AI Industry Daily Report: AI Application Cases Across Various Industries (2025-07-04)

In today's rapidly evolving landscape of artificial intelligence technology, AI applications have permeated every aspect of various industries. From fintech to enterprise automation, and cybersecurity, innovative uses of AI not only enhance efficiency but also bring unprecedented social impact. This article will delve into the latest AI application cases as of July 4, 2025, exploring their technical implementations and value, and forecasting future development trends.

AI Research and Development: Sakana AI's TreeQuest

Sakana AI's TreeQuest project utilizes Monte-Carlo Tree Search (MCTS) to coordinate multiple large language models (LLMs) for collaboration, achieving a 30% performance increase over a single LLM on complex tasks. This concept of multi-model team collaboration is introduced for the first time and demonstrates significant performance improvements, showcasing high originality and novelty.

Technical Implementation: TreeQuest employs Monte-Carlo Tree Search to optimize collaboration among multiple LLMs, selecting the optimal path through simulated decision trees, thereby enhancing the accuracy and efficiency of overall decision-making.

Value: This approach can be applied in fields requiring high precision and complex decision-making, such as financial analysis and medical diagnostics, improving the decision-making capabilities of AI systems, reducing error rates, and providing more accurate predictions and decisions.

Enterprise Automation: Dust's AI Agents

Dust has developed AI agents capable of performing real-world operations within enterprise systems using Anthropic's Claude model and the MCP protocol. This innovation not only automates workflows and boosts efficiency but also drives digital transformation in enterprises, having a significant social impact.

Technical Implementation: Dust's AI agents understand user commands through the Claude model and interact with enterprise systems via the MCP protocol to execute specific operational tasks, such as data processing and report generation.

Value: The application of these AI agents can significantly reduce labor costs, improve operational efficiency in enterprises, and enhance overall economic efficiency.

AI Research and Development: TNG Technology Consulting GmbH's DeepSeek R1-0528

The DeepSeek R1-0528 variant, developed by TNG Technology Consulting GmbH's laboratory in Germany, achieves a 200% speed increase in LLMs using TNG's Assembly-of-Experts (AoE) method. This efficient construction method is highly original and novel, suitable for fields requiring efficient processing of large-scale data.

Technical Implementation: DeepSeek R1-0528 constructs LLMs by selectively merging weight tensors, optimizing computational efficiency and achieving significant speed improvements.

Value: This method can be applied in areas such as data analysis and scientific computing, improving computational efficiency, advancing research and applications in related fields, and delivering faster computational results and higher productivity.

Fintech: OpenAI Rejects Robinhood's Tokenized Shares

OpenAI rejected Robinhood's unauthorized tokenized shares, an event involving blockchain technology and AI applications in financial markets. This incident not only showcases the potential applications and risks of AI and blockchain technology in financial markets but also sparks discussions and regulatory concerns about the use of new technologies in financial markets, having significant social impact.

Technical Implementation: This event involves risk management in financial markets using AI and the application of blockchain technology. OpenAI assessed the risks of Robinhood's tokenized shares using AI technology and decided to reject the transaction.

Value: This event serves as a reminder to participants in financial markets that the application of AI and blockchain technology requires careful consideration, prompting discussions and regulatory attention on the use of new technologies, contributing to the healthy development of financial markets.

Cybersecurity: CyXcel's Research

CyXcel's research found that one-third of UK businesses face AI risks. This study not only reveals the current state and deficiencies in AI risk management for enterprises but also provides recommendations for AI risk management, helping them better address potential threats posed by AI, having significant social impact.

Technical Implementation: CyXcel analyzed the cybersecurity status of UK businesses using AI technology, identifying key factors of AI risks and proposing corresponding risk management strategies.

Value: This research raises awareness of AI risks among enterprises, promoting the formulation and implementation of AI security strategies, helping businesses better protect their cybersecurity, and reducing potential threats posed by AI.

Future Development Trends

From the cases above, it is evident that AI technology applications are deepening and expanding across various industries. In the future, AI technology will continue to make breakthroughs in the following areas:

• Multi-Model Collaboration: As demonstrated by Sakana AI's TreeQuest, multi-model collaboration will become a crucial means of enhancing AI system performance, suitable for fields requiring high precision and complex decision-making.
• Real-World Operational Capabilities of AI Agents: Dust's AI agents showcase the potential of AI in enterprise automation. In the future, more AI agents will be able to perform real-world operational tasks, further driving digital transformation in enterprises.
• Efficient LLM Construction Methods: TNG Technology Consulting GmbH's DeepSeek R1-0528 variant demonstrates efficient methods for constructing LLMs. In the future, more innovative methods will enhance the computational efficiency of AI models.
• AI Applications in Financial Markets: The event of OpenAI rejecting Robinhood's tokenized shares reminds participants in financial markets that the application of AI and blockchain technology requires careful consideration. In the future, more AI technologies will be applied to risk management in financial markets.
• AI Risk Management: CyXcel's research reveals the current state and deficiencies in AI risk management for enterprises. In the future, more research and practices will drive the formulation and implementation of AI security strategies, helping enterprises better address potential threats posed by AI.

Conclusion

Through the analysis of the latest AI application cases on July 4, 2025, we can see the innovative applications and potential social impact of AI technology across various industries. In the future, AI technology will continue to make breakthroughs in multi-model collaboration, real-world operational capabilities of AI agents, efficient LLM construction methods, AI applications in financial markets, and AI risk management, driving digital transformation and social progress across industries. It is hoped that this article will provide valuable insights and inspiration to readers, helping them better understand and apply AI technology.