chatbot that generates responses from a static model is not an agent. An AI agent, in the meaningful sense, continuously improves as it processes more data, and its performance trajectory eventually exceeds what any human could achieve.

This “superhuman performance” criterion filters the agent space dramatically. For fraud detection: yes — the model retrains daily on new behavioral data, continuously improving as fraud patterns evolve. For rug pull detection: yes — the model learns from new confirmed rug pull cases. For behavioral targeting: yes — the system learns which message types convert best for which wallet profiles, improving targeting precision over time. For credit scoring: yes — repayment behavior feeds back into model improvement.

For content generation: no — generating a blog post doesn’t improve the next blog post in any meaningful model sense. For trade routing: no — the optimization algorithm doesn’t learn, it solves the same optimization problem each time. For governance: no — governance decisions are not a learning problem. For smart contract audits: no — the vulnerability patterns are static rules, not learned from data.

As Tarmo concluded: “The space where you have AI agents is actually very small. And most of what we spoke about are not agentic when we use this word ‘agentic.’ These are just tools for one-time activity and you repeat it nine months later. But real AI agents are for continuous activities — activities you integrate into your business processes that provide superior value to customers. The more these agents learn, the higher the value, the higher it gets superhuman performance.”

For the full architecture of ChainAware’s 31 open-source agent definitions and how they map to continuous AI business processes, see our guides on the Web3 Agentic Economy and 12 blockchain capabilities any AI agent can use.

Full Comparison Table: AI Types × Web3 Use Cases

Use Case	AI Type	Tool or Integration	Measurable Accuracy	Integrable by Others via API	AI Agent Viable
Fraud Detection	Predictive ML	Continuous Integration	98%	Yes	Yes
Rug Pull Detection	Predictive ML	Continuous Integration	High	Yes	Yes
Web3 Ad Tech / 1:1 Targeting	Predictive ML + Gen AI	Continuous Integration	Measurable CTR/CVR	Yes	Yes
Credit Scoring	Predictive ML	Continuous Integration	Backtested	Yes	Yes
AML Screening	Rules-based	Continuous Integration	Deterministic	Yes	Partial
Transaction Monitoring	Predictive ML	Continuous Integration	Measurable	Yes	Yes
Content Generation	Generative AI	Tool (sporadic)	Unmeasurable	No (human review needed)	No
Chatbots	Generative AI	Tool (on-demand)	Unmeasurable	Partial	Limited
NFT Generation	Generative AI	Tool (one-time batch)	N/A	No	No
Smart Contract Generation	Generative AI	Tool (one-time)	Unmeasurable	No	No
Smart Contract Audit	Rules-based + partial ML	Tool (sporadic)	Partial	Partial	No
Trade Routing	Optimization (A*)	Continuous but rules-based	Deterministic	Platform-specific only	No
Yield Farming Optimization	Optimization (A*)	Continuous but rules-based	Deterministic	Platform-specific only	No
Portfolio Management	Rules-based (must be auditable)	Continuous but rules-based	Fully explainable	Regulatory constraint	No
Trading Signals	Predictive ML	Continuous Integration	Backtested	Partial (B2C focused)	Possible
Prediction Markets	Predictive ML	Continuous Integration	Measurable	Platform-specific only	Possible

Frequently Asked Questions

What’s the difference between generative AI and predictive AI for Web3?

Generative AI (LLMs like ChatGPT) creates content — text, images, code — but its accuracy is unmeasurable because outputs may be correct or hallucinated, requiring human review before any action is taken. Predictive AI (machine learning models) generates scores and predictions with verifiable, backtested accuracy — enabling fully automated decision-making without human review. For Web3 integration, only predictive AI is suitable for continuous automated business processes. Generative AI is a productivity tool for human employees.

Why does Web3 require 100% AI integration rather than tool usage?

Web3 is defined by 100% digitalization of business processes — end-to-end automation with no manual human intervention between steps. The moment a human employee reviews an AI output and decides what to do with it, the process is Web2-style human-operated software, not Web3. This matters practically because human-in-the-loop processes don’t scale, can’t operate 24/7, introduce latency, and create consistency errors. True Web3 AI integration means the AI acts as an autonomous participant in the process, not as a tool for a human participant.

Is DeFi trade routing actually AI?

No. Trade routing in DeFi is an optimization problem — finding the best path through liquidity pools to execute a trade at minimum cost/maximum value. This is solved by standard optimization algorithms (similar to the A* pathfinding algorithm), with rules manually defined by engineers. No unknown patterns are being discovered, no model is being trained, no accuracy metric applies. Many DeFi protocols call this AI; it is not. Optimization algorithms are powerful tools, but they are not machine learning.

Can smart contract audits be replaced by AI?

Not reliably. Most smart contract vulnerability scanners are rules-based — they check for known vulnerability patterns in the code. The most significant DeFi exploits involve vulnerabilities that emerge from the interaction between contracts and unpredictable external inputs (flash loans, oracle manipulation, MEV extraction) — behaviors that no static code analysis can predict. Multiple audits of the same contract do not make it more secure against runtime attack vectors. AI-powered audit tools add value at the margins but cannot provide the security guarantees their marketing often implies.

What exactly can a Web3 project integrate from ChainAware via API?

Via ChainAware’s Prediction MCP server at prediction.mcp.chainaware.ai/sse, any Web3 project can integrate: predictive fraud detection (98% accuracy), predictive rug pull detection (for contracts), behavioral wallet profiling and intention prediction (for ad tech / personalization), on-chain credit scoring (for lending), and AML scoring. All are accessible as MCP tools or REST API endpoints. 31 open-source agent definitions are available on GitHub. API key required — see chainaware.ai/pricing for access.

Why is the AI agent space in Web3 “actually quite narrow”?

A genuine AI agent learns continuously and achieves superhuman performance — performance that improves beyond human capability over time as the model retrains on new data. Most “AI agents” in Web3 are actually automated scripts (rules-based), one-time generative AI tasks, or optimization algorithms. The narrow space where genuine agents are viable corresponds to the five integrable use cases: fraud detection, rug pull detection, behavioral targeting, credit scoring, and transaction monitoring. All five involve continuous learning, measurable accuracy, and improving performance — the defining characteristics of genuine AI agents.

Why does portfolio management have to remain rules-based?

Regulatory requirements for portfolio management mandate full auditability — every investment decision must be explainable with a clear rationale that can be presented to regulators, auditors, and clients who experience losses. ML models, by their nature, make decisions based on statistical patterns in training data that cannot always be fully explained in natural language terms. In regulated financial contexts, “the model decided” is not an acceptable answer. Portfolio management in DeFi that uses ML is either operating outside regulations or will face enforcement problems when things go wrong.

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This article is based on X Space #32 hosted by ChainAware.ai co-founders Martin and Tarmo. Watch the full recording on YouTube. For questions or integration support, visit chainaware.ai.

The post Real AI Use Cases for Web3: What to Integrate via API first appeared on ChainAware.ai.

Based on X Space #31 — ChainAware co-founders Martin and Tarmo. March 2025. Watch the full recording on YouTube · Listen on X

There are 225,000 crypto projects listed on CoinGecko. Most of them face the same two problems that are quietly killing their growth: user acquisition costs so high the unit economics will never work, and fraud rates so severe that users leave before they convert. These are not new problems. Web2 had them too — and solved them with two specific technologies. In X Space #31, ChainAware co-founders Martin and Tarmo lay out exactly how those technologies map to Web3, what ChainAware has built, and how every Web3 business can start benefiting from AI agents today — not in a white paper, not in theory, but in production right now.

The Web2 Parallel: How Two Technologies Changed Everything

To understand where Web3 is going, Martin and Tarmo start where they always do: with the Web2 analogy that most founders haven’t fully internalized yet, because they weren’t there in the early 1990s to live through it.

In the early days of the commercial Internet, e-commerce was struggling with two existential problems. The first was rampant credit card fraud. Consumers were terrified to type their card numbers into a website. Transaction data was being intercepted by network sniffers — malicious tools that harvested credit card details from unencrypted HTTP traffic. This fear directly suppressed transaction volumes. Online businesses were building products people wanted but couldn’t sell at scale because users were afraid to pay. The result: low revenues, skeptical investors, ecosystem-wide stagnation.

The second problem was catastrophically inefficient user acquisition. There was no targeting infrastructure. To drive traffic to a website, companies put ads in newspapers. They rented billboards beside highways with their domain name printed on them. Martin describes it: “There were companies with transparencies beside car roads — buy food at petshop.com. This was the style of marketing at the beginning of the 90s.” Customer acquisition cost was enormous. The first people attracted to these campaigns were technology enthusiasts — maybe 50 million globally — but getting beyond that initial cohort to mainstream adoption was economically impossible at those costs.

Two technologies solved both problems and triggered the exponential growth of Web2. The first was AI-powered transaction monitoring — machine learning models trained to detect fraudulent behavioral patterns before fraud occurred, not after. This crushed credit card fraud rates and restored consumer confidence in online transactions. The second was Google AdWords and the ad tech infrastructure it spawned — the ability to predict user behavior from search history and browsing patterns, and deliver targeted ads that matched user intent. This reduced cost per acquiring a transacting user from hundreds of dollars to $15–$30 in major markets.

The result was not organic adoption or market maturation. It was a specific technology-enabled phase transition. As Tarmo explained: “It wasn’t like some magic crossing the chasm happened. It was technology which enabled it.” Geoffrey Moore’s famous framework for crossing the technology adoption chasm describes what happened but not how. The how was these two technologies removing the two specific blockers that were holding the ecosystem back.

Why Web3 Is Stuck — The Same Two Problems

Web3 in 2025 is in exactly the same structural position Web2 was in the early 1990s. The problems are identical. The missing technologies are the same. The potential of the ecosystem is enormous — and it’s being held back by the same two blockers that held Web2 back a generation ago.

Problem 1: Fraud. According to Tarmo, fraud in Web3 currently represents approximately 15% of Total Value Locked across DeFi — the same percentage as Web2 credit card fraud before predictive fraud detection was introduced. The specific mechanisms are different: rug pulls on PancakeSwap affect approximately 95% of new liquidity pools, wallet fraud rates are extremely high, and the irreversibility of blockchain transactions means the consequences are permanent. But the ecosystem-level effect is identical to 1990s Web2: users get burned, lose confidence, and leave the sector faster than they can learn its benefits. As Martin described: “People are joining the sector, they are going away. They’re joining, they’re going away.” One X user Martin cited had been rug pulled 128 times.

Problem 2: User acquisition cost. The cost of acquiring one genuinely transacting user in DeFi is approximately $1,000–$3,000 — compared to $15–$30 in Web2. This is not a small gap; it’s a factor of 50–100x worse. At these acquisition costs, the unit economics of virtually every Web3 project are structurally negative. Even with zero fraud losses, a DeFi protocol cannot become cash flow positive when it costs thousands of dollars to acquire each transacting user. The projects that do survive either have token treasury subsidies that mask the unit economics, or they get lucky with viral adoption — neither of which is a sustainable growth strategy.

The math is unforgiving. Every business has a unit cost per customer served and a unit revenue per customer. If acquisition cost exceeds customer lifetime value, the business will not survive when its initial capital runs out. This is the quiet economic reality behind the vast majority of Web3 project failures — not bad products, not bad teams, not bad timing. Bad unit economics driven by a missing infrastructure layer.

The Three Real Pain Points of Every Web3 Founder

Martin synthesizes the founder perspective into three specific pain points that emerge from this structural situation. Understanding these precisely matters because the solutions map directly to them.

Pain Point 1: User Acquisition Cost

The most immediately pressing pain point for most founders: how do you acquire users who actually use your product, at a cost that makes the business viable? This is not about generating website traffic — that’s the easy, expensive, and largely useless version of the problem. It’s about acquiring transacting users — people who connect their wallet, engage with the protocol, and generate revenue. The gap between visitors and transacting users in Web3 is enormous, and most marketing spend goes toward generating the former without converting to the latter.

Pain Point 2: Trust and Fraud

Founders are confronted simultaneously by the audit industry (spend heavily on smart contract audits as a trust signal) and by actual fraud risk (bad actors accessing their platform). Both are real concerns. But Martin makes a subtle point that most founders miss: auditing your source code is one dimension of security, but it doesn’t address the question of who you’re letting use your application. “As a founder you want to exclude the fraudsters from your platform. You have to check who do you let to access your platform — is the trust ranking of this gentleman who is using your application high enough, or maybe he has a predictive fraud risk?” Multi-dimensional, multi-layered security requires addressing both the code layer and the user layer.

Pain Point 3: Competitive Advantage in a Copy-Paste Ecosystem

The open-source ethos of DeFi has created an innovation dilemma. When all code is public and copyable, there’s limited incentive to build genuinely novel protocols. Martin observes that most major DeFi categories now have only four or five actual implementations — and most are copies of copies. Uniswap’s function names appear in DEX code on BNB Chain. Compound’s architecture was cloned dozens of times. “Innovation stopped because there’s no point to invent new source code — everyone copied everyone else’s.”

In this environment, competitive advantage can only come from two sources: a more cost-efficient business process, or a lower user acquisition cost. There is no third option. Product differentiation through novel code is largely unavailable. What remains is operational efficiency and growth efficiency — precisely the domains where AI creates real, sustainable competitive advantage.

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Why Marketing Agencies Are Failing Web3 Founders

Martin spends considerable time in X Space #31 on the marketing agency problem — not because it’s a minor irritation, but because it represents a systematic misallocation of founder capital that directly prevents Web3 projects from reaching viability.

The parallel to early Web2 is precise. In the early 1990s, the gatekeepers of Internet marketing were traditional agencies who charged enormous fees to apply traditional advertising methods to a new medium — putting website URLs on billboards, in newspapers, on television. They made money, their clients generated website traffic, and essentially none of it converted because the targeting was non-existent and the experience wasn’t designed to convert. The agencies collected fees regardless of outcome.

The arrival of Google AdWords didn’t just reduce acquisition costs — it obsoleted the agency model. The agencies that survived became expert users of the new ad tech platforms. The ones that didn’t adapt closed. The technology did what the agencies were supposed to do, but better and cheaper.

“All these magic marketing agencies who are promising all the mana to the founders — they have all these different call strategies, all the different point strategies, or they’re using the crypto ads,” Martin says. “This is not converting. You can create a visitor flow to the website. But you as a founder are less interested in the visitor flow. You are interested about converting the visitor flow.”

The specific failure modes he identifies are common and recognizable to any Web3 founder: KOL (Key Opinion Leader) campaigns that drive traffic lasting 12–15 seconds before users bounce, with essentially zero conversion to transacting users. Coin ad networks (Coinzilla, Bitmedia) that are expensive, ad-blocker vulnerable, and disproportionately attract inexperienced users. Point/task systems that create artificial engagement metrics that don’t translate to protocol usage. All of these generate activity. None of them reliably generate transacting users at viable unit economics.

The solution isn’t to find better marketing agencies. It’s to adopt the ad tech infrastructure that makes targeting behavioral rather than demographic — the same shift that Google enabled in Web2. For a full breakdown of why KOL marketing specifically fails, see our guide on why influencer marketing isn’t working in Web3. For the alternative approach, see our guide to intention-based marketing in Web3.

ChainAware’s Five Live AI Products for Web3 Businesses

ChainAware’s response to these pain points is a suite of five live, production products — not white papers, not roadmap items, not beta features. These are systems that have been running for months to years, serving real clients, generating real intelligence. What follows is a detailed breakdown of each, drawing on Martin and Tarmo’s explanations in X Space #31.

1. AI Marketing Agents — 1:1 Behavioral Targeting

The marketing agent is ChainAware’s flagship growth product and the most direct implementation of the Web3 ad tech thesis. It solves the conversion problem — not the traffic problem — through real-time behavioral targeting at the wallet connection event.

The mechanism is a two-stage process that happens automatically every time a visitor connects their wallet to a DApp. Stage one: ChainAware’s predictive ML models analyze the wallet address and calculate the user’s behavioral profile — their DeFi experience level, risk tolerance, protocol history, and — most importantly — their predicted intentions: what are they likely to want to do next? Are they a yield farmer looking for the best APY? A trader hunting for leverage? A newcomer exploring DeFi for the first time? Stage two: based on those calculated intentions, the system generates personalized marketing messages — embedded content on the DApp’s website — that speak directly to what that specific user is trying to accomplish.

The contrast with conventional Web3 marketing is stark. Conventional: “Buy now and get 10% off” — the same message to every visitor, regardless of who they are or what they want. ChainAware: “You’ve been actively yield farming on ETH and BNB for 18 months, and you tend to favor low-risk positions. Here’s why our stable-yield vault might be exactly what you’re looking for.” The message is generated for that specific wallet’s profile. As Martin puts it: “You don’t know who the user is, but based on his blockchain history you can predict and create much higher attachment, much higher likeliness, much higher resonance.”

Tarmo makes a comparison to Amazon that every founder should understand: “If you go to Amazon, everybody sees it differently. It is calculated on the fly. Everybody sees his personalized UI what is generated for him.” This is what adaptive web interfaces look like in Web2. ChainAware brings the equivalent capability to Web3 — without cookies, without identity, using only public blockchain data.

The setup time is two minutes via Google Tag Manager — the same integration used for Google Analytics and other web tracking tools. No code changes required. The marketing agent begins generating personalized messages immediately. Founders can review, adjust, and refine the messages at any time — but even without any manual editing, the auto-generated content based on behavioral profiles substantially outperforms generic mass messaging in engagement and conversion metrics. A documented example of this in action: SmartCredit.io achieved 8x engagement and 2x conversions using ChainAware Growth Agents.

One aspect that Martin emphasizes repeatedly: the AI is embedded in the website, invisible to users. “To the outside it’s not visible that AI technology is behind there. It creates resonating messages for you.” This is a crucial design principle — not a chatbot that announces itself and that users dismiss, but ambient personalization that improves the user experience without friction. For the full technical guide to the analytics layer that powers this, see the Web3 Behavioral User Analytics complete guide. For the personalization philosophy, see why personalization is the next big thing for AI agents in Web3.

2. AI Transaction Monitoring Agent

The transaction monitoring agent addresses the fraud dimension of Web3’s structural problem — the 15% of TVL being lost to fraudulent activity. It operates as a continuous surveillance system that monitors wallet addresses connecting to or transacting with a DApp, flags behavioral changes that indicate emerging fraud risk, and delivers real-time notifications to platform operators.

The key architectural insight — one that ChainAware returns to consistently across their X Spaces — is the difference between forensic analysis and predictive monitoring. Most crypto security tools operate forensically: they document what has already happened, analyze blockchain history after the fact, and produce reports on completed fraud events. This is useful for investigation but useless for prevention, especially given blockchain’s irreversibility.

ChainAware’s monitoring is predictive: it evaluates behavioral patterns and predicts whether a wallet is trending toward fraud before any fraud occurs. Tarmo describes the mechanism: “You download addresses you want to monitor, you select notification mechanism, and the ChainAware agent just monitors it. As soon as an address does some strange behaviors you get notification. Strange behavior means the trust score of address is reduced — you get notification in real time.”

The quantitative context matters here. Fraud in Web3 — combining hacking, impersonation, scamming, and rug pulls — represents approximately 15% of TVL. This is not an edge case; it’s a systemic tax on every DeFi protocol. And it’s not inevitable: Web2’s credit card fraud rate was similarly approximately 15% of online transaction value in the early 1990s, before AI-powered transaction monitoring was introduced. Post-implementation, it dropped to well below 1%. This is the trajectory ChainAware is working to replicate for Web3.

The monitoring currently operates on Ethereum, BNB Smart Chain, and Polygon, with Telegram notifications being added to the existing API delivery system. For a detailed technical breakdown of how the monitoring agent works, the alert thresholds, and the integration path, see the complete Transaction Monitoring Agent guide and our AML vs transaction monitoring comparison.

3. AI Credit Scoring Agent

ChainAware’s credit scoring agent is the oldest product in the portfolio — the model has been live for more than four years, having originated in SmartCredit.io’s DeFi lending platform before being abstracted into a standalone service. It is the most mature, most backtested, and most validated AI model in the suite.

The core function is straightforward: given a wallet address, calculate a credit score that reflects the financial ability and creditworthiness of the person controlling that address. Tarmo describes it as the Web3 equivalent of a FICO score — “the same credit score what we calculate based on your on-chain data and your social data. We calculate a credit score and we monitor it.”

But as Tarmo carefully emphasizes, credit scoring in traditional finance is used for much more than lending decisions. “Credit score is not only used for borrowing lending — it’s used generally as an indicator of your financial ability. Higher credit score means your financial ability is higher. It’s a general indicator.” In Web3, this translates to what he calls “ABC filtering” — identifying your top A clients (high credit score, financially able), your B clients (moderate capability), and your C clients (low capability), and allocating resources accordingly. The Pareto principle operates here: “With 20% of clients you generate 80% of your revenue. If you know the credit score of your clients, you know which 20% to focus on.”

The monitoring aspect is equally important for lending protocols specifically: the agent continuously tracks credit score changes for existing borrowers. If a borrower’s credit score deteriorates — their financial behavior is showing signs of stress — the platform gets an early warning before any default occurs. This is the credit equivalent of the transaction monitoring agent’s fraud alerts: proactive intelligence that enables action before the problem manifests, not after. For the full technical guide, see the complete Web3 credit scoring guide and the Credit Scoring Agent guide.

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4. Web3 User Analytics — Free Forever

Web3 User Analytics is ChainAware’s most accessible product — free forever for any Web3 platform, with no enterprise commitment required. It is also, arguably, the most immediately valuable product for founders who have never had reliable data on who their users actually are.

The problem it solves is fundamental. Most Web3 founders make strategic decisions based on assumptions about their users rather than data. They assume their DeFi protocol attracts experienced DeFi users. They assume their marketing is reaching the right audience. They assume their token holders are protocol users. Often, all three assumptions are wrong.

Martin gives a specific example from a DeFi platform that discovered through the analytics dashboard that their users — whom they assumed were DeFi-experienced participants — were actually predominantly low-risk traders who had minimal DeFi protocol experience. “They realized they had to change their marketing strategy. But if you want to change your strategy, first you have to know who your actual users are — not who is holding which token, but who is using which protocols.”

The dashboard shows eight dimensions of aggregate behavioral intelligence across all wallets connecting to the DApp: wallet intentions (what users plan to do next), experience distribution (Web3 sophistication level), risk willingness (how aggressively they engage with on-chain risk), protocol categories used, top specific protocols in user history, predicted fraud probability distribution, Wallet Rank distribution (overall quality of user base), and wallet age distribution (how long users have been in Web3). All of this is derived from public blockchain data with zero KYC, zero identity collection, and zero cookie dependency. For the complete walkthrough of all eight dimensions and how to use them, see our Web3 Behavioral User Analytics complete guide.

The integration is identical to the marketing agent: a Google Tag Manager pixel, no code changes, no engineering involvement. The dashboard begins populating with aggregate data within 24–48 hours of first wallet connections. This is your user base as it actually is — not as you assumed it was.

5. Marketing Strategy (Preferred Clients)

The fifth product is the most selective and the least publicly advertised: a comprehensive marketing strategy service available only to a small number of preferred clients. Martin is direct about why it’s not offered broadly: “It doesn’t make sense to offer it to everyone. There’s no benefit.”

The service combines both sides of the growth problem: acquiring a convertible visitor flow to the DApp, and converting that visitor flow into transacting users once they arrive. This is the distinction Martin returns to repeatedly — most marketing spend addresses only the first half (getting visitors to the website) while the second half (converting visitors to users) is ignored or addressed inadequately.

The approach uses ChainAware’s full behavioral intelligence stack: identifying which types of wallet addresses are most likely to be your high-value users, finding the acquisition channels that reach those wallets, and deploying the personalization infrastructure to maximize conversion once they arrive. It is the complete loop that replaces the traditional agency model — not just traffic generation, but traffic-generation targeted at wallets pre-qualified by behavioral profile.

The Roadmap: Base, Solana, and What’s Next

Martin outlines the near-term product roadmap across two dimensions: chain expansion and feature enhancement.

Chain Expansion

The marketing agent and user analytics currently run on Ethereum and BNB Smart Chain, with Base chain launching “in the next few days” at the time of the X Space, and Solana following shortly after. “Because on Solana there is so much activity, we’re launching it as well on Solana.” This brings the supported chains for the growth products to: ETH, BNB, BASE, and SOL — covering the four highest-activity chains for DeFi and DApp activity in 2025.

The fraud detection and transaction monitoring models already cover a broader set: ETH, BNB, BASE, HAQQ, SOL, TON, TRX, and POL — eight chains in total for the full behavioral intelligence stack. The Prediction MCP server exposes all of this intelligence as callable tools for AI agent integration.

Feature Enhancement: Telegram Notifications

The transaction monitoring agent is adding Telegram notification support alongside the existing API delivery. This removes the need for engineering work to receive fraud alerts — instead of building a notification system, compliance contacts and COOs can simply receive direct Telegram messages when wallets crossing fraud thresholds connect to or transact with their platform.

Compute Infrastructure

Tarmo mentions ChainAware’s compute infrastructure partnership, which is relevant context for understanding the scale of what these models require: “We are in Google Cloud Web3 Startup Program. We have enormous compute power from Google and this is how we can do all these calculations.” Predictive behavioral AI at the scale ChainAware operates — 14M+ wallet profiles, continuous retraining, real-time inference — requires significant compute infrastructure that most startups couldn’t self-fund. The Google Cloud partnership enables the daily model retraining and real-time prediction latency that make the products practically useful. For more on why compute scale matters for model quality, see our guide on AI-powered blockchain analysis: machine learning for crypto security.

Crossing the Chasm: How Web3 Gets to Exponential Growth

The X Space #31 concludes with the big picture framing that gives the entire product roadmap its context: what needs to happen for Web3 to cross the chasm into exponential growth?

Martin and Tarmo’s analysis, grounded in their observation of Web2’s growth trajectory and their years of building in Web3, converges on a specific thesis: the crossing-the-chasm moment for Web3 will be enabled by exactly the same two technologies that enabled it for Web2. Not by a sudden surge in public interest. Not by a killer app that everyone suddenly wants. Not by regulatory clarity. By two specific infrastructure technologies that remove the two specific blockers that are currently holding the ecosystem back.

Technology 1: Predictive fraud detection at scale, integrated across platforms, reducing Web3’s 15% fraud rate toward the sub-1% rate that Web2 achieved after AI-powered monitoring was deployed. This restores user trust and removes the “I’ll get burned if I engage” fear that drives users out of the ecosystem faster than organic growth can replace them.

Technology 2: Behavioral ad tech for Web3 — 1:1 behavioral targeting based on on-chain wallet data, reducing the cost of acquiring a transacting user from the current $1,000–$3,000 toward the $15–$30 that Web2 achieves. This makes the unit economics of Web3 platforms viable and enables sustainable growth rather than treasury-subsidized user acquisition.

Tarmo’s summary: “JNAware is the company which has technologies which brought Web2 to exponential growth, and we can bring also Web3 to exponential growth.” This isn’t marketing language — it’s an architectural thesis grounded in specific historical analysis and specific technology claims. The technologies that solved Web2’s problems exist. They work. They’re running in production. The question is how quickly Web3 projects adopt them.

For the broader context of where AI agents fit into the long-term evolution of Web3, see our articles on the Web3 agentic economy and why 90% of connected wallets never transact — and how AI agents fix it.

Comparison: ChainAware vs Traditional Web3 Growth Approaches

The fundamental difference in the table is targeting. Every traditional Web3 growth approach operates without behavioral targeting — it reaches people, but not the right people at the right moment with the right message. ChainAware’s approach targets based on what each specific wallet is likely to want next, derived from their actual on-chain history. This is the difference between billboard advertising and Google AdWords — the same conceptual gap that defined the transition from Web1 to Web2.

For an in-depth comparison of Web3 analytics and growth platforms, see our Web3 analytics tools comparison and Web3 growth platforms compared.

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Frequently Asked Questions

Why is Web3 user acquisition cost so much higher than Web2?

Web2 has decades of behavioral targeting infrastructure built on top of identity-linked data (cookies, login IDs, device fingerprints) that enables highly precise user targeting. Web3 currently lacks equivalent infrastructure — most growth campaigns use mass broadcast methods (KOLs, crypto ad networks, airdrop campaigns) that generate traffic but not behaviorally-qualified transacting users. ChainAware’s behavioral targeting infrastructure closes this gap by using on-chain wallet data to predict user intentions and deliver resonating messages, reducing acquisition cost toward Web2 levels.

How is ChainAware’s fraud detection different from AML screening?

AML (Anti-Money Laundering) screening is rules-based — it checks whether a wallet has interacted with sanctioned addresses, mixers, or other flagged entities. The rules are public and sophisticated fraudsters can work around them by using clean funds. ChainAware’s fraud detection is predictive ML — it identifies behavioral patterns that predict future fraudulent activity, even from wallets with no AML flags. 98% accuracy on held-out test data. Predicts fraud before it occurs, not after. See the complete Fraud Detector guide for full methodology.

What does the marketing agent actually show users?

The marketing agent generates embedded content — messages, callouts, feature highlights — on your DApp’s website that are personalized to each connecting wallet’s behavioral profile. Think of the difference between a generic “Earn up to 12% APY” banner and a message tailored to a wallet that has been actively yield farming on Aave and Compound for two years, showing moderate risk tolerance: “For experienced yield farmers: our 3-month fixed-rate vault currently offers competitive stable returns, with no liquidation risk.” The second message resonates because it matches what that specific user is actually looking for. The messages are auto-generated, reviewed/edited by your team if desired, and embedded into your existing website without UI changes.

Is the Web3 User Analytics dashboard really free?

Yes — free forever for existing integrations. Martin is explicit: “We offer it for free for any Web3 platform if you want to use it. Free forever.” The free tier shows aggregate behavioral data across your user base across all eight dimensions. Individual wallet targeting (the marketing agent) is an enterprise subscription. The free analytics tier is ChainAware’s goodwill contribution to the Web3 ecosystem — giving every founder the data they need to understand their actual users, rather than operating on assumptions. Subscribe at chainaware.ai/subscribe/starter.

Which blockchains does ChainAware support?

At the time of X Space #31: ETH and BNB for the full product suite, with Base launching imminently and Solana to follow. The fraud detection and transaction monitoring models cover a broader set: ETH, BNB, BASE, POL, SOL, TON, TRX, and HAQQ — 8 blockchains total. Check chainaware.ai for the current chain coverage across each product.

How does ChainAware target users without knowing their identity?

All intelligence is derived from public blockchain transaction data. ChainAware never requires KYC, never collects personal information, and never links wallet addresses to real-world identities. The behavioral profile — experience level, risk tolerance, protocol history, intentions — is calculated entirely from the public on-chain transaction history associated with the wallet address. This is actually more privacy-preserving than Web2 targeting (which requires identity-linked data) while being more accurate for Web3 use cases (because on-chain behavior is a more direct signal of DeFi intent than browsing history or demographics).

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This article is based on X Space #31 hosted by ChainAware.ai co-founders Martin and Tarmo. Watch the full recording on YouTube · Listen on X . For questions or integration support, visit chainaware.ai.

The post AI Agents for Web3: The ChainAware Roadmap first appeared on ChainAware.ai.

X Space with AILayer — ChainAware co-founder Martin joins YJ from Cluster Protocol, Sharon from SecuredApp, and Val from Foreverland in a wide-ranging discussion on AI and Web3: the opportunities, the risks, and which industries AI will disrupt first. Hosted by AILayer — a Bitcoin Layer 2 ZK rollup platform powering the next generation of AI-native blockchain applications. Listen to the full recording on X

Four projects at the intersection of AI and Web3 infrastructure sit down for one of the most practically grounded conversations about what AI agents can actually do in blockchain — and what the real barriers to doing it well are. The discussion covers decentralized compute, predictive AI versus LLMs, the risk profile of autonomous financial agents, which industries AI will disrupt first, and the core argument that Web3 marketing — not trading or portfolio management — represents the single largest AI opportunity in the space. Each speaker brings a distinct vantage point: infrastructure orchestration (Cluster Protocol), behavioral prediction and marketing agents (ChainAware), DeFi security and smart contract auditing (SecuredApp), and Web3 cloud computing (Foreverland). Together they map an honest, multi-perspective picture of where AI and Web3 are heading.

The Speakers: Four Perspectives on AI and Web3 Infrastructure

AILayer, the host of this X Space, is a Bitcoin Layer 2 solution built on advanced ZK rollup technology. It is EVM compatible, supports staking of BTC, BRC20, Inscription Ordinals, and VM assets including BNB, MATIC, USDT, and USDC, and aims to serve as a foundational platform for AI projects building across DeFi, SoFi, and DePIN sectors. Bringing together four project builders for this conversation about the next wave of AI and Web3 creates a natural complementarity: each speaker addresses a different layer of the stack.

YJ from Cluster Protocol brings the infrastructure orchestration perspective. Cluster Protocol is building a coordination layer for AI agents on top of Arbitrum’s orbit stack, providing the backbone infrastructure for hosting and running AI agents — including distributed datasets, models, and compute alongside a personalized AI agent filter layer. Sharon from SecuredApp brings the security lens: SecuredApp began as a blockchain security company and has expanded into token launchpad, NFT marketplace, and DAO community services, with a team that has audited major DeFi projects globally and holds membership in the DeFi Security Alliance. Val from Foreverland brings a pragmatic, experience-grounded view from three years of Web3 cloud computing operations serving over 100,000 developers. Martin from ChainAware brings the behavioral prediction and marketing agent perspective — the practical application of predictive AI to the user acquisition problem that is currently limiting every Web3 project’s growth. For the complete ChainAware platform overview, see our product guide.

AI and Decentralized Computing: Solving the Wrong Problem?

The opening question asks how AI can help Web3 break free from reliance on centralized computing power. YJ’s answer from the Cluster Protocol perspective frames decentralized compute as a meaningful alternative to cloud monopolies for certain use cases — specifically the ability to access individual GPU configurations (like a single RTX 4090) that major cloud providers like AWS don’t offer, at lower cost because there are no middlemen between compute contributors and users. DePIN projects like Akash Network, IO.net, and Cluster Protocol’s own proof-aggregated compute system represent real progress in this direction.

Martin’s response, however, challenges the framing of the question itself. Rather than asking how to decentralize the massive compute requirements of LLMs, he argues that the better question is whether those requirements are necessary in the first place. Specifically, he distinguishes between two fundamentally different types of AI that require very different compute profiles — and makes the case that the AI most valuable for blockchain applications is the type that requires far less compute than the LLM narrative suggests. For a deeper exploration of this distinction, see our generative vs predictive AI guide.

LLMs vs Predictive AI: Two Entirely Different Compute Profiles

Martin’s core argument on the compute question deserves careful attention because it reframes what “AI on the blockchain” actually requires. LLMs — large language models like ChatGPT, Claude, and Gemini — are, in his words, “huge computing engines, statistical autoregression models.” They require massive GPU clusters to run inference, enormous memory bandwidth to load model weights, and significant latency even with optimized infrastructure. Furthermore, they are fundamentally linguistic processing systems: they predict the most probable next token in a text sequence. Applying LLMs to blockchain behavioral analysis means using a linguistic tool on data that is inherently numerical and transactional — a fundamental mismatch between tool and problem.

Predictive AI models, by contrast, are domain-specific. They train on labeled behavioral datasets to classify future states — which wallet will commit fraud, which pool will rug pull, which user will borrow next. Once trained, these models execute extremely quickly against new input data: feeding a wallet’s transaction history into a pre-trained neural network takes milliseconds, not seconds. As Martin explains: “When you train predictive models, the executions are pretty fast. You don’t need to go into these topics of decentralized computing power. You can execute the predictive models in real time.” ChainAware’s fraud detection model — 98% accuracy, 2+ years in production — runs against standard wallets in under a second with no decentralized compute infrastructure required. The implication is that much of the debate about decentralized compute for AI is relevant to LLMs specifically, not to the predictive AI systems that are most useful for on-chain behavioral analysis. For the full technical breakdown, see our real AI use cases guide and our predictive AI guide.

The Smart Approach: Build Better Models, Not Bigger Infrastructure

Martin frames the choice explicitly: “Two ways to address the problem. One is to build even bigger, bigger computing and decentralized computing. The other way is to build smart predictive models which are actually maybe much better.” This is not an argument against decentralized compute per se — YJ’s point about GPU accessibility and cost reduction is valid for teams that genuinely need LLM-scale compute. Rather, it is an argument that many blockchain AI use cases should not require LLM-scale compute in the first place. Fraud detection, behavioral segmentation, rug pull prediction, and user intention calculation are all problems that well-trained predictive models solve efficiently without the resource overhead of general-purpose language models. Sharon from SecuredApp reinforces this view from the security side: decentralized AI models are more viable and feasible when they are specialized and domain-specific rather than attempting to decentralize the infrastructure of general-purpose LLMs.

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The Limits of AI Decentralization: Val’s Honest Assessment

Val from Foreverland offers the most candid perspective on the decentralized AI compute question, and it deserves full consideration precisely because it challenges the consensus view. Her core argument is that AI models themselves — as opposed to the applications built on top of them — are inherently centralizing in their current form. The training of large AI models requires concentrated compute, centralized datasets, and significant coordination that distributed systems have not yet replicated at competitive quality. She points to DeepSeek as the only meaningful open-source LLM currently available, observing that “this is only one LLM, and it is not the rule for other developer teams to create open-source, decentralized LLMs.”

Val’s further point is that decentralization and AI solve different problems. Decentralization addresses security, immutability, and trust. AI addresses efficiency, pattern recognition, and automation. These goals are not inherently aligned, and conflating them creates confusion about what each technology can actually deliver. As she puts it: “Decentralization is not about efficiency — it’s more about security and reliance and immutability.” A decentralized AI model is not necessarily better at prediction than a centralized one; it is different in its trust properties. Whether those trust properties are necessary for a given application is a design question that each project must answer for itself, rather than assuming that decentralization is always the goal. For context on the blockchain trust and verification model, see our behavioral analytics guide.

The Real Risks of AI in Web3: Privacy, Bias, and Autonomous Trading

The second discussion topic shifts from opportunity to risk, and produces some of the most practically important observations in the entire conversation. Three distinct risk categories emerge across the speakers’ responses: privacy risks from AI data requirements, algorithmic bias inherited from training data, and the unique risks of fully autonomous financial agents operating on-chain.

Sharon from SecuredApp addresses privacy and bias with technical precision. AI models require large datasets for training — and in a blockchain context, that data can include sensitive information about user financial behavior, protocol interactions, and asset holdings. If not properly managed, that data creates exposure risks. On algorithmic bias, she notes that AI models inherit the biases present in their training data, which could lead to unfair decisions in DeFi contexts — particularly in automated trading or lending decisions where biased models might systematically disadvantage certain user categories. Her proposed mitigations are technically sophisticated: zero-knowledge proofs and secure multi-party computation to enable AI inference on private data without exposing the underlying information, combined with decentralized and auditable model governance. For the complete regulatory compliance framework, see our blockchain compliance guide and the FATF virtual assets recommendations .

Backtesting as Risk Mitigation: How ChainAware Publishes Accountability

Martin’s approach to AI risk in Web3 centers on a specific and actionable practice that he argues the entire industry should adopt: published backtesting. The concern is that many AI products in blockchain claim high accuracy without providing any verifiable evidence of how that accuracy was measured, on what data, and with what methodology. This opacity makes it impossible for users and clients to evaluate whether the claimed accuracy reflects real-world performance or optimistic in-sample testing on data the model was trained on.

ChainAware’s approach is to publish its prediction rates and backtesting methodology explicitly, with one specific and important constraint: the backtesting data must not overlap with the training data. Using training data for backtesting is a fundamental methodological error that produces artificially inflated accuracy figures — the model is being tested on data it has already learned from. As Martin states: “Everyone should publish just prediction rates, prediction occurrences, and backtesting — and backtesting should always be on obviously public data, and backtesting data should not be used for the training data.” ChainAware uses CryptoScamDB as its backtesting source for fraud detection — a publicly available database of confirmed scam addresses that provides an objective, independent test set for validating the 98% accuracy claim. This standard, if adopted industry-wide, would enable genuine comparison between competing AI products and eliminate the category of vague accuracy claims that currently makes evaluation difficult. For the complete fraud detection methodology, see our fraud detection guide and our fraud detector guide.

The Opportunity Side: Risks in Context

Martin also makes an important point about proportionality when thinking about AI risks in Web3. Risks exist and deserve serious mitigation — but they should be evaluated against the scale of the opportunity. Properly backtested predictive AI that achieves 98% fraud prediction accuracy has been in production at ChainAware for over two years. The value that system delivers in preventing fraudulent interactions — protecting new users, cleaning the ecosystem, enabling sustainable project growth — is enormous relative to the risks of a probabilistic system occasionally producing false positives. As Martin puts it: “I think the potential that we’re getting from AI agents — the potential of real products that are working — is so huge that even these risks, when they are mitigated properly, are not so significant.” The framework is not to minimize risks, but to ensure that risk mitigation is commensurate with risk severity rather than allowing edge-case concerns to block deployment of systems that deliver substantial real-world value. For more on the ecosystem-level impact of fraud reduction, see our Web3 growth guide.

Autonomous Trading Agents: The Highest-Risk AI+Web3 Scenario

Both YJ and Val converge on automated trading as the highest-risk application of AI in Web3 — and their concerns are worth examining in detail because they identify specific threat vectors rather than making vague warnings about AI in general.

YJ’s concern centers on the combination of full financial autonomy and decentralized operation. When an AI agent has been given funds and full discretion over trading decisions, any vulnerability in the agent’s decision-making logic, training data, or execution environment can result in financial loss at machine speed. He references the documented case of two AI chatbots developing their own communication patterns when left interacting without supervision — and extrapolates this to the financial context: “With full autonomy, the trust on the AI might reduce a bit, because you need to run these AI in specific environment conditions, but then that would not be truly decentralized.” The tension is real: full autonomy and full decentralization together create an attack surface that neither fully centralized AI (which can be monitored and corrected) nor manual DeFi (which requires human initiation) presents. For how ChainAware’s fraud detection integrates into DeFi security workflows, see our fraud detection guide.

The Attack Surface of Autonomous Trading Infrastructure

Val extends the autonomous trading risk analysis to the infrastructure layer. Autonomous trading agents rely on data feeds, model weights, and execution endpoints — all of which represent potential attack surfaces for threat actors who want to manipulate trading outcomes. As she explains: “I’m afraid that would be the most risky part of the AI story integrating with Web3 because probably there would be some attacks coming from threat actors in order to manipulate the trading vaults or models.” This is a specific and legitimate concern: data poisoning attacks that subtly bias a trading agent’s model toward favorable outcomes for an attacker are significantly harder to detect than direct fund theft and could persist undetected across many transactions. The mitigation is not to avoid autonomous trading agents entirely — the efficiency gain is too large — but to implement the kind of behavioral monitoring that ChainAware’s transaction monitoring agent provides: continuous surveillance that detects anomalous patterns before they result in irreversible on-chain losses. For the transaction monitoring approach, see our transaction monitoring guide and our AML and monitoring guide.

Zero-Knowledge Proofs and Privacy-Preserving AI Inference

Sharon’s proposed technical solution to the AI privacy problem in Web3 introduces one of the most significant emerging research areas at the intersection of cryptography and machine learning: privacy-preserving AI inference using zero-knowledge proofs and secure multi-party computation.

Standard AI inference requires the model to access the input data — which means that any AI system analyzing a user’s financial behavior must, in the conventional architecture, have access to that user’s transaction history. This creates a privacy risk: the entity running the model learns about the user’s behavior as a byproduct of providing a service. Zero-knowledge proofs offer a cryptographic solution: they allow a computation to be verified as correctly executed without revealing the inputs to the computation. Applied to AI inference, this means a user could submit their transaction history to an AI model and receive a behavioral profile output — without the model operator ever seeing the raw transaction data. As Sharon describes: “We can implement zero-knowledge proofs and secure multi-party computations to allow AI models to process data without exposing private information.” For broader context on cryptographic privacy in blockchain, see the Ethereum Foundation’s zero-knowledge proof documentation and our Web3 trust and verification guide.

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Which Industries Will AI Disrupt First in Web3?

The third discussion question generates significant diversity of opinion, reflecting the genuinely different vantage points of each speaker. Sharon from SecuredApp argues for DeFi as the first-disrupted sector, citing the ongoing boom in decentralized finance adoption, several countries moving toward Bitcoin reserves and crypto as legal tender, and the natural fit between AI automation and DeFi’s already highly automated infrastructure. She also points to supply chain and healthcare as secondary targets where blockchain transparency, combined with AI analysis, creates particularly strong efficiency gains.

Val from Foreverland makes the contrarian argument that no industry will be “eliminated” by Web3 going mainstream — because Web3 going mainstream in the replacement sense simply will not happen. Her point is more sociological than technical: technology adoption in human society is not characterized by binary replacement but by coexistence and layered adoption. Computers did not eliminate calculators or watches. The internet did not eliminate physical retail. Web3 will not eliminate Web2. Instead, it will serve an expanding base of users who have chosen to engage with it, coexisting with Web2 infrastructure rather than supplanting it. This is a realistic framing that many Web3 maximalists resist but that history consistently validates. For more on the Web3 adoption trajectory, see our Web3 growth guide.

Web3 Marketing: The Biggest AI Opportunity Nobody Is Talking About

Martin’s answer to the “which industry will AI disrupt first” question is deliberately specific and counterintuitive — and it is worth examining precisely because it diverges from the consensus responses that focus on trading, portfolio management, and DeFi automation. His argument is that Web3 marketing represents the largest addressable AI opportunity in the space, specifically because the current state of Web3 marketing is so far behind where it needs to be that the improvement potential is enormous.

The framing is direct: “The current Web3 marketing level is pretty stone age. It hasn’t reached Web2 marketing. We are still like before the Internet hype.” Every major marketing channel in Web3 — KOL campaigns, crypto media banners, Telegram ads, exchange listings, Discord announcements — delivers identical messages to heterogeneous audiences. A DeFi-native yield optimizer with five years of complex protocol history receives the same promotional content as someone who connected their first wallet last week. The conversion rate from this undifferentiated approach is predictably poor, which directly causes the prohibitively high user acquisition costs that prevent Web3 projects from achieving financial sustainability. As Martin explains: “If you have Web3 marketing agents, and the marketing agents predict the behavior of the users based on predictive models and know which content to create, which resonating content — we get much higher engagement.” For the complete Web3 personalization framework, see our AI marketing guide and our intention-based marketing guide.

Why Marketing Beats Trading as the Primary AI Application

The reasoning for prioritizing marketing over trading as the highest-impact AI application is both commercial and structural. Trading AI agents face significant technical challenges — the risk of adversarial attacks on model weights, the difficulty of maintaining performance across changing market conditions, and the regulatory uncertainty around fully autonomous financial agents. Marketing AI agents, by contrast, operate in a lower-stakes environment where errors are recoverable (a suboptimal marketing message has much lower consequence than an erroneous trade), the feedback loops are clear and measurable, and the infrastructure (wallet behavioral profiles, content generation) is already mature. Furthermore, marketing AI solves a universal problem that affects every Web3 project regardless of sector — every protocol, every DApp, every service needs to acquire users. Solving user acquisition efficiently through personalization therefore amplifies the success of every other AI+Web3 application by ensuring those applications can reach the users who would benefit from them. For more on how personalization addresses the Web3 growth bottleneck, see our high-conversion marketing guide and our Web3 personas guide.

The User Acquisition Cost Crisis: 10-20x Higher Than Web2

Martin provides the specific quantification that makes the Web3 marketing problem concrete. Web2 platforms — after the AdTech revolution driven by Google’s behavioral targeting innovation — achieved user acquisition costs in the $30-40 range for transacting customers. Web3 platforms today face user acquisition costs that are 10-20 times higher. This is not a minor operational inefficiency — it is a fundamental business model failure. No project can build sustainable revenue when acquiring each customer costs hundreds of dollars but the economics of blockchain transactions produce relatively thin margins per user in the early growth phase.

The reason for this disparity is structural, not accidental. Web3 marketing has not yet developed the behavioral targeting infrastructure that Web2 deployed through AdTech. Every dollar spent on Web3 marketing reaches an undifferentiated audience and converts at a rate that reflects that lack of targeting precision. As Martin states: “In Web2, a user acquisition cost is maybe $30-35-40. In Web3, we are speaking a user acquisition cost factor 10-20x higher. So this is what you’re facing in Web3 now.” The solution is identical to what Web2 deployed: behavioral targeting based on demonstrated user intentions, delivering personalized messages to users whose behavioral profiles indicate genuine interest in the specific product being promoted. For the historical Web2 parallel, see our ChainAware vs Google Web2 guide and Statista’s Google advertising revenue data .

The Iteration Argument: Why Cash Flows Are the Real Bottleneck

Martin makes a foundational product development argument that connects user acquisition costs directly to the innovation velocity of the entire Web3 ecosystem. The argument has a clean logical structure: no product is perfect in its first version — every product becomes better through iteration informed by real user feedback. To iterate, founders need users. To get users sustainably, founders need cash flows. To generate cash flows, the economics of user acquisition must be viable. Currently, they are not viable because acquisition costs are too high.

The consequence of this economic trap is a predictable pattern: Web3 projects launch with genuine innovation, fail to acquire users at sustainable cost, conduct a token sale to fund ongoing operations, watch the token price decline as speculative interest fades without sustainable utility, and eventually wind down — never having had the chance to iterate toward the product-market fit that was potentially within reach. As Martin explains: “The projects need to get users. The projects need to get, from users, the cash flows. There has to be a much higher user conversion rate. For the cash flows you need user acquisition — you have to bring massively down, by a factor of tens, the user acquisition cost in Web3.” Reducing that cost is therefore not merely a marketing efficiency improvement — it is the prerequisite for the entire Web3 ecosystem’s ability to evolve from first-generation products to mature, market-validated applications. For more on the sustainable Web3 business model argument, see our unit costs and AdTech guide.

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Coexistence vs Replacement: Val’s Case for a Realistic Web3 Future

Val’s contribution to the industry disruption discussion extends well beyond a list of sectors to a philosophical framework for thinking about technological transitions that is grounded in historical pattern recognition rather than ideological preference. Her core observation is that technology adoption does not work through binary replacement — one paradigm eliminating the previous one — but through coexistence and layered adoption where different populations, with different needs, trust levels, and educational backgrounds, adopt new technologies at different rates and to different degrees.

Her examples are deliberately mundane: computers did not eliminate calculators or watches, even though they can perform the functions of both. The internet did not eliminate physical retail, print media, or telephone communication, even though it is technically superior for many of their functions. People continue using the less optimal technology because habit, preference, familiarity, and comfort are also real factors in technology adoption decisions. Web3 faces the same social reality. As Val observes: “Even if we may see that more and more people are utilizing Web3, it doesn’t mean that the majority of them are utilizing it. Just look at the older generation — look at your dads, moms, grannies. How will they get the tokens? How will they use them?” The realistic near-term vision is therefore not mainstream Web3 adoption replacing Web2, but expanding Web3 adoption alongside continuing Web2 infrastructure — with AI accelerating Web3’s ability to serve its growing user base more effectively. For the broader adoption trajectory discussion, see our DeFi onboarding guide.

AI-Powered Smart Contract Security: SecuredApp’s Approach

Sharon’s final contribution to the growth question focuses on one of the most practically valuable applications of AI in the Web3 security space: automated smart contract auditing. Smart contracts are the execution layer of all DeFi protocols, and their vulnerability to exploits has resulted in billions of dollars of losses over the history of the space. Traditional smart contract auditing is time-consuming, expensive, and dependent on the expertise of individual human auditors who may miss subtle vulnerability patterns in complex codebases.

AI-powered audit automation changes this equation significantly. Models trained on historical vulnerability patterns can scan smart contract code in seconds, flagging categories of vulnerability — reentrancy attacks, integer overflows, access control failures, flash loan attack vectors — that match known exploit signatures. Crucially, AI can also do this in real time during deployment and operation, not just in pre-launch audits. As Sharon explains: “Smart contracts are prone to vulnerabilities and exploits. We can use AI to automate smart contract audits, detect vulnerabilities and prevent hacks in real time.” SecuredApp’s integration of AI into its security tooling — including the Solidity Shield Scanner — represents exactly this approach: using AI to make high-quality security screening more accessible and more continuous. For ChainAware’s complementary approach to on-chain security through behavioral fraud prediction, see our fraud detection guide and our rug pull detection guide. For broader context on DeFi security best practices, see ConsenSys Diligence’s smart contract security resources .

DAO Governance and AI-Assisted Decision-Making

Sharon also raises a less frequently discussed AI application in Web3: improving DAO governance decision-making. DAOs face a well-documented governance problem — proposal participation rates are low, voting is often uninformed because voters lack the context to evaluate complex technical or economic proposals, and decision-making velocity is slow because each governance action requires manual coordination. AI systems that analyze on-chain data, model proposal impacts, and surface relevant context for voters could dramatically improve governance quality without requiring any change to the underlying decentralized structure. This remains a nascent application area, but the combination of transparent on-chain governance data and AI analytical capability makes it a natural fit. For more on how behavioral analytics supports governance quality, see our behavioral analytics guide.

Comparison Tables

LLMs vs Predictive AI for Blockchain Applications

AI Risk Categories in Web3: Assessment and Mitigation

Frequently Asked Questions

What is AILayer and why did it host this X Space?

AILayer is an innovative Bitcoin Layer 2 solution that uses advanced ZK rollup technology to enhance Bitcoin transaction performance and scalability. It is EVM compatible, supports a broad range of assets including BTC, BRC20, Inscription Ordinals, BNB, MATIC, USDT, and USDC, and aims to serve as a foundational platform for AI projects building across DeFi, SoFi, and DePIN sectors. The X Space brought together builders from across the AI+Web3 ecosystem to discuss the opportunities and challenges at this intersection — directly relevant to AILayer’s mission of enabling AI-native applications on a Bitcoin-secured foundation.

Why does ChainAware use predictive AI instead of LLMs for blockchain analysis?

LLMs are linguistic processing systems — they predict the most probable next text token based on patterns in training data. Blockchain behavioral analysis requires a completely different type of intelligence: classifying future financial actions from numerical transactional history. Using an LLM for blockchain analysis is a category mismatch — like using a language translator to perform chemical synthesis. Beyond the functional mismatch, LLMs require massive computational resources that make real-time blockchain inference impractical. ChainAware’s domain-specific predictive models, trained specifically on blockchain behavioral data, execute against new wallet addresses in under a second with no heavy compute infrastructure. This is why ChainAware achieves 98% fraud detection accuracy in real-time production rather than near-real-time inference with a general-purpose model.

How does ChainAware verify and publish its 98% fraud detection accuracy?

ChainAware backtests its fraud detection model against CryptoScamDB — a publicly available database of confirmed scam and fraud addresses that is entirely separate from the training data used to build the model. Using independent test data (not training data) is essential for producing accuracy figures that reflect real-world performance rather than in-sample overfitting. The 98% figure means that when ChainAware’s fraud model is applied to addresses in the CryptoScamDB test set, it correctly classifies 98% of them as fraudulent before their fraud was documented. This specific methodology — published, independent backtesting on verified public data — is what Martin argues the entire AI+blockchain industry should adopt as a minimum standard for accuracy claims.

What is the Web3 user acquisition cost problem and how does AI fix it?

Web3 user acquisition costs are currently 10-20x higher than equivalent Web2 acquisition costs ($300-800+ per transacting user vs $30-40 in Web2). The root cause is mass marketing: every marketing channel in Web3 delivers identical messages to heterogeneous audiences, producing low conversion rates that drive up the effective cost per acquired user. AI fixes this by enabling personalization at scale — using each connecting wallet’s on-chain behavioral history to calculate their specific intentions and generate matched content automatically. A borrower sees borrowing content; a trader sees trading content; an NFT collector sees NFT-relevant messaging. Higher relevance produces higher conversion rates, which reduces the effective cost per acquired user — the same transformation that Google’s AdTech delivered in Web2 through behavioral targeting. ChainAware’s Web3 marketing agents implement this personalization using predictive AI models trained on 18M+ wallet profiles across 8 blockchains.

Will AI replace Web3 or Web2? What does the future look like?

Val from Foreverland’s historical perspective offers the most grounded answer: neither technology replaces the other. Technology adoption follows patterns of coexistence and layered usage rather than binary replacement. Computers did not eliminate calculators; the internet did not eliminate physical retail; Web3 will not eliminate Web2. Different populations adopt new technologies at different rates, and many people will continue using Web2 infrastructure for reasons of habit, education, and preference even as Web3 usage expands. The realistic future is an expanding Web3 user base — accelerated by AI improvements in onboarding, fraud reduction, and user experience — coexisting alongside continuing Web2 infrastructure. AI’s role in this trajectory is to make Web3 more accessible, more trustworthy, and more capable of delivering sustainable value to both new and existing participants.

This article is based on the X Space hosted by AILayer featuring ChainAware co-founder Martin alongside YJ from Cluster Protocol, Sharon from SecuredApp, and Val from Foreverland. Listen to the full recording on X . For integration support or product questions, visit chainaware.ai.

The post AI and Web3 — Opportunities, Risks and the Next Wave — X Space with AILayer first appeared on ChainAware.ai.

Based on X Space #30 (two-part session) — ChainAware co-founders Martin and Tarmo. Watch the full recording on YouTube · Listen X-Space #30 X

DeFi AI — the convergence of decentralized finance and artificial intelligence agents — is the topic of X Space #30. Martin and Tarmo, co-founders of ChainAware.ai and veterans of Credit Suisse’s private banking division, argue a straightforward thesis: AI will enter every existing Web3 domain and dramatically increase its utility. DeFi, with its 100% automated processes and freely accessible on-chain data, is the clearest example of where this transformation is already happening. This article covers the full two-part discussion — what DeFi AI actually means, why self-custody matters, what AI agents are doing in DeFi right now, and why the distinction between attention AI and real utility AI determines which projects survive.

The Core Thesis: AI Enters Every Web3 Domain

The central hypothesis of X Space #30 is both simple and significant: AI is a megatrend that will penetrate every existing Web3 domain. It will not create new domains from scratch. Instead, it will enter gaming, NFTs, payments, DeFi, gambling, wallets, analytics, and every other category that already has real users and real utility — and it will make each one dramatically more effective.

Tarmo frames it precisely: “Existing domains have real use cases. AI is not there to invent new use cases. AI is there to improve the utility, to improve the value added of these existing domains even further.” The keyword is existing. Every domain that already generates revenue and serves real users becomes a candidate for AI-driven improvement. DeFi, with its fully automated processes and transparent on-chain data, is the most natural starting point.

Consequently, the term “DeFi AI” — or DeFAI as CoinGecko began categorizing it — represents an evolution, not a new invention. DeFi already has utility. AI makes that utility better. Furthermore, the same pattern will play out in every other Web3 category. There will be no separate “NFT AI” or “gaming AI” as distinct categories — there will simply be AI-enhanced versions of every category that already matters. For the broader context on how ChainAware approaches real utility AI, see our previous X Space discussion on attention AI vs real utility AI.

Attention AI vs Real Utility AI — The Distinction That Matters

Before diving into DeFi AI specifically, Martin and Tarmo revisit the framework they developed in X Space #29. Understanding this distinction matters because it separates projects worth building on from those that will disappear in the next market correction.

Attention AI — what Martin and Tarmo call “fake AI” in plain speech — generates narratives without generating utility. It combines impressive-sounding keywords: “tokenized decentralized AI optimization,” “cross-chain AI energy improvement,” “AI-driven supply chain healthcare.” These phrases attract retail investors because they sound sophisticated. However, behind them typically lies two or three lines of LLM prompts and a website. The product does not solve a specific, measurable problem for real users. As a result, when markets correct, attention AI projects are always the first to collapse.

Real utility AI, by contrast, uses proprietary machine learning models to solve specific, verifiable problems — and produces results that are measurable. ChainAware’s fraud detection achieves 98% accuracy, predicting future fraud before it occurs across 14M+ wallet profiles. That is a measurable claim. Moreover, it requires years of model development and training data that competitors cannot simply copy. This creates genuine competitive moats. For a detailed breakdown of what separates these two categories, see our complete guide to attention AI vs utility AI.

What AI Agents Actually Are — and Two Types You Need to Know

Tarmo defines AI agents with clarity that cuts through the hype: “AI agents are autonomous. They work 24 hours per day, seven days per week. No supervision — they just do it. They are self-running, self-healing, self-learning. They carry out tasks, measure results, and improve the next predictions continuously.”

Crucially, two fundamentally different types of AI agents exist — and confusing them leads to bad investment and integration decisions.

Type 1: LLM-Based Agents

LLM-based agents use large language models (ChatGPT, Claude, Gemini) to automate tasks through prompts. They are fast to build — sometimes just a few lines of prompt — and cover a wide range of use cases. Generating smart contract code, writing marketing copy, creating governance summaries — all of these suit LLM agents well. However, they have two critical limitations.

First, LLMs are statistical autoregression models. They predict the next most probable token in a sequence. They are linguistical models, not decision-making models. Feeding blockchain transaction data into an LLM and asking it to detect fraud produces unreliable results — because the LLM is optimized for language patterns, not for on-chain behavioral signals. Second, anyone can replicate an LLM-based agent quickly. There is no competitive moat. As a result, these agents commoditize rapidly.

Type 2: Predictive AI Agents

Predictive AI agents use proprietary ML models trained on specific data domains. Instead of predicting language sequences, they predict events and behaviors — will this wallet commit fraud, will this user borrow, will this contract rug pull? These models require substantial investment in data, training, and validation. Moreover, they produce measurable accuracy scores that can be backtested and verified. ChainAware’s fraud detection model, for example, achieves 98% accuracy — a number that is independently verifiable and has been validated over four years of production operation.

Tarmo explains the key difference in agent value: “The longer AI agents learn, they get superhuman performance. They go from junior to senior to master to principal to expert. When you let AI agents work and get continuously this feedback and relearn, relearn, relearn, then you will get super employees.” This continuous improvement loop is only possible with predictive ML — not with static LLM prompts. For more on how ChainAware’s predictive agents work in practice, see the Prediction MCP developer guide.

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ChainAware Fraud Detector — 98% Accuracy, Predicts Before It Happens

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Self-Custody vs Custodial: Why DeFi Solves a Real Problem

Before discussing AI agents in DeFi, Martin and Tarmo spend time on a foundational question: why does DeFi matter in the first place? The answer comes down to a single distinction — owning an asset versus owning a claim on an asset.

In traditional banking — and in centralized crypto exchanges — users do not own their assets. They own a record in a database that says the institution owes them those assets. The institution controls the actual assets. This is the custodial model. The bank or exchange holds your funds and gives you an IOU.

DeFi operates on self-custody. Users control their private keys directly. Consequently, they control access to their actual assets — not to a claim. Nobody can rehypothecate those assets, lend them out, or lose them without the user’s direct participation. As Martin explains: “In DeFi you have the asset instead of a claim on the asset. That is the difference between the custodial system — where you deal with claims on assets which belong to you — versus self-custodial, where you own the asset itself.”

This distinction matters enormously for risk assessment. Furthermore, it defines what makes DeFi valuable independent of any AI enhancement. Self-custody eliminates an entire category of counterparty risk that custodial finance inherently carries. For more on how ChainAware protects self-custodial DeFi users from the risks that do remain, see our complete KYT and AML guide for DeFi.

The MF Global Warning: Rehypothecation and Its Risks

Tarmo brings a specific historical case to illustrate the custodial risk. Before Credit Suisse, he worked at Man Investments — described as the largest independent hedge fund in the world at the time. Man Investments had a sister company called MF Global.

MF Global offered brokerage services to retail clients with approximately $600M in client deposits. Everything operated smoothly until the firm decided to speculate with those client assets — taking highly leveraged positions on interest rates. When those positions moved against them, clients logged into their accounts and found nothing. The assets were gone. MF Global had rehypothecated — lent out — the client funds to make its own trades. Rehypothecation in European jurisdictions allows banks to lend out client assets up to 80 times. The same asset can appear on 80 different books simultaneously.

Tarmo describes it vividly: “You have one cow and the bank can lend it out 80 times. The same cow is existing 80 times in the same moment in different books of different organizations.” When one link in that chain fails, nobody knows where the assets actually are. Celsius and other centralized crypto platforms repeated this exact pattern in 2022, with identical consequences for depositors.

DeFi eliminates this risk by design. On a DeFi protocol, the smart contract holds the assets — not a company. No human can decide to rehypothecate them. This is why, despite the volatility and fraud risks that DeFi faces, the fundamental architecture is a genuine improvement over custodial systems for users who want full control. For guidance on how to assess DeFi protocol security before depositing, see our rug pull detector guide.

What DeFi AI Actually Means

With the DeFi foundation established, the discussion turns to DeFi AI — and Tarmo’s definition is precise: “DeFi AI = digitalization by DeFi + superior decision making by AI agents. We add superior decision making to existing DeFi. DeFi already has utility. When we go over to DeFi AI, that utility is massively improved because of the superior decision power of AI agents.”

The evolution follows a clear sequence. First came digitalization — DeFi automated financial processes that previously required human intermediaries. Uniswap automated market-making. Compound automated lending and borrowing. Aave added flash loans. These products created genuine utility. However, decisions within these systems were still either fully deterministic (rules-based smart contracts) or made by human users who were often poorly informed.

On-Chain Data as an AI Advantage

DeFi AI adds a second layer: autonomous, learning agents that make better decisions than either static rules or average human judgment. Crucially, these agents train on freely available on-chain data. Tarmo highlights this advantage explicitly: “This data is free. It’s not like in traditional finance where you have to buy very expensive licenses to get data sources.” Every transaction on Ethereum, BNB, Solana, and other chains is publicly accessible, freely available, and continuously growing. An AI agent trained on this data can improve daily simply by relearning from new on-chain events — no data licensing fees, no API paywalls, no data moats protecting incumbents.

Additionally, the combination creates a win-win for all stakeholders. Users get better products that serve their needs more precisely. Protocols get better performance metrics — higher TVL, better conversion rates, lower fraud losses. Investors benefit from improved cash flows as the products outperform competitors that don’t use AI. As Tarmo notes: “When decentralized finance merges with AI agents, it is a win-win where everybody wins more out of it — which happens very seldom in the real world.”

Six specific AI agent categories are emerging in DeFi. Each one takes an existing DeFi function and replaces human decision-making with AI-driven superiority. For how these agents integrate via API into existing platforms, see our guide to 12 blockchain capabilities any AI agent can use.

1. Trading Agents — Pattern Recognition at Scale

Trading agents are the most widely discussed AI use case in crypto. However, the discussion in X Space #30 cuts through the hype with a sobering baseline: the 90/90/90 rule. Ninety percent of traders lose 90% of their assets in 90 days. This is not speculation — it comes from Martin and Tarmo’s decade-plus experience at Credit Suisse and Man Investments, where professional trading infrastructure operated at a scale most retail participants never encounter.

Man Investments ran automated trading engines managing $20 billion in assets under management over 20 years ago. The systems that outperform human traders use predictive AI for pattern recognition — not LLMs. LLMs analyze language sequences. Trading requires pattern recognition across price data, volume data, liquidity data, and on-chain flow data. These are completely different data types requiring completely different model architectures.

Current trading systems in Web3 are largely rules-based — if/then/else conditions that attempt to encode human intuition as explicit logic. AI trading agents replace the explicit rules with learned patterns, potentially producing accuracy well above the 90/90/90 baseline. Moreover, unlike human traders, they operate 24/7 without fatigue, emotion, or variance. For more on the distinction between rules-based systems and genuine predictive AI, see our guide to real AI use cases for Web3 projects.

2. Portfolio Management Agents — Risk-Adjusted Returns

Portfolio management agents operate at a higher level than trading agents. Rather than managing individual positions, they manage the overall portfolio — balancing asset classes, monitoring correlations, and optimizing risk-adjusted returns according to the Sharpe ratio framework.

Martin and Tarmo bring their CFA (Chartered Financial Analyst) credentials to this discussion explicitly. The core insight from professional portfolio management is simple: generating returns is easy — anyone can take extreme leverage and win in a bull market. Generating risk-adjusted returns consistently is the actual challenge. The Sharpe ratio (return per unit of risk) is the correct metric, not raw return.

Currently, DeFi has no equivalent to the private banking wealth management layer. Users must manually monitor their positions across multiple protocols, rebalance when allocations drift, and manage liquidation risks independently. An AI portfolio management agent automates all of this — watching allocation ratios between asset classes, rebalancing when thresholds are crossed, and applying risk optimization logic continuously. Tarmo calls it “an automated wealth manager that works on your portfolio and rebalances it and keeps the risk minimized.” For context on how SmartCredit already deploys risk monitoring for its preferred clients, see the Credit Scoring Agent guide.

3. Risk Monitoring Agents — Protecting Individual Positions

Risk monitoring agents differ from portfolio management agents in scope. Portfolio management handles the full portfolio — risk monitoring handles individual positions, specifically the risk of liquidation in borrowing and leveraged lending protocols.

The liquidation problem in DeFi is real and costly. Protocols like Aave, Compound, and MakerDAO generate significant revenue from liquidating undercollateralized borrowers. Many of these liquidations happen not because borrowers are insolvent but because they lack tools to monitor their positions in real time and take protective action before the liquidation threshold is crossed.

A risk monitoring agent watches a user’s borrowing position continuously. When collateral value drops toward the liquidation threshold, it triggers alerts — via Telegram, webhook, or automated actions. Furthermore, it can be configured to take protective actions automatically: adding collateral, partially repaying the loan, or executing a hedge. This is the DeFi equivalent of a bank’s margin call team, but operating 24/7 with zero human latency. SmartCredit offers risk monitoring agents to preferred clients as part of their DeFi AI stack. For the technical implementation via MCP, see our guide to 5 ways Prediction MCP turbocharges DeFi platforms.

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4. Marketing Agents — Behavioral Targeting for DeFi

Marketing agents address the most expensive problem in Web3: user acquisition cost. Currently, acquiring one transacting DeFi user costs $1,000–$3,000 — a figure that makes most protocols structurally cash-flow negative. Traditional marketing approaches in Web3 — KOLs, airdrops, crypto ad networks — drive traffic but not conversion. Sessions from KOL campaigns typically last 12–15 seconds. Users arrive, see a generic interface, and leave.

ChainAware’s marketing agents solve this conversion problem through behavioral targeting at the wallet connection event. When a user connects their wallet to a DeFi platform, the marketing agent immediately calculates that wallet’s behavioral profile from on-chain data: what protocols have they used, what is their experience level, what are their predicted next actions? Based on this profile, the agent generates a personalized message — an embedded section of the website that resonates specifically with that user’s intentions.

Resonance, Not Interruption

Martin describes the goal: “You have to resonate with users, not users resonate with you.” A yield-farming-experienced wallet visiting a lending platform should not see a generic “earn up to 15% APY” banner. Instead, it should see messaging tailored to its specific experience and likely next action. This one-to-one targeting — at scale, automated, without cookies or identity — is the Web3 equivalent of what Google AdWords did for Web2.

Additionally, the power law distribution in DeFi — where a small number of protocols capture the vast majority of TVL and users — starts to flatten when effective targeting reaches smaller protocols. Users currently gravitate to large protocols partly because visibility drives familiarity. When a smaller protocol with genuinely better terms can reach exactly the right user with exactly the right message, the competitive dynamic shifts. For a detailed guide on how marketing agents work, see our Web3 behavioral user analytics guide and our analysis of why personalization is the next big thing for AI agents.

5. Transaction Monitoring Agents — Address-Level Security

Transaction monitoring agents provide security at the address level — and the Bybit hack, referenced explicitly in the X Space, illustrates why this matters more than contract-level security.

After major DeFi hacks, discussion typically focuses on smart contract vulnerabilities. Auditing firms audit the code. Protocols get 15 different audits from different firms. Yet hacks continue. Tarmo explains why contract monitoring alone is insufficient: “What you need is monitoring of addresses. Fraudulent addresses are doing nasty things. Avoid transacting with these partners who use those addresses. It is your firewall.”

Behind every malicious contract sits a malicious address. Moreover, regulators increasingly mandate address-level monitoring specifically — not contract monitoring. FATF’s guidance on virtual assets focuses on user addresses as the unit of compliance obligation, not smart contract code. Monitoring addresses catches bad actors before they can deploy or interact with malicious contracts.

ChainAware’s transaction monitoring agent does this continuously. It monitors wallets connecting to or transacting with a DeFi platform, detects when behavioral patterns shift toward pre-fraud signatures, and sends real-time alerts. Critically, this is predictive — it identifies the behavioral change before any fraud occurs, not after. ChainAware integrates via Google Tag Manager pixel, requiring no code changes to existing DeFi front-ends. For the full integration guide, see our AML and transaction monitoring integration guide for DApps.

6. Credit Scoring Agents — Financial Ability Assessment

Credit scoring agents perform a function that traditional finance has relied on for decades — assessing the financial ability of a borrower — but applied to anonymous on-chain wallets without any KYC.

Martin clarifies what credit scoring actually measures: “It’s not just — is someone now paying back what they borrowed? It’s a general financial ability of a person. What is his financial ability?” A FICO score in traditional finance captures income, debt levels, payment history, and account longevity — a composite measure of financial health, not just loan repayment history. ChainAware’s credit scoring agent does the same from on-chain data.

For DeFi lending protocols specifically, credit scoring unlocks a critical capability: undercollateralized lending. Today, nearly all DeFi lending is overcollateralized — borrowers post 150% collateral to receive a 100% loan. This constraint exists precisely because there is no credit infrastructure to assess borrower quality. By integrating credit scoring agents, lending protocols can offer better terms to high-creditworthiness wallets and tighter terms to lower-quality ones — personalizing risk management the same way traditional banks do for customers with different credit scores. Furthermore, credit scoring extends beyond lending to ABC client filtering, growth targeting, and collateral decisions across any DeFi protocol. For the complete guide, see our complete Web3 credit scoring guide.

SmartCredit: A Live Example of DeFi AI

Throughout X Space #30, SmartCredit.io serves as the concrete live example of what a fully integrated DeFi AI platform looks like. Martin and Tarmo built SmartCredit before ChainAware — and it incorporates AI agents across every function the X Space discusses.

SmartCredit was the first DeFi lending protocol to offer fixed-interest, fixed-term loans — an innovation that Traditional DeFi, with its variable-rate money markets, had never addressed. Fixed terms allow borrowers to plan: “I know exactly what interest I will pay.” Variable rates in DeFi lending are equivalent to a variable-rate mortgage where you never know what next month’s payment will be.

Beyond this core innovation, SmartCredit integrates the full DeFi AI stack. It uses transaction monitoring agents for security. It deploys credit scoring agents for borrower assessment. It leverages Web3 marketing agents for user conversion. Risk monitoring agents protect preferred clients’ individual positions. As Martin summarizes: “It is like an example of what future DeFi systems will look like. Integrate marketing agents, integrate transaction monitoring agents, integrate credit scoring agent, risk monitoring agent — and then you get superior performance compared to platforms which don’t use AI capabilities.” To understand how SmartCredit has applied these tools with measurable results, see the SmartCredit case study.

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The Washing Machine Analogy: AI Frees Humans for Innovation

One of the most memorable moments in X Space #30 is Tarmo’s washing machine analogy for AI’s broader societal impact. He asks: “Which technology enabled cave-to-humans most freedom of time?” His answer: the washing machine. Before it existed, manual laundry consumed enormous amounts of daily time. The washing machine automated that task completely — and the time freed up went toward innovation, not unemployment.

AI agents do the same at the expert level. Tasks that currently require skilled employees — compliance review, fraud analysis, portfolio rebalancing, user targeting — will be taken over by AI agents operating with superhuman accuracy. The freed time then goes toward what humans do best: creative work, new product development, new startup formation, new ideas. Martin adds: “People will have more capacity to do what they are best at. This is creation of new concepts, new startups, new ideas, new products.”

Consequently, the fear that AI creates unemployment is misplaced — at least for builders and founders. The analogy holds precisely because the washing machine did not reduce human activity; it redirected it toward higher-value creation. AI agents in DeFi will similarly redirect human effort from repetitive expert-level tasks toward genuinely creative ones. For more on this transition in the context of AI agent infrastructure, see our article on the Web3 agentic economy.

Comparison: Attention AI vs Real Utility AI in DeFi

Frequently Asked Questions

What is DeFi AI and how is it different from regular DeFi?

DeFi AI combines the automated financial processes of decentralized finance with AI agents that make superior decisions within those processes. Regular DeFi uses deterministic smart contracts — rules that execute the same way every time. DeFi AI adds learning agents that analyze on-chain data, predict user behavior, detect fraud, optimize portfolios, and improve marketing — continuously getting better as they process more data. The result is higher utility for users and better economics for protocols. For a full breakdown, see our guide on real AI use cases for every Web3 project.

What is the difference between attention AI and real utility AI?

Attention AI combines buzzwords — “decentralized AI cross-chain optimization” — to attract investor interest without delivering real utility. Real utility AI uses proprietary ML models to solve specific, verifiable problems with measurable accuracy. The test is simple: can you state what problem the AI solves, measure its accuracy, and verify that the product is live with real users? If yes, it is utility AI. If the answer to any of those questions is no, it is attention AI.

Why are LLMs insufficient for DeFi AI decision making?

LLMs are statistical autoregression models optimized for language patterns — predicting which word comes next in a sequence. They are excellent for generating text, summarizing documents, and answering questions. However, they are not designed for on-chain behavioral prediction, fraud detection, or trading signal generation. Those tasks require predictive ML models trained on specific data types (transaction patterns, behavioral signals, price data) with backtested accuracy scores. Using an LLM for fraud detection is analogous to using a spell-checker to predict stock movements — technically possible to attempt, but structurally wrong for the task.

What is rehypothecation and why does it matter for DeFi?

Rehypothecation is the practice of lending out client assets to generate additional returns. In European banking, a single asset can be lent out up to 80 times simultaneously. MF Global used client deposits (approximately $600M) for speculative trades — when those trades failed, clients lost everything. Celsius repeated this pattern in crypto in 2022. DeFi eliminates this risk structurally: self-custodial protocols cannot rehypothecate user assets because no central entity controls them. Users hold their private keys and retain direct access to their assets at all times.

How does ChainAware’s marketing agent reduce DeFi user acquisition cost?

ChainAware’s marketing agent calculates each connecting wallet’s behavioral profile from on-chain data — experience level, protocol history, predicted intentions — and generates personalized messages that resonate with that specific user. Instead of every visitor seeing the same generic banner, each user sees a message tailored to what they are likely to want to do next. This resonance drives higher engagement, longer session duration, and better conversion rates. The result is a significant reduction in cost-per-transacting-user compared to mass broadcast approaches like KOLs and crypto ad networks. For measured results, see the SmartCredit case study.

What makes ChainAware’s AI cross-category in Web3?

Every Web3 project — regardless of category — needs two things: users and security. Marketing agents reduce user acquisition cost in every category. Transaction monitoring agents improve security in every category. These are not DeFi-specific problems; they are universal Web3 problems. Consequently, ChainAware’s infrastructure applies to gaming, NFTs, payments, gambling, wallets, and every other Web3 domain — not just DeFi. This cross-category applicability is what Martin calls “the real AI revolution”: the same agent infrastructure benefiting every existing Web3 domain simultaneously. For more on ChainAware’s full agent ecosystem, see the MCP integration guide.

Is the free ChainAware analytics useful for DeFi projects?

Yes — the free Web3 User Analytics dashboard is the starting point for any DeFi project wanting to understand its actual user base. It shows the behavioral profile of connecting wallets across eight dimensions: intentions, experience levels, risk profiles, protocol history, fraud distribution, and more. Many DeFi teams discover that their assumed user base (e.g. experienced DeFi participants) and their actual user base (e.g. low-risk retail traders) are completely different — which fundamentally changes marketing and product strategy. The free analytics tier is available to any DeFi project via Google Tag Manager integration. See the complete analytics guide to get started.

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This article is based on X Space #30 hosted by ChainAware.ai co-founders Martin and Tarmo. Watch the full recording on YouTube . For questions or integration support, visit chainaware.ai.

The post DeFAI Explained: How AI Agents Are Transforming Decentralized Finance first appeared on ChainAware.ai.

X Space with Plena Finance — ChainAware co-founder Martin joined Plena Finance’s PlanarPod to discuss predictive AI for Web3 growth and security. Listen to the full X Space recording

Predictive AI is the most misunderstood concept in Web3 today. Most projects claiming to use AI are wrapping OpenAI’s API with a few lines of prompt. They call it an AI agent. They call it intelligent. However, they cannot predict fraud, cannot power a marketing agent, and cannot detect a rug pull — because large language models are not designed for any of these tasks. In this X Space with Plena Finance, ChainAware co-founder Martin explains exactly what predictive AI is, why it is fundamentally different from LLMs, how blockchain data makes it uniquely powerful, and what the two most important use cases are for every Web3 project building for sustainable growth.

Attention AI vs Real Utility AI: The Market Cleanup That Had to Happen

The X Space opens with a candid market assessment. Martin’s view is direct: the AI agent market is flooded with projects grabbing attention without delivering utility. These projects — “attention AI” in ChainAware’s terminology — label themselves as AI-driven, follow the narrative, and raise capital based on trend-surfing rather than genuine technical capability. Their future is short. The correction that followed the AI hype wave was, in Martin’s words, not just expected but required.

“It’s a cleanup phase,” Martin explains. “People have to learn what real AI is, and that attention AI — projects just creating attention — have to face reality.” This framing matters for anyone evaluating Web3 AI projects. When almost every project in a category claims to use AI, the meaningful question shifts from “does it use AI?” to “does it use AI that produces measurable, verifiable results that competitors cannot easily copy?”

ChainAware’s answer to that question is a proprietary predictive AI stack — neural networks trained on blockchain behavioral data, not wrappers around OpenAI’s API. The distinction has technical and commercial consequences that Martin walks through in detail throughout the session. For more background on how this distinction maps to the broader Web3 AI landscape, see our guide on attention AI vs real utility AI and our complete breakdown of real AI use cases for Web3 projects.

LLMs vs Predictive AI: The Critical Technical Difference

The most technically important section of the X Space is Martin’s explanation of why LLMs cannot do what predictive AI does. This is not a marketing claim — it is a structural architectural fact, and understanding it changes how you evaluate every AI-claiming project in Web3.

What LLMs Actually Are

Large language models are statistical autoregression engines. They process enormous text datasets and learn to predict the most probable next word — or sequence of words — given a preceding input. They do not understand meaning. They do not reason about causality. They produce statistically likely output based on patterns in training data. As Martin describes it: “LLMs don’t understand what they are saying. They’re just producing output. It’s statistical autoregression based on the previous input.”

This architecture makes LLMs excellent at certain tasks: generating text, summarizing content, writing smart contract code templates, answering knowledge-based questions, creating Twitter posts. Consequently, content generation, code generation, and chatbot tasks suit LLMs well. Furthermore, the barrier to building LLM-based products is low — three to five lines of prompt code, an API key, and a website. This is precisely why 95% of “Web3 AI” projects are LLM wrappers. They require minimal technical investment, which also means they create no defensible competitive advantage.

What Predictive AI Does Instead

Predictive AI uses dedicated neural networks trained on domain-specific labeled data to forecast future events and behaviors. Rather than predicting the next word in a sequence, it predicts the next action of a wallet address, the probability that a contract will rug pull, or whether a user is likely to borrow, lend, trade, or buy NFTs. These predictions carry measurable accuracy scores, can be backtested against historical data, and improve continuously as the models retrain on new behavioral data.

Critically, you cannot substitute an LLM for a predictive model in behavioral tasks. Martin is explicit: “With LLMs you cannot create marketing agents because with LLMs you cannot predict behavior. With LLMs you can generate content. If you want to go into marketing agents or transaction monitoring agents, you have to develop your own models. It’s an unavoidable step.” This means any project claiming to offer behavioral targeting, fraud prediction, or rug pull detection using only LLMs is either mistaken or misleading. For the full technical breakdown, see our article on which AI use cases Web3 projects can actually integrate via API.

Property	LLMs (e.g. ChatGPT, Claude)	Predictive AI (ChainAware)
Core mechanism	Statistical autoregression on text	Neural networks trained on behavioral data
Output type	Text, code, summaries	Probabilities, scores, predictions
Accuracy measurement	Not measurable — may hallucinate	Measurable, backtested (98% fraud, 80%+ behavioral)
Can predict fraud?	No	Yes — 98% accuracy
Can power marketing agents?	No — cannot predict behavior	Yes — predicts then generates
Can detect rug pulls?	No	Yes — before they happen
Build barrier	Low — 3–5 lines of prompt	High — years of model development
Competitive moat	None — easily copied	Strong — proprietary models + training data
Improves over time	No — static model	Yes — continuous retraining on new data
Requires own models	No — API wrapper	Yes — mandatory

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Why Blockchain Data Is Better Than Google’s Browsing History

Before explaining how ChainAware’s models work, Martin addresses a question that many people implicitly ask: what makes blockchain data good enough to build predictive models on? The answer is more compelling than most realize — and it comes from a direct comparison to the data Google uses for its advertising system.

Google’s advertising targeting relies on two primary data sources: browsing history and search history. These signals are useful but imprecise. Browsing history captures what pages a person visits — not what they intend to do, how much money they have, or what financial decisions they are considering. Search history is somewhat better as a signal of intent, but it remains ambiguous. Someone searching “crypto lending” might be a professional researcher, a curious student, or an active DeFi user — the signal does not distinguish them reliably.

Blockchain data is fundamentally different. Every transaction on a blockchain reflects a real financial decision that the person made. They thought about the amount, the counterparty, the protocol, the timing, and the risk. They executed the transaction because they intended to. Consequently, blockchain transaction history captures a person’s actual financial intentions and behaviors with extraordinary precision. As Martin explains: “People are thinking before they’re doing financial transactions. Because they are thinking, this part of their thinking leaves patterns on the blockchain.”

The Signal Quality Advantage

Additionally, blockchain data is permanent and tamper-proof. A browsing history can be cleared, a VPN can mask it, and cookies can be blocked. An on-chain transaction history cannot be altered or hidden. Every protocol interaction, every borrowing position, every yield farming deposit, every NFT purchase — all permanently recorded, publicly accessible, and freely available for analysis.

This combination of permanence, financial significance, and public availability makes blockchain data a uniquely powerful foundation for behavioral prediction models. Furthermore, the data is free — no licensing fees, no data partnerships required. Any organization building predictive models on blockchain data starts with a dataset that is higher quality than what Google uses for AdWords, at zero marginal data cost. For a deeper look at how ChainAware uses this data across 8 blockchains, see our Web3 behavioral user analytics guide.

How Predictive AI Models Actually Work

Martin provides a clear, non-technical explanation of how ChainAware’s predictive models are built — covering the training process, the data labeling approach, and why the result is meaningfully different from anything achievable with LLMs.

The core process involves training dedicated neural networks on two categories of labeled behavioral data. The first category is “good behavior” — wallet addresses with established histories of legitimate DeFi activity: responsible borrowing, protocol participation, normal trading patterns. The second category is “bad behavior” — wallet addresses associated with confirmed fraud, scam activity, hacking, phishing, and rug pull execution. These bad-actor addresses left behavioral patterns on-chain in the weeks and months before their attacks. Those patterns become the training signal for detecting future fraud.

The resulting model does not check a list of known bad addresses — it identifies behavioral patterns that match the pre-fraud signature, even for addresses that have never been flagged before. This is why ChainAware’s fraud detection is genuinely predictive rather than reactive. A conventional AML system checks whether an address appears on a sanctions list. ChainAware’s system predicts whether a new address is exhibiting the same behavioral patterns that confirmed fraudsters exhibited before they committed fraud. For more on the distinction between these approaches, see our guide on crypto AML vs transaction monitoring and our analysis of forensic vs AI-based blockchain analytics.

Predictive Fraud Detection: 98% Accuracy Before It Happens

ChainAware’s fraud detection model was launched on February 4, 2023 — at the time of this X Space, exactly two years prior. The 98% accuracy figure is not self-reported but backtested against CryptoScamDB, an independent database of confirmed crypto scam addresses and events. This means the model correctly identified fraudulent behavior 98% of the time on data it had never seen during training.

The practical implications for DeFi protocols, NFT platforms, and Web3 projects are significant. Consider the standard approach to security: deploy smart contract audits, display security badges, and hope that sophisticated attackers don’t find exploits the auditors missed. This approach addresses code-layer vulnerabilities but ignores the most common attack vector — malicious users who interact with otherwise secure protocols.

Stopping Fraud at the Wallet Connection Event

ChainAware’s fraud detection integrates at the wallet connection event — the moment a user connects their wallet to a DApp, before any transaction occurs. At that moment, the model scores the connecting wallet address for fraud probability. If the score exceeds the configured threshold, the platform can block the connection, shadow-ban the user, apply tiered restrictions, or trigger an alert to the compliance team — all before any damage can occur.

Moreover, the model operates continuously. A wallet that passes a fraud check today might develop suspicious behavioral patterns over the following weeks. ChainAware’s transaction monitoring agent watches for these changes and sends real-time notifications when a previously clean wallet begins exhibiting pre-fraud signals. This combination — predictive screening at entry plus continuous monitoring — is the complete security picture that blockchain transactions’ irreversibility demands. For the full technical integration guide, see our AML and transaction monitoring integration guide and the Transaction Monitoring Agent guide.

Rug Pull Detection: The 95% PancakeSwap Reality

Martin introduces one of the most striking statistics in the X Space: approximately 95% of liquidity pools on PancakeSwap end in a rug pull. This is not fringe activity — it is the overwhelming norm for new token launches on one of the largest DeFi platforms in the world. Consequently, the default outcome for a new token investor on PancakeSwap is a total loss.

The mechanism is social engineering at scale. Scam factories — organized groups running coordinated fraud operations — create Telegram groups that attract new Web3 users. These newcomers receive “tips” about promising new tokens, join the channels, buy the tokens, and almost always lose everything when the liquidity is pulled. Martin is direct: “The buyers are the newbies. They’re getting social engineered into these Telegram channels and buying. In 95% of cases you end up with rug pulls.”

What Rug Pull Prediction Looks Like

ChainAware’s rug pull detection model analyzes the contract address, the liquidity pool structure, the creator wallet’s behavioral history, and trading patterns to predict whether a contract will execute a rug pull. Importantly, this is prediction — not a rules-based checklist. The model identifies behavioral signatures that confirmed rug-pull contracts exhibited before the event, not just known vulnerability patterns in the code.

At the time of the X Space, the model predicts whether a rug pull will occur but not precisely when. Martin acknowledges this as a future development goal: “We don’t have the ability to say when the rug pull happens. Maybe in the future we will build our model so we can see the timeframe when it will happen.” This planned capability — not just detecting rug pull probability but predicting timing — would give investors and platforms an even more actionable early warning system. Free rug pull checks are available at chainaware.ai/rug-pull-detector. For the complete methodology, see our Rug Pull Detector guide and our guide on how to identify fake crypto tokens.

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Marketing Agents: One-to-One Personalization Without Cookies

The second major application of ChainAware’s predictive AI — alongside fraud and rug pull detection — is behavioral marketing. Martin describes the current state of Web3 marketing as “1930s style”: every visitor to every Web3 platform sees the same message, regardless of who they are, what they have done on-chain, or what they are likely to do next. The result is low conversion, low engagement, and enormous user acquisition costs.

ChainAware’s marketing agents change this by predicting each connecting wallet’s behavioral profile and generating personalized content that resonates with their specific intentions. The process has two stages that work in sequence.

Stage One: Behavioral Prediction

At wallet connection, the predictive model analyzes the wallet’s complete on-chain history and calculates its behavioral profile. Some wallets are active borrowers. Others are NFT collectors, yield farmers, high-frequency traders, cautious long-term holders, or complete newcomers. Each profile implies different intentions, different risk tolerance, and different messaging that will resonate. The model does not ask the user to fill out a preferences form. It reads their blockchain history and infers their profile automatically.

Stage Two: Content Generation

Once the behavioral prediction is complete, the generative layer creates personalized content matched to that profile. This is where generative AI (including LLMs) appropriately enters the process — not to predict behavior, but to generate text that speaks to the predicted intentions. A yield-farming-oriented wallet visiting a lending protocol sees messaging about yield optimization opportunities. A newcomer wallet sees explanatory content that reduces friction. A high-experience DeFi user sees advanced product features. Each message targets that specific user’s likely next action.

Martin describes the outcome: “Web3 visitors start to see resonated content. It’s not one content for everyone — it’s one-to-one content for everyone. People start to get much more attached to the websites using our technology. Because this resonance creates attachment.” Higher attachment leads to longer sessions, higher conversion, and better retention — the metrics that determine whether a Web3 project can build sustainable revenue. For a detailed walkthrough of how this works in practice, see our Web3 behavioral user analytics guide, the guide to personalization as the next big AI agent opportunity, and the SmartCredit case study showing 8x engagement and 2x conversions.

The Google AdTech Parallel: Why Web3 Is Where Web2 Was

Throughout the conversation, Martin returns to a historical parallel that explains both the opportunity and the urgency for Web3 marketing transformation. Before Google invented AdWords, online advertising was mass broadcasting. Companies bought banner ads, newspaper placements, and billboard space to drive traffic. Cost per acquisition was extremely high. Conversion was extremely low. The economics barely worked, even for well-funded companies.

Google changed everything by using search history to predict user intent and match ads to that intent at the moment of expression. The result was a dramatic collapse in cost per acquiring a transacting user — from hundreds of dollars to $15–$30 in mature markets. This enabled the online economy to scale. Companies that adopted Google AdWords early gained compounding advantages in growth economics over competitors that remained on broadcast advertising.

Web3 is in the pre-AdWords phase right now. Every project uses the same broadcast approach: KOLs, airdrops, Telegram groups, crypto ad networks. Conversion is abysmal. User acquisition costs are $1,000–$3,000 per transacting user in DeFi. The unit economics are structurally negative for almost every protocol.

Blockchain History as a Superior Signal

ChainAware’s behavioral targeting uses blockchain transaction history as the targeting signal — which Martin argues is actually superior to search history. “Google uses the browsing history and the search history to predict behavior. In Web3, we have a blockchain. All the data is there. We predict behavior based on blockchain history.” The deliberate, financial nature of blockchain transactions means this signal carries far more predictive weight than browsing data. Consequently, the targeting precision available to Web3 projects that adopt behavioral AI is higher than what Google achieved with search data — if they build the infrastructure to use it. For more on how this transforms user acquisition economics, see our guide on ChainAware’s AI agent roadmap for Web3 businesses.

AI Use Cases in DeFi, NFTs, and Portfolio Management

Beyond fraud detection, rug pull prediction, and marketing agents, Martin covers several additional AI use cases that are emerging in DeFi and the broader Web3 ecosystem. Each one represents an existing DeFi function where AI-driven decision making adds measurable value over human judgment or static rules.

Trading Agents

Trading agents represent the most discussed AI use case in crypto. Martin draws on his experience at Man Investments — the largest independent hedge fund in the world at the time, managing $20 billion in automated trading systems two decades ago — to contextualize the competition. Professional algorithmic trading is not a new field. Retail traders competing with institutional systems face very high odds against them. AI-based pattern recognition can improve those odds by identifying market structure patterns that rules-based systems miss, but the competitive bar is already extremely high. Additionally, most current Web3 “trading bots” are rules-based, not AI-based — the “AI” label is often applied to if/then logic that predates machine learning.

Portfolio Optimization Agents

Portfolio optimization agents address a different and arguably more accessible problem: risk-adjusted asset allocation. People have fundamentally different risk tolerances. Some accept high volatility for high expected returns. Others need stability. A portfolio optimization agent continuously monitors a user’s holdings, rebalances when allocations drift from target ratios, and applies risk management logic appropriate to the user’s profile — without requiring the user to manually track positions across multiple protocols. This is effectively an automated version of the wealth management service that high-net-worth clients receive at banks like Credit Suisse, now accessible to any Web3 user.

NFT Market Intelligence

In the NFT sector, predictive AI applies to collection valuation, trait rarity scoring, and market trend prediction. Rather than relying on floor price as the primary signal, behavioral models can analyze trading patterns, wallet holder profiles, and collection liquidity dynamics to provide more nuanced intelligence about asset value and market direction. As NFT markets mature beyond speculative peaks, these tools become increasingly relevant for collectors and platforms managing inventory risk.

Smart Contract Review: Where LLMs Hit Their Limit

Smart contract review is a use case where Martin explicitly acknowledges the role of AI tools — while also identifying a hard technical ceiling on what LLMs can achieve. Using AI tools for initial smart contract screening is genuinely useful. LLMs can identify common vulnerability patterns: reentrancy issues, integer overflow risks, access control gaps. For preliminary pre-audit screening, they accelerate the process and reduce the manual work required of human auditors.

However, the ceiling is real. “Hackers are very advanced,” Martin notes. “You can do some pre-screening, but there is again a limit to how far LLMs can go. LLMs do not understand what they are doing — it’s statistical autoregression.” The most sophisticated exploits — flash loan attacks, oracle manipulation, MEV extraction — arise from the interaction between contracts and external real-time conditions that no static code analysis can reliably anticipate. As a result, AI-augmented auditing improves baseline security but does not replace expert human review for high-value protocols.

Furthermore, the more critical security layer is what happens after deployment — who interacts with the contract. A perfectly audited contract can still be exploited if it serves malicious users whose wallets have been pre-screened as fraudulent by ChainAware and ignored. Address-level screening complements code-level auditing. Neither alone is sufficient. For the detailed argument on why address monitoring matters more than contract monitoring for preventing major hacks, see our complete DApp AML and transaction monitoring guide.

The Two Core Problems Every Web3 Project Must Solve

Martin’s conclusion in the X Space distills everything into a simple framework that applies to every Web3 project regardless of category. Two problems determine whether a project survives long enough to become sustainable. Solve both and the project has a viable growth trajectory. Fail at either and the project faces structural headwinds that no amount of token incentives or marketing spend can overcome.

Problem 1: User Conversion

Every Web3 project needs transacting users — not visitors, not token holders, not Telegram community members. Transacting users who actively use the protocol generate revenue. Without revenue, even well-funded projects eventually close. Getting from visitor to transacting user requires resonance. The user must feel that the platform understands their goals and speaks to their intentions. Currently, almost no Web3 project achieves this because all use mass broadcast messaging. ChainAware’s marketing agents solve this with behavioral targeting at the wallet connection event — the first point where a visitor becomes an identifiable potential user. For more on how to deploy this, see the guide on why 90% of connected wallets never transact and how AI agents fix it.

Problem 2: Trust and Security

The second problem is fraud. Every Web3 project interacts with a user base that contains a meaningful percentage of bad actors — fraudsters, scammers, rug pull operators, and exploiters. Without active screening, these actors cause direct financial damage, reputational damage, and regulatory exposure. ChainAware’s fraud detection and transaction monitoring solve this at the address level — not through code auditing, but through behavioral prediction of the humans behind the wallets. As Martin summarizes: “These are the core issues. User conversion — that’s the marketing. Every Web3 project needs it. And fraud — that’s the other one. These are the core issues.” For a complete view of how both problems connect to the Web2→Web3 growth transition, see our X Space recap on AI agents for Web3 businesses.

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Comparison Table: LLMs vs Predictive AI for Web3

Use Case	LLM Suitable?	Predictive AI Suitable?	ChainAware Tool
Fraud detection	No — cannot predict behavior	Yes — 98% accuracy	Fraud Detector
Rug pull detection	No	Yes — before it happens	Rug Pull Detector
1:1 marketing agents	No — needs prediction first	Yes — predict then generate	Growth Agents
Transaction monitoring	No	Yes — continuous behavioral watch	Transaction Monitoring Agent
Credit scoring	No	Yes — 4+ years live	Credit Scoring Agent
Behavioral analytics	No	Yes — 8 dimensions	Web3 User Analytics (free)
Content generation	Yes	Partial (step 2 of marketing)	Growth Agents (combined)
Smart contract code	Yes — with limitations	N/A	—
Smart contract pre-screening	Partial — limited depth	Better — behavioral analysis	Fraud Detector
Portfolio optimization	No	Yes — risk-adjusted rebalancing	Risk Monitoring Agent
Trading signal generation	No — not pattern recognition	Yes — ML on market data	External integration

Frequently Asked Questions

Why can’t LLMs predict fraud or power marketing agents?

LLMs are statistical autoregression models — they predict the most probable next word given a preceding input. They are optimized for language patterns, not behavioral prediction. Predicting whether a wallet will commit fraud requires a neural network trained on labeled good/bad behavioral examples — a fundamentally different architecture with a fundamentally different training process. Feeding blockchain data into an LLM and asking it to detect fraud produces unreliable results because the model was never designed for this task. Marketing agents require behavioral prediction first (to know what the user intends) and content generation second (to craft a message matching that intent). LLMs can only perform the second step. For the full explanation, see our guide on real AI use cases for Web3 projects.

How is 98% fraud detection accuracy measured?

ChainAware’s 98% accuracy is backtested against CryptoScamDB — an independent database of confirmed crypto scam addresses and events. The model is tested on labeled data it has never seen during training. Of wallets the model flags as fraudulent, 98% are confirmed as fraudulent in the ground truth dataset. This is a standard machine learning validation methodology. It is not self-reported performance but independently verifiable against a third-party database. For more on how this compares to traditional AML approaches, see our Fraud Detector complete guide.

What percentage of PancakeSwap pools are rug pulls?

According to ChainAware’s analysis, approximately 95% of PancakeSwap liquidity pools end in a rug pull. This reflects the reality of new token launches on high-volume, permissionless DEX platforms where creating a liquidity pool requires minimal effort and the social engineering infrastructure (Telegram groups, influencer promotion) to attract victims is well-developed. New Web3 users are disproportionately targeted because they lack the experience to identify rug pull indicators. ChainAware’s rug pull detector provides free contract analysis to help users check any pool before depositing at chainaware.ai/rug-pull-detector.

What makes blockchain data better for behavioral prediction than browsing data?

Blockchain transactions reflect deliberate, financially significant decisions. Every on-chain transaction required the user to think about amount, counterparty, risk, and timing — then actively execute the transaction. Browsing history, by contrast, captures passive page visits that may have no intentional significance. Furthermore, blockchain data is permanent and tamper-proof — it cannot be cleared, masked, or manipulated. Additionally, it is freely and publicly accessible with no licensing fees. The combination of high signal quality, permanence, and zero data cost makes blockchain behavioral data uniquely powerful for prediction models.

Does ChainAware use OpenAI or other LLM providers?

No. ChainAware builds its own proprietary neural networks trained on blockchain behavioral data. It does not wrap OpenAI, Anthropic, or any other LLM provider for its core prediction capabilities. The fraud detection model, rug pull detection model, behavioral profiling model, and credit scoring model are all proprietary — developed and trained by ChainAware’s team over multiple years. Generative AI may be used in the second stage of marketing agent content generation, but the critical predictive layer is entirely in-house. This is what creates the competitive moat that LLM-based competitors cannot replicate by switching API providers.

How does ChainAware’s free tier work?

ChainAware offers several free products. The Fraud Detector and Rug Pull Detector are free for individual wallet and contract checks at chainaware.ai/fraud-detector and chainaware.ai/rug-pull-detector — no signup required. The Web3 User Analytics dashboard is free forever for any Web3 project that integrates via Google Tag Manager — showing aggregate behavioral profiles of all connecting wallets across eight dimensions. Enterprise products — marketing agents, transaction monitoring, credit scoring — are subscription-based. See chainaware.ai/pricing for full details.

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This article is based on the ChainAware x Plena Finance X Space. Listen to the full recording on X . For questions or integration support, visit chainaware.ai.

The post Predictive AI for Web3: Growth and Security Without LLM Wrappers first appeared on ChainAware.ai.

X Space #29 — ChainAware co-founders Martin and Tarmo. Watch the full recording on YouTube · Listen on X

This is X Space #29 — and the topic cuts straight to the most important question in Web3 AI right now: what separates real AI from fake AI? Not in abstract technical terms, but in the concrete, practical language that any founder, investor, or user can apply immediately to every project they encounter. Martin and Tarmo, co-founders of ChainAware.ai, have spent 25–30 years each in technology — building natural language processing systems, designing banking infrastructure at Credit Suisse, running AI models in production for over four years. Their framework for distinguishing attention AI from real utility AI is grounded in that experience, not in white papers.

What Is Attention AI — and Why It Flooded the Market

Attention AI is a term Martin and Tarmo coined to describe a specific and widespread phenomenon in the Web3 AI market. It refers to projects that use AI terminology, AI narratives, and AI branding to capture investor attention and generate positive emotions — without building actual AI products that create real value for real users.

The definition is precise: attention AI targets investors’ emotions, not users’ needs. Tarmo explains the mechanism clearly: “You explain something to investors that they don’t understand, and then they have your attention. You give them every day, every week, more attention. You explain cool stuff, sci-fi, technologically unfeasible things, dreams. They get very strong positive emotions, they invest, and they have attention. Emotions — not products.”

The scale of this phenomenon is visible in the data. When ChainAware first appeared on CoinGecko’s AI category list, there were approximately 20 projects. By the time of X Space #29, that number had grown to 447. Most of these projects did not appear because they built real AI products — they appeared because they successfully attached AI terminology to their narratives. Furthermore, when Tarmo and Martin analyzed the original list of around 120 projects, only 20 had running products of any kind — and some of those were barely AI-related. The ratio of narrative to substance was approximately 5:1 in favor of attention.

Attention AI is not a new phenomenon — it is the same dynamic that previously played out with blockchain supply chain projects, DAOs, and NFTs. Each cycle generates a wave of projects that combine credible technology with incredible claims, attract capital during the hype phase, and then collapse when investors realize there are no products. The AI cycle, however, is larger than previous cycles because AI genuinely is an unstoppable megatrend — making the attention AI problem both more pervasive and more dangerous. For more on the X Space #28 discussion that preceded this, see our earlier article on predictive AI for Web3 growth and security.

The AI Market Correction: What It Was Really Correcting

One of the most clarifying insights in X Space #29 is the reframing of the AI market correction that happened in early 2025. Many observers interpreted the sharp decline in AI token prices as evidence that AI in crypto was overhyped as a category. Martin and Tarmo argue this interpretation is wrong — and importantly, that misinterpreting the correction leads to the wrong investment conclusions.

The correction was specifically an attention AI correction. Projects that captured investor emotions without building products collapsed because investor sentiment corrected toward substance. Real utility AI — projects with running products, proprietary models, and measurable outcomes — did not face the same correction because their value is anchored in utility, not narrative. As Martin notes: “It’s a correction of attention AI, of these projects who were creating narratives. It’s not a correction of real AI.”

Ignite Capital, a venture capital firm whose analysis both Martin and Tarmo reference approvingly, articulated this same thesis. Their documentation provided external validation that the attention AI vs real utility AI distinction was not just an internal ChainAware framework but a recognized analytical lens in serious investment circles. Additionally, Martin makes a sharp observation about investor behaviour during this period: many retail investors who had avoided meme coins because they “had no utility” moved into AI tokens believing they were choosing the safer, more substantive category. In reality, much of the AI category was simply a more sophisticated version of the same attention-based narrative economy.

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The Full List: Attention AI Narratives and Why They Don’t Work

Martin and Tarmo use DeepSeek to generate a comprehensive list of attention AI narrative categories — the recurring “buzzword bingo” combinations that attract investment without delivering utility. Going through this list is instructive because it reveals the specific technical and business reasons why each category fails, not just the assertion that it does.

Decentralized AI Marketplaces

Over 100 decentralized AI marketplaces exist, claiming to connect AI model producers with AI consumers in a decentralized way. The ratio problem is immediately obvious: there are roughly 20 companies producing genuinely proprietary AI models worldwide in the Web3 space — and over 100 marketplaces waiting to list them. The supply of real product is dwarfed by the infrastructure claiming to distribute it. Furthermore, the “products” these marketplaces propose to list — data sets, computational resources, AI models — are not actually decentralizable in the ways their narratives suggest. Data sets require provenance and quality assurance. Computational nodes require proximity for performance. AI models run as unified neural networks, not distributed tokens.

AI-Driven Smart Contracts

The narrative: use AI to make smart contracts “dynamic and adaptive.” Tarmo identifies this as “total technological science fiction.” Smart contract integration with external APIs remains one of the hardest unsolved problems in blockchain engineering — oracle problems, timing issues, and determinism requirements make even basic API calls technically complex. Layering AI decision-making on top of these unresolved integration challenges produces not dynamic contracts but elaborate failure modes. Additionally, LLM-generated smart contract code introduces entropy: the longer the generated artifact, the more errors accumulate, and none of them pass security audits reliably.

Data Privacy AI + Blockchain

The narrative: use blockchain to secure AI training data. Martin’s response is direct: “You don’t need blockchain to secure training data. You put them into a secure file system, you encrypt them.” This category combines two technologies without identifying a problem that requires both. It creates the impression of technical sophistication by stacking terminology, but the actual security problem has standard solutions that predate blockchain by decades.

AI-Optimized Blockchain Networks

The narrative: use AI to optimize consensus protocols, solve scalability, and improve efficiency. This category targets people who don’t understand that consensus protocols are deterministic systems with specific mathematical properties — properties that AI optimization algorithms cannot improve without fundamentally changing what the consensus mechanism guarantees. Tarmo identifies this as combining “CSP words” — complex-sounding phrases — for the sole purpose of generating investor attention.

Tokenized AI Assets

The narrative: tokenize AI intellectual property, AI models, or AI datasets on-chain. Martin challenges this directly: “If you are an AI model producer, you let others subscribe to your AI models. You don’t need to tokenize your AI at all.” A neural network running inference is a single computational process — it cannot be meaningfully distributed across token holders without destroying the performance that makes it valuable. Tokenizing an AI model is equivalent to tokenizing a software process: the tokenization adds complexity and friction without adding capability.

DAOs with AI Governance

The narrative: use AI to improve DAO decision-making and governance. Martin applies Bill Gates’s maxim: “If you take a process that is inefficient and automate it, you get automated inefficiency.” DAOs have well-documented governance problems — low participation, plutocratic dynamics, coordination failures. Adding AI to a non-functioning governance process does not fix the governance problem. It simply adds technical complexity to an already dysfunctional system.

Supply Chain Transparency with AI and Blockchain

Supply chain projects have been a recurring attention AI category for five years — first with blockchain alone, now with blockchain plus AI. The fundamental problem remains unchanged: international trade documentation is legally required to use wet signatures. As long as physical signature requirements exist in trade law, the supply chain cannot achieve the 100% digitalization that would make blockchain transparency meaningful. Technology does not solve regulatory and legal constraints.

Decentralized AI Training

The narrative: distribute AI training across decentralized compute nodes. Tarmo identifies the fundamental architectural contradiction: “All industries are now building computing centers to have compute nodes as close together as possible. The new processor architecture merges memory and compute nodes. And now we want to separate them?” Modern AI training performance depends critically on memory bandwidth and inter-node communication latency. Decentralization, by definition, increases both. The result is not decentralized AI training — it is inefficient AI training with a compelling story.

AI for Cross-Chain Interoperability and Healthcare

Both categories follow the same pattern: take a real, hard problem in a large industry, add AI and blockchain as proposed solutions, and create a narrative. Healthcare AI is real — but it has nothing to do with blockchain, because healthcare data has military-grade security requirements that make public blockchain storage legally impossible in most jurisdictions. Cross-chain interoperability is also a real problem, but the proposed AI solutions are undefined beyond the combination of words.

The LLM Problem: Why Prompt Engineering Is Not AI

A theme running through the entire X Space is the distinction between LLM-based products and genuine AI. This distinction matters enormously for investors and founders evaluating AI projects, because it determines whether a project has a defensible competitive position or is one copy-paste away from obsolescence.

What LLMs Enable

LLMs — large language models like OpenAI’s GPT series, Google’s Gemini, and Anthropic’s Claude — are powerful tools for specific tasks: generating text, summarizing content, writing code templates, answering questions, creating marketing copy. In the Web3 context, they enable rapid creation of Telegram bots, Discord bots, smart contract templates, Twitter content, and chatbot interfaces. All of these have genuine utility. None of them create competitive advantage.

Martin explains why: “LLM means you’re creating a prompt. Anyone can use OpenAI or Gemini. You create a prompt, test it, adjust it until you get the output you want. There is no competitive advantage. It’s just prompt engineering with a user interface on top.” The moment a product’s core functionality is an LLM prompt, that product can be replicated in hours by any developer with an API key. Consequently, any business built exclusively on LLM wrappers has no moat, no defensibility, and no long-term competitive position.

What Proprietary Models Enable

Proprietary ML models — neural networks trained on domain-specific data — create genuine competitive advantages because they are not replicable without the training data, the model architecture choices, and years of iterative development. ChainAware’s fraud detection model was first launched in February 2023 and has been continuously retrained on blockchain behavioral data ever since. The credit scoring model has been running for over four years. Replicating these models requires not just the architecture but the labeled training data, the validation methodology, and the production track record.

Furthermore, proprietary models improve continuously. Each new on-chain event adds signal to the training data. Each production deployment generates feedback that improves the next model version. This compounding improvement is unavailable to LLM wrappers, which are fundamentally limited by whatever the LLM provider chose to train their model on. For the full technical breakdown of why predictive AI differs fundamentally from LLMs, see our guide to predictive AI for Web3 and the complete breakdown of real AI use cases for Web3 projects.

What Real Utility AI Looks Like

Tarmo provides a clean definition that cuts through all the narrative complexity: “Utility AI has value for users who use it. We are not talking about investors — we are talking about users who use it. They get a benefit from it.” This user-centricity is the core distinguishing feature. Attention AI is designed to generate investor attention. Real utility AI is designed to create user value.

Beyond user value, real utility AI requires competitive advantage. A product that uses LLMs to generate content has user value — but no competitive advantage, because anyone can build the same thing. Real utility AI combines user value with proprietary technology that competitors cannot easily copy. As Tarmo puts it: “If you don’t have competitive advantage, then someone else will do it. Even with big marketing power, they will do it — maybe faster, maybe bigger. There is no protection.”

Additionally, real utility AI requires a live, working product — not a white paper, not a roadmap, not a token launch. Martin and Tarmo are explicit about ChainAware’s own positioning: “We don’t have a white paper. We just have products. We don’t write white papers — we have no time. People ask where is your white paper. We say: use our product.” This is not false modesty — it is a deliberate prioritization of delivery over documentation, of working software over speculative narratives. For more on how this philosophy applies to the specific products ChainAware has built, see our Web3 behavioral user analytics guide.

ChainAware’s Live AI Products: What Real Utility Looks Like in Practice

Throughout X Space #29, Martin and Tarmo walk through ChainAware’s live product portfolio as a concrete illustration of what real utility AI delivers. Each product addresses a specific, verifiable problem with a proprietary solution that produces measurable outcomes. None of them exist as white papers — all are in production.

AI Marketing Agent — 8x Conversion Improvement

The marketing agent addresses the most expensive problem in Web3 growth: converting website visitors into transacting users. It analyzes each connecting wallet’s on-chain history, predicts their behavioral intentions, and generates personalized content that resonates with those specific intentions. The result is a reported 8x improvement in conversion ratio for integrated platforms. Critically, this requires proprietary predictive models — not LLMs — because predicting future user behavior from blockchain data requires a neural network trained specifically on that task. For full details, see our AI agents for Web3 businesses guide and the guide to why personalization is the next AI agent opportunity.

Transaction Monitoring Agent — Compliance and Fraud Prevention

The transaction monitoring agent continuously watches wallet addresses for behavioral patterns that indicate emerging fraud risk. It serves two audiences simultaneously: compliance teams at virtual asset service providers (VASPs) who must meet regulatory requirements under MiCA and similar regulations, and security teams who want to proactively exclude bad actors from their platforms. Real-time Telegram notifications alert operators when a previously clean wallet begins exhibiting pre-fraud behavioral signatures. For the technical integration guide, see our AML and transaction monitoring integration guide.

Credit Scoring Agent — Financial Ability Assessment

ChainAware’s credit scoring model has been running for over four years — the oldest component of the product suite. It calculates a composite credit score from a wallet’s on-chain transaction history, enabling DeFi lending protocols to assess borrower quality without KYC. This is the foundational technology that makes undercollateralized DeFi lending viable. For the complete guide, see our Web3 credit scoring guide.

Predictive Fraud Detector — Free, 98% Accuracy

The fraud detector predicts whether a wallet address will exhibit fraudulent behavior in the future — not what it has done in the past, but what it will do next. At 98% accuracy backtested on CryptoScamDB, it outperforms human compliance officer accuracy. It is free to use for individual checks and available via API for enterprise integration. For methodology details, see our Fraud Detector complete guide.

Rug Pull Detector — Predicting Before the Exit

The rug pull detector analyzes contract addresses to predict whether a pool will execute a rug pull. Tarmo emphasizes the key distinction: “It’s not a rug pull detector documenting a rug pull that has happened. We predict there will be a rug pull in the future.” Given that approximately 95% of PancakeSwap pools end in rug pulls, this tool provides critical protection for new users who lack the experience to identify rug pull patterns manually. See our Rug Pull Detector guide for full details.

Wallet Auditor — Beyond Fraud to Full Behavioral Profile

The wallet auditor goes beyond fraud detection to produce a complete behavioral profile of any wallet address: experience level, risk willingness, likely next actions (will this address borrow, lend, trade, use leverage?), protocol categories used, and more. Martin describes the use case: “If someone claims they have a lot of experience, check the address. Look — do they really have experience? Maybe yes, maybe not. It’s free to use — check it and share the results.” Additionally, this behavioral intelligence feeds directly into the marketing agent, allowing platforms to deliver precisely targeted messages to each user. For the full guide, see our behavioral user analytics guide.

Free Pixel + Analytics Dashboard

ChainAware offers a free analytics pixel — equivalent to Google Analytics but for Web3 behavioral intelligence — to any Web3 project. Rather than showing geographic user distribution, it shows the behavioral profile of wallet connections: user intentions, experience levels, risk profiles, and protocol categories. Martin describes a revealing client example: “One of our clients is a trading platform. But their users are actually NFT users. They were thinking they are targeting traders — their actual users are NFT collectors.” This kind of insight is only available from behavioral on-chain analysis, not from conventional web analytics. The free tier is available to any project at chainaware.ai/subscribe/starter. For the complete guide, see the Web3 behavioral analytics guide.

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The Four Questions Every Investor Must Ask

Martin and Tarmo close their attention AI analysis with a practical checklist for evaluating any AI project. These four questions apply equally to investment decisions, partnership decisions, and integration decisions. Tarmo frames the context: “If you invest into technology, you have to understand technology. Otherwise you will be scammed. Attention AI is an example — it promises everything. It is just a bubble.”

Question 1: Is There a Running Website?

The baseline test. A project without a running website exists only as a Telegram group, a Twitter account, and a promise. Remarkably, a significant portion of CoinGecko’s AI-listed projects do not have working websites at all. No website means no product, no team accountability, and no verifiable claim about what the project actually does.

Question 2: Is There a Running MVP?

Beyond a website, is there a minimum viable product — something that can actually be used to perform the function the project claims to perform? Many attention AI projects have websites with impressive graphics and whitepapers but no clickable, working product. A running MVP is the minimum evidence that the technology exists beyond its description. Moreover, for AI specifically, a running MVP enables verification of the accuracy claims — you can test it yourself.

Question 3: Does It Provide Real Value to Users?

This is the utility question. Who benefits from this product? Is there a specific, concrete problem that real users have, and does this product solve it measurably? Attention AI often describes benefit in abstract terms — “improves efficiency,” “enhances security,” “optimizes performance” — without identifying a specific user group, a specific problem, and a specific measurable outcome. Real utility AI says: “This wallet has a 98% probability of fraudulent behavior, and here is the verification methodology.”

Question 4: Is There a Competitive Advantage That Cannot Be Copied?

This is the moat question — and for AI specifically, it usually comes down to whether the core technology relies on proprietary models or LLM wrappers. If the answer is LLM wrapper, the competitive advantage is essentially zero: anyone with an API key and a week of development time can replicate it. If the answer is proprietary models trained on domain-specific data over years of production operation, the competitive advantage is substantial and compounding. Martin puts it plainly: “If you go to your MBA, spend two years on your MBA, just listen to what we are telling: every project needs a competitive advantage. If you don’t have it, it’s just copy-paste.”

The Social Psychology Behind Attention AI

One of the most revealing sections of X Space #29 is the explicit discussion of the psychological mechanisms that make attention AI work. Understanding these mechanisms is not just intellectually interesting — it is practically important because they operate regardless of technical knowledge. Even sophisticated investors fall for attention AI because the mechanisms exploit psychological patterns rather than technical ignorance.

The Serotonin Response

Tarmo describes the mechanism precisely: “Emotions, they get from it very strong emotions. And they like it and they invest. It’s all about giving cool positive emotions to investors.” When someone hears a phrase like “decentralized AI training on blockchain with cross-chain interoperability,” the brain processes it as sophisticated, future-oriented, and potentially transformative. Serotonin and dopamine responses fire before any critical evaluation occurs. By the time skepticism kicks in, the emotional investment has already happened.

Attention AI projects understand this mechanism and engineer their communications around it deliberately. They use specific word combinations — decentralized, autonomous, AI, blockchain, optimized, transparent — that trigger positive associations in their target audience. Consequently, the more technically jargon-dense the narrative, the more effective it is at generating emotional responses in people who don’t fully understand the terms but respond to the pattern of sophistication they suggest.

The 10x Psychology Trap

Martin identifies a specific psychological pattern that makes investors particularly vulnerable: the 10x/100x mindset. “Investors are looking for the hot thing. They are looking for the 10x, for the 100x. And if people are in this mode, they will get bombarded with attention AI.” The 10x mindset creates urgency — the fear of missing the next big thing — that overrides the slower, more deliberate evaluation that would reveal the absence of utility. Furthermore, the move from meme coins to AI tokens felt like a rational upgrade to many investors. Both categories turned out to produce similar outcomes for the same reason: narrative without utility.

Importantly, the people creating attention AI projects understand this psychology. Martin is direct: “The guys behind these projects — they know what they are doing. They know there is no real value. They know they are just creating a narrative to get retail, to get VCs, to get people to believe.” This asymmetry of knowledge between project creators and investors is the defining feature of the attention AI ecosystem. Protecting against it requires applying the four questions above rather than relying on the emotional response to a compelling narrative.

Real Utility AI Use Cases That Actually Work in Web3

Having catalogued what does not work, Martin and Tarmo turn to what does. The real utility AI categories they identify share specific characteristics: they address a problem that exists in the current Web3 ecosystem, they use AI in a way that is technically appropriate for the task, and they produce outcomes that can be verified and measured.

Fraud Detection and Transaction Monitoring

Predicting fraudulent behavior from blockchain transaction history is a perfect use case for predictive AI. The data is high-quality (financial transactions reflect deliberate thinking), the labels are verifiable (confirmed fraud cases are publicly documented on-chain), and the benefit is concrete (98% prediction accuracy before fraud occurs). Moreover, regulatory requirements under frameworks like FATF’s guidance on virtual assets make transaction monitoring a compliance obligation for VASPs — creating a stable, non-speculative demand for the product.

AI-Powered DeFi (DeFAI)

Combining AI with existing DeFi primitives — trading, lending, yield farming, portfolio management — produces genuine utility improvements. Trading agents using pattern recognition on price and on-chain data can outperform rules-based systems. Portfolio management agents applying Sharpe ratio optimization provide the kind of risk-adjusted return management that private banking clients pay substantial fees for. Risk monitoring agents protect individual positions from liquidation. Each of these applies predictive AI to a well-defined DeFi problem with a measurable outcome. For the full breakdown, see our DeFAI explained guide.

Regulatory Compliance and AML

AML (Anti-Money Laundering) monitoring is legally mandated for virtual asset service providers in the EU under MiCA and increasingly in other jurisdictions. AI-powered transaction monitoring that identifies suspicious behavioral patterns — not just checks against static AML lists — represents a genuine utility improvement over rules-based compliance systems. Additionally, the combination of AML screening with predictive fraud detection provides a more complete compliance picture than either alone. See our complete KYT and AML guide for DeFi for the full compliance architecture.

Web3 Marketing and Behavioral Targeting

One-to-one behavioral targeting using on-chain wallet data is a Web3-native marketing capability that has no equivalent in Web2. Google’s ad targeting uses browsing and search history. ChainAware’s marketing agent uses financial transaction history — a higher-quality signal that reflects actual financial behavior and intentions. The result is content that resonates with each specific user, creating attachment, engagement, and conversion at rates that mass-broadcast marketing cannot approach. For measured results, see the SmartCredit case study showing 8x engagement improvement.

Credit Scoring for DeFi Lending

Credit scoring from on-chain data enables undercollateralized DeFi lending — the mechanism that would unlock the majority of the global credit market for blockchain platforms. Without reliable credit scoring, DeFi lending must remain overcollateralized (borrowers post 150% to borrow 100%), which makes it capital-inefficient and inaccessible to most potential borrowers. AI-based credit scoring addresses this directly with a four-year production track record. See our DeFi credit score platform comparison for the full market landscape.

Comparison Table: Attention AI vs Real Utility AI

The New Wave: Utility AI as the Next Narrative

X Space #29 ends on a constructive note. Despite the extensive critique of attention AI, both Martin and Tarmo are unambiguous that AI itself is an unstoppable megatrend — and that the correction in AI token prices represents not the failure of AI but the market’s maturation toward recognizing genuine utility.

Tarmo’s closing thesis: “Utility AI is the new narrative. The old narrative was attention AI — get serotonin for your biochemistry, feel good, invest. The new narrative is utility AI, where you create a real benefit. It serves customers. It is about delivering running software to customers which brings value to customers.”

Martin identifies the specific transition mechanism: as investors understand what real AI can accomplish — having seen ChainAware’s products, having seen what the technology actually delivers — their expectations shift. They stop accepting narratives and start demanding evidence. “When the client sees the technologies, what we can do with AI, their world, their imagination is changing. It’s changing in the moment when you see what is possible. There is no way back. You just go forward.”

Furthermore, the transition from attention AI to utility AI is not just a market preference shift — it is a technological inevitability. Attention AI projects cannot improve because they have nothing to improve. Real utility AI projects improve continuously, compounding competitive advantage with every new data point, every new production deployment, every new client integration. Over time, the gap between attention AI and real utility AI widens until it becomes impossible to bridge with narrative alone.

The blockchain-specific version of this transition is particularly important. Tarmo identifies what makes Web3 uniquely suited for real utility AI: “Blockchain data is so perfect. And now: blockchain data with AI — that’s new. That’s where you get value added. Value added for real businesses, real utility.” Public on-chain data — free, permanent, high-quality, financially significant — is the raw material for AI models that cannot be built from any other data source. The moats that real utility AI builds in Web3 are therefore doubly defensible: proprietary models plus proprietary on-chain data insights that are available to all but interpreted well by very few. For more on how this plays out across all Web3 AI domains, see our DeFAI explained guide and our complete guide to real AI use cases for Web3 projects.

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Frequently Asked Questions

What is attention AI in Web3?

Attention AI refers to Web3 projects that use AI terminology and narratives to generate investor attention and positive emotions — without building real AI products that deliver measurable value to users. The term was coined by ChainAware co-founders Martin and Tarmo to describe projects whose primary output is investor excitement rather than user utility. Common attention AI patterns include decentralized AI marketplaces, tokenized AI assets, AI-optimized blockchain consensus, and supply chain transparency with AI and blockchain. None of these categories has produced working products at scale because the underlying technical premise of each is either infeasible or unnecessary.

How do you tell the difference between attention AI and real utility AI?

Apply four questions: (1) Is there a running website? (2) Is there a running MVP — a product you can actually use? (3) Does the product provide real, measurable value to specific users? (4) Does it have a competitive advantage that cannot be copied by anyone with an API key? Real utility AI answers yes to all four. Attention AI typically fails at question two and always fails at question four, because most attention AI projects are LLM wrappers — prompt engineering dressed up as AI innovation.

Why does the 2025 AI market correction not mean AI is overhyped?

The 2025 correction was specifically an attention AI correction — projects built on narratives rather than products collapsed as investors recognized the absence of utility. Real utility AI projects with running products, proprietary models, and paying enterprise clients were not affected in the same way because their value is anchored in recurring revenue from genuine utility delivery. AI as a technology remains an unstoppable megatrend. The correction filtered attention AI — it did not invalidate the megatrend.

Why do LLMs not create competitive advantage in Web3?

LLMs are accessible to anyone through an API key. Building a product on an LLM means building on a foundation that any competitor can also access immediately. The prompt engineering that differentiates one LLM-based product from another can typically be replicated in hours. Proprietary ML models trained on domain-specific data — like ChainAware’s fraud detection models trained on blockchain behavioral data — create competitive advantages that require years to replicate because they need both the model architecture and the training data. LLMs are useful tools for specific tasks like content generation and code templates, but they do not create defensible competitive positions.

What is ChainAware’s free offering for Web3 projects?

ChainAware offers several free products. The Fraud Detector and Rug Pull Detector are free for individual checks at chainaware.ai/fraud-detector. The Web3 User Analytics dashboard — including the free pixel integration via Google Tag Manager — is free forever for any Web3 project, showing the behavioral profile of connecting wallets across eight dimensions. Individual wallet audit at chainaware.ai/audit is also free. Enterprise products (marketing agents, transaction monitoring, credit scoring) are subscription-based — see chainaware.ai/pricing.

Is blockchain data actually better than the data Google uses for advertising?

For financial behavioral prediction, yes. Google’s targeting relies on browsing history and search queries — passive signals of potential interest that may not reflect actual financial intentions or capabilities. Blockchain transaction data reflects deliberate financial decisions: amounts chosen, protocols used, timing selected, counterparties trusted. Because financial transactions require conscious thought before execution, the resulting data carries significantly higher predictive signal for financial behavior than passive browsing signals. Additionally, blockchain data is permanent and tamper-proof — it cannot be cleared, masked, or manipulated the way browsing history can.

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This article is based on X Space #29 hosted by ChainAware.ai co-founders Martin and Tarmo. Watch the full recording on YouTube · Listen on X . For questions or integration support, visit chainaware.ai.

The post Attention AI vs Real Utility AI: How to Spot the Difference in Web3 first appeared on ChainAware.ai.

X Space AMA with ChainGPT Pad — ChainAware co-founder Martin joins Timo from ChainGPT to cover the full ChainAware story: origin, products, AI architecture, and the Web2 parallel that explains why Web3 is at a turning point. Listen to the full recording on X

Few projects in Web3 can trace a clean line from first product decision to full platform architecture. Most pivot reactively, following market trends rather than internal logic. ChainAware is different. In this AMA with ChainGPT Pad, co-founder Martin walks through the complete chain of reasoning that led from a DeFi lending platform to a fraud detection engine, from fraud detection to rug pull prediction, from behavioral data to marketing automation, and ultimately to the recognition that Web3 is standing at exactly the inflection point Web2 occupied in the year 2000. Every product ChainAware built answered a question the previous product raised. Understanding that chain is the key to understanding what the platform is and why it matters.

Two Twin Founders, One Decade at Credit Suisse, and Twenty-Five Years in AI

ChainAware was built by Martin and Tarmo — twin brothers who each spent ten years at Credit Suisse in Zurich before entering the blockchain space. Their backgrounds are unusually deep for a Web3 project. Tarmo holds a PhD from a Nobel Prize winner’s program, multiple master’s degrees, and both the CFA and CAIA charters. Before Credit Suisse, Martin spent seven years building a startup that deployed natural language processing AI models 25 years ago — when neural networks were still a niche academic concern rather than an industry standard. That combination of applied AI experience and institutional financial risk management is not decorative. It directly shaped every architectural decision ChainAware made.

Timo from ChainGPT Pad notes during the AMA that another project he hosted — Omnia — was also co-founded by twin brothers. Both cases illustrate the same dynamic: the trust baseline between co-founders who have known each other their whole lives differs structurally from that between professional co-founders who met at a hackathon. As Martin explains: “There is always a little unsync somewhere in a startup — everything moves so fast. If founders don’t have a good relationship, these small misalignments can create serious issues later. For us as twin brothers, it is much easier.” That trust advantage becomes practically significant when making dozens of judgment calls per week about model training strategies, product priorities, and resource allocation — all decisions where honest, fast disagreement matters more than formal process. For the complete platform overview, see our ChainAware product guide.

SmartCredit to ChainAware: The Organic Chain of Discovery

ChainAware did not begin as a fraud detection platform. Three years before this AMA, it began as a credit scoring subsystem inside SmartCredit.io — the fixed-term, fixed-interest DeFi lending marketplace that Martin and Tarmo built first. SmartCredit’s core innovation was predictability: unlike every other DeFi lending protocol of the era, which offered variable money-market rates, SmartCredit gave borrowers and lenders fixed terms at fixed rates. Users knew exactly what they would pay and exactly when — something no other DeFi platform provided at the time.

Building a fixed-term lending platform immediately raised a credit assessment question. Over-collateralised lending protocols like Aave or Compound do not need to assess borrower creditworthiness because collateral backstops all losses automatically. Fixed-term lending introduces counterparty risk — the borrower might default before the term expires. Consequently, Martin and Tarmo began building on-chain credit scoring models. Credit scoring, in turn, requires fraud scoring: a borrower with excellent cash flow history but a fraudulent behavioral profile remains a bad credit risk. Building the fraud component revealed that the fraud detection capability itself was far more broadly applicable and commercially valuable than the credit score. As Martin describes it: “We realised our fraud detection system had much higher value. And so we tuned it — we realised we can use it not only for fraud detection, but also for rug pull detection.” For the full credit scoring architecture, see our credit score guide.

Step by Step, Without a Master Plan

The product evolution that followed was entirely driven by what the data made calculable — not by a pre-designed roadmap. Rug pull detection followed fraud detection naturally. The wallet auditor followed rug pull detection, expanding the behavioral parameter set from fraud probability alone to experience levels, risk willingness, and behavioral intentions. Marketing agents emerged when the team recognised that behavioral intention data could drive personalised content generation. Transaction monitoring agents emerged from the commercial need for businesses to watch address sets continuously. Each product raised a question that the next answered. As Martin summarises: “There was no master plan. It just looked: we can calculate it, let’s calculate. We can calculate this other thing, let’s calculate that. What we always looked for was to predict — not price, but behavior.” For how this stack fits together today, see our behavioral analytics guide.

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Why Fraud Detection Proved More Valuable Than Credit Scoring in DeFi

One of the clearest strategic insights in the AMA concerns why fraud detection became the core product while credit scoring was deprioritised — even though credit scoring was the original goal. The answer lies entirely in DeFi’s structural architecture.

Virtually all DeFi lending today runs on over-collateralisation. Borrowers must deposit more in collateral than they borrow — typically 150% or higher. Under this structure, creditworthiness is operationally irrelevant: if the borrower fails to repay, the smart contract automatically liquidates their collateral without any human intervention or dispute process. Therefore, DeFi protocols have no immediate commercial incentive to invest in credit scoring models because the collateral mechanism already eliminates credit risk by design. Fraud risk, by contrast, affects every on-chain interaction regardless of collateralisation. Whether a protocol is a DEX, a lending platform, a launchpad, or a gaming application, every interaction with a fraudulent address carries real risk that the collateral mechanism cannot address. As Martin explains: “We realised our fraud detection system had much higher value — because DeFi uses overcollateralisation. If someone is not paying, so be it — collateral liquidated, no questions asked.” For the broader context of fraud costs in Web3, see our fraud detection guide.

The Blockchain Data Advantage: Why Gas Fees Create Better Training Data Than Google

A central argument throughout the AMA — and in ChainAware’s broader thesis — concerns why blockchain behavioral data produces more accurate predictions than the web browsing and search data underpinning Web2’s entire AdTech industry. The argument is straightforward but surprisingly underappreciated, even within the blockchain industry itself.

Google builds user profiles from search queries and page visits — actions that cost nothing to perform. A user can search for “DeFi lending” because a friend mentioned it in conversation, with no intention of ever using a DeFi lending protocol. That search nonetheless creates a behavioral signal that Google’s systems interpret as genuine interest and act on for weeks. The signal is noisy precisely because it requires zero commitment. Blockchain transactions, however, require gas fees — real money, however small. That financial barrier acts as a behavioral filter: people think before executing transactions, which means every transaction reflects a genuine financial decision rather than a casual click. As Martin explains directly in the AMA: “Ethereum data is beautiful data because people have to pay for the gas. That means they think about which transactions they do. And these transactions say so much about the persons themselves. If transactions were fully free, anyone could do anything. But having this little gas fee puts people to think — and this data has such a high basis for prediction.” For more on blockchain data quality, see our blockchain data guide.

Free, Public, and Higher Quality Than Bank Data

Beyond quality, blockchain data carries two additional advantages over every other behavioral data source available. First, it is entirely public and permissionless — any team can access it without licensing costs or negotiation. Second, it is significantly richer than anything banks share externally: the equivalent behavioral transaction dataset from a traditional financial institution would cost approximately $600 per user if licensed commercially. ChainAware accesses the same quality of financial behavioral data for free, at scale, across 8 blockchains simultaneously. That advantage compounds continuously as more chains and more transaction history accumulate. For the technical analysis, see our AI-powered blockchain analysis guide and the Ethereum Foundation’s data documentation .

60% to 99% to 98%: The Counterintuitive Model Accuracy Decision

ChainAware’s fraud detection model accuracy history tells a story that most AI project founders would not share publicly — because it exposes the messy, non-linear reality of building production machine learning systems from scratch on novel data.

The initial model achieved approximately 60% prediction accuracy on fraud detection. For roughly 12 months, the team was unable to improve beyond this baseline despite continuous iteration. Then a breakthrough came, pushing accuracy to 80%. Further work eventually reached 98%, and a push to 99% was also achieved. However, the 99% model presented a specific production problem: it required processing so much data per address that large wallets with extensive transaction histories took 25 seconds to evaluate. Martin uses Vitalik Buterin’s Ethereum address as the standard test case throughout ChainAware’s development — and at the 99% model level, even that address took 25 seconds to process. As he explains in the AMA: “We said we have 99% prediction rate of something happening in the future. But this is not real-time. It takes 25 seconds. And we downgraded the algorithm — we went from 99 down to 98%. We said having real-time is more important than having near-real-time.”

Why the 1% Downgrade Was the Right Decision

The decision to downscale from 99% to 98% accuracy in exchange for real-time response is not a compromise — it reflects a clear understanding of the product’s purpose. Fraud detection only protects users if results arrive before they interact with a fraudulent address. A system that takes 25 seconds produces its warning after the interaction window has already closed. Consequently, real-time availability at 98% accuracy is far more useful in production than near-real-time at 99%. Interestingly, Timo from ChainGPT Pad makes a perceptive marketing observation during the AMA: “I think if you advertise something with 98%, it looks more real than if you advertise a higher percentage. It’s a psychological thing — and the fact that it’s real-time is a massive benefit.” The deliberate downgrade to 98% turns out to be both the correct engineering decision and the more credible marketing claim. For how CryptoScamDB is used to backtest this accuracy, see our fraud detector guide.

AI Model Training Is Art, Not Engineering: What That Means in Practice

Martin’s characterisation of AI model training as art rather than engineering is one of the most practically useful observations in the entire AMA — particularly for founders evaluating blockchain AI projects that claim high accuracy without explaining how they achieved it.

Engineering implies a reproducible process: follow the documented steps, get the specified output. Model training does not operate this way. Every model presents a set of judgment questions with no universal answers: which behavioral features to include in training, how to preprocess raw transaction data, how to balance the ratio of positive to negative examples, when a training plateau represents a genuine ceiling versus a solvable constraint, and which architectural variations to explore next. The 12-month period that ChainAware spent at 60% accuracy before breaking through to 80% was not 12 months of delay — it was 12 months of applied judgment on a genuinely hard problem that had not been solved before for this specific data domain. As Martin states: “Training the models is like an art. It’s not engineering. Somehow you’re just looking — you reach a certain level and then you have to start to analyse. Which training data? Do I have to change the training data? Do I have to pre-process data? Because there is positive data, there is negative data used for training. It’s a continuous iterative process.” For the distinction between genuine predictive AI and LLM wrappers, see our generative vs predictive AI guide and our predictive AI guide.

Why “Just Add More Data” Does Not Solve the Problem

A common misconception about AI model development — one Martin directly addresses in the AMA — is that accuracy improves automatically by adding more training data. While volume matters, the quality of data preprocessing, feature selection, and the balance of positive versus negative examples typically matters more for fraud detection specifically. Beyond this, the requirement for real-time response creates a hard constraint that pure data volume cannot resolve: a model can always be made more accurate by processing more features per address, but each additional feature adds latency. Navigating that accuracy-latency tradeoff requires judgment, not a formula — which is precisely what Martin means by calling it art rather than engineering.

How Fraud Detection Actually Works: Neural Networks on Positive and Negative Behavior

For community members who wanted a non-technical explanation of the fraud detection system, Martin provides the clearest walkthrough in the entire AMA. The explanation is fully accessible without any background in machine learning.

The foundation is a neural network trained on labeled examples of on-chain behavioral history. Two categories of examples feed the training process: addresses with confirmed legitimate, trustworthy histories (positive examples) and addresses associated with confirmed fraud, scams, or illicit activity (negative examples). CryptoScamDB — a public database of confirmed scam addresses — serves as ChainAware’s backtesting source to validate accuracy, though not as training data directly. Training iterates repeatedly through these examples, adjusting the neural network’s internal parameters until it reliably distinguishes between the two behavioral categories.

Once training completes, the network deploys to evaluate new addresses — wallets not present in the training data at all. When a new address arrives, the system analyses its complete transaction history and automatically calculates how closely its behavioral patterns match the positive category versus the negative category. The output is a single probability score between 0 and 1 representing the likelihood of future fraudulent behavior. As Martin describes: “This AI model that you trained — technically you’re creating a neural network in the background with the training. Then it automatically analyses: how many of the positive behaviors are on the address, how many of the negative behaviors? And then you’re getting the output value.” For the complete fraud detection methodology, see our fraud detector guide.

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Rug Pull Detection: Why the Code Is Not the Problem

Rug pull detection extends the fraud detection neural network to a fundamentally different problem structure. Where fraud detection evaluates a single wallet address, rug pull detection evaluates a contract ecosystem — and because professionally executed rug pulls specifically deploy clean, audited contract code to avoid automated detection, the contract code itself is almost never where the risk signal lives.

ChainAware’s rug pull detection operates by tracing the behavioral history of the people behind the contract rather than the contract itself. The process follows two parallel tracks simultaneously. First, it traces upstream through the contract creation hierarchy: who created this contract? If that creator is itself another contract, who created that second contract? The trace continues until reaching externally owned accounts with meaningful transaction histories — the actual humans operating the scheme. Second, it analyses every address that has provided or removed liquidity from the associated pool, evaluating each one’s behavioral history against the trained negative pattern library. As Martin explains: “Rug pull means someone created a contract — there’s a contract creator. We look on the contract creator’s transaction history. If the contract creator is another contract, we look who created that other contract. And rug pull means liquidity is added and removed — so we look on the liquidity adders and look on their histories.”

Clean Contracts, Dirty Creators: The Category Static Analysis Misses

The practical consequence of this architecture is that ChainAware catches exactly the category of rug pull that every static analysis tool misses: the professionally executed operation where the contract code is intentionally clean. Sophisticated rug pull operators know that potential investors use contract scanners, so they deliberately write code that passes every automated check. Their fraudulent intent exists not in the contract but in their behavioral history — previous rug pulls, interactions with known scam infrastructure, and patterns of liquidity manipulation all leave permanent traces in on-chain transaction history that cannot be removed or forged. ChainAware’s behavioral approach reads those traces precisely where static tools see nothing. For the complete rug pull detection methodology, see our rug pull detection guide and our rug pull detector guide. For broader context on crypto fraud scale, see Chainalysis’s annual crypto crime report .

Transaction Monitoring Agent: The Regulatory Requirement Most Web3 Projects Ignore

ChainAware’s business product suite is structured around AI agents that companies subscribe to rather than individual free tools. The transaction monitoring agent is the most compliance-critical of these offerings — and Martin’s explanation in the AMA clarifies a distinction that causes widespread confusion across the Web3 compliance industry.

AML (Anti-Money Laundering) analysis and transaction monitoring are not the same thing, despite being treated as interchangeable by most blockchain compliance vendors. AML is backward-looking and static: it tracks the movement of funds that have already been flagged as illicit through the on-chain ecosystem, following contaminated money as it passes through intermediate wallets. Essentially, AML documents what happened. Transaction monitoring is forward-looking and AI-based: it analyses behavioral patterns of active addresses to predict future fraudulent behavior before any transaction executes. As Martin states precisely in the AMA: “AML is backward-looking static analysis and transaction monitoring is a required AI-based forward predictive analysis. AML is backward, transaction monitoring is forward.” For the complete distinction and regulatory context, see our AML and transaction monitoring guide.

MiCA and FATF Make Transaction Monitoring Non-Optional

Critically, European MiCA regulation and FATF Recommendation 16 both require AI-based transaction monitoring — not AML alone. The compliance community in Web3 has widely deployed AML tools because they are simpler to implement and were the first compliance requirement that centralised exchanges encountered. Transaction monitoring — the more powerful and directly user-protective mechanism — has been largely ignored despite being equally mandated for any entity classified as a Virtual Asset Service Provider. ChainAware’s transaction monitoring agent closes this gap directly: it accepts a set of addresses to monitor, watches them continuously with AI behavioral analysis, and issues automated notifications when behavioral patterns indicate elevated risk — enabling operator intervention before harm occurs. For the full regulatory context, see our transaction monitoring agent guide and our blockchain compliance guide.

Web3 Marketing Agents: The Starbucks Principle Applied to DApp Conversion

Beyond security, ChainAware’s most commercially compelling product for DApp operators is the Web3 marketing agent — the growth-side tool that addresses the catastrophic customer acquisition cost problem across the entire industry. Martin introduces it through an analogy that cuts through the technical complexity immediately and makes the concept accessible to any founder or community member.

Consider how different people choose where to get coffee. Some prefer Starbucks — the consistency, the predictable environment, the specific aesthetic. Others prefer a local independent café with completely different qualities. Neither preference is objectively right or wrong. Each person feels comfortable in their preferred environment because something about it resonates with who they are and what they are looking for in that moment. Web3 platforms today serve a single version of their interface to every visitor — the same message, the same content, the same calls-to-action — regardless of whether the visitor is an experienced DeFi yield farmer, a complete newcomer exploring the space for the first time, or an institutional counterparty evaluating a position. The marketing agent changes this dynamic entirely. As Martin explains: “Users are coming to this website and they’re like — I feel myself good here. There are the colors which I like, the fonts, the messages I like. It’s like coming to a café where you like to be. We are matching user interest with the website — and that’s how the agents are doing it.” For the full marketing agent methodology, see our Web3 AI marketing guide.

How the Marketing Agent Creates Personalised Experiences

The operational sequence of the marketing agent is straightforward at the integration level. When a wallet connects to a platform, the agent immediately queries ChainAware’s Prediction MCP with that wallet address. The MCP returns a behavioral profile derived from 18M+ Web3 Personas: experience level (1–5), risk willingness, predicted intentions (borrower, lender, trader, staker, gamer, NFT collector), and Wallet Rank. Based on this profile, the agent generates content matched to that specific behavioral type — the right messages, the right emphasis, and the right calls-to-action for what this person is actually likely to want next. Two wallets with similar profiles will see similar content. Two wallets with very different behavioral profiles see meaningfully different experiences from the same platform — entirely automatically, with no human intervention per visitor. No identity information is required. No cookies are involved. The only input is the public wallet address and the public transaction history it represents. For how this translates to conversion rate improvements, see our high-conversion marketing guide and our Web3 personas guide.

Credit Scoring Agent: The Product That Is Early — But Coming

The credit scoring agent holds an unusual position in ChainAware’s product roadmap. Unlike fraud detection and marketing agents — which address immediate, urgent, and universal problems — the credit scoring agent addresses a need that is currently suppressed by DeFi’s structural architecture. Nevertheless, Martin is clear and specific: this suppression is temporary.

DeFi’s current over-collateralisation requirement is a structural constraint born of distrust, not of design preference. The reason that Aave, Compound, and every other major DeFi lending protocol requires 150%+ collateral is that they lack both a way to assess borrower creditworthiness and any enforcement mechanism for loan repayment. The collateral backstop is a workaround for a missing infrastructure layer — exactly the infrastructure ChainAware’s credit scoring model provides. Both Martin and Tarmo are Chartered Financial Analysts who have spent careers in credit risk management. Their view is that on-chain credit scoring will become a standard financial trust indicator — applied not just to lending but to any high-value counterparty interaction where financial reliability matters. As Martin explains: “We think there will be a time in 12, 18, 24 months where credit score will be used as a general financial trust indicator — because we are seeing it in Web2. It will be there in Web3 too.” For the complete credit scoring framework and current implementation, see our credit score guide and our credit scoring agent guide.

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The Web2 Parallel: How the Internet Crossed the Chasm and What It Means for Web3

The most strategically significant part of the AMA comes in response to Timo’s closing question: what has ChainAware been “gatekeeping” — what insight would most increase community understanding of where the project is going? Martin’s answer draws a precise historical parallel that reframes everything ChainAware is building within a framework that makes the outcome feel inevitable rather than speculative.

Around the year 2000, the internet had approximately 50 million active users — a technically enthusiastic early adopter cohort who understood the technology and saw its potential but represented a tiny fraction of the eventual addressable market. Web2 faced two specific barriers preventing mainstream expansion beyond those 50 million users. First, credit card fraud was so widespread that a significant portion of consumers refused to enter payment details online at all — stifling e-commerce adoption and forcing early companies to devote enormous engineering resources to fraud problems before they could focus on growth. Second, customer acquisition costs were catastrophic: companies spent thousands of dollars per acquired customer because mass marketing was the only available mechanism. Billboards, TV spots, magazine ads, and press releases all served the same undifferentiated audience at the same cost per impression regardless of stated intent. As Martin recalls: “I saw the Internet hype, I saw the Web2 hype. What happened in Web2 — there were 50 million users. But the acquisition costs were horrific because everything was mass marketing. And on the other side, there was so much credit card fraud that regulators mandated transaction monitors.” For the complete Web2 parallel analysis, see our Web3 growth guide.

Two Technologies Solved Both Web2 Problems — Both Are Now Available for Web3

Web2 solved its dual crisis through two specific technology innovations deployed in sequence. Transaction monitoring — mandated by financial regulators for all payment processors — dramatically reduced credit card fraud and restored consumer confidence in online transactions. AdTech — pioneered by Google with search-based intent targeting and micro-segmentation — reduced customer acquisition costs from thousands of dollars to tens of dollars by matching advertisements to users whose behavioral signals indicated genuine intent. Both technologies are now available for Web3 in a superior form. Web3 transaction monitoring operates on higher-quality proof-of-work financial data than any payment processor ever had access to. Web3 AdTech can target individual wallets by their complete financial behavioral history rather than by cookie-based proxy signals. The only difference between Web2 in 2005 and Web3 in 2025 is that Web3 hasn’t yet deployed either technology at scale. ChainAware is building exactly that deployment layer. According to Statista’s internet industry data , the global digital advertising market grew from near zero in 2000 to over $600 billion annually — powered entirely by this AdTech transition from mass marketing to intent-based targeting.

From Cash-Burn to Cash-Flow Positive: Why the Iteration Argument Changes Everything

Martin’s closing argument in the AMA moves from historical parallel to practical consequence for individual Web3 projects and founders. Solving fraud and customer acquisition costs simultaneously does not just create a better ecosystem in aggregate — it changes the fundamental unit economics of each individual project in a way that enables long-term survival and genuine product iteration.

Currently, most Web3 projects face a structural trap with two reinforcing failure modes. High customer acquisition costs mean that every user acquired costs more than they return in revenue during their first engagement period — making the business mathematically unprofitable at the unit level regardless of how technically excellent the product is. High fraud rates mean that new users who enter the ecosystem through legitimate channels frequently have their first significant experience be a loss from a scam or rug pull — and they leave permanently, reducing both the size of the addressable market and the word-of-mouth dynamics that drive organic growth. The combination creates enormous pressure on treasury management and forces founders toward token-based exit strategies rather than genuine product iteration cycles. Resolving both pressures simultaneously changes this equation fundamentally: lower fraud rates mean new users stay and become real participants; lower acquisition costs mean user acquisition can be profitable at reasonable scale. Together, they create the unit economics that make sustainable product development possible. As Martin concludes: “New people join the ecosystem, they get scammed, they leave — they should stay. By bringing fraud rates down and acquisition costs down, Web3 businesses will become cash-flow positive. They will have more chances to innovate, better chances to stay long term — not just doing a one-shot. You need a first, second, third, tenth iteration. Same as in AI models.” For how this translates to specific growth strategy, see our AI agents acceleration guide and our ChainAware vs Google Web2 guide.

Comparison Tables

ChainAware Product Evolution: What Each Product Solved and What It Discovered Next

Product	Problem Solved	Discovery It Triggered	Status in 2025
SmartCredit.io	Variable DeFi lending rates — nobody knows their cost of borrowing	Fixed-term lending requires credit scoring	Live — external project
Credit Scoring	On-chain creditworthiness assessment for DeFi borrowers	Credit scoring requires fraud scoring as a subsystem	Live — limited DeFi demand (overcollateralised)
Fraud Detector	Predict wallet fraud probability before interaction	Same architecture extends to contract fraud (rug pulls)	Live — 98% accuracy, real-time, 6 chains
Rug Pull Detector	Predict rug pulls by tracing creator and LP behavioral chains	Behavioral data encodes user intentions beyond fraud	Live — ETH, BNB, BASE, HAQQ
Wallet Auditor	Complete behavioral profile: fraud, experience, risk, intentions	Behavioral intentions can drive personalised marketing content	Live — free, no signup, 5 chains
Marketing Agents	1:1 personalised website experience per connecting wallet	Businesses need continuous address monitoring for compliance	Live — GTM 2-line pixel, free analytics tier
Transaction Monitoring Agent	Forward-looking AI surveillance of business address sets	Credit scoring demand will grow as DeFi matures	Live — subscription, MiCA-compliant
Credit Scoring Agent	Financial trust indicator for under-collateralised DeFi	Foundation for mainstream DeFi credit infrastructure	Live on ETH — 12-18-24 month demand timeline
Prediction MCP	Single developer access point for all models via natural language	32 open-source agents enable ecosystem-wide adoption	Live — SSE-based, 18M+ Personas, 8 chains

AML vs Transaction Monitoring: The Distinction That Determines Compliance Effectiveness

Frequently Asked Questions

How did ChainAware evolve from SmartCredit into a full Web3 security platform?

ChainAware emerged organically from SmartCredit.io — the fixed-term, fixed-interest DeFi lending platform that co-founders Martin and Tarmo built three years before this AMA. Building a lending platform required credit scoring. Building credit scoring required fraud scoring as a subsystem. The fraud detection capability proved more broadly valuable and commercially applicable than the credit score itself, particularly given DeFi’s over-collateralised structure where credit scores are not urgently needed across the market. From fraud detection, rug pull detection followed using the same neural network architecture. Wallet auditing followed by expanding the behavioral parameter set. Marketing agents followed by applying behavioral intention data to personalised content generation. Transaction monitoring agents followed from commercial client demand for continuous address surveillance. There was no master plan — each product discovered the next through one consistent question: what else can we calculate from this behavioral data?

Why did ChainAware deliberately downgrade from 99% to 98% fraud detection accuracy?

The 99% accuracy model required 25 seconds to process large addresses like Vitalik Buterin’s Ethereum wallet — making it unusable in a real-time transaction context where users need results before any interaction. The team deliberately downscaled to 98% accuracy to achieve sub-second real-time response. Fraud detection only provides meaningful user protection if results arrive before an interaction occurs, not after. Therefore, 98% accuracy delivered in real-time is far more valuable in production than 99% accuracy delivered in near-real-time. The 98% figure also happens to be a more credible marketing claim — exactly as Timo from ChainGPT Pad observed during the AMA.

Why can’t professional rug pulls be caught by smart contract analysis alone?

Sophisticated rug pull operators understand that potential investors use automated contract scanners before investing. Consequently, they deliberately write contract code that passes every static analysis check — clean code, no honeypot flags, no obvious backdoors. Their fraudulent intent exists not in the contract code but in their behavioral history: previous rug pulls, interactions with known scam infrastructure, and liquidity manipulation patterns all leave permanent traces in on-chain transaction history. ChainAware’s rug pull detection traces the complete funding chain — from contract creator through upstream contract deployers to all liquidity providers — evaluating every address’s behavioral history against trained negative patterns. This approach catches clean-contract rug pulls that static tools miss entirely.

What is the Web2 parallel that ChainAware draws for Web3?

Around the year 2000, Web2 had approximately 50 million internet users — the same number as Web3 has DeFi users today. Web2 faced two specific barriers to mainstream adoption: widespread credit card fraud that prevented consumer trust in online transactions, and catastrophic customer acquisition costs from mass marketing approaches. Both problems were solved by specific technologies: regulators mandated transaction monitoring for payment processors, which reduced fraud and restored consumer confidence; Google’s AdTech innovation replaced mass marketing with intent-based targeting, reducing CAC from thousands of dollars to tens of dollars. Web3 today faces the identical dual challenge. ChainAware provides both solutions in a form specifically designed for blockchain — predictive AI fraud detection and behavioral targeting marketing agents — using data that is higher quality than anything Web2 ever had.

What makes blockchain data better for behavioral prediction than Web2 data?

Every blockchain transaction on Ethereum and similar chains requires a gas fee — a real financial cost that forces deliberate action before any transaction executes. This proof-of-work filter removes casual, accidental, and performative behavior from the dataset, leaving only genuine committed financial decisions. Google’s data consists of search queries and page visits — both generated at zero cost in response to external stimuli with no financial commitment required. A user can search for anything without any intention of acting. On-chain, every action involves spending real money. That fundamental difference means blockchain behavioral data delivers significantly higher prediction accuracy from a smaller number of data points than anything Google can build from browsing history — and it is entirely public and free.

This article is based on the X Space AMA between ChainAware.ai co-founder Martin and Timo from ChainGPT Pad. Listen to the full recording on X . For integration support or product questions, visit chainaware.ai.

The post Enabling Web3 Security with ChainAware first appeared on ChainAware.ai.

X Space with Klink Finance — ChainAware co-founder Martin in conversation with Philip, co-founder of Klink Finance, on AI-driven AdTech for Web3 finance platforms. Listen to the full recording on X

Two Web3 founders with very different perspectives on user acquisition sit down to map the honest state of Web3 marketing. Philip from Klink Finance brings three years of operating a 350,000-member crypto wealth creation platform — real experience running campaigns across Twitter, Telegram, and Discord through the full cycle of channel migration and community building. Martin from ChainAware brings the data layer: behavioral analytics across 18M+ wallets, AI-powered fraud detection at 98% accuracy, and the conviction that Web3 marketing is about to undergo the same AdTech transformation that Web2 underwent in the early 2000s. Their conversation covers the gap between traffic generation and user conversion, the 15x uplift that personalization delivers over mass marketing, why AI agents are not the next evolution of prompt engineering but something structurally different, and why the wallpaper analogy explains what resonating content actually means in practice. Together, they arrive at the same conclusion from different directions: the most important unsolved problem in Web3 growth is not reaching users — it is converting the right users at sustainable cost.

Klink Finance: Building Crypto Wealth Creation from Zero

Philip, co-founder of Klink Finance, opens the conversation with a platform overview that immediately establishes the scale of the Web3 user acquisition challenge from the operator’s perspective. Klink Finance is a crypto wealth creation platform — specifically designed to let anyone start building a crypto portfolio from $0 of personal investment. Rather than requiring users to bring capital, Klink enables participants to earn crypto rewards through completing quests, participating in airdrops, playing games, answering surveys, and engaging with various platform activities. Rewards are distributed in stablecoins (primarily USDT) as well as newly listed tokens and other airdrop opportunities.

Since launch, Klink Finance has grown to over 350,000 community members — accessible through a mobile app, a web app, and a Telegram mini app. That multi-platform presence reflects a deliberate strategic adaptation: Klink has observed firsthand how rapidly Web3 user communities migrate between channels, and has built infrastructure to follow users wherever they concentrate. As Philip explains: “The trends are changing so quickly in the crypto space and also user interest changes rapidly. Over the course of building Clink, we had different channels that worked better or worse over time.” For more on understanding Web3 user behavior patterns, see our behavioral analytics guide.

Web3 Marketing in 2025: 30 Years of Web2 Practice Meets Six Years of Web3 Native

One of the most practically useful observations Philip makes early in the conversation concerns the false dichotomy many Web3 founders hold about their marketing approach. Early in the crypto industry’s history, a significant faction believed that Web3 marketing was fundamentally different from Web2 marketing — that it required entirely new channels, tactics, and frameworks. Experience has proven this view too simple. As Philip puts it: “If you look at how it evolved over the years, it is very much a mixture of strategies that have worked extremely well in the Web2 space and adding things on top that are very much Web3 native.”

The asymmetry of the situation is significant: Web2 marketing has 30 years of accumulated best practices, tested frameworks, conversion rate data, and channel-specific expertise. Web3 marketing has approximately six years as a serious discipline. Rather than rejecting those 30 years, the most effective Web3 marketing operators layer Web3-native elements — wallet behavioral targeting, on-chain audience segmentation, token incentive structures — on top of the proven Web2 foundation. The projects that succeed are those that understand both layers and know which tool applies in which context. For how wallet behavioral data creates a Web3-native targeting layer, see our intention-based marketing guide.

Agility as the Core Marketing Competency

Beyond the hybrid approach, Philip identifies agility as the single most valuable marketing competency for Web3 operators. The speed at which trends, user concentrations, and effective channels shift in the crypto space is dramatically faster than in Web2. A marketing strategy that worked in Q1 may be significantly less effective by Q3 — not because the product changed, but because the ecosystem migrated. The operators who sustain growth are those who monitor channel effectiveness continuously and reallocate resources quickly when the data signals a shift. Rigidity — committing to a single channel because it worked previously — is one of the fastest ways to lose momentum in Web3.

Channel Migration: From Twitter Dominance to the Telegram Ecosystem

Klink Finance’s own channel history provides a concrete illustration of why agility matters. For an extended period after launch, Twitter (now X) was their primary user acquisition channel — leveraging the platform’s dense Web3 community and its culture of crypto discussion, alpha sharing, and community building. That approach worked well. Over the course of 2024, however, Klink’s primary acquisition channel shifted decisively toward Telegram — both the broader Telegram ecosystem and the specific advertising capabilities that Telegram provides to reach its 900+ million monthly active users.

This migration reflects a broader pattern visible across the Web3 industry: community infrastructure has been moving from Discord (which dominated the 2020-2022 era as the go-to community building platform for NFT and DeFi projects) toward Telegram as both a community platform and a distribution channel. Telegram mini apps have created an entirely new product category — lightweight applications running natively within Telegram that can reach users directly inside their primary communication environment. Klink’s Telegram mini app captures this opportunity directly. As Philip explains: “We also launched the Telegram mini app to leverage advertising on Telegram directly. Because you see a lot of migration also where Web3 communities are built up from being only on Discord initially to a lot more reliance on Telegram.” For more on channel strategy and conversion optimisation, see our Web3 marketing guide.

Mass Marketing Generates Traffic. Personalization Converts It.

Martin introduces the structural distinction at the heart of ChainAware’s approach to Web3 marketing — one that Philip quickly validates from Klink’s operational experience. The distinction separates two entirely different problems that most Web3 marketing discussions conflate: traffic generation and user conversion.

Mass marketing — banner ads, KOL campaigns, Telegram ads, Twitter promotions — is reasonably effective at generating traffic to a platform. It brings visitors to the website or application. However, it is almost entirely ineffective at converting those visitors into active, transacting users. The reason is structural: mass marketing sends the same message to everyone, regardless of their behavioral profile, experience level, risk tolerance, or actual intentions. People are different. A DeFi trader who arrives at a borrowing and lending platform has completely different needs, vocabulary familiarity, and conversion triggers than a crypto newcomer who arrived through the same campaign. Sending both of them an identical onboarding experience means neither gets a particularly relevant one. As Martin frames it: “Visitors are coming to your website. Everyone is seeing the same message. People are different. We have to give to people different messages.” For the complete framework on personalized Web3 marketing, see our AI marketing for Web3 guide.

Philip adds an important operational dimension to this framework. Reducing customer acquisition cost is not only about targeting better acquisition channels — it equally requires optimising the conversion from first landing to first transacting action. As he explains: “It’s not only about spending an amount of money and driving users into your platform. Because then you actually enter the next phase of facilitating a very easy onboarding towards the user. The simpler it is to use your product and to convert from first landing into becoming an actual user, the cheaper it will get also to grow your community.” The implication is clear: personalisation is the conversion layer that makes the acquisition spend worthwhile. Without it, the traffic generated by mass marketing leaks out of the funnel before reaching the transacting stage. For how behavioral segmentation enables the conversion layer, see our user segmentation guide.

Know Who Is Landing on Your Platform

ChainAware Web3 Analytics — Free, 2 Lines of Code, Results in 24 Hours

Before you can personalise, you need to know your real users — not the marketing persona you imagined, but the actual behavioral profiles of wallets connecting to your platform today. ChainAware Analytics shows you experience level, risk willingness, intentions (trader, borrower, staker, gamer), and Wallet Rank distribution. Two lines in Google Tag Manager. Results in 24-48 hours. Free.

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The Email Marketing Proof Point: 1% vs 15% — a 15x Conversion Multiplier

Martin introduces a specific data point that quantifies the personalization premium with enough precision to be immediately actionable for any Web3 founder evaluating their marketing strategy. The comparison comes from email marketing — a channel with decades of conversion rate data across millions of campaigns.

Mass email marketing achieves approximately 1% conversion across general audiences — dropping to 0.5% in the crypto sector, where inbox competition from project newsletters, airdrop announcements, and exchange promotions is particularly intense. Personalised email marketing — where message content is generated based on additional data about the recipient from LinkedIn, Twitter history, and behavioral signals — achieves open rates of approximately 15%. That is not a marginal improvement. At 15x the conversion rate of mass email, personalisation fundamentally changes the economics of every marketing investment. As Martin states directly: “Mass email marketing conversion ratio is 1%, in crypto 0.5%. Now if you go personalised, meaning the emails are generated based on additional information available about you via LinkedIn and Twitter, then you get open rates of 15%. And this shows how much personalisation impacts the conversion. 1% versus 15% — that’s 15x.” For the complete conversion framework applied to Web3 platforms, see our high-conversion Web3 marketing guide.

Blockchain Behavioral Data Outperforms LinkedIn and Twitter Signals

The 15x personalization premium in email marketing uses relatively shallow data sources — LinkedIn profile information, Twitter activity patterns, and basic demographic signals. Blockchain behavioral data is structurally richer and more reliable than any of those signals. Every on-chain transaction reflects a deliberate financial decision that cost real money (gas fees) to execute. The resulting behavioral profile captures actual financial behavior, not self-reported professional credentials or social media activity that may be entirely performative. A wallet with a three-year history of leveraged trading on multiple chains tells you far more about that person’s risk profile, experience level, and likely next action than their LinkedIn job title ever could. Consequently, the personalization premium that blockchain-based targeting enables is likely to exceed the 15x email marketing benchmark — because the underlying data quality is higher.

The Onboarding Aha Moment: How Klink Reduced CAC by Optimising the First Reward

Philip provides a concrete case study from Klink Finance’s own growth history that illustrates how onboarding optimisation directly reduces customer acquisition cost — without changing a single marketing channel or campaign budget. The concept centres on what product teams call the “aha moment” — the specific point in a new user’s first experience where they genuinely understand the product’s value, decide they like it, and commit to continued engagement.

For Klink Finance, that aha moment is precisely defined: it is when a new user earns their first crypto reward starting from zero. Not when they register. Not when they download the app. Not when they complete a profile. The specific moment they see their first crypto balance appear — earned without any prior investment — is when they truly understand what Klink is and why it is valuable. As Philip explains: “For us, this key moment of being a Klink community member is when you earn your first crypto rewards starting from zero. Over time we more and more optimise this flow of getting someone to land on the website or application and getting them to earn their first rewards. And the more you understand how to optimise this onboarding flow, that will have a direct impact on your Web3 marketing strategy and the types of users you are targeting.” For how behavioral profiling enables personalised onboarding at scale, see our DeFi onboarding guide.

Personalisation Reduces Onboarding Noise

Philip makes a specific practical observation about personalised onboarding that connects directly to ChainAware’s approach. If a platform builds a single onboarding flow suitable for both complete crypto beginners and experienced DeFi natives, both groups receive significant irrelevant content. The beginner needs education about private keys and basic wallet concepts. The experienced DeFi user finds that same education condescending and time-wasting. As Philip explains: “If you understand they have been in the crypto space for years already, you don’t need to educate them about what a private key is or how to stake tokens. But you can get straight to the point of the key benefits of your specific solution.” ChainAware’s experience level parameter (1–5 scale derived from transaction history) enables exactly this distinction to be made at wallet connection — before the user interacts with any onboarding content at all. For how ChainAware calculates experience levels, see our wallet auditor guide.

The User Journey from CEX to DeFi: 90%, 10%, and Why It Matters

The conversation surfaces a data point that has significant implications for how Web3 platforms should think about their addressable market. Philip observes that Klink Finance’s community sits at the intersection of Web2 and Web3 — serving users who interact with crypto applications but are not necessarily DeFi natives. Martin provides the broader industry context: approximately 90% of crypto users conduct their activity exclusively on centralised exchanges, with only around 10% actively using DeFi wallets and interacting with on-chain protocols.

Rather than viewing this 90/10 split as a limitation, Martin frames it as a predictable stage in a user journey that is directionally clear and commercially important. New crypto users almost universally start on centralised exchanges — the user experience is familiar, the custodial model removes the complexity of key management, and the fiat on-ramps are straightforward. Over time, as users gain experience and confidence, they begin exploring Web3 applications. Typically, they encounter rug pulls or other fraud events on platforms like PancakeSwap or pump.fun, temporarily retreat to centralised exchanges, then return to DeFi with more caution and more knowledge. Eventually, experienced users often exit centralised exchanges entirely. As Martin describes the arc: “It’s like a personal development upon every Web3 user. It was as well my journey. I started on the central exchanges. I don’t want to use central exchanges anymore.” For more on the user journey and how behavioral analytics tracks it, see our Web3 growth guide.

The Commercial Implication: Protect New Entrants or Lose Them Permanently

The user journey analysis has a specific commercial implication that Martin emphasises throughout the conversation: new users who encounter fraud in their first DeFi experiences frequently leave the ecosystem permanently. They do not pause and try again — they associate the entire Web3 space with the negative experience and return to centralised exchanges as their permanent solution. Every fraudulent interaction that drives a new user out is not just a lost transaction — it is a permanently lost ecosystem participant who will never contribute to DeFi liquidity, governance, or growth again. Reducing fraud rates therefore directly expands the addressable market for every DeFi platform by keeping new entrants in the ecosystem long enough to become genuine participants. For the full fraud reduction argument, see our fraud detection guide.

Address History as Trust Infrastructure: Your Best Business Card in Web3

Martin introduces an underappreciated use case for on-chain behavioral data that extends beyond fraud detection and marketing personalisation: address history as a trust infrastructure for peer-to-peer and business-to-business interactions in the Web3 ecosystem. The argument is both practical and elegant — blockchain’s combination of transparency and pseudonymity creates a unique opportunity to project verifiable trustworthiness without sacrificing privacy.

In a traditional business context, trust is established through credentials — CVs, references, LinkedIn profiles, company registrations. All of these can be falsified. On-chain transaction history, by contrast, is cryptographically immutable and permanently public. A wallet with a five-year history of sophisticated DeFi interactions, consistent protocol usage, and zero fraud associations tells a more reliable story about its owner than any self-reported credential. Furthermore, the history cannot be retrospectively altered — it stands as a permanent, verifiable record. As Martin explains: “Address history is a way to create trust in the ecosystem. You can stay anonymous but you can still calculate the trust level — how much you can trust other persons. Your address history is my credit score, my business card, my visit card. I don’t need to pretend to be someone — I say that’s my address, look who I am, look at the predictions, look at my behavior. I am who I am.” For the complete Share My Wallet Audit implementation, see our Share My Audit guide.

Your Wallet Is Your Reputation

ChainAware Share My Audit — Your Web3 Business Card

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KOL Accountability: Why Share My Wallet Would Change Everything

The trust infrastructure argument leads Martin to a pointed application: Key Opinion Leaders (KOLs) — the influencers who shape investment decisions across the Web3 space — should be required to share their wallet audits alongside their investment calls and project promotions. The logic is direct: if a KOL claims to be an experienced trader who got into a memecoin at a specific early price, their on-chain transaction history either confirms or refutes that claim with cryptographic certainty.

Philip acknowledges the principle but highlights the practical barrier: most KOLs would resist because public wallet history would expose the gap between their public claims and their actual behavior. As Philip explains: “I think that would be beneficial but I also feel like there is still a very big barrier from creators in the economy to start sharing that. Because I personally believe that we would see a lot of false X tweets and Telegram posts of people saying I only bought it at this price, whilst they already got it a lot earlier or even didn’t even buy it but just got paid by projects to present.” The resistance to wallet-based KOL accountability is itself revealing — it confirms the extent to which the current KOL marketing ecosystem relies on unverifiable claims to function. For more on KOL marketing accountability, see our KOL marketing guide.

Address Clustering: Finding One Entity Across Many Wallets

Philip raises a challenge that represents one of the genuine technical limitations of wallet-based behavioral analytics: many sophisticated Web3 users deliberately distribute their activity across multiple wallet addresses — sometimes for privacy reasons, sometimes for tax management, and sometimes simply because different wallets serve different purposes. This multi-wallet behavior limits the completeness of behavioral profiles derived from any single address.

Martin’s response introduces address clustering — a technique that partially addresses this limitation by identifying circular dependencies between addresses that appear unrelated on the surface. Even when a user routes through centralised exchanges between DeFi interactions, or regularly creates fresh wallet addresses to separate their activity, they inevitably leave interaction patterns that connect those addresses: shared funding sources, common counterparties, timing correlations, or token flow patterns that form identifiable clusters. As Martin explains: “Even if you look on the first side that addresses are not interrelated, you will still find the circular dependencies. And then you realise — wow, it’s actually one person behind these addresses. So with the analytics, even if you have centralised exchanges between them, still many things can be calculated, much more than people think.” For more on the analytics capabilities across multi-wallet scenarios, see our blockchain analysis guide.

AI Agents Defined: What Separates Autonomous Agents from Prompt Engineering

As the conversation shifts toward AI agents — the topic Philip explicitly identifies as dominating X and generating enormous community interest — Martin provides one of the clearest definitions of what differentiates a true AI agent from the prompt engineering paradigm that preceded it. The distinction matters because “AI agent” has become one of the most overloaded terms in technology marketing, applied to everything from simple chatbot wrappers to genuinely autonomous systems.

Prompt engineering, which dominated the two years following the emergence of large language models, requires a human at every interaction. A prompt engineer designs clever input sequences that extract useful outputs from an LLM — but that process requires a person to initiate each query, evaluate the response, and decide on the next step. Furthermore, the LLMs available during that period operated on training data that was 18-24 months old, limiting their usefulness for time-sensitive applications. An AI agent, by contrast, removes the human from the loop entirely. It runs autonomously, operates continuously (24/7), learns from feedback loops without human intervention, and processes real-time data rather than static training datasets. As Martin defines it: “AI agent is not the next level of prompt engineering. Prompt engineering still needs a person who is creating the prompt. In the case of an AI agent, it means it’s autonomous, it runs from itself. You don’t need this person. There it’s continuous, it’s 24/7. It’s not like an employee who in the evening goes home. And it’s a continuous self-learning when they integrate the feedback loops.” For the complete AI agent taxonomy applied to Web3, see our Web3 AI agents guide.

How ChainAware Built Agents Without Knowing It

Martin’s account of how ChainAware arrived at its agent architecture is instructive precisely because it was not planned. The team built fraud detection, then rug pull detection, then wallet auditing, then AdTech targeting — each product emerging organically from the previous one. At some point, the combination of real-time behavioral prediction and automated content generation produced a system that ran continuously, learned from results, and required no human intervention per user interaction. That is, by any rigorous definition, an AI agent. As Martin puts it: “We got to the agent without knowing that we built an agent. We just kept building and then we realised other people are calling it AI agents and we were like — oh, we like the name, that’s great.” The organic emergence reflects both the genuineness of ChainAware’s agent architecture and the fact that most legitimate Web3 AI agents were built from solving real problems, not from top-down narrative construction.

Generative AI vs Predictive AI: Two Entirely Different Engines

Before explaining how ChainAware’s marketing agents work, Martin establishes the foundational distinction between the two types of AI that are frequently conflated in Web3 marketing discussions. This distinction is critical because the two types are not interchangeable — they solve different problems with different architectures and different value propositions.

Generative AI — the category that includes ChatGPT, Claude, Gemini, and most of the AI tools that became mainstream in 2022-2023 — is fundamentally a statistical autocorrelation engine. It processes enormous volumes of text and learns the probabilistic relationships between words, sentences, and concepts. When asked a question, it generates the statistically most probable response given its training data. This makes it extremely capable at content creation, summarisation, translation, and conversational interaction. However, it cannot make deterministic predictions about specific future events from numerical behavioral data, cannot classify fraud with 98% accuracy, and cannot calculate a specific wallet’s likelihood of borrowing in the next 30 days. As Martin explains: “Generative AI is just an autocorrelation engine. It produces the most probable answer based on the data that it has. It doesn’t think, it just gives you statistically the most probable response.” Predictive AI, by contrast, uses supervised learning on labeled behavioral data to classify future states — which wallets will commit fraud, which will borrow, which will trade. For the full generative vs predictive AI analysis, see our generative vs predictive AI guide.

The Marketing Agent in Practice: The Wallpaper Analogy

Having established the distinction between generative and predictive AI, Martin explains how ChainAware’s marketing agents use both in combination to create what he calls a “resonating experience” — a website interaction that feels personally relevant to each visitor without revealing why.

The operational sequence begins at the moment a wallet connects to a platform. If the wallet is entirely new with no transaction history, the platform shows its default messages — the same experience every user receives today. However, as soon as transaction history is available, the agent processes the wallet’s behavioral profile and generates matched content. An NFT collector arriving at a DeFi lending platform sees messages framed around the NFT ecosystem and how lending connects to it. A leverage trader arriving at the same platform sees messages about collateral usage and leveraged position opportunities. Neither visitor has explicitly requested this personalised experience — the agent inferred it from their transaction history and generated the appropriate content automatically. As Martin describes the mechanic: “You get an NFT guy at a borrowing lending platform — the NFT guy sees messages cut for him. You get a trader there — the trader gets messages like you can leverage up, you can use your funds as collateral, you can borrow more and go long trades.” For the detailed marketing agent implementation guide, see our AI marketing guide.

The Wallpaper Analogy: You Like It But You Don’t Know Why

Martin uses a memorable analogy to explain the user experience created by resonating content. Imagine walking into a living room where some guests see blue wallpaper and others see green wallpaper — each person sees the colour they prefer, but nobody explains this or draws attention to it. They simply feel comfortable in the space. Web3 marketing agents create the equivalent effect on a website: each visitor experiences content that resonates with their specific behavioral profile, generating a feeling of relevance and comfort without any explicit personalisation signal. As Martin explains: “Some people see blue wallpapers, other people see green wallpapers — they see a wallpaper what they like. And the same will be on the website. If you’re resonating with someone, you like them, you spend more time there. If you’re not resonating, probably you could have a website where you speak to someone else. It’s about resonance.” For how this resonance mechanism drives conversion, see our Web3 personas guide and our high-conversion guide.

The Transaction Monitoring Agent: Expert-Level Compliance Running 24/7

The second agent Martin describes in detail is the transaction monitoring agent — a fundamentally different use case from the marketing agent but sharing the same architectural characteristics of autonomy, real-time operation, and continuous learning. Where the marketing agent operates at the acquisition and conversion layer, the transaction monitoring agent operates at the compliance and security layer.

The agent’s function is straightforward to describe: it takes a defined set of wallet addresses — the connected users of a Web3 platform — and continuously monitors all of their on-chain transactions across every blockchain it has access to. When behavioral patterns emerge that match the fraud signature library (not just fund flow from blacklisted addresses, but forward-looking behavioral indicators of future fraud), the agent automatically flags the address and sends a notification to the relevant compliance officer via Telegram or the platform’s interface. The compliance officer then decides what action to take — shadow ban, full restriction, or further investigation. As Martin explains: “This agent is continuously, autonomously analyzing all these wallets all the time. If there’s a new transaction — not on your platform, but on any platform — it analyses these transactions and if it sees fraud patterns, it will automatically flag it. Then a compliance officer gets the notification: watch out this address, there’s a probability that something will happen there.” For the full transaction monitoring methodology and regulatory context, see our transaction monitoring guide and our AML and transaction monitoring guide.

Expert-Level Workers at a Fraction of the Cost

Martin frames both agents through an employment analogy that makes their commercial value immediately tangible. Both the marketing agent and the transaction monitoring agent perform work that would otherwise require expert human professionals — senior marketers who understand behavioral segmentation and personalisation strategy, and compliance analysts who monitor transaction activity and identify fraud patterns. Both roles typically cost significant salaries, operate only during business hours, require management overhead, and cannot physically monitor thousands of addresses simultaneously. The agents eliminate all of these constraints: they operate at expert level, run continuously 24/7, require no management beyond initial configuration, and can monitor unlimited addresses in parallel. As Martin puts it: “These are like expert workers who are doing work for you — transaction monitoring agents or marketing agents. Expert-level workers, 24/7.” For how these agents fit into the broader Web3 agentic economy, see our Web3 agentic economy guide.

Deploy Both Agents on Your Platform

ChainAware Growth Agents + Transaction Monitoring — One Integration

Marketing Agent: calculates each wallet’s behavioral profile at connection, generates resonating 1:1 content automatically. Transaction Monitoring Agent: continuously monitors your user address set, flags fraud patterns before damage occurs, alerts compliance via Telegram. Both run 24/7. Both integrate via Google Tag Manager. Both powered by 18M+ Web3 Personas across 8 blockchains.

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Amazon, eBay, and the Mechanism Behind Web2 Crossing the Chasm

Martin returns in the conversation’s closing section to the historical parallel that contextualises everything ChainAware builds: the mechanism by which Web2 crossed from 50 million technical early adopters to mainstream adoption affecting hundreds of millions of users and generating trillions of dollars of commerce annually. The crossing the chasm framework, popularised by Geoffrey Moore’s influential book on technology adoption, describes the phenomenon but does not fully explain the mechanism. Martin’s argument is that the mechanism is now identifiable in retrospect and directly applicable to Web3.

Web2 companies in the early 2000s faced the same cost structure Web3 faces today: catastrophically high customer acquisition costs from mass marketing, combined with user trust being eroded by credit card fraud. The crossing of the chasm happened when two specific technologies were deployed at scale. First, AI-based fraud detection — mandated by regulators for payment processors — reduced credit card fraud to the point where consumers felt safe transacting online. Second, and more structurally transformative, was AdTech: Google’s micro-segmentation and intent-based targeting, followed by the adaptive interface infrastructure deployed by Amazon, eBay, and eventually every major Web2 platform. As Martin explains: “If you go on Amazon.com, eBay, everyone is seeing his own version of a website. No two people are seeing the same website. Everything is super personalised, super calculated for you. And people think I can personalise the color — no, no, no. The platform provider personalises it for the visitor so that every visitor is getting the most resonating experience.” For the complete Web2-Web3 parallel analysis, see our ChainAware vs Google Web2 guide and Statista’s internet industry data for AdTech growth figures.

The CAC Reduction That Made Web2 Companies Viable

The reason adaptive interfaces and micro-segmentation mattered commercially was not just better user experience — it was the reduction in customer acquisition cost to levels that made business models viable. When Web2 platforms could target users whose behavioral signals indicated genuine intent to purchase, the conversion rate per dollar of marketing spend increased dramatically. Reaching a user who has already demonstrated relevant purchase intent costs the same advertising dollar as reaching a random mass audience — but the conversion from that targeted reach is ten or twenty times higher. Consequently, the effective CAC dropped from hundreds or thousands of dollars to tens of dollars. That reduction was what made it mathematically possible for Web2 companies to acquire users profitably and, as Philip frames it, “build ventures that can sustain themselves and generate revenue.” Web3 is standing at the equivalent inflection point. For more on the CAC reduction framework for Web3, see our unit costs and AdTech guide and the IAB Internet Advertising Revenue Report .

Comparison Tables

Mass Marketing vs Personalized Marketing: The Conversion Economics

Prompt Engineering vs AI Agents: What Actually Changed

Frequently Asked Questions

What is Klink Finance and how does it relate to Web3 user acquisition?

Klink Finance is a crypto wealth creation platform that enables users to start building a crypto portfolio from $0 of personal investment by earning crypto rewards through quests, airdrops, games, and surveys. With over 350,000 community members across mobile, web, and Telegram mini app platforms, Klink operates at the exact intersection of Web3 user acquisition and retention where the challenges Martin and Philip discuss are most practically felt. Klink’s experience illustrates both the effectiveness of multi-channel agility (migrating from Twitter to Telegram as community infrastructure shifted) and the importance of onboarding optimisation in reducing effective customer acquisition cost — specifically by identifying and optimising toward the aha moment when a user earns their first crypto reward.

What is the difference between mass Web3 marketing and personalised Web3 marketing?

Mass Web3 marketing sends identical messages to every visitor regardless of their experience level, risk profile, behavioral history, or actual intentions — exactly as Web2 billboard or TV advertising did in the 1990s. Personalised Web3 marketing uses each connecting wallet’s on-chain transaction history to calculate their behavioral profile and generate matched content automatically. The conversion rate difference is substantial: mass email marketing achieves 0.5-1% conversion in crypto, while personalised email marketing achieves approximately 15% — a 15x multiplier. ChainAware’s marketing agents extend this personalisation to the full website experience: each wallet sees different content, messages, and calls-to-action based on their behavioral intentions, without requiring any identity disclosure or cookie tracking.

How do AI marketing agents differ from prompt engineering?

Prompt engineering requires a human to write an input for every query and evaluate every output. AI agents run autonomously without human intervention per interaction. The key distinctions are: autonomy (agents run continuously without a human initiating each step), real-time data (agents process live blockchain data, not 18-24 month old training sets), continuous learning (agents improve performance through feedback loops), and scale (agents can process unlimited parallel interactions simultaneously). ChainAware’s marketing agent, for example, autonomously calculates each connecting wallet’s behavioral profile, generates matched content, and serves it — all without any human involvement beyond the initial configuration.

Why does blockchain transaction history make a better behavioral dataset than Web2 data?

Every blockchain transaction requires a gas fee — a real financial cost that forces deliberate action before execution. This proof-of-work filter ensures that every data point in a wallet’s transaction history represents a genuine, committed financial decision rather than casual browsing or search activity generated at zero cost. By contrast, Google’s behavioral data derives from search queries and page visits that anyone can generate without spending anything. The financial commitment filter embedded in blockchain data produces substantially higher behavioral signal quality, which is why ChainAware achieves 98% fraud prediction accuracy from transaction history alone — an accuracy level that would be significantly harder to achieve from Web2 behavioral proxies.

What is the resonating experience and why does it improve conversion?

A resonating experience is a website interaction where the content, messages, and calls-to-action precisely match what that specific visitor is looking for — without the visitor knowing why it feels relevant. ChainAware’s marketing agents create this by analysing each connecting wallet’s behavioral profile (experience level, risk willingness, intentions) and generating matched content automatically. An NFT collector sees content framed around NFT use cases; a leverage trader sees content about collateral and position management. Neither has explicitly requested this personalisation — the agent inferred it from their transaction history. The commercial result is increased time on site, higher engagement with key actions, and improved conversion from visitor to transacting user. This is the Web3 equivalent of the adaptive interfaces Amazon and eBay built in the early 2000s to drive Web2 adoption.

This article is based on the X Space between ChainAware.ai co-founder Martin and Philip from Klink Finance. Listen to the full recording on X . For integration support or product questions, visit chainaware.ai.

The post AI-Driven AdTech for Web3 Finance Platforms first appeared on ChainAware.ai.