How can InfoFi break out of the prisoner’s dilemma? | Bee Network

How can InfoFi break out of the prisoner’s dilemma? | Bee Network Login Актуальные новости Запуск мемов Агенты искусственного интеллекта DeSci TopChainExplorer Для Ньюби 100-кратное количество монет Игра "Пчелка Основные веб-сайты Must-Have APP Криптознаменитости DePIN Новички насущные Детектор-ловушка Основные инструменты Продвинутые веб-сайты Обмен Инструменты NFT Привет, Выйти Вселенная Web3 Игры DApp Пчелиный улей Растущая платформа AD Поиск Английский язык Монеты для пополнения запасов Вход в систему Скачать Web3 Uni Игры DApp Пчелиный улей AD дом•Анализ•Основной текст How can InfoFi break out of the prisoner’s dilemma?Анализ8 месяцев назадUpdateУайатт 30 182 22 Автор оригинала: KarenZ, Foresight News

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

Economist and USV Managing Partner Albert Wenger further revealed a fundamental transformation 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 the problem of food scarcity, but gave rise to land competition;

Industrial Revolution: dedicated to solving the problem of land scarcity, but shifted to resource competition and capital accumulation;

The digital revolution: The battle for attention.

The underlying driving force behind this transformation stems from two major characteristics of digital technology: zero marginal cost of information replication and dissemination, and the ubiquity of AI computing (but human attention cannot be replicated).

Whether it is the popularity of Labubu in the trendy toy market or the live streaming of top anchors, in essence, it is a competition for the attention of users and viewers. However, in the traditional attention economy, users, fans, and consumers contribute their attention as data fuel, but the excess profits are monopolized by platforms, scalpers, etc. InfoFi in the Web3 world attempts to subvert this model – through blockchain, token incentives, and AI technology, it makes the production, dissemination, and consumption of information transparent, and attempts to return value to the participants.

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

What is InfoFi? InfoFi is a combination of Information + Finance. Its core lies in transforming difficult-to-quantify and abstract information into dynamic and quantifiable value carriers. This not only covers 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.

InfoFis core advantages are:

Value redistribution mechanism: Return the value monopolized by platforms in the traditional attention economy to the real contributors. Through smart contracts and incentive mechanisms, information producers, disseminators and consumers can share the benefits.

Ability to value information: Convert abstract attention, insights, reputation, narrative activity, etc. into tradable digital assets, creating a trading market for information value that was originally difficult to circulate.

Low threshold for participation: Users can participate in value distribution through content creation with just a social media account.

Innovation in incentive mechanism: rewards are not only given to content creation, but also to dissemination, interaction, verification and other links, so that niche content and long-tail users can also be rewarded. High-quality content will receive more rewards, which encourages the continuous production of high-quality information;

Cross-domain application potential: For example, the introduction of AI provides InfoFi with advantages such as content quality assessment and predictive market optimization.

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

Prediction Рынок As a core component of InfoFi, the prediction market is a mechanism for predicting the outcome of future events through group wisdom. Participants express their expectations for future events (such as election or policy results, sports events, economic forecasts, price expectations, product release dates, etc.) by buying and selling shares linked to the results of specific events, and the market price reflects the collective expectations of the group on the outcome of the event. Polymarket is a representative application that promotes the concept of InfoFi.

Vitalik has always been a loyal supporter of the prediction market Polymarket. In his November 2024 article From prediction markets to info finance, he said, Prediction markets have the potential to create better applications in social media, science, news, governance and other fields. I call this type of market info finance. Vitalik also pointed out the two sides of Polymarket: one is a gambling website for participants, and the other is a news website for everyone else.

In the framework of InfoFi, the prediction market is not just a tool for speculation, but a platform for mining and revealing real information through financial incentives. This mechanism takes advantage of the efficiency of the market and encourages participants to provide accurate information, because correct predictions will bring financial rewards, while incorrect predictions may lead to losses. Musk himself also forwarded the data of Trump leading with 51% support on Polymarket a month before the 2024 US election, and commented: Because it involves real money investment, this data is more accurate than traditional polls.

Prediction market representative platforms include:

Polymarket: The largest decentralized prediction market, Polymarket is built on the Polygon network and uses USDC stablecoin as a trading medium. Users can predict political elections, economy, entertainment, product launches, and other events.

Kalshi: It is a prediction market platform in the United States that is fully regulated by the CFTC. Through cooperation with Zero Hash, a криптовалютаcurrency and stablecoin infrastructure provider, it supports accepting USDC, BTC, WLD, SOL, XRP and RLUSD deposits, but receives legal currency settlement. Kalshi focuses on event contracts, allowing users to trade the results of political, economic and financial events. Due to regulatory compliance, Kalshi has a unique advantage in the US market.

Yap-to-Earn InfoFi Zuilu is a nickname for Yap-to-Earn in the Chinese crypto community, which means earning rewards by expressing opinions and sharing content. The core concept of Yap-to-Earn is to encourage users to post high-quality posts or comments related to crypto projects on social platforms. Most of them use AI algorithms to evaluate the quantity, quality, interaction and depth of content, and then allocate points or token rewards. This model is different from traditional on-chain activities (such as trading or staking), and pays more attention to the information contribution and influence of users in the community.

Features of Mouth Strike:

No on-chain transactions or high capital are required, just an X account to participate.

Enhance the activity of the project community by rewarding valuable discussions.

AI algorithms reduce human intervention, filter out robots and low-quality content, and ensure more transparent reward distribution.

Points may be converted into token airdrops or ecological privileges, and early participants may obtain higher returns.

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

Kaito AI: It is the representative platform of Yap-to-Earn and has cooperated with multiple projects. It uses AI algorithms to evaluate the quantity, quality, interactivity and depth of crypto-related content posted by users on X, and rewards Yap points for users to compete in the rankings to earn token airdrops.

In this way, creators can not only effectively prove their influence and content value through Yaps, but also attract accurate and high-quality attention; ordinary users can use the Yaps system to efficiently discover high-quality content and KOLs; and project parties have achieved the dual goals of accurately reaching target users and expanding brand influence, forming a virtuous ecological cycle of win-win for all parties.

Kaito AI has distributed over $90 million worth of tokens to various communities (excluding Kaito鈥檚 own airdrops), and has over 200,000 active Yappers per month.

Source: https://dune.com/queries/5088750/8397899

Cookie.fun: Cookies track the mindshare, interactions, and on-chain data of AI agents to generate a comprehensive market overview, and also track the mindshare and sentiment of crypto projects. Cookie Snaps has a built-in reward and airdrop activity system to reward Cookie creators for contributing to project attention.

Cookie has launched Snaps activities in cooperation with three projects, namely Spark, Sapien and OpenLedger. Among them, the number of participants in the Spark activity exceeded 16,000, and the number of participants in the latter two projects was 7,930 and 6,810 respectively.

Virtuals: Virtuals itself is not a Yap-to-Earn focused platform, but rather an AI agent launch platform, but a new launch mechanism, Genesis Launch, was launched on Base in mid-April, and one of the ways to earn points required to participate in the launch includes Yap-to-Earn (powered by Kaito).

The top AI agent projects with high subscription rates on Virtuals, source: https://dune.com/queries/5195678/8548951

Loud: As an attention value experiment in the Kaito AI ecosystem, Loud once occupied more than 70% of the Kaito attention rankings through the Yap-to-Earn event before officially releasing tokens through the Initial Attention Offering (IAO) at the end of May 2025. The LOUD operating mechanism also revolves around the attention economy. The transaction fees collected after the opening of transactions are mainly distributed in the form of SOL to the top 25 users in the attention rankings.

Wallchain Quacks: Wallchain is a programmatic AttentionFi project based on Solana, supported by AllianceDAO. Wallchain X Score evaluates the overall influence of users, while Wallchain Quacks rewards high-quality content and valuable interactions. Currently, Wallchain Quacks custom LLM will evaluate creator content every day, and valuable and insightful content creators will be rewarded with Quacks.

Mouth + Tasks/On-chain Activities/Verification: Multi-dimensional Contribution Value There are also some projects that comprehensively evaluate users multi-dimensional contributions by combining content contributions with on-chain behaviors (such as transactions, staking, NFT minting) or tasks.

Galxe Starboard: Galxe is a Web3 growth platform, and its newly launched Galxe Starboard is committed to rewarding real contributions in both off-chain and on-chain actions. Projects can define multiple contribution tiers, and what matters is not just how many tweets are sent, but the value brought to the entire project, 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-selected data that can learn from real-time contributions from Web3 users. Specifically, creators post high-quality content on X, which is equivalent to submitting AI verification data; Scouts identify high-value content on X and tag @MirraTerminal in replies to submit insights, which determines what AI learns and helps shape intelligent AI.

Reputation-based InfoFi Ethos is an on-chain reputation protocol that is completely based on open protocols and on-chain records, and combines social proof of stake (Social PoS) to generate a credibility score (Credibility Score) through a decentralized mechanism to ensure the reliability, decentralization and Sybil resistance of its reputation system. Currently, Ethos adopts a strict invitation system. The core function of Ethos is to generate a credibility score, a numerical indicator that quantifies the users trust in the chain. The score is based on the following on-chain activities and social interactions: comment mechanism (with cumulative utility), guarantee mechanism (staking Ethereum to endorse other users).

Ethos also released a reputation market that allows users to speculate on the reputation of individuals, companies, DAOs, and even AI entities by buying and selling trust votes and no-confidence votes, that is, going long or short on reputation.

GiveRep: Mainly built on Sui, it aims to convert users social influence and community participation into quantifiable on-chain reputation through their activities on the X platform, and motivate users to participate through rewards. Comment on the GiveRep official Twitter under the creators post, and the commenter and the creator will each get one reputation point. In order to limit abuse, GiveRep limits users comment mentions to 3 times per day (including 3 times), while creators can receive unlimited points per day. Comment mentions from Sui ecological projects and ambassadors will get more points.

Attention Market/Forecast Noise: It is a trend discovery and trading platform based on MegaETH. Currently, you need an invitation code to experience it. Users can go long or short the attention of the project.

Upside: Upside is a social prediction market (investors include Arthur Hayes) that rewards discovering, sharing and predicting valuable content and links, creating a dynamic market through the like mechanism. The proceeds are distributed proportionally to voters, creators and curators. To prevent manipulation of the prediction pool, the weight of likes will be reduced in the last 5 minutes of each round.

YAPYO: An attention market infrastructure for the Arbitrum ecosystem. YAPYO says that the rewards in its coordination mechanism are not just earnings, but lasting influence.

Trends: X posts can be tokenized and become a trend on the bonding curve (call it Trend it). Creators are eligible to receive 20% of the bonding curve transaction fees for each trend.

Токен-gated content access: filtering out the noise Backroom: Creators can launch tokenized spaces to provide curated content such as market insights, alpha, and analysis, without the need for management and social pressure; users can unlock low-noise, high-value information by purchasing on-chain keys bound to each creator space. Keys are not just for access – they are tradable assets with dynamic pricing curves driven by demand. At the same time, AI processes chat data and signals into actionable insights.

Xeet: A new protocol on the Abstract network, which has not yet been fully launched, but a referral program has been launched, and inviting KOLs will receive bonus points. Xeet founder @Pons_ETH mocked InfoFi for evolving into NoiseFi, and said, Its time to reduce noise and enhance signals. The current public information is that Xeet will be integrated with the use of Ethos scoring. Apart from this, Xeet has not disclosed more information.

Data Insight InfoFi Arkham Intel Обмен: Arkham is an on-chain data query tool, intelligence trading platform, and exchange. Arkham Intel Intel Exchange is a decentralized intelligence trading platform where on-chain detectives can earn rewards.

InfoFi Dilemma Prediction Market Regulation and compliance: Prediction markets may be viewed as markets similar to binary options and gambling, and face regulatory pressure. For example, Polymarket was deemed to be operating illegally by the CFTC in the United States because it was not registered as a designated contract market (DCM) or swap execution facility (SEF). In 2022, it was fined $1.4 million and required to block US users. The investigation by the US Department of Justice and the FBI in 2024 further highlighted its regulatory dilemma.

Insider Trading and Fairness: Prediction markets may be disrupted by insider information. Large funds may distort prices in the short term. Designing fair rules and mechanisms is one of the key challenges of the InfoFi prediction market.

Liquidity and participation: The effectiveness of prediction markets depends on sufficient participants and liquidity. Prediction markets often face the long-tail liquidity problem on niche topics, that is, insufficient participants lead to unreliable market information. The introduction of AI agents may partially solve this problem, but further optimization is still needed.

Oracle design: Polymarket has suffered from oracle manipulation attacks, resulting in heavy losses for users who bet on the correct results. In February 2025, UMA, Polymarket, and EigenLayer said they were working together to build prediction market oracles. Some research ideas include developing an oracle that can support multiple tokens to resolve disputes. Other features being studied include dynamic binding, AI agent integration, and enhanced security against bribery attacks.

Mouth Information noise has intensified, and AI content advertising accounts have flooded, obscuring real signals. It is difficult for users to filter value from massive amounts of content, community trust has declined, and the marketing effect of the project has been discounted. According to KOL CryptobraveHQ (@cryptobraveHQ), Several project owners have complained that they spent 150,000 USDT service fees on Kaito, allocated 0.5%-1% of tokens to KOLs, and ended up with more than half of AI content advertising accounts participating. If the project wants to attract top KOLs and ICT to participate, they have to pay extra, and then Kaito will contact the top KOLs to participate.

The algorithms of most ZuiLu projects lack a public explanation of how to evaluate content quality, interactivity, and depth, causing users to question the fairness of point distribution. If the algorithm favors specific accounts (such as big Vs or matrix accounts), it may lead to the loss of high-quality creators. Kaito has recently made some new upgrades to the algorithm based on community feedback. The upgrade focuses on defaulting to quality over quantity, posts that do not provide project insights and comments will not receive attention, and further crackdowns on interactive manipulation and group brushing behaviors.

Matthew effect of income distribution: In most cases, projects and KOLs win-win, but tail content creators and interactive retail investors still face the dilemma of low income and fierce competition. Yu Hu, founder of Kaito, said on June 8 that out of the approximately 1 million registered users on Kaito, less than 30,000 users have received yaps, which is less than 3%. The next growth stage of the network is to maximize conversion rates. In addition, poor management of airdrop expectations will lead to community dissatisfaction. Magic Newton is a relatively successful case of mouth-pushing on Kaito AI. Kaito ecosystem recommendations account for 1/3 of all Newton verification agents. Mouth-pushing users have made a lot of money, but they also face questions about being unfriendly to retail investors. In contrast, Humanity was directly accused by the community of backstabbing users and extreme anti-pushing. This imbalance in distribution has triggered a crisis of trust.

The initial LOUD activity attracted users to participate, but after the rewards were distributed, the attention dropped drastically and lacked sustainability. The market value of LOUD tokens on the day of its launch was close to 30 million US dollars, but now it is less than 600,000 US dollars.

Attention does not equal market capitalization share.

reputation Reputation InfoFi projects such as Ethos use an invitation system to control user quality and reduce Sybil attacks. However, this mechanism raises the participation threshold, limits the entry of new users, and makes it difficult to form a broad network effect.

Risk of malicious operations.

The problem of cross-platform mutual recognition of reputation scoring is that scoring systems of different protocols are difficult to communicate with each other, forming information islands.

InfoFi Trends Prediction Market Combination of AI and prediction markets: AI can significantly enhance the efficiency of prediction markets. For example, AI can provide more accurate predictions in complex scenarios by analyzing massive amounts of data; AI agents can also be explored to solve long-tail problems.

The combination of social media and prediction markets: Prediction markets have the potential to become the core infrastructure of the future information economy. On June 6, X officially announced a partnership with Polymarket, which became Xs official prediction market partner. Polymarket founder and CEO Shayne Coplan said: Combining Polymarkets accurate, fair, and real-time prediction market probabilities with Groks analysis and Xs real-time insights will be able to instantly provide contextualized, data-driven insights to millions of Polymarket users around the world.

Decentralized governance: Prediction markets can be applied to the governance of DAOs, companies, and even society, the so-called Futarchy. Vitalik said in 2014 that Futarchy is a governance model proposed by economist Robin Hanson. The core idea is to vote to express values and use the market to predict beliefs. It works as follows: the community determines a measure of success (such as GDP, company stock prices, etc.) through voting; for specific policy proposals, two prediction markets are created (such as approval and rejection). Participants trade these two tokens, and the price reflects the markets expectations of whether the policy can optimize the goal; ultimately, the policy with a higher average price is selected, and the token income is settled based on the actual results. The advantage of Futarchy is that it relies on data rather than political propaganda, personal charm or promotion.

Content, news tools for everyone.

Mouth + Reputation InfoFi Introducing social graph and semantic understanding technologies will improve the accuracy of AI鈥檚 assessment of content value, ultimately moving towards high-quality content.

Incentivize high-quality long-tail creators.

Add slashing or penalty mechanisms.

The launch of the Web3-specific InfoFi LLM.

Evaluate contribution from multiple dimensions.

Reputation-based InfoFi is combined with DeFi, and reputation scores are used as the credit basis for lending and staking.

The tokenization of abstract assets such as attention, reputation, and trends will give rise to more types of derivatives.

Not just based on X social platform.

Integration with more social platforms and news media will drive the formation of an attention-seeking, Alpha discovery tool for everyone.

Data Insight InfoFi Combine data analysis charts with creator insights, and add incentive mechanisms for creation, distribution, etc.

Combination of data analysis charts and AI analysis.

резюме The core contradiction of the digital age is the separation between attention creators and value holders. This separation is the driving force behind the Web3 InfoFi revolution.

The core contradiction of InfoFi is that if the value of information and the incentive for participation cannot be balanced, it may repeat the mistakes of SocialFi, which started high and ended low. The key to InfoFi is to establish a trinity balance mechanism, information mining, user participation, and value return, so as to drive the formation of a better knowledge sharing and collective decision-making infrastructure. This requires not only the quantification of attention at the technical level, but also the mechanism design to ensure that ordinary participants can obtain reasonable returns from information dissemination and avoid serious skew in value distribution.

More importantly, the revolution of InfoFi needs to be promoted from both top to bottom and bottom to truly realize the fairness and efficiency of the attention economy. Otherwise, the Matthew effect of the income pyramid will make InfoFi a gold-digging game for a few people, which runs counter to the original intention of universal benefit of attention value.

Reference: https://vitalik.eth.limo/general/2024/11/09/infofinance.htmlhttps://www.un.org/sites/un2.un.org/files/attention_economy_feb.pdf

This article is sourced from the internet: How can InfoFi break out of the prisoner’s dilemma?

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