Author: danny
It was around the winter of 2020 when the goal of projects mutated from "creating value and serving users" to "getting listed on exchanges and serving studios." The core driver behind this phenomenon is the contradiction between the rigid demand for data from exchanges and the cold start of early projects. Lacking genuine initial users and data, yet needing to provide this data to exchanges, projects are forced into a "collusion" with studios, creating false prosperity through wash trading to meet market expectations.
This model has led projects to directly "start businesses for exchanges" (To Exchange) and "for airdrop hunters" (To Airdrop Hunter). In this context, the industry has seen a phenomenon of "bad money driving out good," where fake, profit-driven interactions (bad money) crowd out network resources, dilute rewards, and increase usage costs, thereby driving away genuine, utility-oriented users (good money).
The "airdrop" mechanism, initially designed as a marketing activity to attract new users, has completely lost its original purpose and instead become a blood transfusion mechanism feeding studios and bots. Projects and exchanges are intoxicated by this data facade built by scripts, leading not only to a massive waste of resources but also fundamentally misleading the industry's development direction.
This article aims to discuss the roots, mechanisms, and impact of this phenomenon on the industry's future. We will explore how top-tier exchanges like Binance and OKX have unintentionally become the "baton" of this distorted incentive mechanism through their listing standards; analyze how venture capital institutions, through tokenomics designs of "high FDV, low circulation," have formed a hidden symbiotic relationship with "airdrop farming studios" to jointly complete this grand play of false prosperity.
I. The Incentive Structure of the "Fake" Economy: Alienation from Value Creation to Listing-Only
The proliferation of airdrop farming studios is not accidental chaos but a rational economic response to the established incentive structure of the current cryptocurrency market. To understand why projects even "tolerate" the existence of studios, one must first dissect the survival rules set by the industry's gatekeepers—CEXs, VCs, and KOLs.
1.1 The Gatekeeper Effect of Exchanges: Data as the Entry Ticket
In the current token economic model, for the vast majority of infrastructure and middleware protocols, achieving a "Grand Slam" listing on a top-tier exchange (like Binance, OKX, Coinbase) is the definition of project success. This is not only a necessary liquidity event for early investors to exit but also a mark of mainstream market recognition for the project. However, the listing standards of exchanges objectively create a demand for fake data.
Exchanges rely on quantitative metrics to review listing applicants. Binance, as the exchange with the largest market share, publicly emphasizes "strong community support" and "sustainable business models" in its listing standards, but in practice, trading volume, daily active addresses, on-chain transaction count, and TVL are often given high weight. OKX also explicitly states that, besides technical aspects, they are extremely concerned with "adoption rate metrics" and "market competitive position."
This mechanism creates a typical "cold start paradox": a new Layer 2 or DeFi protocol needs real users to qualify for listing, but it is difficult to attract real users without the liquidity and token incentive expectations that come with listing. Airdrop farming studios恰好 fill this vacuum, offering a "growth as a service" solution. Through automated scripts, studios can generate hundreds of thousands of daily active addresses and millions of transactions in a short time, drawing a perfect growth curve that meets the data requirements of the exchange's due diligence teams.
This pressure is also reflected in the rumors of so-called "listing fees." Although top exchanges like Binance often deny charging high listing fees and emphasize fee transparency, in reality, projects often need to commit to a certain level of trading volume liquidity or provide a large number of tokens as marketing budgets. If the project itself lacks sufficient organic traffic, it must rely on market makers and studios to maintain this false prosperity to avoid being delisted or put on a watchlist by the exchange.
1.2 The Pressure Cooker of VCs: Vanity Metrics and Exit Liquidity
VCs play a role in fueling this ecosystem. In the past cycle, tens of billions of dollars flooded into the infrastructure track. The business model of VCs dictates that they must seek exit paths. A standard lifecycle for a crypto project includes seed round, private round, and finally TGE and listing.
At the TGE stage, the project's valuation is highly correlated with market heat and discussion. Because the crypto industry lacks traditional P/E or discounted cash flow models, valuation often relies on proxy metrics:
- Active address count is directly interpreted as "number of users."
- Transaction count is interpreted as "demand for block space," "user activity."
- TVL is interpreted as "trusted capital scale," "cold start capital."
Influenced by industry washouts and previous wealth creation myths, the crypto industry has attracted many speculators with short attention spans, who prioritize these "vanity metrics" over substantive value. VCs know they are competing with retail for limited liquidity, so they pressure their portfolio companies to maximize these data points before TGE.
This creates a serious moral hazard: VCs have an incentive to turn a blind eye to Sybil activity, or even promote it behind the scenes, because it is the data contributed by these studios that supports their high-valuation exits. This is why you see TGE projects' Twitter accounts with nearly a million followers, nearly a hundred million interactive addresses, and tens of billions of transactions, etc.
Although total registered users or raw transaction volume may seem convincing on the surface, they often lack correlation with the long-term success of the business. However, at the primary market negotiation table, these metrics are the standard conditions, a barrier to entry. A project with 500,000 "active addresses" (even if 99% are bots) is often valued much higher than a project with 500 real high-net-worth users.
1.3 The Alienation of Marketing Activities: From User Acquisition to Feeding Bots
Airdrops were originally designed as a decentralized marketing tool to distribute tokens to real users and kickstart network effects. However, under the current incentive structure, the nature of airdrops has fundamentally changed.
Project teams found that instead of spending budgets to educate the market and find real users (a slow and expensive process), it's better to attract studios by hinting at airdrop expectations. These "points-based" or "task-based" marketing activities are essentially a transaction for purchasing data (some also say it's a form of远期 discounted token purchase). The project pays (or promises to pay) tokens, and the studio delivers on-chain data, gas fees, and transaction fees. This transaction is beneficial to both parties in the short term: the project gets beautiful data to show exchanges and VCs, and the studio gets the expected token rewards.
But the victim of this collusion is the entire industry's product culture and real users. Because studios only need to meet the minimum interaction threshold (e.g., interact once a week, amount greater than $10), the project's product iteration also begins to optimize for these bot and script interaction logics, rather than optimizing the real user experience. This has led to the birth of a large number of "zombie protocols" that are useless except for wash trading—their functions are designed for bots. Come on, no one would跨链 from chain A to chain B just to swap $10 worth of tokens, right?
II. The Industrialized Operation Mechanism of Airdrop Farming Studios (Supply-Side Analysis)
The term "airdrop farming studio" carries a certain grassroots flavor, even containing some online self-mockery from the community. But in the context of 2024-2025, it refers to a highly specialized, capitalized, even professionally software development-capable high-tech industry. These entities operate with the efficiency of software companies, utilizing complex tools, sophisticated algorithms, and infrastructure to maximize the exploitation of reward mechanisms.
2.1 Industrial-Grade Infrastructure and Automation
The barrier to participating in Sybil attacks has been significantly lowered, thanks mainly to the proliferation of professional tools. Fingerprint browsers like AdsPower and Multilogin allow operators to manage thousands of independent browser environments on one computer. Each environment has an independent digital fingerprint (User Agent, Canvas Hash, WebGL data, etc.) and an independent proxy IP address. This renders traditional Web2 anti-cheat measures (like detecting same device login) completely ineffective.
A typical studio operation process involves the following highly industrialized steps:
Identity Masking and Isolation: Using fingerprint browsers to isolate the local storage and cookies of thousands of wallets, ensuring they appear to the frontend as independent users from all over the world with no relation.
Batch Wallet Generation and Management: Using Hierarchical Deterministic (HD) wallet technology to generate addresses in bulk. To avoid on-chain cluster analysis, studios use CEXs that support sub-accounts for fund distribution. Since the CEX's hot wallet address is common, this severs the on-chain fund source association, breaking the fund tracking graphs commonly used by "Sybil hunters." (Advanced versions also stagger transfer times and amounts, etc.)
Scripted Interaction Execution: Writing Python or JavaScript scripts, combined with automation testing frameworks like Selenium or Puppeteer, to perform on-chain interactions 24/7. These scripts can not only automatically complete Swap, Bridge, Lending and other operations but also incorporate random modules to simulate human operation intervals and amount fluctuations, deceiving behavioral analysis algorithms.
KYC Supply Chain: For projects that try to block studios by enforcing KYC (like CoinList public sales or certain project verifications), an underground market for KYC data has matured. Studios can purchase real identity information and biometric data in bulk from developing countries at very low cost, even passing liveness detection using AI technology, completely breaching the Proof of Personhood防线.
2.2 "Task Platforms": Training Grounds and Accomplices for Industrialized Wash Trading
A key development this cycle is that besides Web3 task platforms like Galxe, Layer3, Zealy, Kaito,正规军 like wallet providers, project teams, such as Binance alpha, various Perp Dexes, and various emerging L1s have also joined the fray. These platforms ostensibly position themselves as tools to educate users or build communities, rewarding users with points or NFTs for completing "tasks" (e.g., "Bridge ETH to Base," "Perform a swap on Uniswap").
However, these platforms have become "training grounds" and "task lists" for airdrop farming studios.
Layer3 essentially operates a "growth as a service" marketplace. Protocols pay fees to Layer3 in exchange for traffic, and Layer3 distributes these tasks to users. For studios, Layer3 clearly lists the interaction paths endorsed by the project teams. Studios only need to write scripts targeting these specific paths to obtain "officially certified" interaction records at the lowest cost.
Kaito is another service market for renting clout (media buy). It is filled with大量AI bot voices, indirectly causing Twitter to be flooded with various AI comments and无效 tweets.
Galxe allows project teams to create tasks involving on-chain interactions and social media follows. Although Galxe offers some anti-Sybil features (like Galxe Passport), these are often paid premium options, and many project teams intentionally do not enable strict filtering to maximize participation numbers data.
More ironically, these platforms unintentionally (or perhaps intentionally) train bots. By standardizing complex interactions into linear "Task A + Task B = Reward," they create a deterministic logic that scripts are best at handling. The result is a large number of "mercenary users" who mechanically perform the minimum actions required to obtain rewards and immediately cease all activity once the tasks are complete.
2.3 The Economics of Farming: ROI-Driven Capital Allocation
The essence of an airdrop farming studio is a capital allocation strategy. On the studio's ledger, Gas fees, slippage losses, and capital occupancy costs are seen as customer acquisition costs. They calculate the Return on Investment (ROI).
If spending $100 in Gas fees on a cluster of 50 wallets ultimately yields airdropped tokens worth $5,000, the ROI is a staggering 4,900%. Such huge profits have been common historically:
Starknet Case: An ordinary GitHub developer account could receive about 1,800 STRK tokens. At the token's initial release, the price exceeded $2, meaning the收益 per account exceeded $3,600. If a studio used scripts to批量 register and maintain 100 GitHub accounts, its total收益 would exceed $360,000.
Arbitrum Case: The Arbitrum airdrop distributed about 12.75% of the total token supply. Even wallets with minimal interaction records received ARB worth thousands of dollars. This massive liquidity injection not only validated the studio model's feasibility but also provided them with ample ammunition (capital) to launch larger-scale attacks in the next cycle (like zkSync, LayerZero, Linea).
This high return creates a positive feedback loop: successful airdrops provide studios with funds to develop more complex scripts, buy more expensive fingerprint browsers and proxy IPs, thereby capturing a larger share in the next project, further squeezing the living space of real users.
III. The Ruins Beneath the Data Facade:币发。人去。楼空。 (Tokens Distributed. People Gone. Building Empty.)
The consequences of the studios' "victory" are赤裸裸地 displayed in the dismal performance of major protocols post-airdrop. This reveals a clear pattern: Manufactured Growth -> Airdrop Snapshot -> Retention Collapse.
3.1 Starknet: Avalanche in Retention Rate and Extremely High Customer Acquisition Cost
Starknet, as a highly anticipated ZK-Rollup network, conducted a large-scale STRK token airdrop in early 2024. Its distribution criteria were quite broad, aiming to cover developers, early users, and Ethereum stakers.
This data is staggering. On-chain analysis after the airdrop showed that among the users who claimed the airdrop, only about 1.1% of addresses remained active subsequently. This means 98.9% of the profiting addresses were mercenary in nature, ceasing their contribution to the ecosystem immediately after taking the reward.
Starknet essentially spent approximately $100 million (calculated based on token value) to acquire about 500,000 users. However, considering the 1.1% retention rate, its cost per retained user skyrocketed to over $1,341. For any Web3 protocol or Web2 company, this is an economically completely unsustainable, catastrophic figure.
This selling pressure caused the STRK token price to plummet 64% after launch. Although the total market cap appeared to grow due to the token unlock schedule, the purchasing power of the token itself had shrunk significantly.
The Starknet case provides a textbook反面教材: Users "bought" through airdrop expectations are merely phantoms. The studios extracted the value and moved to the next battlefield, leaving the protocol with only inflated historical data and empty block space.
3.2 zkSync Era: The End of an "Era" and a Cliff in Data
zkSync Era's trajectory was identical to Starknet's. Before the airdrop snapshot, the network's active address count showed exponential growth, often even surpassing Ethereum mainnet, being touted as the leader among L2s.
With the announcement of the airdrop and the confirmation of the snapshot date, network activity on zkSync Era immediately collapsed. The 7-day average active addresses fell from a peak of 455,000 in late February 2024 to 218,000 in June, a drop of 52%. Daily transaction volume plummeted from 1.75 million to 512,000. Notably, this暴跌 occurred *before* the token distribution.
Data from Nansen showed that among the top 10,000 wallets receiving the airdrop, nearly 40% of addresses sold their entire token holdings within 24 hours. Only about 25% of recipients chose to hold the tokens.
This collapse starting even before distribution confirms that the previous prosperity was entirely driven by external incentives. Once the "snapshot" was deemed complete by the studios, they immediately stopped script operation. The data drop is just the表象; the slap in the face to the project's "ecosystem prosperity" narrative is the truth.
3.3 LayerZero: Community Civil War and Trust Crisis Triggered by Self-Reporting Mechanism
The cross-chain interoperability protocol LayerZero attempted a radical measure to combat studios: introducing a "self-reporting" mechanism. The project team proposed a deal: if you admit to being a Sybil, you can keep 15% of the airdrop allocation; if you conceal it and are caught, you get nothing.
LayerZero ultimately identified and flagged over 800,000 addresses as potential Sybil attackers. This strategy caused a huge rift within the community. Critics pointed out that it was unfair for LayerZero to directly label users of "wash trading tools" like Merkly as Sybils, as LayerZero had previously benefited from the cross-chain fees and transaction volume data generated by these users.
Although this "purge" redistributed tokens to so-called "persistent users," $ZRO still faced a 23% price drop in the week following its listing. More seriously, the "Sybil bounty hunter" program led to community members reporting each other, creating an extremely toxic atmosphere of surveillance and confrontation, severely damaging the project's brand reputation.
IV. The Phenomenon of Bad Money Driving Out Good Money in the Digital Asset Space
In economics, when the exchange rate is fixed, bad money drives out good money. In the context of crypto user acquisition, this phenomenon manifests as: fake users drive out real users.
4.1 Several Ways the Driving-Out Mechanism Works
Reward Dilution: Airdrops are often a zero-sum game. The project allocates a fixed percentage (e.g., 10%) of tokens to the community. If a studio controls 10,000 wallets, they take a huge slice from the reward pool,极大地 diluting the share of a real user who only has one wallet. When real users find that a year of normal use only yields negligible rewards, their willingness to participate in the ecosystem approaches zero.
Network Congestion and Soaring Fees: Industrialized wash trading consumes precious block space. During peak wash trading periods (like during Linea Voyage or Arbitrum Odyssey campaigns), Gas fees soar. Real users, unable to afford the high transaction costs, are forced to migrate to other chains or stop using. The network eventually只剩下 robots—because robots can amortize the high Gas fees through the expected high airdrop returns, while the utility收益 of real users cannot cover this cost.
Complex Mechanisms: Some TGE projects, in an attempt to block bots, intentionally design interaction tasks to be extremely complex,殊不知 the complexity of the mechanisms has deterred natural persons, and only tireless bots can complete them. Interestingly, some comments suggest the 2025 Perp Dex war has evolved into a script war.
4.2 "Noise Floor" and Signal Loss
The proliferation of studios raises the Noise Floor of the entire ecosystem. With 80%-90% of the traffic being inorganic, project teams simply cannot judge the real Product-Market Fit.
Amidst this high-intensity data pollution and toxic transactions, traditional A/B testing, user feedback loops, and feature adoption rate metrics completely fail. Ultimately, project teams start optimizing UI/UX based on script preferences (e.g., reducing click counts for easier script execution, not for human usability).
The market falls into a "Market for Lemons" dilemma. High-quality projects that refuse wash trading and appear "quiet" in data are undervalued by the market; while low-quality projects that actively cooperate with wash trading and appear "hot" in data receive funding and attention. Eventually, high-quality projects are forced to exit or conform, leading to an overall decline in market quality.
4.3 The "Intoxication" and Collusion of Project Teams
Under the influence of the general environment and the tacit approval of exchanges, some project teams begin to become "intoxicated" with the data facade. Beautiful data is the only proof project teams can present to investors and the public. Admitting that 90% of their users are fake would lead to valuation collapse and potentially not only failure to get listed but also lawsuits from investors.
Therefore, project teams fall into a state of "performative ignorance." They implement some seemingly strict anti-Sybil measures (like banning低级 scripts) but intentionally leave "backdoors" for advanced studios. The co-founder of Layer3 even publicly admitted that some projects do not want strict bot filtering because they are optimizing for the scale metrics that drive narratives and fundraising.
This collusion completes the closed loop—project teams need fake data to sell to VCs/exchanges; studios provide fake data to sell to project teams; VCs/exchanges sell the packaged projects to retail.
V. Conclusion
The current industry is like an athlete who has taken too many stimulants (fake data). While the muscles (TVL, user count)膨胀 in the short term, the internal organs (real revenue, community consensus) have failed.
What was once a cyberpunk path to change the world, the crypto ecosystem has regressed into a Performative Economy, where project teams pay fees or sign options to studios to "produce" data that meets the arbitrarily set standards of exchanges and VCs.
It's not that the studios are wrong or bad—after all, it's commercial behavior, where there's demand, there's supply—but when the entire market is filled with traces of studios and incentive-driven traffic, things change.
This "Project-VC-Exchange-Studio" interest闭环 is a typical negative-sum game. It sustains short-term账面 prosperity by consuming the industry's credit reserves. To break this vicious cycle, the industry must undergo a painful "deleveraging" process.
For project teams, the pursuit of exchange listing qualifications has replaced the exploration of product-market fit (PMF). Projects are designed to "be farmed," not "be used." Furthermore, hundreds of billions of dollars in token incentives—originally intended to bootstrap genuine communities—have been siphoned off and arbitraged by professional extraction machines, ultimately abandoned.
This is not just bad money driving out good money; it is the fake driving out the real. Unless the industry can shift its focus from vanity metrics like "active addresses" and "transaction count" to attracting real use cases and creating real economic value, we will only go further down the path of bad money driving out good.
The studios may have won the battle of the airdrops, but their victory could cause the crypto industry to lose the war of mass adoption.
Perhaps only when the收益 of "using the product" outweighs the收益 of "farming data" can good money return, and the crypto industry truly emerge from the quagmire of false prosperity financial games and move towards the彼岸 of technological implementation.
2026, may we be clumsy players in this "data is king" era.
