Subnet Directory

With the amount of new subnets being added it can be hard to get up to date information across all subnets, so data may be slightly out of date from time to time

Subnet 42

Gopher

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ABOUT

What exactly does it do?

Bittensor Subnet 42 – “Gopher” is a specialized decentralized real-time data pipeline for AI applications. It replaced the previous “Real-Time Data” subnet on Bittensor (formerly run by Masa) but is an independent project built from the ground up for high-performance data delivery. Gopher’s subnet continuously scrapes, structures, and streams public web data in real-time, providing AI developers with high-quality, reliable, and verifiable data from multiple sources. In essence, it eliminates the need for expensive, rate-limited centralized APIs by offering an enterprise-grade data acquisition and storage infrastructure focused initially on social media (especially X/Twitter) and web content.

Practically, Gopher SN42 gathers data from platforms like X (Twitter), websites, and even multimedia sources (e.g. TikTok transcripts), converts them into structured JSON or embeddings, and makes them accessible via a simple API and dashboard. This real-time data feed can be used to train or augment AI models (for example, providing up-to-date context for LLMs or powering AI agents). The subnet has already processed hundreds of millions of data records for various AI apps and protocols, demonstrating its capacity. Importantly, Trusted Execution Environments (TEE) are used in the network’s nodes, meaning data is collected and validated within secure hardware enclaves – ensuring the data’s provenance is cryptographically verifiable and tamper-proof. In short, Gopher SN42 provides a permissionless, real-time data firehose for AI, delivering fresh and trustworthy information to models and developers on demand.

 

Bittensor Subnet 42 – “Gopher” is a specialized decentralized real-time data pipeline for AI applications. It replaced the previous “Real-Time Data” subnet on Bittensor (formerly run by Masa) but is an independent project built from the ground up for high-performance data delivery. Gopher’s subnet continuously scrapes, structures, and streams public web data in real-time, providing AI developers with high-quality, reliable, and verifiable data from multiple sources. In essence, it eliminates the need for expensive, rate-limited centralized APIs by offering an enterprise-grade data acquisition and storage infrastructure focused initially on social media (especially X/Twitter) and web content.

Practically, Gopher SN42 gathers data from platforms like X (Twitter), websites, and even multimedia sources (e.g. TikTok transcripts), converts them into structured JSON or embeddings, and makes them accessible via a simple API and dashboard. This real-time data feed can be used to train or augment AI models (for example, providing up-to-date context for LLMs or powering AI agents). The subnet has already processed hundreds of millions of data records for various AI apps and protocols, demonstrating its capacity. Importantly, Trusted Execution Environments (TEE) are used in the network’s nodes, meaning data is collected and validated within secure hardware enclaves – ensuring the data’s provenance is cryptographically verifiable and tamper-proof. In short, Gopher SN42 provides a permissionless, real-time data firehose for AI, delivering fresh and trustworthy information to models and developers on demand.

 

PURPOSE

What exactly is the 'product/build'?

Gopher’s product is an end-to-end AI data platform and blockchain combined. On the user side, it offers a unified Data API and a web-based dashboard of one-click tools to search, scrape, and store data from various platforms (e.g. X/Twitter, web pages, Reddit, TikTok, YouTube) in real time. Developers can query this API to instantly fetch structured data (like JSON records or embeddings) for their AI models, with features like advanced search filters and caching in a vector database for fast retrieval. Gopher even provides “AI Insights” services, where it runs AI models on the scraped data to produce summaries or analyses, helping turn raw data into useful information. A free tier is available (with rate-limited credits) to encourage adoption, and higher volumes are offered via enterprise plans.

Under the hood, the “build” consists of a sovereign Layer-1 blockchain (Gopher Chain) and the Bittensor Subnet 42 integration. Gopher Chain is built on the Cosmos SDK and is described as “the truth layer for the AI economy,” designed for speed and scalability in handling data transactions. It uses an emission-driven token economy (the $GOAI token, formerly $MASA) to reward participation: data providers (“miners”) and application builders earn emissions for contributing and utilizing valuable data. Meanwhile on Bittensor SN42, a network of miners continuously fetch and upload fresh data (e.g. live social media posts, website text) while validators run inside TEEs to cryptographically verify the data’s integrity and serve it to requesters. Miners on SN42 earn Bittensor TAO rewards (and previously also earned MASA/GOAI rewards) for high-performance data feeds. This dual setup lets Gopher leverage Bittensor’s distributed AI network while also developing its own blockchain tailored for data.

In summary, Gopher’s product is a decentralized data pipeline and marketplace: on one side, a suite of easy-to-use tools and API for AI developers to get live, clean data; on the other side, a blockchain protocol where participants compete to supply and verify that data, with economic incentives for quality and truth. All data transactions are secured by design (encryption, on-chain proofs of origin), aiming to replace “black-box” data APIs with an auditable, trustless data supply chain.

 

Gopher’s product is an end-to-end AI data platform and blockchain combined. On the user side, it offers a unified Data API and a web-based dashboard of one-click tools to search, scrape, and store data from various platforms (e.g. X/Twitter, web pages, Reddit, TikTok, YouTube) in real time. Developers can query this API to instantly fetch structured data (like JSON records or embeddings) for their AI models, with features like advanced search filters and caching in a vector database for fast retrieval. Gopher even provides “AI Insights” services, where it runs AI models on the scraped data to produce summaries or analyses, helping turn raw data into useful information. A free tier is available (with rate-limited credits) to encourage adoption, and higher volumes are offered via enterprise plans.

Under the hood, the “build” consists of a sovereign Layer-1 blockchain (Gopher Chain) and the Bittensor Subnet 42 integration. Gopher Chain is built on the Cosmos SDK and is described as “the truth layer for the AI economy,” designed for speed and scalability in handling data transactions. It uses an emission-driven token economy (the $GOAI token, formerly $MASA) to reward participation: data providers (“miners”) and application builders earn emissions for contributing and utilizing valuable data. Meanwhile on Bittensor SN42, a network of miners continuously fetch and upload fresh data (e.g. live social media posts, website text) while validators run inside TEEs to cryptographically verify the data’s integrity and serve it to requesters. Miners on SN42 earn Bittensor TAO rewards (and previously also earned MASA/GOAI rewards) for high-performance data feeds. This dual setup lets Gopher leverage Bittensor’s distributed AI network while also developing its own blockchain tailored for data.

In summary, Gopher’s product is a decentralized data pipeline and marketplace: on one side, a suite of easy-to-use tools and API for AI developers to get live, clean data; on the other side, a blockchain protocol where participants compete to supply and verify that data, with economic incentives for quality and truth. All data transactions are secured by design (encryption, on-chain proofs of origin), aiming to replace “black-box” data APIs with an auditable, trustless data supply chain.

 

WHO

Team Info

Gopher (previously known as Masa) was co-founded by Brendan Playford (CEO) and Calanthia Mei (Co-Founder) in the United States, with a mission to democratize AI data access. Brendan Playford was also the founder of Masa Finance, and brought his expertise in decentralized data and blockchain to the Gopher project. Calanthia Mei co-leads the project, likely focusing on strategy and partnerships (she was also co-founder of Masa Finance). The team’s Head of Marketing, Amateo Ra, helps drive community and growth efforts.

Originally launched as “Masa” in 2021, the project underwent a major rebrand to Gopher in September 2025. Along with the name change, the focus expanded from being a dApp on Bittensor to developing a full Layer-1 blockchain for the AI data economy. Gopher’s team and initiative are well-backed by notable investors – the project has raised approximately $17–18 million in funding (across pre-seed, seed, and a public token sale) from firms including Digital Currency Group (DCG), GSR, and Anagram Ventures, among others. This strong backing and a growing developer community reflect a high level of confidence in the team’s vision. The Gopher team is active in the open-source and AI community (hosting its datasets on Hugging Face and engaging developers on Discord/X) and continues to expand as the platform grows.

 

Gopher (previously known as Masa) was co-founded by Brendan Playford (CEO) and Calanthia Mei (Co-Founder) in the United States, with a mission to democratize AI data access. Brendan Playford was also the founder of Masa Finance, and brought his expertise in decentralized data and blockchain to the Gopher project. Calanthia Mei co-leads the project, likely focusing on strategy and partnerships (she was also co-founder of Masa Finance). The team’s Head of Marketing, Amateo Ra, helps drive community and growth efforts.

Originally launched as “Masa” in 2021, the project underwent a major rebrand to Gopher in September 2025. Along with the name change, the focus expanded from being a dApp on Bittensor to developing a full Layer-1 blockchain for the AI data economy. Gopher’s team and initiative are well-backed by notable investors – the project has raised approximately $17–18 million in funding (across pre-seed, seed, and a public token sale) from firms including Digital Currency Group (DCG), GSR, and Anagram Ventures, among others. This strong backing and a growing developer community reflect a high level of confidence in the team’s vision. The Gopher team is active in the open-source and AI community (hosting its datasets on Hugging Face and engaging developers on Discord/X) and continues to expand as the platform grows.

 

FUTURE

Roadmap

Gopher’s roadmap is centered on expanding data sources and scaling its blockchain infrastructure. Key upcoming milestones include:

Reddit Integration (Q4 2025): Launching full API support for scraping and streaming Reddit content (posts, comments, trends) in real time. This will mirror the existing Twitter data tool, allowing AI models to ingest community discussions and sentiment from Reddit.

Unified Data Search (Q4 2025): Developing a cross-platform search capability that spans X/Twitter, TikTok, Reddit (and potentially more) in one query interface. This will enable users to retrieve AI-ready data across multiple sources with a single request, improving convenience and data fusion for model training.

Layer-1 Ecosystem Expansion (2026): Transitioning fully to the Gopher sovereign blockchain and growing its ecosystem. This involves scaling the Gopher Chain (post-testnet toward mainnet launch), and deepening integrations with other networks and partners. The team has noted plans to collaborate further with platforms like Base (Coinbase’s L2), BNB Chain, and Spectral to broaden Gopher’s reach and utility. As the Gopher L1 matures, expect enhanced scalability, refined tokenomics, and new “Gophernets” (sub-networks/modules) where data providers and AI apps compete and cooperate for emissions in specific domains.

Beyond these, Gopher will continue to iterate on its data tooling (e.g. adding more sources such as full Reddit support, YouTube, Discord, etc., and improving its AI insight services). The overarching goal is to solidify Gopher’s role as the provable data layer for AI. By delivering on its roadmap, Gopher aims to outpace centralized data providers and become foundational infrastructure for the AI agent and decentralized AI economy.

 

Gopher’s roadmap is centered on expanding data sources and scaling its blockchain infrastructure. Key upcoming milestones include:

Reddit Integration (Q4 2025): Launching full API support for scraping and streaming Reddit content (posts, comments, trends) in real time. This will mirror the existing Twitter data tool, allowing AI models to ingest community discussions and sentiment from Reddit.

Unified Data Search (Q4 2025): Developing a cross-platform search capability that spans X/Twitter, TikTok, Reddit (and potentially more) in one query interface. This will enable users to retrieve AI-ready data across multiple sources with a single request, improving convenience and data fusion for model training.

Layer-1 Ecosystem Expansion (2026): Transitioning fully to the Gopher sovereign blockchain and growing its ecosystem. This involves scaling the Gopher Chain (post-testnet toward mainnet launch), and deepening integrations with other networks and partners. The team has noted plans to collaborate further with platforms like Base (Coinbase’s L2), BNB Chain, and Spectral to broaden Gopher’s reach and utility. As the Gopher L1 matures, expect enhanced scalability, refined tokenomics, and new “Gophernets” (sub-networks/modules) where data providers and AI apps compete and cooperate for emissions in specific domains.

Beyond these, Gopher will continue to iterate on its data tooling (e.g. adding more sources such as full Reddit support, YouTube, Discord, etc., and improving its AI insight services). The overarching goal is to solidify Gopher’s role as the provable data layer for AI. By delivering on its roadmap, Gopher aims to outpace centralized data providers and become foundational infrastructure for the AI agent and decentralized AI economy.

 

MEDIA

Novelty Search is great, but for most investors trying to understand Bittensor, the technical depth is a wall, not a bridge. If we’re going to attract investment into this ecosystem then we need more people to understand it! That’s why Siam Kidd and Mark Creaser from DSV Fund have launched Revenue Search, where they ask the simple questions that investors want to know the answers to.

Gopher (ex-Masa) / Subnet 42 provides cryptographically verified, AI-ready data via a network of ~256 miners running trusted execution environments to scrape and normalise web/X/Reddit/TikTok plus large financial price feeds. Devs use a UI + API (credit-based billing) and a built-in vector DB to aggregate/search topics and power apps. Traction: ~77k users across products (≈54k on AI Insights; ≈20k on a new trading tool), ~$1M ARR, ~1k paying on the trading app launched ~2 weeks ago. That app ingests multi-timeframe price data, generates trade setups, and can execute on Hyperliquid; team claims ~65% win rate with ~1:4 risk-reward on internal accounts and will publish on-chain trading wallets.

Strategy & token alignment: Gopher is migrating Masa to a Cosmos L1 (“Gopher”) focused on data aggregation/apps (Q1 launch target). Subnet 42 remains the data engine; enterprise and app customers pay fiat credits, and usage-based revenue from Gopher’s stack flows to SN42 for the data it supplies. The plan is for Alpha holders on SN42 to govern revenue use (buybacks, treasury, growth, etc.), keeping value with the miners who create it. Near-term focus: expand financial feeds (~100k assets), improve low-latency delivery (~26 ms), add social/news signals into trading, and explore a fund/vaults that trade the signals—while continuing to court market makers, prop funds, and other subnets that need dependable, verifiable data.

NEWS

Announcements

Gopher Follow 590 220,412

A Layer-1 for the AI data economy. Access AI-ready data on demand. The fastest & simplest way to scrape and stream data straight into models, apps & agents.

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