赛道分化加剧，2026年人工智能最强风口来袭

Core Insights - The article emphasizes that 2026 will be a pivotal year for artificial intelligence (AI), marking a shift from "AI+" to "AI native," where AI fundamentally redefines system architectures and operational logic [1][3]. Group 1: AI Native Revolution - AI native signifies a complete redesign of systems with AI as the core logic and capability, leading to a comprehensive transformation across technology architecture, business processes, organizational roles, and value creation methods [3][4]. - The transition from "AI+" to "AI native" is not merely an enhancement but a fundamental restructuring that makes intelligence an inherent attribute of applications rather than an added feature [3][4]. - Key characteristics of a true AI native system include natural language interaction, autonomous learning and adaptation, and the ability to complete tasks independently based on large language models and knowledge bases [4][5]. Group 2: Development Trends and Tools - The rise of low-code/no-code platforms allows individuals without programming skills to create custom AI tools, fostering a surge in "one-person company" models [8]. - Major companies like Microsoft and ByteDance are embedding AI agents into office suites, creating end-to-end workflows that enhance productivity [8]. - The development of AI native applications requires a productized approach to various tools, such as platforms for deploying large models and automated fine-tuning tools, which are essential for widespread adoption [8]. Group 3: Physical AI Integration - By 2026, AI will extend beyond screens into physical environments like cities, factories, hospitals, and homes, marking the era of Physical AI [10][11]. - Physical AI is characterized by its ability to connect digital and physical worlds, enabling actions based on real-time data and physical interactions [10][11]. - The evolution of AI has progressed through three stages: perceptual AI, generative AI, and now Physical AI, which can reason, plan, and act like humans [10][11]. Group 4: World Models and Their Impact - World models are becoming crucial for AI's integration into the real world, allowing AI to shift from data-driven to rule-driven approaches, enabling predictive decision-making [19][21]. - These models enhance generalization capabilities, allowing AI to apply learned knowledge to new, unseen scenarios, which is vital for applications like autonomous driving [22][23]. - The development of world models involves understanding physical laws and simulating environments, which can significantly improve the performance of AI systems in complex real-world situations [24][25]. Group 5: Multimodal AI Capabilities - The emergence of multimodal large models (MLLMs) will redefine industries by enabling AI to process and integrate various data types, such as text, images, and audio [15][17]. - MLLMs will enhance cross-modal understanding and generation, allowing for more sophisticated content creation and problem-solving capabilities [15][16]. - By 2026, MLLMs are expected to drive significant advancements across various sectors, including cultural heritage preservation, security, and intelligent driving [17][18].