What is Grass?

Grass is a decentralized protocol that enables individuals to earn rewards by contributing their unused internet bandwidth. Through a lightweight browser extension or a future desktop application, users share bandwidth with the network, which is then used to collect publicly available web data. This data is structured and made useful for training artificial intelligence (AI) models, with a focus on large language models and similar technologies that require vast datasets. Unlike traditional data aggregators that rely on centralized infrastructure, Grass distributes this task across a global network of participants, thereby lowering costs and increasing accessibility. Contributors receive Grass Points in exchange for their participation, which can be converted into GRASS tokens—creating a clear incentive mechanism for everyday users to take part in the protocol.

The Grass protocol operates on the Solana blockchain, which supports high-speed, low-cost transactions and is well-suited for scalable applications. This choice of infrastructure allows Grass to handle a large number of contributors without bottlenecks or cost inefficiencies. To maintain trust and security, Grass uses zero-knowledge proofs—cryptographic techniques that confirm data integrity and contribution validity without exposing personal user information. This design protects the privacy of contributors while ensuring that only genuine, valuable bandwidth contributions are rewarded. As a result, Grass builds a privacy-respecting system that avoids the surveillance or intrusive data collection often seen in centralized web scraping operations.

One of the protocol’s core innovations lies in how it transforms passive internet connections into productive infrastructure. Most users pay for more bandwidth than they actually use, particularly during idle hours. Grass taps into this latent capacity and allows it to be redirected for a specific purpose—harvesting structured web data from the public internet. The network prioritizes data sources that are widely available and useful for AI training, with an emphasis on content-rich pages, APIs, and media files that require constant updating. This model provides value on both sides: users earn rewards for bandwidth they weren’t using, and developers access fresh, organized data without needing their own scraping infrastructure.

Beyond individual users, Grass is also designed to integrate with routers, mobile apps, and potentially hardware devices in the future. These integrations are intended to extend availability and ease of use, making bandwidth sharing more seamless and accessible across a wider range of devices. In doing so, the network becomes more resilient and continuous, as users don’t need to manually activate participation—they can simply plug in and stay connected, contributing passively around the clock. The team is exploring ways to enable this through partnerships with telecom providers and hardware manufacturers.

History of Grass

The idea for Grass emerged from the increasing demand for high-quality training data in the field of artificial intelligence. AI models, particularly large language models, require immense volumes of structured, real-world data to perform effectively. Traditional data providers often rely on centralized scraping infrastructure or pre-licensed datasets, which can be expensive, difficult to update, and restricted by licensing terms. Wynd Labs, the team behind Grass, recognized this limitation and aimed to create a decentralized protocol that could collect, organize, and deliver this data at scale using the bandwidth contributions of individuals around the world. The solution was to reward users directly for participating in the data collection process through tokenized incentives.

Grass launched publicly in mid-2023, starting with a small-scale browser extension that allowed users to contribute their internet bandwidth. The onboarding process was straightforward, which helped the project attract early adopters who were already familiar with bandwidth-sharing platforms like Honeygain. Unlike those platforms, however, Grass connected its economic incentives to blockchain-based tokens, offering a native crypto reward system through Grass Points. This initial rollout focused on gathering user feedback, refining backend infrastructure, and preparing for larger-scale distribution.

A major milestone came in December 2023, when Grass completed a $3.5 million seed funding round led by well-known venture capital firms Polychain Capital and Tribe Capital. Other participants included Bitscale, Big Brain Holdings, Mozaik Capital, Advisors Anonymous, and Typhon V. The round gave the team the resources needed to scale its infrastructure, improve reward systems, and expand global outreach. The funding also supported the development of advanced validation tools, deeper integrations with AI labs, and efforts to improve reward transparency across the network.

As of early 2024, Grass reported over 3 million users worldwide. This rapid adoption was driven in part by the clear value proposition—users could earn tokens simply by running a lightweight extension and sharing bandwidth. The project gained additional attention within the Solana ecosystem, where Grass positioned itself as a leading DePIN application utilizing the chain’s high-performance architecture. The ease of participation and strong community engagement helped the project quickly build momentum and begin planning for its mainnet token launch.

Grass has also focused heavily on partnerships to increase both adoption and utility. Integrations with Solana Mobile and other consumer-facing hardware platforms aim to bring Grass into devices by default, making it easier for users to contribute without added effort. On the demand side, partnerships with AI data companies like Alignment Lab AI help establish real-world use cases for the data collected, ensuring that the network isn’t just a passive data sink, but a functional pipeline that feeds valuable content into AI model pipelines.

Team and Founders of Grass

Grass was founded by Andrej Radonjic, who also leads Wynd Labs, the company behind the protocol. Radonjic has an academic background in engineering physics and mathematics, having studied at McMaster University and York University. His technical foundation, paired with experience in web scraping and data infrastructure, positioned him to understand the limitations of traditional data pipelines and the inefficiencies they introduce in the AI development process. Rather than continue operating within centralized frameworks, Radonjic set out to build a model where ordinary internet users could collectively form the backend of a scalable, decentralized data collection layer. This approach formed the foundation of what would become the Grass protocol.

Unlike many crypto startups that immediately spotlight a full executive board, Grass has kept most of its development team anonymous. The reasoning behind this decision lies in the project’s commitment to decentralization and in letting the protocol speak for itself. Despite the anonymity of the broader team, their engineering capabilities have been made evident through the platform’s smooth scaling, its early ability to manage millions of participants, and the deployment of smart contracts and backend infrastructure that meet both security and usability standards. The consistent updates and responsiveness to community feedback also suggest an agile development cycle, with product management and engineering tightly aligned.

While Radonjic is the public face of the project, he often uses community interviews and forums to answer questions and set expectations. His approach has been measured and data-driven, emphasizing that Grass is not just a passive income tool, but part of a broader thesis: that the AI industry’s demand for data is growing faster than traditional infrastructure can supply it. In interviews, Radonjic has also acknowledged the privacy and ethical challenges of AI data gathering, reiterating Grass’s use of zero-knowledge proofs and on-chain validation to ensure ethical participation.

Wynd Labs, the parent company, plays a critical role in maintaining the balance between fast-paced product development and long-term ecosystem design. The lab’s work has extended into partner negotiations, tokenomics design, and exploratory research into the application of Grass data across AI use cases. The company’s internal documentation and Gitbook provide regular technical updates, which have become an important resource for those following the project’s progress or building on top of its infrastructure.

Investors and Backers of Grass

In December 2023, Grass secured a $3.5 million seed round to fund the development and expansion of its decentralized bandwidth-sharing protocol. The round was led by two of the most active and reputable venture firms in the Web3 ecosystem—Polychain Capital and Tribe Capital. These firms have previously backed infrastructure and protocol-layer projects, often placing bets on early-stage teams building foundational tools for decentralized systems. Their involvement with Grass suggested early validation of the project’s technical model, scalability prospects, and potential to capture value in both the crypto and AI markets.

The seed round also included participation from Bitscale Capital, Mozaik Capital, Advisors Anonymous, Typhon V, and Big Brain Holdings. This mix of generalist and crypto-native investors gave Grass access to capital as well as strategic networks across the Solana ecosystem, data infrastructure, and Web3-native distribution. These investors brought more than funding—they also supported the team with advice on token strategy, go-to-market alignment, and community engagement. The diversity of backers also reflects a shared conviction that the future of AI tooling will be tied to decentralized protocols, particularly in areas like data collection and compute access.

Earlier in 2023, Grass conducted a smaller pre-seed round led by No Limit Holdings. This initial capital injection helped the team build out the MVP, test the early extension with a small user group, and prepare documentation for both users and potential partners. The funds were used to support engineering sprints, backend validation infrastructure, and the early formation of the rewards model. By the time the seed round closed, Grass had already demonstrated product-market fit, onboarding hundreds of thousands of users who were actively contributing bandwidth and earning Grass Points.

The funds raised across both rounds, totaling roughly $4.5 million, have been allocated toward infrastructure development, cross-platform integrations, and user support systems. A portion of the funds also supported the airdrop and distribution mechanisms, which were a major driver of user participation and community growth during the protocol’s initial expansion phase. The team emphasized a fair and wide-reaching airdrop model that rewarded early contributors without concentrating token supply in the hands of a few insiders.

Grass’s ability to secure funding during a time of general investor caution in the crypto market further reinforced confidence in its long-term vision. While many projects struggled to raise capital post-2022, Grass attracted tier-one backers by anchoring itself in a real and growing demand: the need for structured, updated data pipelines for AI systems.

Roadmap of Grass

The Grass roadmap is structured around three primary objectives: improving infrastructure reliability, expanding user accessibility, and integrating deeper into AI data pipelines. Each of these goals addresses a core limitation in existing decentralized data networks while preparing the protocol for long-term scale and adoption. From a technical standpoint, the team has prioritized replacing temporary or manual processes—such as Chrome extensions—with more stable, high-performance infrastructure like standalone desktop applications and dedicated hardware devices. These improvements are designed to eliminate friction for contributors and ensure that participation does not require technical knowledge or frequent maintenance.

One of the most significant upgrades in the roadmap is the development of dedicated hardware, such as plug-and-play routers, that allow users to contribute bandwidth passively. Unlike browser-based extensions, which only operate while a device is online and running, these routers could function continuously without user intervention. This development aims to increase average uptime across the network, improve data collection consistency, and attract a wider range of users who prefer set-it-and-forget-it systems. By expanding the types of devices that can serve as nodes, Grass also improves its resilience, reducing dependency on any single participation method.

In parallel, the roadmap outlines a shift toward supporting more complex forms of data validation and categorization. The team is building tools to automatically verify the quality, freshness, and relevance of the data collected through the network. These validation layers are critical for Grass to serve AI labs and data companies that require structured inputs for training models. Grass is not just aggregating raw data—it’s attempting to organize it in a way that makes it useful for fine-tuning and inference tasks in AI systems. By introducing mechanisms for node scoring, automated content classification, and metadata tagging, the protocol adds a layer of reliability that many Web2 scrapers currently lack.

Another area of focus is semantic and multimodal search integration. Grass is developing features that will enable AI systems to search across the collected dataset using semantic understanding—finding contextually relevant results across text, image, and video data. This feature will allow the network to support the growing class of multimodal AI models, which need mixed-format data to train effectively. If successful, this expansion would move Grass beyond traditional scraping use cases and into the territory of AI-native infrastructure. It would also provide a unique value proposition for data consumers looking for timely, pre-labeled, and queryable content.

Grass also plans to broaden distribution across mobile platforms, especially as part of its collaboration with Solana Mobile and related hardware partners. By bundling Grass with mobile operating systems or integrating it into default phone configurations, the team hopes to tap into a previously underutilized bandwidth source: smartphones. Mobile data contributions could supplement desktop and router-based sharing, particularly in regions with high cellular coverage but limited fixed broadband. These integrations also simplify onboarding, as users could begin contributing the moment they activate their phone, without needing to install extra software.

Highlights

• Grass allows users to earn by sharing unused internet bandwidth, contributing to the collection of public web data used for AI model training.
• Founded by Andrej Radonjic and developed by Wynd Labs, Grass focuses on building decentralized infrastructure for structured data sourcing at scale.
• The project raised $4.5 million across pre-seed and seed rounds, with backing from Polychain Capital, Tribe Capital, and other leading Web3 investors.
• Over 3 million users have joined the network since its launch in 2023, participating via a browser extension and, soon, other devices like routers and mobile apps.
• The roadmap includes plans for dedicated hardware, advanced data validation, and integrations with AI systems, ensuring Grass becomes a reliable data layer for decentralized AI pipelines.