Original Title: "The InfoFi Dilemma in the Attention Economy"

Original Source: Tiger Research Reports

Key Points Summary

· InfoFi is a structured attempt to quantify user attention and activity and link it to rewards.

· However, there are currently some structural issues, including a decline in content quality and reward centralization.

· These are not limitations of the InfoFi model itself but rather design issues in evaluation criteria and reward distribution that urgently need improvement.

1. The Era of Attention as Currency

Attention has become one of the scarcest resources in the modern industry. In the age of the Internet, information is abundant, while human capacity to process information is extremely limited. This scarcity has led numerous companies to engage in fierce competition, where the ability to capture user attention has become a core competitive advantage.

The crypto industry has demonstrated the intensity of attention competition in a more extreme form. Attention share plays a vital role in token pricing and liquidity formation, making it a key factor in determining project success or failure. Even technologically superior projects are often eliminated from the market if they fail to attract market attention.

This phenomenon is rooted in the structural characteristics of the crypto market. Users are not just participants but also investors, where their attention directly translates into token purchases, creating greater demand and network effects. Liquidity is created where attention is concentrated, and narratives develop on this liquidity foundation. These established narratives then attract new attention, forming a virtuous cycle that drives market development.

2. InfoFi: A Systemic Attempt to Tokenize Attention

The market operates based on attention. This structure raises a critical question: who can truly benefit from this attention? Users generate attention through community activities and content creation, but these behaviors are difficult to measure and lack a clear direct reward mechanism. So far, ordinary users can only gain indirect benefits through token trading. There is currently no reward mechanism for those who truly create attention.

Kaito's InfoFi network, Source: Kaito

InfoFi is an attempt to address this issue. InfoFi combines information with finance, creating a mechanism that evaluates user contribution based on user-generated content engagement metrics (such as views, comments, and shares) and ties it to token rewards. Kaito's success has widely popularized this structure.

Kaito evaluates social media activity, including posting and commenting, through AI algorithms. The platform rewards users with tokens based on their scores. The more attention user-generated content attracts, the greater the project's exposure. Capital views this attention as a signal and makes investment decisions based on it. As attention grows, more capital flows into the project, increasing rewards for participants. Participants, projects, and capital collaborate through attention data as a medium, forming a virtuous cycle.

The InfoFi model has made significant contributions in three key areas.

First, it quantifies user contribution activities with unclear evaluation criteria. Based on a point system, people can structurally define their contribution, helping users predict what rewards they can earn through specific actions, thereby enhancing the sustainability and consistency of user engagement.

Second, InfoFi transforms attention from an abstract concept into quantifiable and tradable data, shifting user engagement from passive consumption to productive activities. Most existing online participation involves investment or content sharing, while platforms profit from the attention generated by these activities. InfoFi quantifies users' market responses to this content and rewards them based on this data, leading to participants' behaviors being seen as productive work. This shift empowers users as creators of network value, not just community members.

Third, InfoFi lowers the barrier to entry for information production. In the past, Twitter influencers and institutional accounts dominated information distribution, capturing most of the attention and rewards. Now, ordinary users, after gaining a certain level of market attention, can also receive tangible rewards, creating more opportunities for participation for users from diverse backgrounds.

3. The Attention Economy Trap Triggered by InfoFi

The InfoFi model is a new reward design experiment in the crypto industry that quantifies user contribution and ties it to rewards. However, attention has become an overly centralized value, and its side effects are becoming increasingly evident.

The first issue is excessive attention competition and a decline in content quality. When attention becomes the standard for rewards, the purpose of creating content shifts from providing information or encouraging meaningful engagement to solely seeking rewards. Generative AI has made content creation easier, and bulk content lacking authentic information or insights spreads rapidly. This so-called "AI Slop" content is proliferating throughout the ecosystem, raising concerns among people.

Loud Mechanism, Source: Loud

The Loud project clearly exemplifies this trend. Loud attempts to tokenize attention, with the platform choosing to reward the top users who receive the most attention within a specific time period. This structure is intriguing in theory, but attention became the sole criterion for rewards, leading to overheated competition among users and resulting in the generation of a large amount of repetitive low-quality content, ultimately leading to content homogenization within the entire community.

Source: Kaito Mindshare

The second issue is reward centralization. Attention-based rewards began to focus on specific projects or topics, causing content from other projects to effectively disappear or decrease, as evidenced by Kaito's shared data. Loud once dominated over 70% of the crypto content on Twitter, leading the ecosystem's information flow. When rewards focus on attention, content diversity decreases, and information gradually revolves around projects offering high token rewards. Ultimately, the scale of marketing budgets determines influence within the ecosystem.

4. Structural Constraints of InfoFi: Evaluation and Distribution

4.1. Limitations of Simple Methods for Content Evaluation

An attention-centric reward structure raises a fundamental question: how should content be evaluated, and how should rewards be distributed? Currently, most InfoFi platforms rely on simplistic metrics (such as views, likes, and comments) to assess content value. This structure assumes that "high engagement equals good content."

Highly engaging content may indeed have better information quality or delivery effectiveness, but this structure mainly applies to very high-quality content. For most mediocre content, the relationship between feedback quantity and quality is not yet clear, leading to the phenomenon where repetitive formats and overly enthusiastic content receive high ratings. Meanwhile, content presenting diverse perspectives or discussing new topics struggles to receive due recognition.

Addressing these issues requires a more robust content quality assessment system. Relying solely on engagement-based evaluation standards is rigid, as content value evolves over time or in different contexts. For example, AI can identify meaningful content, and community-based algorithm adjustment methods can be introduced. The latter can involve algorithms adjusting evaluation criteria based on periodically provided user feedback data, thus helping the evaluation system flexibly adapt to changes.

4.2. Reward Structure Centralization and Balance Requirements

The limitations of content evaluation coexist with reward structure issues, which also exacerbate information flow bias. The current InfoFi ecosystem usually operates individual leaderboards for each project, each using its own token for rewards. In this structure, projects with large marketing budgets can attract more content, leading users' attention to be concentrated on specific projects.

To address these issues, adjustments to the reward distribution structure are needed. Each project can retain its own rewards, and the platform can monitor content centralization in real time and make adjustments using the platform token. For example, when content becomes too concentrated on a specific project, the platform token rewards can temporarily decrease, while topics with relatively lower coverage can receive additional platform tokens. Content covering multiple projects can also receive additional rewards. This will create an environment with diverse themes and viewpoints.

Evaluation and rewards are at the core of the InfoFi structure. How content is evaluated determines the ecosystem's information flow, and who receives what kind of reward is also crucial. The current structure relies on a single standard evaluation system combined with a marketing-centric reward structure, accelerating the dominance of attention while also weakening information diversity. Flexibility in evaluation criteria is crucial for sustainable operation, and balancing adjustments to the allocation structure are a key challenge facing the InfoFi ecosystem.

5. Conclusion

InfoFi's structured experiment aims to quantify attention and transform it into economic value, shifting the existing one-way content consumption structure into a producer-centered participatory economy, which is of extraordinary significance. However, the current InfoFi ecosystem is facing structural side effects in the attention tokenization process, including a decline in content quality and biased information flow. These side effects are more a dilemma inherent in the initial design stage than limitations of the model.

The evaluation pattern based on simple feedback has exposed its limitations, and the reward structure influenced by marketing resources has also revealed issues. Urgent improvements are needed to develop systems that correctly evaluate content quality, as well as community-based algorithm adjustment mechanisms and platform-level balance adjustment mechanisms. InfoFi aims to create an ecosystem where members can receive fair rewards through participating in information production and dissemination. To achieve this goal, technical improvements are needed, as well as encouraging community involvement in design.

In the crypto ecosystem, attention operates much like a token. InfoFi is a crucial experiment in designing and operating a new economic structure. Only when it evolves into a structure where valuable information and insights are shared, can its full potential be realized. The results of this experiment will accelerate the development of the quantified information economy in the digital age.

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