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.

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.

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.

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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

Connect your wallet, sign a message to prove ownership, and generate a shareable link showing your complete behavioral profile: experience level, risk willingness, fraud probability, intentions, and Wallet Rank. Share it with counterparties, partners, or investors. Stay anonymous. Prove trustworthiness. No KYC. No identity disclosure.

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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.

X Space #28 — ChainAware AI Agents and Roadmap for Individual Users. Watch the full recording on YouTube · Listen on X

X Space #28 focuses on a single, practical question: what tools does ChainAware offer individual Web3 users right now, and where is the product roadmap heading? However, to answer that question meaningfully, Martin and Tarmo spend the first half of the session establishing the strategic framework that explains why these tools exist at all. The answer reaches back 20 years to the early Web2 era — when two technologies transformed the internet from a 50-million-user enthusiast network into the global economy it became. Those same two technologies are now needed in Web3, and ChainAware is building them.

The Web2→Web3 Parallel: Two Technologies That Drive Exponential Growth

Martin opens X Space #28 with a historical observation that sets the entire session in context: Web3 today is in exactly the same position Web2 was approximately 20 years ago. At that point, the internet had around 50 million users — predominantly technology enthusiasts, early adopters, and people comfortable with command-line protocols. The transition from 50 million to billions of users did not happen through better technology alone. It happened because two specific problems were solved.

The first problem was fraud. In early Web2, credit card data transmitted over the internet was routinely intercepted. E-commerce fraud rates reached 8–9% of total transaction volume. Consumers were genuinely afraid to transact online — and that fear prevented the ecosystem from growing. Payment processors eventually solved this by introducing predictive transaction monitoring: AI systems that analyzed purchasing patterns in real time and flagged transactions that deviated from expected behavior before they completed. The fraud rate collapsed, trust recovered, and the ecosystem began its exponential growth phase.

The second problem was user acquisition. Before Google invented AdWords, online advertising consisted of banner ads, roadside signs with URLs printed on them (Martin recalls literally seeing newspaper advertisements with website URLs), and mass broadcast campaigns with conversion rates so low that Web2 businesses could not become cash-flow positive. Google solved this by developing micro-segmentation using search and browsing history — targeting users with advertising precisely matched to their demonstrated intentions. User acquisition costs collapsed, conversion rates rose, and Web2 businesses finally had sustainable economics.

The Invisible Hand Was Google

Tarmo draws a memorable parallel to economics: business schools teach the concept of the “invisible hand” — the market mechanism that matches buyers with sellers — without ever fully explaining what it is. In Web2, the invisible hand turned out to be Google’s ad technology. It created the matching layer between supply and demand that made the Web2 economy work at scale. Web3 currently lacks this matching layer entirely. Furthermore, Web3’s fraud problem is even more acute than Web2’s was, because blockchain transactions are irreversible — there is no credit card chargeback mechanism, no dispute resolution, no reversal. When fraud happens in Web3, the loss is permanent. For more on how ChainAware applies this framework to Web3 businesses, see our AI agents for Web3 businesses guide.

The Web3 Fraud Crisis: 7–8% of TVL Lost Annually

The scale of Web3’s fraud problem is not well understood even within the industry. Tarmo provides the calculation that puts it in perspective: the annual “hackers fee” — smart contract exploits, protocol hacks, and direct theft — amounts to approximately 2–3% of Total Value Locked (TVL) across all blockchains. Adding impersonation scams, direct fraud, social engineering attacks, and rug pulls brings the total to approximately 7–8% of TVL annually.

This figure is strikingly similar to Web2’s pre-fraud-detection era. In early e-commerce, approximately 8–9% of all online transactions involved fraud. The parallel is not a coincidence — it reflects a structural reality: when a financial ecosystem lacks effective fraud detection, bad actors expand to fill whatever space the absence creates. Consequently, the fraud problem in Web3 is not a temporary phase that the industry will naturally grow out of. It is a structural deficit that requires specific technological intervention to close.

Moreover, the human cost extends beyond the financial numbers. Tarmo emphasizes that when new Web3 users get defrauded or rug-pulled in their first interactions with the ecosystem, they leave and never return — and they tell others to stay away. Every fraud victim is a permanent loss to the ecosystem’s potential user base. Reducing fraud is therefore not just about protecting existing users — it is about creating the conditions under which new users can enter Web3 and stay. For more context on how this plays out in DeFi specifically, see our DeFAI guide and our complete KYT and AML guide for DeFi.

Why Conventional AML Systems Fail in Web3

Before explaining what ChainAware does, Martin and Tarmo explain why existing approaches to Web3 fraud prevention are insufficient — and why the word “prevention” rarely applies to them at all.

Most existing “fraud detection” systems in Web3 are, in Martin’s precise terminology, “documentation systems” or “accounting technologies.” They maintain databases of known bad addresses — wallets that have been associated with confirmed hacks, scams, or other fraud events. When a new address interacts with a platform, these systems check whether the address appears in the database. If it does, the address is flagged. If it doesn’t, the address passes.

The Documentation Problem

This approach has a fatal structural flaw in the Web3 context: blockchain transactions are irreversible. In Web2, documentation-based fraud detection was supplemented by reversal mechanisms — credit card chargebacks, payment holds, dispute resolution processes. If a fraudulent transaction slipped through the filter, the victim could often recover their funds. In Web3, there is no recovery mechanism. A transaction that completes cannot be undone. Therefore, the only fraud detection that provides real protection is forward-looking prediction — identifying fraud risk before a transaction occurs, not documenting that fraud occurred after the fact.

The Clean Wallet Problem

Additionally, documentation-based systems are trivially circumvented by sophisticated fraudsters. A bad actor simply funds a new wallet through a clean route — withdrawing from a centralized exchange like Binance — and starts with a completely clean on-chain history. The documentation database has no record of this new wallet. AML checks pass. The fraudster proceeds. As Martin describes: “You just go to the central exchange and route it out from the exchange to a new wallet. You start from there. You cannot lose AML there. It doesn’t work.”

Predictive AI solves both problems simultaneously. By analyzing behavioral patterns rather than address identity, it identifies fraud risk from the way a wallet behaves — not from whether its address appears on a list. A newly created wallet that begins behaving like a pre-fraud wallet (specific transaction patterns, interaction with certain contract types, timing signatures of known attack preparation) receives a high fraud probability score regardless of whether it has any prior history. For a detailed comparison of these approaches, see our article on forensic vs AI-based crypto analytics and our guide to crypto AML vs transaction monitoring.

Stop Documenting Fraud — Start Predicting It

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ChainAware Predictive Fraud Detector: 98% Accuracy, Free to Use

ChainAware’s fraud detector launched publicly on February 4, 2023 — exactly two years before X Space #28 was recorded. At the time of the session, Martin notes that BNB Smart Chain had just been announced and retweeted by BNB Chain’s official account (3+ million followers), marking a significant expansion of the product’s chain coverage.

The tool is straightforward to use: enter any wallet address, and receive a real-time fraud probability score. Regular addresses process in 0.5–1 second. Larger addresses with extensive transaction histories (Martin uses Vitalik Buterin’s ETH address as a demo benchmark) take slightly longer due to the volume of transaction data being analyzed — but still return results in real time.

The 98% accuracy figure requires context to be meaningful. It is not a self-reported claim — it is backtested against CryptoScamDB, an independent public database of confirmed crypto scam and fraud addresses. The model was tested against labeled data it had never seen during training (to prevent overfitting, also known as “curve optimization” in quantitative finance terminology). Of wallets the model identified as high fraud probability, 98% matched confirmed fraudulent addresses in the ground-truth dataset.

Chain Coverage

At the time of X Space #28, fraud detection covers Ethereum (the first chain supported), BNB Smart Chain (just announced), Polygon, and TON. Base is the next chain in active development, driven by client demand. Martin explains the prioritisation logic: “When clients are screaming for Base, of course we format to the clients.” Additional chains are added continuously as client demand warrants. For the current full list of supported chains, see chainaware.ai/fraud-detector.

Dynamic Detection — Not a Static List

One of the most practically important properties of ChainAware’s fraud detector is that it produces changing scores over time. Martin describes it explicitly: “You check some address, he’s good. Then you check him again some transactions later, he’s bad.” This reflects the model detecting behavioral pattern changes — an address that was behaving normally begins exhibiting pre-fraud behavioral signatures. The score changes accordingly, providing early warning of addresses that are transitioning from clean to risky behavior. Static AML databases cannot do this — an address either is or isn’t on the list, and that status changes only when a fraud event is confirmed and documented after the fact. For the complete methodology guide, see our Fraud Detector guide.

How the Predictive AI Models Actually Work

Martin provides a clear explanation of the model training process — important context for understanding why ChainAware’s approach is defensible and why competitors cannot simply copy it overnight.

The process begins with labeled training data in two categories. The first category is “positive behavior” — wallet addresses with confirmed histories of legitimate, clean activity across various DeFi protocols, exchanges, and use cases. The second category is “negative behavior” — wallet addresses associated with confirmed fraud, hack preparation, scam activity, and other malicious patterns, including the behavioral history those wallets exhibited in the weeks and months before their fraudulent events.

Training Takes Time — By Design

The model training process is iterative and time-consuming — not just because of computational requirements, but because improving a predictive model resembles learning mathematics: it requires repeated cycles of hypothesis, testing, refinement, and validation. As Martin explains: “It’s like learning mathematics. It’s a very iterative process.” ChainAware has been refining these models since before the public launch in February 2023, meaning the current production models represent over four years of iterative development.

Crucially, ChainAware builds its own proprietary neural networks. The company does not use OpenAI, DeepSeek, or any other third-party LLM provider for its prediction models. As Martin states explicitly: “We are not using OpenAI, we are not using DeepSeek. We have our own AI models, our own predictive AI models.” This is what creates the defensible competitive moat — not just the model architecture, but the years of labeled training data specific to blockchain behavioral patterns across multiple chains and millions of addresses. For more on why proprietary models matter, see our guide on attention AI vs real utility AI and our predictive AI for Web3 guide.

Google Cloud Partnership: The Road to 99%+ Accuracy

One of the significant announcements in X Space #28 is ChainAware’s acceptance into the Google Cloud Web3 Startup Program — an elite program providing compute credits to selected Web3 and AI startups. ChainAware received $250,000–$350,000 in Google Cloud compute credits, enabling a substantial expansion of its calculation capacity.

The practical implications are significant and specific. First, pre-calculation: rather than calculating fraud probability only when a query is received, ChainAware can pre-calculate scores for known addresses and update them continuously. This is analogous to the difference between on-demand rendering and cached rendering in software — faster response times, higher accuracy, and the ability to run more computationally intensive models.

The 99% Accuracy Target

Martin explains a specific trade-off the team encountered: a more data-intensive version of the fraud model (incorporating approximately 57 times more data per address) achieves 99%+ accuracy but at the cost of real-time performance. Instead of 0.5–1 second response times, the higher-accuracy model requires longer computation. With the Google Cloud compute credits, ChainAware can run pre-calculations that bridge this gap — computing the higher-accuracy scores in advance and serving them in real time when queries arrive.

Furthermore, the additional compute capacity opens the door to a capability that Tarmo describes as particularly valuable: timing prediction. Currently, ChainAware predicts whether fraud will occur — but not when. With sufficient compute power, the model can potentially predict the approximate timeframe of a fraud event. As Tarmo puts it: “We can even go over and start predicting when fraud is going to happen or when rug pull is going to happen.” This would transform the tool from a binary risk indicator into a temporal early-warning system. For context on how this fits into ChainAware’s broader product vision, see our guide to 5 ways Prediction MCP turbocharges DeFi platforms.

The Rug Pull Industry: Organised Crime With Social Psychologists

Before introducing the rug pull detector, Tarmo provides context that reframes how most people think about rug pulls. The common mental model is a small-scale scam — one bad actor creates a token, pumps it with fake activity, and pulls the liquidity. The reality is considerably more sophisticated and alarming.

Rug pulls are run by a professional industry. This industry employs social psychologists who design the social campaigns — the Telegram group strategies, the influencer playbooks, the FOMO messaging that convinces new users to buy. It employs engineers who design the rug pull contracts themselves, building in hidden mechanisms that allow the developer to drain liquidity at any chosen moment while appearing legitimate to cursory inspection. It employs marketing teams who manage the communication infrastructure. Tarmo describes it plainly: “It is huge industry and very profitable industry.”

The 95% PancakeSwap Statistic

The scale is illustrated by data ChainAware collected during a monitoring exercise: between 1,400 and 1,800 new liquidity pools are created on PancakeSwap daily. Of these, approximately 95% end in a rug pull. This is not a fringe phenomenon — it is the dominant outcome for new token launches on one of the largest DeFi platforms in existence. The 5% of legitimate new launches are effectively camouflaged within a sea of professionally engineered fraud.

Tarmo makes the decisive technical point about why static approaches to rug pull detection are inadequate: “You have to understand that your adversary is very well organised. They have very highly paid social psychologists, engineers. It is an industry, run very very well. And you have to answer to this fraud industry with behavioural analytics algorithms.” A static algorithm — one that checks contracts against lists of known rug pull patterns — is fighting a dynamic adversary with a fixed weapon. The rug pull industry simply adapts, creating new contract structures that bypass the known patterns. As Tarmo frames it: “One guy goes to war with an automated weapon, the other goes with a sword. It doesn’t work this way. If you respond with a static algorithm, you just lose.” For more on this dynamic, see our Rug Pull Detector guide and our guide to identifying fake crypto tokens.

ChainAware Rug Pull Detector: Predicting Before You Lose Everything

The rug pull detector operates on the same predictive behavioral principle as the fraud detector, but applies it to smart contracts rather than wallet addresses. Where fraud detection asks “will this wallet commit fraud?”, rug pull detection asks “will this contract execute a rug pull?”

The key distinction — which Tarmo emphasizes repeatedly — is that the tool predicts rug pulls, it does not document them. Documenting a rug pull is useful for reporting purposes but irrelevant to the investor who has already lost their funds. Prediction before the event is the only form of protection that matters given the irreversibility of blockchain transactions.

The practical implication is direct: before investing in any new token or liquidity pool, paste the contract address into ChainAware’s rug pull detector. The model analyzes the contract structure, the developer wallet’s behavioral history, the liquidity dynamics, and the trading patterns to produce a risk assessment. If the result indicates high rug pull probability, the investment decision is clear regardless of how compelling the project’s marketing appears.

Chain Coverage for Rug Pull Detection

At the time of X Space #28, rug pull detection covers Ethereum and BNB Smart Chain. Rug pull analysis requires somewhat more computation than wallet-level fraud detection — the contract analysis is more extensive — making chain expansion a more incremental process. Additionally, pre-calculation capabilities from the Google Cloud partnership will be applied to rug pull detection as well, increasing both speed and accuracy. For a full walkthrough of what the rug pull risk indicators mean, see our complete Rug Pull Detector guide.

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Wallet Auditor and Share My Wallet Audit

The wallet auditor extends the fraud detector from a binary risk score to a comprehensive behavioral profile. When a user runs a wallet audit on any address, they receive not just a fraud probability but a complete picture of the wallet’s on-chain identity: experience level, risk willingness, behavioral intentions, protocol categories used, forensic analysis, and hundreds of individual attributes and summary metrics.

Martin explains the distinction between risk “willingness” and risk “ability”: “It’s the willingness to take a risk — slightly different. It’s like how banks are working. They are looking on your willingness to take a risk.” This distinction matters for behavioral prediction. Two wallets might have similar portfolio sizes (ability to take risk) but very different behavioral patterns — one consistently takes maximum leverage, the other consistently de-risks. The behavioral willingness metric captures this difference, making it a useful signal both for fraud assessment and for marketing targeting.

Share My Wallet Audit: Trust Without KYC

The most innovative feature of the wallet auditor is the Share My Wallet Audit capability. Web3’s pseudonymous nature creates a genuine verification problem: anyone can claim to be an experienced DeFi participant, a trustworthy counterparty, or a legitimate service provider. Without KYC, there is no identity verification mechanism. Consequently, fraud through impersonation is endemic — people claim expertise or trustworthiness they don’t have, or impersonate known legitimate actors.

Share My Wallet Audit solves this without requiring KYC. The process is simple: the wallet owner connects their wallet to ChainAware and generates a unique shareable link. Generating this link requires signing a transaction, which cryptographically proves that the person generating the link controls the wallet. The link recipient can then view the complete wallet audit — fraud score, experience level, risk profile, behavioral history, intentions — and verify that the audit belongs to the wallet the other party claims to own.

Martin draws the parallel to Web2 social verification: “In Web2, we have a word — ‘I Googled you.’ When I heard it first time, I was like… you what? But it’s like — trust but verify. Check the address.” The equivalent in Web3 is asking a business partner or counterparty to share their wallet audit before proceeding with any transaction or agreement. Furthermore, the person sharing cannot manipulate the result — the audit is calculated by ChainAware’s models from public on-chain data. It reflects actual behavior, not self-reported information. For more on how this applies to peer-to-peer and B2B contexts, see our behavioral user analytics guide.

AI Credit Score: Web3’s Missing Financial Layer

ChainAware’s credit score is the oldest component of the product suite — it predates the public launch of the fraud detector, having been developed first for SmartCredit.io’s DeFi lending platform. The credit score model has been running in production for over four years at the time of X Space #28.

Martin provides the clearest explanation of what a credit score actually measures — important context because the term is often misused: “Credit score displays the person’s financial ability to conform to, to respect his financial obligations. It’s their financial ability.” In traditional finance, FICO and similar scores are calculated from cash flow patterns, repayment history, debt levels, account longevity, and credit utilization. ChainAware’s Web3 credit score calculates equivalent metrics from on-chain transaction data — without any KYC, without any personal data collection, entirely from public blockchain history.

The credit score is currently less widely used in Web3 than fraud detection — Martin acknowledges that Web3 is “less focused on financial ability at the moment.” However, this is a temporary state of the market rather than a permanent feature. As DeFi lending matures and moves toward undercollateralized products, credit scoring becomes essential infrastructure. The protocols that build credit assessment infrastructure early will have a significant advantage when market demand catches up to the capability. For the full credit scoring guide, see our complete Web3 credit scoring guide and our DeFi credit score platform comparison.

Telegram Mini App, Bots, and Discord Integration

A significant practical convenience announcement in X Space #28 is the Telegram Mini App — which Martin notes was launched but not yet formally announced. The motivation is direct: Web3 users spend enormous amounts of time in Telegram groups discussing projects, sharing contract addresses, and making investment decisions. Previously, checking an address required leaving Telegram, navigating to the ChainAware website, pasting the address, and returning to Telegram with the result. This friction reduces the likelihood that users will actually check addresses before acting.

No Context Switch — Stay in Telegram

The Telegram Mini App eliminates this friction entirely. Users can paste any wallet or contract address directly into the TMA’s interface without leaving the Telegram environment. The result appears immediately within Telegram, making fraud and rug pull checks effortless at the exact moment they are most relevant — when an address is being shared in a group conversation. As Martin describes: “No context switch. Super effective.”

Additionally, ChainAware supports TON chain fraud detection — directly relevant to Telegram users given Telegram’s deep integration with the TON ecosystem. The timing is significant: Telegram updated its terms and conditions in early 2025 to require that all Telegram Mini Apps support TON chain. Martin notes this with evident satisfaction: “For us it’s like — thank you guys. Now only the real TMAs will stay there.” This requirement filters out the many TMAs that are unrelated to Web3 and ensures that genuine blockchain tools like ChainAware’s TMA remain prominent and accessible. ChainAware also offers Telegram and Discord bots with command-line interfaces for users who prefer typed commands to graphical interfaces.

Decentralised Ecosystem Self-Cleaning: The Big Picture

One of the most compelling ideas in X Space #28 is the concept of decentralised ecosystem self-cleaning through widespread adoption of free fraud detection tools. The logic is elegant and worth laying out explicitly.

Currently, most Web3 users check addresses only occasionally — when they are already suspicious, or after they have had a bad experience. Bad actors thrive in this environment because the overwhelming majority of their potential victims do not check. Consequently, the expected cost of attempting fraud is low: the probability of being caught before the fraud occurs is minimal.

If, however, checking addresses becomes a standard behavior — as automatic as Googling someone before a meeting in Web2 — the dynamic reverses entirely. Every bad actor’s address is continuously scrutinized. High fraud probability scores become visible to potential counterparties before any interaction occurs. The bad actor gets excluded from interactions not by a central authority but by individual users making informed decisions.

Martin describes the mechanism: “If you allow any user to check anyone — is it bad or good — you are introducing a fully decentralised system to verify users. Users are verifying each other.” This is not blockchain surveillance or KYC compliance — it is a peer-verification system built on public data and open tools. Furthermore, the more people who use the free tools, the more effective the ecosystem cleaning becomes. Unlike most network effects where early adopters benefit most, this one benefits late adopters equally — every new user who starts checking addresses adds to the collective protection of the ecosystem.

Web3 Ad Tech: The Second Key to Exponential Growth

While X Space #28 focuses primarily on individual user tools, Martin and Tarmo repeatedly reference the second pillar of ChainAware’s vision: Web3 ad tech. This is covered in detail in X Space #29 and subsequent sessions, but its importance to the overall framework warrants explanation here.

The user acquisition problem in Web3 is severe. Martin describes a real client scenario: 3,000 monthly website visitors → 600 connected wallets → 6–8 transacting users. This 0.2% conversion rate makes Web3 user acquisition unit economics fundamentally unworkable. No business can become cash-flow positive acquiring transacting users at this conversion rate, regardless of how low the cost-per-click is.

The root cause is the same mass-broadcast approach that Web2 used before Google AdWords: every visitor sees the same message, regardless of who they are and what they want. Web3’s blockchain data makes this unnecessary — every connecting wallet brings a complete financial behavioral history that can be used to deliver precisely targeted messages. An experienced DeFi borrower and a first-time crypto user visiting the same lending protocol should see completely different messaging. Currently, they see the same thing.

Meme Coin or Cash-Flow Positive — The Binary Choice

Martin frames the strategic choice facing every Web3 project founder with characteristic directness: “Either you are a meme coin or you will become cash-flow positive. And to become cash-flow positive, first you have to eliminate fraud. Second, apply ad tech like Google for Web3.” There is no third option. Projects that continue using ineffective mass-broadcast marketing while failing to address fraud will exhaust their token treasury without achieving sustainable economics. For the full detail on how ChainAware’s marketing agents solve this, see our guides on the personalization opportunity in Web3, the DeFi onboarding problem, and our SmartCredit case study showing 8x engagement improvement.

Comparison Table: ChainAware Individual User Tools

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Frequently Asked Questions

Why is ChainAware’s fraud detection free for individual users?

ChainAware keeps individual user tools free because widespread adoption creates ecosystem-level value that benefits the company indirectly. When every Web3 user checks wallet addresses before transacting, bad actors get excluded organically — the ecosystem self-cleans. This increased trust and safety benefits all ChainAware clients, including enterprise customers who pay for the transaction monitoring agent and marketing agent. Additionally, free tools attract users who eventually become enterprise clients or refer enterprise clients. The infrastructure costs are real — ChainAware pays for the compute — but the Google Cloud partnership helps offset these costs substantially.

What is the difference between AML screening and ChainAware’s fraud detection?

AML (Anti-Money Laundering) screening checks whether a wallet address appears on lists of known bad actors — sanctioned entities, confirmed fraud addresses, mixer service users. It is backward-looking and documentation-based: it can only flag addresses that have already been identified as problematic. ChainAware’s fraud detection is forward-looking: it predicts whether an address will exhibit fraudulent behavior in the future based on its behavioral patterns, regardless of whether it has any prior documented history. In Web3 where transactions are irreversible, only forward-looking prediction provides meaningful protection. For the detailed comparison, see our AML vs transaction monitoring guide.

How does the Share My Wallet Audit feature prove wallet ownership?

When a user generates a Share My Wallet Audit link, they must sign a message with their private key — the same mechanism used to sign any blockchain transaction. This cryptographic signature proves that the person generating the link controls the private key associated with the wallet address. The link is then unique to that signing event and cannot be generated by anyone who doesn’t control the wallet. The recipient of the link can therefore be confident that the wallet audit they see corresponds to the wallet whose ownership the sender has proven.

What does ChainAware’s 98% accuracy actually mean?

The 98% accuracy figure is derived from backtesting against CryptoScamDB — an independent public database of confirmed crypto scam and fraud addresses. The model was tested on labeled data it had never seen during training (using held-out test sets, not training data, to prevent overfitting). Of all wallet addresses the model flagged as high fraud probability, 98% matched confirmed fraudulent addresses in the ground-truth dataset. Backtesting methodology is essential for any predictive AI claim — as Martin notes in the X Space, “an algorithm without backtesting is like a ship without a captain.” For more on ChainAware’s methodology, see our Fraud Detector guide.

Why does ChainAware use its own AI models rather than OpenAI or other LLMs?

LLMs (large language models) like ChatGPT are statistical autoregression engines designed to predict the next word in a text sequence. They are not designed for behavioral prediction from structured blockchain data — and cannot provide measurable accuracy for fraud detection tasks. Building proprietary neural networks trained specifically on blockchain behavioral data (labeled examples of pre-fraud and pre-rug-pull wallet behavior) produces models with verifiable, backtested accuracy that continuously improves as more labeled data accumulates. Using LLMs would provide no competitive advantage since any competitor could build the same wrapper. For the full explanation, see our predictive AI vs LLMs guide.

What is the Google Cloud Web3 Startup Program partnership?

ChainAware was accepted into Google’s Cloud Web3 Startup Program, receiving $250,000–$350,000 in compute credits. The program is selective — Google verified that ChainAware had real calculation needs rather than just wanting to make an announcement. With these credits, ChainAware can run pre-calculations that push fraud and rug pull prediction accuracy above 99%, reduce latency, and begin developing timing prediction capabilities (predicting when a fraud event will occur, not just whether it will occur). This compute partnership is a significant enabler of the roadmap milestones discussed in X Space #28.

How is this X Space series structured?

ChainAware co-founders Martin and Tarmo have been hosting weekly (previously biweekly) X Spaces since January 2024. X Space #28 focuses on individual user tools and roadmap. X Space #29, covered in our article on attention AI vs real utility AI, discusses the broader Web3 AI landscape. Subsequent sessions cover business-facing tools and DeFi AI applications — see our full series including the real AI use cases guide, the AI agents for Web3 businesses guide, and the DeFAI explained guide.

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ChainAware.ai — Web3 Agentic Growth Infrastructure

Fraud Detector (98% accuracy) · Rug Pull Detector · Wallet Auditor · Share My Audit · Credit Score · Telegram Mini App — all free for individual users. 14M+ wallet profiles. 8 blockchains. Proprietary ML models. 2+ years live. No KYC required.

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This article is based on X Space #28 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 How ChainAware Is Doing for Web3 What Google Did for Web2 first appeared on ChainAware.ai.

X Space #24 — AI-Based Web3 Marketing Agents: How to End Mass Marketing and Start Converting Users. Watch the full recording on YouTube · Listen on X

Web3 marketing is broken — and most founders know it but can’t articulate exactly why. They spend significant portions of their treasury on KOLs, banners, media articles, and crypto ad networks. Traffic arrives. Wallets connect. Almost nobody transacts. Marketing agencies suggest doing more of the same. X Space #24 is ChainAware co-founders Martin and Tarmo’s most focused session on this problem: why Web3 marketing fails structurally, what solved the exact same problem in Web2, and how AI marketing agents deliver the Web3 equivalent of what Google AdTech did for the internet economy. The session connects twenty years of experience in financial services, startup product development, and predictive AI to the most pressing sustainability challenge every Web3 project faces.

Web3 Marketing Is Still in the 1930s — Literally

Martin and Tarmo open X Space #24 with a historical comparison that is simultaneously uncomfortable and precise. Web3 marketing in 2024 operates on the same principles as Madison Avenue advertising in the 1930s. Both use mass distribution of identical messages to everyone in the target population, with zero personalisation based on the recipient’s individual profile, needs, or intentions.

The 1930s version involved newspaper advertisements, travelling salespeople, and in-store displays at department stores like Macy’s. Every customer walking into Macy’s saw the same store layout. Every newspaper reader saw the same print ad. The communication was one-directional, undifferentiated, and incapable of adapting to the individual receiving it. As Tarmo describes: “1930s — there was a newspaper. The ads were printed in newspaper. People took the newspapers, went to Macy’s. Everyone saw the same newspaper, went to Macy’s separately, individually. Then there was Macy’s and everyone saw the same shopping flow.” Web3 in 2024: “Everyone sees the same banners. Everyone gets the same messages from KOLs. Everyone is reading the same articles. Everyone gets the same content. Like in the 1930s. Then they get to the application — and everyone sees the same application screen. Zero personalisation. Zero.”

The comparison is not a rhetorical flourish. It identifies a structural reality: 90 years of marketing evolution happened in Web2, producing micro-segmentation, intent targeting, and personalised user journeys. None of that evolution transferred to Web3. Consequently, every Web3 project that relies on mass marketing is operating with tools that Web2 abandoned decades ago. For the broader context on why this matters for ecosystem growth, see our guide to why AI agents will accelerate Web3.

The Three Pillars of Web3 Mass Marketing — and Why None of Them Work

Martin identifies the three primary marketing channels that Web3 projects currently use — and explains why all three are mass marketing with the same structural flaw.

KOLs — Key Opinion Leaders

KOL campaigns send the same message to an influencer’s entire follower base. The influencer’s audience may be large — millions of followers — but the message is identical for every person in that audience. An NFT collector and a yield farmer and a first-time crypto user all receive the same promotional content, regardless of their completely different needs and intentions. This is, by definition, mass marketing. The cost per follower reached may seem low, but the cost per converted transacting user is enormous precisely because undifferentiated messaging converts at near-zero rates.

Banner Advertising

Display advertising on platforms like CoinGecko, CoinMarketCap, and Etherscan shows identical banner creatives to every visitor. There is no targeting by wallet behavior, DeFi experience level, or behavioral intention. An experienced yield farmer visiting Etherscan sees the same banner as a complete beginner who has never used a DeFi protocol. Furthermore, projects pay enormous sums for these placements — on platforms where the same banner is shown to the entire user base without any intention-matching whatsoever.

Crypto Media Articles

Press releases and editorial coverage in publications like Cointelegraph and CoinDesk reach broad audiences but without any personalisation. Every reader of the same article gets the same content regardless of their specific interest, experience level, or likelihood to convert to the featured project. Media coverage generates awareness — which is valuable — but awareness alone does not produce converting users. Additionally, the cost of premium crypto media placement has escalated significantly, making the economics of media-driven acquisition increasingly unworkable for projects without substantial treasuries. For more on the structural economics of this problem, see our ChainAware AI agents roadmap.

The Conversion Crisis: 3,000 Visitors, 6 Transacting Users

Martin presents a real-world example from a ChainAware client that makes the conversion problem concrete. This DeFi platform had 3,000 monthly website visitors. Of those visitors, 600 connected their wallets. Of those wallet connectors, 6–8 completed actual transactions. That represents a 0.2% end-to-end conversion rate from visitor to transacting user.

The question Martin poses is simple and devastating: “If you get 3,000 visitors, 600 wallet connects, and 7–8 transactions — will you ever be cash flow positive? Actually never.” At $1,000–$2,000 per transacting user in DeFi acquisition costs (a realistic figure given the combination of KOL fees, banner placements, and media costs), acquiring 8 transacting users costs between $8,000 and $16,000. If each transacting user borrows $100 on a platform with a 0.5% fee, the revenue from those 8 users is $4. The unit economics are not marginal — they are structurally impossible.

The Two-Problem Structure

Tarmo clarifies that two distinct problems exist within user acquisition, and confusing them leads to wasted resources. The first problem is traffic — getting visitors to the website at all. Web3 has partially solved this through quest platforms, loyalty systems, token incentives, and community building. Projects can generate substantial visitor numbers. The second problem is conversion — turning visitors into transacting users. This problem remains almost entirely unsolved. Marketing agencies typically conflate the two, measuring success by traffic metrics while ignoring conversion rates. As Martin describes: “Marketing agencies are saying your website doesn’t convert. Your website is bad — keep giving us money, we’ll fix your website. Like a drug dealer: more of the same.” For the full analysis of why conversion remains broken, see our DeFi onboarding guide.

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Why the AIDA Framework Fails in Web3

Tarmo introduces the AIDA marketing framework — Attention, Interest, Desire, Action — to explain why the structural timeline of mass marketing makes Web3 conversion impossible, regardless of the quality of the product being marketed.

In a functioning personalised marketing environment, AIDA collapses to seconds. A user sees a message that immediately resonates with their specific intentions — attention is captured, interest is triggered, desire forms almost simultaneously, and action follows. The entire sequence completes within a single session. This is what makes personalised web commerce work: when a user encounters something that genuinely matches what they were looking for, the conversion happens naturally and quickly.

In Web3’s mass marketing environment, the sequence stretches over months. A user sees a KOL post (attention). Perhaps they visit the website briefly (interest starts, weakly). They leave without converting. Over the following weeks, they encounter more generic messaging that doesn’t specifically address their needs (desire fails to build). By the time they might theoretically convert, they have completely forgotten the initial attention signal — overwhelmed by the constant stream of identical mass marketing messages from hundreds of competing projects.

The Sensory Overload Problem

Tarmo identifies the neurological mechanism: “Our brains have cognitive limits. Our brains are not working in a way that we will remember some attention which happened four months ago because of the brain’s sensory overload. Like everyone is doing the mass marketing in Web3 today — everyone does the mass marketing and the potential clients get sensory overload.” When every project broadcasts to everyone simultaneously, users cannot retain or act on any individual message. Furthermore, the attention captured by one project’s mass marketing is immediately displaced by the next project’s mass marketing message. The solution is resonance — delivering messages so precisely matched to a user’s intentions that they generate instant desire rather than fleeting attention. For a deeper analysis, see our guide on why personalisation is the next big AI agent opportunity.

The Missing Invisible Hand: What Web2 Solved That Web3 Hasn’t

Martin introduces the economic concept that frames his entire analysis: the invisible hand. In classical economics, the invisible hand describes the market mechanism that allocates resources efficiently without central coordination — buyers and sellers find each other and transact at prices that reflect their respective values. The invisible hand is the matching technology underlying every functional market.

In technology markets, the invisible hand is not abstract — it is a specific piece of infrastructure. Web3 has extraordinary innovation on both sides of the market: 50,000–80,000+ projects creating valuable products and services, and millions of users who would benefit from those products and services. However, the mechanism that connects them efficiently — the technology that routes the right users to the right platforms at the right moment — does not exist in Web3.

Consequently, the market is deeply inefficient. Projects with good products cannot find their users. Users who would benefit from a protocol never discover it. The economic value of the innovation goes unrealised not because the product is bad but because the matching infrastructure is missing. Tarmo puts it directly: “What is the point of a pricing if a user doesn’t know about you? I have three offerings — Starter, Advanced, Premium — and the user doesn’t know you exist, although you will bring so much value.” For more on this dynamic, see our guide to how ChainAware is doing for Web3 what Google did for Web2.

The Google AdTech Innovation: How Web2 Crossed the Chasm

Web2 faced an identical problem in its early phase. E-commerce companies had genuine value to offer — lower prices, greater convenience, wider selection — but could not reach the users who would benefit from their products at sustainable acquisition costs. Web2 companies started with the same mass marketing approaches Web3 uses today: billboard advertising, print media, television commercials. The economics were equally broken: customer acquisition costs were too high for the unit economics of the internet to survive.

Google solved this with a specific technical innovation: micro-segmentation based on behavioral data. By analyzing search history and browsing patterns, Google calculated user intentions — what someone was actively looking for, what problems they were trying to solve, what products they were likely to purchase. This enabled targeted advertising that reached users at the moment of maximum receptivity, with messages specific to their demonstrated intentions rather than general demographics. User acquisition costs collapsed from hundreds of dollars to $15–35 per transacting user in mature markets. Web2 businesses finally had viable unit economics. As Martin notes: “Google is not a search engine. Google gets 95% of revenues via ad tech.” Similarly, Twitter, Facebook, and every large Web2 platform generates its core revenue through intention-based advertising technology.

The Technology Paradigm Law

Martin articulates a principle he calls the technology paradigm law: every technology paradigm requires its own targeting system. Web1 had its own approach. Web2 had Google AdWords. The physical retail economy before Web1 had Madison Avenue and travelling salespeople. Each paradigm creates new user behavior patterns — and matching technology must be purpose-built for those patterns. You cannot port Web2’s Google AdWords to Web3 and expect equivalent results, because Web3 users behave differently, interact through different interfaces, and leave different behavioral traces than Web2 users do. Web3 needs its own paradigm-native targeting technology — and that technology is AI marketing agents powered by blockchain behavioral data. For how this connects to the broader Web3 growth thesis, see our guide to the three levers that accelerate Web3.

Why Blockchain Data Is More Accurate Than Google’s Data

The comparison between blockchain data and Google’s search/browsing data reveals a crucial insight: Web3 actually has access to higher-quality behavioral data than Google had when it invented AdWords. This is a significant advantage that Web3 has not yet exploited.

Google’s targeting accuracy is limited by the quality of its data sources. Search queries reflect momentary curiosity more than settled behavioral patterns. Browsing history captures passive scrolling and incidental visits that carry weak signal about genuine intentions. Tarmo explains the fundamental limitation: “You can search anything. You get some little input, you speak with someone, you see something, a car is driving by, weather — and then you’re curious to search something. So actually search queries don’t really define who you are as a person.” The signal-to-noise ratio in search and browsing data is relatively low.

The Financial Transaction Signal

Blockchain transactions are fundamentally different. Every on-chain transaction required the user to consciously decide to commit real financial value to a specific action. Nobody accidentally borrows $500 on Aave or buys an NFT on OpenSea. The decision process involves real money, MetaMask signature confirmation, and often significant deliberation. As Martin describes: “Will I do this borrow transaction? Will I do this buy transaction? People are thinking. In the case of search, it’s pretty much arbitrary — the kind of searches people are doing during the day.” The deliberateness of financial transactions means that on-chain history reveals genuine behavioral commitments — not momentary curiosity — making it vastly more predictive of future behavior.

Furthermore, the data is permanent, tamper-proof, and publicly available at zero cost. Unlike Google’s data, which is proprietary and not accessible to third parties, blockchain behavioral data is a public good. Any organisation can build predictive models on this data — giving Web3 projects access to a targeting intelligence infrastructure that, in quality terms, surpasses what Web2’s richest ad tech platforms have. ChainAware’s fraud prediction achieves 98–99% accuracy precisely because blockchain data is so high-quality — and the same data quality advantage applies to behavioral intention prediction for marketing. For more on this data advantage, see our guide to predictive AI for Web3 and our comparison of forensic vs AI-based blockchain analytics.

How Web3 Marketing Agents Actually Work

With the problem and the data advantage established, Tarmo and Martin walk through the precise mechanism of ChainAware’s marketing agents — making clear that this is a live production system with actual clients, not a theoretical concept.

The process begins at wallet connection. The moment a user connects their wallet to a Web3 platform, the marketing agent accesses the wallet’s complete public on-chain transaction history and runs it through ChainAware’s behavioral prediction models. The output is a detailed profile: what is this wallet likely to do next? Are they a borrower, a yield farmer, an NFT collector, a trader, a complete newcomer? What is their experience level with DeFi? How risk-tolerant are they based on their historical behavior? What protocol categories have they used?

From Profile to Resonating Content

Based on this profile, the agent generates content specifically tailored to the wallet’s predicted intentions. The content is not just text — it encompasses layout, colour, messaging tone, and call-to-action framing. Tarmo’s example of personality types illustrates why this depth matters: there are at least 16 distinct personality types in standard psychometric frameworks, each of which responds to different visual and textual presentations. Additionally, cultural background and social environment shape aesthetic preferences. A single user interface cannot resonate with 16 different personality types simultaneously. However, a dynamically generated interface can present each user with the specific combination of visual and textual elements that matches their profile.

Martin describes the user experience outcome: “You come to the screen, you look on the screen, and the screen is cut for you. It feels for you at home. It resonates with you. You like some cafe, you like some website — they resonate with you.” When a user experiences genuine resonance, the AIDA framework collapses from months to seconds. Attention, interest, desire, and action all happen in a single session because the content the user sees is precisely matched to what they were already looking for. SmartCredit.io, ChainAware’s lending platform, was among the first to deploy this system — with measurable improvements in wallet engagement visible immediately upon deployment. For the full measured impact, see our SmartCredit case study.

Setup Simplicity

The technical integration is deliberately minimal. Deploying a ChainAware marketing agent requires four lines of JavaScript — the same complexity as adding Google Analytics to a website. Additionally, the marketing team provides URLs pointing to existing content (blog posts, product pages, announcements), and the agent uses these to generate intention-matched messages for each user profile. No custom development, no design team involvement, no ongoing campaign management. The agent operates continuously and autonomously — 24/7, across all time zones, without breaks. For the complete setup walkthrough, see our behavioral user analytics guide.

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The Self-Learning Loop: From 8x to 80x Cost Reduction

The most powerful aspect of the marketing agent architecture is not its initial performance — it is its trajectory. Every interaction with a converting or non-converting user generates feedback that updates the agent’s models. Did the content delivered to a borrower-intent wallet produce a transaction? If yes, that content-profile mapping is reinforced. If not, the agent adjusts its content selection for similar profiles in future interactions.

This feedback loop runs in real time — not in the monthly campaign review cycles of traditional marketing agencies, not in the quarterly retrospective analysis of enterprise marketing teams. As Martin emphasises: “The campaign is finished, it’s over, it’s finita, it’s gone — it’s too late. You need learning in the same moment.” The agent learns from each user interaction immediately, applying the lesson to the very next user it encounters with a similar profile. Consequently, the agent that has processed 10,000 wallet connections is demonstrably more accurate than the agent that processed 1,000 — because each of those 10,000 interactions has contributed to model refinement.

The Compound Improvement Projection

Martin’s quantitative projection illustrates the trajectory. At deployment, marketing agents reduce acquisition costs by at least 8x compared to mass marketing — through immediate behavioral targeting that eliminates the mismatch between message and recipient. After multiple self-learning cycles — six months, nine months, twelve months of continuous operation — the projected improvement reaches 80x or more. The model continues improving as long as it operates, because each user interaction adds to the training set from which it learns. Furthermore, an agent that has been running for 18 months on a specific platform has learned the unique behavioral patterns of that platform’s specific user base — knowledge that is not transferable to a competitor who deploys a generic agent without that training history. For the full theoretical framework, see our complete guide to how Web3 projects benefit from AI agents.

Breaking the Power Law: Why Best Innovation Should Win

Martin and Tarmo spend considerable time on the revenue distribution problem in Web3 — which they identify as both a symptom of broken marketing and a structural barrier to innovation. The revenue distribution across Web3 projects follows a power law, not a normal distribution. This is verifiable: go to DeFi Llama, navigate to the revenue section, sort by annual revenue, and observe the distribution. A small number of protocols capture the vast majority of revenue, while thousands of other projects generate insufficient revenue to sustain themselves.

The critical question is whether this concentration reflects the quality distribution of innovation — or simply the distribution of marketing reach. Tarmo argues, with conviction, that it does not reflect innovation quality: “Some technologies, some systems which don’t deserve to be so much in the focus have cannibalized the market. The real innovations have no chance because the others have created such strong brands. These real innovations coming on next and next — they have no chance.” In other words, the current power law rewards projects with existing brand visibility and shilling capacity, not necessarily those with the most genuinely valuable products.

Marketing Agents as a Levelling Force

Marketing agents address this directly by giving every project — regardless of treasury size or brand visibility — access to the same conversion efficiency. When a small, genuinely innovative DeFi protocol can deliver the same precision-targeted experience as a large, heavily-funded incumbent, the conversion advantage of the incumbent’s mass marketing spend disappears. Users make decisions based on which product actually resonates with their needs — which is which product’s marketing agent best identifies their intentions and delivers matching content. As Tarmo argues: “The best innovation will get the highest conversion of users. The best innovation wins — not some solution that maybe is not the best innovation but the best innovation. Marketing agents bring a kind of normality into the ecosystem. Innovation is incentivised.” For the detailed analysis of the power law mechanism, see our three levers guide.

Adaptive Applications: Beyond Text to Personalised Interfaces

The discussion in X Space #24 extends beyond marketing messages to a broader concept that Tarmo calls “adaptive applications.” This is the logical extension of personalised content: not just what a user reads, but how the entire application presents itself.

Tarmo is direct in addressing the UX designer community’s objection: “Of course, now there are thousands of UX designers coming and saying — no, it’s not true, we design perfect UX. We are saying — guys, you cannot create perfect UX. Let’s think on this. We are all persons, we are different, we have different psychometrics.” The fundamental challenge of UX design is that it must serve an enormously diverse user population with a single interface — and average design, by definition, resonates with nobody in particular while approximately fitting everyone.

Adaptive applications solve this by generating interface elements dynamically based on the user’s behavioral profile. Colors, layouts, typography weight, call-to-action intensity, content hierarchy — all of these adjust to match the specific psychological and behavioral profile the marketing agent has calculated for the connecting wallet. A risk-tolerant trader gets a high-intensity, action-oriented interface with prominent position-taking CTAs. A cautious newcomer gets a gentler, more educational interface with lower-pressure progression. Both users interact with the same underlying protocol, but each sees an interface specifically calibrated to produce the resonance that drives conversion for their specific profile. For more on how ChainAware implements this, see our behavioral user analytics guide.

The Innovation Bandwidth Effect

X Space #24 closes with a reflection on what happens to Web3 innovation when marketing is no longer a manual, time-consuming, human-operated function. Martin identifies the founder time allocation problem: a significant proportion of every Web3 founder’s time goes to marketing coordination, community management, content production, and campaign management — all supplementary activities relative to product innovation.

When marketing agents automate these activities, founders recover bandwidth for the work that only they can do: identifying unmet user needs, designing innovative product mechanisms, iterating on user feedback, and building the features that create genuine competitive differentiation. This bandwidth recovery has a compounding effect: more innovation cycles produce better products, better products attract more users through marketing agents, more users generate more data for agent learning, better agent learning produces higher conversion, higher conversion generates more revenue, and more revenue funds more innovation cycles.

Martin’s conclusion in X Space #24 is a direct prediction: “AI marketing agents will be the new Google. What Google did for Web2, AI marketing agents will do for Web3. The crossing of the chasm for Web3 will happen because of this technology — the same way the Crossing the Chasm in Web2 happened because of Google technology.” The session is not abstract theorising — ChainAware’s marketing agent is live, running on client platforms including SmartCredit.io, generating measurable conversion improvements. For the ecosystem-level implications, see our full ChainAware AI agents roadmap and our guide on the Web3 Agentic Economy.

Comparison Tables

Web3 Mass Marketing vs AI Marketing Agents

Web2 AdTech vs Web3 Marketing Agents: The Parallel

Frequently Asked Questions

Why is Web3 marketing called “1930s marketing” in this X Space?

Because the underlying approach is identical: one message broadcast to everyone with zero personalisation. In the 1930s, this was a newspaper advertisement or in-store display at Macy’s — the same content seen by every customer regardless of their individual preferences or intentions. In Web3 in 2024, this is a KOL tweet, a banner ad on CoinGecko, or a Cointelegraph article — the same content delivered to every member of the audience regardless of whether they are an NFT collector, a yield farmer, a first-time user, or an experienced DeFi participant. The digital delivery mechanism is different; the absence of personalisation is identical.

What makes blockchain data better than Google’s search data for marketing?

Blockchain transactions require deliberate financial decisions. Before executing a transaction, users consciously evaluate whether to commit real money, confirm the transaction in their wallet, and accept the gas cost. This deliberateness means on-chain history reflects genuine behavioral commitments rather than momentary curiosity. Search queries, by contrast, are costless and often arbitrary — triggered by passing conversations, casual curiosity, or algorithmic prompts. As a result, behavioral predictions from on-chain data carry significantly higher accuracy than predictions from search data. ChainAware’s fraud detection achieves 98–99% accuracy specifically because blockchain data is so high quality — and the same quality advantage applies to intention prediction for marketing purposes.

How quickly does a ChainAware marketing agent start producing results?

Immediately. From the first wallet connection after deployment, the agent delivers personalized content based on that wallet’s behavioral profile. The initial 8x improvement in acquisition efficiency applies from day one — because personalised content targeting outperforms mass marketing regardless of how long the agent has been running. The self-learning improvement compounds over time: the longer the agent runs, the more accurately it learns which content variants convert which profiles on that specific platform. After six to nine months of continuous operation, Martin projects conversion improvements of 80x or more relative to mass marketing baselines. For deployment instructions, see our behavioral user analytics guide.

Why does the power law distribution in Web3 revenues persist?

Because marketing reach, not innovation quality, determines which projects acquire users at scale. Projects that secured early market positions through aggressive mass marketing — regardless of their technical merit — benefit from accumulated brand visibility and community trust that makes continued user acquisition easier. Smaller, potentially more innovative projects cannot compete for users using the same mass marketing tools because the economics are prohibitive. Marketing agents change this by giving every project access to the same conversion efficiency — making product quality, rather than marketing budget, the primary determinant of user acquisition success. Verify the power law yourself at DeFi Llama by sorting protocols by annual revenue.

Are marketing agents a replacement for all other marketing?

Marketing agents optimise the conversion of visitors who are already on a platform. They do not replace top-of-funnel awareness generation — some level of traffic acquisition (community building, content marketing, social presence) is still required to get visitors to the platform in the first place. However, marketing agents make every unit of traffic investment dramatically more productive: when 8x more visitors convert to transacting users, the effective cost per transacting user falls 8x, and the economics of awareness-generation activities improve proportionally. The combination — awareness generation to drive traffic, marketing agents to convert that traffic — produces sustainable acquisition economics that pure mass marketing never can.

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This article is based on X Space #24 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-Based Web3 Marketing Agents: How to End Mass Marketing and Start Converting Users first appeared on ChainAware.ai.

X Space with Magic Square — ChainAware co-founder Martin joins the Magic Square community to discuss Web3 AdTech, predictive fraud detection, user acquisition costs, and why the same two forces that drove Web2’s growth will determine whether Web3 crosses the chasm. Listen to the full recording on X

Most Web3 projects excel at building technology and fail at finding users. The unit cost of a blockchain business process has dropped to near zero through full automation — yet customer acquisition costs remain brutally high, hovering around $1,000 per transacting DeFi user. Meanwhile, new entrants burn their fingers on rug pulls and leave the ecosystem permanently, shrinking the addressable market every day. In this X Space hosted by Magic Square, ChainAware co-founder Martin maps exactly why this situation exists, what history tells us about how to fix it, and how ChainAware’s predictive AI platform addresses both problems simultaneously. The conversation covers the intellectual property moat of custom AI models, the critical distinction between predictive AI and LLMs, the mechanics of wallet-based behavioral targeting, and why the Web2 AdTech revolution is the most relevant precedent for where Web3 goes next.

From SmartCredit to ChainAware: How Each Product Discovered the Next

ChainAware did not start as an AI fraud detection company. It started as a DeFi lending platform. Martin and his twin brother Tarmo — both former Credit Suisse Vice Presidents with over ten years at the institution in Zurich — built SmartCredit.io first: a fixed-term, fixed-interest DeFi borrowing and lending marketplace. Before joining Credit Suisse, Martin had already launched four successful products with a combined user base that has grown to somewhere between 250,000 and 500,000 users over the years. That product-building instinct defined how ChainAware was built — through direct observation of what each product needed, not through top-down strategic planning.

SmartCredit required credit scoring. Credit scoring required fraud detection. Fraud detection, once built, revealed it could be applied to smart contract rug pull prediction. Rug pull detection expanded into a full wallet auditing capability. Wallet auditing created the behavioral data foundation needed for personalized user targeting. Each step answered a question raised by the previous one. As Martin explains: “What is Chain Aware? We are practically a prediction engine now. We are predicting behavior. We are predicting who is doing fraud on the blockchain, who is doing rug pulls, who is borrowing next, who is lending next, who is doing trading next. We are predicting behavior.” For the complete product architecture overview, see our ChainAware product guide.

The Prediction Engine: Fraud Detection, Rug Pull Detection, and Wallet Auditing

ChainAware’s platform operates across three interconnected prediction layers, each serving a distinct use case while sharing the same underlying behavioral data infrastructure. Understanding how these layers work together clarifies why they are more powerful as a combined system than as standalone tools.

Fraud detection addresses the most immediate trust problem in Web3: interacting with unknown addresses. On a pseudonymous blockchain, you cannot know whether the person behind an address has a history of scams, money laundering, or protocol manipulation. ChainAware’s fraud detection model analyzes the complete transaction history of any address and produces a real-time fraud probability score — with 98% backtested accuracy against confirmed fraud cases from CryptoScamDB. The prediction is forward-looking, not backward-looking: it tells you what this address is likely to do next, not just what it has done in the past. For the complete fraud detection methodology, see our fraud detection guide.

Rug Pull Prediction: 100% Loss Prevention

Rug pull detection operates on a different threat model. While fraud detection evaluates individual wallets, rug pull detection evaluates the people behind smart contracts and liquidity pools. The distinction matters commercially: a trading loss might cost 20-50% depending on stop losses, but a rug pull results in 100% loss — “chairman total shard” as Martin describes it. ChainAware traces both the contract creator’s funding chain and the behavioral histories of all liquidity providers, identifying the fraud signature in their prior on-chain activity rather than in the contract code itself. This approach catches the sophisticated rug pulls that static contract scanners miss entirely, because sophisticated operators deliberately write clean code while their behavioral history remains permanently on-chain. For the complete rug pull methodology, see our rug pull detection guide.

Wallet Auditing: The Full Behavioral Profile

Wallet auditing combines all prediction layers into a single behavioral profile for any address. The audit calculates experience level, risk tolerance, behavioral intentions (borrower, lender, trader, staker, gamer), and fraud probability — constructing what Martin calls a “human Persona behind the blockchain.” This profile requires no KYC, no identity disclosure, and no data sharing beyond the address itself and its public transaction history. Beyond security, the wallet auditor serves a commercial function: it enables Web3 platforms to understand exactly who is visiting their platform, what those users are likely to do next, and how to reach them with resonating content. For the wallet auditor implementation, see our wallet auditor guide and our behavioral analytics guide.

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The Intellectual Property Moat: Why Custom AI Models Cannot Be Copied

One of the most commercially significant points Martin makes in the conversation concerns the structural difference between building on open-source smart contract code and building proprietary AI models. Most DeFi projects are built on copied foundations — and Martin names this directly with specific examples. Compound wrote the original lending protocol source code. Aave copied Compound’s source code. Then every other lending protocol copied Compound or Aave. PancakeSwap copied the PancakeSwap predecessor. Uniswap then copied or iterated on that, and subsequently the entire DEX ecosystem copied Uniswap. As Martin states clearly: “If you take Uniswap, Uniswap copied a pancreas source code and then everyone copied Uniswap. Everyone copied everyone else’s source code.”

This copying dynamic made DeFi protocols highly replicable but also highly commoditized. Any team with basic Solidity skills can deploy a fork of an existing protocol in days. By contrast, ChainAware’s fraud detection, rug pull prediction, and behavioral analytics models are proprietary intellectual property built over more than two years of model training, backtesting, and iteration. Nobody can fork a trained neural network the way they can fork a GitHub repository. As Martin explains: “If you have AI models, these are not public. This is your intellectual property that you have built. And this intellectual property no one can copy. They can try to redevelop it — meaning it’s a very strong entry barrier.” When competitors claim comparable AI capabilities, ChainAware’s response is direct: specify your prediction accuracy, your data set, and your backtesting methodology. So far, no challenger has provided those details. For more on the competitive positioning, see our predictive AI guide.

98% Accuracy in Real-Time: The Deliberate Downgrade from 99%

ChainAware’s fraud model journey from 60% to 98% accuracy took over two years of iterative development. The path was not linear: initial models achieved roughly 60% prediction accuracy, then improved to 70%, then eventually reached 98%. During that progression, the team also achieved 99% accuracy — and deliberately rejected it. The reason was operational: the 99% model required processing so much additional data that it crossed the threshold from real-time to near-real-time response. For fraud detection specifically, that latency distinction is consequential. A warning that arrives after an interaction has completed offers significantly less user value than one that arrives in time to prevent the interaction entirely.

The decision to stabilize at 98% real-time rather than 99% near-real-time reflects a clear product philosophy: accuracy that arrives too late is less valuable than slightly lower accuracy that arrives in time to act on. As Martin explains: “We had to decide — do we offer 98% real-time or 99% near-real-time? We just say okay, time to scale down. We offer 98% real-time.” The 98% figure is also, as it happens, a more credible claim than 99% — precisely because it acknowledges the real trade-offs involved in production AI systems rather than overpromising. For the complete model accuracy discussion, see our fraud detector guide and our generative vs predictive AI guide.

Predictive AI vs LLM: Two Different Tools for Two Different Jobs

A community member asks whether AI might at some point be turned against users — whether the technology that protects could also harm. Martin’s answer reframes the question entirely by separating two fundamentally different types of AI that the public currently conflates under a single term.

Large Language Models — the category that includes ChatGPT, Claude, Gemini, and the AI tools that became mainstream from 2022 onward — are fundamentally statistical autoregression engines. They learn probabilistic relationships between tokens in text and generate the most statistically probable continuation given the input. Martin is precise about what this means: “LLM is just a statistical auto regression engine, meaning you’re predicting the next word, the next words, the next paragraph, the next sequence.” LLMs are excellent at content generation, conversation, summarisation, and translation. They are not designed to make deterministic numerical predictions about future behavioral events from structured transactional data.

Predictive AI — the category ChainAware operates in — uses supervised learning on labeled behavioral datasets to classify and predict future states. Rather than generating probable text, it produces probability scores for specific outcomes: this address will commit fraud with 0.87 probability, this pool will rug pull with 0.93 probability, this wallet’s next action will be a leveraged trade with 0.74 probability. These are deterministic numerical outputs trained on domain-specific financial behavioral data. As Martin frames it: “Predictive AI will help you to see Personas behind these bits and bytes.” The Matrix analogy is apt — most people see raw transaction data, while ChainAware’s models see the person behind it. For a full breakdown of the two AI categories, see our generative vs predictive AI guide and our real AI use cases guide.

Building Trust in the Web3 Ecosystem: Verification Without KYC

Martin’s argument about ecosystem-level fraud impact extends well beyond individual user protection. The case he makes is structural: the rate at which new users enter and stay in the Web3 ecosystem is directly constrained by the rate at which they encounter fraud, and every user who burns their fingers on rug pulls and leaves permanently represents a permanent reduction in the ecosystem’s growth ceiling.

The pattern Martin describes is familiar to anyone who has tried to onboard non-crypto-native users. A new participant joins, gets exposed to shilling groups, buys into promoted tokens, experiences one or more rug pulls, and concludes that the entire space is fraudulent. They do not try again. They become negative advocates who discourage others from entering. This cycle compounds over time: high fraud rates reduce new user retention, which reduces liquidity and ecosystem vitality, which makes the space less attractive to the next wave of entrants. Conversely, reducing fraud rates creates a trust environment where new users can explore, learn, and eventually become committed participants. As Martin states: “Solving the fraud issue — giving all users possibilities first to verify themselves anonymously. Verification doesn’t mean that you have to open your KYC. You just have to open your address and show who you are. Via this verification, we will create trust in a blockchain.” For the complete trust infrastructure argument, see our Share My Audit guide and our Web3 trust guide.

Anonymous Trust: The Address as Identity

ChainAware’s approach to trust infrastructure rests on a specific insight about blockchain’s properties. On-chain transaction history is immutable, permanent, and public — yet it requires no personal identity disclosure to read or share. This creates a unique opportunity: an address can prove its trustworthiness without ever revealing who owns it. A wallet with five years of sophisticated DeFi interactions, zero fraud associations, and consistent protocol usage tells a compelling story about its owner’s reliability — purely from public behavioral data, without KYC, without identity documents, and without any centralized verification authority. Martin’s practical application is direct: when someone approaches with a business proposal, ask them to sign their wallet and share the audit. If their transaction history is clean and their behavioral profile is consistent with their claims, the interaction can proceed. If it is not, the evidence is cryptographic and permanent. For how this translates into the Share My Wallet product, see our Share My Audit guide.

The Web3 Unit Cost Revolution and the User Acquisition Paradox

One of the most analytically precise arguments in the conversation concerns what Martin calls the unit cost paradox. Web3 has achieved something genuinely revolutionary: it has automated business processes end-to-end, eliminating the back-office operations, settlement delays, counterparty risk, and institutional intermediaries that make financial services expensive in traditional systems. The unit cost of a DeFi lending transaction, a token swap, or a yield farming interaction is a fraction of the equivalent traditional finance operation — and in many cases, the costs shift to the user in the form of gas fees, making the protocol’s marginal cost effectively zero.

Yet despite this dramatic unit cost reduction, Web3 projects consistently fail to become sustainable businesses. The reason is that user acquisition costs are completely disconnected from operational costs. While protocol operations cost pennies, acquiring a genuine transacting DeFi user costs approximately $1,000 or more through existing marketing channels. That asymmetry makes unit economics non-viable at every scale. As Martin explains: “There is no point if your unit cost of a business process is $1, $5, $10 and your customer acquisition costs are $1,000. You have to balance it out, you have to fix it.” Web2 faced the same paradox in the early 2000s — business process costs had dropped dramatically through digitization, but customer acquisition costs remained in the thousands of dollars until AdTech changed the equation. For more on the unit economics framework, see our unit costs and AdTech guide.

The Google Parallel: How Web2 Solved AdTech and What Web3 Must Do Next

Martin’s historical framing of the Web3 problem draws a precise and instructive parallel to Web2’s experience. In Web2’s early growth phase, two specific problems prevented mainstream adoption: rampant credit card fraud that made consumers reluctant to transact online, and prohibitively expensive user acquisition costs driven by mass marketing. Both problems had to be solved for Web2 to cross the chasm from early adopters to mass market.

Fraud was suppressed through mandated transaction monitoring systems — every bank and payment processor was required to deploy real-time AI-based monitoring that could detect new fraud patterns as they emerged. User acquisition costs were reduced through AdTech — Google’s innovation of using search history and browsing behavior to infer user intentions and target advertising accordingly. The critical insight Martin emphasizes is that it was not the search engine itself that made Google the most valuable company in advertising history. Rather, it was the AdTech layer built on top of it. As Martin states directly: “It wasn’t the search engine, it was the AdTech that they created. Twitter, Facebook — let’s be transparent — these are AdTech companies. Google gets 95% of its revenues from AdTech. It’s user targeting.” For the complete Web2-Web3 parallel, see our ChainAware vs Google Web2 guide and Statista’s Google advertising revenue data .

Blockchain History as the Web3 Equivalent of Search History

Google’s AdTech revolution worked because search queries and browsing behavior provided a proxy for user intent — imperfect and easily gamed, but vastly better than demographic targeting. ChainAware’s approach to Web3 AdTech uses a data source that is structurally superior: on-chain transaction history. Every blockchain transaction reflects a deliberate, paid financial decision — not a casual query or accidental page visit. The behavioral signal is higher quality precisely because the gas fee filter removes casual, performative, and accidental behavior. A wallet that has executed twenty leveraged trades on a derivatives protocol has demonstrated its preferences through real money, not just search terms. Predicting its next action with 98% accuracy and targeting it accordingly produces a dramatically higher return on marketing spend than sending the same message to every visitor. For how this translates into the marketing agent product, see our AI marketing for Web3 guide and our intention-based marketing guide.

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Mass Marketing vs Targeted Marketing: Why Web3 Is Stuck in the 1990s

Martin’s critique of Web3 marketing is specific and data-driven. Every major marketing channel in the current Web3 ecosystem delivers the same message to every recipient regardless of their behavioral profile, intentions, or experience level. CoinGecko banner ads reach DeFi veterans and complete beginners simultaneously, showing both the identical creative. CMC listings present the same project overview to retail speculators and sophisticated protocol researchers. KOL posts go out to entire follower bases whether those followers are stakers, traders, NFT collectors, or people who bought their first token last week. Cointelegraph articles are read by everyone who arrives at that headline, regardless of what they are actually looking for.

This mass marketing approach has two compounding problems. First, it generates traffic without generating relevant traffic — visitors arrive at a platform, find messaging that does not speak to their specific needs, and leave without converting. Second, the cost per impression is identical regardless of whether the impression lands in front of a highly qualified prospect or a completely unqualified one. The combination produces terrible unit economics: high spend, low conversion, enormous effective cost per acquired user. As Martin observes: “Crypto media — you go to Cointelegraph, same message for everyone. You see the crypto banners, same message for everyone. But same message for everyone doesn’t resonate with everyone. People are different, people have different intentions, people have different behavior. So you have to resonate with the users.” For more on how personalization addresses this, see our high-conversion Web3 marketing guide and our Web3 personas guide.

The Amazon Landing Page: No Two Visitors See the Same Website

Martin uses Amazon.com as the most vivid illustration of what genuinely personalized user experience looks like at scale. Amazon’s personalization infrastructure means that every visitor to the site sees a different version of the homepage, different product recommendations, different pricing emphasis, and different promotional content — all calculated in real time based on that specific visitor’s browsing history, purchase history, and behavioral signals inferred from millions of comparable user journeys.

This personalization is not cosmetic. It is not about color schemes or font choices. It is about matching the product surface to the specific intent each visitor brings to that session. A user who has been browsing professional photography equipment sees professional camera recommendations. A user who has been researching home office setups sees ergonomic furniture. Neither visitor is served generic “bestsellers” — they are each served a version of Amazon optimized for their specific, data-derived intention profile. Web3 today operates at the opposite extreme: every visitor to every DApp sees the same landing page, the same hero message, the same call-to-action, regardless of whether they are a DeFi native with three years of leveraged trading history or someone connecting a wallet for the first time. As Martin states: “Go on Amazon.com and compare your landing page with others. Every landing page is different because it’s calculated based on your intentions. There’s no two same landing pages. Go in Web3 — everyone gets the same landing page. Every single user.” For how ChainAware’s marketing agent creates this Amazon-style experience for Web3 platforms, see our Web3 adaptive UX guide and our user segmentation guide.

The Web3 AdTech Competitive Landscape: Underdeveloped and Misunderstood

In response to a question about competitors, Martin describes the state of the Web3 AdTech market in precise terms that reveal both the opportunity and the misconception that characterizes most of it. The reference point is the Safary Web3 Growth Landscape — a regularly maintained map of Web3 marketing and analytics companies that ChainAware joined in August, listed in the attribution and AdTech sectors. The landscape contains over 100 companies that have collectively received more than $1 billion in investment.

Looking closely at the companies in the AdTech category, however, reveals a significant mismatch between label and function. Most of them are publisher networks — platforms like Coinzilla and BitMedia that distribute crypto advertising inventory across publisher sites. These are ad distribution networks, not AdTech companies in the behavioral targeting sense. They can deliver impressions but cannot calculate user intentions, segment audiences by behavioral profiles, or serve personalized content based on on-chain history. Real AdTech requires two components: an analytics layer that calculates user behavioral intentions from their history, and a targeting layer that delivers content matched to those intentions. The combination of both in a Web3-native form, using on-chain transaction history as the data source, is what Martin describes as nearly absent from the current market. As he explains: “If you’re looking at the AdTech sector and analyzing these companies, you see that the part of real targeting — intention calculation, behavior calculation, combined with targeting — is pretty underdeveloped.” For a breakdown of how ChainAware fits into the Web3 growth landscape, see our behavioral analytics guide.

Why Wallet-to-Wallet Messaging Fails as a Targeting Method

One approach that some companies have tried is wallet-to-wallet messaging: sending communications directly to wallet addresses via on-chain protocols or aggregator interfaces. Martin dismisses this approach with a specific data point: only approximately 5% of users have enabled wallet-to-wallet messaging. The 95% who have not enabled it either never see the message or find it in a spam folder they rarely check. Beyond the reach problem, there is a consent and relevance problem: unsolicited wallet messages are widely perceived as spam, which actively damages brand perception rather than improving conversion. Effective targeting requires reaching users in the contexts where they are already engaged — not inserting messages into communication channels they mostly ignore. For more on effective Web3 user acquisition approaches, see our Web3 marketing guide.

AML vs Transaction Monitoring: The Regulatory Distinction Most Projects Ignore

Martin addresses the compliance landscape with a technical distinction that has significant practical consequences for any Web3 project that needs to meet regulatory requirements. The two primary compliance tools in the blockchain space — AML (Anti-Money Laundering) analysis and transaction monitoring — are fundamentally different technologies that solve different problems, yet most projects and even most compliance vendors treat them as interchangeable.

AML analysis is a rules-based algorithm. It traces the flow of known-illicit funds through the blockchain ecosystem, following contaminated money from flagged sources through intermediate addresses to identify who may have received proceeds from criminal activity. The rules that define “illicit” are codified based on known past cases. This makes AML analysis effective at tracking funds connected to previously identified bad actors, but structurally incapable of detecting genuinely new fraud patterns that have not yet been flagged. Regulators under MiCA and FATF frameworks require both AML compliance and real-time AI-based transaction monitoring — not one as a substitute for the other. As Martin explains: “AML is a rules-based algorithm. But the regulator mandates transaction monitoring because the same happened in Web2. Every bank, every virtual asset service provider has to do actually both.” For the complete regulatory context and compliance implementation, see our AML and transaction monitoring guide, our blockchain compliance guide, and the FATF virtual assets recommendations .

Why Fraud Farms Stay Ahead of Static Tools

Martin introduces the concept of “fraud farms” — sophisticated organizations that operate fraud as a professional business, continuously adapting their methods to circumvent the detection systems their targets deploy. These operations know what tools their counterparties use. They design their fraud patterns specifically to pass rules-based AML checks while remaining active. Static rules-based systems, by their nature, can only detect patterns that have already been codified — which means they are always behind the current state of fraud innovation. AI-based transaction monitoring learns from new patterns continuously, updating its detection capability as new fraud techniques emerge. This continuous learning capability is what makes it mandated rather than optional under forward-looking regulatory frameworks. For the transaction monitoring agent implementation, see our transaction monitoring agent guide.

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ChainGPT Partnership and IDO: Why the Right Ecosystem Partner Matters

The conversation covers ChainAware’s IDO plans, with Martin providing both the commercial details and the strategic reasoning behind choosing ChainGPT as the exclusive launchpad and lead investor. The IDO was announced the day before this recording, with ChainGPT as lead investor alongside Koinix. The launch would use ChainGPT’s launchpad exclusively. At the time of listing, the fully diluted valuation was set at $3.5 million, with an initial market cap of $80,000 before liquidity — a structure Martin described as deliberately attractive to genuine participants rather than optimized for opening-day hype.

Beyond the economics, Martin’s assessment of ChainGPT as a partner reflects a specific philosophy about which relationships create long-term value. ChainGPT’s investment thesis focuses explicitly on projects with real technology and genuine use cases, screening out the category of project that combines copied source code with a large shilling army. As Martin explains: “ChainGPT is looking for the real stuff. They’re not looking for someone like what we had in DeFi summer — 95% of projects copied someone and put a shilling army on top. ChainGPT is focused on AI, analytics, predictions. That’s what they focus on. We are very happy to be in this family.” The contrast Martin draws with anonymous VC relationships — where partners may not understand the technology they are backing — highlights how partnership quality affects both credibility and long-term project sustainability.

Crossing the Chasm: The Two Requirements for Web3 Mainstream Adoption

Martin’s closing remarks synthesise everything discussed into a single, clear framework for Web3 mainstream adoption. The framework has exactly two components, both historically demonstrated in Web2, both currently unresolved in Web3.

First, fraud rates must decrease significantly. High fraud rates prevent new users from establishing positive experiences in the ecosystem. Every rug pull experienced by a newcomer is a permanent ecosystem exit. Building trust through accessible, anonymous behavioral verification — making it possible for any participant to verify any address without KYC — is the mechanism by which fraud rates fall. When bad actors know they can be identified by their on-chain behavior before they execute the next scam, the cost-benefit calculation of fraud changes. When potential victims can check an address before they interact, the success rate of fraud attempts drops. Both effects compound over time to create a more trustworthy ecosystem that retains new entrants rather than driving them away. For the full fraud ecosystem argument, see our Web3 growth guide and Chainalysis’s crypto crime data .

Innovation Cannot Scale Without Sustainable Unit Economics

Second, user acquisition costs must fall to sustainable levels through targeted, intent-based marketing. Web3 has solved the operational cost problem — business process unit costs are already at levels that make the technology structurally superior to traditional finance. However, solving the operational side while leaving acquisition costs at $1,000 per user creates a business model that cannot reach sustainability regardless of how elegant the technology is. Projects in this situation have two options: raise more capital and burn it on mass marketing, or launch a token and use speculation to subsidize acquisition. Neither path leads to the sustainable revenue generation that enables long-term product iteration. As Martin states in his closing remarks: “From one side we have to introduce the AdTech systems which reduce mass-related user acquisition costs. From the other side, we have to create much higher trust in the ecosystem. That’s all the same that happened in Web2. We are not inventing anything new — we are just repeating what Web2 did.” For how ChainAware’s complete platform addresses both requirements simultaneously, see our product guide and our Web3 agentic economy guide.

Comparison Tables

Web3 Mass Marketing vs ChainAware Intent-Based Targeting

AML Tools vs Transaction Monitoring: What Regulators Actually Require

Frequently Asked Questions

What is Magic Square and why did they host this X Space with ChainAware?

Magic Square is a Web3 app store and launchpad that curates and distributes decentralized applications to its community. The X Space series they run brings Web3 projects to their audience for educational conversations about technology, use cases, and ecosystem development. ChainAware’s focus on fraud detection and Web3 AdTech aligned directly with topics relevant to Magic Square’s community of Web3 users and builders — specifically the questions of how to verify project legitimacy and how Web3 projects can find users sustainably.

Why did ChainAware build its own AI models instead of using OpenAI or other LLMs?

ChainAware’s core use cases — fraud detection, rug pull prediction, and behavioral intention calculation — require deterministic numerical outputs trained on structured financial transaction data. LLMs are designed to generate probable text sequences, not to classify future behavioral events from on-chain data with 98% accuracy. Beyond the technical mismatch, building proprietary AI models creates a defensible intellectual property moat. DeFi smart contract code can be forked in hours. A trained neural network with 2+ years of iteration, carefully curated training data, and validated backtesting results cannot be replicated without equivalent investment of time and expertise. This IP moat is one of ChainAware’s core competitive advantages.

How does ChainAware’s wallet verification work without KYC?

ChainAware analyzes only publicly available on-chain transaction data — no personal identity information is required at any point. A user who wants to verify themselves shares their wallet address and cryptographically signs a message proving they control it. ChainAware’s models then analyze the public transaction history of that address and produce a behavioral profile: fraud probability, experience level, risk tolerance, and predicted intentions. The profile proves trustworthiness through demonstrated financial behavior without revealing who the person behind the address is. This maintains the pseudonymity that blockchain users value while enabling the trust signals that counterparties, investors, and platforms need.

What chains does ChainAware currently support, and which are coming next?

At the time of this X Space, fraud detection was live on four chains and rug pull detection was live on two. ChainAware was actively working on full-package integrations for new chains — adding fraud detection, rug pull detection, and behavioral intention calculation together rather than piecemeal. The next chain announced was HAQQ Network (Islamic Coin). The team aims to add a new chain approximately every one to two months, with the goal of delivering the complete product suite on each new chain rather than partial capabilities. For the current chain coverage, see the chainaware.ai platform directly.

Why are Web3 user acquisition costs so high, and how does ChainAware help reduce them?

Web3 user acquisition costs are high because the entire marketing ecosystem operates on mass marketing — sending the same message to everyone regardless of behavioral profile, experience level, or intent. Mass marketing generates impressions but not conversions, because undifferentiated messages do not resonate with the specific needs of diverse user segments. ChainAware calculates each visiting wallet’s behavioral profile from their on-chain transaction history and uses that profile to serve matched, resonating content automatically. The result is that the marketing message reaching a DeFi trader speaks to their trading context, while the message reaching a first-time user speaks to their entry-level needs. Higher relevance produces higher conversion rates, which reduces the effective cost per acquired user — exactly as Google’s AdTech reduced Web2’s acquisition costs from thousands of dollars to tens of dollars.

This article is based on the X Space hosted by Magic Square featuring ChainAware co-founder Martin. Listen to the full recording on X . For integration support or product questions, visit chainaware.ai.

The post Web3 AdTech and Fraud Detection — X Space with Magic Square first appeared on ChainAware.ai.

X Space with ChainGPT and Datai — ChainAware co-founders Martin and Tarmo join Ellie from Datai and ChainGPT Labs host Chris for a wide-ranging conversation on AI agents in Web3: what they actually are, what they can already do, and why they mark the beginning of the biggest innovation wave the industry has ever seen. Listen to the full recording on X

Three projects at the frontier of Web3 AI infrastructure sit down to talk honestly about what is actually being built. ChainAware brings two production-ready AI agents — a fraud detection agent and a Web3 marketing agent — built on proprietary predictive models trained over two years. Datai brings three years of blockchain data aggregation and a smart contract categorization AI that translates raw on-chain transactions into the behavioral language that intelligent agents need to function. ChainGPT Labs, which incubates both, provides the ecosystem context that connects these tools to the broader question every Web3 builder faces: how do you get real users, build sustainable revenue, and focus on the innovation that actually matters? Together, they map out why AI agents are not a hype narrative — they are the infrastructure layer that finally makes Web3 businesses viable.

Three Projects, One Mission: What ChainAware, Datai, and ChainGPT Are Building

ChainGPT Labs brought together two of its incubated projects — ChainAware and Datai — for this X Space precisely because their work is complementary. Both teams identified the same fundamental gap in Web3 infrastructure from different directions, and both arrived at AI agents as the solution. Understanding what each brings to the table clarifies why the combination matters.

ChainAware is a prediction engine. Starting from SmartCredit’s DeFi lending platform, Martin and Tarmo built iteratively: credit scoring required fraud detection, fraud detection extended to rug pull prediction, behavioral modeling followed, and marketing personalization emerged from behavioral data. Today the platform produces real-time behavioral profiles for any wallet address — predicting fraud probability, rug pull risk, experience level, risk tolerance, and future behavioral intentions (borrower, lender, trader, gamer, NFT collector). Two production AI agents sit on top of that infrastructure: the fraud detection agent and the Web3 marketing agent. As Martin explains: “We are a big calculation engine. Not just a calculation engine — we are a prediction engine. We predict what wallets are doing in the future.” For the complete ChainAware architecture, see our product guide.

Datai: Making Blockchain Data Readable for AI

Datai approaches the same problem from the data infrastructure layer. Ellie explains the core challenge: when you look at any blockchain transaction explorer, you see addresses interacting with other addresses. However, you do not see what the user was doing. That address could be connecting to a DeFi lending protocol, minting an NFT, bridging assets between chains, signing a contract, purchasing a gaming asset, or investing in a real-world asset. The transaction looks identical at the address level regardless of which of these activities is occurring. Datai spent three years manually aggregating blockchain data and building categorization for the smart contracts that users interact with — then invested 1.5 years building an AI model that can automatically categorize smart contracts at scale. The result is data that, as Ellie puts it, reads “like English” — structured behavioral context that AI agents can actually understand and act on. For how clean behavioral data enables better AI agent decisions, see our behavioral analytics guide.

What AI Agents Actually Are: Beyond the Hype

The X Space opens with an accessible definition that cuts through the significant volume of AI agent hype circulating in the Web3 space. AI agents are autonomous systems that run continuously, learn from feedback, and execute defined functions without requiring human initiation at each step. They differ from chatbots and simple automations in three specific ways: they operate on real-time data rather than static training sets, they learn continuously from outcomes rather than remaining fixed, and they execute consequential actions (transactions, content generation, risk flags) rather than just producing text responses.

Ellie offers the most accessible definition in the conversation: “Just a friend. Like it’s a robot friend who’s living inside your PC. This robot friend will listen to what you say, what you do, and then it will start telling you things — find my best pictures, find my best song. It can understand a lot of information really quickly. It’s like having a super helper that is always ready.” This analogy captures the operational reality well: an agent that has been configured for a specific task runs in the background, continuously analyzing the information relevant to that task and taking defined actions when conditions are met. No human needs to ask it to start or tell it when to act. For more on how AI agents differ from prompt engineering, see our Web3 AI agents guide.

Why Web3 Is the Ideal Environment for AI Agents

Both Ellie and Martin make a specific structural point about why Web3 enables AI agents more powerfully than Web2. In Web2, building agents is technically simpler because the data is in natural language — tweets, messages, Netflix viewing history, search queries. However, that data is locked behind proprietary APIs, fragmented across closed platforms, and requires individual permission agreements with each company. Web3’s data is structurally different: every transaction is public, every interaction is permanently recorded on open ledgers, and no permission is required to read any of it. The challenge in Web3 is not access — it is interpretation. Raw blockchain data is not readable without smart contract categorization. Once that categorization layer exists (which is what Datai provides), the behavioral signal quality is dramatically superior to anything Web2 has — because every transaction represents a real financial decision with real cost attached. For how this connects to ChainAware’s behavioral prediction models, see our generative vs predictive AI guide.

The Founder Bandwidth Problem: Why 90% of Time Goes to the Wrong Things

One of the most practically resonant arguments in the entire conversation comes from Tarmo’s opening on what AI agents mean for Web3 founders. The observation is simple and verifiable by anyone who has run a startup: the actual innovation a founder set out to build receives a small fraction of their working time. The rest goes to the operational overhead that every business requires — marketing, sales, compliance monitoring, tax reporting, transaction auditing, customer support, legal coordination. None of these activities are the core innovation. All of them are essential. Together, they consume the majority of a founder’s calendar.

Tarmo frames this precisely: “Just imagine when you are doing now a startup. You can spend maybe a real innovation for a small piece of time. The rest of time goes into tax reporting, into marketing, into sales, into transaction monitoring. What AI agents do — they take over all these tasks which you have to do supplementary to the real innovation, so that you can focus on the innovation.” Martin reinforces this with a specific observation about Web3 marketing: most founders end up devoting enormous energy to mass marketing campaigns that produce poor conversion because the personalization infrastructure does not exist yet. Building that infrastructure, running it, and optimizing it manually consumes resources that should be going toward product iteration. For more on how marketing agents specifically address the founder bandwidth problem, see our AI marketing guide and our Web3 agentic economy guide.

The Innovation Multiplier Effect

The second-order argument is even more significant than the immediate bandwidth gain. If AI agents remove the supplementary task burden from every Web3 founder simultaneously, the aggregate increase in innovation output across the entire ecosystem is enormous. Currently, thousands of talented teams spend the majority of their time on activities that provide no competitive differentiation — mass marketing to undifferentiated audiences, manually configuring compliance monitoring, preparing tax reports. All of this effort produces zero innovation. Redirecting even half of that effort toward core product development would compound into a wave of new capability that Martin describes as the biggest the industry has seen: “This will be a massive wave of innovation that is coming. All these supplementary activities — what the founders have to do at the moment — it blocks their time. Take it over with agents. That means focus on innovation, create real innovation.” For how this connects to the broader Web3 growth trajectory, see our AI agents acceleration guide.

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The Web3 Marketing Agent: From Mass Messaging to 1:1 Personalization

Marketing was personalized before it became mass. Before broadcast advertising, before mass media, before the internet — merchants knew their customers individually, knew their needs, and tailored their communication accordingly. Mass marketing was an economic compromise: reaching millions of people with identical messages was cheaper per impression than reaching each person with a relevant one, even though conversion rates were dramatically lower. The internet initially intensified mass marketing rather than solving it, because the data layer needed for personalization at scale did not exist yet.

Google changed that equation in Web2 by using search and browsing history to infer behavioral intent and serve matched advertising. Web3 today sits at the same pre-AdTech position that Web2 occupied before Google’s innovation. Every major marketing channel — KOL promotions, crypto media banners, Telegram ads, CMC listings — delivers identical messages to heterogeneous audiences. A DeFi native with five years of sophisticated protocol usage receives the same onboarding content as someone who created their first wallet last week. The conversion rate from this misalignment is predictably terrible. As Martin explains: “What is website’s role? Website’s role is to convert users. Website’s role is to resonate with users. So you have to create personalized websites.” For the full Web3 personalization framework, see our Web3 personalization guide and our intention-based marketing guide.

How the Marketing Agent Works in Practice

ChainAware’s marketing agent operates at the moment a wallet connects to a platform. The sequence is: wallet connects → ChainAware’s behavioral models calculate the wallet’s profile in real time → the agent generates content matched to that profile → the visitor sees messaging that resonates with their specific behavioral type. A high-probability borrower arrives at a lending platform and sees content about borrowing terms and collateral optimization. A leverage trader at the same platform sees content about position management and leverage tools. A first-time DeFi user sees content that addresses their onboarding needs. None of these visitors know that the content was generated for them specifically — they simply experience a platform that feels relevant. As Martin explains: “You calculate the user’s behavior, experience, risk willingness. You calculate who are the future borrowers with probabilities, who are the future lenders, who are the future leverage takers, who are the gamers, who are the NFT collectors. Based on these behavioral parameters, it’s automated targeting.” For the complete marketing agent implementation, see our Web3 personas guide.

From Manual Configuration to Auto-Generation

ChainAware’s banner system — which delivers personalized messages to platform visitors based on behavioral profiles — is already in production with clients. Currently, the system includes a significant manual configuration step: a team member specifies which messages should appear for which behavioral profiles, designs the content variants, and sets the targeting parameters. This manual configuration creates a startup cost for each new client deployment. The next evolution underway is auto-generation: the agent itself generates the content variants based on the behavioral profiles it identifies, requiring only human review rather than human creation. As Martin notes: “We have a lot of manual configuration there. What we are doing now is we are moving from manual configuration to auto generation.” Once auto-generation is complete, deploying the full personalization system requires minimal setup time — and the agent runs continuously from that point without ongoing human involvement.

The Orchestrator Shift: How Marketers Evolve in an AI Agent World

The host Chris, who works in marketing and community management for ChainGPT Labs, asks the question that many marketing professionals privately wonder: do AI agents replace the marketer? The answer from both Ellie and Tarmo is thoughtful and specific — and it reframes the question in a way that is both reassuring and clarifying.

Ellie’s observation is precise: AI agents in Web3 marketing will make the marketer’s work “a bit similar to Web2.” The comparison is apt. In Web2, sophisticated marketers do not write every word of copy, design every visual, or manually A/B test every subject line — they use tools, platforms, and workflows that handle execution while the marketer focuses on strategy, brief writing, and judgment about what is and is not resonating. Web3 marketing currently operates below that level because the data layer and personalization infrastructure do not yet exist. AI agents bring Web3 marketing up to Web2 sophistication, and then push further toward genuine 1:1 personalization that Web2 never fully achieved. For the marketing professional, the transition is from manual execution to strategic orchestration. As Tarmo describes the shift: “You become like an orchestrator. You have highly specialized agents — one agent is preparing nice illustrations which resonate with specific personas, one agent is preparing your texting, one agent is calculating a psychological profile. All you do is orchestrate them.” For more on how this orchestration model works in practice, see our Web3 growth guide.

High-Value Creation vs Low-Value Execution

The practical consequence of the orchestrator shift is a redistribution of human cognitive effort from low-value execution tasks toward high-value creative and strategic work. Currently, a significant portion of any marketing team’s time goes to tasks that require skill to do but that produce no strategic differentiation: writing variations of the same message for different channels, manually segmenting audience lists, resizing images for different ad formats, reporting on campaign performance. These tasks require time and training but not genuine creative judgment. AI agents can execute all of them. What they cannot replace is the judgment about which message strategy actually resonates with a specific community, which product narrative builds genuine trust, and which creative approach communicates a technical value proposition clearly. As Tarmo explains: “We are taken out of these daily operating activities where we spend 90% of our time. Instead we focus on these high, very high value creation activities. We use our creativity, our intellectual power to create something new.” For more on how ChainAware’s agent stack supports this reallocation, see our DeFi onboarding guide.

Datai: The Data Layer That Makes Intelligent Agents Possible

For an AI agent to make intelligent decisions, it needs to understand the context of the data it is acting on. In Web2, context is relatively accessible: user behavior is expressed in natural language — search queries, messages, reviews, social posts. AI systems trained on language can interpret this behavior without additional translation layers. In Web3, the equivalent behavioral data is expressed in a format that is opaque by default: hexadecimal addresses interacting with hexadecimal contracts, with transaction values in token units. None of this raw data tells you what the user was doing in any meaningful behavioral sense.

Datai’s core product solves this interpretation problem. By categorizing the smart contracts that users interact with, Datai transforms raw transaction histories into behavioral narratives. A series of transactions that looks like “0x4f…a2 interacted with 0x7d…c8” becomes “this wallet borrowed USDC on Aave, provided liquidity on Uniswap, bridged to Arbitrum, and purchased a gaming asset on Immutable X.” That translated narrative is what Ellie means by data that reads “like English” — structured, categorized behavioral context that AI agents can process, segment, and act on without requiring custom interpretation for each new protocol or chain. As Ellie explains: “When a user is interacting with a smart contract, there can be a thousand ways of what they’re doing — connecting to a DeFi protocol, interacting with NFT, bridging, signing a contract, maybe buying a gaming asset, investing in real world assets. If you look at the scanner, you see only addresses. But what are those addresses? What is the user doing? This is exactly what we’re trying to solve.” For how ChainAware’s models use behavioral data, see our blockchain analysis guide.

Smart Contract Categorization: Translating Addresses into Behavior

The practical value of smart contract categorization becomes clear when you consider the analytics problem any DApp operator faces. A platform operator knows everything about what users do inside their own protocol — how much liquidity they add, how long they stay, what assets they prefer. However, they know nothing about what those same users do everywhere else on the blockchain. A lending platform does not know whether its users also trade on derivatives protocols, whether they are active NFT collectors, whether they bridge frequently to other chains, or whether they have significant capital sitting idle in other protocols that they might potentially move. All of that behavioral context exists in public blockchain data — it is simply not interpretable without the categorization layer that tells you what each smart contract interaction represents.

Datai’s categorization layer makes this cross-platform behavioral picture available. As Ellie explains: “We can tell you that 10% of your customers are using lending-borrowing platforms on the same chain or on different chains. What assets are they lending and borrowing that you don’t have internally? So you can adjust your product strategy based on the behavior of what your customers are doing outside of the platform.” This external behavioral view is the Web3 equivalent of Google Analytics combined with competitor research — understanding not just what users do on your platform but who they are in the broader behavioral ecosystem. For how ChainAware’s wallet auditor provides a similar behavioral picture for individual wallets, see our wallet auditor guide and our user segmentation guide.

The Fraud Detection Agent: Protecting the Ecosystem, Not Just One Platform

Martin frames ChainAware’s fraud detection agent not as a product that protects individual users, but as ecosystem infrastructure that affects whether Web3 grows at all. The argument connects directly to the new user retention problem: every time a new participant enters Web3 and encounters a rug pull or scam, there is a meaningful probability they leave permanently. They do not distinguish between one bad project and the broader ecosystem — they associate the negative experience with the entire space and return to centralised exchanges or exit crypto altogether. Experienced participants — the OGs Martin refers to — have developed instincts for avoiding the worst situations. But new users have not.

The scale of the fraud problem in DeFi is significant. ChainAware’s data on PancakeSwap pools is striking: 95 to 98% of new pools end in rug pulls. That number means the base rate expectation for a new user exploring DeFi liquidity provision is almost certain loss. No amount of excellent UX or product innovation can overcome a user experience where the majority of initial interactions result in total loss of funds. Reducing that fraud rate — not just for individual users but across the ecosystem — is therefore a prerequisite for Web3 mainstream adoption. As Martin states: “It’s not just for one person, it’s not just for one DApp — it’s for the full ecosystem. If you clean up the ecosystem, we increase the trust, we get much more users, we get much more usage.” For the complete fraud detection methodology, see our fraud detection guide and our fraud detector guide.

Free Tools as Ecosystem Infrastructure

ChainAware’s decision to offer fraud detection and rug pull detection tools free to individual users reflects this ecosystem logic directly. If the goal were purely commercial, these tools would be paywalled to maximize revenue per user. The actual goal, however, is ecosystem trust improvement — which requires maximum adoption. Every user who checks an address before interacting with it, and every user who avoids a rug pull because they checked the pool contract, represents one fewer negative experience that might have driven a new participant out of Web3 permanently. At scale, widespread adoption of free fraud detection tools changes the ecosystem-level new user retention rate. For the free tools, see our fraud detector guide and our rug pull detection guide. For context on crypto fraud scale, see Chainalysis’s annual crypto crime data .

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Transaction Monitoring Agent: The Regulatory Requirement That Protects Everyone

Beyond the individual user tools, ChainAware’s transaction monitoring agent serves a specific regulatory function for platform operators. Under MiCA regulation and FATF recommendations, Virtual Asset Service Providers — which includes most DeFi protocols — must implement both AML analysis and AI-based transaction monitoring. These are not the same thing, and Martin is precise about the distinction throughout the conversation.

AML analysis is a rules-based system that tracks the flow of known-illicit funds through the blockchain. It is inherently backward-looking and static: it can only flag addresses connected to previously identified fraud. Transaction monitoring, by contrast, uses AI to analyze behavioral patterns in real time and predict which currently legitimate-appearing addresses are likely to commit fraud in the future. The operational difference matters because sophisticated fraud operations design their activity specifically to pass AML checks while their behavioral history already contains the patterns that predictive AI identifies. As Martin explains: “Scammers and hackers — it’s a dynamical system. You cannot go with rules against a dynamical system. You need AI to interact with this dynamical system. That’s why you need transaction monitoring.” For the full regulatory context, see our AML and transaction monitoring guide and the FATF virtual assets recommendations .

The Transaction Monitoring Agent in Operation

The operational model for the transaction monitoring agent is straightforward to implement. A platform operator uploads a list of wallet addresses — the connected users of their protocol — ranging from a few hundred to several thousand. The agent monitors all of these addresses continuously across all supported blockchains. When behavioral patterns emerge that match the fraud signature library (patterns that have historically preceded fraudulent activity, even in addresses that have not yet committed visible fraud), the agent flags the address and notifies the relevant compliance contact via Telegram or the platform interface. The compliance officer then makes the decision about what action to take — shadow restriction, investigation, or automated exclusion. The human remains in the decision loop, but the detection and notification happens automatically, continuously, without any ongoing human monitoring effort. For the complete transaction monitoring implementation, see our transaction monitoring guide.

Datai’s Trading Use Case: From Pre-Packaged Strategies to Personalized AI Agents

Ellie’s description of Datai’s trading AI agent use case traces a clear evolutionary arc in how DeFi users interact with complex financial strategies. DeFi began as a series of raw protocol interactions — users manually navigating Aave, Uniswap, Compound, and other protocols to construct their own yield strategies. In 2020, platforms began packaging these interactions into pre-built strategies: users could select from a menu of two to ten defined approaches, each representing a different combination of protocols, assets, and risk parameters. This was an improvement, but it created a different problem: the strategies were designed for generic user profiles, not for individual behavioral histories.

A user who primarily trades stable pairs and never touches leveraged positions faces the same menu of strategies as a user who actively manages high-risk leveraged portfolios across multiple chains. Neither user gets a strategy actually calibrated to their risk tolerance, behavioral history, or current asset holdings. The AI agent approach changes this entirely. As Ellie describes: “Wallet providers are developing agents that will go and analyze all your trading history — did you trade meme coins, stablecoins, add liquidity, borrow, leverage yourself? Based off this deep understanding, they create strategies that are fit to the user’s behavior.” The agent additionally considers what other users with similar behavioral profiles have done — a peer comparison layer that makes the recommendation more robust than individual history alone. For more on how behavioral profiling enables this personalization, see our behavioral analytics guide.

The Pool Comparison Product: A Practical Agent Application

Ellie shares a concrete product example that illustrates how data infrastructure enables AI agent functionality. Datai built an internal tool that tracks a single liquidity pool (for example, ETH/USDT) across all major protocols — Uniswap, Sushiswap, PancakeSwap, and others — comparing APY performance, liquidity depth, and security parameters simultaneously. A crypto fund initially used this to track their own portfolio performance. Then an external company building a trading AI agent contacted Datai to integrate this data: the agent needed to know which version of a given pool across which protocol and chain offered the best combination of yield and security at any given moment, then use bridging to route the user’s capital to the optimal destination automatically. As Ellie explains: “You want to invest in the same pool. You have maybe 100 possibilities. AI agents are built to help you better guide your choices. You just say: I want to add ETH/USDT to a pool. I don’t care if I’m on Ethereum or Base. It’s funneled to the right chain and the protocol with acceptable liquidity and highest APY.” For a parallel example using ChainAware’s Prediction MCP for agent decision-making, see our Prediction MCP guide.

The Web2 Parallel: Two Technologies That Drove the Crossing of the Chasm

Both ChainAware and Datai converge on the same historical framework for understanding Web3’s current position. The Web2 internet went through an identical phase before mainstream adoption: a technically sophisticated early-adopter community, significant innovation in business process efficiency, but brutal user acquisition costs driven by mass marketing and a persistent trust problem driven by widespread fraud. Web2 crossed from niche to mainstream through two specific technological interventions — and both Martin and Ellie name them explicitly.

The first was fraud detection. Credit card fraud was so pervasive in Web2’s early commercial phase that consumer reluctance to transact online constrained the entire e-commerce sector. Web2 companies collectively spent enormous development resources fighting fraud before they could focus on growth. The solution was transaction monitoring systems — mandated by financial regulators for payment processors, implemented in AI-based real-time pattern detection. Once fraud rates dropped, consumer trust increased and new users stopped burning their fingers and leaving. Ellie frames this directly: “Web2 became real. Web2, before what we know now, developed two very important technologies. One of them was fraud detection. It was fighting of credit card fraud.” For the complete historical parallel, see our ChainAware vs Google Web2 guide.

AdTech: The Second Technology That Made Web2 Viable

The second technology was AdTech. Before Google’s innovation, Web2 marketing was mass marketing — banner ads, email blasts, and press releases that reached everyone identically regardless of intent. Customer acquisition costs were prohibitively high because undifferentiated messages produced low conversion rates. Google used search history and browsing behavior as a proxy for intent, combined micro-segmentation with targeted delivery, and reduced customer acquisition costs from thousands of dollars to tens of dollars. Twitter, Facebook, and every major Web2 platform followed with their own behavioral targeting systems. The business models that power the modern internet — $600+ billion annually in digital advertising — exist because AdTech made user acquisition economically viable. As Ellie summarises: “The second crucial technology that Web2 had before it became mainstream was AdTech. Web2 used AdTech to match in an invisible way buyers and sellers. These were two key technologies which were the basis of our current Web2 world.” For AdTech scale data, see Statista’s Google advertising revenue data . For how ChainAware replaces Google’s role in Web3, see our Web3 AdTech unit costs guide.

Web3 Is at the Same Inflection Point

Web3 today mirrors Web2 at the pre-chasm moment almost exactly. There is a sophisticated early-adopter community, significant innovation in business process automation (unit costs of financial operations have fallen dramatically), persistent fraud that drives new users away, and catastrophic user acquisition costs driven by mass marketing that does not convert. The two solutions that worked in Web2 — AI-based fraud detection and behavioral targeting AdTech — are now available for Web3 in a form that is structurally superior to what Web2 had, because blockchain transaction data carries higher behavioral signal quality than search history. As Martin concludes: “It happened because the fraud was taken down in the ecosystem. And from the other side, the crossing was introduced by Google. Google was the innovator. Now we are in Web3, exactly in the same situation as Web2 once was. How do we cross the chasm? Reduce fraud. Bring in personalized AdTech.” For more on how this two-part solution maps to ChainAware’s product roadmap, see our Web3 growth guide and Geoffrey Moore’s Crossing the Chasm framework .

The Coming Innovation Wave: What Happens When Founders Get Their Time Back

The conversation closes with both Martin and Tarmo making a forward-looking argument that goes beyond the near-term benefits of individual AI agent deployments. The second-order effect of AI agents removing supplementary task burdens from every Web3 founder simultaneously is not incremental improvement — it is a step-change in the industry’s aggregate innovation capacity.

Currently, the Web3 ecosystem contains thousands of technically capable teams building genuinely novel infrastructure. Most of them spend the majority of their working time on activities that require skill but produce no differentiation — the same mass marketing campaigns, the same compliance monitoring procedures, the same administrative overhead. When AI agents absorb those tasks, the collective human creative capacity that was previously consumed by execution gets redirected toward product ideation, architectural decisions, and genuine innovation. Tarmo’s framing is direct: “With AI agents in marketing, AI agents in trust systems and fraud detection, we can bring the entire Web3 ecosystem to a new level.” This is not a marginal improvement to existing trajectories — it is a qualitative shift in what Web3 can produce. For context on the AI agent economy’s growth trajectory, see the Grand View Research AI agents market report and our real AI use cases guide.

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Every ChainAware capability — fraud detection (98%), rug pull prediction, behavioral profiling, marketing personalization, transaction monitoring — accessible via a single Prediction MCP. Any AI agent queries it in natural language and gets real-time behavioral predictions. 32 MIT-licensed agents on GitHub. SSE-based integration in minutes.

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Comparison Tables

ChainAware vs Datai: Complementary AI Agent Infrastructure Layers

Pre-Packaged DeFi Strategies vs AI Agent Personalized Strategies

Frequently Asked Questions

What is ChainGPT Labs and why did it incubate both ChainAware and Datai?

ChainGPT Labs is the incubation and investment arm of ChainGPT, a blockchain-focused AI platform and IDO launchpad. The incubation thesis focuses on projects building real AI infrastructure for Web3 — specifically those with proprietary technology, genuine use cases, and measurable product traction rather than narrative-driven projects. Both ChainAware and Datai fit this thesis: ChainAware with its proprietary predictive AI models (fraud detection, rug pull prediction, behavioral profiling) and Datai with its three-year smart contract categorization dataset and AI model. The X Space brought both together specifically because their capabilities are complementary — ChainAware predicts future wallet behavior while Datai provides the historical behavioral context that makes predictions richer and more accurate.

How does ChainAware’s marketing agent protect user privacy?

ChainAware’s marketing agent operates exclusively on publicly available on-chain transaction data. No personal identity information is required at any point. When a wallet connects to a platform, the agent calculates a behavioral profile from that wallet’s public transaction history — experience level, risk tolerance, intentions — and generates matched content accordingly. The user remains fully anonymous throughout: the agent knows behavioral patterns but not personal identity. This means the personalized experience is delivered without any KYC process, without cookie tracking, and without any data that could identify the individual behind the address. As Martin notes in the conversation: “Anonymity is still there, but we know the behavior of a person behind this address.”

What problem does Datai solve that wallet analytics tools do not?

Standard wallet analytics tools show you what transactions a wallet executed — the addresses it interacted with, the values transferred, the timing. They do not tell you what the wallet was doing in any behavioral sense. A wallet that interacted with 0x4f…a2 could have been borrowing USDC, providing liquidity, bridging ETH, or purchasing an NFT — the address looks identical in all cases. Datai’s smart contract categorization layer solves this interpretation problem by mapping every smart contract address to its functional category and behavioral context. The result is that wallet transaction histories become readable behavioral narratives: “this user borrowed on Aave, traded on Uniswap, bridged to Arbitrum, and purchased a gaming asset” — context that AI agents can act on meaningfully.

Will AI agents replace Web3 marketing professionals?

The consensus from both ChainAware and Datai is no — but the role changes significantly. AI agents take over execution tasks: generating content variants, segmenting audiences by behavioral profile, serving personalized messages, monitoring campaign performance, and optimizing targeting parameters. What they do not replace is strategic judgment: deciding which product narrative builds genuine community trust, identifying which behavioral segments represent the highest-value users, designing the creative brief that agents execute from, and evaluating whether the overall strategy is achieving its goals. The marketer becomes an orchestrator of specialized agents rather than a manual executor — which is, as Ellie notes, similar to how sophisticated Web2 marketing professionals already work with marketing technology platforms today.

What is the crossing the chasm requirement for Web3 mainstream adoption?

Both ChainAware and Datai identify the same two requirements, directly parallel to what drove Web2’s crossing of the chasm. First, fraud rates must decrease significantly through widespread deployment of AI-based fraud detection — making the ecosystem safe enough for new users to stay and build positive experiences rather than burning their fingers and leaving permanently. Second, user acquisition costs must drop from the current ~$1,000 per transacting DeFi user to something closer to Web2’s $15-30 benchmark — achievable through behavioral targeting AdTech that replaces mass marketing with intent-matched personalization. Both ChainAware’s production agents and Datai’s data infrastructure directly address both requirements. When both are solved simultaneously, the conditions for mainstream adoption are in place — exactly as they were when Web2 deployed transaction monitoring and AdTech in the early 2000s.

This article is based on the X Space hosted by ChainGPT Labs featuring ChainAware co-founders Martin and Tarmo alongside Ellie from Datai. Listen to the full recording on X . For integration support or product questions, visit chainaware.ai.

The post AI Agents in Web3: From Hype to Production Infrastructure — X Space with ChainGPT and Datai first appeared on ChainAware.ai.

X Space #20 — Intention-Based Web3 AdTech: The Invisible Hand That Will Take Web3 Mainstream. Watch the full recording on YouTube · Listen on X

X Space #20 is ChainAware’s most comprehensive session on the Web3 user acquisition crisis — and the most historically grounded. Co-founders Martin and Tarmo spend the full hour making a case that most people in Web3 have never heard articulated clearly: the reason Web3 can’t scale is not missing innovation. The innovation is extraordinary. The reason is missing user acquisition technology — and that gap has a precise historical precedent, a known solution, and a live implementation available today. The session covers the economics of why most Web3 projects will never reach cash flow positive, why Geoffrey Moore’s Crossing the Chasm never answered its own central question, and how blockchain data creates a higher-quality AdTech foundation than anything Google ever had access to.

The Two Unit Costs Every Web3 Founder Must Innovate

Martin opens X Space #20 with a framework that most Web3 founders have never applied to their own businesses: every successful company must innovate two distinct unit costs simultaneously, and failing to innovate either one guarantees failure regardless of how good the product is.

The first unit cost is the cost of the business process — how cheaply and efficiently the core product delivers its value to users. DeFi has achieved extraordinary innovation here. Lending, borrowing, trading, and staking through smart contracts costs a fraction of what equivalent services cost in traditional finance. Martin draws on ten years at Credit Suisse to make the contrast vivid: “Guys, just check what Credit Suisse’s business processes look like, how long they take. There is no comparison with DeFi.” The automation of financial processes that DeFi achieves is genuinely revolutionary in unit economics terms.

The Second Unit Cost Nobody Is Innovating

The second unit cost is the cost of customer acquisition — how cheaply the company reaches users who will transact with the product. Web3 is producing almost no innovation in this area. Founders treat customer acquisition as a necessary operational expense managed by external agencies rather than as a core technical problem requiring the same level of innovation as the product itself. As Martin states directly: “You have to innovate both processes. The unit cost of your business process and the unit cost of your customer acquisition. You cannot delegate one part — the customer acquisition — to marketing agencies with different motivations.” The mathematical result of failing to innovate customer acquisition while succeeding brilliantly at business process innovation is a business that delivers enormous value to the tiny fraction of users who find it while remaining structurally unable to scale. For the full analysis of how this plays out across the ecosystem, see our Web3 AI marketing guide.

The Web3 Acquisition Cost Mathematics: Why $1,000 Per User Kills Every Project

Martin walks through a specific, reproducible calculation that any Web3 project can run against its own marketing spend. The numbers are not hypothetical — they reflect real conversion rates from real campaigns.

Start with a banner ad campaign on Etherscan, CoinGecko, or CoinMarketCap. High-quality Web3 traffic costs approximately $5 per click. With a $1,000 budget, a project gets roughly 200 website visitors. Of those 200 visitors — each arriving through a paid click, each therefore showing some initial interest — approximately 10 will connect their wallets. Of those 10 wallet connections, approximately 1 will complete an actual transaction. The result: $1,000 spent to acquire one transacting user.

Why This Math Guarantees Failure

Now apply the customer lifetime value test. A DeFi lending platform earns revenue from transaction fees, spread, or interest — typically a fraction of a percent per transaction. If the average transacting user performs transactions totalling $10,000 and the platform earns 0.5%, the lifetime revenue from that user is $50. The project spent $1,000 to generate $50 in revenue. This is not a business — it is a loss mechanism. As Martin summarises: “This one transacting user should generate total lifetime revenues of at least $1,000 — will they generate that in DeFi? No, probably not. Maybe some whales. But 99% of users are not generating this.” The mathematics of current Web3 acquisition economics make cash flow positive essentially unachievable for all but the most established protocols. For more on how this connects to the broader Web3 growth crisis, see our guide on why AI agents will accelerate Web3.

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The Mass Marketing Trap: Why Projects Are Forced to Do What Doesn’t Work

One of the most insightful observations in X Space #20 is Martin’s analysis of why mass marketing persists in Web3 despite its demonstrable failure. The answer is not that founders are irrational — it is that they are in a prisoner’s dilemma where stopping is worse than continuing.

Consider the situation from a single project’s perspective. Mass marketing produces poor results — high cost, low conversion, cognitive overload for potential users. However, if competitors are all doing mass marketing and a project stops, it loses even the minimal attention that mass broadcasting generates. The project that opts out of the mass marketing race disappears from potential users’ awareness entirely. Consequently, every project is forced to participate in a system they know is inefficient because opting out is even worse than participating.

The Cognitive Overload Effect

From the user’s perspective, the result of 50,000+ projects all doing mass marketing simultaneously is catastrophic cognitive overload. Telegram channels fill with generic project announcements. Twitter feeds flood with KOL promotions. CoinGecko and CoinMarketCap banners rotate through identical calls-to-action. As Martin describes: “First you get this first mass marketing message, then the second, then the next 10,000 mass marketing messages. You will run away. You say these messages don’t resonate — you close your Telegram, your Discord, you stop going to Twitter. You say enough is enough.” The ecosystem is simultaneously over-broadcasting to potential users and under-converting them — spending enormous resources to generate a backlash. For more on this dynamic and its connection to the trust problem, see our guide to building trust in Web3 anonymous ecosystems.

The KOL Reality: 60 Out of 625 Generate Positive Returns

Martin cites a specific data point from Alphascreener that quantifies the ineffectiveness of KOL-based marketing more precisely than any qualitative criticism could. Using the 10-day delayed free version of the platform, he checked KOL call performance and found that approximately 60 out of 625 calls generate positive returns within 30 days. That is a 9.6% success rate — meaning over 90% of KOL promotional campaigns produce no positive price action for the project within a month.

Projects pay significant fees for these campaigns regardless of outcome. The payment structure is typically upfront — cost per million impressions or a flat promotional fee — with no performance accountability. The KOL receives payment whether the campaign generates users or not. As Martin notes: “You have to pay these KOLs and you have to pay them a lot. And if you don’t pay them, they are not tweeting about you — they tweet about someone else and generate sensory overload for someone else instead.” The incentive structure of KOL marketing is fundamentally misaligned with project success, yet it remains one of the largest budget line items in Web3 marketing spend.

Attribution vs Intention: Last Week’s Weather vs Tomorrow’s Forecast

Tarmo introduces a conceptual distinction that is essential for understanding why most Web3 analytics tools — and the marketing strategies built on them — are fundamentally limited: the difference between attribution and intention.

Attribution describes what a user has done in the past: which protocols they used, what transactions they completed, which tokens they held. This is descriptive data — it tells you what happened but says nothing reliable about what will happen next. Tarmo’s analogy is precise: “It’s like reading a weather forecast from five days ago. Okay, it was the weather. But you are interested in the future weather, not what the weather was in the past.” Attribution data tells you that a user interacted with Aave, Uniswap, and Compound. However, that tells you almost nothing actionable — it doesn’t tell you whether they’re currently looking to borrow, trade, or exit the market entirely.

What Intention Actually Is

Intention is forward-looking behavioral prediction. Tarmo defines the core dimensions: “Are you a high risk taker, low risk taker, medium risk taker? These are psychological preferences. It is your investment behavior — how you invest. What is your innovation attitude — are you an innovator, early adopter, late adopter? What is your experience?” These behavioral characteristics allow prediction of what the user will do next. A high-risk-tolerance, experienced DeFi user who has been staking for two years but hasn’t made a new position in three months is exhibiting pre-borrowing behavioral signals. Showing that specific user a targeted borrowing offer at that moment creates resonance — the offer matches what they’re already considering. Showing them a generic “join our community” banner creates noise.

Furthermore, Tarmo distinguishes intention from simple protocol attribution: “User uses protocols, but corresponding to his intentions. If a user wants something, he does it. It’s not that he did it in the past so he will repeat it in the future.” Behavioral intentions are deeper than usage patterns — they are psychological and economic states that drive behavior across multiple protocol categories. For the full analysis of what ChainAware calculates, see our behavioral user analytics guide and our personalisation guide.

The Invisible Hand: What Actually Took Web2 Mainstream

Economics textbooks describe the “invisible hand” as the market mechanism that coordinates buyers and sellers efficiently — the spontaneous coordination of supply and demand through price signals. Martin and Tarmo argue that this description obscures the actual mechanism, and that understanding the real mechanism is the key to understanding what Web3 is missing.

The invisible hand in economics is usually presented as self-generating — markets coordinate themselves without central planning. But Martin points out the practical reality: “Things never happen from themselves. There has to be something for things to happen.” In every functioning market at scale, a specific coordination technology exists that matches buyers with relevant sellers efficiently. Before digital markets, this was the travelling salesman, the newspaper classified section, and the local market stall. In Web1, it was banner advertising and directory listings. In Web2, it was AdTech — the technology that calculated user intentions and matched them to relevant products at the moment of maximum receptivity.

AdTech Is the Invisible Hand

Google’s AdWords, Facebook’s news feed targeting, and Twitter’s promoted tweets are all implementations of the same mechanism: calculate what a user wants, show them the relevant offer at the right time. As Tarmo summarises: “AdTech is the invisible hand. The invisible hand which brings right users to right platforms at the right time.” The Web2 ecosystem scaled from tens of millions of users to billions not because the underlying products got dramatically better (they were already good), but because AdTech created the coordination layer that made discovery efficient, acquisition economical, and conversion reliable. For more on this parallel, see our guide to how ChainAware is doing for Web3 what Google did for Web2.

Crossing the Chasm: The Question Geoffrey Moore Never Answered

Martin makes a pointed critique of one of the most influential business books ever written on technology adoption: Geoffrey Moore’s Crossing the Chasm. The book describes the transition from early adopters to mainstream users in technology markets — the “chasm” that most innovative technologies fail to cross — with considerable analytical sophistication. Martin recalls reading it during the early internet era: “I was so excited. I remember reading this book even during the night because it was so cool.”

However, Moore’s book describes the phenomenon without explaining its mechanism. He identifies that a chasm exists and that crossing it requires specific strategies, but he doesn’t answer the fundamental question of how the crossing actually happens at the market infrastructure level. As Martin notes: “He had maybe 200 pages. He was all the time speaking about the crossing the chasm — this transformation from early innovators to early maturity. And he never said how it happened.” The answer, which neither Moore’s book nor conventional business education explains clearly, is AdTech. The crossing of the chasm in Web2 happened specifically because Google created the coordination mechanism that matched products to users at scale — reducing acquisition costs from $500-700 to $15-30 and enabling the mass-market economics that made Web2 companies viable.

Web1 to Web2: The Exact Transition Web3 Must Now Make

The historical parallel that structures the entire X Space #20 discussion is precise: Web3 in 2024 is at the same stage as Web1 in approximately 2000-2002. The user numbers are similar (50 million), the project count is similar (tens of thousands), and the acquisition cost problem is identical in structure — high costs preventing viable unit economics, mass marketing failing to convert, and a coordination layer missing that would bring the right users to the right platforms.

Web1’s 50 million users and thousands of Web1 companies faced an almost unsolvable mapping problem: how do you get 50 million people to find the specific products relevant to their needs among thousands of options? The answer to the mapping problem was not better products, not more content, not more conferences. The answer was AdTech — technology that computed user intentions from available data and matched users to relevant products automatically. Once that technology existed, user acquisition costs collapsed, mass-market economics became viable, and Web2 scaled globally.

Web3 Has the Same Mapping Problem

Web3 currently has 50 million users and 50,000-70,000 projects. That means approximately 1,000 potential users per project on average — enough to build viable businesses if the matching worked efficiently. The problem is that matching doesn’t work. KOLs, banner ads, and crypto media broadcast to undifferentiated audiences. The right users never find the right platforms. Platforms that would create enormous value for specific user profiles waste their entire marketing budget on users who will never convert. As Martin frames it: “We need the mapping. We need to get the right people to the right platforms at the right time. So and Web2 solved this problem.” For how this applies to the DeFi sector specifically, see our DeFi onboarding guide.

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Google Is Not a Search Company: Every Big Tech Platform Is AdTech

Martin makes an observation that reframes the entire Web2 technology landscape: the companies that are typically described as search, social media, or communication companies are all, at their revenue core, AdTech companies. Understanding this is essential for understanding what Web3 needs to build.

Google generates approximately 95% of its revenues through advertising. Twitter (now X) generates essentially all of its revenues through advertising. Facebook/Meta generates essentially all of its revenues through advertising. None of these companies is primarily in the business they describe themselves as being in. Each is fundamentally in the business of calculating user intentions and matching those intentions to relevant offers — the definition of AdTech. As Martin states: “Google is not a search company. They call themselves a search company. Well, they are not. They’re an ad tech company. Twitter, Facebook — they call themselves social media. We are asking: how does Facebook generate revenues? With ad tech, with the targeting system.”

Why This Matters for Web3

If the central value creation mechanism in Web2 was intention calculation and user-product matching, then Web3’s missing piece is precisely the Web3-native equivalent of that mechanism. Building better DeFi protocols, launching more NFT collections, and creating more GameFi experiences all produce more supply — but supply without matching infrastructure doesn’t scale. Web3 needs its own AdTech layer, built on the data source native to its ecosystem: on-chain transaction history. As Tarmo summarises: “Each technological paradigm needs its own coordination mechanism. We cannot create a new technological paradigm based on the old coordination mechanism.” For the full parallel, see our complete Web3 AI marketing guide.

Why Blockchain Data Produces Better AdTech Than Google Ever Had

Martin and Tarmo make a counterintuitive but well-grounded argument: blockchain data is actually a higher-quality input for intention calculation than any data source Google or Facebook has ever had access to. This is not a marginal difference — it is a fundamental data quality advantage that makes Web3 AdTech more precise than anything Web2 built.

Google calculates user intentions primarily from search queries and browsing history. Search queries reflect momentary curiosity — they are triggered by passing conversations, random associations, and deliberate research in roughly equal measure. Browsing history captures passive content consumption with weak behavioral signal. Both data sources are noisy, easily influenced by context, and imprecise about actual behavioral intent.

Financial Transactions as Pure Behavioral Signal

Blockchain transactions are financial decisions. Every on-chain transaction required a deliberate choice, deliberate execution in a wallet interface, and real financial cost (gas fees). As Tarmo explains: “Blockchain data is not like social metadata or some browsing history. These are financial transactions where you pay gas. It is really very high quality data. And we can calculate very precise user intentions.” Nobody accidentally borrows $500 on Aave, stakes in a liquidity pool, or purchases an NFT. Each of these actions reveals deliberate behavioral commitments that Google’s data cannot match in precision. The result is that ChainAware’s intention calculations from blockchain data achieve accuracy that Web2 AdTech systems took years of data accumulation to approach — because the data source is inherently more signal-dense. For the full data quality analysis, see our predictive AI for Web3 guide.

How ChainAware’s Intention-Based Marketing Works

With the theoretical case established, Martin explains ChainAware’s specific implementation — which is live, scaling, and immediately available to any Web3 project.

The process starts at wallet connection. When a user connects their wallet to a Web3 platform, ChainAware reads the wallet’s complete on-chain transaction history across Ethereum (2,000+ protocols monitored) and BNB Chain (800+ protocols monitored). ChainAware’s AI models process this history to generate a behavioral profile: is this wallet likely to borrow? Trade with leverage? Provide liquidity? Buy NFTs? What is their experience level? What is their risk tolerance? What stage of the technology adoption cycle are they in?

From Profile to Resonating Message

Based on this profile, the marketing agent selects or generates content specifically matched to the user’s predicted intentions. A borrower-profile wallet visiting a lending platform sees messaging about the platform’s loan terms and benefits. A yield farmer profile visiting the same platform sees messaging about liquidity provision returns. A first-time DeFi user sees educational content about how to get started safely. Every user sees something different — content designed to resonate with what they were already planning to do.

The email marketing parallel makes the conversion difference concrete. Mass email in crypto achieves below 0.05% open rates — so degraded by cognitive overload that almost nobody reads it. Personalised email marketing — even using imprecise data sources like LinkedIn — achieves 15% open rates. That is a 300x improvement from personalisation alone. ChainAware’s intention-based targeting uses blockchain data that is more precise than LinkedIn profiles, applied at the highest-intent moment (wallet connection), to deliver content that matches the user’s demonstrated behavioral history. The conversion improvement compounds accordingly. For the full implementation guide, see our behavioral user analytics guide and the SmartCredit case study.

The Marketing Agency Problem: Misaligned Incentives

Martin delivers a pointed analysis of why delegating customer acquisition to Web3 marketing agencies systematically fails — not because agencies are incompetent, but because their incentives are structurally misaligned with their clients’ success.

Web3 marketing agencies charge upfront fees for traffic generation, campaign management, and KOL coordination. Their revenue model is fee-based, not performance-based. They receive payment whether or not the campaigns generate transacting users. Their business interest is to maximise their fee revenue, which means maximising the scope of campaigns and minimising accountability for conversion outcomes. As Martin states: “Founders think that marketing agencies will do their job. Marketing agencies have a different intention — their intention is to get money, a lot of money from founders. Their intention is not to acquire users.” This is not a moral critique — it is a structural incentive observation. Agencies operating under this model will always recommend more spending (more campaigns, more KOLs, more media placements) rather than diagnosing the fundamental problem of user acquisition technology.

The Web1 Marketing Agency Parallel

Critically, this situation is not unique to Web3 — it is a predictable feature of any technology paradigm that lacks an efficient coordination mechanism. Web1 had identical marketing agencies charging founders for “guerrilla marketing,” media placements, and conference presence — all of which generated noise without solving the mapping problem. When Google’s AdTech emerged and made intention-based targeting viable, the Web1 marketing agencies went through one of two fates: they disappeared, or they evolved into AdTech consultants who helped their clients use the new targeting tools effectively. Martin predicts the same transformation is coming for Web3 marketing agencies: “The same transformation will happen in Web3 with the marketing agencies. The ones who remain will start using advanced AdTech solutions for their clients — instead of telling founders stories, give us the money, we solve all your problems.” For the full context on where this transition stands today, see our guides on how Web3 projects benefit from AI agents.

Why Pump-and-Dump Is a Rational Response to Broken Economics

One of X Space #20’s most uncomfortable but analytically important arguments is Martin’s claim that pump-and-dump is not simply malicious behavior — it is a rational economic response to the impossibility of reaching cash flow positive under current acquisition cost conditions.

The logic runs as follows: VCs invest in Web3 projects knowing that the probability of the project reaching cash flow positive is extremely low given current acquisition costs. The mathematical outcome of $1,000 acquisition cost against $50-100 user lifetime value is negative regardless of product quality. Consequently, the rational exit for both founders and VCs is to capture value through token appreciation before the unit economics reality becomes undeniable. Token pump-and-dump is not a failure of ethics — it is a rational adaptation to a structural economic problem that has not been solved.

The Ecosystem Implication

This analysis has an important implication: attacking pump-and-dump behavior without solving the underlying acquisition cost problem will not change the ecosystem’s dynamics. The incentive to pump and dump exists because sustainable long-term business building is economically irrational under current conditions. Solving the acquisition cost problem — through intention-based targeting that reduces costs from $1,000 to $50-150 per transacting user — changes the incentive calculation. Sustainable business building becomes viable, and pump-and-dump loses its comparative advantage. As Martin argues: “VCs know the probabilities for Web3 companies to become cash flow positive are pretty low. And if you know this information, what do you do? We see pump-and-dumps. Because the long-term perspective is not there.” The solution is not regulation — it is innovation in user acquisition technology. For more on how this connects to the ecosystem trust problem, see our guide on AI-based predictive fraud detection in Web3.

The Ecosystem Cleanup: What Happens When AdTech Arrives

Martin and Tarmo close X Space #20 with a prediction about the market structure transformation that will follow when intention-based AdTech becomes widely adopted in Web3. The prediction is grounded in the Web2 precedent: the arrival of efficient targeting technology does not just improve individual company performance — it restructures the entire competitive landscape.

In Web2, the arrival of Google AdWords and its successors created a bifurcation between companies that adopted the new targeting capabilities and companies that continued relying on mass marketing. Companies that adopted AdTech gained sustainable acquisition economics, could iterate on products with reliable user feedback, and accumulated the user bases needed for network effects and defensibility. Companies that didn’t adopt died — not from bad products but from unsustainable acquisition costs. The same selection dynamic is coming to Web3.

First-Mover Advantage Is Significant

Tarmo is direct about what this means for projects that adopt ChainAware’s intention-based marketing early: “Message to other founders — use this opportunity. Be the first. You get competitive advantage and you get your acquisition costs down. Innovative solutions that you have built find their real users — users who are proud to use these innovative solutions.” The competitive dynamics of AdTech adoption in Web3 will mirror Web2: early adopters gain sustainable economics while competitors continue burning capital on mass marketing. The gap compounds over time as early adopters reinvest acquisition savings into product development, generating better products that convert even better. As Martin summarises: “This leads to a kind of market cleanup, ecosystem cleanup, where the focus goes away from pump-and-dump over to sustainable positive cash flow generating businesses. And AdTech is the key — it was the key in Web2, it is the key in Web3.” For how ChainAware’s agents support this ecosystem transformation, see our full AI agents roadmap.

Comparison Tables

Web3 Mass Marketing vs Intention-Based Marketing

Web1 to Web2 vs Web2 to Web3: The Parallel Transition

Frequently Asked Questions

Why is Web3 user acquisition so much more expensive than Web2?

Web2 built an efficient coordination layer — AdTech — that matched users’ demonstrated intentions to relevant products at the moment of maximum receptivity. This matching mechanism reduced acquisition costs from $500-700 (Web1-era mass marketing) to $15-30 (mature Web2). Web3 currently uses Web1-era mass marketing tactics (KOLs, banner ads, crypto media placements) without any intention-matching layer, producing Web1-era acquisition costs of $1,000+ per transacting user. The gap is not a product quality issue — it is a missing infrastructure layer. For the full analysis, see our Web3 AI marketing guide.

What is the difference between attribution and intention in Web3 marketing?

Attribution describes what a user has already done — which protocols they used, what transactions they completed, what tokens they held. Tarmo’s analogy: it is like reading last week’s weather forecast. Intention predicts what a user will do next — based on their behavioral patterns, risk profile, experience level, and investment psychology. Showing a user content matched to their attribution history is marginally better than mass marketing. Showing a user content matched to their behavioral intentions — what they are actively considering doing — creates genuine resonance and drives conversion. ChainAware calculates intentions, not just attribution.

Why can’t founders delegate customer acquisition to marketing agencies?

Because marketing agencies have structurally different incentives from their clients. Agencies earn fees based on campaign scope and upfront payments — not on acquisition outcomes. Their business interest is to maximise fee revenue, which means recommending more spending rather than solving the underlying acquisition technology problem. Additionally, customer acquisition is one of two unit costs that determine whether a business becomes cash flow positive — delegating either unit cost to a party with misaligned incentives is a structural mistake. Founders must own acquisition cost innovation the same way they own product development.

Why does blockchain data produce better AdTech than Google’s data?

Google’s targeting relies on search queries and browsing history — signals that reflect momentary curiosity, passive consumption, and incidental exposure. Blockchain transactions are deliberate financial decisions made with real money at stake. Every on-chain action (borrowing, trading, staking, purchasing an NFT) required conscious evaluation and execution. This deliberateness makes blockchain history a substantially higher-quality behavioral signal than browsing patterns, producing more accurate intention predictions. ChainAware achieves 98% accuracy in fraud prediction from blockchain data — a precision level that reflects the inherent signal quality of the underlying data source.

How does solving the acquisition cost problem affect pump-and-dump behavior?

Pump-and-dump is a rational economic response to a situation where sustainable long-term business building is mathematically impossible — when acquisition costs ($1,000+) permanently exceed user lifetime value ($50-100 in DeFi). When intention-based AdTech reduces acquisition costs to $50-150, sustainable business building becomes viable. Founders and VCs who previously had rational incentives to pump-and-dump now have rational incentives to build. The ecosystem cleanup follows naturally from the economics, without requiring regulatory intervention or changes in founder behavior. For more on the complementary role of fraud reduction in this ecosystem transformation, see our guide on AI-based predictive fraud detection in Web3.

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This article is based on X Space #20 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 Intention-Based Web3 AdTech: The Invisible Hand That Will Take Web3 Mainstream first appeared on ChainAware.ai.

X Space #16 — Do You Still Believe in Web3 KOL Marketing? Why Mass Marketing Fails and Web3 AdTech Wins. Watch the full recording on YouTube · Listen on X

X Space #16 is ChainAware co-founder Martin’s most comprehensive solo breakdown of the Web3 marketing crisis. With Tarmo experiencing connection difficulties, Martin delivers an extended analysis covering every major Web3 marketing channel, the data on KOL effectiveness from AlphaScan, a deep dive into how Web2 real-time bidding actually works, why Web3 projects cannot access Web2 advertising infrastructure, and precisely how blockchain history enables the Web3 AdTech alternative. The session frames everything around one central question: if the goal of marketing is to reduce user acquisition cost, are any of the tools Web3 projects currently use actually achieving that?

The Purpose of Marketing: User Acquisition, Not Hype

Martin opens X Space #16 by establishing the single purpose that all marketing should serve — a definition that most Web3 founders never explicitly articulate but that determines whether any marketing activity is money well spent or money wasted.

Marketing is not a purpose in itself. It is a tool for user acquisition. Every channel, every campaign, and every budget allocation should be evaluated against one question: does this activity bring down the cost of acquiring a transacting user? As Martin states: “It’s not just marketing — this is not self-glorification. It’s all about user acquisition. We need to acquire users. We need to get users to the platform. Marketing is a tool for user acquisition.” The implication is immediate and uncomfortable: if a marketing activity generates impressions, engagement, and community noise without producing transacting users at an acceptable cost, it is not marketing — it is an expensive entertainment purchase.

The Two Unit Costs Every Project Must Optimise

Martin connects marketing purpose to unit economics. Every sustainable business has two critical unit costs that must both be optimised: the cost of the business process itself, and the cost of customer acquisition. DeFi protocols have achieved extraordinary innovation on the first — smart contracts eliminate intermediaries, automate settlement, and reduce transaction costs to a fraction of traditional finance equivalents. However, achieving near-zero business process costs is irrelevant if the cost of acquiring users who actually transact remains prohibitively high. As Martin explains: “You need both. You need both processes and you need to bring your user acquisition cost down. That is the challenge for most Web3 founders.” For the full unit economics framework, see our intention-based Web3 marketing guide.

The KOL Landscape: Why Call Marketing Dominates Web3

Understanding why KOL marketing became Web3’s dominant promotional approach requires understanding the structural constraints that pushed projects toward it. Martin identifies the core issue: Web3 projects cannot access the marketing infrastructure that Web2 companies use, so they built a parallel universe of alternatives — with KOLs at the centre.

KOL marketing, as it currently operates in Web3, involves paying influencers to post messages about a project to their followers. The project pays upfront, the influencer broadcasts promotional content, and the project hopes that a percentage of the influencer’s audience visits the platform and transacts. This model became standard because it is one of the few options available: crypto advertising is banned from most mainstream publisher platforms, DeFi projects cannot obtain Google ad accounts, and the Web2 targeting infrastructure that enables microsegmentation is entirely inaccessible for non-compliant financial services.

The False Security of KOL Ubiquity

Because every Web3 project uses KOL marketing, its use creates a false sense of legitimacy. Launch pads offer special KOL packages. VCs ask about KOL relationships. Exchanges evaluate project Twitter scores partly based on which influencers engage with the project. This systemic embedding of KOL marketing in Web3’s evaluation infrastructure makes opting out feel dangerous even when the data shows it is ineffective. Tarmo — before his connection issues — frames it precisely: “It is a kind of escape from reality. It is wishful thinking. It is the last hope. People think that if they cannot use real AdTech, then let’s use this virtual call marketing. It is the last hope for all Web3.” The problem is not that founders are irrational. The problem is that the rational-seeming alternative — doing what everyone else does — is collectively destroying value across the entire ecosystem. For more on why the ecosystem is trapped in this cycle, see our crossing the chasm in Web3 analysis.

The AlphaScan Reality: Max 10% of KOLs Produce Positive Returns

Rather than relying on qualitative critique, Martin checks AlphaScan — a KOL performance tracking tool — immediately before X Space #16 and reports the results live. AlphaScan tracks 650 crypto influencers and measures the average token return for projects they promote within a defined measurement window. Sorting all 650 by 30-day positive return reveals a striking data point.

Of 650 tracked KOLs, 29-30 produced positive 30-day token returns at the time of the session. That represents approximately 4.5% of the total. Martin notes that he checks AlphaScan regularly and that the positive count fluctuates between 30 and 60 — meaning the upper bound is approximately 10% of tracked influencers producing positive outcomes. As he explains: “Max 10% of them are producing positive returns for you. So projects are paying money, paying quite some money. But somehow it is standard now in Web3 that everyone is doing call marketing. Everyone is doing call marketing.”

The 90% Problem

The inverse of the 10% positive rate is a 90% neutral-or-negative rate. Projects that hire KOLs from the majority of the tracked pool are paying upfront fees for campaigns that produce either no measurable positive effect on token price or an actively negative effect. Martin notes that AlphaScan uses a 10-day delay in its free version, making the data slightly lagged but still directionally reliable. The key takeaway is not that all KOLs are ineffective — 10% genuinely produce positive results. Rather, without the analytical tools to identify which 10%, projects default to hiring from the full pool and get the weighted average outcome: mostly negative, occasionally positive, never reliably predictable. For the deeper analysis of KOL economics, see our comprehensive KOL vs AdTech comparison.

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The Hype Addiction: Why KOL Spend Compounds Without Compounding Results

Beyond the static performance data, Martin identifies a dynamic problem with KOL marketing that makes it structurally unsustainable even for the minority of projects that see initial positive results: hype is an addiction that requires ever-increasing doses to maintain the same effect.

Hype, by definition, is a temporary elevation above baseline attention. Generating it requires novelty — the first announcement of a project creates hype; the third announcement of the same project creates considerably less. Maintaining elevated attention therefore requires escalating inputs: more KOLs, more frequent posts, larger paid promotions. As Martin explains: “For hype to become stronger, you need more of hype, and then you need again more of hype, and then you need even more hype. It is a drug, it is an addiction. So that means if you start, you have to do it more and more and more.”

The Herd Movement Problem

KOL followings behave as herds — they move as a collective toward the most engaging current narrative and away from yesterday’s story. A project that paid for KOL promotion in month one has no residual audience attention by month three. The influencer’s followers have moved on to four other narratives since then. Stopping KOL payments means immediate disappearance from the herd’s attention entirely. As Martin observes: “One day you stop. One day you stop paying. And the KOLs, they have their own followers — this herd is going somewhere else. They were one day following you and next day they will follow someone else.” This means KOL marketing produces no compounding value: every month of spend delivers exactly one month of attention, with nothing carrying forward into subsequent months. The economics are permanently linear — while the goal of user acquisition requires compounding growth. For the broader strategic analysis, see our Web3 AI marketing guide.

All Web3 Marketing Is Mass Marketing — KOLs, Media, Banners, Guerrilla

Martin’s most important structural argument is that KOL marketing is not a unique problem — it is just the most expensive symptom of a broader disease. Every major Web3 marketing channel shares the same fundamental failure: it is mass marketing that delivers one message to many recipients regardless of their individual needs, intentions, or likelihood to convert.

Crypto media — CoinDesk, Bitcoin.com, Cointelegraph, and dozens of others — charges projects for articles that reach the publication’s entire readership. Every reader receives the same content regardless of whether they are a DeFi power user, a complete newcomer, or someone whose interests have no overlap with the featured project. The publication’s credibility transfers to the project through association — a genuine but fleeting benefit that fades without ongoing spend. Martin’s assessment is direct: prices are “ridiculous, especially for startups.”

Guerrilla Marketing: A Nice Term for the Same Problem

Beyond KOLs and media, agencies sell “guerrilla marketing” to Web3 projects — a term that Martin identifies as primarily a rebranding exercise. “Some agencies are selling guerrilla marketing, whatever it means. It is always a nice term to sell. Like — we do guerrilla marketing. It is a guerrilla. And some projects are paying for this in the hope they get results.” Guerrilla marketing in this context typically means creative social media stunts, community infiltration, and non-conventional promotional activities — all of which share the mass marketing flaw: undifferentiated audiences receiving undifferentiated messages. Martin’s recommendation is memorable: “If you hear guerrilla marketing, you better run — not do guerrilla marketing, but away.” For the full landscape analysis of what does and doesn’t work, see our crossing the chasm in Web3 guide.

The Banner Problem: $8 CPM for Untargeted Impressions

Banner advertising on crypto platforms — Etherscan, CoinGecko, CoinMarketCap, BSCScan — represents the clearest illustration of what Web3 mass marketing costs relative to what it delivers. Martin provides a specific price point that frames the inefficiency precisely.

The standard banner CPM (cost per thousand impressions) on major crypto platforms is approximately $8. This means a project pays $8 for every 1,000 times its banner appears to a visitor — regardless of whether that visitor is a DeFi power user, a trader looking for price data, a developer checking a contract, or someone who accidentally clicked a link. Every visitor to Etherscan or CoinGecko sees the same banner creative regardless of their individual profile, current needs, or likelihood of ever using the advertised platform. Martin describes the pricing directly: “The banner prices are like $8 CPM — $8 per 1,000 impressions — which are, using an English word, ridiculous, very high prices.”

Why $8 CPM Is Actually Expensive

At first glance, $8 per 1,000 impressions might seem affordable. However, the cost-per-acquisition calculation reveals the problem. If a banner generates a 0.1% click-through rate (optimistic for an untargeted banner), $8 CPM produces approximately 1 click per $8 spent — or $8 per click. From those clicks, if 5% connect a wallet (generous), and 20% of those transact (also generous), the effective acquisition cost is $8 / (0.001 × 0.05 × 0.20) = $8,000 per transacting user. Mass marketing economics make the nominal CPM irrelevant — what matters is conversion rate, and untargeted mass marketing achieves conversion rates that make every apparent cost metric misleading. For the complete acquisition cost calculation showing how Web3 compares to Web2’s $30-40, see our user acquisition cost breakdown.

How Web2 AdTech Actually Works: RTB, Microsegmentation, and €30B Markets

To understand what Web3 AdTech needs to build, Martin explains how Web2 actually reduced user acquisition costs — not through better creative or more media spend, but through a technological infrastructure that most Web2 marketers themselves don’t fully understand.

The foundation of Web2 AdTech is microsegmentation: the division of users into extremely precise audience clusters based on thousands of behavioural attributes. As Martin explains: “Microsegmentation means that when I am sending messages to my users, I am sending to specific segments. The segments are very, very specifically calculated — like the company shows Nike shoes to a lot of technology companies. We are speaking like zillions of different segments and people are assigned to these segments.”

Real-Time Bidding: The €30B Market Most Marketers Don’t Know About

On top of microsegmentation sits RTB — Real-Time Bidding — the technology that determines which advertiser’s creative reaches which user in real time. When a user visits a publisher website, an automated auction runs in milliseconds: multiple advertisers simultaneously bid to show their ad to that specific user based on their segment membership. The advertiser willing to pay the most to reach that specific microsegment wins the impression. The entire auction completes before the page finishes loading. Martin emphasises that this market is enormous and almost invisible to most practitioners: “RTB is a real-time bidding market — Europe alone, annual 30 billion euro. 30 billion euro. That is this market. It is a data market where technology is running. It is an ad technology. That is where it is decided which customer is getting which ad. You probably never heard about it.” The implication is that Web2’s $30-40 per user acquisition cost was not achieved by better banners or smarter KOL choices — it was achieved by a technological infrastructure that matches specific users to specific offers at the millisecond level. For the broader historical context, see our Web3 crossing the chasm guide.

Web2’s $30-40 Per User: What Microsegmentation Achieves

The concrete output of Web2’s microsegmentation and RTB infrastructure is a user acquisition cost that makes sustainable business building possible. Martin cites the Web2 benchmark: $30-40 per transacting user. This compares directly with Web3’s current reality of hundreds to thousands of dollars per transacting user from mass marketing approaches.

The mechanism behind the Web2 cost advantage is precision: showing the right message to the right user at the right moment dramatically increases conversion probability. A user who searches “DeFi lending rates” and then sees a targeted lending platform ad is far more likely to click, visit, connect their wallet, and transact than a user who sees the same ad banner while checking their portfolio value on CoinGecko. The same ad creative, the same landing page, and the same product produces radically different conversion rates depending entirely on how well the targeting matches the message to the recipient’s current intentions.

Where Web2 Gets Its Intention Data

Web2’s microsegmentation relies on three main data inputs. Google uses search history and browsing history — the latter collected partly through reCAPTCHA, which transmits browsing data to Google as part of bot verification. Facebook uses social interactions, content consumption patterns, video watch time, and the explicit data users provide through their profiles. Twitter uses engagement patterns and dwell time. Each platform builds a virtual identity for every user consisting of hundreds to thousands of behavioural attributes, which then feeds both the microsegmentation and the RTB bidding logic. As Martin notes: “In Web2, we have browsing history, search history. Google is using a lot of browsing history. This identity — some virtual identity somewhere — with the microsegmentation and with the intention calculations, with hundreds slash thousands attributes about each of us.” For how blockchain data compares to these sources, see our predictive AI for Web3 guide.

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Why Web3 Projects Cannot Use Web2 Ad Technology

The natural question following any description of Web2’s superior targeting infrastructure is: why don’t Web3 projects simply use it? Martin addresses this directly, explaining two structural barriers that prevent Web3 DeFi projects from accessing Web2 ad platforms — barriers that are not technical limitations but regulatory and policy constraints.

The first barrier is publisher access. Approximately 99% of Web2 publishers — news sites, content platforms, social networks outside of Twitter — do not accept cryptocurrency advertising. The closed ecosystem of “crypto media” that Web3 projects use for banner advertising and sponsored content exists precisely because mainstream publishers reject crypto ad spend. Martin frames it clearly: “The number of publishers who accept crypto ads at all is very limited. The amount of publishers is limited, plus you need an ads account.”

The Google Ad Account Problem for DeFi

The second barrier is the ad account requirement. Google Ads requires financial service advertisers to hold a relevant license — a reasonable requirement for consumer protection in regulated financial markets. Centralised exchanges like Binance, OKX, and Coinbase can obtain these licenses and therefore qualify for Google ad accounts. Decentralised Finance protocols, by contrast, have no legal entity operating the protocol in most cases and therefore cannot obtain the required financial services licence. No licence means no Google ad account. No ad account means no access to Google’s targeting infrastructure, RTB participation, or search advertising. As Martin explains: “If you want to get an ads account from Google, of course you can make some little steps, but Google is probably telling you to show them a licence. But there is no licensing for DeFi. There is only licensing for centralised finance companies.” The result is that the most powerful and cost-effective marketing infrastructure ever built is structurally inaccessible to the most innovative financial sector currently operating.

The Twitter Exception: When Web3 AdTech Access Is Possible

Within the broadly inaccessible Web2 ad landscape for crypto projects, Twitter/X represents a meaningful exception — with important conditions that determine which Web3 projects can benefit. Martin notes that ChainAware itself uses a Twitter ad account, using it to promote X Space announcements.

Twitter’s policy on crypto advertising is more permissive than Google’s or Facebook’s, but it still draws a line at financial services. Projects that are not classified as financial service providers — AI tools, developer infrastructure, analytics platforms, community tools — can obtain Twitter ad accounts and use Twitter’s targeting capabilities. Projects that provide direct financial services — lending, borrowing, trading, or investment products — face the same licence requirements that block Google access. As Martin explains: “In Twitter, it is a little bit easier if you are not doing financial transactions. If you are doing advertisements for AI and Web3, you can — you will get an answer from Twitter, and in ChainAware we have an ads account. We are using it and it is very effective.” For Web3 projects that qualify, Twitter’s targeting represents a genuine partial alternative to the fully closed mainstream ad infrastructure.

Blockchain History as the Web3 Data Source for Microsegmentation

With Web2 ad infrastructure inaccessible, Martin establishes the data source that makes Web3-native microsegmentation possible: blockchain transaction history. This data source is not only accessible — it is public, free, and arguably more accurate for predicting financial behaviour than anything Google or Facebook has ever collected.

Web2 AdTech uses browsing history, social interactions, and search queries to infer what a user is likely to do next. These are indirect signals — someone who searches “DeFi lending” might be a researcher, a journalist, a curious student, or an active lender looking for better rates. The signal is noisy because the same query serves many different purposes. Blockchain transaction history, by contrast, records actual financial decisions made with real money at stake. A wallet that has borrowed on Aave, provided liquidity on Uniswap, and staked on multiple protocols over two years is not ambiguously interested in DeFi — it is an active, experienced DeFi participant with a specific behavioral profile that predicts future actions with high confidence.

Pattern Matching at Scale Enables Prediction

ChainAware’s approach to intention calculation from blockchain history mirrors the pattern-matching methodology behind all predictive AI: train models on historical data from wallets with known outcomes, identify the patterns that reliably preceded those outcomes, and apply the identified patterns to new wallets to predict their likely next actions. Martin explains the process: “You create the models, you train them with your data, training with negative data, training with positive data. It is a very iterative process. Most interestingly — we can predict fraud 98% before it happens, because there are some patterns in addresses which are saying there are other addresses with the same patterns that committed fraud. This address here, which has not yet committed fraud, probably will commit fraud.” The same pattern-matching logic applies to non-fraud intentions: borrower patterns, trader patterns, gamer patterns, NFT collector patterns — all extractable from transaction history with high confidence. For the full methodology, see our behavioral user analytics guide.

The Two-Step Web3 AdTech Framework: Calculate and Target

Martin distils the entire Web3 AdTech approach into a two-step framework that mirrors the structure Web2 AdTech already uses — but replaces Web2’s browsing and social data with blockchain transaction history as the input.

Step one is calculating user intentions from blockchain history. This produces a behavioral profile for each wallet address: what is the wallet owner likely to do next? Are they a likely borrower? A potential liquidity provider? An active NFT trader considering their next purchase? A newcomer who has never used DeFi protocols? Each profile represents a different set of needs, motivations, and messages that will resonate. As Martin explains: “What is the web three AdTech? It is the same as we have in Web2. From one side, we need to predict user behavior. We have to do this microsegmentation. And from the other side, we have to place messages for the users.”

Step Two: Matching Messages to Intentions

Step two is connecting the calculated intentions to a targeting system that delivers matched messages to each persona. This is the component that transforms static user profiles into dynamic, conversion-optimised interactions. A project defines which messages to show each persona — not a single message for all visitors, but a matrix of persona-message pairings that ensures every user receives content relevant to their specific behavioral profile and likely next action. Martin describes the mechanics: “From one side, we calculate who is this user, what is his behavior. And from the other side, we are connecting the calculated intentions with the messaging. Two parts: we calculate user intentions, and we connect it with a targeting system so that you can target users with proper messages.” For the implementation guide, see our personalisation in Web3 guide and the Web3 AI agents guide.

Persona Examples: NFT Collector, Gamer, Leverage Staker on a Lending Platform

To make the abstract framework concrete, Martin walks through a specific scenario that illustrates why persona-based messaging produces fundamentally different conversion outcomes than mass messaging. The scenario involves a lending and borrowing platform — one of the most common DeFi product types — receiving three different types of visitors.

Visitor type one is an NFT collector. Their blockchain history shows active trading in NFT marketplaces, token holdings associated with NFT communities, and minimal interaction with lending protocols. The right message for this visitor is not the lending platform’s general interest rate — it is the possibility of borrowing against NFT collateral to fund new purchases without selling existing holdings. Without personalised targeting, this visitor sees a generic lending pitch that doesn’t connect to their actual use case. Consequently, they leave without converting.

Gamer and Leverage Staker

Visitor type two is a gamer whose blockchain history shows GameFi token holdings, in-game asset transactions, and play-to-earn protocol interactions. Their lending platform use case is different from the NFT collector’s: they may want to borrow stablecoins against GameFi assets to fund game purchases or amplify in-game earnings. Generic lending messaging misses this framing entirely. Visitor type three is a leverage staker — an experienced DeFi participant whose history shows repeated loop borrowing strategies on multiple protocols. For this visitor, the technical details of the platform’s leverage mechanics, collateralisation ratios, and yield optimisation features are exactly what they need to see. As Martin states: “For all these three personas, you give fully different messages. If he is an NFT dealer on the borrowing platform, we give him fully different messages. If he is a gamer, fully different. If he is a leverage taker, of course — then it is easy, he is used to borrow-lend and looping.” For more on persona calculation and marketing strategy, see our behavioral analytics guide.

ChainAware On-Site Targeting: Personas from Blockchain History

ChainAware implements the two-step framework as a live product that Web3 platforms can integrate in minutes. When a user connects their wallet to a platform running ChainAware’s targeting system, their blockchain address is immediately evaluated against ChainAware’s behavioral models to generate a persona assignment. The platform then displays messaging configured for that specific persona rather than the generic content every other visitor sees.

Martin describes the persona development process as iterative: “You calculate, you start maybe five personas, you get more experience, you have ten personas, you get even more experience, twenty personas. And you just define which messages you are showing to different personas.” Projects begin with a small number of broad persona categories and refine them over time as more conversion data accumulates. Each iteration produces more precise persona definitions and better-performing message variants, creating a compounding improvement cycle that mass marketing can never achieve.

The Conversion Impact

The conversion impact of switching from generic messaging to persona-matched messaging is significant. When each visitor sees content that matches their behavioral profile and addresses their specific use case, the proportion who take the target action increases substantially. Martin frames the outcome: “Then the wonders will happen because the conversion starts to change. It is not anymore that one magic message is converting every possible user. One magic message is converting the NFT dealer and the gamer and the leverage taker. No — if you are this platform, everyone is getting his own magic message. And that is how you start to convert the users.” For the specific conversion rate benchmarks — and how Web3 personalisation compares to Web2’s 10-15% AI-segmented conversion — see our full AdTech comparison guide.

The Unit Cost Conclusion: Why Personalisation Is Not Optional

Martin closes X Space #16 by returning to the unit economics framework that opened the session, tying the entire analysis together into a conclusion about business sustainability.

Innovation in the business process — the technology that powers a DeFi protocol, the smart contracts, the automated settlement — is necessary but not sufficient for sustainable business building. Every innovative Web3 project also needs innovation in user acquisition. Without both, the business process innovation produces value that cannot reach the users who need it, the project burns through capital, and the logical outcome is closure regardless of product quality. As Martin states: “You need both. One is your cost of business process, other is cost of user acquisition. You need both processes and you need to bring your user acquisition cost down. And that is the challenge for most Web3 founders.”

The Web2 Crossing of the Chasm — Repeated for Web3

The transition Martin describes is not unprecedented. Web2 faced the identical situation: thousands of innovative platforms, limited user budgets, and mass marketing as the only available tool. The moment Web2 solved user acquisition through AdTech — microsegmentation, RTB, intention-based targeting — was the moment Web2 crossed from niche technology to mainstream adoption. As Martin summarises: “Web two had exactly the same situation. There were all these technology innovators who created all these beautiful new platforms. But how do you get the right people to the right platforms? We have two steps: get the right people to the right platform, and then on the platform, convert them. When Web2 solved this, that was the moment when Web2 crossed the cosmos.” Web3 is at the same inflection point now, and blockchain data provides the foundation for the same transition. For the complete historical analysis and what it means for Web3 in 2025, see our crossing the chasm in Web3 guide.

Comparison Tables

Web3 Mass Marketing Channels vs Web3 AdTech (ChainAware)

Web2 AdTech Data vs Blockchain Intention Data

Frequently Asked Questions

Why do only 10% of KOLs produce positive returns?

Because KOL marketing is mass marketing — the same message delivered to an undifferentiated audience regardless of individual intentions, needs, or likelihood to convert. The 10% who produce positive results likely have audiences with higher concentrations of users whose profiles happen to match the promoted project, or the timing of their promotion coincides with positive broader market sentiment. Without a systematic way to identify which KOLs have relevant, authentic audiences for a specific project, the majority of campaigns will miss their target entirely. AlphaScan’s data — 29-30 positive outcomes out of 650 tracked — reflects this structural mismatch. For the full analysis, see our KOL vs AdTech comparison.

Why can’t DeFi projects use Google Ads?

Google requires financial services advertisers to hold a relevant jurisdiction-specific licence. Centralised exchanges and regulated crypto brokers can obtain these licences. Decentralised Finance protocols — which typically operate without a central legal entity and are not regulated as financial services in most jurisdictions — cannot obtain them. Without the required licence, DeFi projects cannot get a Google Ads account, which means no access to Google’s search advertising, display network, or YouTube targeting infrastructure. Twitter/X is more permissive for non-financial-service Web3 projects.

What is Real-Time Bidding and why does it matter for Web3?

Real-Time Bidding (RTB) is the auction technology that determines which advertiser’s creative reaches which specific user when they load a web page. Advertisers bid simultaneously for each impression in milliseconds, with the highest bidder’s ad displayed. RTB operates on top of microsegmentation — advertisers bid specifically for users in defined micro-audience segments rather than for generic page impressions. This combination produces the $30-40 per transacting user acquisition cost that makes Web2 businesses sustainable. Europe’s RTB market alone is €30 billion annually. Web3 projects are currently structurally excluded from this infrastructure — which is why blockchain-based Web3 AdTech is the necessary alternative. For more, see the RTB Wikipedia overview.

How does ChainAware create user personas from blockchain data?

ChainAware’s AI models analyse a wallet’s complete transaction history across 2,000+ Ethereum protocols and 800+ BNB Smart Chain protocols to identify behavioral patterns that reliably predict future actions. Pattern matching against known outcomes — the same technique that achieves 98% fraud detection accuracy — produces behavioral profiles: NFT collector, gamer, leverage staker, yield farmer, newcomer, experienced DeFi user. These profiles are then connected to a targeting system that delivers matched messages for each persona when users connect their wallets to integrated platforms. The entire process runs in real time at wallet connection. For the implementation guide, see our behavioral user analytics guide.

Is blockchain data actually better than Google’s data for targeting?

For Web3 use cases, yes — substantially. Google’s data reflects browsing and search behaviour, which includes passive curiosity, research, and incidental exposure. A user who searches “DeFi lending rates” might be a journalist, a student, or an active DeFi participant — the search query alone doesn’t distinguish them. Blockchain transactions are financial decisions made with real money, requiring deliberate evaluation and action. They leave behind high-confidence behavioral signals that predict future financial actions with far greater precision than browsing history. Additionally, blockchain data is completely public and free to access — it doesn’t require building a massive data collection platform or paying licensing fees to a data marketplace.

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This article is based on X Space #16 hosted by ChainAware.ai co-founder Martin. Watch the full recording on YouTube · Listen on X . For questions or integration support, visit chainaware.ai.

The post Do You Still Believe in Web3 KOL Marketing? Why Mass Marketing Fails and Web3 AdTech Wins first appeared on ChainAware.ai.