Grass Depth Research Report: A Bright New Star in the DePIN Field, AI Data Bank is Expanding

Key Points

• How can Grass stand out among numerous DePIN projects?

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

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

With the explosive growth of AI data demand, the popularity of Solana and the DePIN market, and reasonable operational strategies, Grass has established itself as a leader in AI data-related DePIN.

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

Short-term: Will the decentralized transformation be successfully completed by 2025?

Mid-term: AI enterprise procurement data scale verification requirements;

Long-term: The compliance game of data privacy and ownership rules.

The current biggest risk lies in the "token frenzy masking the demand vacuum" - if future AI client orders fail to ramp up, the perfect business closed loop may degenerate from a positive cycle of "data-capital" into a supply-side bubble.

1. Industry Background

DePIN integrates global idle resources ( computing power, storage, bandwidth ) through token incentives to build a distributed infrastructure network; at the same time, the AI industry faces a structural shortage of data, monopolization by giants, privacy disputes, and data silos, 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 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 Web3 concepts, various industries are exploring decentralized transformation paths. DePIN is a reflection of this trend in the infrastructure field. DePIN(, short for 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.

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

Industry Drivers

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.

Niche Areas and Typical Cases

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

• Physical Infrastructure: Represented by a certain decentralized wireless network project, a globally covered communication network is built through community deployment of hotspot devices;

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

Market Potential

According to Messari data, by 2024, the number of DePIN devices worldwide has exceeded 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 the improvement of resource efficiency ), such as the utilization of idle bandwidth ( and the demand explosion ), such as the bilateral effect of AI's demand for computing power and data (.

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

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) 1.2 AI Data Demand: Explosive Growth and Structural Contradictions

"数据是新时代的石油###Data is the new oil("

The acquisition and processing of AI data is the core driving force 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 are crucial for the performance of AI models.

Data Demand Scale and Features

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

• Cost Proportion: In AI development, the costs of data collection, cleaning, and labeling account for more than 40% of the total budget, becoming a 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/major entities control extensive data sources, while small and medium developers face high entry barriers and unfair pricing;

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

• Data Privacy: Data collection often involves privacy and copyright disputes, such as a certain social platform API charge event that sparked protests from developers;

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

• Value Chain Disruption: Individual contributors creating data are unable to profit from the subsequent use of the data.

The Breakthrough Path of DePIN

• Distributed Data Collection: Collecting public data ) through a network of nodes such as social media, public databases (, 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 data and improving the generalization ability of AI models.

• Decentralized Cleaning and Annotation: Community collaboration to complete data preprocessing, combined with zero-knowledge proof )ZK( to ensure data authenticity;

• Tokenized Incentive Closed Loop: Data contributors receive token rewards, and demanders purchase 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 has constructed a decentralized data scraping network aimed at providing a more economical, efficient, and reliable data source for AI model training.

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

![Grass Depth Research Report: DePIN Shining Star, Expanding AI Data Bank])https://img-cdn.gateio.im/webp-social/moments-53ff22e3333759cdc38081bea3e4148f.webp(

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 high cost-effective and diverse data sources for AI training. Users only need to install the client to contribute bandwidth and earn token rewards - in just one year online, it has attracted over 2.5 million nodes, and the token has increased over 5 times in the first 10 days of launch, validating its business logic.

The project has received backing from a top-tier capital, leveraging the high-performance Solana chain to achieve data certification 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 through the residential proxy network ), allowing companies to use users' internet connections 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.

• Problems Addressed: Traditional web scraping is typically carried out 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 the data provided by decentralized users inherently possessing diversity, multi-regional publishing, and real-time characteristics.

• 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 rewards mechanism.

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

In summary, the key features and advantages of Grass are: low cost of data extraction 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 Phase: Mid-2022, the project was proposed by Wynd Labs.

Development Phase: The product construction began in early 2023, marking the project's entry into the actual development phase.

Seed Round Financing: In 2023, Grass completed a $3.5 million seed round financing, led by some capital and some capital, totaling $4.5 million ) including the pre-seed round ### led by some capital.

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 over 2 million connected node devices, which are growing rapidly. According to DePIN Scan data, as of March 2025, its active users have surpassed 2.5 million.

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

Exchange Launch: Launched on a certain exchange on October 28, 2024, the price rose steadily from $0.6 to $3.89 in 10 days, increasing approximately 5 times.

Current Status: The project continues to expand, and the second phase of user incentive for挂机 is underway; plans to launch Android and iPhone mobile applications are in place to increase network scale and user participation.

( 2.3 Team Situation

According to Rootdata, Grass was developed by Wynd Labs, and the founder is Andrej Radonjic, who is the CEO of Wynd Labs. He 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 technology, with relevant experience in the field. However, specific member information has not been widely disclosed, and only Radonjic's identity has been revealed.

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

) 2.4 Financing and Key Partners

Investors and Support

Seed Round: Completed a $3.5 million seed round in 2023, led by certain capital firms. According to Rootdata, total financing after the seed round reached $4.5 million, including the pre-seed round led by certain capital.

Series A Financing: Completed Series A financing in September 2024, led by a certain capital, with participation from certain capitals and certain capitals, the amount undisclosed.

Investor Support: Certain Capital, Certain Capital, Certain Capital, Certain Capital, and Certain Capital are all well-known investors in the industry. Gaining their support also demonstrates the project's recognition within the industry.

Partners

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

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

![Grass Depth Research Report: DePIN Shining Star, Expanding AI Data Bank]###https://img-cdn.gateio.im/webp-social/moments-5a5dc433d77affb341f2409a4573ace1.webp###

3.