Grass

Introduction to Grass

Grass transforms unused internet bandwidth into AI training data, creating a network where users earn by sharing their internet connection. The platform scrapes public web data through distributed residential connections, providing AI companies with diverse, geographically distributed training data that would be difficult to obtain otherwise.

The project taps into the growing demand for AI training data while offering everyday users a way to monetize a resource they already have—bandwidth they’re paying for but not fully using. This creates a marketplace matching AI data needs with distributed supply.

How Grass Works

Bandwidth Sharing

User participation:

• Install browser extension
• Share unused bandwidth
• Network accesses web data
• Users earn GRASS tokens

Data Collection

Network function:

• Scrape public websites
• Residential IP diversity
• Geographic distribution
• Clean data output

AI Data Pipeline

Value creation:

• Web data collected
• Processed and cleaned
• Sold to AI companies
• Revenue to network

Technical Specifications

Metric	Value
Network	Solana
Users	Millions registered
Data Type	Public web scraping
Reward	GRASS token

The GRASS Token

Utility

GRASS serves multiple purposes:

• Rewards: Bandwidth sharing payment
• Staking: Network participation
• Governance: Protocol decisions
• Access: Data marketplace

Tokenomics

Distribution:

• User rewards
• Community airdrop
• Ecosystem development
• Team allocation

Airdrop

Token launch:

• Large community distribution
• Based on network participation
• Points conversion
• Retroactive rewards

The AI Data Thesis

Data Demand

Market opportunity:

• AI models need training data
• Diverse data valuable
• Web-scale information
• Growing demand

Residential Advantage

Why distributed:

• Diverse IP addresses
• Geographic spread
• Anti-blocking benefits
• Data quality

Market Size

Business potential:

• AI market massive
• Data costs significant
• B2B revenue opportunity
• Growing market

User Experience

Browser Extension

Participation method:

• Chrome extension
• Background operation
• Minimal resource use
• Automatic earning

Earning Mechanics

Reward system:

• Bandwidth contribution
• Uptime requirements
• Quality factors
• Token distribution

Desktop App

Enhanced participation:

• More bandwidth share
• Higher rewards
• Always-on capability
• Deeper integration

Data Products

AI Training Data

Core offering:

• Web scraping at scale
• Cleaned datasets
• Diverse sources
• AI-ready format

Structured Data

Enhanced products:

• Organized information
• Domain-specific data
• Custom collection
• Premium pricing

Competition and Positioning

vs. Other Data Networks

Network	Focus	Method
Grass	AI training data	Bandwidth sharing
Ocean	Data marketplace	Token economy
Streamr	Data streaming	Real-time data

Grass Differentiation

Key advantages:

• Simple user experience
• Residential IP network
• AI market focus
• Scale achieved

DePIN Positioning

Infrastructure Category

Network type:

• Physical resource (bandwidth)
• Token incentives
• Decentralized operation
• Real utility

vs. Other DePIN

Comparison:

• Simpler than hardware DePIN
• Lower barrier to entry
• Browser-based
• Mass participation

Revenue Model

B2B Sales

Business model:

• Sell data to AI companies
• Subscription models
• Custom data services
• Enterprise contracts

User Payments

Participant economics:

• GRASS token rewards
• Proportional to contribution
• Quality bonuses
• Consistency rewards

Challenges and Risks

Privacy Concerns

User questions:

• What traffic flows through?
• Data usage transparency
• Security implications
• Trust requirements

Regulatory Uncertainty

Legal considerations:

• Web scraping legality
• Terms of service issues
• Jurisdictional questions
• Evolving regulations

Competition

Market dynamics:

• Other data networks
• Traditional data providers
• AI company capabilities
• Market share

Sustainability

Long-term questions:

• Revenue vs. rewards
• Token economics
• User retention
• Market demand

Security Model

User Protection

Safety measures:

• Traffic encryption
• Limited access scope
• Privacy controls
• Transparency claims

Network Security

Infrastructure protection:

• Distributed architecture
• Sybil resistance
• Quality verification
• Abuse prevention

Recent Developments

Token Launch

GRASS distribution:

• Airdrop execution
• Exchange listings
• Trading activity
• Community reaction

Network Growth

Expansion:

• User acquisition
• Data volume
• Partnership announcements
• Product development

Future Roadmap

Development priorities:

• Data Products: Enhanced offerings
• Enterprise: B2B growth
• Network: Scale expansion
• Features: New capabilities
• Ecosystem: Partner integration

Conclusion

Grass created a clever arbitrage: AI companies need diverse web data, and millions of users have unused bandwidth. By connecting these markets, the network generates value from a previously untapped resource.

The simplicity of participation—just install an extension—enables mass adoption that more complex DePIN projects struggle to achieve. However, questions about data usage, privacy implications, and long-term economics deserve consideration.

For users curious about earning from unused bandwidth and for those interested in the AI data market, Grass offers accessible participation—though understanding what you’re sharing and trusting the network’s claims is essential.