Codex

Revolutionising AI with Decentralised Storage

Nasrudin

Feb 20, 2025 • 4 min read

Global storage demands continue to grow rapidly, and the advent of artificial intelligence only amplifies this surge in the need for data capacity.

The powerful, transformative machine learning algorithms that underlie this new, exciting field of applications and power globally adopted generative pre-trained transformers (GPTs) are trained on massive amounts of data.

Traditional centralised storage paradigms, where data is stored on cloud platforms controlled by a handful of organisations, are struggling to meet the demands of these new applications. They operate monolithically, building large, expensive data centres that take a long time to construct and bring online.

In contrast, decentralised storage networks like Codex store data securely and reliably over a distributed network of node operators.

Codex is a decentralised durability engine (DDE) designed to create a decentralised archive of durable knowledge. It allows files to be efficiently stored and accessed on a peer-to-peer network.

Decentralised storage is the key to unlocking artificial intelligence's true power. It forms a foundational component of the concept of decentralised AI (DAI), which will incorporate global data markets, verifiable models, and broad accessibility.

Below are a few ways decentralised storage networks such as Codex can revolutionise AI tools and models.

Durable, Accessible Storage for AI Applications

AI and machine learning (ML) require massive datasets for training and validation, creating a growing need for cost-effective and durable storage solutions. Traditional storage models often struggle with these requirements, offering limited scalability at high costs.

AI training data is invaluable to researchers and AI developers, and decentralised storage networks offer a way to preserve this information immutably and cost-efficiently by creating a distributed archive of information stored across many individual nodes. The Codex storage network expands horizontally, with its total data storage growing in proportion to the number of clients on the network. This allows for more cost-effective and scalable storage, removes the risks of centralisation, and ensures data is persisted in a durable and censorship-resistant manner.

AI workflows often involve multiple stakeholders, from data providers and model developers to businesses and end-users. Traditional storage models create inefficiencies in collaboration, requiring centralised intermediaries to manage data access and distribution.

Decentralised storage networks simplify this process by enabling direct peer-to-peer interactions. AI developers can securely share training datasets, collaborate on model development, and deploy AI services without relying on third-party storage providers. Access permissions can ensure that only authorised parties can retrieve and utilise stored data.

Decentralised storage also enhances the efficiency of federated learning, where AI models are trained across multiple devices or nodes without centralising sensitive data. This approach improves data privacy and reduces the risk of exposing proprietary or personal information during AI training.

Monetising Training Data and Providing Transparency

Decentralised storage networks offer the unique opportunity to provide public access to AI training data and monetise the use of this data to create AI applications. This offers a potential solution to the longstanding question of the value of training data and how it may be returned to its sources.

By recording data transparently on a distributed storage network, AI companies can quickly achieve reliable traceability to declare the provenance of their training data. This enhances accountability and gives AI users greater confidence in the outputs of generative models and automated decision-making systems.

AI developers may also publish monetised models to a decentralised network, where businesses and users can access them using smart contracts. This allows anyone to use that AI model without fear of censorship and to have a clear picture of its provenance and training data.This approach fosters an open, collaborative ecosystem for AI development while ensuring that models and datasets remain secure and tamper-resistant.

As regulation around AI models and datasets is debated and drawn up worldwide, AI companies may be required to make their training data available and transparent to regulators or users. This would inform the biases behind generated content and provide the sources of training data for accuracy and compliance.Using Codex’s decentralised storage infrastructure, AI developers can create a robust and immutable record of their training datasets. This enhances compliance with emerging AI regulations and strengthens the reliability of AI-generated outputs.

Auditable AI Agents

Centralised storage solutions are vulnerable to censorship, outages, and third-party control, limiting the potential of AI applications to function in a truly decentralised manner.

As AI-driven applications become more autonomous and widely adopted, it will become increasingly important to ensure they can operate independently without reliance on centralised infrastructure.

The rapid development and adoption of AI agents have demonstrated the trend towards AI autonomy. If these agents are to fairly serve all users and assume responsibility for sensitive tasks or important operations, their mechanics must be predictable and transparent.

Decentralised storage networks like Codex enable AI agents to persistently store, retrieve, and share data in a trustless and secure environment. Their character files and configurations can be securely stored on a decentralised network, providing a transparent account of their programming and intended behaviour. This will allow AI agents to remain resilient and independent, free from malicious interference and error through public audits.

Join the Codex Testnet

As AI continues pushing the boundaries of technological innovation, its data storage and transparency needs will only grow. Decentralised storage solutions like Codex provide the scalability, transparency, and resilience required to support the next generation of AI applications.

By integrating decentralised storage into AI development, we can create a more open, verifiable, and censorship-resistant ecosystem for artificial intelligence. Codex’s peer-to-peer network ensures that AI models and datasets remain secure, accessible, and future-proof.

The Codex public testnet is now live, allowing anyone to help build the decentralised, durable data archive for the future Internet. Join the Codex Testnet today and experience the future of decentralised storage for AI applications. To get started, read our step-by-step guide on how to install and run Codex.