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of new subnets being added it can be hard to get up to date information across all subnets, so data may be slightly out of date from time to time
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of new subnets being added it can be hard to get up to date information across all subnets, so data may be slightly out of date from time to time
AI Factory (Subnet 80) is a specialized subnetwork in the Bittensor ecosystem designed as an “AI factory,” essentially AIs that create AIs. In other words, it focuses on automatically developing customized AI models and solutions for specific applications. The project’s vision is to democratize AI development – allowing even non-technical users to obtain tailored AI solutions – by leveraging a decentralized network of miners and validators that collectively build and refine new AI models. This makes Subnet 80 a unique “meta-AI” marketplace where the product is not just AI output (like text or images), but actual AI models and algorithms generated by the network itself.
AI Factory (Subnet 80) is a specialized subnetwork in the Bittensor ecosystem designed as an “AI factory,” essentially AIs that create AIs. In other words, it focuses on automatically developing customized AI models and solutions for specific applications. The project’s vision is to democratize AI development – allowing even non-technical users to obtain tailored AI solutions – by leveraging a decentralized network of miners and validators that collectively build and refine new AI models. This makes Subnet 80 a unique “meta-AI” marketplace where the product is not just AI output (like text or images), but actual AI models and algorithms generated by the network itself.
Like all Bittensor subnets, AI Factory is an incentive-driven marketplace composed of two participant roles: miners (who perform the work) and validators (who evaluate and score the work). The incentive mechanism of Subnet 80 defines what tasks miners do and how validators assess them:
Miner role: In AI Factory, miners are essentially AI model creators. Given a task or problem specification (potentially an application request or a dataset), miners will build or train AI models that attempt to solve that specific problem. This could involve generating a neural network architecture, training a model on provided data, or even using AI agents to write new model code. The miner then provides the resulting model or its outputs for evaluation. In essence, the miners (AI agents) produce new AI systems, aligning with the subnet’s theme of “AIs creating AIs.” (For example, in a similar subnet for model training, miners produced pretrained language models on a web dataset; AI Factory generalizes this concept to various custom AI tasks.)
Validator role: Validators in Subnet 80 serve as the quality assurance and scoring mechanism. They independently test the models or solutions produced by miners against predefined benchmarks or criteria. A validator might provide a set of evaluation tasks or datasets to the miner’s model (e.g. if the task is an image classifier, the validator can run the miner’s model on a hidden test set and measure accuracy). They then quantify the performance – effectively “scoring” each miner. The incentive code of AI Factory defines how these scores are computed (the reward model), ensuring that models are judged on relevant metrics (accuracy, efficiency, etc.) and discouraging any gaming of the system. The goal is to reward models that truly solve the given task best.
Consensus & rewards: Bittensor’s on-chain Yuma consensus takes the validators’ scores and allocates network rewards accordingly. Miners whose AI models perform better (as per the collective validator scoring) earn higher TAO token rewards, and validators are also rewarded based on the fidelity of their evaluations. Thus, the subnet creates a competitive dynamic: miners compete to evolve the best AI solutions, and validators are incentivized to fairly measure quality, aligning everyone’s interests. Over time, this should drive the subnet to produce increasingly powerful or efficient AI models for a variety of tasks.
Model format and functionality: The “product” of AI Factory is typically an ML model or AI system. These could range from machine learning models (like a trained neural network for a specific dataset) to AI agents or pipelines. Because the subnet is meant to handle “customized AI solutions,” it is likely flexible in terms of model modality – e.g. miners might create an NLP model for a text-based task, or a computer vision model for an imaging task, depending on what the validators request. In practice, the subnet may support multiple model competitions in parallel (for different model sizes or problem types) to ensure fairness and specialization. (In a previous Bittensor experiment, for instance, separate categories for 700M vs 3B vs 7B parameter models were used so that smaller models could compete among themselves.) This kind of multi-track competition could be employed in AI Factory as well, to generate AI solutions across various scales and domains.
Overall, Subnet 80’s architecture allows it to function as an AutoML factory – distributed miners explore model architectures, hyperparameters, and training strategies, while validators evaluate and guide the evolution of those models. Successful patterns are reinforced by rewards. The end result is a decentralized pipeline that can output high-quality AI models as a commodity of the network.
Integrations and Ecosystem Connections
AI Factory is envisioned not just as an isolated subnet, but as part of a larger AI service ecosystem. Key integrations and interactions include:
Bittensor Network Integration: By design, Subnet 80 plugs into the broader Bittensor blockchain (Subtensor). It uses Bittensor’s unified token (TAO) for its incentive and reward system. This means its performance affects and is affected by Bittensor’s dynamic TAO allocation – if AI Factory produces high-quality output and attracts many miners/validators, it will earn a larger share of daily TAO emissions, benefiting its participants. Conversely, it competes with other subnets for those rewards, which drives continual improvement across the network.
Cross-Subnet Collaboration: In the future, AI Factory could collaborate with other specialized subnets. For example, it might use a data subnet (like SN13 Dataverse) to source large datasets for training models, or leverage a compute subnet for heavy GPU power if needed. The “factory” might also output models that are then served by other service subnets. While no explicit cross-subnet pipeline is confirmed yet, the “one-stop hub for all marketplaces” vision implies a platform where various subnet services are unified. It’s reasonable to expect that AI Factory will interface with subnets for data, compute, or model deployment as those become available (for instance, a subnet that provides decentralized storage could store AI Factory’s model artifacts, etc.).
Ethereum and DeFi Integration: A distinctive integration is with Ethereum. FacTAO has implemented a Bridge between Ethereum and Bittensor. This bridge allows the $FAO token and possibly TAO to move between chains. The rationale is to tap into Ethereum’s user base and DeFi liquidity to support Subnet 80. On Ethereum, $FAO can be staked or traded, and then bridged to Bittensor to participate in the subnet. The FacTAO docs highlight staking and bridging as part of the user experience. This integration lowers entry barriers: users can acquire $FAO on regular crypto markets and indirectly support or utilize the AI Factory without needing deep knowledge of Bittensor’s native chain. It also gives the project additional funding avenues (for example, liquidity pools, etc., which is unusual for most subnets that only use TAO).
End-User Applications: Although still under development, AI Factory will integrate with front-end applications (web platforms, possibly chat interfaces or APIs). The idea is that an end-user might interact with a simple interface – for example, a web app where they describe the AI solution they need – and behind the scenes the request is fed into Subnet 80’s miners/validators to generate that solution. This kind of integration (front-end to subnet via perhaps an API gateway) is part of making the AI Factory output accessible to non-technical consumers. It effectively turns the subnet into a cloud service. The team’s focus on “ultimate convenience” underscores that user-friendly integrations (browser apps, bots, etc.) are on the horizon.
In summary, AI Factory is deeply integrated with the Bittensor network by design, and the FacTAO team is extending those integrations to Ethereum and user-facing platforms. This multi-faceted approach helps bridge decentralized AI with mainstream usage and liquidity.
Use Cases and End-User Functionality
The core use case for Subnet 80 is “AI Generation-as-a-Service.” It allows people to obtain custom AI models without having to build them from scratch. Some potential use cases and functionalities include:
Customized Model Generation: An individual or organization could request an AI model tailored to their needs – for instance, “a sentiment analysis model for tweets about my product” or “an image recognition model to detect manufacturing defects from these sample images.” AI Factory’s miners would train or assemble models to meet that request, and the best model (as determined by validators) would essentially be the output. This greatly lowers the barrier for accessing specialized AI; you don’t need an in-house ML team, you can let the decentralized network create it for you.
Automated AI Development Pipelines: AI Factory can serve as an AutoML pipeline. Instead of manually tuning hyperparameters or trying many model architectures, you rely on the competitive swarm of miners to do that exploration. The end-user functionality might be as simple as uploading data or describing a task, then waiting for the “factory” to output a ready-to-use model. This could save time in AI development and yield high-quality results by harnessing many approaches in parallel.
AI Model Marketplace: Once models are generated, they could be made available for use by others. For example, if someone requested a model for a certain task, that model (if generic enough) might be published on an AI marketplace for others to download or query. This creates a library of AI solutions. The monetization of digital content is mentioned as a goal – likely meaning that contributors (miners) whose models are popular could earn additional rewards or that usage of models might involve microtransactions (possibly via the FAO token or TAO). End-users could thus either own the models created for them or pay per use for existing models on the marketplace.
Domain-Specific AI Factories: Over time, AI Factory might cater to multiple domains: e.g. generating text-based AI (chatbots, translators), vision AI (object detectors, art generators), audio AI (speech recognizers, music generators), etc. In its current form it’s a single subnet, but it could spawn specialized tracks. For an end-user, this means the service could handle a wide array of AI solution requests – truly a general AI manufacturing plant. For example, a small game development team might use AI Factory to generate NPC behaviors (AI agents) for their game, while a medical researcher might use it to create a model that analyzes X-ray images. The subnet’s flexible, community-driven approach aims to support such diverse applications.
No-Code AI Solution Creation: A big part of AI Factory’s promise is enabling non-programmers to create AI. By integrating with a simple user interface, it provides a no-code or low-code experience. This functionality opens up AI development to a much broader audience. Imagine a web portal where a user answers a few questions or uploads some example data, and clicks “Generate AI Model.” The backend (Subnet 80) then does the heavy lifting. The user could then download the model or access it via an API. This democratization of AI development is at the heart of Subnet 80’s value proposition.
In essence, AI Factory turns AI development itself into a decentralized, on-demand service. The immediate use cases revolve around automated model building for those who need custom AI solutions. As the platform grows, it could become a marketplace of AI capabilities – a place one can go to either commission a new model or find an existing one, all powered by the collective intelligence of the Bittensor network. This could dramatically accelerate AI adoption, especially for niche or long-tail applications that big tech companies may not focus on, but which a decentralized community can tackle.
Like all Bittensor subnets, AI Factory is an incentive-driven marketplace composed of two participant roles: miners (who perform the work) and validators (who evaluate and score the work). The incentive mechanism of Subnet 80 defines what tasks miners do and how validators assess them:
Miner role: In AI Factory, miners are essentially AI model creators. Given a task or problem specification (potentially an application request or a dataset), miners will build or train AI models that attempt to solve that specific problem. This could involve generating a neural network architecture, training a model on provided data, or even using AI agents to write new model code. The miner then provides the resulting model or its outputs for evaluation. In essence, the miners (AI agents) produce new AI systems, aligning with the subnet’s theme of “AIs creating AIs.” (For example, in a similar subnet for model training, miners produced pretrained language models on a web dataset; AI Factory generalizes this concept to various custom AI tasks.)
Validator role: Validators in Subnet 80 serve as the quality assurance and scoring mechanism. They independently test the models or solutions produced by miners against predefined benchmarks or criteria. A validator might provide a set of evaluation tasks or datasets to the miner’s model (e.g. if the task is an image classifier, the validator can run the miner’s model on a hidden test set and measure accuracy). They then quantify the performance – effectively “scoring” each miner. The incentive code of AI Factory defines how these scores are computed (the reward model), ensuring that models are judged on relevant metrics (accuracy, efficiency, etc.) and discouraging any gaming of the system. The goal is to reward models that truly solve the given task best.
Consensus & rewards: Bittensor’s on-chain Yuma consensus takes the validators’ scores and allocates network rewards accordingly. Miners whose AI models perform better (as per the collective validator scoring) earn higher TAO token rewards, and validators are also rewarded based on the fidelity of their evaluations. Thus, the subnet creates a competitive dynamic: miners compete to evolve the best AI solutions, and validators are incentivized to fairly measure quality, aligning everyone’s interests. Over time, this should drive the subnet to produce increasingly powerful or efficient AI models for a variety of tasks.
Model format and functionality: The “product” of AI Factory is typically an ML model or AI system. These could range from machine learning models (like a trained neural network for a specific dataset) to AI agents or pipelines. Because the subnet is meant to handle “customized AI solutions,” it is likely flexible in terms of model modality – e.g. miners might create an NLP model for a text-based task, or a computer vision model for an imaging task, depending on what the validators request. In practice, the subnet may support multiple model competitions in parallel (for different model sizes or problem types) to ensure fairness and specialization. (In a previous Bittensor experiment, for instance, separate categories for 700M vs 3B vs 7B parameter models were used so that smaller models could compete among themselves.) This kind of multi-track competition could be employed in AI Factory as well, to generate AI solutions across various scales and domains.
Overall, Subnet 80’s architecture allows it to function as an AutoML factory – distributed miners explore model architectures, hyperparameters, and training strategies, while validators evaluate and guide the evolution of those models. Successful patterns are reinforced by rewards. The end result is a decentralized pipeline that can output high-quality AI models as a commodity of the network.
Integrations and Ecosystem Connections
AI Factory is envisioned not just as an isolated subnet, but as part of a larger AI service ecosystem. Key integrations and interactions include:
Bittensor Network Integration: By design, Subnet 80 plugs into the broader Bittensor blockchain (Subtensor). It uses Bittensor’s unified token (TAO) for its incentive and reward system. This means its performance affects and is affected by Bittensor’s dynamic TAO allocation – if AI Factory produces high-quality output and attracts many miners/validators, it will earn a larger share of daily TAO emissions, benefiting its participants. Conversely, it competes with other subnets for those rewards, which drives continual improvement across the network.
Cross-Subnet Collaboration: In the future, AI Factory could collaborate with other specialized subnets. For example, it might use a data subnet (like SN13 Dataverse) to source large datasets for training models, or leverage a compute subnet for heavy GPU power if needed. The “factory” might also output models that are then served by other service subnets. While no explicit cross-subnet pipeline is confirmed yet, the “one-stop hub for all marketplaces” vision implies a platform where various subnet services are unified. It’s reasonable to expect that AI Factory will interface with subnets for data, compute, or model deployment as those become available (for instance, a subnet that provides decentralized storage could store AI Factory’s model artifacts, etc.).
Ethereum and DeFi Integration: A distinctive integration is with Ethereum. FacTAO has implemented a Bridge between Ethereum and Bittensor. This bridge allows the $FAO token and possibly TAO to move between chains. The rationale is to tap into Ethereum’s user base and DeFi liquidity to support Subnet 80. On Ethereum, $FAO can be staked or traded, and then bridged to Bittensor to participate in the subnet. The FacTAO docs highlight staking and bridging as part of the user experience. This integration lowers entry barriers: users can acquire $FAO on regular crypto markets and indirectly support or utilize the AI Factory without needing deep knowledge of Bittensor’s native chain. It also gives the project additional funding avenues (for example, liquidity pools, etc., which is unusual for most subnets that only use TAO).
End-User Applications: Although still under development, AI Factory will integrate with front-end applications (web platforms, possibly chat interfaces or APIs). The idea is that an end-user might interact with a simple interface – for example, a web app where they describe the AI solution they need – and behind the scenes the request is fed into Subnet 80’s miners/validators to generate that solution. This kind of integration (front-end to subnet via perhaps an API gateway) is part of making the AI Factory output accessible to non-technical consumers. It effectively turns the subnet into a cloud service. The team’s focus on “ultimate convenience” underscores that user-friendly integrations (browser apps, bots, etc.) are on the horizon.
In summary, AI Factory is deeply integrated with the Bittensor network by design, and the FacTAO team is extending those integrations to Ethereum and user-facing platforms. This multi-faceted approach helps bridge decentralized AI with mainstream usage and liquidity.
Use Cases and End-User Functionality
The core use case for Subnet 80 is “AI Generation-as-a-Service.” It allows people to obtain custom AI models without having to build them from scratch. Some potential use cases and functionalities include:
Customized Model Generation: An individual or organization could request an AI model tailored to their needs – for instance, “a sentiment analysis model for tweets about my product” or “an image recognition model to detect manufacturing defects from these sample images.” AI Factory’s miners would train or assemble models to meet that request, and the best model (as determined by validators) would essentially be the output. This greatly lowers the barrier for accessing specialized AI; you don’t need an in-house ML team, you can let the decentralized network create it for you.
Automated AI Development Pipelines: AI Factory can serve as an AutoML pipeline. Instead of manually tuning hyperparameters or trying many model architectures, you rely on the competitive swarm of miners to do that exploration. The end-user functionality might be as simple as uploading data or describing a task, then waiting for the “factory” to output a ready-to-use model. This could save time in AI development and yield high-quality results by harnessing many approaches in parallel.
AI Model Marketplace: Once models are generated, they could be made available for use by others. For example, if someone requested a model for a certain task, that model (if generic enough) might be published on an AI marketplace for others to download or query. This creates a library of AI solutions. The monetization of digital content is mentioned as a goal – likely meaning that contributors (miners) whose models are popular could earn additional rewards or that usage of models might involve microtransactions (possibly via the FAO token or TAO). End-users could thus either own the models created for them or pay per use for existing models on the marketplace.
Domain-Specific AI Factories: Over time, AI Factory might cater to multiple domains: e.g. generating text-based AI (chatbots, translators), vision AI (object detectors, art generators), audio AI (speech recognizers, music generators), etc. In its current form it’s a single subnet, but it could spawn specialized tracks. For an end-user, this means the service could handle a wide array of AI solution requests – truly a general AI manufacturing plant. For example, a small game development team might use AI Factory to generate NPC behaviors (AI agents) for their game, while a medical researcher might use it to create a model that analyzes X-ray images. The subnet’s flexible, community-driven approach aims to support such diverse applications.
No-Code AI Solution Creation: A big part of AI Factory’s promise is enabling non-programmers to create AI. By integrating with a simple user interface, it provides a no-code or low-code experience. This functionality opens up AI development to a much broader audience. Imagine a web portal where a user answers a few questions or uploads some example data, and clicks “Generate AI Model.” The backend (Subnet 80) then does the heavy lifting. The user could then download the model or access it via an API. This democratization of AI development is at the heart of Subnet 80’s value proposition.
In essence, AI Factory turns AI development itself into a decentralized, on-demand service. The immediate use cases revolve around automated model building for those who need custom AI solutions. As the platform grows, it could become a marketplace of AI capabilities – a place one can go to either commission a new model or find an existing one, all powered by the collective intelligence of the Bittensor network. This could dramatically accelerate AI adoption, especially for niche or long-tail applications that big tech companies may not focus on, but which a decentralized community can tackle.
Subnet 80 (AI Factory) is spearheaded by the FacTAO project. FacTAO positions itself as an independent team within the Bittensor ecosystem focusing on AI-as-a-service and marketplace solutions. While specific team members’ identities are not widely public, we know the following:
FacTAO Project: FacTAO appears to be the entity behind AI Factory. It has its own token ($FAO) and branding, and describes itself as “an innovative subnet on the Bittensor Network” built to streamline AI tasks for users. The project has an official website and documentation (factao.io) and social presence. It’s not an official OpenTensor Foundation subnet, but a community/third-party subnet developed by this team.
Investors and Community: There are indications of early supporters in the crypto community. For example, the FacTAO X (Twitter) account has mentioned attracting interest for being a hub in the Bittensor ecosystem, and some crypto investors have noted involvement. (One public figure, Jason Calacanis, was indirectly referenced by an early investor as being interested, though this is anecdotal.) The team actively engages on social media under handles like @FacTAO_io and @ai_factory_bit.
Affiliations: FacTAO’s mission to integrate multiple “marketplaces” suggests they are working closely with other Bittensor subnet developers or at least aligning with them. They reference partnerships (e.g. with Virtuals Protocol for AI agents, per some community research) and are likely collaborating within the Bittensor developer community. However, as of now Subnet 80 is primarily the product of the FacTAO team and is not yet publicly linked to any large corporate backers or known AI research institutions. It is a community-driven effort in the decentralized AI space.
Subnet 80 (AI Factory) is spearheaded by the FacTAO project. FacTAO positions itself as an independent team within the Bittensor ecosystem focusing on AI-as-a-service and marketplace solutions. While specific team members’ identities are not widely public, we know the following:
FacTAO Project: FacTAO appears to be the entity behind AI Factory. It has its own token ($FAO) and branding, and describes itself as “an innovative subnet on the Bittensor Network” built to streamline AI tasks for users. The project has an official website and documentation (factao.io) and social presence. It’s not an official OpenTensor Foundation subnet, but a community/third-party subnet developed by this team.
Investors and Community: There are indications of early supporters in the crypto community. For example, the FacTAO X (Twitter) account has mentioned attracting interest for being a hub in the Bittensor ecosystem, and some crypto investors have noted involvement. (One public figure, Jason Calacanis, was indirectly referenced by an early investor as being interested, though this is anecdotal.) The team actively engages on social media under handles like @FacTAO_io and @ai_factory_bit.
Affiliations: FacTAO’s mission to integrate multiple “marketplaces” suggests they are working closely with other Bittensor subnet developers or at least aligning with them. They reference partnerships (e.g. with Virtuals Protocol for AI agents, per some community research) and are likely collaborating within the Bittensor developer community. However, as of now Subnet 80 is primarily the product of the FacTAO team and is not yet publicly linked to any large corporate backers or known AI research institutions. It is a community-driven effort in the decentralized AI space.
AI Factory (Subnet 80) is a relatively new initiative – it was launched in early 2025 as one of the latest Bittensor subnets (it was discussed as a “latest launch” in a March 28, 2025 podcast). Development appears to be led by a team called FacTAO, which has been building out the platform over the past year. Some key milestones and plans include:
2024: The foundations for AI Factory were being laid. The FacTAO team launched an ERC-20 token called $FAO (FacTAO) on Ethereum in early 2024 to bootstrap the project’s ecosystem. Throughout 2024, they likely developed the incentive mechanism off-chain and prepared the subnet’s infrastructure (this period saw many Bittensor subnets in R&D phase).
Q1 2025 – Subnet Launch: Subnet 80 was officially registered and launched on Bittensor in March 2025. This enabled miners and validators to start joining. Early on, the subnet is focused on proving out the concept of AI-generated AI. We have seen the subnet owner actively staking in the network (e.g. small buys of TAO to support the subnet were observed), indicating commitment to growing its share of Bittensor’s emissions.
Near-term Development: A major upcoming component is an AI Marketplace that will interface with AI Factory. According to the project’s documentation, the platform is divided into two parts: (1) the AI-driven creation suite (i.e. the AI Factory subnet services) and (2) DeFi/utility features like staking and bridging. Once the creation engine (Subnet 80) is running, the next step is to build user-facing applications. The team has hinted at making FacTAO “a one-stop hub for all marketplaces on the Bittensor network” – suggesting they plan to aggregate or launch marketplaces where the models produced can be deployed and monetized. An AI Marketplace dApp would allow end-users to request or purchase AI solutions, effectively tapping into Subnet 80’s output. This is likely on the roadmap for 2025.
Future Milestones: As the network matures, we can expect iterative improvements to the incentive mechanism (to handle more complex tasks or multi-modal AI creation) and possibly integration with other subnets (see below). The FacTAO team’s broader vision is to “distribute the wealth of the trillion-dollar AI market on Bittensor” – implying future milestones around scaling up usage (more tasks, more users) and maybe governance mechanisms for community-driven AI development. They may also introduce staking programs with the $FAO token, and expand the bridging between Ethereum and Bittensor to onboard more users and liquidity into the subnet.
AI Factory (Subnet 80) is a relatively new initiative – it was launched in early 2025 as one of the latest Bittensor subnets (it was discussed as a “latest launch” in a March 28, 2025 podcast). Development appears to be led by a team called FacTAO, which has been building out the platform over the past year. Some key milestones and plans include:
2024: The foundations for AI Factory were being laid. The FacTAO team launched an ERC-20 token called $FAO (FacTAO) on Ethereum in early 2024 to bootstrap the project’s ecosystem. Throughout 2024, they likely developed the incentive mechanism off-chain and prepared the subnet’s infrastructure (this period saw many Bittensor subnets in R&D phase).
Q1 2025 – Subnet Launch: Subnet 80 was officially registered and launched on Bittensor in March 2025. This enabled miners and validators to start joining. Early on, the subnet is focused on proving out the concept of AI-generated AI. We have seen the subnet owner actively staking in the network (e.g. small buys of TAO to support the subnet were observed), indicating commitment to growing its share of Bittensor’s emissions.
Near-term Development: A major upcoming component is an AI Marketplace that will interface with AI Factory. According to the project’s documentation, the platform is divided into two parts: (1) the AI-driven creation suite (i.e. the AI Factory subnet services) and (2) DeFi/utility features like staking and bridging. Once the creation engine (Subnet 80) is running, the next step is to build user-facing applications. The team has hinted at making FacTAO “a one-stop hub for all marketplaces on the Bittensor network” – suggesting they plan to aggregate or launch marketplaces where the models produced can be deployed and monetized. An AI Marketplace dApp would allow end-users to request or purchase AI solutions, effectively tapping into Subnet 80’s output. This is likely on the roadmap for 2025.
Future Milestones: As the network matures, we can expect iterative improvements to the incentive mechanism (to handle more complex tasks or multi-modal AI creation) and possibly integration with other subnets (see below). The FacTAO team’s broader vision is to “distribute the wealth of the trillion-dollar AI market on Bittensor” – implying future milestones around scaling up usage (more tasks, more users) and maybe governance mechanisms for community-driven AI development. They may also introduce staking programs with the $FAO token, and expand the bridging between Ethereum and Bittensor to onboard more users and liquidity into the subnet.
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