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.

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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.

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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 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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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.