Grass Depth Analysis: The Bright New Star in the DePIN Field

Grass is a project that has recently garnered significant attention in the DePIN field, innovatively applying the DePIN model to the AI data collection sector. This article will conduct an in-depth analysis of Grass from multiple angles, exploring its innovations, development potential, and the challenges it faces.

1. Industry Background

When the democratization of DePIN’s computing power meets the data dilemma of AI, a silent data equity movement erupts.

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

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

The disruption of Grass lies in the realization of the integration of these two.

1.1 DePIN: Reconstructing the Global Paradigm of Infrastructure

DePIN( is a decentralized physical infrastructure network), which is a new economic model that integrates globally dispersed physical resources( such as computing power, storage, bandwidth, and energy) through blockchain technology.

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

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

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 ), achieving supply-demand matching and incentive mechanisms through blockchain technology.

• Physical infrastructure: Taking a certain wireless network project as a representative, build a globally covered communication network through community deployment of hotspot devices;

• Digital Resource Network: includes a certain decentralized storage project, a certain distributed computing project, etc., which forms a sharing economy model by integrating idle resources.

According to Messari data, as of 2024, the number of DePIN devices worldwide has surpassed 13 million, with a market size of $50 billion, but the penetration rate is less than 0.1%. It is expected to grow 100-1000 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 improving resource efficiency (, such as utilizing idle bandwidth ) and the explosion of demand (, like the demand for computing power and data by AI ), creating a bilateral effect.

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 oil of the new era”

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

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

Data demand scale and characteristics:

• Leap in Scale: 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 share: The data collection, cleaning, and labeling costs in AI development account for more than 40% of the total budget, becoming a core bottleneck for commercialization.

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

Pain points of traditional data supply:

• Data barriers: Core enterprises/major players control extensive data sources, and small to medium developers face high thresholds and unfair pricing;

• Data silos: Data is often scattered among different institutions and enterprises, and data sharing and circulation face numerous obstacles, resulting in inadequate utilization of data resources.

• Data Privacy: Data collection often involves privacy and copyright disputes, such as the incident where API charges from a social media platform sparked protests from developers.

• Inefficient circulation: Data silos and lack of standardization lead to redundant collection, with a global data utilization rate of less than 20%;

• Value chain disruption: individual contributors who create data cannot profit from the subsequent use of that data.

The breakthrough path of DePIN:

• Distributed Data Collection: Capture publicly available data ( through a network of nodes, such as social media and public databases ), to reduce the cost of data collection and improve the efficiency and scale of data collection;

• Improve data quality and diversity: The DePIN incentive mechanism can attract more participants to contribute data, thereby enhancing the quality and diversity of 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, and demand-side purchases structured datasets with tokens, forming a direct match of supply and demand.

The Grass project is located at the intersection of DePIN and the AI data industry, innovatively applying the DePIN concept to the field of AI data collection, and building 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 deep analysis of the specific mechanisms, technical characteristics, application scenarios, and future development prospects of the Grass project.

2. Basic Project Information

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

Grass builds a decentralized data scraping network through the DePIN architecture, providing cost-effective and highly 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’s initial launch increasing over 5 times in the first 10 days, validating its business logic.

The project has received backing from top-tier capital such as Polychain and Hack VC, relying on the high-performance Solana chain to achieve data ownership and circulation.

The current team’s anonymity is still controversial, 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, specifically supporting the development of artificial intelligence )AI###.

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

• Problems 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, with data provided by decentralized users naturally characterized by diversity, multi-regional publication, and real-time features.

• 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 way. Its mission is to empower users to contribute to the data layer and incentivize participation through a reward mechanism.

• User participation method: Users can start 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 retrieval in a decentralized network, richer data diversity; users earn rewards by contributing bandwidth, achieving data value recovery; using blockchain technology to verify data, ensuring data transparency and reliability.

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) 2.2 Development History

Concept Phase: 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 a pre-seed round led by No Limit Holdings ).

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

Milestone: In April 2024, the project announced that it has surpassed 2 million connected node devices and is experiencing rapid growth. According to DePIN Scan data, 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 (, rewarding early users.

Launched on the exchange: On October 28, 2024, launched on a certain trading platform and other exchanges, the price rose steadily from $0.6 to $3.89 in 10 days, approximately 5 times.

Current status: The project continues to expand and is in the second phase of user incentive for staking; plans to launch Android and iPhone mobile applications to increase network scale and user participation.

) Team Situation

According to Rootdata, 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.

The team members are all from Wynd Labs, focusing on the development of blockchain and AI technologies, with relevant experience in the field. However, specific member information has not been widely disclosed, with only Radonjic’s identity being revealed.

According to Tracxn, Wynd Labs was founded in 2022, and its core product is Grass.

The team’s background demonstrates expertise in the blockchain and AI fields, but a 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 Key Partners

Investors and Support

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

Series A Financing: The Series A financing was completed in September 2024, led by HackVC, with participation from Polychain, Delphi, Lattice, and Brevan Howard. The amount has not been disclosed.

Investor Support: HackVC, Polychain, Delphi, Lattice, and Brevan Howard are all well-known investors in the industry. Gaining their support also demonstrates the project’s recognition within the industry.

Partner

Blockchain platform: Built on the Solana network, the project leverages Solana’s high performance and scalability.

Currently, there is no specific mention of collaboration with AI companies or other projects, but the ecosystem of the Solana network may provide opportunities for future collaborations.

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3. Project Technical Analysis

Grass aims to redistribute the value of data from big tech companies to ordinary users.

The node network, ZKP processing innovation, and data ledger in the Grass technical architecture form a closed-loop workflow, fully supporting its decentralized vision from collection, verification to delivery in a decentralized manner.

However, the current centralized operations need to be addressed, and it is still necessary to track whether the technical implementation can be smoothly executed.

3.1 Core Technology Architecture: Sovereign Data Rollup

Grass is building the first sovereign data aggregator. It simplifies data procurement and transformation through a globally distributed network of Grass nodes, enabling AI universal structured web data access. The infrastructure is supported by a dedicated data Rollup on Solana, designed to manage the complete lifecycle of data - sourcing, processing, validation, and dataset construction. The architecture revolves around the following components:

Deconstructing the technical architecture of Grass.