The Decline of InfoFi and Its Implications for Crypto Market Dynamics

The decline of InfoFi in 2025 has sent shockwaves through the crypto ecosystem, exposing critical vulnerabilities in attention-based models that attempt to monetize user engagement. While these models initially promised to democratize content creation and reward participation, their structural flaws-rooted in the subjective measurement of value-have led to market fatigue, regulatory scrutiny, and a reevaluation of long-term sustainability. As we analyze the implications for crypto market dynamics, it becomes clear that the future of attention-based models hinges on their ability to adapt to evolving user behavior, regulatory frameworks, and technological advancements.

The Structural Challenges of InfoFi

InfoFi's collapse was not a sudden event but a predictable outcome of its core design. Platforms like Kaito and Cookie DAO incentivized users to generate content in exchange for tokens, creating a system where engagement metrics (views, likes, shares) became the primary proxy for value. However, as noted in a 2025 recap, this approach led to a "billboard effect," where platforms prioritized sensationalism over accuracy, eroding trust in the content ecosystem. The reliance on subjective metrics also incentivized AI-generated spam and repetitive, low-quality contributions, further devaluing the attention economy.

The structural fragility of InfoFi was compounded by its dependence on centralized platforms like X (formerly Twitter). When X overhauled its API policies to block applications rewarding users for posting, projects like Kaito saw token prices plummet by 20%, forcing restructurings. This highlighted a critical flaw: attention-based models lacked resilience against policy changes by gatekeepers, a vulnerability that undermined their long-term viability.

The Rise of Attention-Based Crypto Models

Despite these challenges, attention-based crypto models have shown promise in leveraging advanced machine learning to predict market trends. Studies from 2023–2025 demonstrate that attention mechanisms in deep learning models-such as Attention-LSTM and hybrid CNN-LSTM architectures-outperform traditional statistical models like ARIMA and GARCH in cryptocurrency price forecasting. These models excel at capturing the non-linear, volatile nature of crypto markets, offering improved accuracy and interpretability. For instance, a two-stage attention-based CNN-BIGRU model enhanced Ethereum price predictions by integrating feature extraction with long-term dependency modeling according to research.

However, the decline of InfoFi has cast doubt on the sustainability of these models. Critics argue that attention-based systems inherit the same flaws as InfoFi: they prioritize short-term engagement metrics over substantive value creation, leading to market distortions. The proliferation of AI-generated spam and the centralization of attention around specific projects further exacerbate these issues, creating a feedback loop that devalues user contributions.

Post-InfoFi Adaptations and Regulatory Shifts

In response to these challenges, platforms are redefining their approaches. Kaito, for example, has shifted from task-driven incentives to a "user growth operating system" that emphasizes long-term structural engagement according to analysis. By rewarding consistent, high-quality contributions and fostering a "verified content identity," Kaito aims to transform attention into a durable asset as reported. Similarly, Cookie3 and Xeet have introduced tiered scoring systems that prioritize depth over breadth, using on-chain metrics to evaluate influence. These adaptations reflect a broader industry reassessment of how attention can be monetized without degrading content quality as observed.