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The InfoFi Autopsy: Why 98% of Leaderboard Projects Never Survive the Airdrop The systemic collapse of the "Post-to-Earn" model in early 2026 has exposed the structural fragility of the InfoFi sector. While platforms like Kaito, Cookie, and Wallchain provided the infrastructure for attention mining, the projects they launched have largely failed to transition from subsidized engagement to sustainable on-chain utility. →The Success Outliers: Internal Ecosystem Gains Only a small segment of projects achieved what could be termed success, though this remained largely confined to the InfoFi circular economy. → Succnet: Distinguished by providing real-time data utility for researchers. It is one of the few projects where the airdrop felt like a reward for high-signal analysis rather than automated farming. → Newton (NEWT): The primary success story for Kaito "Yappers." It converted social mindshare into a liquid asset, but its valuation remains heavily tethered to the Kaito ecosystem. → Laudio (LOUD): Success defined by the sheer scale of its initial distribution. While it reached a massive audience, post-airdrop metrics show a steep decline in organic activity. → The Failed Projects: Platforms and Market Rejection The failure rate for leaderboard-driven projects is staggering. Data indicates that projects launched via these platforms have a negative expected value for passive holders. → Kaito The 73% Failure Factory Kaito’s "Attention-to-Earn" model led to a massive influx of low-quality projects that failed post-TGE. → PLAI & XNL: Recorded as the weakest performers on the platform. Both failed to maintain liquidity, leaving community holders with near-worthless allocations. → Espresso: A top-ranked project that has faced repeated TGE delays. The community now views the "snapshot" as a stalled event, leading to accusations of a soft rug. → Arbus ($ARBUS): A Kaito veteran that extracted maximum social attention but failed to deliver a functional product. The token price flatlined immediately after the hype phase concluded. → Cookie The Snaps Shutdown In January 2026, Cookie DAO announced the termination of "Snaps," its core creator reward platform. → Incentive Collapse: The platform pivot was forced by X API restrictions, effectively killing the "Post-to-Earn" logic. → BubbledMaps: Promoted heavily via Cookie. The incentive program was systematically drained by automated bot networks, leaving genuine creators with negligible value. → AI Agent Tokens: Numerous small-cap AI tokens launched through Cookie failed to reach a TGE. Teams leveraged the platform for free marketing before abandoning the projects. → Wallchain The Liquidity Void Wallchain-backed projects struggled with market depth and technical delivery. → Anti-MEV Solutions: Most projects incubated here failed to gain traction against established protocols like Flashbots. → Token Depletion: Low organic volume led to a "liquidity hole" where price drops triggered massive sell-offs with no institutional or retail buyers to support the floor. → The Monad (MON) Disaster: Technical and Social Failure Despite being one of the most anticipated Layer-1 launches, Monad is now categorized as a failure in execution and community trust. → Exclusionary Airdrop: The distribution excluded over 99% of its Discord community, favoring undisclosed insiders and VC-linked addresses. → Technical Incompetence: Broken claim scripts on launch day caused a user to lose $112,000 in MON to gas fees on failed transactions. → Security Breaches: The claim portal was compromised, allowing hackers to hijack allocations by binding them to attacker-controlled wallets before users could claim. → Market Sentiment: Prominent figures like Arthur Hayes have labeled it a "high FDV, low-float VC coin" with a 99% crash risk. → The Structural Flaw The InfoFi model incentivized attention without requiring retention. Projects used these platforms to purchase a temporary audience, but without a viable protocol, liquidity followed the creators to the next leaderboard. Was the failure of these projects an indictment of the InfoFi distribution model, or simply a reflection of the low-quality teams that gravitate toward attention-based funding?

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