CAEBloX is a decentralized, peer-to-peer (P2P) economy designed to capture the growing CAE data exchange market, forecasted to largely drive the $9.8 billion CAE software market to over $22.2 billion USD by 2030 at a CAGR of 12.3%​. This market expansion is fueled by the rapid fusion between CAE as a modelling & simulation tool with generative AI. A new generation of generative CAE software is currently in the making, with its developers allocating billions to acquire sufficient datasets to train their models. CAEBloX addresses this demand by creating a transparent, secure, and immutable marketplace where engineers can monetize their data, and companies can access high-value datasets for machine learning and simulation needs.

The current landscape of CAE data sharing is inefficient, costly, and plagued by intellectual property (IP) concerns. Developing high-fidelity CAE datasets is a capital-intensive process, with costs ranging from $32,000 to $146,000 per dataset due to data acquisition, cleaning, storage, and validation​. With machine learning models requiring multiple datasets, training costs can rise to between $365,000 and $1.27 million USD for a complete dataset suite. This high cost, coupled with IP risks, prevents companies from fully leveraging CAE data to drive AI and simulation-based innovation. By offering a decentralized marketplace, CAEBloX enables engineers to convert their simulation data into NFTs, facilitating secure, cost-effective access for companies while protecting and regulating the IP rights for all participants. With CAEBloX, the cost per dataset is expected to decrease 5x at least, with a rewarding system that leverages equitable and democratic incentives for CAE engineers across the globe.

CAEBloX is a decentralized economy where engineers transform their unique models into secure digital assets and earn CAE tokens (CAEX). Here, companies access high-value data for AI innovation while directly compensating creators—no middlemen, just seamless value exchange.

Figure 1. CAEBloX transactional economy overview

CAEBloX's mining difficulty is designed to intrinsically drive innovation and hedge against misuse. Using the Similarity State Tensor (SST), as shown in figure 1, the platform rewards novel contributions with lower difficulty, making it easier for engineers to earn CAEX. Redundant uploads, however, face higher difficulty, filtering out low-value data. This system keeps the network fresh, data-driven, and high-quality—an economy where originality is not just rewarded; it's essential.

CAE developers and software companies can buy access to unique engineering models using CAEX tokens, directly compensating engineers. This demand for high-value data incentivizes originality, as engineers are rewarded for novel contributions, creating a decentralized marketplace where companies drive innovation through transparent, efficient, and direct transactions.

Figure 2 illustrates how CAEBloX processes and verifies CAE model submissions. First, an engineer submits their CAE model to the CAEBloX Platform, which then sends it to a Validator for a Proof of Design (PoD) check. The Validator assesses the model's uniqueness by comparing it to existing models through the Similarity State Tensor (SST), which calculates a Uniqueness Score. This score is sent back to the platform, and if the model is confirmed as unique, the platform tokenizes it as an NFT, rewarding the engineer with CAE Coins (CAEC). This system ensures that each model is original, securely recorded, and that the engineer is rewarded for their valuable contribution.

Figure 2. Diagram of the CAE file tokenization process

Federated Learning on CAEBloX allows CAE developers to train AI models on valuable engineering data without accessing or moving the raw data itself. This "privacy-first" approach is especially crucial for sectors with strict data security requirements, such as healthcare, aerospace, and defense. Here's how it works.

Instead of transferring sensitive CAE datasets to CAE software development companies, CAEBloX sends the machine learning algorithms to where the data resides, as shown in figure 3. These algorithms then learn from the data locally, updating their parameters based on patterns observed without actually viewing or transferring the raw data. This setup ensures that intellectual property and sensitive data remain protected while companies still benefit from data insights for their AI models. It provides a secure way for data to "stay in place," enabling organizations to leverage engineering data for innovation while meeting data privacy standards.

Figure 3. CAE developers access CAEBloX by hosting a validator node or by staking CAEX to incentivises CAE tokenization.

CAEBloX operates with CAET (CAEX) tokens, structured as ERC-20 tokens, each with 16 decimal places, and available in three states: unlocked, staked, and locked. These states support various functions within the CAEBloX economy—unlocked tokens for purchases, staked tokens for validators, and locked tokens for management decisions and dividends. The tokenomics strategy includes two sequential mining pools (Pool I and Pool II), with Pool I having a lower mining difficulty to incentivize early adoption. The network strategy aims to leverage the robust Similarity State Tensor (SST) algorithm for managing mining difficulty and rewarding unique CAE contributions.

Figure 4. Tokens are allocated across Pre-Sale, STO, Network Management, and two Mining Pools, promoting early adoption, governance, and sustained participation in CAEBloX’s decentralized engineering data economy.

CAEBloX begins with a pre-sale of CAEX tokens to fund the platform. After launching the token, CAEBloX will start raise additional capital through a Security Token Offering (STO). This allows the network to easily onboard more engineers and scale the network accessibility globally. Finally, the platform's economy goes live, enabling engineers to tokenize models, validators to secure the network, and companies to access data in a decentralized marketplace. Each phase builds the robust CAEBloX ecosystem.