InfoFi: A New Landscape of Attention Market Empowered by AI

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

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

• Agricultural Revolution: Aimed at solving food scarcity issues, but led to land disputes.
• Industrial Revolution: Aimed at addressing the issue of land scarcity, but shifted towards resource competition and capital accumulation.
• Digital Revolution: Competing for Attention

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

Whether it is the booming market for trendy toys or the live streaming sales led by top influencers, it essentially reflects a significant competition for the attention of users and viewers. However, in the traditional attention economy, users act as "data fuel" contributing attention, while the excess profits are monopolized by platforms. The InfoFi in the Web3 world attempts to disrupt this model—by leveraging 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 the 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 not only encompasses traditional prediction markets but also includes 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: Returning the value monopolized by platforms in the traditional attention economy to the true contributors. Through smart contracts and incentive mechanisms, allowing information producers, disseminators, and consumers to share the profits.
• Information monetization capability: Transforming abstract elements such as attention, insights, reputation, and narrative activity into tradable digital assets, thereby creating a trading market for the value of information that is otherwise difficult to circulate.
• Low barrier to entry: 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 rewards. 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 for forecasting future event outcomes through collective intelligence. Participants express their expectations for future events (such as election or policy outcomes, sports events, economic forecasts, price expectations, product release dates, etc.) by buying and selling "shares" linked to specific event outcomes, and the market price reflects 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 lead to economic rewards, while incorrect predictions may result in losses.

Predictive market representative platforms include:

• A certain prediction market: the largest decentralized prediction market built on the Polygon network, using USDC stablecoin as the trading medium. Users can make predictions about events such as political elections, economics, entertainment, and product launches.
• A regulated platform: It is a prediction market platform fully regulated by the CFTC in the United States, supporting deposits in USDC, BTC, SOL, XRP, etc., through partnerships with cryptocurrency and stablecoin infrastructure providers, but settlements are received in fiat currency. The platform focuses on event contracts, allowing users to trade the outcomes of political, economic, and financial events. Due to regulatory compliance, it has a unique advantage in the U.S. market.

Mouth Licking Type InfoFi (Yap-to-Earn)

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

Characteristics of "Zui Lu":

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

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

An AI platform: It is a representative platform for Yap-to-Earn, having collaborated with multiple projects to evaluate the quantity, quality, interactivity, and depth of crypto-related content posted by users on social platforms through AI algorithms. It rewards points for users to compete for a leaderboard to earn token airdrops. The platform has distributed tokens worth over $90 million to various communities, with more than 200,000 active users each month.

A tracking platform: Tracks the mind share, interaction status, and on-chain data of AI agents, generating a comprehensive market overview, and also tracks the mind share and sentiment of crypto projects. It features a built-in reward and airdrop activity system to provide rewards to creators who contribute to project attention.

A certain AI agency launch platform: it is not specifically a Yap-to-Earn platform, but rather an AI agency launch platform. However, it has introduced a new launch mechanism on a certain public chain, and one of the ways to earn points for participation in the launch includes Yap-to-Earn.

A certain attention experiment project: As a "Attention Value Experiment" within an AI ecosystem, it officially issues tokens through initial attention allocation. The operating mechanism revolves around the "Attention Economy", and the transaction fees collected after trading is opened are primarily distributed in a certain cryptocurrency form to the top 25 users on the attention leaderboard.

A Solana-based project: a programmatic AttentionFi project based on Solana. It assesses the overall influence of users and rewards high-quality content and valuable interactions. A customized LLM will evaluate creator content daily, and content creators who provide valuable and insightful content will be rewarded.

Mouth Lick + Task/On-chain Activity/Verification: Multidimensional Contribution Valueization

Some projects also combine content contributions with on-chain behaviors (such as trading, staking, NFT minting) or tasks to comprehensively assess users' multidimensional contributions.

A certain Web3 growth platform: It is a Web3 growth platform that has recently launched features aimed at rewarding real contributions in off-chain and on-chain actions. Projects can define multiple contribution layers, where what matters is not just how many tweets were sent, but the value brought to the entire project, including post engagement, sentiment, viral spread, interaction with dApps, token holding, minting NFTs, or completing on-chain tasks.

A certain decentralized AI model: It is a decentralized AI model trained on community-selected data that can learn from the real-time contributions of Web3 users. Specifically, creators post high-quality content on social platforms, which is akin to submitting AI validation data; scouts identify high-value content and mark submissions of insights, determining what content the AI learns from, thus helping to shape intelligent AI.

Reputation-based InfoFi

A Reputation Protocol: It is an on-chain reputation protocol, entirely based on open protocols and on-chain records, combined with social proof of stake, which generates credibility scores through a decentralized mechanism, ensuring the reliability, decentralization, and Sybil attack resistance of its reputation system. Currently, it employs a strict invitation system. The core function is to generate credibility scores, a quantifiable indicator of users' on-chain trust. The scoring is based on the following on-chain activities and social interactions: a review mechanism (with cumulative utility), and a guarantee mechanism (staking Ethereum to endorse other users). The protocol has also launched a reputation market, allowing users to speculate on the reputation of individuals, companies, DAOs, and even AI entities by buying and selling "trust tickets" and "distrust tickets."

A reputation project based on Sui: primarily built on Sui, it aims to convert users' social influence and community participation on social platforms into quantifiable on-chain reputation through their activities, and incentivize user participation through rewards. Commenting on the creator's post by mentioning the official account allows both the commenter and the creator to earn one reputation point each. To limit abuse, users are restricted to a maximum of 3 such comment mentions per day, while creators can receive unlimited points daily. Mentions from Sui ecosystem projects and ambassadors will earn additional points.

Attention Market/Prediction

Trend discovery and trading platform: It is a trend discovery and trading platform based on MegaETH, currently requiring an invitation code to experience. Users can long or short the attention of the project.

A social prediction market: It is a social prediction market that rewards the discovery, sharing, and prediction of valuable content and links, creating a dynamic market through a liking mechanism. Earnings are proportionally distributed among voters, creators, and curators. To prevent manipulation of the prediction pool, the weight of likes will decrease in the last 5 minutes of each round.

A certain Arbitrum ecosystem project: An attention market infrastructure within the Arbitrum ecosystem. It indicates that the rewards in its coordination mechanism are not just profits, but also enduring influence.

A tokenized social content platform: it allows the tokenization of social posts, becoming a trend on the bonding curve. Creators are eligible to receive 20% of the bonding curve transaction fees for each trend.

Token gated content access: Filter noise

A certain creator platform: Creators can launch tokenized spaces, providing curated content such as market insights, Alpha, and analysis, without the need for management or social pressure; users can unlock low-noise, high-value information by purchasing on-chain Keys linked to each creator's space. Keys are not just for access—they are tradable assets with dynamically priced curves driven by demand. At the same time, AI processes chat data and signals into actionable insights.

A new protocol: A new protocol on the Abstract network that has not been fully launched yet, but a referral program has been introduced, inviting KOLs to earn reward points. The founder stated, "It's time to reduce the noise and enhance the signal." Currently available information indicates that the protocol will integrate with a certain reputation scoring system.

Data Insights InfoFi

An intelligence trading platform: is an on-chain data query tool, intelligence trading platform, and exchange. Its intelligence trading platform 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 similarly to binary options and gambling markets, facing regulatory pressure.
• Insider Trading and Fairness: Predictive markets may be affected by insider information.