Core Insights - Generative AI is transitioning from "technically feasible" to "value feasible," entering a critical validation period for its practical application [1] Group 1: Consensus on AI Implementation - The bottleneck for AI deployment has shifted from the supply side to the demand side, with 88% of surveyed medium to large enterprises using AI in at least one business function, but only one-third achieving large-scale deployment [2] - The high customization requirement for AI solutions poses challenges, with about 70% needing customization and only 30% being standardizable, leading to difficulties in monetization and product capability accumulation [3] - The commercial model for AI applications remains unproven, with significant price competition pressures, particularly in the B2B sector, where API prices have dropped by 95%-99% since 2024 [4][5] Group 2: Divergences in AI Development - The extent to which intelligent agents can evolve by 2026 is uncertain, with significant advancements in task completion capabilities but still facing challenges in high-risk scenarios like finance and healthcare [6] - The competition for computing power is shifting from training to inference, with a focus on optimizing inference efficiency and cost, which will redefine market dynamics for chip manufacturers and cloud service providers [7][8] - The evolution of the AI ecosystem is complex, with debates on data flow rules and privacy concerns, indicating a need for a new regulatory framework to address these challenges [9][10] Group 3: Recommendations for Future Actions - Companies should prioritize application scenarios that demonstrate real value, focusing on areas with good data foundations and manageable risks [11] - Standardization efforts are needed to reduce customization costs and foster replicable product capabilities, particularly in key industries [12] - High-risk AI applications require robust quality supervision and safety audits to mitigate systemic uncertainties [13] - Encouraging diverse commercial models is essential to avoid detrimental price competition and foster long-term industry health [14]

Investment Rating - The report does not explicitly provide an investment rating for the industry or specific companies involved in the forum [2][3]. Core Insights - The forum emphasized the need to reduce AI computing costs and promote technology adoption to accelerate the implementation of AI in the real economy, marking a shift from foundational model capabilities to practical applications that create tangible value [2][3][18]. - The concept of "Open Symbiosis" was highlighted, indicating that no single company can dominate the AI landscape, and collaboration among over 20,000 partners is essential for building a cohesive ecosystem [3][19]. - The focus is shifting towards inference solutions, with a significant demand for cost-effective computing, as the ratio of inference to training computing is expected to reach 1000:1 in the future [4][20]. Summary by Sections Event Overview - On July 28, 2025, ZTE hosted the "Computing Power for All, AI for Reality" forum as part of the World Artificial Intelligence Conference (WAIC), gathering leaders from government, academia, and industry [16][17]. Industry Trends - The forum's discussions reflected a consensus on the transition from "Model is King" to "Application is Core," emphasizing the practical value of AI in real-world scenarios [18]. - The launch of a medical all-in-one machine that enhances doctor efficiency tenfold exemplifies the industry's focus on creating real value through AI [18]. Technological Developments - The report noted advancements in AI inference architecture, particularly through technologies like "Mooncake" that optimize memory usage and reduce hardware costs, making powerful AI models more accessible [21][20]. - Companies like JD Group and China Southern Power Grid showcased their large-scale applications of AI in various sectors, highlighting the importance of green electricity in supporting AI computing [21]. Ecosystem Collaboration - The importance of an open ecosystem and hardware-software synergy was reiterated by representatives from major cloud providers, indicating a collective effort to build large models and ultra-large-scale computing clusters [21].