Grooming Economy: The Hidden Symbiotic Chain Between Project Teams, VCs, and Studios

Author: danny

Around the winter of 2020, the project’s goal shifted from “creating value, serving users” to “getting listed and serving the studio well.” The core driving force behind this phenomenon lies in the conflicting demands: exchanges have a rigid need for data, while early-stage projects lack authentic initial users and data. As a result, project teams are forced to “collude” with studios and use fake volume to create a false prosperity, satisfying market expectations.

This model leads to a direct “To Exchange” and “To Airdrop Hunter” approach by project teams. In this context, a phenomenon of “bad money driving out good” has emerged in the industry, where fake, arbitrage-driven interactions (bad money) crowd out genuine, utility-oriented users (good money) by diluting rewards and increasing usage costs.

Initially, airdrops were designed as marketing activities to attract new users, but their original purpose has been completely undermined, turning into a blood transfusion mechanism feeding studios and bots. Both project teams and exchanges are intoxicated by this superficial data façade, which not only causes massive resource waste but also fundamentally misleads the industry’s development direction.

This article aims to discuss the root causes, mechanisms, and future impacts of this phenomenon. We will explore how top-tier exchanges like Binance and OKX, through their listing standards, unintentionally become “conductors” of this distorted incentive mechanism; analyze how venture capital (VC) firms, with “high FDV and low circulating supply” tokenomics, form a covert symbiotic relationship with “grinding studios,” jointly fueling this false prosperity.

1. “Fake” Economy Incentive Structure: From Value Creation to Listing-Driven Alienation

The proliferation of grinding studios is not accidental chaos but a rational economic response to the current crypto market’s established incentive structure. To understand why project teams even “tacitly accept” the existence of studios, we must first analyze the “gatekeepers”—CEXs, VCs, and KOLs—who hold the industry’s lifeblood and set the survival rules.

1.1 Exchange Gatekeeper Effect: Data as an Entry Ticket

In the current token economy model, for most infrastructure and middleware protocols, getting “grand slam” listed on top exchanges (like Binance, OKX, Coinbase) is considered 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. However, the listing standards of exchanges objectively create a demand for fake data.

The review process for listing applicants relies on quantitative metrics. Binance, as the largest market share exchange, emphasizes “strong community support” and “sustainable business models,” but in practice, metrics like trading volume, daily active addresses, on-chain transaction count, and TVL are heavily weighted. OKX also explicitly states that beyond technical factors, they focus intensely on “adoption rate” and “market position.”

This mechanism creates a classic “cold start paradox”: a new Layer 2 or DeFi protocol needs real users to qualify for listing, but without the liquidity and token incentives that come with listing, attracting genuine users is difficult. Grinding studios fill this vacuum—they offer a “growth-as-a-service” solution. Using automation scripts, studios can generate 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 exchange due diligence teams.

This pressure is also reflected in the so-called “listing fee” rumors. While top exchanges like Binance often deny charging high listing fees and emphasize transparency, in reality, project teams usually need to promise certain trading volume liquidity or provide large token reserves as marketing budgets. If the project lacks enough natural traffic, it must rely on market makers and studios to sustain this fake prosperity, avoiding delisting or being placed on watchlists.

1.2 VC Pressure Cooker: Vanity Metrics and Exit Liquidity

VCs play a catalytic role in this ecosystem. Over past cycles, billions of dollars have flowed into infrastructure projects. Their business model compels them to seek exit routes. A typical crypto project lifecycle includes seed rounds, private placements, TGE, and listing.

At the TGE stage, valuation is highly correlated with market hype and discussion volume. Due to the lack of traditional P/E or cash flow discount models in crypto, valuations often rely on proxy indicators:

• Active addresses are directly interpreted as “users.”
• Transaction count is seen as “demand for on-chain space” and “user activity.”
• TVL is viewed as “trusted capital” and “cold start funding.”

Influenced by industry cleansing and the myth of quick riches, crypto attracts many short-term speculators who prioritize these “soil metrics” over substantive value. VCs know they are competing with retail for limited liquidity, so they push their portfolio companies to maximize these metrics before TGE.

This creates a serious moral hazard: VCs are incentivized to overlook or even secretly promote Sybil activities, because the data contributed by these studios supports their high valuation exits. Hence, some TGE projects’ Twitter accounts boast nearly a million followers, with billions of addresses and tens of billions of transactions.

While total registered users or raw transaction volume may seem convincing on the surface, they often lack correlation with long-term business success. Yet, at the primary market negotiation table, these metrics are the standard prerequisites for entry. A project with 500,000 “active addresses” (even if 99% are bots) often has a valuation far exceeding a project with 500 genuine high-net-worth users.

1.3 Alienation of Marketing Activities: From Customer Acquisition to Feeding Bots

Originally, airdrops aimed to be a decentralized marketing tool to distribute tokens to real users and initiate network effects. However, under current incentive structures, the nature of airdrops has undergone a fundamental transformation.

Project teams find it more cost-effective to attract studios by hinting at airdrop expectations rather than spending budgets to educate the market and find genuine users (a slow and expensive process). This “points system” or “task-based” marketing activity is essentially a data purchase transaction (some call it a forward discount buy-in). The project pays (or promises to pay) tokens, and studios deliver on-chain data, gas fees, and transaction costs. This short-term mutually beneficial transaction allows the project to showcase impressive data to exchanges and VCs, while studios receive expected token rewards.

However, the victims of this collusion are the entire industry’s product culture and genuine users. Studios only need to meet minimal interaction thresholds (e.g., once a week, with amounts over $10), and product iterations begin to revolve around these scripted, bot-driven interactions rather than genuine user experience. This leads to the birth of “zombie protocols” with functions designed solely for bots. Come on, who would cross-chain a $10 token just to swap it?

Second, the Industrialized Operation Mechanism of Grinding Studios (Supply Side Analysis)

The term “grinding studio” carries a grassroots connotation, even some internet slang, as a form of self-deprecating community humor. But in the 2024-2025 context, it refers to a highly professionalized, capitalized, and even software-capable high-tech industry. These entities operate with the efficiency of software companies, utilizing complex tools, sophisticated algorithms, and infrastructure to maximize reward mechanism exploitation.

2.1 Industrial-Grade Infrastructure and Automation

The threshold for participating in Sybil attacks has been significantly lowered, thanks 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 machine. Each environment has its own digital fingerprint (User Agent, Canvas Hash, WebGL data, etc.) and independent proxy IPs. This renders traditional Web2 anti-cheat measures (like device login detection) completely ineffective.

A typical studio operation involves several highly industrialized steps:

Identity Disguise and Isolation: Using fingerprint browsers to isolate local storage and cookies for thousands of wallets, ensuring they appear as unrelated, independent users from around the world.

Bulk Wallet Generation and Management: Using hierarchical deterministic (HD) wallets to generate addresses in bulk. To avoid on-chain clustering analysis, studios often use CEXs supporting sub-accounts for fund distribution. Since CEX hot wallet addresses are shared, this cuts off the link to on-chain fund sources, breaking common “witch-hunter” tracking graphs. (Advanced versions also stagger transfer times, amounts, etc.)

Scripted Interaction Execution: Writing Python or JavaScript scripts, combined with automation frameworks like Selenium or Puppeteer, to perform on-chain interactions 24/7. These scripts can automatically execute swaps, bridges, lending, and even incorporate randomness to simulate human-like intervals and amount fluctuations, deceiving behavior analysis algorithms.

KYC Supply Chain: For projects attempting to block studios via mandatory KYC (e.g., CoinList public offerings or certain project verifications), an underground KYC data industry has formed. Studios can buy real identity info and biometric data in bulk from developing countries at very low costs, even using AI-based liveness detection to break through Proof of Personhood defenses.

2.2 “Task Platforms”: Industrial-Scale Volume Grinding Training Grounds and Conspirators

Another key development this cycle is that, besides Web3 task platforms like Galxe, Layer3, Zealy, Kaito, etc., legitimate wallets and project teams—such as Binance alpha, various Perp DEXs, and emerging L1s—have joined this arena. These platforms ostensibly serve as tools for user education or community building, rewarding users with “tasks” (e.g., “Cross-chain ETH to Base,” “Trade on Uniswap”) with points or NFTs.

However, these platforms have become “training grounds” and “task lists” for grinding studios.

Layer3 actually operates a “growth-as-a-service” marketplace. Protocols pay fees to Layer3 for traffic, and Layer3 distributes these tasks to users. For studios, Layer3 clearly lists the interaction paths approved by project teams. They only need to write scripts targeting these specific paths to obtain “officially certified” interaction records at minimal cost.

Kaito is another service market for leasing media volume. It is flooded with AI bot voices, indirectly fueling the proliferation of AI-generated comments and spam on Twitter.

Galxe allows projects to create tasks involving on-chain interactions and social media follows. While Galxe offers some anti-witch features (like Galxe Passport), these are often paid premium options, and many projects deliberately disable strict filtering to maximize participation data.

Ironically, these platforms inadvertently (or perhaps intentionally) train bots. By standardizing complex interactions 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 perform only the minimum actions needed for rewards, then immediately stop all activity once the task is complete.

2.3 Economics Ledger of Grinding Studios: ROI-Driven Capital Allocation

The essence of grinding studios is capital allocation strategy. On their books, gas fees, slippage losses, and capital occupation costs are viewed as customer acquisition costs. They calculate ROI (Return on Investment).

For example, spending $100 on gas fees across a cluster of 50 wallets, to obtain $5,000 worth of airdrop tokens, yields an ROI of 4,900%. Such high returns are historically common:

Starknet case: An ordinary GitHub developer account can earn about 1,800 STRK tokens. Early in the token release, with prices over $2, this means over $3,600 in profit per account. If a studio scripts and maintains 100 GitHub accounts, total profit exceeds $360,000.

Arbitrum case: The Arbitrum airdrop distributed about 12.75% of total tokens. Even wallets with minimal interaction records received thousands of dollars worth of ARB. This massive liquidity injection not only proved the feasibility of the studio model but also provided ample capital for larger-scale attacks in subsequent cycles (like zkSync, LayerZero, Linea).

This high ROI creates a positive feedback loop: successful airdrops fund studios, enabling them to develop more complex scripts, buy more expensive fingerprint tools and proxies, and further dominate future projects, squeezing out genuine users.

3. Data Illusions and Ruins: Coins Disappear. People Leave. Buildings Empty.

The “victories” of studios are starkly reflected in the poor post-airdrop performance of major protocols. This reveals a clear pattern: growth driven by artificial means -> snapshot for airdrop -> retention collapse.

3.1 Starknet: Retention Avalanche and Extremely High Customer Acquisition Cost

Starknet, a highly anticipated ZK-Rollup network, launched a large-scale STRK token airdrop in early 2024. Its distribution was broad, targeting developers, early users, and ETH stakers.

The data is shocking. On-chain analysis shows that only about 1.1% of addresses that claimed the airdrop remained active afterward. This means 98.9% of profit addresses are mercenaries who stop contributing immediately after collecting rewards.

Starknet spent roughly $100 million (based on token value) to acquire about 500,000 users. But with a 1.1% retention rate, the cost per retained user skyrockets to over $1,341. For any Web3 protocol or Web2 company, this is an economically unsustainable disaster.

This selling pressure caused STRK’s price to plummet 64% post-launch. Although the total market cap appears to have increased due to token unlocks, the tokens’ actual purchasing power has greatly diminished.

Starknet’s case is a textbook example: users “bought” via airdrops are just illusions. Studios extract value and shift to the next battlefield, leaving protocols with only inflated historical data and empty on-chain space.

3.2 zkSync Era: “Era” Ends and Data Plummets

zkSync Era’s trajectory mirrors Starknet’s. Before the snapshot, active addresses grew exponentially, often surpassing Ethereum mainnet, hailed as a leader in L2.

After the airdrop announcement and snapshot confirmation, network activity on zkSync Era collapsed immediately. The 7-day average active addresses dropped from a peak of 455,000 at the end of February 2024 to 218,000 in June—a 52% decline. Daily transaction count plummeted from 1.75 million to 512,000. Notably, this decline occurred before token distribution.

Nansen data shows that nearly 40% of the top 10,000 wallets that received the airdrop sold all their tokens within 24 hours. Only about 25% chose to hold.

This activity drop before distribution confirms that the previous boom was entirely driven by external incentives. Once the “snapshot” was deemed complete by studios, they immediately stopped scripts. The data decline is superficial; the real truth is in the collapse of the “ecosystem prosperity” narrative.

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

Cross-chain interoperability protocol LayerZero attempted a radical measure against studios: launching a “self-reporting” mechanism. Projects could submit a transaction: admit to being a witch and keep 15% of the airdrop; hide and be caught, and get nothing.

LayerZero ultimately identified and marked over 800,000 addresses as potential witch attackers. This strategy caused a huge rift in the community. Critics argue that labeling users of tools like Merkly as witches is unfair, as LayerZero previously benefited from cross-chain fees and transaction data generated by these users.

Although this “clean-up” redistributed tokens to “long-term users,” LayerZero’s price still fell 23% within a week after listing. More seriously, the “Witch Hunter Bounty” plan led to community members reporting on each other, creating a toxic surveillance and confrontation atmosphere that severely damaged the project’s reputation.

$ZRO 4. The Phenomenon of Bad Money Driving Out Good in Digital Assets

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

4.1 Several Methods of Displacement

Reward Dilution: Airdrops are often zero-sum games. Projects allocate a fixed percentage (e.g., 10%) of tokens to the community. If a studio controls 10,000 wallets, it takes a huge slice from the reward pool, greatly diluting the share of genuine users with only one wallet. When real users see that a year of normal use yields negligible rewards, their willingness to participate plummets.

Network Congestion and Fee Spikes: Industrial-scale volume grinding consumes precious block space. During peak periods (e.g., Linea Voyage or Arbitrum Odyssey), gas fees surge. Genuine users, unable to afford high transaction costs, are forced to migrate to other chains or stop using. The network ends up dominated by bots—since bots can amortize high airdrop returns against high gas costs, while real users’ utility no longer justifies the expense.

Complex Mechanisms: Some TGE projects intentionally design extremely complex interaction tasks to block bots, but the complexity itself deters natural users, leaving only tireless bots capable of completing them. Interestingly, some commentaries suggest that by 2025, the Perp DEX wars have evolved into script wars.

4.2 “Noise Floor” and Signal Loss

The proliferation of studios raises the entire ecosystem’s Noise Floor. With 80%-90% of traffic being inorganic, project teams cannot accurately gauge true product-market fit.

In such high-volume data pollution and toxic transaction environments, traditional A/B testing, user feedback loops, and adoption metrics become completely invalid. Ultimately, project teams optimize UI/UX based on script preferences (e.g., reducing clicks for easier script automation rather than human usability).

The market falls into a “Lemons Market” dilemma. High-quality projects with “quiet” data are undervalued; meanwhile, low-quality projects that actively grind volume and appear “hot” receive funding and attention. This leads to the decline of overall market quality as high-quality projects exit or collude.

4.3 Project Teams’ “Enchantment” and Collusion

Under the influence of the macro environment and exchange tacit approval, some project teams become “enamored” with superficial data. Beautiful data is the only credential they can show to investors and the public. Admitting that 90% of users are fake would cause valuation collapse, possibly preventing listing and risking lawsuits.

Thus, project teams fall into a “performative ignorance”: they implement seemingly strict anti-witch measures (e.g., banning low-level scripts) but deliberately leave “backdoors” for advanced studios. Layer3’s co-founder even openly admits that some projects prefer not to enforce strict bot filtering because they are optimizing metrics that support narratives and fundraising.

This collusion completes a closed loop—project teams sell fake data to VCs/exchanges; studios provide fake data to project teams; VCs/exchanges package and sell projects to retail investors.

5. Conclusion

The industry today resembles an athlete overdosed on stimulants (fake data): muscles (TVL, user count) swell in the short term, but internal organs (real revenue, community consensus) are failing.

It was once a path to change the world in a cyberpunk style, but the crypto ecosystem has degenerated into a Performative Economy, where project teams pay or sign options with studios to “produce” data that meets the arbitrary standards set by exchanges and VCs.

It’s not that studios are doing wrong or poorly—after all, it’s business. Demand creates supply. But when the entire market is flooded with studios and incentive-driven traffic, the situation changes.

This “project-VC-exchange-studio”利益闭环 is a classic negative-sum game. It sustains short-term superficial prosperity by consuming the industry’s credit reserves. To break this vicious cycle, the industry must undergo a painful “deleveraging” process.

For project teams, chasing exchange listing qualifications has replaced exploring product-market fit (PMF). Projects are designed to be “grinded” rather than “used.” Moreover, hundreds of billions of dollars in token incentives—originally meant to bootstrap genuine communities—are siphoned off by professional extraction machines and arbitrage, ultimately abandoned.

This is not just bad money driving out good, but falsehood displacing truth. Unless the industry shifts 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.

Studio victories in airdrops may win battles, but their success could cause the entire crypto industry to lose the war for mass adoption.

Perhaps only when “using the product” yields greater benefits than “grinding data” can good money return, and the crypto industry truly emerge from the quagmire of false prosperity into the realm of real technological implementation.

2026, may we become clumsy players in this “data is king” era.