Written by: Jay Jo, Tiger Research
Compiled by: AididiaoJP, Foresight News
TL;DR
InfoFi is a structured attempt to quantify user attention and activities and link them to rewards.
InfoFi currently faces 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 of assessment criteria and reward distribution mechanisms that urgently need improvement.
The Era of Attention as Tokens
Attention has become one of the scarcest resources in modern industries. In the age of the internet, information is flooded, while human capacity to process information is extremely limited. This scarcity has led many businesses to engage in fierce competition, and the ability to capture user attention has become a core competitive advantage for companies.
The crypto industry exhibits the competition for attention in a more extreme form. Attention market share plays a crucial role in token pricing and liquidity formation, becoming a key factor in determining project success or failure. Even technically advanced projects can often be eliminated from the market if they fail to attract market attention.
This phenomenon stems from the structural features of the crypto market. Users are not only participants but also investors, and their attention directly leads to actual token purchasing behavior, creating greater demand and network effects. In places where attention is concentrated, liquidity is created, and narratives develop based on this liquidity. These established narratives then attract new attention, forming a virtuous cycle that drives market development.
InfoFi: A Systematic Attempt to Tokenize Attention
The market operates based on attention. This structure raises a key question: who can truly benefit from this attention? Users generate attention through community activities and content creation, but these actions are difficult to measure and lack a clear direct reward mechanism. So far, ordinary users can only obtain indirect benefits through buying and selling tokens. There is currently no reward mechanism for contributors 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 contributions based on the attention generated by user content (e.g., views, comments, and shares) and links it to token rewards. Kaito's success has allowed this structure to be widely disseminated.
Kaito evaluates social media activities through AI algorithms, including posts and comments. The platform offers token rewards based on scores. The more attention user-generated content attracts, the greater exposure the projects can achieve. Capital views this attention as a signal and makes investment decisions based on it. As attention grows, more capital flows into the projects, and participants' rewards increase accordingly. Participants, projects, and capital work together through attention data as a medium, creating a virtuous cycle.
The InfoFi model makes outstanding contributions in three key areas.
First, it quantifies user contribution activities with unclear assessment criteria. The point system allows people to define contributions in a structured way and helps users predict what rewards they can earn through specific actions, thereby improving the sustainability and consistency of user participation.
Secondly, InfoFi transforms attention from an abstract concept into quantifiable and tradable data, shifting user participation from mere consumption to productive activities. Most existing online participation involves investment or content sharing, and the platform monetizes the attention generated from these activities. InfoFi quantifies users' market responses to this content and distributes rewards based on this data, leading participants' actions to be seen as productive work. This transformation empowers users to become network value creators rather than just community members.
Thirdly, InfoFi lowers the barriers to information production. In the past, major Twitter personalities and institutional accounts dominated information distribution and captured most attention and rewards, but now ordinary users can also receive tangible rewards after gaining a certain level of market attention, creating more opportunities for participation for users from different backgrounds.
The Attention Economy Trap Induced by InfoFi
The InfoFi model is a new reward design experiment within the crypto industry, quantifying user contributions and linking them to rewards. However, attention has become an overly centralized value, and its side effects are gradually becoming apparent.
The first issue is excessive competition for attention 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 participation to merely seeking rewards. Generative AI has made content creation easier, and bulk content lacking genuine information or insights spreads rapidly. This so-called 'AI Slop' content is proliferating throughout the ecosystem, raising concerns.
Loud Mechanism, Source: Loud
The Loud project clearly illustrates this trend. Loud attempts to tokenize attention, with the platform choosing to distribute rewards to the top users who receive the most attention within a specific time frame. This structure is experimentally interesting, but attention has become the sole standard for rewards, leading to intense competition among users and resulting in a large amount of repetitive low-quality content, ultimately causing content homogenization across the entire community.
Source: Kaito Mindshare
The second issue is reward centralization. Attention-based rewards begin to focus on specific projects or topics, causing content from other projects to effectively disappear or diminish in the market, as Kaito's shared data clearly indicates. Loud once accounted for over 70% of crypto content on Twitter, dominating the information flow within the ecosystem. When rewards focus on attention, content diversity declines, and information gradually centers around projects offering high token rewards. Ultimately, the scale of marketing budgets determines influence within the ecosystem.
Structural Limitations of InfoFi: Evaluation and Distribution
4.1. Limitations of Simple Methods for Content Evaluation
The attention-centered reward structure raises a fundamental question: how should content be evaluated, and how should rewards be distributed? Currently, most InfoFi platforms assess content value based on simple metrics (e.g., views, likes, and comments). This structure assumes that 'high engagement equals good content.'
Content with high engagement may indeed possess better information quality or transmission effects; however, this structure mainly applies to very high-quality content. For most mid to low-end content, the relationship between the quantity of feedback and quality remains unclear, leading to a phenomenon where repetitive formats and overly positive content receive high scores. Meanwhile, content that presents diverse perspectives or explores new topics struggles to receive the recognition it deserves.
Addressing these issues requires a more robust content quality assessment system. Simple evaluation criteria based solely on engagement are fixed, while content value can change over time or according to context. For instance, AI can identify meaningful content, and community-based algorithmic adjustment methods can also be introduced. The latter can involve adjusting assessment criteria based on regularly provided user feedback data, thereby helping the evaluation system adapt flexibly to changes.
4.2. Concentration of Reward Structure and Balancing Needs
The limitations of content evaluation coexist with issues in the reward structure, which also exacerbates biases in information flow. The current InfoFi ecosystem typically runs separate leaderboards for each project, using their own tokens for rewards. In this structure, projects with large marketing budgets can attract more content, and users' attention often focuses on specific projects.
To address these issues, adjustments to the reward distribution structure are necessary. Each project can retain its own rewards, while the platform can monitor content concentration in real-time and make adjustments using platform tokens. For example, if content is overly concentrated on specific projects, platform token rewards can be temporarily reduced, while topics with relatively low coverage can receive additional platform tokens. Content covering multiple projects can also receive extra rewards. This will create an environment with diverse themes and viewpoints.
Evaluation and rewards form the core of the InfoFi structure. How content is evaluated determines the information flow within the ecosystem, and who receives what kind of rewards is also crucial. The current structure combines a single standard evaluation system with a marketing-centered reward structure, accelerating the dominance of attention while also weakening the diversity of information. The flexibility of assessment criteria is vital for sustainable operation, and balancing the distribution structure is also a key challenge faced by the InfoFi ecosystem.
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 great significance. However, the current InfoFi ecosystem faces structural side effects in the attention tokenization process, including a decline in content quality and biases in information flow. These side effects are more about the dilemmas faced during the initial design phase than limitations of the model.
The evaluation model based on simple feedback exposes its limitations, and the reward structure influenced by marketing resources also reveals issues. There is an urgent need to improve a system that can accurately assess content quality, as well as community-based algorithm adjustment mechanisms and platform-level balancing regulation mechanisms. InfoFi aims to create an ecosystem where members can obtain fair rewards through participation in information production and dissemination. To achieve this goal, technical improvements are needed, as well as encouraging community participation in the design process.
In the crypto ecosystem, attention operates like tokens. InfoFi is an important experiment in designing and operating a new economic structure. Its potential can only be fully realized when it evolves into a structure where valuable information and insights can be shared. The results of this experiment will accelerate the development of a quantified economy of information in the digital age.
