5.1 Development Advantages of DePIN Combined with AI and Crypto

Control of AI applications returns to end-users. In recent years, although applications like intelligent assistants have begun to adopt edge computing, key modules are still deployed in the cloud, and AI core capabilities are still controlled by a few giants. To truly achieve the decentralization of AI, edge intelligence is undoubtedly the best path. The current mainstream approach is to first train large models with massive amounts of data, then encapsulate them as APIs to provide services to users. This centralized model has many problems. First, users cannot view the details of the model's operation, and the output is not transparent enough. Second, user data is likely to be used by the model training party, and privacy rights cannot be guaranteed. Finally, the leadership of AI innovation is concentrated in the hands of a few resource-rich companies, forming a monopoly.

Edge Intelligence Achieves True AI Openness and Autonomy.

Firstly, the barrier to computing power no longer limits participants. Ordinary users can run open-source pre-trained models on their local devices and fine-tune the models based on their own data. Secondly, there is no need to connect to the network, which ensures user data privacy. Customized models can meet specific needs, significantly lowering the barrier to innovation. Blockchain incentive networks can be built based on commercial application scenarios, allowing different users to share resources and protect intellectual property rights.

Edge intelligence brings AI back to its open and decentralized roots, and its advantages cannot be overlooked. It represents the general direction of AI development and will inevitably dismantle the current oligarchic structure, releasing the control of AI applications to every participant. We are confident that edge intelligence will truly bring AI back to its open and inclusive essence.

Web3 Economic Model: Allowing Users to Participate in the Co-governance of AI Large Models

In Silicon Valley, a circle composed of artificial intelligence researchers and activists has been issuing warnings: AI systems are becoming too powerful, and out-of-control AI could pose a threat to human survival. Former board member and Chief Scientist Ilya believes that general AI should be "super-aligned" with human values. AI will bring about a prosperous new era, but its dangers must be prevented first.

Therefore, how to deal with the risks and challenges of AI has become a hot topic in the industry, and the Web3 economic model provides an important idea. Currently, mainstream AI platforms are controlled by a few tech giants, and users cannot influence their functional design and value orientation. This centralized model poses risks. In contrast, Web3, based on blockchain, decentralizes power to each user through its economic incentive mechanism.

In the Web3 model, different user organizations can independently train AI models, and the platform aggregates different models. Users participate in providing computing power and contributing data through the economic model, obtaining platform management rights based on their contributions and achieving reasonable governance. Users can vote on functional design and directly participate in decision-making, rather than passively accepting. This highly open and user-autonomous model helps to avoid the risk of algorithmic out-of-control, ensuring that the direction of AI development returns to user needs and ensures safety.

The Web3 economic model transforms the user's role from a service recipient to a principal. Users no longer passively adapt to AI but actively adjust and guide AI according to their needs. This model is highly commendable and worth promoting as it allows users to collectively determine the direction of AI development, reducing risks in a human-centric manner and enhancing the social utility of AI. In terms of specific practices, different organizations can independently develop models, and the platform aggregates these models to form a comprehensive AI capability.

Users can vote on functional settings, directly determining the final application direction, rather than passively accepting it. The platform will also set corresponding governance weights based on node contributions. This highly open and user-autonomous model can effectively reduce the risk of algorithmic out-of-control, ensuring that AI meets societal needs and achieves safe co-governance. The industry is actively exploring related practices, and the promotion of this model has positive significance.