2026年人工智能+的共识与分歧

Core Viewpoint - Generative AI is transitioning from "technically feasible" to "value feasible," entering a critical validation period for its practical application, with significant industry consensus on its implementation but deep divisions on key pathways that will determine its potential as a new productive force [2]. Three Consensus Points - The bottleneck for AI implementation 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. Key obstacles include unclear goals and insufficient integration readiness [4]. - Approximately 70% of current AI solutions require customization, with only 30% being standardizable. High customization leads to challenges in monetization and the inability to create reusable product capabilities, resulting in a reliance on "API calls + customization services" for enterprise AI delivery [5]. - The commercial model for AI remains unproven, with significant price competition pressures. While C-end AI applications have high user engagement, revenue conversion rates are low. B-end AI faces even greater challenges, with API prices dropping by 95%-99% since 2024, leading to a highly competitive low-price environment [6][7]. Three Divergence Points - The capabilities of intelligent agents are evolving from "answering questions" to "completing tasks," with significant advancements in long-term task execution and tool utilization. However, accuracy in complex tasks remains inconsistent, particularly in high-risk sectors like finance and healthcare [9][10]. - The focus of computing power competition is shifting from training to inference, with demand for AI applications driving exponential growth in inference calls. Companies are optimizing algorithms to enhance inference efficiency, indicating a shift in market dynamics [11][12]. - The evolution of the AI ecosystem is complex, with debates on data flow rules and user privacy. The transition from mobile internet to AI necessitates new structural solutions to address data sharing and privacy concerns, with no clear answers yet established [13][14]. Next Steps - Companies should prioritize real value and carefully select application scenarios, focusing on areas with strong data foundations and manageable risks, such as quality inspection in manufacturing and AI-assisted diagnosis in healthcare [16]. - Standardization efforts should be promoted to reduce customization costs and foster reusable product capabilities, particularly in key industries like finance and manufacturing [17]. - Quality supervision and safety audits should be strengthened in high-risk AI applications, establishing a governance framework to mitigate systemic uncertainties [18]. - Diverse commercial models should be encouraged to avoid detrimental price competition, supporting differentiated pricing strategies based on technical capabilities and industry expertise [19].