In the past month, three major trends have emerged in this sector: practical technology, segmented scenarios, and capital focusing on cash flow. Let's take a look at the highlights and challenges of popular projects.

Written by: Haotian

A review of several popular projects in the Crypto+AI sector over the past month reveals three significant trend changes, along with brief introductions and comments on the projects:

1) Project technology paths are becoming more pragmatic, focusing on performance data rather than pure conceptual packaging;

2) Specialized segmented scenarios are becoming the focus of expansion, with generalized AI giving way to specialized AI;

3) Capital is placing more emphasis on business model validation, with projects that have cash flow being significantly more favored;

Attached: Project introductions, highlight analyses, and personal comments:

1. @yupp_ai

Project Introduction: A decentralized AI model evaluation platform that completed a $33 million seed round in June, led by a16z, with participation from Jeff Dean.

Highlight Analysis: It applies the advantages of human subjective judgment to the shortcomings of AI evaluation. By crowdsourcing, it scores over 500 large models, and user feedback can be redeemed for cash (1000 points = $1). It has attracted companies like OpenAI to purchase data, demonstrating real cash flow.

Personal Comment: This project has a relatively clear business model and is not purely a burn-money model. However, preventing fraudulent orders is a significant challenge, and the anti-sybil attack algorithm needs continuous optimization. The $33 million financing scale indicates that capital is clearly more focused on projects with monetization validation.

2. @Gradient_HQ

Project Introduction: A decentralized AI computing network that completed a $10 million seed round in June, led by Pantera Capital and Multicoin Capital.

Highlight Analysis: With the Sentry Nodes browser plugin, it has gained some market consensus in the Solana DePIN field. The team members come from Helium, and they have newly launched the Lattica data transmission protocol and Parallax inference engine, making substantial explorations in edge computing and data verifiability, reducing latency by 40% and supporting heterogeneous device access.

Personal Comment: The direction is correct, aligning well with the trend of AI localization "sinking." However, when handling complex tasks, efficiency needs to be compared with centralized platforms, and the stability of edge nodes remains an issue. Nevertheless, edge computing is a new demand emerging from the web2AI competition and is a distributed framework advantage of web3AI. I am optimistic about advancing practical performance with specific products.

3. @PublicAI_

Project Introduction: A decentralized AI data infrastructure platform that incentivizes global users to contribute multi-domain data (medical, autonomous driving, voice, etc.) through tokens, with cumulative revenue exceeding $14 million and a network of over a million data contributors.

Highlight Analysis: Technically, it integrates ZK verification and BFT consensus algorithms to ensure data quality, and it also uses Amazon Nitro Enclaves privacy computing technology to meet compliance requirements. Interestingly, it has launched the HeadCap brainwave collection device, expanding from software to hardware. The economic model is also well-designed, allowing users to earn $16 + 500,000 points for 10 hours of voice annotation, while the cost for enterprises subscribing to data services can be reduced by 45%.

Personal Comment: I feel that the greatest value of this project lies in addressing the real demand for AI data annotation, especially in fields like medical and autonomous driving, which have extremely high data quality and compliance requirements. However, a 20% error rate is still higher than the 10% of traditional platforms, and data quality fluctuations are an ongoing issue that needs to be resolved. The brain-computer interface direction has considerable imaginative potential, but the execution difficulty is also significant.

4. @sparkchainai

Project Introduction: A distributed computing network on the Solana chain that completed $10.8 million in financing in June, led by OakStone Ventures.

Highlight Analysis: It aggregates idle GPU resources through dynamic sharding technology, supporting inference for large models like Llama3-405B, with costs 40% lower than AWS. The design of tokenized data trading is quite interesting, directly turning computing power contributors into stakeholders and incentivizing more people to participate in the network.

Personal Comment: This is a typical "aggregating idle resources" model, which makes logical sense. However, a 15% cross-chain validation error rate is indeed a bit high, and technical stability needs further refinement. Nevertheless, it has advantages in scenarios like 3D rendering, where real-time requirements are not as high; the key is whether the error rate can be reduced, as even the best business model can be dragged down by technical issues.

5. @olaxbt_terminal

Project Introduction: An AI-driven cryptocurrency high-frequency trading platform that completed a $3.38 million seed round in June, led by @ambergroup_io.

Highlight Analysis: The MCP technology can dynamically optimize trading paths, reducing slippage, with a measured efficiency improvement of 30%. It aligns with the #AgentFi trend, finding an entry point in the relatively blank segment of DeFi quantitative trading, thus filling a market demand.

Personal Comment: The direction is correct; DeFi indeed needs smarter trading tools. However, high-frequency trading requires extremely high demands for latency and accuracy, and the real-time synergy between AI predictions and on-chain execution still needs validation. Additionally, MEV attacks pose a significant risk, and technical protective measures must keep pace.

免责声明：本文章仅代表作者个人观点，不代表本平台的立场和观点。本文章仅供信息分享，不构成对任何人的任何投资建议。用户与作者之间的任何争议，与本平台无关。如网页中刊载的文章或图片涉及侵权，请提供相关的权利证明和身份证明发送邮件到support@aicoin.com，本平台相关工作人员将会进行核查。