Prospects and Challenges at the AI x Crypto Crossroads

In recent years, the AI x Crypto has undergone significant growth and change. This emerging field combines two transformative technologies, blockchain and AI, to explore how decentralized approaches can enhance AI applications, increasing transparency, security, and user control. The AI x Crypto is rapidly advancing due to the rise of generative AI and the growing demand for decentralized solutions. This exciting frontier of innovation in the technology sector is gaining momentum.

A Novel Perspective on Assetization in the AI x Crypto Domain: The Path to Innovation in Data, Models, and Arithmetic

The primary use case of Crypto is assetization. In the field of AI x Crypto, there are three major scenarios: arithmetic assetization, model/Agent assetization, and data assetization.

Arithmetic assetization has two main directions: decentralized computing and decentralized reasoning for AI Agents. Decentralized computing focuses on using distributed networks for AI model training, while AI Agents mainly use trained AI models for decentralized reasoning.

However, from a technical perspective, training large AI models requires extensive data processing and high-speed communication bandwidth, which places significant demands on hardware facilities.

In contrast, AI reasoning performed by AI agents makes decentralization more feasible and practical due to its low computing power and communication bandwidth requirements.

Assetization of models and agents is an important direction, particularly driven by large language models like GPT.

Data assetization is an important direction in the AI x Crypto track. It focuses on using decentralized technologies and incentives to release and leverage the large amount of data resources that are usually confined to the private domain.

Decentralized data annotation, as a part of data assetization, improves data availability and quality while reducing cost and time. It incentivizes community members to participate in data annotation through the 'Label to Earn' model or crowdsourcing platform.

Currently, there are few scenarios in which AI and Crypto are actually combined, and most of these directions have low thresholds.

Several Core Challenges

The immaturity of the business model: The combination of AI and Crypto is still in its early stages, and many projects attempting to merge the two are not yet mature enough to fully utilise their respective strengths.

Interdisciplinary expertise and practitioner preference: One side may have a deep background in AI, while the other may have a deep understanding of Web3 and cryptocurrencies.

Technical challenges of internal empowerment: When Crypto attempts to empower AI from within, the main obstacle it faces is the poor scalability of these technologies, which limits their practical application. Similarly, when AI tries to empower Crypto from within, it must address not only the complex engineering issues of integrating AI into existing systems but also ensure that this integration works efficiently and does not hinder system performance.

Despite the current difficulties, the combination of AI and Crypto remains one of the most important tracks of this cycle. AI is widely recognized as the key force driving the next round of tech revolution, and the combination of AI and Crypto not only demonstrates strong technological potential and application promise but also occupies a unique and important position in the current technology and investment landscape.

Compared to the previous round, which focused on the concept of the meta-universe, this round requires more practical applications on the ground, and there are challenges in validating user data.

Additionally, in the AI x Crypto project, the importance of arithmetic power is self-evident. Arithmetic power is not only directly related to the efficiency and effectiveness of AI model training, but also an important indicator of the project's technical strength and market consensus.

Representative projectS: Worldcoin, Arkham, Render Network, Arweave, Akash Network, Bittensor, and io.net ect.