❓ The core contradictions and dilemmas of the current airdrop farming track
1️⃣ The imbalance between 'data users' and 'real users'
The relationship between project parties and 'farming' groups has completely turned from 'symbiosis' to 'confrontation'🤺. To prevent witch attacks, project parties continue to complicate airdrop rules:
• Mandatory high-frequency interactions🔗
• Set high capital thresholds🚪
• Require multi-chain cross-platform operations💻
This makes it difficult for ordinary users to meet the standards, while studios monopolize profits through scaling and automation. For example, some projects require users to stake ETH and continuously participate in ecological DApps for airdrops, and ordinary users often exit helplessly due to insufficient funds or cumbersome operations🙅♂️.
2️⃣ Cost-benefit inversion and uncontrollable risks
Ordinary users engaging in airdrop farming must invest significant Gas fees, time, and funds, but the value of the airdropped tokens is often far lower than the investment cost, resulting in much lower returns than expected💸. Worse still, there are many security risks in on-chain interactions now:
• Frequent project protocol vulnerabilities🤯
• Continuous incidents of stolen coins due to ADS fingerprint browser vulnerabilities etc.😫
• Phishing links are difficult to guard against😵💫
Some users may lose all their funds if their private keys are leaked or they make errors😱.
3️⃣ Information disparity and strategy lag
Traditional airdrop farming relies on manual project screening and tracking community dynamics, which is extremely inefficient and particularly prone to missing key time windows🕙, leading to missed opportunities.
🚀 The breakthrough path of AI large models: From 'human-intensive competition' to 'intelligent efficiency enhancement'
1️⃣ Core directions empowered by AI🧭
Project screening and risk assessment🔍
• Automated intelligence gathering: Using AI (such as DeepSeek) to capture project financing information (investors, amounts), token economic models, on-chain data (TVL, user growth, etc.) in real time, training models based on historical airdrop cases to accurately predict project airdrop probabilities📈.
• Example operation: Enter keywords like 'unlaunched token public chain' 'A16z investment/Paradigm investment', and AI can quickly generate a list of potential projects (such as @LineaBuild @monad_xyz @EclipseFND @StoryProtocol @SaharaLabsAI @soneium @AbstractChain @initia @0G_labs @tradeparadex @_kaitoai, etc.), and mark the difficulty level🌟.
• Strategy automation execution⚙️
• Cross-chain interaction scripts: AI automatically differentiates task execution based on preset rules, significantly reducing manual operation costs👷♂️. For example, set specific weekly cross-chain token transfers (ETH/BTC/SOL, etc.) to designated platforms, or stake corresponding assets daily on specific networks.
• Dynamic adjustment of Gas fees: AI analyzes on-chain congestion to intelligently choose low Gas periods for batch transaction submission, saving 30%-50% costs💰.
• Anti-witch🧙♀️ and compliance optimization💡
• Behavior simulation algorithms: AI accurately simulates real user behavior (random interaction times, multi-DApp switching, etc.), effectively avoiding witch detection by project parties🛡️. Through deep analysis and summarizing past project airdrop rules, AI can also automatically assign reasonable interaction frequencies and capital amounts for different addresses.
• Security monitoring: AI scans wallet authorizations and contract code risks in real time, intercepting phishing links as soon as they are detected, and can manage assets through multi-signature wallets, providing comprehensive asset security🔐.
2️⃣ A practical plan to get started quickly in a week
Day 1-2: Build AI tool chain🔧
• Configure DeepSeek smart assistant: Input the command 'filter potential airdrop projects for Q1 2025, requirements: financing over $5 million, testnet/mainnet launched, no tokens', AI quickly outputs a list, synchronizing detailed interaction strategies📋; it can also generate automated scripts using preset templates for easier subsequent operations.
• Deploy a multi-wallet management system💰: Generate 100 addresses through AI, allocate initial funds to each address (0.05 ETH per address), isolate on-chain correlations using privacy technology🔗, ensuring operation security and discretion.
Day 3-5: Execute high-priority tasks🧐
• Focus on AI+, high-financing L1 public chains/🔥L2 chains and Bitcoin ecology: Use AI to complete high-quality ecological interactions en masse, such as cross-chain asset transfers and daily Swap transactions on the Linea network, staking BTC on Babylon's testnet for early points, etc., to more efficiently earn project loyalty rewards🥇.
• Dynamic monitoring and strategy iteration: AI automatically scans project Discord announcements once an hour, accurately extracts key information (such as airdrop snapshot times), and timely pushes alerts📱, ensuring no important messages are missed.
Day 6-7: Optimization and review♾️
• Revenue📊 analysis and risk review: AI generates detailed interactive reports, comprehensively statistics on Gas consumption, address activity, expected airdrop value, etc., accurately eliminating inefficient projects (such as projects with no token commitments on testnets), providing references for subsequent operations.
🤔 Long-term strategy: From 'farming' to 'ecological co-construction'
1️⃣ AI-driven value capture🎯
By deeply training on-chain data models, accurately identify early quality protocols (such as DEX with TVL growth over 200%), proactively provide liquidity, and achieve ✅ higher airdrop weights for more stable earnings.
2️⃣ Community collaboration and data sharing⛓️
Establish an AI analysis platform where users contribute interaction data while receiving model optimization feedback, creating a positive 'data flywheel' effect💫 that promotes community development.
3️⃣ Compliance and sustainability📈
Utilize AI to identify public opinion or regulatory risks (closely monitor SEC compliance warnings, project founders' past controversies, etc.), and timely avoid 🚫 controversial projects to ensure compliance and sustainability.
Conclusion👇
AI does not simply replace human labor, but upgrades 'farming' from 'manual labor' to 'cognitive competition'🧠. With the help of tool-driven, strategic, and compliant intelligent paths, ordinary users can break through information barriers and accurately capture Alpha in the wave of 'anti-farming'. In the future, who can quickly integrate AI and on-chain data in the airdrop field will seize the opportunity and reap rich rewards✨.
