Analysis of the Three Major Trends and Popular Projects in the Crypto+AI Track Recently

In the past month, the Crypto+AI sector 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 pays more attention to the validation of business models, and projects with cash flow are clearly more favored.

Here is a brief introduction and analysis of several popular projects:

1. Decentralized AI Model Evaluation Platform

The platform completed a $33 million seed round financing in June. It applies the human advantage of subjective judgment to the shortcomings of AI evaluation, scoring over 500 large models through human crowdsourcing. User feedback can be redeemed for cash, with every 1,000 points equal to $1. The platform has attracted several well-known companies to purchase data, creating actual cash flow.

This is a project with a relatively clear business model, and it is not purely a burn money model. However, preventing brush orders is a major challenge, and the anti-witch attack algorithm needs continuous optimization. From the perspective of financing scale, capital clearly places more importance on projects with monetization validation.

2. Decentralized AI Computing Network

The project completed a $10 million seed round of financing in June. It has already gained some market consensus in the Solana DePIN field through a browser plugin. Team members come from several well-known projects, and the newly launched data transmission protocol and inference engine have made substantial explorations in edge computing and data verifiability, reducing latency by 40% and supporting access from heterogeneous devices.

The direction of this project is correct, as it precisely aligns with the "downward" trend of AI localization. However, when handling complex tasks, it needs to compete with centralized platforms in terms of efficiency, and the stability of edge nodes remains a concern. Nonetheless, edge computing is a new demand arising from the internal competition of Web2 AI, and it is also an advantage of the distributed framework of Web3 AI, making it worthwhile to pay attention to the subsequent specific product implementation.

3. Decentralized AI Data Infrastructure Platform

The platform incentivizes global users to contribute data across multiple fields, including healthcare, autonomous driving, and voice. It has accumulated over $14 million in revenue and established a network of millions of data contributors. Technically, it integrates ZK verification and BFT consensus algorithms to ensure data quality, and also employs privacy computing technology to meet compliance requirements.

The project has also launched a brainwave acquisition device, achieving an expansion from software to hardware. Its economic model design is good, allowing users to earn $16 and 500,000 points for 10 hours of voice annotation, while the cost of enterprise subscription data services can be reduced by 45%.

The greatest value of this project lies in addressing the real needs of AI data annotation, especially in fields such as healthcare and autonomous driving, where there are extremely high demands for data quality and compliance. However, the 20% error rate is still higher than the 10% of traditional platforms, and the fluctuation in data quality remains an ongoing issue to be resolved. The brain-computer interface direction, although rich in imaginative potential, presents considerable execution challenges.

4. Distributed Computing Network on Solana Chain

The project completed a $10.8 million financing in June. It aggregates idle GPU resources through dynamic sharding technology, supporting large language model inference at a cost 40% lower than certain cloud service providers. Its design for tokenized data trading turns computing power contributors into stakeholders, helping to incentivize more people to participate in the network.

This is a typical "aggregated idle resources" model, which makes sense logically. However, a 15% cross-chain validation error rate is indeed too high, and the technical stability needs to be further improved. It has advantages in scenarios like 3D rendering where real-time requirements are not high; the key is whether the error rate can be reduced, otherwise even the best business model will be hindered by technical issues.

5. AI-Driven Cryptocurrency High-Frequency Trading Platform

The platform completed a seed round financing of 3.38 million USD in June. Its MCP technology can dynamically optimize trading paths, reducing slippage, with actual efficiency improvements of 30%. This project aligns with the AgentFi trend, finding a foothold in the relatively untapped subfield of DeFi quantitative trading, filling a market demand.

The direction is undoubtedly correct; DeFi indeed needs smarter trading tools. However, high-frequency trading has extremely high demands for latency and accuracy, and the real-time synergy of AI predictions and on-chain execution still needs to be validated. Additionally, MEV attacks pose a significant risk, and technological safeguards need to keep up.