Grass Depth Research Report: A Rising Star in the DePIN Ecosystem, The Emergence of Artificial Intelligence Data Banks

Key Points Overview

• How does Grass stand out among numerous DePIN projects?

The core factor is zero-threshold participation, users are the cornerstone, and other factors are all leverage.

Grass breaks through the DePIN industry competition through the "technology + model" dual-drive - utilizing zero-knowledge proofs and the Solana Layer2 architecture to ensure data authenticity, addressing the "dirty data" pain point in the AI industry; at the same time, adopting the "bandwidth mining → points incentive" model to convert 2.5 million users into data nodes, forming a supply-side crushing advantage.

The surge in AI data demand, the popularity of Solana and DePIN, and reasonable operational strategies have shaped the leading position of AI data DePIN.

• What key factors should be considered for the future development of Grass?

Short-term outlook on technological implementation: Can the decentralized transformation be successfully completed by 2025?

Medium-term demand validation: AI enterprise procurement data scale;

Long-term view of compliance games: data privacy and ownership rules.

The biggest risk at present is that "the token frenzy masks a demand vacuum" - if future AI client orders do not ramp up, the perfect business closed loop may degenerate from a positive cycle of "data-capital" into a supply-side bubble.

1. Industry Background

When the democratization of computing power in DePIN encounters the data dilemma of AI, a silent data equity movement quietly erupts.

DePIN integrates global idle resources (computing power, storage, bandwidth) through token economics to build a distributed infrastructure network; at the same time, the AI industry faces a structural shortage of data, with monopolies, privacy disputes, and island barriers leading to 80% of data value being unreleased.

The future competition in AI is essentially a dual game of data acquisition efficiency and ethical compliance, and DePIN provides the optimal technical solution.

The disruptive nature of Grass lies in the realization of the integration of these two.

1.1 DePIN: A Global Paradigm for Reconstructing Infrastructure

Definition and Core Logic

In recent years, with the maturity of blockchain technology and the rise of the Web3 concept, various industries are exploring decentralized transformation paths. DePIN is a manifestation of this trend in the infrastructure sector. DePIN (full name Decentralized Physical Infrastructure Networks) is a new economic model that integrates globally distributed physical resources (such as computing power, storage, bandwidth, energy, etc.) through blockchain technology.

The core logic lies in: driving community contributions of idle resources through token incentives, building a decentralized infrastructure network to replace the high-cost, low-efficiency model of traditional centralized service providers.

Industry Drivers

Compared to centralized models, the decentralized transformation of physical infrastructure has greater advantages in terms of cost structure, governance models, network resilience, and ecological scalability.

Subfields and Typical Cases

According to Messari's definition, DePIN encompasses two major categories: physical infrastructure (such as wireless networks and energy networks) and digital resource networks (such as storage and computing), and it achieves supply-demand matching and incentive mechanisms through blockchain technology.

• Physical infrastructure: represented by Helium (decentralized wireless network), building a globally covered communication network through the deployment of hotspot devices by the community;

• Digital Resource Network: including Filecoin (decentralized storage), Aethir (distributed computing), etc., forming a shared economy model by integrating idle resources.

Market Potential

According to Messari data, by 2024, the number of DePIN devices worldwide has surpassed 13 million, with a market size reaching $50 billion, but the penetration rate is less than 0.1%. It is expected to grow by 100 to 1000 times in the next decade.

In 2024, the total market value of the DePIN track will reach 50 billion USD, covering more than 350 projects, with an annual growth rate exceeding 35%.

Its core driving force lies in the bilateral effects of resource efficiency improvement (such as the utilization of idle bandwidth) and demand explosion (such as AI's demand for computing power and data).

Of course, the scalability, data privacy, and security verification of decentralized networks remain key challenges in the development of DePIN.

1.2 AI Data Demand: Explosive Growth and Structural Contradictions

"Data is the new oil"

The acquisition and processing of AI data are the core driving forces behind the development of artificial intelligence, especially when training large language models (such as GPT) and generative neural networks (such as MidJourney).

The performance and effectiveness of AI models largely depend on the quality and quantity of the training data. High-quality, diverse, and geographically representative data is crucial for the performance of AI models.

Data Demand Scale and Characteristics

• Magnitude leap: Taking GPT-4 as an example, training requires over 45TB of text data, while the iteration speed of generative AI demands real-time updates and diversification of data;

• Cost proportion: The data collection, cleaning, and labeling costs in AI development account for more than 40% of the total budget, becoming the core bottleneck for commercialization;

• Scenario differentiation: Autonomous driving requires high-precision sensor data, medical AI relies on privacy-compliant case databases, and social AI depends on user behavior data.

Traditional Data Supply Pain Points

• Data Barriers: Core enterprises/industry giants control extensive data sources, while small and medium developers face high thresholds and unfair pricing;

• Data Silos: Data is often scattered across different institutions and enterprises, facing numerous obstacles to data sharing and circulation, which leads to the underutilization of data resources.

• Data Privacy: Data collection often involves privacy and copyright disputes;

• Inefficient circulation: Data silos and lack of standardization lead to duplicate collection, with global data utilization rate below 20%;

• Value chain disruption: individual contributors who create data are unable to profit from the subsequent use of that data.

The Breakthrough Path of DePIN

• Distributed data collection: Capture public data (such as social media, public databases) through a node network, reducing the cost of data collection and improving the efficiency and scale of data collection.

• Improve data quality and diversity: Through the DePIN incentive mechanism, more participants can be attracted to contribute data, thereby enhancing the quality and diversity of the data and improving the generalization ability of AI models.

• Decentralized cleaning and labeling: Community collaboration completes data preprocessing, combined with zero-knowledge proofs (ZK) to ensure data authenticity;

• Tokenized incentive closed loop: Data contributors receive token rewards, while demanders purchase structured datasets with tokens, forming a direct match between supply and demand.

The Grass project is positioned at the intersection of DePIN and the AI data industry, innovatively applying the DePIN concept to the field of AI data collection, and has built a decentralized data scraping network aimed at providing a more economical, efficient, and reliable source of data for AI model training.

In the following chapters, we will conduct a detailed analysis of the specific mechanisms, technical features, application scenarios, and future development prospects of the Grass project.

2. Project Basic Information

The rapid expansion of Grass is inseparable from its extremely low entry barrier. It allows every user to become a "miner" of AI data, exchanging idle bandwidth for future dividends.

Grass builds a decentralized data fetching network through the DePIN architecture, providing cost-effective and diverse data sources for AI training. Users only need to install the client to contribute bandwidth and earn token rewards - attracting over 2.5 million nodes in its first year, with the token increasing more than 5 times in the first 10 days of its launch, validating its business logic.

The project has secured investment from top-tier capital such as Polychain and Hack VC, leveraging the high-performance Solana chain to achieve data verification and circulation.

The current anonymity of the team is still under dispute, and the progress of decentralized data processing needs to be followed up.

2.1 Scope of Business

Grass is a DePIN project that collects and verifies internet data through the unused bandwidth of user devices, providing support specifically for artificial intelligence (AI) development.

Its core is to allow companies to use users' internet connections through a residential proxy network to access and scrape internet data from different geographical locations, which is very useful for AI model training that requires diverse and geographically representative data.

• Problem addressed: Traditional web scraping is typically done by centralized systems, which are inefficient and prone to errors or biases. Grass aims to provide reliable, verified internet data through a decentralized approach, and the data provided by decentralized users inherently possesses characteristics of diversity, multi-regional distribution, and real-time availability.

• Vision and Mission: Grass's vision is to create a decentralized internet data layer where data is collected, verified, and structured in a trust-minimized manner. Its mission is to empower users to contribute to the data layer and incentivize participation through a reward mechanism.

• How to participate: Users can get started in just three steps: visit the Grass official website, install the extension/client, connect, and start earning Grass Points. This way of contributing bandwidth to earn rewards provides ordinary users with an opportunity to share in the AI growth dividend.

In summary, the key features and advantages of Grass are: low cost of data acquisition in a decentralized network, richer data diversity; users earn rewards by contributing bandwidth, realizing the return of data value; using blockchain technology to verify data, ensuring the transparency and reliability of the data.

2.2 Development History

Concept Stage: In mid-2022, the project was proposed by Wynd Labs.

Development Stage: Product construction began in early 2023, marking the project's entry into the actual development stage.

Seed Round Financing: In 2023, Grass completed a $3.5 million seed round financing, led by Polychain Capital and Tribe Capital, totaling $4.5 million (including the pre-seed round led by No Limit Holdings).

User Testing: By the end of 2023, launch the Chrome browser extension, begin user testing, and attract early users to participate.

Milestone: In April 2024, the project announced that it had surpassed 2 million connected node devices and is experiencing rapid growth. According to DePIN Scan, as of March 2025, its active users have exceeded 2.5 million.

First Airdrop: The first airdrop will be announced on October 21, 2024, distributing 100 million GRASS tokens (10% of the total supply) as rewards for early users.

Listing on exchanges: On October 28, 2024, it will be listed on a certain trading platform and other exchanges. The price increased steadily from $0.6 to $3.89 within 10 days, approximately 5 times.

Current status: The project continues to expand, and the second phase of user挂机 incentives is underway; plans to launch Android and iPhone mobile applications to increase network scale and user participation.

2.3 Team Situation

According to data from the data platform, Grass was developed by Wynd Labs, founded by Andrej Radonjic, who is the CEO of Wynd Labs and holds a master's degree in Mathematics and Statistics from York University and a bachelor's degree in Engineering Physics from McMaster University.

Team members are all from Wynd Labs, focusing on blockchain and AI technology development, with relevant experience in the field. However, specific member information has not been widely disclosed, only Radonjic's identity has been revealed.

According to the data platform, Wynd Labs was established in 2022, and its core product is Grass.

The team's background shows professional capabilities in the blockchain and AI fields, but the lack of information transparency may affect the trust of investors and users. Radonjic's experience lends credibility to the project, but the anonymity of other members may raise concerns.

2.4 Financing and Important Partners

Investors and Support

Seed Round: Completed a $3.5 million seed round financing in 2023, led by Polychain Capital and Tribe Capital. According to the data platform, total financing after the seed round reached $4.5 million, including the pre-seed round led by No Limit Holdings.

Series A Financing: Completed Series A financing in September 2024, led by HackVC, with participation from Polychain, Delphi, Lattice, and Brevan Howard, amount undisclosed.

Investor Support: HackVC, Poly