Analysis of Popular Projects and Trends in the Crypto+AI Sector

Recently, we have sorted out the popular projects in the Crypto+AI track and found three significant trend changes:

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, with specialized AI applications replacing generalized AI.
3. Capital is increasingly focused on business model validation, and projects with cash flow are obviously more favored.

The following is a brief introduction and analysis of several representative projects:

Decentralized AI Model Evaluation Platform

The platform completed a $33 million seed round of financing in June. It applies the advantage of human subjective judgment to the shortcomings of AI evaluation, scoring more than 500 large models through human crowdsourcing. User feedback can be redeemed for cash, which has attracted multiple companies to purchase data, creating a real cash flow.

This is a project with a relatively clear business model, not just a pure money-burning model. However, preventing fake orders is a major challenge, and the anti-witch attack algorithm needs continuous optimization. From the perspective of fundraising scale, capital clearly values projects that have verified monetization.

Decentralized AI Computing Network

The project completed a $10 million seed round of financing in June. The project has achieved a certain market consensus in the Solana DePIN field through a browser plugin. 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 for heterogeneous devices.

The project direction aligns with the "downward" trend of AI localization. However, when handling complex tasks, there is a need to compete with centralized platforms in terms of efficiency, and the stability of edge nodes remains a concern. Nevertheless, edge computing is not only a new demand for the internal competition of Web2 AI but also an advantage of the distributed framework of Web3 AI, making it worth paying attention to its practical performance through specific products for implementation.

Decentralized AI Data Infrastructure Platform

The platform incentivizes global users to contribute multi-domain data (such as healthcare, autonomous driving, voice, etc.) through tokens, accumulating over $14 million in revenue and establishing a network of millions of data contributors.

The technology integrates ZK verification and BFT consensus algorithms to ensure data quality, and also employs privacy computing technology to meet compliance requirements. Interestingly, the project has also launched an EEG collection device, expanding from software into the hardware domain. Its economic model is well-designed, allowing users to earn cash and points through voice labeling, while the cost for enterprises subscribing to data services can be reduced by 45%.

The greatest value of the project lies in addressing the real needs of AI data annotation, especially in fields such as healthcare and autonomous driving that have extremely high data quality and compliance requirements. However, the 20% error rate is still higher than the 10% of traditional platforms, and fluctuations in data quality remain a problem that needs to be continuously addressed. There is potential in the direction of brain-computer interfaces, but the execution difficulty is not small.

Distributed Computing Network on Solana Chain

The project completed a $10.8 million funding round 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 services. Its tokenized data trading design turns computing power contributors into stakeholders, helping to incentivize more people to participate in the network.

This is a typical "aggregated idle resource" model, which makes logical sense. However, a 15% cross-chain validation error rate is relatively high, and technical stability needs further optimization. It indeed 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 burdened by technical issues.

AI-Driven Cryptocurrency High-Frequency Trading Platform

The platform completed a $3.38 million seed round financing in June. Its MCP technology can dynamically optimize trading paths, reducing slippage and improving efficiency by 30% in practice. The project aligns with the AgentFi trend and has found a foothold in the relatively untapped subfield of DeFi quantitative trading, filling a market demand.

The project direction is correct, and DeFi indeed needs smarter trading tools. However, high-frequency trading requires extremely high levels of latency and accuracy, and the real-time synergy of AI prediction and on-chain execution still needs to be verified. In addition, MEV attacks pose a significant risk, and technical protective measures need to keep pace.