21世纪经济报道记者骆轶琪 其中，AI原生开发平台(AI-Native Development Platforms)已经逐渐成为现实，即使是非技术背景的员工，也能借助AI工具自主开 发应用。 Gartner预测，到2030年，80%的企业将通过AI原生开发平台将大型软件工程团队转变为更小、更敏捷的团队并通过AI赋能。 AI大模型技术正加速进入大众生活，但同时潜在威胁也相伴相生。 近日，Gartner发布2026年十大战略技术趋势，其中有超过一半都与AI相关。Gartner研究副总裁高挺在接受21世纪经济报道等媒 体采访时指出，出现这一现象，反映出一个"由AI驱动、超连接的世界"正走向现实。 在他看来，AI占据主导地位，因为它同时扮演了两个角色。一方面，AI是创新的基础，在企业运营中应用AI将带来新的商业价 值和创新产品；另一方面，广泛采用AI也带来传统工具无法解决的新安全风险。 报告特别提到，由AI驱动的攻击在速度和复杂性上都在增长，这迫使企业必须采用新的防御趋势。"企业必须在利用AI创造价值 的同时，还要防范AI带来的威胁，这使其成为2026年战略布局的绝对核心。"高挺补充道。AI底层演进 如今的AI功能远 ...

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]

Core Insights - Gartner identifies ten strategic technology trends that organizations need to focus on by 2026, emphasizing the unprecedented speed of innovation and transformation in the current year [1][3]. Group 1: AI Supercomputing Platforms - AI supercomputing platforms integrate various computing resources to manage complex workloads, enhancing performance and innovation potential [6]. - By 2028, over 40% of leading companies will apply hybrid computing paradigms to critical business processes, a significant increase from the current 8% [8]. Group 2: Multi-Agent Systems - Multi-agent systems consist of multiple AI agents that interact to achieve complex individual or collective goals, enhancing automation and collaboration [10]. Group 3: Domain-Specific Language Models (DSLM) - DSLMs are tailored AI models trained on specific industry data, providing higher accuracy and compliance for specialized tasks compared to general models [11]. - By 2028, over half of generative AI models used by enterprises will be domain-specific [13]. Group 4: AI Security Platforms - AI security platforms offer unified protection mechanisms for AI applications, helping organizations monitor activities and enforce usage policies [16]. - By 2028, over 50% of enterprises will utilize AI security platforms to safeguard their AI investments [16]. Group 5: AI Native Development Platforms - AI native development platforms enable rapid software development through generative AI, allowing non-technical experts to create applications [19]. - By 2030, 80% of enterprises will transform large software engineering teams into smaller, agile teams empowered by AI [19]. Group 6: Confidential Computing - Confidential computing protects sensitive data by isolating workloads in trusted execution environments, crucial for regulated industries [20]. - By 2029, over 75% of business processes handled in untrusted infrastructures will be secured through confidential computing [22]. Group 7: Physical AI - Physical AI empowers machines and devices with perception, decision-making, and action capabilities, providing significant benefits in automation and safety [23]. Group 8: Proactive Cybersecurity - Proactive cybersecurity is becoming a trend as organizations shift from passive defense to active protection, with AI-driven solutions playing a key role [26]. Group 9: Digital Traceability - Digital traceability is essential for verifying the source and integrity of software and data, especially as reliance on third-party software increases [30]. Group 10: Geopolitical Repatriation - Geopolitical repatriation involves moving data and applications to local platforms to mitigate geopolitical risks, a trend expected to grow significantly by 2030 [33].

Core Viewpoint - The article discusses the ten strategic technology trends that enterprises need to focus on in 2026, emphasizing the integration of AI and the necessity for responsible innovation, operational excellence, and digital trust in a rapidly evolving technological landscape [5]. Group 1: AI Supercomputing Platforms - AI supercomputing platforms integrate various computing technologies to manage complex workloads, enhancing performance and innovation potential [8]. - By 2028, over 40% of leading enterprises will adopt hybrid computing paradigms in critical business processes, a significant increase from the current 8% [9]. Group 2: Multi-Agent Systems - Multi-agent systems consist of multiple AI agents that interact to achieve complex individual or collective goals, enhancing automation and collaboration [10][11]. Group 3: Domain-Specific Language Models (DSLM) - DSLMs are specialized language models trained on specific industry data, providing higher accuracy and compliance for business needs [12][13]. - By 2028, over half of generative AI models used by enterprises will be domain-specific [13]. Group 4: AI Security Platforms - AI security platforms offer unified protection mechanisms for AI applications, helping organizations monitor AI activities and enforce usage policies [14][15]. - By 2028, over 50% of enterprises will use AI security platforms to safeguard their AI investments [15]. Group 5: AI-Native Development Platforms - AI-native development platforms enable faster software development through generative AI, allowing smaller teams to create more applications efficiently [16][17]. - By 2030, 80% of enterprises will transition to smaller, AI-augmented teams for software development [17]. Group 6: Confidential Computing - Confidential computing transforms how enterprises handle sensitive data by isolating workloads in trusted execution environments [18][20]. - By 2029, over 75% of business operations in untrusted infrastructures will be secured through confidential computing [20]. Group 7: Physical AI - Physical AI empowers machines and devices with perception, decision-making, and action capabilities, providing significant benefits in automation and safety [21][23]. Group 8: Proactive Cybersecurity - Proactive cybersecurity is becoming a trend as organizations shift from passive defense to active protection strategies [24][26]. - By 2030, proactive defense solutions will account for half of enterprise security spending [24]. Group 9: Digital Traceability - Digital traceability is crucial for verifying the source, ownership, and integrity of software and data, especially as reliance on third-party software increases [27][28]. - By 2029, enterprises lacking investment in digital traceability may face significant financial penalties [28]. Group 10: Geopolitical Repatriation - Geopolitical repatriation involves moving data and applications to local platforms to mitigate geopolitical risks, a trend that is gaining traction across various industries [29][30]. - By 2030, over 75% of enterprises in Europe and the Middle East will migrate workloads to solutions that reduce geopolitical risks [30]. Summary of Trends Evolution - The trends indicate a shift towards AI being central to all technology strategies, with a focus on specialized applications and security measures as enterprises scale AI and digital technologies [33].