Kaito Adjusts Crypto Mindshare Algorithm After Backlash: What Users Need To Know

Kaito AI has rolled out a fresh update to its crypto mindshare algorithm. The move follows weeks of criticism over the quality of content dominating its InfoFi dashboards.

For months, users have questioned whether Kaito’s mindshare scores reflect real influence or manufactured hype. Critics argue that influencers and projects have gamed, manipulated, and leveraged the system to inflate visibility without actual value.

Influencers and Projects Exploiting Kaito Loopholes

The new changes aim to curb spammy engagement farming and reward insightful, long-term contributions across Crypto Twitter.

Crypto and DeFi researchers called out Kaito and other InfoFi platforms for fueling misaligned incentives in the ecosystem.

Among them is Louround, co-founder at Redacted Research, who publicly called out current leaderboards for rewarding content that drives views and comments regardless of quality or depth. The researcher says many top-ranked KOLs know little about the projects they boost.

“We’ve seen through the Loud experimentation that mindshare does not equal protocol interest, nor value creation,” Louround wrote in a post.

This concern aligns with a recently launched feature on Arkham that sought to track influencer portfolios. As BeInCrypto reported, the feature would reveal if they genuinely back tokens or engage in paid advertising.

For perspective, Louround referenced the Loud project, whose mindshare surged to 60%. It peaked at a $30 million FDV before collapsing to just $1.4 million within two weeks.

The criticism goes beyond content quality, with accusations that centralized exchanges (CEXs), VCs, and even Kaito participate in a circular system of incentives.

“Projects are leveraging views, engagement, and ‘traction’ (without filtering the quality) to then justify to VCs and listing platforms,” Louround added.

The researcher called it a “feedback loop” where no party is incentivized to change the broken system. Other users pointed to the Caldera leaderboard as a “perfect example” of what’s wrong with current InfoFi models.

Another Redacted Research executive and Hyperliquid maxi, Zero Knowledge on X, noted how “low-quality engagement farmers” dominate the top ranks, while genuine contributors get drowned out.

“A guy who drops 900+ replies in a day is not an advocate for your tech or brand. It’s an extractor that wants to dump tokens on day one,” the user said.

While acknowledging the potential of InfoFi and the mindshare concept, experts said the current form “doesn’t build or foster” meaningful community, content, or education.

Against this backdrop, users call for a return to Crypto Twitter’s roots, where community members can learn and share knowledge.

Community Demands Force Kaito AI to Act

In response, Kaito said it was implementing changes to prioritize quality over quantity. Changes include excluding posts focused solely on rewards or rankings without insight.

Kaito will also limit weekly mindshare tweets to prioritize valuable content and crack down on engagement farming. Further, the blockchain aggregator will enhance loyalty rewards for consistent contributors without penalizing broader participation.

Despite the criticism, some users see value in Kaito’s efforts. Jeff, a Web 3 content creator, highlighted a shift in crypto trading strategies.

He suggests that valuable insights now come from analyzing social media conversations.

“The new alpha isn’t just hidden in charts, it’s hidden in conversations. The old meta was DYOR. The new meta? Decode the feed,” Jeff wrote.

However, with a reported $1.38 billion FDV and increasing market influence, pressure is mounting on Kaito AI to move beyond surface metrics.

Calls for AI-driven content filtering and tiered dashboards that reward genuine insight are growing louder. Kaito’s move suggests openness to implement changes, with these tweaks geared toward restoring trust.

“Kaito finally factoring in quality contents. now the timeline can appear more solid,” one user remarked.

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