Intro to Bittensor: A Potential Game-Changer in Decentralized AI

Bittensor is emerging as a potentially groundbreaking project that could surpass the combined significance of Bitcoin and Ethereum in the realms of cryptocurrency and artificial intelligence (AI). This decentralized AI platform has been the subject of intense scrutiny for several months, and it’s time to delve into the specifics of what makes Bittensor revolutionary.

Bittensor’s mission is to address the challenge of decentralizing intelligence, not just in terms of GPUs, models, or data collection, but by accelerating the development of digital intelligence and mitigating the risks associated with centralization.

Why Bittensor Matters

1. Programmable Incentives: Unlike Bitcoin, which rewards energy consumption, and Ethereum, which rewards the economic risk taken by node operators, Bittensor offers incentives for more sophisticated and beneficial work in the field of ML development.
2. Accelerating Innovation: Market competition drives innovation, but this process is often slow due to regulatory hurdles and the time it takes to establish new companies. Bittensor leverages a peer-to-peer (p2p) economy to speed up competition, idea validation, and, by extension, capitalism itself. Accelerating AI development could be key to solving many of humanity’s challenges.
3. Rewards for Contributions: Bittensor offers rewards comparable to salaries at OpenAI or potential startup profits to founders, engineers, researchers, MLOps professionals, hardware owners, and service operators in the form of a DAO (Decentralized Autonomous Organization). This approach is open, unrestricted, and non-hierarchical.
4. Overcoming High Costs in ML Development: Producing ML software is costly, with organizations like OpenAI spending millions on a single model. Unlike traditional open-source projects, Bittensor enables a network that aggregates and distributes capital, attracting top talent in ML.
5. Centralization Risks: The future should not be dominated by a handful of AI models controlled by corporations or governments, making decisions behind closed doors with a heavy political agenda.
6. The Failure of Centralized Regulation: AI regulation is ineffective when regulators lack understanding, processes are slow, and a single entity cannot foster conditions conducive to progress and market self-organization.

How Bittensor Works

Subnets: Bittensor allows for the creation of economic mechanisms where rewards are given for various AI-development activities, such as model creation, data collection, computation, bandwidth, and more. These activities are organized into subnets, with each subnet creator earning a percentage of the earnings generated by their network.

Validators: Validators distribute rewards among subnets and verify miners’ work within each subnet. Becoming a validator requires staking tokens, a process that allows validators to directly sell the outcomes of the network’s efforts.

Miners: Miners perform tasks assigned by subnets, ranging from providing computational power to developing and training new models.

Validation: The validation of a miner’s work varies by subnet, with benchmarks set by subnet creators to ensure quality and relevance.

Rewards: Different subnets have unique reward algorithms, focusing on speed (latency), accuracy, or a combination of metrics. Miners are incentivized to continuously optimize their software and hardware. For example, in just one of the subnets one miner is making ~$75k a day by providing a newly pre-trained model to the network.

Users: End-users interact with AI products powered by Bittensor indirectly, similar to how Google users engage with products without knowledge of the internal workings of ML teams. Validators stake tokens, oversee network operations, and can sell results through an API, akin to the business models of OpenAI, Anthropic, and Microsoft, without owning the entire network infrastructure.