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.

See the Platform That Emerged from Three Years of Discovery

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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 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.