Core Insights - Gartner's Vice President, Gao Ting, presented ten strategic technology trends for 2026, focusing on themes of "architects, coordinators, and sentinels," covering areas such as AI-native development, multi-agent systems, physical AI, and cybersecurity [1] Group 1: AI Native Development - AI-native development platforms are seen as the core of next-generation software engineering, utilizing "ambient programming" to generate complete applications or assist developers in coding [2] - Currently, 20%-40% of code in some tech companies is generated by AI, indicating a shift in software development from efficiency tools to a new development paradigm [2] Group 2: AI Supercomputing Platforms - The demand for computing power in AI is growing exponentially, with AI supercomputing platforms characterized by hybrid AI computing and scheduling capabilities [3][7] - Technologies like NVIDIA's NVQLink and CUDA-Q enable the integration of quantum computing with classical supercomputing, enhancing task scheduling across architectures [3] Group 3: Multi-Agent Systems - Multi-agent systems improve reliability in executing complex tasks by breaking down tasks and allowing different agents to collaborate, addressing the limitations of single-agent systems [8][9] - This approach represents a key step in AI evolving from a "tool" to a "collaborator," reflecting a management mindset of "AI teamwork" [9] Group 4: Domain-Specific Language Models - The high failure rate of enterprise AI projects (95%) is attributed to general models lacking business understanding, which domain-specific language models aim to address through retraining with industry data [10] - Companies must invest in data governance and domain training to effectively utilize AI, avoiding the pitfall of having "models without intelligence" [10] Group 5: Physical AI - Physical AI refers to AI systems that interact with the real world, primarily in applications like autonomous driving and robotics, utilizing VLA models and "world models" [11] - This technology serves as a bridge between AI and the real economy, gradually replacing repetitive labor in sectors like manufacturing and logistics [11] Group 6: Proactive Cybersecurity - AI-driven attacks are lowering the barriers for hackers, necessitating the development of proactive cybersecurity systems that include predictive threat intelligence and automated defenses [12][14] - Companies must transition from static defenses to a proactive security framework that integrates prediction, response, and deception [14] Group 7: Digital Traceability - Digital traceability is becoming essential for building trustworthy digital supply chains, especially in light of frequent software supply chain attacks [15][16] - Establishing software SBOM and model MLBOM lists allows companies to track component origins and security, while watermarking and identification technologies for AI-generated content are being standardized [15][16] Group 8: Geopolitical Migration - Geopolitical risks are prompting companies to migrate data and applications from global public clouds to local "sovereign clouds," with European firms being the most affected [17] - Chinese companies are balancing self-sufficiency and global collaboration to avoid becoming "technology islands" [17] Group 9: Confidential Computing and AI Security Platforms - Although not the main focus, "confidential computing" and "AI security platforms" are ongoing trends that protect data and prevent new types of attacks [18] - The emphasis is on embedding AI into business processes and ensuring ecological collaboration rather than chasing technology fads [18]