中国工业大模型行业发展研究报告：靡不有初，鲜克有终

Industry Overview - Industrial large models are in the early stages of development, leveraging advancements in large model technology to gradually penetrate industrial applications [2] - The core of large models lies in their probabilistic nature, relying on statistical inference rather than true understanding or logical reasoning, leading to challenges like lack of interpretability and unavoidable hallucinations [2] - Industrial internet platforms have laid the groundwork for data preparation, making the development of industrial large models potentially faster than industrial internet platforms, provided there are no technical limitations [2] - The market for industrial large models is highly exploratory, with products and services still in the nascent stage, and players are leveraging their unique strengths to explore specific application scenarios [2] Key Players and Market Dynamics - The industrial large model market is dominated by players with overlapping expertise in industrial internet platforms, with a high degree of similarity in growth paths [2] - Key competitive factors include foundational capabilities, model capabilities, and model application depth, with short-term focus on technology and long-term focus on application depth [3] - The relationship between large and small models is complementary, with both coexisting and synergizing to enhance industrial applications [3] - The market is moving towards platformization, with vertical industry large models, intelligent agents, small models, and mechanism models forming the core of platform-based solutions [3] Technological Framework and Model Capabilities - AI frameworks are divided into three layers: foundational, technical, and application layers, with major players like Google, Meta, Microsoft, Amazon, Alibaba, Baidu, and Huawei dominating the foundational and technical layers [8] - Large models are essentially probabilistic systems that learn from vast amounts of data, with their capabilities derived from pre-training, fine-tuning, and reinforcement learning [10] - The relationship between AI, machine learning, deep learning, and large models is hierarchical, with large models representing the application of various algorithmic combinations [12] - Large models differ from small models in terms of data volume, parameter scale, computational requirements, and generalization capabilities, with large models offering better generalization but lower interpretability [13] Industrial Applications and Feasibility - Industrial large models are feasible due to the accumulation of data assets and the readiness of data for feeding into foundational models, enabling the development of industry-specific large models [17] - The application of large models in industrial settings is currently focused on operational scenarios with some tolerance for errors, such as knowledge Q&A and design assistance, while core manufacturing scenarios await further model evolution [2] - The integration of industrial internet platforms and large models is expected to accelerate the development of industrial large models, with data preparation being a key enabler [2] Market Entry Strategies and Revenue Models - Industrial large model players are diverse, covering software and hardware vendors, with market entry strategies focusing on leveraging their unique strengths to explore specific application scenarios [23] - The primary revenue model for industrial large models is customized comprehensive solutions, with additional revenue streams from hardware-software integrated products [29] - API calls and intelligent agent distribution are potential revenue models, but these are still in the exploratory phase and require further market validation [29] Challenges and Future Outlook - The main challenges for industrial large models include model evolution, data quality, application depth, and commercial viability, with these factors being interdependent and mutually reinforcing [2] - The future of industrial large models is uncertain, with potential for significant evolution in capabilities and services, driven by advancements in model technology and the integration of multi-modal models [29] - The platformization of industrial large models is expected to lead to the development of modular solutions that can address 60%-70% of the needs of similar enterprises and scenarios [32]