Latest Trends and Popular Projects Analysis in the Crypto+AI Field

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 gradually replaces general-purpose AI.
3. Capital is paying more attention to the validation of business models, and projects with cash flow are clearly more favored.

Here are brief introductions and analyses of several popular projects:

Decentralized AI Model Evaluation Platform

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

This is a project with a relatively clear business model, rather than a purely money-burning model. However, preventing fraudulent orders is a significant challenge that requires continuous optimization of the anti-witch attack algorithm. In terms of financing scale, capital clearly favors projects with monetization validation.

Decentralized AI Computing Network

The project completed a $10 million seed round of financing in June. The project has gained a certain level of 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 progress in edge computing and data verifiability, achieving a 40% reduction in latency and supporting access from heterogeneous devices.

The direction of this project is very correct, perfectly aligning with the trend of "localization" in AI. However, when handling complex tasks, it needs to compete with centralized platforms in terms of efficiency, and the stability of edge nodes remains an issue. Nevertheless, edge computing is both a new demand arising from the internal competition of Web2 AI and an advantage of the distributed framework of Web3 AI. Looking forward to seeing more concrete products based on actual performance being implemented.

Decentralized AI Data Infrastructure Platform

The platform incentivizes global users to contribute data across multiple domains, including healthcare, autonomous driving, and voice recognition, through tokens. It has accumulated over 14 million dollars 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 it also employs privacy computing technologies to meet compliance requirements.

The project has also launched an EEG collection device, achieving an expansion from software to hardware. The economic model is designed reasonably, allowing users to earn $16 and 500,000 points through 10 hours of voice annotation, while the cost for enterprises to subscribe to data services can be reduced by 45%.

The greatest value of this project lies in meeting the real needs of AI data annotation, especially in fields such as healthcare and autonomous driving, where data quality and compliance requirements are extremely high. However, a 20% error rate is still higher than the 10% of traditional platforms, and the fluctuation in data quality is an ongoing issue that needs to be addressed. There is significant potential in the field of brain-computer interfaces, but the execution challenges are not insignificant.

Distributed Computing Network on Solana Chain

The project completed a financing of 10.8 million dollars in June. It aggregates idle GPU resources through dynamic sharding technology to support large language model inference, at a cost 40% lower than mainstream cloud service providers. The project turns computing power contributors into stakeholders, incentivizing more people to participate in the network.

This is a typical "aggregate idle resources" model, which makes logical sense. However, a 15% cross-chain validation error rate is indeed quite high, and technical stability needs further improvement. It does have 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 hampered by technical issues.

AI-Driven Cryptocurrency High-Frequency Trading Platform

The platform completed a seed round financing of 3.38 million USD in June. Its technology for dynamically optimizing trading paths can reduce slippage, with a measured efficiency improvement of 30%. The platform caters to current trends and has found an entry point in the relatively blank subfield of DeFi quantitative trading, filling a market demand.

There are no issues in this direction; DeFi indeed requires smarter trading tools. However, high-frequency trading has very high demands for latency and accuracy, and the real-time synergy of AI predictions and on-chain execution still needs further validation. In addition, MEV attacks pose a significant risk, and technical protection measures need to be strengthened.