深度｜2026年，AI将从炒作走向务实

Core Insights - The article posits that 2026 will mark the transition of AI from hype to practical application, focusing on deploying lightweight models in real-world scenarios and integrating AI into human workflows [3][4]. Group 1: AI Development Trends - The industry is shifting from large-scale model expansion to new architectural research, emphasizing targeted deployment and collaboration tools that enhance human work [4]. - Many researchers believe the AI industry is nearing the limits of Scaling Law, indicating a need for new approaches beyond merely increasing model size [9]. - Smaller, fine-tuned language models (SLMs) are expected to become standard tools for mature AI enterprises by 2026 due to their cost and performance advantages [10]. Group 2: World Models and Gaming - 2026 is anticipated to be a pivotal year for world models, which learn how objects interact in three-dimensional space, enabling predictive capabilities [14][15]. - The gaming industry is projected to see significant growth, with the world model market expected to increase from $1.2 billion in 2022 to $276 billion by 2030, driven by the technology's ability to create interactive environments [16]. Group 3: Agent Integration and Automation - The introduction of Model Context Protocol (MCP) is seen as a key development that will facilitate the integration of AI agents with real-world systems, potentially marking 2026 as the year these agents transition from demonstration to practical application [18][19]. - There is a belief that AI will enhance rather than replace human workflows, with new job opportunities emerging in AI governance, transparency, and data management [21]. Group 4: Physical AI and Market Adoption - Advances in small models, world models, and edge computing are expected to drive the adoption of physical AI applications, with wearable devices becoming a cost-effective entry point for consumers [24]. - The market for physical AI, including robotics and autonomous vehicles, is projected to grow, although training and deployment costs remain high [24].