Opportunities and Challenges of Web3 AI Development

Recently, as Nvidia's stock price continues to rise, the evolution of multimodal models does not seem to have caused chaos in the Web2 AI field; rather, it has further deepened the technological barriers. From semantic alignment to visual understanding, from high-dimensional embedding to feature fusion, complex models are integrating various modalities of expression at an unprecedented speed, building an increasingly closed AI stronghold.

However, this wave seems to have little connection with the cryptocurrency field. The current attempts in Web3 AI, especially the recent explorations in the Agent direction, show a clear deviation in direction. Attempting to assemble a Web2-style multimodal modular system using decentralized structures is, in fact, a dual misalignment of technology and thinking.

In the current environment where the coupling of modules is extremely strong, feature distribution is highly unstable, and computing power demands are increasingly concentrated, multimodal modularization finds it difficult to take root in Web3. The future of Web3 AI does not lie in simple imitation, but in strategic circumvention. From semantic alignment in high-dimensional space, to information bottlenecks in attention mechanisms, and to feature alignment under heterogeneous computing power, Web3 AI needs to find a new path.

Web3 AI is based on a flattened multi-modal model, facing severe challenges in semantic alignment. The lack of a high-dimensional embedding space makes it difficult for different modal information to effectively integrate, affecting the overall performance of the model. Meanwhile, the low-dimensional space restricts the precise design of the attention mechanism, making it difficult for the model to capture complex cross-modal correlations.

In terms of feature fusion, Web3 AI is currently still at the simple static stitching stage. The lack of a unified high-dimensional representation and dynamic fusion strategies leads to an inability to fully leverage the potential value of multimodal data.

Despite the deepening technological barriers in the AI industry, the opportunities for Web3 AI may lie in a "rural encircling the city" strategy. We should start small-scale trials in edge scenarios, looking for breakthroughs in lightweight structures, easily parallelizable and incentivized tasks. For instance, there may be opportunities in areas such as LoRA fine-tuning, behavior-aligned post-training tasks, crowdsourced data training and labeling, training of small foundational models, and collaborative training on edge devices.

However, Web3 AI projects need to maintain flexibility and quickly adjust to different scenarios to adapt to the dynamically changing market demands. An overly large and rigid network architecture may limit the development potential of the project.

Overall, the development path of Web3 AI is still full of challenges, but through strategic positioning and continuous innovation, there is still hope to find breakthroughs in specific areas, opening up new possibilities for the future of decentralized AI.