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Key Takeaways
Rather than rewarding vanity metrics, InfoFi introduces YAPs to incentivize genuinely valuable content, much like Moneyball used data-driven scouting to find overlooked players.
Aligning “earned reputation” (YAP) with “staked capital” (sKAITO) prevents simple buy-ins from overshadowing merit-based influence, helping ensure that actual contributors rise to the top.
Proposals like YAP-spending actions, claim staking, and slashing mechanisms create real risks for spammers or bad actors, turning InfoFi into a more credible reputation ecosystem.
Defining KOLs along multiple axes (Casual↔Hardcore, Shitposter↔Curator, etc.) helps projects and communities identify the right “players” for their goals, further strengthening InfoFi’s data-driven approach.
1. The Unfair Game
"There is an epidemic failure within the game to understand what is really happening... And this leads people who run Major League Baseball teams to misjudge their players and mismanage their teams." – Billy Beane
CT often rewards clout over quality, where big influencers dominate and genuine insight gets buried. As Billy Beane quips, “If he’s a good hitter, why doesn’t he hit good?.” The old metrics simply aren’t measuring what really matters.
InfoFi, Kaito’s “information finance” initiative, aims to fix this by assigning value (YAPs) to thoughtful, constructive content. Like the Oakland A’s in Moneyball, Kaito is challenging an unfair system of attention, one where sticking to popularity-driven rules spells defeat.
So far, the results have been promising yet not without hurdles. Kaito’s Yaps leaderboard (meant to spotlight high-quality posts) sometimes devolved into a SocialFi-style popularity contest rather than meritocracy. Early on, spam and quick hacks exposed a few cracks in the system, but the Kaito team has been proactive in iterating and refining. Now, with those lessons in hand, it’s time to adapt the strategy and ensure that Yaps truly reward high-quality contributions.
Instead of merely “buying players,” InfoFi participants can focus on “buying wins,” fostering better information flow, tighter community alignment, and more efficient markets. The proposals below outline how to retool InfoFi to emphasize earned reputation, project-specific alignment, and a truly data-driven information marketplace.
2. For Credible Voting
“Adapt or die.” – Billy Beane
YAPs are earned by posting quality content on CT. sKAITO, by contrast, represents staked $KAITO tokens. Currently, sKAITO can skew influence in voting (e.g. deciding which projects appear on Yapper Launchpads), giving token stakers an outsized voice. This dynamic risks overshadowing the real point of InfoFi: surfacing merit-based insight. It mirrors the old “big-budget teams” mistake from Moneyball, overlooking consistent on-base percentages.
Kaito can consider re-aligning YAP and sKAITO in ways that preserve meritocracy:
Rebalance Voting Toward Earned YAP: Governance and curation (e.g. which project gets a leaderboard) might place heavier weight on YAP-based voting since those points reflect genuine content contributions. For instance, let sKAITO votes count fully only if backed by a certain YAP threshold, ensuring people with real track records have more sway than purely financial stakers.
Project-Specific YAP Allocation: Building on Kaito’s hint toward project-level alignment, users could assign a portion of their YAP to specific communities. If someone devotes 60% of their YAP to Solana, they gain extra credibility in that ecosystem. This ensures that top ranks go to those truly invested in a project’s success, not to random engagement farmers.
sKAITO as Long-Term Alignment: sKAITO stakes can still matter, but in a time-weighted fashion that rewards continuous commitment instead of flash voting. For instance, stakers who lock up $KAITO for months might see vote power ramp up gradually, discouraging last-minute “stake-and-vote” strategies.
Even though some KOLs may try to sell YAPs over-the-counter for extra votes, it remains significantly harder to accumulate YAP in large quantities (since it must be earned) than simply buying $KAITO on the open market to sway outcomes. Aligning these two currencies (YAP as “on-base percentage” and sKAITO as “the bankroll”) can protect InfoFi leaderboards from whales and ensure that earned insight remains paramount.
3. Skin in the Game
“The first guy through the wall – he always gets bloody.” – John Henry
InfoFi is pioneering; naturally, it’s drawn spammers who exploit free points. For example, some exploit tactics like AI-generated “gm/gn” posts, then tag ongoing reward programs and pay for engagement networks (such as @mochicircle) to inflate mindshare artificially. Adding skin in the game can address this. When YAP holders face real costs or risks, they’re more likely to produce thoughtful content. Below are proposals to embed accountability:
YAP-Spending Actions: Kaito can’t control polls or tweets on X, but it can control visibility within its own aggregator and leaderboards. If a user wants special exposure like “boosting” a thread or featuring a poll in Kaito Connect, they pay a YAP fee. That cost deters spam (since points are finite) and creates a natural YAP sink. If your content is truly valuable, you’ll recoup YAP via engagement; if it’s junk, you lose out.
YAP Staking on Claims: Similar to prediction markets, KOLs might lock up YAP when making bold predictions (e.g., “ETH to $5k by next month”). An oracle checks the outcome. If correct, the user gains a modest bonus or a “Credibility” badge; if wrong, their staked YAP is burned or redistributed. YAP Staking on Claims can look like gambling, but it’s designed to highlight actual skill rather than luck:
Multipliers are intentionally kept low (e.g., 1.1–1.2x), ensuring random lucky hits don’t overshadow sustained performance. Over repeated calls, consistent winners naturally rise, while those relying on chance lose their staked points.
This lets underrated analysts who consistently call the market correctly earn more YAP and visibility, even without a massive following. It’s not about encouraging blind bets, but about using accountability to reward genuine insight.
Slashing (live): Plagiarism slashing is already in effect, with Kaito’s plagiarism checker revealing that roughly 22% of 50,000 sampled tweets were near-identical copy pasta. While previously only about 0.03% of malicious accounts were slashed, this expanded approach now hints that it addresses repeated plagiarism, misinformation, or spam.
YAP Burn for Misconduct: For serious offenses (sybil attacks, coordinated botting), Kaito can enforce full YAP burns. That ejection-level penalty sends a strong message: cheat, and you’ll lose everything.
These accountability loops shift InfoFi from a simple airdrop farm to a legitimate reputation ecosystem. KOLs with consistent, valuable insights grow their influence; hype-posters risk burning their points.
4. Defining KOL Types
"He gets on base." – Peter Brand
In Moneyball, the Oakland Athletics shifted away from traditional scouts’ “gut-feel” evaluations and adopted quantitative statistics (e.g., on-base percentage) to determine who truly contributed to the team’s success. By using this data-driven approach, they uncovered hidden value in their players and assembled a more efficient roster.
InfoFi similarly needs multi-dimensional metrics instead of single-dimensional rankings like total YAP or follower counts. Below are five personality axes, inspired by player stats in “MLB 9 Innings (CON, POW, EYE, SPD, and FLD),” to gauge how KOLs operate(Kaito already partially implements axes like Casual/Hardcore, Shitposter/Curator, and Copy Pasta/Creative; the following suggestions build upon that foundation). Projects can scan these to decide which KOL best fits their goals:
Source: Naver Blog
Casual ↔ Hardcore: How deep or “hardcore” a KOL goes on research, analysis, and engagement. A Casual KOL might only post brief overviews or broad market vibes; a Hardcore one dives into every dev update and on-chain address.
Shitposter ↔ Curator: The ratio of lowbrow memes and edgy banter (Shitposter) to well-organized, thoughtful content (Curator).
Copy Pasta ↔ Creative: Whether the KOL tends to reuse popular threads and memes (Copy Pasta) or invent original takes and novel memes (Creative).
Tech Brain ↔ Storyteller: The balance between technical, code-level, data-driven perspectives (Tech Brain) vs. narrative-driven, big-picture framing (Storyteller).
Memecoin Degen ↔ Fundamentals Maxi: How much a KOL chases fun, high-risk memecoins (Degen) vs. sticks to fundamentals-based, value-oriented tokens and projects (Maxi).
Personality Tag (Bonus): A short descriptor that captures each KOL’s overall vibe, like “Laser-Eyed Otaku” (BTC maxis who also love anime). It’s a playful nickname that helps the community quickly gauge someone’s flavor.
By labeling KOLs in these dimensions, Kaito can produce a “player card” for each user, echoing Moneyball’s insight that how a player performs can matter more than raw stats. Identifying these traits helps projects and communities target the right “players” for their own rosters.
5. Toward Open Information Markets
"If we win (on our budget with this team) we’ll change the game. And that’s what I want, I want it to mean something.”– Billy Beane
InfoFi’s Endgame is a world where quality information finds its value, and contributors find ownership and income in producing it. By refining YAP and sKAITO dynamics, enforcing skin in the game, introducing Moneyball-style metrics, and supercharging incentives, Kaito can ensure the InfoFi experiment not only survives its initial innings but starts putting up winning numbers on the board.
In Moneyball, a daring strategy led a small team to punch above its weight and change how the sport is played. Similarily, InfoFi can rewrite the rules of crypto social engagement. It can change the entire game of CT, proving that an open, data-driven system of information exchange can outcompete the legacy model of attention casinos and influencer oligopolies.
