Analysis of Recently Popular Projects in the Crypto+AI Sector

In the past month, the Crypto+AI track has shown three significant trend changes:

1. The project's technical path is more pragmatic, focusing on performance data rather than pure conceptual packaging.
2. Vertical segmentation scenarios become the focus of expansion, and specialized AI applications gradually replace generalized AI.
3. Capital is paying more attention to business model validation, and projects with cash flow are obviously more favored.

Here is a brief introduction and analysis of several popular 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 companies like OpenAI to purchase data, creating a real cash flow.

The business model of this project is relatively clear, and it is not purely a money-burning model. However, preventing fake orders and improving the anti-witch attack algorithm remains a significant challenge. From the scale of the $33 million financing, it is evident that capital prefers projects that have validated their monetization capabilities.

Decentralized AI Computing Network

The network has gained some market recognition in the Solana DePIN field through a browser plugin. The team has launched a data transmission protocol and an inference engine, making substantial explorations in edge computing and data verifiability, capable of reducing latency by 40% and supporting access from heterogeneous devices.

The project direction aligns with the "downward" trend of AI localization. However, when handling complex tasks, efficiency still needs to be compared with centralized platforms, and the stability of edge nodes is also an issue to be resolved. Nevertheless, edge computing, as a new demand arising from the web2 AI competition, is also an advantage of the web3 AI distributed framework, and it is worth looking forward to its implementation through specific products that advance actual performance.

Decentralized AI Data Infrastructure Platform

The platform incentivizes global users to contribute data across multiple fields, including healthcare, autonomous driving, and voice recognition, through tokens. It has accumulated over $14 million in revenue and established a network of over a million data contributors.

Technically, the platform integrates ZK verification and BFT consensus algorithms to ensure data quality, and utilizes privacy computing technology to meet compliance requirements. They have also launched brainwave acquisition devices, realizing the expansion from software to hardware. The economic model is designed reasonably, allowing users to earn 16 dollars and 500,000 points through 10 hours of voice annotation, while the cost for enterprises subscribing to data services can be reduced by 45%.

The greatest value of this project 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 the 10% of traditional platforms, and the fluctuation in data quality is an issue that needs ongoing resolution. Although the brain-computer interface direction has a lot of imaginative potential, the execution difficulty is not small.

Distributed Computing Network on Solana Chain

The network aggregates idle GPU resources through dynamic sharding technology, supporting large model inference at a cost 40% lower than certain cloud service providers. The design of its tokenized data trading transforms computing power contributors into stakeholders, helping to incentivize more people to participate in the network.

This is a typical "aggregation of idle resources" model, which makes logical sense. However, a 15% cross-chain verification error rate is indeed quite high, and technical stability still needs improvement. In scenarios such as 3D rendering, where real-time requirements are not critical, this project does have advantages. The key is to reduce the error rate; otherwise, no matter how good the business model is, it will be hampered by technical issues.

AI-Driven Cryptocurrency High-Frequency Trading Platform

The platform utilizes MCP technology to dynamically optimize trading paths, reducing slippage and improving efficiency by 30% in practical tests. In line with the AgentFi trend, it has found an entry point in the relatively untapped niche of DeFi quantitative trading, filling a market need.

The project direction is correct; DeFi indeed needs 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. Furthermore, MEV attacks pose a significant risk, and technical protective measures must keep up.