If InfoFi Part I was about the measurement of attention, Part II is about its monetization. As protocols score and tokenize influence, a new question emerges: can these systems create durable economies—or are they inflating financialized noise?

CoinEx Research will analyze the second-order effects of InfoFi models and assess what it takes to make attention markets truly decentralized, interoperable, and meaningful.

Three Archetypes of Attention Finance: Kaito, Noise, Cookie

Kaito: Narrative Mining and the Feedback Flywheel

Kaito represents the most complete version of the social-to-capital feedback cycle. Its Yaps system rewards users for creating crypto-native content, and its ecosystem includes:

Pre-TGE leaderboards that gauge project mindshare;
Yapper rankings that determine eligibility for token drops;
Kaito Earns modules that link influence to investment opportunities.

The cycle is tight and recursive: content → Yaps → exposure → airdrop → more content. It's not just creator economy infrastructure—it’s a gamified funnel for narrative speculation.

This loop has proven powerful. Berachain and Story Protocol both tied their airdrop criteria to Kaito metrics, legitimizing Yaps as a proxy for influence equity. Nevertheless, it also means that Kaito’s algorithm becomes the de facto arbiter of who deserves capital, which brings us back to the issue of opaque scoring.

Noise: The Attention Derivatives Market

Where Kaito rewards influence, Noise trades it. Users can go long or short on a project’s mindshare—not its token price, but its social capital. If you believe Farcaster is gaining narrative traction, you can take a leveraged long position on its attention score.

This model draws inspiration from prediction markets like Polymarket, but with a twist: the “asset” is not future events, but present perception. Noise even uses Kaito as its sole attention oracle, introducing a dependency chain that is both innovative and fragile.

The implication is radical: attention is no longer just a signal—it becomes a speculative commodity. But when that commodity is priced based on a single data feed, the market is susceptible to manipulation, bias, and distortion.

Cookie: Infrastructure for Data-Native Influence

Cookie takes a different path—less speculative, more infrastructural. Its core thesis is that influence is a function of context-rich data, not raw engagement. Cookie tracks interactions across Twitter, on-chain data to build multi-dimensional KOL profiles, which can be used by AI agents, marketers, and protocols.

Unlike Kaito or Noise, Cookie doesn’t just reward attention—it analyzes and packages it, aiming to become a B2B intelligence layer for the programmable influence economy. Its recent launch of Snaps (a score based on content quality and consistency) begins to close the loop between content and rewards, while its Data Swarm API signals ambitions far beyond social media.

Together, Kaito, Noise, and Cookie represent three InfoFi archetypes:

Kaito: Score, reward, and reallocate attention.
Noise: Financialize and speculate on attention.
Cookie: Organize and synthesize attention for AI agents.

Each approach reveals part of the opportunity—and part of the problem.

Fragile Experiments: Loud, GiveRep, Wallchain

Loud: Incentives Without Endurance

Loud represents one of the earliest pure-play attention tokenization models—rewarding users based on visibility metrics and redistributing trading fees to high performers. The Initial Attention Offering (IAO) concept was bold, flipping traditional capital-first models into influence-first allocation.

However, without mechanisms to sustain quality or filter for long-term engagement, the model struggled to retain durable mindshare. Content surges became short-lived, and once speculative momentum cooled, its token economy lost relevance.

The takeaway is not that Loud “failed,” but that tokenizing visibility requires more than incentives—it demands layered filters for signal persistence and contextual value.

GiveRep: Reputation Without Differentiation

GiveRep’s vision—mapping social interaction into on-chain reputation—is compelling and much-needed in Web3. Yet its current architecture treats all activity equally, without parsing the semantic weight of contributions.

A mention from a long-term contributor and a casual repost receive similar treatment. Without quality-sensitive scoring or cross-platform validation, reputation becomes a surface metric, not a trust layer.

Moreover, its current scope within the Sui ecosystem limits network effects. For such systems to scale meaningfully, interoperability and semantic filters must be core, not optional.

Wallchain: Decentralized Intent, Centralized Substrate

Wallchain aims to quantify real influence via metrics like X Score and Quacks points. The platform emphasizes authentic contribution and daily evaluation via AI. Yet, its strong reliance on X (Twitter) data means that platform-level volatility—algorithm changes, API limits, moderation shifts—can directly affect influence scoring.

This isn't a design flaw, but a reflection of a broader InfoFi constraint: Web3 projects remain deeply reliant on Web2 data surfaces. Until native, verifiable attention substrates emerge, many InfoFi systems will face this same fragility—despite their decentralized aspirations.

The Decentralization Dilemma: Protocols with Platform Dependencies

Centralized Data Feeds, Decentralized Wrappers

Despite InfoFi’s ambitions, most projects build on top of centralized platforms like X (Twitter)—a single point of failure for the entire stack. When user attention is scraped from privately governed networks, algorithmic opacity, moderation policies, and access controls become upstream risks.

Whether it’s Wallchain’s X Score, Kaito’s Yaps, or Fantasy.top’s KOL rankings, the same weakness persists: Web3 metrics built on Web2 terrain.

Scoring Systems: The New Opaque Gatekeepers

Most InfoFi scoring engines remain closed-source, unverifiable, and resistant to challenge. Yaps, Quacks, Snaps—all determine user access to rewards, airdrops, and visibility—yet their inner logic is hidden.

This opacity recreates the same trust asymmetry Web3 set out to dissolve. If influence is to be financialized, the models behind it must be auditable and composable. Otherwise, we’re trading one black box (Web2 algorithms) for another.

Governance Theater, Not Real Ownership

Some platforms adopt community-oriented narratives (e.g., reputation staking, social slashing), but few have meaningful governance frameworks. Decisions around scoring weights, leaderboard thresholds, or airdrop eligibility often remain centralized behind product teams or advisory boards.

True decentralization demands more than token distribution—it requires user-driven control over the data models that define access, capital, and influence.

Protocol Potential: Where InfoFi Must Go Next

AI Agents as Native InfoFi Consumers

The next generation of AI agents will not just process information—they will transact based on it. InfoFi’s datasets—real-time social signals, narrative trends, reputation scores—are ideal inputs for autonomous agents executing investment, curation, or research strategies.

Imagine a portfolio manager AI using Kaito’s mindshare index to adjust exposure to emerging tokens. Or a reputation-scoring bot using Wallchain and GiveRep data to vet protocol contributors. These agents will demand structured, high-integrity data—pushing InfoFi to improve signal quality, transparency, and interoperability.

This is where Cookie’s AI-focused data APIs could become critical middleware, turning noisy human content into machine-usable intelligence.

DeFi: Trustless Finance Needs Trustful Context

DeFi has always lacked a native credit layer. Most protocols rely on overcollateralization—not because they want to, but because they have no way to price risk.

InfoFi can help fix that. On-chain reputation systems like Ethos profiles could allow lenders to evaluate borrowers beyond wallet balances. Predictive metrics from Kaito or Cookie could power attention-based yield strategies. And mindshare indexes could serve as risk inputs for volatility modeling and insurance pricing.

By embedding reputation, influence, and attention into DeFi, InfoFi could finally give Web3 a trust layer that is not blind.

The Crucial Shift: From Platform to Protocol

None of this will happen unless InfoFi players are willing to self-decentralize. That means:

Turning scoring models into open-source modules;
Making governance programmable and inclusive;
Building data ingestion layers that don't rely on legacy platforms.

Conclusion: Influence Is the Asset—But Only If It’s Verifiable

InfoFi opened a new frontier: treating information, attention, and social capital as liquid economic assets. But the first wave of experiments—however ambitious—have revealed the structural costs of incomplete decentralization, opaque scoring, and speculative design.

Still, the thesis remains powerful. If Web3 is to escape the noise of algorithmic media and rebuild trust in its narratives, it must invest in infrastructure that makes influence measurable, transparent, and composable.

The next phase won’t be won through hype mechanics or leaderboard mining. It will be built on verifiable data, AI-native protocols, and trust layers that scale with context. InfoFi’s future depends not on who captures attention—but on who can prove its value.

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