Content is experiencing a rise, but the value system is lagging behind.

The production of global content and models is expanding at an exponential rate. According to IDC data, the total amount of global data is expected to reach 181ZB by the end of 2025, more than three times that of 2020; at the same time, the popularity of generative AI has lowered the barrier to content creation to a historic low. The open interfaces of model platforms such as OpenAI, Anthropic, and Mistral allow the generation capabilities of text, images, audio, and code to be accessed by billions of terminals, creating an unprecedented “flood of content production.” In this process, the boundaries between content, models, and algorithms are increasingly blurred, but the value distribution and rights confirmation system have not evolved accordingly.

In fact, the existing Web2 model is still platform-centric. The text, videos, or algorithm models produced by creators ultimately settle in the closed databases of large content or technology platforms. The platform holds the dominant power over data, algorithms, and revenue distribution, while individuals receive only limited exposure or usage sharing.

From the revenue perspective, approximately 72% of the revenue in the global digital content market is concentrated in less than 5% of the platform ecosystem, while the revenue share of original creators or developers has long remained in single digits. This concentrated structure undermines innovation incentives and keeps the logic of “content as an asset” at a theoretical level.

At the same time, the rapid evolution of AI models has further exacerbated the “value gap.” Model training relies on massive amounts of content and data, but contributors behind it can hardly obtain any rights or revenue return. For example, Stability AI publicly acknowledged in 2023 that its Stable Diffusion model training data included billions of unauthorized images, highlighting the structural asymmetry between “content supply—model revenue.” When content becomes the fuel for AI, its economic value is absorbed by algorithms, yet it is difficult to measure and return within the system.

RWA Achieves New Exploratory Directions

We see that this dilemma has given rise to new exploration directions, namely how to make content and models possess asset attributes that are verifiable, measurable, and yield returns.

The traditional RWA (Real World Asset) narrative focuses on the on-chainization of bonds, real estate, and income certificates to enhance the circulation and transparency of financial assets. At the intersection of AI and the content economy, the connotation of RWA is expanding, extending from physical assets to digital production factors. Digital works, training corpora, algorithm codes, and even model invocation rights are being redefined as “new asset units” that can be entitled, traded, and profit-shared.

The above trend has shown signs at both the capital and regulatory levels. Deloitte's “Digital Asset Outlook” published in 2024 indicates that by 2030, approximately 15% of the global RWA market will be composed of digital content, intellectual property, and data assets, with a total scale reaching $3.2 trillion. This means that “content assetization” is moving from the conceptual stage into the stage of institutional construction.

Against this background, the industry urgently needs a set of underlying infrastructure that can allow content, models, and algorithms to be authenticated in a trustworthy manner, measured through a transparent mechanism, and achieve profit distribution across applications and entities. CodexField emerges at this historical turning point, attempting to use a technological approach to solve the gap between content assetization and institutionalization.

CodexField's RWA Narrative

Under the trend of reshaping the content value chain, CodexField is attempting to establish a Web3 native asset infrastructure aimed at creators and developers, with the goal of enabling structured content to achieve rights verification, accessibility, and financialization. The project focuses on highly reusable content units such as code, models, prompts, corpora, and images, proposing a unified data encapsulation and authorization invocation standard, allowing these contents to be verified, tracked on-chain, and mapped for revenue through smart contracts, thereby promoting the standardization process of “content as an asset.”

The CodexField architecture covers the entire process from storage, authorization to billing and profit sharing. The system is compatible with multi-chain ecosystems such as BSC, Ethereum, Solana, Greenfield, and mainstream storage networks, supporting the definition of content access and commercial strategies through smart contracts, enabling on-chain automatic settlement and distribution for content invocation, subscription, model training, and other actions. This mechanism allows creators and collaborators to directly participate in the revenue path, ensuring the traceability of data usage and the transparency of profit sharing.

As a key intermediary in the path of content assetization, CodexField caters not only to independent creators and AI model developers but also provides standardized “content as a service” interfaces for platform providers, facilitating the flow and combination of content assets between the Web3 and AI ecosystems. Its goal is to establish a transparent, verifiable, and financially-enabled content economic protocol on top of the existing fragmented content system, providing foundational support for the future “measurable content economy.”

Technological Innovation and Implementation Path

The system architecture of CodexField is based on a clear assumption that if content is to truly possess asset attributes, it must have technical verifiability and economic measurability in the three stages of rights confirmation, invocation, and settlement. This logic is more of a cross-level systematic engineering design, unifying the asset registration logic of Web3, the invocation tracking mechanism of AI, and the bookkeeping thinking of traditional finance into a closed-loop technical path.

Structured Mapping from Content to Assets

CodexField uses “content capsules” as the core data structure to structurally encapsulate multiple types of creative units such as text, images, model weights, corpora, prompts, and code modules. Each capsule is accompanied by a unique asset ID upon generation, and records the creator's identity, version information, citation relationships, and timestamps, thereby forming asset atomic units on the chain with independent rights confirmation capabilities.

This structure is not merely a hash registration; it is more of a “computable rights confirmation” system, which allows assets to be partially called, incrementally updated, and referenced in combination.

For example, a model may only call a portion of a dataset's corpus or reference specific segments of others' algorithms, while the system automatically traces its dependency path upon invocation and generates corresponding citation weights. This shifts the attribution of content from relying on total ownership to a more granular structured labeling, which is the technical prerequisite for “content financialization.”

Permissible Programmability and Verifiability

In the traditional content ecosystem, authorization often relies on contracts and manual reviews, lacking a verifiable execution basis. CodexField achieves the standardization and procedural expression of authorization relationships through LexDL and CapToken (capability credentials).

LexDL is a human-readable, machine-executable licensing language that can describe conditions such as access scope, geographic restrictions, types of use, and time dimensions. The system generates CapToken based on this — an access credential bound by authorization rules. Each content call or model training requires the corresponding CapToken to pass contract verification, and upon execution, it is automatically written into usage receipts. This approach transforms “authorization” from paper terms into a necessary prerequisite for technical execution, ensuring the revenue boundaries of creators while allowing enterprises or platform parties to achieve automated compliance management.

It is worth noting that this mechanism is particularly critical in a multi-party collaborative environment: AI model training usually involves different data sources and algorithm contributors. Traditional methods have difficulty defining the rights and interests of each party, while the licensing structure based on LexDL + CapToken can replace “human judgment” with “machine trust,” providing an institutional foundation for future cross-institution training and data collaboration.

Generation of verifiable economic events

The value of content and models in the CodexField system is more quantitatively assessed based on the objective behavior of “usage”. The system generates verifiable records for each genuine call, known as usage receipts, abbreviated as UR.

Each receipt contains key information about the task: calling role, calling content, duration, execution efficiency, and resource consumption. It serves as a digital certificate of an economic event that can be tracked and calculated in real-time by the on-chain system. In this way, the value of the content no longer relies on platform pricing or traffic direction, but is automatically measured based on the frequency and depth of use.

These receipts together constitute the “value accounting layer” of CodexField. When content or models are used, the system calculates revenue distribution based on the calling data and directly sends the profit share to the relevant creators, collaborators, and data providers through smart contracts. The entire process is transparent, auditable, and does not require the involvement of a centralized platform.

In more complex scenarios, such as a model referencing multiple algorithms, datasets, or scripts, CodexField will track the reference relationships through a “royalty graph” and automatically decompose the revenue proportions. This graph records the inheritance and derivative relationships between the content, and when a certain node is used, the system can accurately distribute the returns to all contributors along the path.

This mechanism allows the distribution of content profits to no longer remain at the contract signing or platform rule level, but to become an economic behavior of “protocol execution.” Each call, each training, and each collaboration will leave a clear value trace on the chain. Ultimately, CodexField enables the value flows of content production and model training, which were originally vague, to possess the same measurability and credibility as financial assets—this is a key step in promoting the institutionalization of “content assetization” from concept to reality.

Calculating Trustworthiness and Cross-Domain Consistency

The execution system of CodexField is built on a multi-layer verification architecture, with the core goal of finding a balance between efficiency, reliability, and scalability.

In lightweight task scenarios, such as content retrieval or model invocation, the system generates verifiable results through zero-knowledge proofs (ZK), allowing external nodes to confirm execution authenticity without exposing data details. For large-scale model inference or training tasks, CodexField adopts a trusted execution environment (TEE) combined with a committee verification mechanism, maintaining computational performance while ensuring security.

This design enables CodexField to flexibly allocate resources across tasks with varying intensities and sensitivities, while ensuring that the computational results of the entire system are verifiable and auditable. For developers who need to share computing power across different institutions or regions, this architecture significantly reduces trust costs and enhances the overall reliability of execution.

To further ensure the smoothness of cross-chain collaboration, CodexField has also introduced a “dual bridge mechanism.”

“Receipt Bridge” is responsible for synchronizing execution results and settlement data, while “Mirror Bridge” is used for synchronizing asset status and authorization permissions. This layered structure allows for consistency of data and status across different networks without sacrificing performance due to excessive synchronization.

In scenarios of multi-chain deployment and cross-domain execution, it can ensure the continuity and traceability of the calling behavior—regardless of which chain the content is stored on, the authorization and settlement process can be fully recorded and verified within the framework of CodexField.

From the perspective of system design, this mechanism provides the necessary stability and compatibility for future institutional applications, and allows content assets to be securely circulated in a broader blockchain environment.

Developer Interface

On top of the overall architecture of CodexField, the Gitd toolchain plays the role of the “developer entry point”. It allows creators and engineers to complete the on-chain, authorization definition, and revenue attachment of content or models directly within a familiar Git workflow. In other words, when a developer submits code or a model version, the system can automatically generate the corresponding “content capsule” and authorization information, and automatically track revenue in future calls or references. This transforms the complex process of content rights confirmation and profit sharing into a part of the development action.

This mechanism is particularly important for collaborative projects. Multiple developers can work in the same repository, and each commit or modification generates a corresponding on-chain record. When the project is called or commercialized, the system automatically generates revenue receipts based on these records and distributes profits according to contribution ratios, without the need for additional settlements or manual statistics. This makes collaboration transparent and allows the labor value of each participant to be confirmed in real time.

At the same time, CodexField also integrates the production process of models and agents into the asset system through two extension modules, Model Fabric and Agent Fabric. The former covers the entire lifecycle from training, fine-tuning, to inference and evaluation; the latter focuses on the task execution and feedback loop of agents. Together, they have achieved the assetization of “AI production capacity,” transforming models from mere tools to economic units with rights confirmation, measurement, and revenue return capabilities.

In such a system, developers, creators, and AI agents no longer belong to different ecosystems, but together form a verifiable and revenue-sharing content economy network. CodexField makes this process independent of platforms, relying instead on the protocol itself, which is one of its most structurally significant innovations.

Establish a New Order of Content Assetization

CodexField is reshaping the value order of content and models in a systematic way, making “compliance” a system attribute rather than an external requirement, through on-chain rights confirmation and verifiable execution as the core. Every authorization, invocation, and revenue sharing between creators and institutions is based on on-chain certificates, without relying on centralized audits or platform endorsements, thus naturally aligning with global regulatory frameworks for digital assets, data circulation, and AI model transactions.

On this basis, the measurable value system built by CodexField is using real-time generated call receipts and an automatic profit-sharing model, with “usage” as the core of valuation. This allows the economic value of content, algorithms, and models to no longer depend on platform exposure or contract negotiation, but to be directly determined by actual usage. This mechanism transforms the concept of “content as an asset” into a verifiable economic activity.

From a more macro perspective, the practical path of CodexField is driving the RWA (Real World Assetization) of digital production factors. Code, models, corpora, and algorithms can all exist here in a “verifiable, auditable, and settleable” manner, becoming a new class of verifiable assets. Against the backdrop of a traditional platform economy that still centers around traffic and centralized control, CodexField demonstrates a kind of institutional innovation: replacing platforms with protocols, reconstructing trust through transparent rules, allowing the production relations of the digital economy to truly possess the characteristics of assetization and autonomy.

From Content Assetization to the Infrastructure of Smart Society

The long-term significance of CodexField lies in transforming “digital creation” from an individual act into a part of social infrastructure. In an era of information explosion and the widespread use of intelligent models, content, algorithms, and models are no longer just tools or products, but new factors of production. CodexField, with its core mechanisms of rights confirmation, measurement, and settlement, allows these factors to be managed, circulated, and traded in an institutionalized manner. Just as electricity made industrialization possible, CodexField is enabling intelligent productivity to have measurable, distributable, and accumulable economic attributes, laying the foundation for the next stage of the digital economy.

This structural change will fundamentally alter the relationship between content and agents. AI models will further become open units that can be shared, verified, and rewarded; content creators, model developers, and institutions will no longer be dependent on centralized platforms, but will be able to share profits and governance rights on-chain through rules, forming a truly “autonomous content economy.” In this system, every invocation, training, or citation is a valuable economic event and also a fundamental action of resource allocation in an intelligent society.