InfoFi: A New Paradigm of Attention Economy Empowered by AI

In 1971, psychologist and economist Herbert Simon first proposed the theory of attention economy, pointing out that in a world of information overload, human attention has become the most scarce resource.

Economist and USV managing partner Albert Wenger further reveals a fundamental shift in "The World After Capital": humanity is undergoing a third leap—from the "capital scarcity" of the industrial age to the "attention scarcity" of the knowledge age.

• Agricultural Revolution: Solved food scarcity but gave rise to land disputes.
• Industrial Revolution: Solved land scarcity, but led to resource competition and capital accumulation
• Digital Revolution: The Battle for Attention

The underlying driving force of this transformation stems from two key characteristics of digital technology: the zero marginal cost of information replication and dissemination, and the universality of AI computation (although human attention cannot be replicated).

Whether it is the booming collectible toy market or the livestream sales by top streamers, it essentially revolves around the competition for user and audience attention. However, in the traditional attention economy, users, fans, and consumers contribute their attention as "data fuel," while the excess profits are monopolized by platforms. The InfoFi in the Web3 world attempts to disrupt this model—by utilizing blockchain, token incentives, and AI technology to make the processes of information production, dissemination, and consumption transparent, aiming to return value to the participants.

This article will provide an in-depth introduction to the classification of the InfoFi project, the challenges it faces, and future development trends.

What is InfoFi?

InfoFi is a combination of Information + Finance, with the core focus on transforming difficult-to-quantify, abstract information into dynamic, quantifiable value carriers. This encompasses not only traditional prediction markets but also the distribution, speculation, or trading of information or abstract concepts such as attention, reputation, on-chain data or intelligence, personal insights, and narrative activity.

The core advantages of InfoFi are reflected in:

• Value Redistribution Mechanism: Returns the value monopolized by platforms in the traditional attention economy to the true contributors. Through smart contracts and incentive mechanisms, allows information producers, disseminators, and consumers to share in the profits.
• Information monetization capability: Transforming abstract elements such as attention, insights, reputation, and narrative engagement into tradable digital assets, thereby creating a trading market for the previously difficult-to-circulate value of information.
• Low threshold for participation: Users can participate in value distribution through content creation using only their social media accounts.
• Innovation in incentive mechanisms: not only rewarding content creation, but also including multiple aspects such as dissemination, interaction, and verification, allowing niche content and long-tail users to receive returns as well. High-quality content receives more rewards, incentivizing the continuous production of high-quality information.
• Cross-domain application potential: For example, the introduction of AI provides advantages such as content quality assessment and market optimization for InfoFi.

InfoFi Classification

InfoFi covers a variety of different application scenarios and models, which can mainly be divided into the following categories:

Prediction Market

Prediction markets, as a core component of InfoFi, are a mechanism that uses collective intelligence to forecast the outcomes of future events. Participants express their expectations about future events (such as election or policy outcomes, sports events, economic forecasts, price expectations, product release times, etc.) by buying and selling "shares" linked to specific event outcomes, with market prices reflecting the collective expectations of the crowd regarding the event outcomes.

Under the framework of InfoFi, prediction markets are not just tools for speculation, but platforms that uncover and reveal real information through financial incentive mechanisms. This mechanism leverages market efficiency, encouraging participants to provide accurate information, as correct predictions yield economic rewards, while incorrect predictions may lead to losses.

The representative platforms for prediction markets include:

• Polymarket: The largest decentralized prediction market built on the Polygon network, using USDC stablecoin as the trading medium. Users can make predictions on events such as political elections, economics, entertainment, and the launch of products.

• Kalshi: is a fully CFTC-regulated prediction market platform in the United States that supports the acceptance of multiple cryptocurrency deposits by collaborating with cryptocurrency and stablecoin infrastructure providers, but settles in fiat currency. Kalshi focuses on event contracts, allowing users to trade the outcomes of political, economic, and financial events. Due to regulatory compliance, Kalshi has a unique advantage in the U.S. market.

Mouth-Lick Type InfoFi (Yap-to-Earn)

"Yap-to-Earn" is a nickname used in the Chinese crypto community, referring to earning rewards by sharing insights and content. The core idea of Yap-to-Earn is to encourage users to post high-quality, crypto-related posts or comments on social platforms, mostly evaluated by AI algorithms based on the quantity, quality, interaction, and depth of the content, thus distributing points or token rewards. This model differs from traditional on-chain activities (such as trading or staking), focusing more on users' contributions and influence in the community.

Characteristics of "Zui Lu":

• No on-chain transactions or high capital required, just a social account to participate.
• Enhance the activity of the project community by rewarding valuable discussions.
• AI algorithms reduce human intervention, filter out bots and low-quality content, ensuring a more transparent reward distribution.
• Points may be converted into token airdrops or ecological privileges, and early participants may receive higher returns.

The current mainstream mouth-pulling projects or projects that support mouth-pulling include:

• Kaito AI: It is the representative platform for Yap-to-Earn, having collaborated with multiple projects to evaluate the quantity, quality, interactivity, and depth of users' crypto-related content published on social platforms through AI algorithms. It rewards Yap points, allowing users to compete for token airdrops on the leaderboard.

• Cookie.fun: Cookie tracks the mindshare, interactions, and on-chain data of AI agents, generating a comprehensive market overview, and also tracks the mindshare and sentiment of crypto projects. The Cookie Snaps system includes rewards and airdrop activities, providing rewards to Cookie creators who contribute to project attention.

• Virtuals: Virtuals itself is not a platform focused on Yap-to-Earn, but rather an AI agent launch platform. However, it has introduced a new launch mechanism called Genesis Launch on Base, where one of the ways to earn points required for participation in the launch includes Yap-to-Earn.

• Loud: Loud, as a "Attention Value Experiment" within the Kaito AI ecosystem, officially released its token through an Initial Attention Offering (IAO). The LOUD operating mechanism revolves around the "Attention Economy", where the transaction fees collected after trading are primarily distributed in SOL to the top 25 users on the attention leaderboard.

• Wallchain Quacks: Wallchain is a programmatic AttentionFi project based on Solana. Wallchain Quacks rewards high-quality content and valuable interactions. A customized LLM evaluates creators' content daily, and valuable, insightful content creators will receive Quacks rewards.

Mouth Licking + Tasks/On-chain Activities/Verification: Multi-dimensional Contribution Value Realization

Some projects comprehensively evaluate users' multidimensional contributions by combining content contributions with on-chain behaviors (such as trading, staking, and NFT minting) or tasks.

• Galxe Starboard: Galxe is a Web3 growth platform, and its latest launch, Galxe Starboard, aims to reward real contributions in off-chain and on-chain actions. Projects can define multiple layers of contribution, including post engagement, sentiment, virality, interaction with dApps, holding tokens, minting NFTs, or completing on-chain tasks.

• Mirra: Mirra is a decentralized AI model trained on community-curated data, capable of learning from real-time contributions from Web3 users. Creators publish high-quality content on social platforms, effectively submitting validation data for the AI; Scouts identify high-value content and submit insights, determining what the AI learns, helping to shape intelligent AI.

Reputation-based InfoFi

• Ethos: Ethos is an on-chain reputation protocol that is entirely based on open protocols and on-chain records, combined with Social Proof of Stake (Social PoS), generating a Credibility Score through decentralized mechanisms. Ethos also launched a reputation market that allows users to speculate on the reputation of individuals, companies, DAOs, and even AI entities by buying and selling "trust tokens" and "distrust tokens."

• GiveRep: Primarily built on Sui, it aims to convert users' social influence and community participation into quantifiable on-chain reputation through their activities on social platforms, and incentivize user participation through rewards.

Attention Market/Prediction

• Noise: is a trend discovery and trading platform based on MegaETH, where users can long or short the attention of projects.

• Upside: Upside is a social prediction market that rewards the discovery, sharing, and prediction of valuable content and links, creating a dynamic market through a voting mechanism. Earnings are proportionally distributed to voters, creators, and curators.

• YAPYO: A foundational attention market infrastructure within the Arbitrum ecosystem. YAPYO indicates that the rewards in its coordination mechanism are not just profits, but also lasting influence.

• Trends: Social posts can be tokenized to become a trend on the joint curve, referred to as "Trend it". Creators are eligible to receive 20% of the joint curve transaction fees for each trend.

Token gated content access: filtering noise

• Backroom: Creators can launch tokenized spaces, providing curated content such as market insights, Alpha, and analysis. Users can unlock high-value, low-noise information by purchasing on-chain Keys tied to each creator's space. Keys are not just for access—they are tradable assets with a demand-driven dynamic pricing curve.

• Xeet: A new protocol on the Abstract network that has not been fully launched yet. Xeet will integrate with the Ethos score, and besides that, no further information has been disclosed.

Data Insight InfoFi

• Arkham Intel Exchange: Arkham is an on-chain data query tool, intelligence trading platform, and exchange. Arkham Intel Exchange is a decentralized intelligence trading platform where "on-chain detectives" can earn bounties.

InfoFi Dilemma

Prediction Market

• Regulation and Compliance: Predictive markets may be viewed as similar to binary options or gambling markets, facing regulatory pressure.
• Insider Trading and Fairness: Predictive markets may be affected by insider information. Large funds may distort prices in the short term.
• Liquidity and Participation: The effectiveness of prediction markets relies on sufficient participants and liquidity. Prediction markets often face the "long tail liquidity problem" on niche topics.
• Oracle Design: Prediction markets have faced oracle manipulation attacks, resulting in significant losses for users who bet on the correct outcomes.

Mouth Licking

• Information noise intensifies, AI content advertisements proliferate, obscuring the real signals.
• Most mouth-licking projects lack public explanations of their algorithms for evaluating content quality, interactivity, and depth, raising user concerns about the fairness of points distribution.
• The Matthew effect in profit distribution: In most cases, projects and KOLs achieve a win-win situation, but tail content creators and interacting retail investors still face the dilemma of low earnings and fierce competition.
• The early stage of the mouth stroking event attracted user participation, but attention sharply decreased after the rewards were distributed, lacking sustainability.
• Attention is not equal to market cap ratio.

reputation

• Reputation InfoFi projects such as Ethos use an invitation system to control user quality and reduce the risk of witch attacks. However, this mechanism raises the participation threshold and limits new users.