Focusing on the emerging trends of industry large models, deeply analyzing the implementation and practice of all-domain artificial intelligence in government and enterprise scenarios
The Zenith-Act large model has significantly improved its inference efficiency and industry knowledge injection capabilities. The new version, based on the MoE (Mixed Expert) architecture, reduces the active parameter count to 37B, triples the inference speed, and improves accuracy in vertical scenarios such as government question answering, energy equipment fault diagnosis, and manufacturing process optimization to over 92%, further consolidating the industry's intelligent foundation.
Urban governance is moving from passive response to proactive prediction. Since 2025, dozens of cities including Beijing, Shanghai, Shenzhen, and Hangzhou in China have integrated large models into the 'One Network Management' platform. Taking Shenzhen as an example, the urban operation command center integrates data from over 1200 perception devices, with the large model analyzing the urban operation situation in real-time, increasing the proactive discovery rate of events to 85%, and shortening the average response time by 60%. This article analyzes the key paths for all-domain AI to connect the entire urban governance chain.
Recently, JiuTian Technology has signed cooperative agreements with many local governments and large enterprises to promote the implementation of all-domain intelligent solutions in areas such as smart city governance, energy and power intelligence, and low-altitude economic supervision. One provincial capital city's 'urban brain' project has completed the first phase delivery, connecting 15 departments and bureaus' business systems, and achieving cross-departmental data integration and intelligent scheduling.
In January 2025, the release of DeepSeek-R1 caused a global shake-up in AI stocks, with NVIDIA's market value evaporating about $589 billion in a single day. However, the deeper impact lies in: DeepSeek has proven that high-quality inference models can be trained at extremely low costs, fundamentally breaking the traditional perception of 'computing power as a barrier'. This article analyzes the profound impact of open-source large models on industry pricing, technical routes, and competitive landscape, as well as the evolution direction of the domestic large model ecosystem.
The API prices of domestic large models in 2025 experienced multiple dives, with some manufacturers' input prices dropping below 1 yuan per million tokens. However, industry research shows that 70% of enterprises face core challenges in large model landing not related to cost, but to data quality, scenario adaptation, and security compliance. This article analyzes the key transitions from 'available' to 'useful' for industry large models from three dimensions: data governance, model fine-tuning, and private deployment.
