After Collaborating with 35+ DeFi Projects, Pink Brains Discovers the New KOL Marketing Rule for 2026

foresightnews_apiPublished on 2026-06-05Last updated on 2026-06-05

Abstract

After collaborating with over 35 leading DeFi projects, marketing studio Pink Brains identifies key 2026 trends for effective KOL marketing, emphasizing a user-centric approach over traditional campaign tactics. The core insight is that discovery is social (driven by trusted voices on platforms like X), but conversion is data-driven, requiring verifiable on-chain metrics and robust protocol fundamentals. Major user interests in 2026 include new DeFi narratives like RWAs, perp DEXs, and crypto×AI (focused on agentic payments and aligned incentives), real yield from protocol fees, value-capturing tokenomics (e.g., buyback/burn mechanisms), and new trading venues like prediction markets and collectibles. User retention depends on real-world utility, sustainable tokenomics tied to product usage, and incentives rewarding genuine contribution over Sybil activity. Effective KOL strategies involve partnering with specific creator types—educators, analysts, vertical experts—at different user journey stages, avoiding generic content and audience mismatch. The most successful marketing mirrors actual user behavior: discovery via trusted KOLs, validation through data and research, and long-term retention through solid product design and economic alignment.


Author: Pink Brains

Compiled by: AididiaoJP, Foresight News


Over the past three years, we have collaborated with more than 35 leading DeFi projects on their marketing initiatives. We found that the most effective marketing campaigns are not designed from the project's perspective, but from the user's perspective: how users discover products, how they build trust, and how they genuinely engage.


Note: Pink Brains is a marketing studio focused on DeFi, providing services like KOL marketing and content creation (threads, analyses) for DeFi projects.


The logic of most crypto marketing guides goes like this: select KOLs, allocate budget, launch campaign, track exposure.


We've completely flipped this process. Instead of starting with tactics, we first study user behavior: How do DeFi users discover new protocols? What convinces them to try? What makes them stay?


How Do DeFi Users Discover New Protocols?


Crypto users typically spot opportunities on Twitter first, then go to platforms like DefiLlama, DeBank, Artemis, Token Terminal, Moni to verify data, check the protocol's official documentation, and finally consider depositing funds.


The discovery process is socially driven, but the decision-making process is data-driven.


The actual path usually looks like this:


A trusted account posts about a new perpetual DEX—this post rarely triggers an immediate deposit.


The user will first check the project's official account, browse posts and reviews from other KOLs, look at data like trading volume, TVL, incentive programs, skim the documentation and guides, and finally deposit a small test amount.


That X post merely introduces the protocol to the user, but what truly drives the decision is the KOL's content and verifiable data.


This is why X remains the core battleground for DeFi: it's where narratives form, vulnerabilities are exposed in real-time, and founders and researchers fiercely debate in the comments.


The practical takeaway for protocol teams is: The goal in the discovery phase is not virality, but to be mentioned by accounts already trusted by data-driven users, and for all data to hold up when users go to verify. A powerful X mention paired with thin TVL or a weak audit page cannot convert truly valuable users.



What Are DeFi Users Focusing on in 2026?


This year, DeFi users are primarily drawn to several clear themes:


  • New DeFi trends (tokenization, perpetual contracts, RWAs, pre-IPO perps, and the crypto×AI wave)
  • Airdrops that require real contribution but carry higher risks
  • Yields backed by real revenue
  • Value-capturing tokens directly linked to product usage
  • New types of trading venues


The common thread is: verifiable mechanisms, not marketing speak.


New Narratives: Perpetual Contracts, RWAs, Crypto×AI


What people are trading is changing.


Hyperliquid's HIP-3 upgrade enabled permissionless perpetual listings, leading to over 100 RWA markets (stocks, commodities, indices, forex, even pre-IPO assets) with a cumulative trading volume exceeding $130 billion. By the end of Q1 2026, RWA markets accounted for over 90% of HIP-3 open interest.


@Ostium (a dedicated RWA perpetual DEX on Arbitrum) proposed the 'perpification' theory: a perpetual contract only needs a price oracle and a liquidity pool, not a full tokenization stack. This brings traditional market exposure on-chain faster than tokenized spot markets.


@tradexyz and @ventuals focus on commodities and forex on Hyperliquid; Trade.xyz's Cerebras pre-IPO perpetual contract almost perfectly 'priced' the stock hours before its Nasdaq debut.


Another major narrative is crypto×AI. Users care not about the narrative, but about agentic payments and token incentive mechanisms aligned with AI.


  • @opentensor ($TAO) completed its first halving in late 2025, now runs 120+ active subnets, and generates real revenue demand.
  • @virtuals_io (VIRTUAL) reported over $400 million in agent GDP and $60 million in protocol revenue in Q1 2026, deployed 17,000+ agents, and co-authored a cross-chain agent commerce standard with the Ethereum Foundation.
  • @NEAR Protocol and @AskVenice occupy core positions in private inference and data sovereignty.


Additionally, early connections in crypto×robotics (like @xmaquina, Robotics Capital Markets) are emerging.


This sector is volatile with many low-quality projects, but what users truly care about is revenue and actual usage of the leading projects.


Airdrops, but the Bar is Much Higher Now


Airdrops remain a significant driver, with many airdrop hunters still seeking the next HYPE, but the easy days are over.


Projects increasingly demand real contributions: sustainable trading, genuine liquidity provision, community education content, etc. Sybil filtering is now standard, and tokens often face immediate selling pressure post-TGE.


Points programs value generated fees more than locked capital; testnet rewards emphasize sustained, qualitative participation over mere transaction count.


Real Yield


Users now clearly distinguish between 'yield generated from real revenue' and 'yield printed via inflation,' strongly preferring the former.


Real yield takes many forms, but only a few are meaningful: fees from economic activities like trading, lending, funding rates, liquidations; infrastructure usage fees; and yield backed by RWAs.


Yield trading platforms like @pendle_fi, vaults managed by risk managers like @veda_labs, @gauntlet_xyz, @MEVCapital, @SteakhouseFi, and on-chain capital allocators like @sparkdotfi have become main entry points for capital deployment, channeling liquidity into fixed-income strategies based on real yield sources.



For example, @ethena's sUSDe generates yield through delta-neutral basis trading, with a supply nearing $5.8 billion.


@SkyEcosystem's sUSDS pays ~4-4.5% yield, backed by RWA collateral and stability fees—S&P even issued its first credit rating to a DeFi protocol, Sky.


The overall trend is moving from a market that 'creates yield via inflation' to one that 'imports and allocates yield from real sources.'


Value-Capturing Tokenomics


Beyond yield, users increasingly favor tokens whose value is directly linked to product adoption—often through buybacks, buyback-and-burn, supply deflation, protocol revenue sharing, etc.


Hyperliquid's HYPE is a classic case: its Assistance Fund uses ~99% of trading fee revenue for open market buybacks, totaling over $1.16 billion. Since TGE, 4.45% of the total supply has been bought back and burned.


Venice's VVV ties demand to staked AI inference compute; part of the protocol revenue is used to buy back and burn VVV, with ~40% of the supply burned so far, and the price up 400% YTD.


Bittensor's TAO adopts a Bitcoin-like halving mechanism, shifting from inflation to scarcity.


The pattern users look for is the same: the token must be tightly coupled with the actual activity the product generates, where increased activity adds value, not dilutes it.



New Types of Trading Venues


Finally, attention is spreading to new types of trading venues:


  • Prediction markets (Polymarket and Kalshi had massive cumulative volume in 2025)
  • Physical card and collectible trading markets
  • Crypto-enabled gamification (Crypto iGaming)


These are more speculative but do bring real trading volume and revenue.


Logan Paul publicly stated his portfolio holds no stocks, only Pokémon cards. The Pokémon card market reached $75 billion in 2026 (compared to under $15 billion in 2016).


@Collector_Crypt (a card trading market on Solana) has become the second-largest revenue dApp on Solana, with $1.9M in daily revenue.



GameFi is passé, but GambleFi is quietly exploding. Crypto gambling revenue reached $81.4B in 2024, a 5x increase from 2022. In Q1 2025 alone, crypto betting volume hit $26B, almost double year-over-year. Non-KYC, global reach, and provably fair mechanisms are driving a new wave of on-chain gamification.


Centralized crypto iGaming like @Stake, @shufflecom, and provably fair on-chain iGaming like @nardotbet, though rarely mentioned by DeFi KOLs, see very strong real trading.


The commonality across these areas: users can independently verify their appeal. Interest stems from the mechanisms themselves, not marketing language.


What Makes DeFi Users Stay?


DeFi users stick with a protocol when it is genuinely useful in real life, generates profits, and creates value for token holders. Simultaneously, it must remain reliable through market ups and downs.


The key differentiator is: protocols that retain capital do so through trust, distribution, and reliability, not temporary APY or TVL.


Real-World Use Cases


The strongest reason for users to stay is simple: the protocol is genuinely useful in daily life. Products like crypto cards, neobanks, and vaults give users a reason beyond speculation to remain in the ecosystem.


Ether.fi Cash is a good example: users earn cashback on spending while also earning staking rewards. The specific rates matter less than the fact that 'daily financial activity itself becomes a reason to stay in the ecosystem.'


The same logic applies to crypto neobanks and capital allocators: they are embedded in users' regular financial habits, not just places users occasionally visit for yield.


Tokenomics Reflecting the Real Product


Users are more willing to stay when tokens genuinely capture the value generated by the product, rather than relying on narratives.


The 2026 textbook case is HYPE. Its Assistance Fund uses 99% of trading fee revenue for open market buybacks. The Bitwise CIO stated plainly: this token's design means that platform activity growth directly benefits holders. This is a value loop users can verify themselves, thus sustaining attention, not just short-term market attention at launch.


@AskVenice's VVV is another concrete model: staking VVV grants a proportional share of the platform's daily AI inference compute, which can be locked to mint DIEM (representing $1 of daily API credit). Venice has burned over 42% of the initial supply and drastically cut inflation. Demand is purely from real usage.


Airdrops and Incentives, but Not Empty Promises


Airdrops can still bring users back, but the easy days are largely over. Projects increasingly reward real usage and rigorously filter Sybils.


@monad skipped traditional points programs entirely, opting to reward real contributors. Its testnet airdrop was based on 5 contributor tracks with strong anti-Sybil measures, ultimately rewarding only 5,500 wallets for community building, support, content creation, and ecosystem growth.


Points programs remain difficult to get right. A recent example is MegaETH's Terminal program: launched with TGE in April 2026 as an 8-week rewards campaign, it was terminated early just 3 weeks later (May 21).


Even well-designed programs struggle to convert short-term activity into long-term retained users.


How Do DeFi Projects Retain Users?


DeFi retention relies on four elements working together:


  • A product experience good enough for daily use
  • Responsive customer support
  • Tokenomics aligned with community interests long-term
  • Community building beyond TG and Discord (product experience, support, tokenomics, strategic community building)


Types of KOLs DeFi Projects Should Collaborate With


DeFi KOLs roughly fall into four categories: Educators, Content Creators, Airdrop Practitioners, and Vertical Experts.


Each type suits different stages of the user journey. Treating them as interchangeable 'exposure tools' is a common and costly mistake.


What Kind of DeFi KOL Content Performs Best?


Top-performing DeFi content is typically specific and verifiable: on-chain proofs, step-by-step strategy threads, balanced protocol analyses, and timely breakdowns of exploits or new mechanisms.


Poor-performing content is often vague, undisclosed, or unverifiable.


Common Mistakes in DeFi KOL Marketing


  • Using creators who don't understand the product
  • Generic content (hollow terms like 'revolutionary,' 'game-changing')
  • Audience mismatch
  • Over-reliance on a few top KOLs (concentration risk)
  • Fake exposure metrics
  • One-off promotions instead of building long-term relationships
  • Ignoring product readiness


Conclusion


The most effective DeFi marketing plans are those that truly mirror actual user behavior: discovery comes from trusted voices, interest comes from verifiable mechanisms, retention comes from strong tokenomics and product design—not mere marketing talk.


The best-performing protocols don't rely solely on internal marketing. KOLs bring awareness, research validates the thesis, users share real results, and ultimately, enduring on-chain data proves the product's value far exceeds incentives.

Related Questions

QAccording to Pink Brains' analysis, what is the key insight into DeFi user discovery of new protocols, and why is this important for marketing?

AThe key insight is that DeFi users discover new protocols socially (often on X/Twitter) but make decisions data-drivenly. The importance lies in the fact that marketing should not just aim for viral mentions but ensure that when users verify the protocol on data platforms like DefiLlama, the data (TVL, audits, etc.) is solid. Discovery starts a user's journey, but trust and conversion are built on verifiable data.

QBased on the article, what are the main themes attracting DeFi user attention in 2026? List at least three.

AThe main themes attracting DeFi user attention in 2026 are: 1) New DeFi trends (Perpetuals, RWA, Crypto x AI), 2) Airdrops with higher barriers requiring genuine contribution, 3) Real yield backed by actual protocol revenue, 4) Value-capturing tokenomics directly tied to product usage, and 5) New trading venues like prediction markets and collectibles trading.

QHow does Pink Brains define the difference between protocols that attract capital versus those that retain users?

AProtocols attract capital through high APYs, temporary incentives, or marketing narratives. However, protocols retain users by building trust through real-world utility (e.g., crypto cards, neobanks), having tokenomics that genuinely capture product value (like HYPE's buybacks), providing reliable support, and fostering a strong community that goes beyond Discord/TG. Retention relies on embedded usefulness and aligned long-term incentives.

QWhat are the four main categories of DeFi KOLs mentioned, and what is the common mistake projects make regarding them?

AThe four main categories of DeFi KOLs are: Educators, Content Creators, Airdrop Hunters, and Niche Experts. The common and costly mistake projects make is treating all KOLs as interchangeable 'exposure tools' for the same purpose, rather than strategically matching each KOL type to different stages of the user journey (e.g., awareness, education, deep-dive analysis).

QAccording to the article, what are key characteristics of high-performing DeFi KOL content versus low-performing content?

AHigh-performing DeFi KOL content is specific and verifiable, such as on-chain proofs, step-by-step strategy threads, balanced protocol analyses, and timely breakdowns of exploits or new mechanisms. Low-performing content is typically generic, uses unsubstantiated claims (e.g., 'game-changing,' 'revolutionary'), lacks disclosure, or cannot be independently verified by the audience.

Related Reads

From Ethereum to AI's 'CROPS': What Exactly is This Set of 'Slow Variables' That Vitalik Repeatedly Emphasizes?

In recent discussions, Vitalik Buterin has frequently emphasized the concept of "CROPS," a framework defining core values for Ethereum's development. CROPS stands for Censorship Resistance, Capture Resistance, Open Source, Privacy, and Security. Initially outlined in the Ethereum Foundation's "EF Mandate," it represents a commitment to user sovereignty, ensuring that the network resists external control, remains open, protects privacy, and prioritizes security. The relevance of CROPS extends beyond Ethereum's foundational principles, becoming crucial in the context of AI integration. As AI agents begin handling wallet operations and automated transactions, the risk increases that users may cede control over their digital assets, privacy, and intentions to centralized AI service providers. A "CROPS AI" would therefore emphasize local execution where possible, privacy-preserving remote model calls (e.g., using zero-knowledge proofs), and transparent, verifiable processes to maintain user agency. Vitalik highlights a significant convergence between "CROPS Ethereum access layer" and "CROPS AI." Both address the same fundamental challenge: how users can access powerful services—be it blockchain data via RPCs or AI models—without exposing sensitive information or relinquishing ultimate control. This intersection points toward a future digital entry point that is more private, secure, and user-controlled. Ultimately, CROPS is not merely an abstract ideal but a practical guidepost. It steers development—from protocol resilience and wallet design to AI agent safety—towards a future where users retain self-sovereignty even as digital systems grow more complex and powerful. In an era of accelerating AI adoption, these "slow variables" of censorship resistance, openness, privacy, and security may define Ethereum's enduring value.

marsbit7m ago

From Ethereum to AI's 'CROPS': What Exactly is This Set of 'Slow Variables' That Vitalik Repeatedly Emphasizes?

marsbit7m ago

Silicon Valley 'Startup Guru' Steve Hoffman: Web3 + AI Could Be a Trap

Silicon Valley investor and "Godfather of Startups" Steve Hoffman warns that combining Web3 with AI is likely a trap, not a promising venture. In an interview, Hoffman argues that while AI is a foundational technology touching all industries, Web3 adds complexity, friction, and regulatory risk without solving mainstream consumer or business needs. He advises founders to focus on deep, specialized applications where startups can out-iterate giants, rather than on generic features easily replicated by large tech companies. Hoffman observes that Silicon Valley will lead foundational AI research, while China excels at rapid, large-scale application and commercialization, particularly in robotics. He stresses that AI-driven autonomous agents capable of collaborative, multi-step tasks are 2-4 years away, which will cause significant job displacement. The solution is not to slow AI but to redesign business models around human-AI collaboration and reform social systems like education and retraining. For startups, Hoffman recommends focusing on vertical, expertise-heavy domains to build defensibility. He sees major opportunities in AI fraud detection and cybersecurity. Key founder mindsets include systemic thinking over feature-focus, relentless customer centricity, building adaptive teams, and deeply understanding AI's capabilities and limits. Hoffman is also leading a non-profit initiative to establish university centers aimed at training future leaders in responsible, human-value-aligned AI innovation.

marsbit1h ago

Silicon Valley 'Startup Guru' Steve Hoffman: Web3 + AI Could Be a Trap

marsbit1h ago

Token Inefficient, Economy Tokenless

The article "Tokens Aren't Economical, Economics Aren't Tokenized" analyzes a pivotal shift in the AI industry from a technology-driven narrative to one dominated by capital efficiency. It highlights two concurrent trends: a severe capital shortage due to the exorbitant and recurring costs of compute (e.g., OpenAI's high burn rate) and a wave of corporate spin-offs where major tech companies are separating their AI units (like Kuaishou's Kling and Baidu's Kunlunxin). The core argument is that AI's "anti-internet" business model, where user growth increases costs rather than profits, has created a disconnect between high valuations and actual cash flow. Spin-offs address this by allowing AI assets to be valued independently. Within a parent company, they are seen as cost centers, but as standalone entities, they are priced based on their growth potential and scarcity in the primary market, leading to massive valuation premiums (e.g., Kling's estimated value tripling post-spin-off). The industry is at an inflection point, moving from "model worship" to "value realization." The competition is evolving from a pure compute (GPU) race to a broader focus on systemic efficiency and full-stack engineering (involving CPUs and orchestration) to achieve viable commercialization. The year 2026 is framed as a critical moment where the industry must definitively answer how to economically translate AI capability into tangible business value, reshaping the sector's future power structure.

marsbit1h ago

Token Inefficient, Economy Tokenless

marsbit1h ago

Crossing the 'Memory Wall': The Wafer-Level Revolution and Computing Power Routes in the AI Inference Era

In 2026, a historic shift occurred in AI as major cloud providers' inference spending surpassed training spending for the first time, signaling a move from "building large models" to "using large models." This shifts the core challenge from computing power to the "memory wall"—the bottleneck of data movement (model weights, activations, KV Cache) between external DRAM and processors, where energy and latency from data transfer far exceed computation itself. Companies like Nvidia face GPU idle time due to bandwidth limits. In contrast, Cerebras Systems adopts a radical "wafer-scale" approach with its Wafer-Scale Engine (WSE). Instead of cutting a silicon wafer into many chips, Cerebras uses almost the entire wafer as one massive chip (WSE-3). This design provides 44GB of on-chip SRAM, delivering memory bandwidth thousands of times higher than traditional HBM (e.g., 21 PB/s vs. Nvidia B200). For LLM inference, weights are streamed layer-by-layer from external MemoryX storage to the chip, avoiding HBM bottlenecks. This results in token generation speeds 1.5–5 times faster than Nvidia's B200 in some models and significant advantages in first-token latency and long-context tasks. Additionally, Cerebras's architecture offers much lower interconnect power consumption (0.15 pJ/bit vs. GPU's ~10 pJ/bit). However, Cerebras faces challenges: SRAM scaling has slowed with advanced nodes, limiting future capacity gains; the chip requires specialized liquid cooling and custom software stacks; and its external I/O bandwidth (150 GB/s) is low compared to NVLink, hindering multi-system scaling for very large models. Competition is intensifying. Major players are pursuing three paths: 1) Developing proprietary inference ASICs (e.g., Google TPU, Microsoft Maia), 2) Leveraging advanced packaging (e.g., TSMC's SoW) to democratize wafer-scale-like integration, potentially eroding Cerebras's process advantage within a few years, and 3) Exploring optical interconnects for ultimate bandwidth. Commercially, Cerebras is transitioning from a hardware vendor to a service provider, facing the immense challenge of building high-power, specialized data centers to meet large contracts (e.g., 250MW/year from 2026–2028). In conclusion, the AI inference era presents a fundamental architectural trade-off. Cerebras opts for extreme physical optimization for low-latency, single-task performance, while Nvidia prioritizes versatility and massive cluster throughput. The path forward remains uncertain, with technology and business models still evolving in the race toward advanced AI.

marsbit1h ago

Crossing the 'Memory Wall': The Wafer-Level Revolution and Computing Power Routes in the AI Inference Era

marsbit1h ago

Trading

Spot
Futures
活动图片