Author: danny

Around the winter of 2020, the project's goal shifted from "creating value and serving users" to "listing and serving studios." The core driving force behind this phenomenon lies in the rigid demand for data from exchanges and the contradiction of early project cold starts. Due to a lack of real initial users and data, but the exchanges needing this data, project teams were forced to "collude" with studios, creating false prosperity through volume manipulation to meet market expectations.

This model led project teams to directly "start businesses for exchanges" (To Exchange) and "start businesses for airdrop hunters" (To Airdrop Hunter). In this context, the industry witnessed the phenomenon of "bad money driving out good," where false, arbitrage-driven interactions (bad money) crowded out network resources, diluting rewards and increasing usage costs, thereby driving away real, utility-oriented users (good money).

Initially designed as a marketing activity to attract new users, the "airdrop" mechanism has completely lost its original intent and has instead become a blood supply mechanism for studios and bots. Project teams and exchanges, intoxicated by this data facade built by scripts, not only led to a massive waste of resources but fundamentally misled the direction of industry development.

This article aims to discuss the roots, mechanisms, and future impacts of this phenomenon on the industry. We will explore how leading exchanges like Binance and OKX inadvertently became the "baton" of this distorted incentive mechanism through their listing standards; analyze how venture capital firms formed a hidden symbiotic relationship with "arbitrage studios" through "high FDV, low circulation" token economics design, jointly completing this false prosperity spectacle.

I. The Incentive Structure of the "False" Economy: From Value Creation to Listing-Only Alienation

The proliferation of arbitrage studios is not a random chaos but a rational economic response to the established incentive structure of the current cryptocurrency market. To understand why project teams even "tolerate" the existence of studios, one must first analyze the survival rules set by the "gatekeepers" who hold the industry's life and death power—CEX, VC, and KOL.

1.1 The Gatekeeper Effect of Exchanges: Data as an Entry Ticket

In the current token economic model, for the vast majority of infrastructure and middleware protocols, achieving a "grand slam" listing on leading exchanges (such as Binance, OKX, Coinbase) defines 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 false data.

Exchanges rely on quantitative indicators to review listing applicants. Binance, as the largest exchange by market share, publicly emphasizes "strong community support" and "sustainable business models" in its listing standards, but in practice, trading volume, daily active address count, on-chain transaction count, and TVL are often given higher weight. OKX also clearly states that, in addition to technical aspects, they are extremely focused on "adoption 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. Arbitrage studios conveniently fill this vacuum, providing a "growth as a service" solution. Through automated scripts, studios can create hundreds of thousands of daily active addresses and millions of transactions in a short time, drawing a perfect growth curve to meet the data requirements of the exchanges' due diligence teams.

This pressure is also reflected in the rumors of "listing fees." Although leading exchanges like Binance often deny charging high listing fees and emphasize fee transparency, in reality, project teams often need to commit to a certain trading volume liquidity or provide a large number of tokens as a marketing budget. If the project itself does not have enough natural traffic, it must rely on market makers and studios to maintain this false prosperity to avoid being delisted or placed on a watchlist by the exchanges.

1.2 The Pressure Cooker of VCs: Vanity Metrics and Exit Liquidity

VCs play a role in this ecosystem by pushing the wave forward. In the past cycle, billions of dollars flowed into the infrastructure sector. The business model of VCs dictates that they must seek exit paths. The standard lifecycle of a crypto project includes seed rounds, private rounds, and ultimately TGE and listing.

At the TGE stage, a project's valuation is highly correlated with market heat and discussion. Due to the lack of traditional P/E or cash flow discount models in the crypto industry, valuations often rely on proxy metrics:

• Active address count is directly interpreted as "user count."
• Transaction count is interpreted as "demand for block space" and "user activity."
• TVL is interpreted as "trusted capital scale" and "cold start funds."

Influenced by industry cleansing and previous wealth myths, the crypto industry attracted many short-attention-span speculators who prioritize these "soil metrics" over substantive value. VCs are well aware that they are competing with retail investors for limited liquidity, so they pressure their portfolio companies to maximize these data points before TGE.

This creates a serious moral hazard: VCs have the incentive to turn a blind eye to Sybil activities, even promoting them behind the scenes, as it is the data contributed by these studios that supports their high valuation exits. Thus, one might see certain TGE projects with Twitter accounts boasting nearly a million followers, interaction address counts nearing a hundred million, and transaction counts reaching billions, etc.

While the total registered user count or original transaction volume may seem convincing on the surface, they often lack correlation with the long-term success of the business. However, at the negotiation table in the primary market, these metrics are standard conditions and a threshold for 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 initially designed as a decentralized marketing tool aimed at distributing tokens to real users to 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 was more effective to attract studios by hinting at airdrop expectations. This "points system" or "task system" marketing activity is essentially a transaction for purchasing data (some say it is a form of forward discounted token buying). Project teams pay (or promise to pay) tokens, and studios deliver on-chain data, gas fees, and transaction fees. This transaction is mutually beneficial in the short term: project teams obtain attractive data to showcase to exchanges and VCs, while studios receive the expected token rewards.

However, the victims of this collusion are the product culture of the entire industry and real users. Because studios only need to meet the minimum interaction threshold (e.g., interacting once a week with an amount greater than $10), project teams' product iterations also begin to optimize around these bots and script interaction logic rather than optimizing real user experiences. This has led to the birth of numerous "zombie protocols" that serve no purpose other than volume manipulation—because the functionalities within are designed for bots. Come on, no one would go through the trouble of swapping a $10 token across chains from chain A to chain B, right?

II. The Industrialized Operation Mechanism of Arbitrage Studios (Supply-Side Analysis)

The term "arbitrage studio" carries a grassroots connotation and even includes some internet humor, reflecting the community's self-deprecation. However, in the context of 2024-2025, it refers to a highly specialized, capitalized, and even software development-capable high-tech industry. These entities operate with the efficiency of software companies, utilizing complex tools, refined algorithms, and infrastructure to maximize their exploration of reward mechanisms.

2.1 Industrial-Grade Infrastructure and Automation

The threshold for participating in Sybil attacks has been significantly lowered, mainly due to the proliferation of professional tools. Fingerprint browser tools like AdsPower and Multilogin allow operators to manage thousands of independent browser environments on a single computer. Each environment has its own digital fingerprint (User Agent, Canvas Hash, WebGL data, etc.) and independent proxy IP addresses. This renders traditional Web2-based anti-cheat measures (like detecting logins from the same device) completely ineffective.

A typical studio operation process includes the following highly industrialized steps:

Identity disguise and isolation: Using fingerprint browsers to isolate the local storage and cookies of thousands of wallets, ensuring they appear as unrelated independent users from around the world on the front end.

Batch wallet generation and management: Utilizing hierarchical deterministic (HD) wallet technology to generate addresses in bulk. To avoid on-chain clustering analysis, studios use CEXs that support sub-accounts for fund distribution. Since CEX hot wallet addresses are universal, this cuts off the correlation of on-chain fund sources, breaking the funding tracking maps commonly used by "witch hunters." (Advanced versions may stagger transfer times, transfer amounts, etc.)

Scripted interaction execution: Writing Python or JavaScript scripts, combined with automation testing frameworks like Selenium or Puppeteer, to execute on-chain interactions around the clock. These scripts can not only automatically complete operations like Swap, Bridge, and Lending but also introduce random modules to simulate human operation intervals and amount fluctuations to deceive behavior analysis algorithms.

KYC supply chain: For projects attempting to block studios through mandatory KYC (like CoinList public offerings or certain project verifications), an underground market has formed a mature KYC data industry chain. Studios can purchase real identity information and biometric data in bulk from developing countries at extremely low costs, even using AI technology for live detection to completely breach the defenses of Proof of Personhood.

2.2 "Task Platforms": Training Grounds and Collaborators for Industrialized Volume Manipulation

Another key development in this cycle is that besides Web3 task platforms like Galxe, Layer3, Zealy, and Kaito, regular wallets and project teams, such as Binance alpha, various Perp DEXs, and emerging L1s, have also joined this trend. These platforms ostensibly position themselves as tools for educating users or building communities, rewarding users with points or NFTs by publishing "tasks" (e.g., "cross-chain ETH to Base," "make a swap on Uniswap").

However, these platforms have become "training grounds" and "task lists" for arbitrage studios.

Layer3 is essentially operating a "growth as a service" market. Protocol parties pay fees to Layer3 in exchange for traffic, and Layer3 distributes these tasks to users. For studios, Layer3 clearly outlines the interaction paths recognized by project teams. Studios only need to write scripts for these specific paths to obtain "officially certified" interaction records at the lowest cost.

Kaito is another service market for leasing media (media buy). It is filled with the voices of numerous AI bots, indirectly leading to a flood of various AI comments and ineffective tweets on Twitter.

Galxe allows project teams to create tasks that include on-chain interactions and social media follows. Although Galxe offers some anti-Sybil features (like Galxe Passport), these features are often paid premium options, and many project teams deliberately do not enable strict filtering to maximize participation data.

Ironically, these platforms have inadvertently (or perhaps intentionally) trained bots. By standardizing complex interaction behaviors into linear "Task A + Task B = Reward," they create a deterministic logic that scripts excel at handling. The result is a large number of "mercenary users" who mechanically complete the minimum actions required to earn rewards, and once the tasks are completed, they immediately cease all activity.

2.3 The Economics of Arbitrage: ROI-Driven Capital Allocation

The essence of arbitrage studios is capital allocation strategy. On the studio's ledger, gas fees, slippage losses, and capital occupation costs are viewed as customer acquisition costs. They calculate the return on investment (ROI).

If $100 in gas fees is spent on a cluster of 50 wallets, ultimately obtaining airdrop tokens worth $5,000, then the ROI is as high as 4,900%. Such exorbitant profits have been seen throughout history:

Starknet Case: An ordinary GitHub developer account can earn about 1,800 STRK tokens. In the early days of the token release, the price exceeded $2, meaning a single account's earnings exceeded $3,600. If a studio uses scripts to batch register and maintain 100 GitHub accounts, their total earnings will exceed $360,000.

Arbitrum Case: Arbitrum's airdrop distributed about 12.75% of the total token supply. Even wallets with only minimal interaction records could receive ARB tokens worth thousands of dollars. This massive liquidity injection not only validated the feasibility of the studio model but also provided ample ammunition (capital) for launching larger-scale attacks in the next cycle (such as zkSync, LayerZero, Linea).

Such high returns create a positive feedback loop: successful airdrops provide studios with funds, enabling them to develop more complex scripts, purchase more expensive fingerprint browsers and proxy IPs, thus capturing a larger share in the next project and further squeezing the survival space of real users.

III. The Ruins Beneath the Data Facade: Tokens Issued. People Gone. Buildings Empty.

The consequences of the studios' "victory" are starkly displayed in the dismal performance of major protocols after airdrops. This reveals a clear pattern: manufactured growth -> airdrop snapshot -> retention collapse.

3.1 Starknet: Avalanche of Retention Rates and Extremely High Customer Acquisition Costs

Starknet, a highly anticipated ZK-Rollup network, implemented a large-scale STRK token airdrop in early 2024. Its distribution criteria were quite broad, aiming to cover developers, early users, and Ethereum stakers.

The data is astonishing. On-chain analysis after the airdrop shows that among users who claimed the airdrop, only about 1.1% of addresses remained active afterward. This means that 98.9% of the profit addresses are mercenary in nature, stopping their contributions to the ecosystem immediately after taking the rewards.

Starknet actually spent about $100 million (based on token value) to acquire around 500,000 users. However, considering the 1.1% retention rate, the cost of acquiring a single retained user skyrocketed to over $1,341. For any Web3 protocol or Web2 company, this is a catastrophic number that is economically unsustainable.

This selling pressure caused the STRK token price to plummet by 64% after its release. Although the total market value seemed to grow due to the token unlocking plan, the purchasing power of the token itself had significantly diminished.

The Starknet case provides a textbook example of a cautionary tale: users "purchased" through airdrop expectations are merely illusions. Studios extracted value and moved on 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 the Cliff of Data

The trajectory of zkSync Era mirrors that of Starknet. Before the airdrop snapshot, the number of active addresses on the network exhibited exponential growth, often surpassing the 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 address count plummeted from a peak of 455,000 at the end of February 2024 to 218,000 in June, a drop of 52%. Daily transaction volume fell from 1.75 million to 512,000. Notably, this crash occurred before the token distribution.

Data from Nansen shows that among the first 10,000 wallets to receive the airdrop, nearly 40% of addresses sold all their tokens within 24 hours. Only about 25% of recipients chose to hold the tokens.

This activity volume crash that began before distribution confirms that the previous prosperity was entirely driven by external incentives. Once the "snapshot" was deemed complete by studios, they immediately ceased script operations. The data decline is merely a facade; the real truth is a slap in the face to the project's narrative of "ecological prosperity."

3.3 LayerZero: Community Civil War and Trust Crisis Triggered by the Self-Reporting Mechanism

The cross-chain interoperability protocol LayerZero attempted to take a radical approach to combat studios: launching a "self-reporting" mechanism. The project team proposed a deal: if you admit to being a bot, you can keep 15% of the airdrop share; if you conceal it and are discovered, you get nothing.

LayerZero ultimately identified and marked over 800,000 addresses as potential bot attackers. This strategy sparked significant rifts within the community. Critics pointed out that it was unfair for LayerZero to directly label users employing tools like Merkly as bots, as LayerZero had previously benefited from the cross-chain fees and transaction volume data generated by these users.

Although this "cleansing" redistributed tokens to so-called "persistent users," $ZRO still faced a 23% price drop within a week of its listing. More seriously, the "bot 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 in the Digital Asset Space

In economics, when exchange rates are fixed, bad money drives out good money. In the context of crypto user acquisition, this phenomenon manifests as: fake users driving out real users.

4.1 Mechanisms of Expulsion

Reward Dilution: Airdrops are typically zero-sum games. Project teams allocate a fixed percentage (e.g., 10%) of tokens to the community. If a studio controls 10,000 wallets, they carve out a huge piece of the reward pool, significantly diluting the share of real users who only have one wallet. When real users find that their normal usage over a year only yields trivial rewards, their willingness to participate in the ecosystem approaches zero.

Network Congestion and Fee Surge: Industrialized volume manipulation consumes valuable block space. During peak volume periods (such as during Linea Voyage or Arbitrum Odyssey events), gas fees surge. Real users, unable to bear the high transaction costs, are forced to migrate to other chains or stop using the service. The network ultimately ends up with only bots—because bots can amortize the high gas fees through anticipated high airdrop returns, while the utility gains for real users cannot cover this cost.

Complex Mechanisms: Some TGE projects intentionally design interaction tasks to be exceedingly complex to block bots, unaware that the complexity of the mechanisms has deterred natural persons, leaving only tireless bots to complete them. Interestingly, some comments suggest that the Perp DEX battle in 2025 has evolved into a script battle.

4.2 "Noise Floor" and Signal Loss

The proliferation of studios has raised the overall noise floor of the ecosystem. With 80%-90% of traffic being inorganic, project teams are unable to determine the real product-market fit.

In this high-intensity data pollution and toxic trading environment, traditional A/B testing, user feedback loops, and feature adoption metrics become completely ineffective. Ultimately, project teams begin to optimize UI/UX based on script preferences (e.g., reducing clicks for easier script execution rather than for human usability).

The market falls into a "Market for Lemons" dilemma. High-quality projects that refuse to engage in volume manipulation and appear "quiet" are undervalued by the market; while low-quality projects that actively cooperate with volume manipulation and appear "booming" receive funding and attention. Ultimately, high-quality projects are forced to exit or compromise, leading to an overall decline in market quality.

4.3 The "Intoxication" and Collusion of Project Teams

Under the influence of the broader environment and the tacit approval of exchanges, some project teams begin to "intoxicate" themselves with data facades. Beautiful data is the only proof that project teams can present to investors and the public. Admitting that 90% of their users are fake would lead to a collapse in valuation and could not only prevent them from being listed but also expose them to lawsuits from investors.

As a result, project teams fall into a state of "performative ignorance." They may implement some seemingly strict anti-Sybil measures (such as banning some low-level scripts) but deliberately leave "backdoors" for advanced studios. The co-founder of Layer3 even publicly admitted that some projects do not want to implement strict bot filtering because they are optimizing scale metrics that can drive narratives and funding.

This collusion completes a 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 investors.

V. Conclusion

The current industry resembles an athlete who has consumed too many stimulants (fake data); although in the short term, their muscles (TVL, user count) are swollen, their internal organs (real income, community consensus) have already deteriorated.

What was once a cyberpunk path to change the world has devolved into a Performative Economy, where project teams pay fees or sign options to studios to "produce" data that meets the arbitrary standards set by exchanges and VCs.

It's not that studios are doing something wrong or bad; after all, it's all commercial behavior. Where there is demand, there is supply. However, when the entire market is filled with studios and traces of incentivized traffic, things change.

This "project team - VC - exchange - studio" interest closed loop is a typical negative-sum game. It maintains short-term superficial prosperity by consuming the industry's credit reserves. To break this vicious cycle, the industry must undergo a painful "de-leveraging" process.

For project teams, the pursuit of listing qualifications on exchanges has replaced the exploration of product-market fit (PMF). Projects are designed to be "gamed" rather than "used." Moreover, hundreds of billions of dollars in token incentives—originally intended to kickstart real communities—are siphoned off and arbitraged by professional extraction machines, ultimately leading to abandonment.

This is not just bad money driving out good money; it is falsehood driving out reality. Unless the industry can shift its focus from vanity metrics like "active addresses" and "transaction counts" to attracting real use cases and creating genuine economic value, we will only continue down the path of bad money driving out good.

Studios have won the battle for airdrops, but their victory may lead the crypto industry to lose the war for mass adoption.

Perhaps only when the benefits of "using products" outweigh the benefits of "gaming data" can good money return, and the crypto industry can truly emerge from the quagmire of false prosperity financial games, moving towards the shore of technological implementation.

In 2026, may we be clumsy players in this "data-driven" era.

免责声明：本文章仅代表作者个人观点，不代表本平台的立场和观点。本文章仅供信息分享，不构成对任何人的任何投资建议。用户与作者之间的任何争议，与本平台无关。如网页中刊载的文章或图片涉及侵权，请提供相关的权利证明和身份证明发送邮件到support@aicoin.com，本平台相关工作人员将会进行核查。