How to combine AI and cryptocurrency_What projects are there in AI Crypto?

AI projects are divided into five categories: 1. The platform category builds decentralized AI infrastructure, such as Bittensor, NEAR, and Sahara; 2. The tool resource category provides markets for production materials such as computing power, such as Render, Akash, and Ocean; 3. The application agent category encapsulates AI into composable components on the chain, such as Fe tch.ai, SingularityNET, AAN; 4. The data sovereignty category ensures that the data is trustworthy and verifiable, such as Modulus, Privy, and Verida; 5. The protocol upgrade category embeds AI into consensus and governance, such as EigenLayer, Worldcoin, and Celestia.

1. AI platform projects

This type of project builds decentralized AI infrastructure and provides model training, reasoning and data processing capabilities, which is equivalent to the underlying public chain in the AI ​​field. Developers can deploy and optimize models without relying on centralized cloud services.

1. Bittensor (TAO) adopts a utility proof mechanism to connect global AI nodes through more than 90 subnets, and rewards model output for real contributions.

2. NEAR Protocol (NEAR) integrates the Chain Abstraction module to support AI agents to directly call inference services and settle fees on the chain.

3. Sahara AI (SAHARA) establishes a multi-layered value attribution system so that user data can obtain token profits when participating in model training.

Mainstream virtual currency exchanges in 2025:

Binance official website entrance: www.binance.com

Binance official app:

Oux yi official website entrance: www.oux yi.com

Ouyi official app:

Huobi official website entrance:

Huobi official app:

2. AI tools and resource projects

Such projects focus on the decentralized supply of key production materials such as computing power, data, and bandwidth required for AI operation, forming a verifiable and tradable resource market.

1. Render Network (RNDR) aggregates idle GPUs around the world to provide distributed rendering and computing services for AI image generation and large model fine-tuning.

2. Akash Network (AKT) allows users to rent or lease server resources and supports the bidding and execution of LLM training tasks on the chain.

3. Ocean Protocol (OCEAN) builds a data asset exchange to enable the authorized use of sensitive data under the protection of zero-knowledge proof.

3. AI application and intelligent body projects

This type of project encapsulates AI capabilities into composable components on the chain, supporting complex behaviors such as autonomous agents executing transactions, managing assets, and coordinating tasks.

1. Fetch.ai (FET) develops an autonomous agent framework and has implemented automatic contract fulfillment on the chain in supply chain scheduling, energy trading and other scenarios.

2. SingularityNET (AGIX) provides an AI model as a service (AIaaS) market, and users can use tokens to call APIs such as speech recognition and medical diagnosis.

3. Autonomous Agents Network (AAN) supports multi-agent collaboration to complete closed-loop operations such as DeFi strategy backtesting and cross-chain arbitrage.

4. Data sovereignty and verification projects

These projects use cryptography and blockchain to ensure that the source of AI training data is credible, the process is auditable, and the results are verifiable, solving the trust bottleneck of the black box model.

1. Modulus Labs (MOD) builds a model verification layer on the chain and attaches ZK-SNARKs proof to the AI ​​output to ensure logical consistency.

2. Privy (PRIVY) provides private computing SDK for Web3 applications, allowing users to participate in federated learning without exposing original data.

3. Verida (VDA) creates a decentralized identity data layer, allowing users to independently control which data can be used for which AI model training.

5. AI-driven on-chain protocol upgrade projects

This direction embeds AI into the core logic of the protocol to improve consensus efficiency, risk identification and governance response speed, and enhance system-level adaptability.

1. EigenLayer (EIGEN) introduces AI monitoring node behavior mode and dynamically adjusts the pledge penalty threshold to prevent new attacks.

2. Worldcoin (WLD) combines iris recognition and zero-knowledge proof to achieve witch-resistant unique personality authentication on the chain.

3. Celestia (TIA) tests the AI ​​compression block header verification path to reduce light client synchronization overhead and improve L2 access efficiency.

The above is the detailed content of How to combine AI and cryptocurrency_What projects are there in AI Crypto?. For more information, please follow other related articles on the PHP Chinese website!