Analysis of Popular Projects in the Crypto+AI Track Recently

In the past month, the Crypto+AI sector has shown three significant trends.

1. The project’s technical approach is more pragmatic, focusing on performance data rather than pure conceptual packaging.
2. Vertical segmentation scenarios become the focus of expansion, and specialized AI gradually replaces generalized AI.
3. Capital is more focused on validating business models, and projects with cash flow are more favored.

Here are introductions and analyses of several representative projects:

Decentralized AI Model Evaluation Platform

The platform scores over 500 large models through human crowdsourcing, and user feedback can be exchanged for cash. The platform has attracted well-known AI companies to purchase data, creating actual cash flow.

This business model is relatively clear and not purely a money-burning model. However, preventing fraudulent orders and countering witch attacks remain major challenges, and related algorithms need continuous optimization. From the scale of the $33 million financing, it is evident that capital is more favorably inclined towards projects with monetization validation.

Decentralized AI Computing Network

The project has achieved a certain market consensus in the DePIN field of Solana. The newly launched data transmission protocol and inference engine have made breakthroughs in edge computing and data verifiability, reducing latency by 40% and supporting access from heterogeneous devices.

The project direction aligns with the "sinking" trend of AI localization. Although there is still a need to compete with centralized platforms in terms of efficiency when handling complex tasks, and there are challenges regarding the stability of edge nodes, edge computing, as a new demand arising from the involution of web2 AI, happens to be an advantage of the web3 AI distributed framework.

Decentralized AI Data Infrastructure Platform

The platform incentivizes global users to contribute multi-domain data through tokens and has established a network of millions of data contributors. Technically, it integrates ZK verification and BFT consensus algorithms to ensure data quality, and employs privacy computing technology to meet compliance requirements.

The project has also launched EEG collection devices, achieving an expansion from software to hardware. Its economic model is designed reasonably, providing users with considerable returns while reducing data service costs for enterprises.

The project's greatest value lies in meeting the real demand for AI data annotation, especially in fields such as healthcare and autonomous driving, where data quality and compliance requirements are extremely high. However, the 20% error rate is still higher than traditional platforms, and the fluctuation in data quality requires ongoing improvement.

Distributed Computing Network on the Solana Chain

The project aggregates idle GPU resources through dynamic sharding technology, supporting large AI model inference at a cost 40% lower than traditional cloud services. Its tokenized data trading design transforms computing power contributors into stakeholders, helping to incentivize more participation.

This is a typical "aggregation of idle resources" model, which is logically feasible. However, a 15% cross-chain validation error rate is still relatively high, and technical stability needs further improvement. It has advantages in scenarios such as 3D rendering, where real-time requirements are not high; the key is to reduce the error rate and avoid technical issues that could impact the implementation of the business model.

AI-Driven Cryptocurrency High-Frequency Trading Platform

The platform uses MCP technology to dynamically optimize trading paths, reducing slippage and improving efficiency by 30% in actual tests. This project has entered the relatively untapped subfield of DeFi quantitative trading, filling the market demand.

The direction is correct; DeFi does need smarter trading tools. However, high-frequency trading has extremely high requirements for latency and accuracy, and the real-time synergy of AI predictions and on-chain execution still needs to be verified. In addition, MEV attacks present a significant risk, necessitating enhanced technical protective measures.