2026 Kaito Marketing Guide: Turning "Attention" into Tradable Assets

Over the past year, Web3 projects have increasingly been doing one thing when it comes to “growth”:

Spending more and more money to buy shorter and shorter attention spans.

While most Web3 growth tools still operate within a task-driven model of “launch—share—airdrop,” user growth in practice is often simplified into a rapid amplification process: first spend money on advertising to generate exposure, then increase engagement through sharing and completing tasks, and finally convert via airdrops or points. This approach may produce impressive short-term data feedback, but it fundamentally revolves around one-time actions, with growth heavily dependent on continuous investment, making long-term accumulation difficult.

Unlike this, @KaitoAI is not just optimizing efficiency within an existing task system but gradually evolving into a highly structured user growth operating system (Growth OS). It’s not merely scoring content or distributing points, but through a comprehensive, quantifiable, competitive, and compoundable attention allocation mechanism, reorganizing user expression and interaction behaviors originally scattered across Twitter (X) into a sustainable growth system.

This article will start from Kaito’s internal mechanisms, systematically dissecting how it helps projects achieve user growth. Later, with the case studies of @Calderaxyz and @berachain, we will verify how these mechanisms are applied within the projects themselves.

1. The essence of Kaito: not a marketing tool, but an “attention distribution system”

The first step to understanding Kaito is to step out of the “marketing platform” perspective. Kaito’s true positioning is: an InfoFi system that transforms “attention, content contribution, and user behavior” into quantifiable assets.

In traditional growth models, projects usually focus on three core metrics: exposure, clicks, and conversion rate. These metrics are not inherently problematic, but they assume that as long as users complete designated actions, growth has occurred.

In the Web3 context, this assumption often does not hold. Growth mechanisms based on task completion can only confirm “whether an action occurred,” but struggle to understand why users act or whether they have long-term engagement intentions. This leads to growth data being easily inflated by low-effort behaviors, appearing lively but often limited in retention and genuine recognition. Additionally, such mechanisms tend to attract efficiency-oriented participants, such as airdrop farmers or bots. To counteract sybil attacks, projects keep increasing task complexity and participation thresholds, which raises growth costs while potentially excluding valuable users.

Against this backdrop, Kaito redefines growth metrics. Instead of focusing on immediate data points from single actions, it emphasizes long-term and structural participation quality. For example, whether a project is repeatedly mentioned and maintains stable mindshare in long-term information flows, whether it can consistently reinforce a core narrative rather than being diluted by fragmented voices (Narrative Control), and whether users are willing to continuously produce content with informational value around the same project over time (Consistent Contribution).

This also means that Kaito’s goal is not to generate short-term data spikes but to help projects occupy a stable, accumulative position within the long-term information flow of Crypto Twitter.

2. How does Kaito’s growth system operate: three core mechanisms

The first key design of Kaito is Yaps / Yapper Points. Before Kaito, the lifespan of a high-quality tweet was very short; apart from likes and retweets, it rarely generated long-term value. After Kaito, each content output enters the user’s long-term contribution record, continuously influencing future rewards through points, rankings, and historical weightings. This long-term accounting mechanism directly shifts creators’ objectives: they no longer pursue a single “viral tweet,” but start cultivating a content identity that can be validated over time.

Meanwhile, Kaito’s algorithm does not treat all interactions equally. Yap scoring assesses whether a piece of content truly adds informational value to the project, considering semantic depth and originality, its relevance to the project narrative, and whether interactions come from influential crypto users. This step makes a critical correction at the growth level—prioritizing traffic quality over quantity, systematically reducing the space for click farms, bot farms, and invalid interactions. Content in Kaito is no longer just a one-time expression but gradually evolves into a long-term growth asset that can be valued over time.

If Yaps is responsible for “asset-izing” content, then the Yapper Leaderboard is responsible for transforming these assets into growth engines. Its value lies not in rankings themselves but in guiding user behavior toward long-term, high-quality, and high-coherence contributions through continuous competition and clear rules.

Rankings depend heavily on the continuity of posting, narrative consistency, and long-term contribution accumulation. This makes short-term rank-chasing behaviors difficult to sustain long-term advantage, while genuinely understanding the project and willing to invest continuously will naturally rise. Meanwhile, Kaito’s algorithm weights and incentive design decentralize the dissemination authority from centralized operations to the community, amplifying positive narratives and in-depth analysis without losing control. Over time, this mechanism also organizes scattered tweets into a recognizable content pool, enabling new users to quickly identify core voices and thus lay the foundation for ongoing Mindshare accumulation.

Finally, Kaito pushes growth into a closed loop through Yapper Launchpad and Capital Launchpad, with the core logic being: enabling “people who speak for the project” to have real influence in resource allocation. Content contributions are converted into quotas and airdrops via the Leaderboard, ultimately translating into tokens and participation rights, turning attention into real benefits, and making high-quality users long-term stakeholders.

3. Case studies: When Kaito is used as a “growth system”

Among all successful cases of Kaito, Caldera and Berachain are highly representative, not because of their size or popularity, but because they exhibit a high degree of alignment among growth goals, content structure, incentive design, and platform mechanisms. This makes Kaito not just a “traffic amplifier,” but embedded into the project’s own growth logic.

Below, we will analyze these two projects from three levels: mechanism adaptation, shaping user behavior, and growth outcomes.

1. Caldera: Filtering and consolidating high-quality users during Pre-TGE with Kaito

Caldera’s case is especially instructive: when a project has a complex technical narrative, how does Kaito help achieve high-quality user growth rather than simple exposure?

Pre-understanding and leveraging Kaito’s algorithm preferences: Before entering the Kaito system, Caldera already recognized a key fact: Kaito’s Yap Points and Leaderboard mechanisms do not inherently favor “dissemination-oriented content,” but are more likely to reward content with high semantic density, strong narrative consistency, and long-term value.

Based on this understanding, Caldera consciously avoided encouraging community to produce “project introduction” or “emotion mobilization” tweets. Instead, it actively guided the community to create around a series of highly structured topics, such as the architecture principles of Rollup-as-a-Service, its positioning within the modular Rollup ecosystem, and its technical relationships with EigenLayer, Data Layer, and execution layers. These topics are information-dense, require understanding, and naturally reduce the likelihood of spam or superficial content.

From a growth perspective, the core is: proactively guiding community creation into an “algorithm-friendly zone,” rather than letting users burn enthusiasm through trial and error.

Using the Leaderboard to systematically filter high-investment users: Caldera’s use of the Kaito Yapper Leaderboard is not just as a results display tool but as a user behavior shaping mechanism. During Pre-TGE, Caldera deliberately extended the leaderboard’s operational cycle, making it difficult for users attempting short-term arbitrage to secure stable positions; only those willing to produce content continuously over weeks or months and deepen their understanding could steadily gain advantage.

This creates a clear filtering effect at the user level: impatient, low-cognition users are naturally eliminated; high-cognition, high-investment users gradually concentrate at the top of the leaderboard. From a growth system perspective, Caldera effectively conducts a “community quality filter,” allocating limited incentive resources to the groups most likely to become long-term users and ecosystem participants.

Structurally binding content contribution with real usage: Unlike many projects that only incentivize content, Caldera consciously avoids turning Kaito into a “talking contest.” During the Leaderboard operation, Caldera continuously incorporates real interactions such as Testnet deployments, developer tools usage, and ecosystem DApp engagement into community discussion and content creation. This binds “participation in the product” and “participation in the narrative” within the same incentive logic.

These behaviors are not always directly reflected in Yap Points, but they are repeatedly referenced, analyzed, and reviewed in content, forming a subtle bonus mechanism: users who have actually used the product tend to produce high semantic density content, which is more likely to be rewarded by the algorithm.

The result is a highly positive feedback loop: using the product → gaining understanding → producing high-quality content → earning higher weight in Kaito → gaining more resources and attention → deepening participation. This allows Caldera to pre-accumulate a core user base that understands the technology and has dissemination ability even before TGE.

2. Berachain: How to use Kaito to maintain long-term Mindshare rather than one-time hype

If Caldera demonstrates Kaito’s capability in “growth during the Pre-TGE phase of a technical project,” then Berachain’s case better illustrates: how Kaito can be used to sustain long-term Mindshare rather than a one-off narrative burst.

Treat Kaito as a long-term narrative infrastructure rather than a short-term activity tool: Berachain regards Kaito as a long-running narrative infrastructure. From the start, the project accepts the natural fluctuations of the leaderboard rather than trying to create short-term hype through incentives. This design allows community content to gradually form a division of labor: some creators focus on deep analysis of the Proof-of-Liquidity (PoL) mechanism, others continuously track ecosystem projects and incentive changes, while some translate technical narratives into more communicable culture and memes. Kaito’s algorithm does not enforce uniform content formats but, through long-term weight accumulation, allows different but “continuously relevant” content to find its place in the system.

Leverage Smart Followers weight to amplify core community structure: Berachain’s community already has a highly interconnected core account network with frequent interactions. Kaito’s Smart Followers mechanism amplifies this advantage, giving additional weight to interactions from core crypto users and high-reputation accounts, pushing Berachain discussions into more influential social networks. Ultimately, this transforms the implicit “core community structure” into algorithm-recognized and rewarded growth resources. This is one of the key reasons Berachain can maintain high Mindshare over multiple time points.

Use stable incentive expectations to foster long-term rather than speculative participation: Berachain does not promise explicit material rewards at every node but instead conveys a signal through the long-term, predictable Kaito incentives: that long-term narrative participation is systematically recorded and recognized. Under this expectation, user participation decisions are less driven by short-term ROI and more akin to long-term investment. This psychological shift is crucial for building a highly sticky community.

3. The common logic behind these two cases

Although Caldera and Berachain differ significantly in stage, narrative, and product form, they follow highly consistent principles when utilizing Kaito: growth is not about “amplification,” but “screening”; algorithms are not adversaries but require understanding and proactive adaptation; incentives aim to shape long-term behaviors rather than stimulate short-term participation.

四、Mechanism elevation: the “value re-evaluation” and reputation shift in 2026

In early 2026, Kaito officially launched a paradigm-level evolution—shifting from ‘attention distribution’ to ‘reputation assetization’—a comprehensive upgrade. The core of this upgrade is that the system no longer only focuses on “content creation,” but begins defining “what kind of participation is worth long-term valuation.”

The most symbolic move was on January 4, 2026, when Kaito officially announced an upgrade to all leaderboard admission standards. This update introduced reputation data and on-chain holdings, fundamentally reconstructing influence weight logic. This means that in Kaito’s ecosystem, the “false prosperity” driven by AI scripts and automated spam has no room to survive. The system now combines on-chain metrics and social reputation weights to filter out low-quality activities, ensuring that every influence is backed by real capital. Kaito is shifting from measuring “who is speaking” to “who is qualified to be taken seriously.”

Complementing this algorithm reshuffle is the formal implementation of the gKAITO governance mechanism. This marks Kaito’s evolution from a growth tool into a reputation-based governance system. Community members are no longer just traffic contributors but participate deeply in token issuance quality control through a “five-dimensional model” assessing thought leadership, engagement, and cultural contribution. Under gKAITO, content production has transitioned from “traffic behavior” to “reputation assets,” with influence formally anchored to governance rights, profit rights, and investment priority.