Attention AI vs Real Utility AI: How to Spot the Difference in Web3

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

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

What Is Attention AI — and Why It Flooded the Market

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

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

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

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

The AI Market Correction: What It Was Really Correcting

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

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

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

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

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

Decentralized AI Marketplaces

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

AI-Driven Smart Contracts

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

Data Privacy AI + Blockchain

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

AI-Optimized Blockchain Networks

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

Tokenized AI Assets

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

DAOs with AI Governance

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

Supply Chain Transparency with AI and Blockchain

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

Decentralized AI Training

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

AI for Cross-Chain Interoperability and Healthcare

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

The LLM Problem: Why Prompt Engineering Is Not AI

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

What LLMs Enable

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

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

What Proprietary Models Enable

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

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

What Real Utility AI Looks Like

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

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

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

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

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

AI Marketing Agent — 8x Conversion Improvement

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

Transaction Monitoring Agent — Compliance and Fraud Prevention

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

Credit Scoring Agent — Financial Ability Assessment

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

Predictive Fraud Detector — Free, 98% Accuracy

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

Rug Pull Detector — Predicting Before the Exit

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

Wallet Auditor — Beyond Fraud to Full Behavioral Profile

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

Free Pixel + Analytics Dashboard

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

The Four Questions Every Investor Must Ask

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

Question 1: Is There a Running Website?

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

Question 2: Is There a Running MVP?

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

Question 3: Does It Provide Real Value to Users?

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

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

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

The Social Psychology Behind Attention AI

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

The Serotonin Response

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

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

The 10x Psychology Trap

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

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

Real Utility AI Use Cases That Actually Work in Web3

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

Fraud Detection and Transaction Monitoring

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

AI-Powered DeFi (DeFAI)

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

Regulatory Compliance and AML

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

Web3 Marketing and Behavioral Targeting

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

Credit Scoring for DeFi Lending

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

Comparison Table: Attention AI vs Real Utility AI

Dimension	Attention AI	Real Utility AI
Primary target	Investor emotions and attention	User problems and needs
Core technology	LLM wrappers or no technology	Proprietary ML models
Competitive advantage	None — easily copied	Strong — years of training data and iteration
Has running product?	Usually no	Yes — required
Measurable accuracy	No — output may hallucinate	Yes — e.g. 98% fraud prediction
User benefit	None or trivial	Concrete and verifiable
Business model	Token speculation	Enterprise subscription, API access
Market cycle resilience	Collapses in corrections	Survives — utility anchors demand
White paper	Central to the product	Secondary or absent — product is the proof
Example categories	Decentralized AI marketplaces, tokenized AI assets, AI consensus protocols	Fraud detection, marketing agents, credit scoring, transaction monitoring
Replicability	High — copy the prompt	Low — requires proprietary data + models
Investor risk	Very high — narrative-driven valuation	Lower — utility-anchored valuation

The New Wave: Utility AI as the Next Narrative

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

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

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

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

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

Frequently Asked Questions

What is attention AI in Web3?

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

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

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

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

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

Why do LLMs not create competitive advantage in Web3?

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

What is ChainAware’s free offering for Web3 projects?

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

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

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

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