InfoFi In-Depth Report: A Attention-Finance Experiment in the Age of AI

I. Introduction: From Information Scarcity to Attention Scarcity — The Rise of InfoFi

The information revolution of the 20th century sparked an explosion in human knowledge. Yet, this also gave birth to a paradox: when information becomes abundant and virtually free, it is no longer the scarce resource. Instead, our cognitive capability to deal with information——attention has become scarce. Nobel laureate Herbert Simon foresaw this in 1971, first introducing the idea of the “attention economy,” where “a wealth of information creates a poverty of attention.” Nevertheless, the modern society is in the middle of the stage. In today’s world of endless content — on Weibo, X (Twitter), YouTube, short videos, and news apps — our cognitive limits are constantly tested, making it harder to filter, evaluate, or assign value to what we consume.

In the digital age, this scarcity of attention has turned into a battle for resources. In traditional Web2 models, platforms use algorithms to predominantly control attention distribution. The true creators of attention — be it users, content creators or community advocates — are often just “free fuel” for platform monetization. Top platforms and capital owners capture most of the value, while the individuals who produce and spread information rarely share in the rewards. This structural imbalance has become a central contradiction in the evolution of digital civilization.

InfoFi (Information Finance) emerges as a response. It is not a random buzzword, but a paradigm shift powered by blockchain, token incentives, and AI, aiming to redefine the value of attention. InfoFi seeks to turn unstructured cognitive behaviors of users— opinions, information, reputation, interactions, trend spotting — into quantifiable, tradable digital assets. Through decentralized incentives, InfoFi aims to reward everyone who creates, spreads, or evaluates information. This is more than technological innovation — it’s a redistribution of power: who owns attention, and who controls information?

Within the Web3 narrative, InfoFi bridges social networks, content creation, market incentives, and AI. It inherits financial designs from DeFi, social dynamics from SocialFi, and incentive models from GameFi, while adding AI’s ability to analyze, interpret, and predict signals, thereby constructing a novel market structure centered on the financialization of cognitive resources. At its core, InfoFi isn’t just about content distribution or tipping — it’s a system that revolves "Information → Trust → Investment → Returns", enabling value discovery and redistribution.

From agricultural societies where "land" was the scarce factor, to the industrial era driven by "capital", and now in today’s digital civilization where "attention" has become the core means of production, the focal resource of human society is undergoing a profound shift. InfoFi aptly represents this macro-paradigm shift in the on-chain world. It’s not just an emerging trend in the crypto market, but also a potential new frontier for digital governance, IP structures, and financial pricing mechanisms.

However, no paradigm shift is linear. Bubbles, speculation, hype, and confusion are inevitable. Whether InfoFi could become a real user-oriented attention revolution will depend on its ability to strike a dynamic balance between incentive models, value capture, and real user needs. Otherwise, it will just be another illusion slipping from an "inclusive narrative" into a "centralized harvesting" dream.

II. The InfoFi Ecosystem: A Tri-Layered Market of Information × Finance × AI

Essentially, InfoFi is a compound system that integrates financial logic, semantic computing, and gamified incentives into a new kind of market within today's network landscape, where information abounds but its value is hard to capture. Its ecological architecture is not a "content platform" or a "financial protocol"; instead, it’s the convergence point of an information-value discovery mechanism, a behavior‑incentive system, and an intelligent distribution engine—forming a full‑stack ecosystem that integrates information trading, attention incentives, reputation scoring, and intelligent prediction.

At its core, InfoFi is about the "financialization" of information — turning previously unpriceable cognitive activities such as opinions, insights, trend predictions, interactions into measurable “quasi-assets” with market value. The intervention of finance means that information—no longer fragmented, isolated "content scraps" in the production, circulation, and consumption processes—is instead transformed into "cognitive products" endowed with game-theoretic attribute and the ability to accumulate value. This means that a comment, a prediction, or a trend analysis can not only be an expression of individual cognition, but also become a speculative asset with risk exposure and potential future returns. The boom of prediction markets like Polymarket and Kalshi is a prime example of this logic materializing in both public opinion and market expectations.

However, financial mechanisms alone are far from sufficient to resolve the deluge of noise and the problem of "bad money driving out good" caused by the information explosion. This is where AI steps in and serves as the second pillar of InfoFi. It serves two major roles: 1. Semantic filtering — the first line of defense against low-quality information and content. 2. Behavioral modeling — evaluating information sources with precision by analyzing multidimensional data such as users’ social interactions, content engagement patterns, and originality of their perspectives. Platforms like Kaito AI, Mirra, and Wallchain are textbook examples of integrating AI into content evaluation and user profiling. In their Yap‑to‑Earn models, they act as "algorithmic referees"—using AI to determine who merits token rewards and who should be filtered out or demoted. In a sense, AI in InfoFi functions just like market makers and clearing mechanisms in a traditional exchange—it’s the core component that maintains ecosystem stability and credibility.

Information is the foundation of this ecosystem. It is not just a tradable commodity, but the source of market sentiment, social connection and consensus building. Unlike DeFi, where assets are anchored in on-chain hard tokens such as USDC, BTC, InfoFi assets are cognitive ones, consisting of more fluid, loosely structured, but more timely opinions, trust, trends, insights. This also means that the operational mechanism of the InfoFi market is not a linear stack but a dynamic ecology that heavily relies on social graphs, semantic networks, and psychological expectations. Here, creators are market makers, offering opinions for valuation. Users are investors, engaging with content through likes, shares, betting and comments to express perceived value, driving its rise and fall across the entire network. Platforms and AI act as exchanges and regulators, ensuring fairness and efficiency of the whole market.

The synergistic opertation of the tri-layered structure has gave rise to new models: Prediction markets for signal-based trading; Yap-to-Earn where speaking = mining; Reputation protocols like Ethos turn behavior into trust scores; Attention markets like Noise and Trends track "emotional swings"; Token-gated platforms like Backroom reimagine paid content via access economics. Together, they form a multifaceted ecosystem of InfoFi including value discovery tools, value distribution mechanisms, identity, and integrating multidimensional identity systems, participation thresholds, and anti-Sybil mechanisms.

It is within this intersecting structure that InfoFi transcends being merely a market; it evolves into a complex information game system: utilizing information as a transactional medium, finance as an incentive engine, and AI as a governance core, with the ultimate aim of constructing a self-organizing, distributable, and adjustable cognitive collaboration platform. In a certain sense, it aims to become a "cognitive financial infrastructure"—not merely for content distribution, but to provide the entire crypto society with more efficient information discovery and collective decision-making mechanisms.

Yet, such complexity and diversity also brings fragility. Subjective information resists uniform valuation. The gamified nature of finance introduces risks of manipulation and herd behavior. AI’s opacity challenges transparency. The InfoFi ecosystem must continuously balance and self-heal within its triadic tension; otherwise, under capital-driven pressures, it risks slipping into a "disguised form of gambling" or becoming a "gamified attention trap".

The construction of the InfoFi ecosystem isn’t the isolated work of a single protocol or platform—it’s the co‑creation of a full socio‑technical system. It marks a profound Web3‑level attempt to govern information, rather than merely assets. It will define the way information is priced in the next era—and even help build a more open and autonomous cognitive market.

III. The Core Game-theoretic Mechanism: Incentive Innovation vs. Extraction Traps

At the heart of InfoFi is the design of its incentive systems. Whether it’s predictions, posts, trust building, or attention mining — it all boils down to: who contributes? Who gets rewarded? Who bears the risk?

From an external perspective, InfoFi appears to be an "innovation in production-relation" in the transition from Web2 to Web3: it seeks to dismantle the exploitative "platform–creator–user" chain of traditional content platforms and return value to the original contributors of information. But from an internal-structure perspective, this value redistribution isn’t inherently fair—it relies on a delicate balance anchored in a series of incentive, verification, and game-theoretic mechanisms. At best, InfoFi can become a win-win innovation hub. At worst, it could devolve into a capital- and algorithm-driven “retail trap”.

The first aspect to examine is the positive potential of "incentive innovation". The fundamental innovation across all InfoFi subdomains is transforming "information"—an intangible asset that was previously difficult to measure and financialize—into a clearly tradable, competitive, and liquid asset. This transformation relies on two key engines: the traceability of blockchain and the assessability of AI.

Prediction markets monetize cognitive consensus through market pricing mechanisms; the Yap-to-Earn ecosystem transforms speech into economic activity; reputation systems build inheritable and mortgageable social capital; attention markets redefine content value by treating trending topics as tradable assets, following the logic of “information discovery → signal betting → arbitrage gains.” Meanwhile, AI-driven InfoFi applications leverage large-scale semantic modeling, signal recognition, and on-chain interaction analysis to construct a data- and algorithm-powered information financial network. These mechanisms endow information with "cash flow" attributes for the first time, transforming actions like "uttering a statement, retweeting a post, or endorsing someone" into genuine economic activities.

However, the more incentive-driven a system is, the more susceptible it becomes to "gaming abuse". The most significant systemic risk faced by InfoFi lies in the distortion of incentive mechanisms and the proliferation of arbitrage chains.

Take Yap-to-Earn as an example: on the surface, it rewards users for content creation through AI algorithms. In practice, however, many projects quickly descend into an "information smog"—characterized by bot-driven spam, early access by influencers, and manipulation of interaction weights by project teams. One leading KOL candidly commented: "If you don't farm engagement, you will never rank. The AI is trained to identify buzzwords and ride trends." Another project team revealed: "We invested $150,000 in a Kaito Yap campaign, only to find that 70% of the traffic was from AI and fake accounts engaging in clickbait. Genuine KOLs weren't participating. There's no way we'd invest again."

Under opaque point systems and unfulfilled airdrop expectations, many users have become "unpaid workers": posting tweets, interacting, onboarding, and building communities, only to find themselves ineligible for airdrops. Such "backstabbing" incentive designs not only damage the platform's reputation but also risk the collapse of the long-term content ecosystem. The contrasting cases of Magic Newton and Humanity serve as particularly illustrative examples: the former established a clear distribution mechanism during the Kaito Yap phase, offering substantial token value returns; whereas the latter faced a community trust crisis and accusations of "gaming the system" due to an imbalanced distribution mechanism and lack of transparency. This structural inequity under the Matthew Effect significantly dampens the participation enthusiasm of tail-end creators and ordinary users, even giving rise to the ironic identity of "algorithm-sacrificing Yap players".

More importantly, the financialization of information does not equate to consensus on its value. In attention and reputation markets, content, individuals, or trends that are "longed" may not necessarily be genuine signals of long-term value. Without real demand and scenario support, once incentives wane and subsidies cease, these financialized "information assets" often rapidly depreciate, even forming a Ponzi-like dynamic of "short-term speculation and long-term collapse". On its launch day, the LOUD project achieved a market capitalization exceeding $30 million; however, just two weeks later, it plummeted to under $600,000, epitomizing the InfoFi version of the "pass-the-parcel" game.

Moreover, in prediction markets, if the oracle mechanism lacks transparency or is susceptible to manipulation by large stakeholders, it can easily lead to pricing distortions. Polymarket has previously faced disputes from users over "unclear event resolutions", and in 2025, it suffered a significant payout controversy triggered by a vulnerability in its oracle voting system. This underscores the need for prediction mechanisms—especially those based on "real-world information"—to strike a better balance between technology and governance.

Ultimately, whether InfoFi's incentive mechanisms can transcend the narrative of "financial capital vs. retail attention" depends on their ability to construct a triple-positive feedback system: accurately identifying information production behaviors ->, transparently executing value distribution mechanisms ->, and genuinely incentivizing long-tail participants. This is not just a technical issue; it is also a test of institutional engineering and product philosophy.

In summary, InfoFi’s incentive mechanisms are both its greatest strength and its biggest source of risk. In this market, every design of incentives can either spark an information revolution or trigger a collapse of trust. Only when the incentive system transcends being a mere game of traffic and airdrops—and instead becomes an infrastructure that can identify genuine signals, reward quality contributions, and sustain a coherent ecosystem—will InfoFi truly evolve from “hype economy” to “cognitive finance.”

IV. Typical Project Analyses and Recommended Focus Areas

The InfoFi ecosystem currently presents a rich and rapidly shifting landscape. Different projects, following the core path of "information → incentives → market," have evolved distinct product frameworks and user acquisition strategies. Some have already validated their business models and emerged as key narrative anchors in InfoFi while others remain in the proof‑of‑concept stage, still seeking breakthroughs through user education and mechanism optimization. Amid this diverse array of tracks, we’ve selected representative projects across five directions for detailed analysis—and identified promising camps worth following.

4.1 Prediction Markets: Polymarket + Upside

Polymarket is one of the most mature and iconic projects in the InfoFi ecosystem. Its core model revolves around buying and selling outcome shares of events using USDC, effectively enabling collective pricing of real-world expectations. The reason Vitalik called it “a prototype of information finance” isn’t just because its trading logic is clear and its financial design robust—but because it has begun to take on the role of a "media function" in the real world. For example, during the 2024 U.S. election, Polymarket’s probability signals for who would win frequently outperformed traditional polling, sparking widespread attention and reposts, including from Elon Musk.

With its official partnership with X (formerly Twitter), Polymarket has enhanced both its user growth and data visibility, positioning itself as a potential “superhub” platform where social sentiment and information pricing converge. However, Polymarket still faces challenges, including regulatory pressure from the CFTC, oracle disputes, and low participation in niche markets.

In contrast, Upside is an emerging, socially-driven prediction platform backed by well-known investors like Arthur Hayes. It uses a like-vote mechanism to turn content into marketable predictions, allowing creators, readers, and voters to share in the rewards. Upside emphasizes lightweight interactions, low barriers to entry, and a de-financialized user experience—exploring a hybrid model between InfoFi and traditional content platforms. It’s worth tracking how it performs in terms of user retention and content quality over time.

4.2 Yap-to-Earn: Kaito AI + LOUD

Kaito AI is one of the most representative platforms in the Yap-to-Earn model and currently the largest InfoFi project by user base, with over 1 million registered users and more than 200,000 active Yappers. Its innovation lies in using AI algorithms to evaluate the quality, engagement level, and project relevance of user posts on X (formerly Twitter). Based on these evaluations, it distributes Yaps (points), which are then used to rank users and determine token airdrops or rewards in partnership with crypto projects.

Kaito forms a closed loop: projects use tokens to incentivize community sharing, creators compete for attention through content, and the platform manages distribution and order via data and AI models. However, with the surging number of users, Kaito has encountered structural issues like signal pollution, bot proliferation, and disputes over point allocation. The founder has begun iterating on its algorithms and optimizing its community mechanisms to address these problems.

LOUD was the first project to conduct an Initial Attention Offering (IAO) based on a Yap-to-Earn leaderboard. Before launch, it dominated 70% of Kaito’s leaderboard attention through aggressive yap campaigns. While its airdrop strategy generated short-term buzz, the rapid token price collapse post-launch drew criticism, with the community accusing it of being a "musical chairs" extraction scheme. LOUD’s rise and fall underscore that the Yap-to-Earn sector is still in its experimental phase, and the fairness and maturity of its mechanisms require further refinement.

4.3 Reputation Finance: Ethos + GiveRep

Ethos is currently the most systemic and decentralized attempt in the reputation finance sector. Its core concept is to build a verifiable, on-chain “trust score”, generated through interaction history, comment evaluations, and a unique "guarantee mechanism"—where users can stake ETH to endorse others, bearing risk and forming a Web3-native trust network.

One of Ethos’s most novel innovations is its reputation speculation market, where users can long or short someone’s reputation, effectively turning trust into a tradable asset. This unlocks future possibilities in integrating trust scores into lending markets, DAO governance, and social identity systems. However, its invite-only model currently limits user growth, and improving accessibility and Sybil resistance will be key to its future development.

Compared to Ethos, GiveRep is more lightweight and community-oriented. It allows users to rate content creators and commenters simply by tagging an official account in replies. With a daily cap on comments and high engagement on X, GiveRep has already achieved notable adoption on the Sui network. This model is well-suited for viral social growth and lightweight trust testing—and could serve as a foundational layer for distributing governance weight or project airdrops in the future.

4.4 Attention Markets: Trends, Noise, and Backroom

Trends is a platform exploring the assetization of content. It allows creators to mint their X posts as tradable “Trends", assign trading curves, and let community members buy shares to go long on the post’s popularity. Creators then earn a cut of the trading volume. This innovative model transforms viral posts into liquid assets—making it a prime example of social financialization.

Noise is a futures platform for attention, built on MegaETH. Users can bet on the rising or falling popularity of certain topics or projects, directly speculating on attention dynamics. In its invite-only closed beta, some of its prediction models have shown early signs of market discovery. With future AI integrations to forecast attention trends, it could evolve into a “sentiment index” for the InfoFi ecosystem.

Backroom represents an InfoFi product model that combines “token-gated access with high-value content curation". Creators can publish premium content gated behind token-based Keys. Users can purchase these Keys to unlock access—and since Keys are tradable and price-sensitive, they form a closed-loop financial layer around content. In an era of NoiseFi at its height , this model is gaining popularity among knowledge creators who value signal over noise.

4.5 Data Insight & AI Agent Platforms: Arkham, Xeet, and Virtuals

Arkham Intel Exchange has become synonymous with the financialization of blockchain intelligence. It enables users to post bounties that reward “on-chain detectives” for deanonymizing wallet addresses. While its model mirrors traditional intelligence markets, it introduces decentralization and tradability for the first time. Though controversial (e.g., privacy concerns, witch-hunting accusations), Arkham has set the standard for data-intelligence-driven InfoFi platforms.

Xeet is still in early development, but its founder Pons has publicly stated his goal to make it a “signal cleaner” for InfoFi. By integrating Ethos reputation scores, KOL endorsements, and curated private feeds, Xeet aims to build a more authentic, spam-resistant signal market—positioning itself as a direct counter to Yap-to-Earn’s noise problem.

Virtuals brings a new twist by introducing AI agents as InfoFi-native participants. These agents can initiate tasks, perform evaluations, and generate interaction data—effectively injecting non-human productivity into the InfoFi ecosystem. During its Genesis Launch, Virtuals also collaborated with Kaito in a Yap-to-Earn phase, showcasing the emerging interconnectivity of InfoFi projects.

V. Future Outlook and Risk Assessment: Can Attention Become the “New Gold”?

In the deep waters of the digital economy, information is no longer scarce—but useful information and credible attention are more valuable than ever. Against this backdrop, InfoFi has been hailed by many as the “next narrative engine” and even as a potential “new gold”. The logic is clear: in an era where AI-generated content is abundant and costless, what’s scarce is not content, but "signals" that drive action—and the real attention that follows them. Whether InfoFi can evolve from a concept into a full-fledged asset class—from short-term “Yap-to-Earn” rewards to" long-term on-chain influence standards"—depends on the interplay between three major trends and three systemic risks.

Trend 1: AI + Prediction Markets → Rise of “Reasoning Capital” The integration of AI and prediction markets will usher in a new era of “reasoning capital.” Polymarket’s ongoing partnership with X and Grok has already piloted this model: real-time public sentiment + AI analysis + monetary stakes = a feedback loop grounded in validity, truth, and market signals. If future InfoFi projects can leverage AI to model events, extract signals, and price dynamically, prediction markets could gain significant credibility in governance, news verification, and trading strategies. For instance, Futarchy-style governance could adopt AI + prediction markets to formulate DAO policies.

Trend 2: The Convergence of Reputation, Attention, and Finance → Decentralized Credit Boom Current reputation-based InfoFi Projects like Ethos and GiveRep are constructing on-chain “trust scores” that bypass traditional credit intermediaries. In the future, reputation points could serve as the basis for DAO voting power, DeFi collateral, and content distribution priority—ushering in true on-chain "social capital". If cross-platform reputation recognition, Sybil resistance, and traceable trust histories can be achieved, the attention-reputation system could shift from a secondary metric to a core asset.

Trend 3: Tokenization and Derivatives of Attention Assets → The Ultimate InfoFi Form Today’s Yap-to-Earn models still operate on point-based content reward systems. A mature InfoFi, however, should tokenize every valuable piece of content, treat each KOL’s “attention bond” or chain-based signal as a tradable asset, and allow users to long, short, or even build ETFs around attention trends. This will open a new financial frontier—from narrative-driven Meme Tokens to derivative products based on attention dynamics.

However, for InfoFi to truly achieve sustainability, it still faces three major structural risks.

Risk 1: Poorly Designed Incentives → The “Yap Trap” If incentives focus solely on quantity over quality, with opaque algorithms and unrealistic airdrop expectations, platforms may experience a surge of early hype followed by a cliff-like collapse in attention—what some call “airdrop is the peak” typical of SocialFi. LOUD’s short-lived cycle is a prime example: it used Yap leaderboards to lure users pre-launch, but post-token, its market cap tanked and engagement dropped, revealing a fragile ecosystem.

Risk 2: The Matthew Effect → Ecosystem Fragmentation Data from most Yap-to-Earn platforms already reveals this: over 90% of rewards go to the top 1% users. Long-tail users neither earn much nor break into the KOL class—and eventually exit. If this structural inequality couldn't be addressed via mechanisms like reputation-weighted scoring or credit mobility, InfoFi may devolve into just another "platform-dominated oligarchy".

Risk 3: Dual Dilemma of Regulatory Risk and Information Manipulation Emerging products like prediction markets, reputation trading, and attention speculation currently lack a unified regulatory framework across major jurisdictions. Once a platform gets involved in gambling, insider trading, deceptive advertising, or market manipulation, it can quickly come under heavy regulatory scrutiny. For instance, Polymarket in the U.S. has faced simultaneous investigations by both the CFTC and the FBI , while Kalshi leveraged its compliance-centric design—successfully navigating the CFTC to pioneer U.S.-based election contracts. These cases signal that InfoFi projects must adopt “reg-friendly” strategies from Day One to avoid operating on illegal fringes.

In summary, InfoFi isn’t merely the next-generation content distribution protocol—it represents a bold new attempt to financialize attention, information, and influence. It challenges the traditional value-capture model of platforms and serves as a collective experiment in “everyone as an Alpha discoverer”. Whether InfoFi can become the “new gold” of the Web3 world hinges on its ability to find the optimal balance across fair mechanisms, incentive design, and regulatory frameworks—truly transforming the “attention dividend” from a trophy for the few into an asset for the many.

VI. Conclusion: The Revolution Has Just Begun—Proceed with Cautious Optimism

InfoFi’s emergence signifies another step in Web3’s cognitive evolution after waves of DeFi, NFTs, and GameFi. It seeks to answer a long-neglected core question: in an era of information overload, free content, and algorithmic proliferation, what is truly scarce? The answer is human attention, genuine signals, and trusted subjective judgment.They are precisely the values InfoFi aims to instantiate through incentives, mechanisms, and market structures.

In a sense, InfoFi represents a “reverse-power revolution” in the attention economy—no longer allowing platforms, big tech, and advertisers to monopolize data and traffic incentives; instead, it attempts to reallocate the value of attention back to the real creators, disseminators, and signal-detectors via blockchain, tokenization, and AI protocols. This structural redistribution empowers InfoFi with the potential to transform content industries, platform governance, knowledge collaboration, and even public discourse.

However, potential is not reality. We must remain cautiously optimistic.

The revolution is underway—but far from complete. The future of InfoFi won’t be defined by a single platform or vertical; it will be shaped by all who create, observe, and recognize attention. If DeFi was the revolution of value flow, then InfoFi is the revolution of value perception and distribution. On the path toward decentralization and disintermediation, we must maintain clear judgment, participate responsibly, and stay alert—while recognizing the possibility that InfoFi could be the fertile ground for the next generation of Web3 narratives.