Perps Dex Competition, Distribution, And Monetization
Key takeaways
- Lighter has bought back about 1.4 million LIT (about $2.8 million) since December 29.
- Twitter blacklisting specific InfoFi apps is argued to be a non-solution because similar spam incentives can reappear in new forms and require repeated blacklisting.
- Kalshi was sued over a TikTok ad that portrayed the product as a financial tool by implying users could pay rent after winning on predictions.
- A crypto-native marketing agency could be a strong business for a small team but may not justify a publicly traded token valuation.
- Explicit on-chain token calls tend to be gamed by bots that automatically snipe the ticker and then drip supply to manual buyers, degrading user experience.
Sections
Perps Dex Competition, Distribution, And Monetization
The deltas jointly update the competitive picture for Lighter vs Hyperliquid (share shift and category rank), while also updating the business model (zero-fee with API/PFOF-like monetization) and distribution (mobile app shipped; competitor lacks native app). At the same time, the corpus supplies two non-exclusive mechanisms for declining volumes: sector-wide drawdown and incentive-rotation post-launch, which reduce confidence that launch-era volumes were durable. The buyback magnitude is a concrete capital-action datapoint, but the corpus does not provide a direct link between buybacks and current revenue generation.
- Lighter has bought back about 1.4 million LIT (about $2.8 million) since December 29.
- Hyperliquid does not have a native mobile app, though its iPhone Safari progressive web app experience is described as usable.
- Lighter perps activity and market share declined from a peak around November 21 to roughly about $1.2 million per week recently.
- Perps volumes have been declining across the sector since mid-2024, including Hyperliquid.
- Only about 37% of Lighter's airdrop is still held, per tracker estimates, with a caveat that transfers may be miscounted as sales.
- Liquidity-mining and farming behavior rotates between perp DEXes post-launch, producing transient volume spikes that later drain.
Incentive-Driven Spam And Platform Moderation Constraints (Twitter And Adjacent Infofi)
The corpus attributes spam/bot behavior to strong attacker economics and engagement-tied incentives (Kaito allocation mechanics; Twitter engagement incentives; recommender feedback loops). It also identifies a repeated constraint: identity verification is framed as necessary to ban bots at scale but undesirable for many users, and the existence of scam-like behavior among verified accounts undermines verification as a clean solution. Together these deltas update the mental model from 'moderation problem' to 'incentive-alignment problem' where mitigation often shifts burden to users or creates new loopholes.
- Twitter blacklisting specific InfoFi apps is argued to be a non-solution because similar spam incentives can reappear in new forms and require repeated blacklisting.
- Recommendation algorithms are highly sensitive to clicks and dwell time, so engaging with undesirable posts causes the system to show more similar posts.
- SPEAKER_01 reports not using Twitter for two months aside from tweeting once or twice.
- Twitter Blue requires identity verification, according to SPEAKER_01.
- Kaito farming and botting were driven by incentives where staking Kaito tokens and/or being an active "yapper" could influence allocations into sales/raises hosted on Kaito's platform.
- Twitter boosts views and engagement due to conflicting incentives that reward attention even when users try to avoid low-quality content.
Prediction Markets: Marketing, Legal Risk, And Regulatory Classification
The corpus reports a specific legal event (Kalshi lawsuit over ad framing) and a broader mechanism claim that operators seek regulatory advantage by framing as 'predictions' rather than gambling. It also includes a dispute about misleading 'bond-like' marketing of near-certain contracts and a constraint that sports revenue concentration makes product-scope changes difficult. These deltas collectively update the mental model toward 'growth via aggressive positioning' bounded by consumer-protection and classification risk.
- Kalshi was sued over a TikTok ad that portrayed the product as a financial tool by implying users could pay rent after winning on predictions.
- Marketing prediction markets as risk-free 'bond-like' instruments is argued to be misleading because stacking many binary bets can still result in total loss when any unexpected outcome occurs.
- Prediction market operators attempt to position themselves as 'predictions' rather than 'gambling' to obtain a regulatory advantage even though the underlying activity is still gambling.
- Some prediction-market participants promote 'prediction market bonds' intended to capture small mispricings on near-certain outcomes and claim theoretically up to about 40% APR.
- These platforms generate a large share of revenue from sports markets, making it difficult to remove sports offerings to comply with jurisdictional restrictions.
Kaito/Kaido Product Pivot Toward Curated Marketing And Its Token-Model Tension
The corpus reports a clear product/GTM pivot: sunsetting Yaps and moving to a curated, tiered marketing model. A dispute is introduced about whether an agency-like business supports token valuation expectations, and a valuation repricing is noted. The potential pre-announcement unstaking is flagged as a watch item but remains unresolved within the corpus.
- A crypto-native marketing agency could be a strong business for a small team but may not justify a publicly traded token valuation.
- A large Kaido unstaking occurred about four days before the program-sunsetting announcement, raising suspicion of advance knowledge though it could be coincidence.
- Kaito announced it is sunsetting the Yaps program and shifting toward a curated, tiered marketing model connecting vetted key opinion leaders with campaigns and analytics tooling.
- Kaido's fully diluted valuation dropped to roughly $550–$560 million following commentary around a Nikita tweet.
Social Trading Features Vs Bot-Driven Market Microstructure (Pump Call-Outs)
The corpus adds a concrete feature delta (call-outs) and a concrete monetization delta (recent revenue increase). It pairs these with a mechanism-level warning: explicit calls can be sniped by bots, and even gating/KYC is framed as circumventable via identity markets. The net update is that social/copy trading product surfaces can amplify existing microstructure adversaries unless paired with robust anti-bot design, which is not specified here.
- Explicit on-chain token calls tend to be gamed by bots that automatically snipe the ticker and then drip supply to manual buyers, degrading user experience.
- Pump's revenue has been rising recently, moving from around $1.0 million in late December to roughly $1.6–$1.8 million over the past two days.
- Pump's new 'call outs' feature can disintermediate paid alpha groups by letting traders broadcast token calls to followers inside the app.
- Preventing bot exploitation in live microcap token calls likely requires gating who can trade, but gating can be circumvented via purchased or rented KYC identities.
Watchlist
- A large Kaido unstaking occurred about four days before the program-sunsetting announcement, raising suspicion of advance knowledge though it could be coincidence.
- Active policy discussions include whether stablecoins will be allowed to pay yield and how tokenized equities will be regulated.
- There is increasing need for clearer rules requiring influencers to disclose whether and how much they are paid when promoting financial strategies or platforms, even when platforms are not directly paying them.
Unknowns
- For Lighter, what are the actual revenue sources and magnitudes under the zero-fee, API/PFOF-like monetization model, and how do they map to buybacks?
- For Lighter, did the mobile app materially change user acquisition, retention, or the composition of flow (e.g., retail vs professional) relative to before the release?
- What is the quality of perps volume across ranked venues (including EdgeX), using independent wash-trade or user-count heuristics?
- For Kaido, what are the economics of the new curated, tiered marketing model (take rates, customer retention, headcount scaling) and how—if at all—does the token accrue value?
- For the Kaido pre-announcement unstaking, which wallets were involved and what were subsequent actions (selling, transfers, links to insiders) around the announcement window?