Current Status and Future Prospects of Web3 AI

Recently, NVIDIA's stock price has reached new highs, and the advancements in multimodal models have further deepened the technological barriers of Web2 AI. From semantic alignment to visual understanding, from high-dimensional embeddings to feature fusion, complex models are integrating various modalities of expression at an unprecedented speed, constructing an increasingly closed AI stronghold. The U.S. stock market has also demonstrated its recognition of this trend through actual actions, with both cryptocurrency-related stocks and AI stocks showing a mini bull market.

However, this wave seems to have no connection to the cryptocurrency field. The Web3 AI attempts we have seen, especially the evolution in the Agent direction in recent months, have a significant deviation in direction: trying to assemble a Web2-style multimodal modular system with a decentralized structure is, in fact, a dual misalignment of technology and thinking. In the current environment where modular coupling is extremely strong, feature distribution is highly unstable, and computing power requirements are increasingly centralized, multimodal modularity finds it difficult to establish itself in the Web3 domain.

The future of Web3 AI lies not in 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 adopt a tactical approach of "surrounding the city from the countryside."

Challenges Faced by Web3 AI

Semantic Alignment and High-Dimensional Embedding

In modern Web2 AI multimodal systems, "semantic alignment" is a key technology for mapping information from different modalities into the same semantic space. This requires a high-dimensional embedding space as a foundation to enable effective collaboration between modules. However, the Web3 Agent protocol struggles to achieve high-dimensional embeddings because they often merely encapsulate off-the-shelf APIs, lacking a unified central embedding space and cross-module attention mechanisms.

To achieve a full-link intelligent agent with industry barriers, it is necessary to start with end-to-end joint modeling, cross-module unified embedding, and systematic engineering of collaborative training and deployment. However, the current market lacks sufficient demand for this, and naturally, there are also corresponding solutions.

Limitations of Attention Mechanism

High-level multimodal models require precisely designed attention mechanisms. Web2 AI has made significant progress in this regard, such as self-attention and cross-attention mechanisms in Transformers. However, modular Web3 AI struggles to achieve unified attention scheduling. This is because attention mechanisms rely on a unified Query-Key-Value space, while the data formats and distributions returned by independent APIs vary, making it impossible to form interactive Q/K/V.

Feature Fusion Shallowization

Web3 AI is still at the simple static stitching stage in terms of feature fusion. This is because dynamic feature fusion requires high-dimensional space and sophisticated attention mechanisms as prerequisites. Web2 AI tends to favor end-to-end joint training, while Web3 AI often adopts a discrete module stitching approach, lacking a unified training objective and cross-module gradient flow.

Barriers to Entry and Future Opportunities in the AI Industry

The technical barriers in the AI industry are deepening, but the opportunities of Web3 AI have not yet truly emerged. The core advantage of Web3 AI lies in decentralization, with its evolutionary path characterized by high parallelism, low coupling, and compatibility with heterogeneous computing power. This gives Web3 AI an advantage in scenarios such as edge computing, making it suitable for lightweight structures, easily parallelizable, and incentivizable tasks.

In the future, the development of Web3 AI should adopt the strategy of "surrounding the city from the countryside":

1. Cut in from the edges and establish a foothold in small-scale scenarios.
2. Combining point and surface, advancing in a circular manner, continuously iterating and updating the product in small application scenarios.
3. Stay flexible and agile, able to quickly adjust strategies according to different scenarios.

Only when the dividends of Web2 AI disappear will the pain points it leaves behind possibly become the entry opportunities for Web3 AI. Before that, Web3 AI practitioners need to carefully discern projects with real potential, focusing on those that can steadily develop in niche areas and possess sufficient flexibility in their protocols.