中信证券分析指出，AI原生开发平台正推动软件行业进入"效率革命" 新阶段。其认为，AI不仅将 软件开发效率提升，更通过低代码/无代码化显著降低专业门槛，使业务专家可直接参与应用构建。这 一变革一方面加速了软件在金融、医疗等垂直领域的渗透，另一方面推动行业竞争从"代码实现能力"转 向 "场景理解与架构设计能力" 。机构强调，具备平台化能力、拥有行业知识库且能实现"需求-设计-开 发"闭环的企业，有望在AI重构软件价值链的过程中占据主导地位。 相关产品：创业板软件ETF华夏（159256）、创业板200ETF华夏（159573）、人工智能AIETF （515070）。 每日经济新闻 今日A股三大股指调整，但软件相关个股迎来逆势大涨，盘面上，能源金属、IT服务、通信服务、 软件开发等板块表现较为强势。创业板软件ETF华夏（159256）盘中大涨超1.12%，持仓股东方国信大 涨超12%，软通动力、润和软件、诚迈科技、北信源等纷纷上涨超3%。 消息面上，全球权威IT研究与顾问咨询公司Gartner近期发布的《2026年十大战略技术趋势》报告 中，AI原生开发平台被列为核心趋势之一，标志着软件开发范式将迎来根本性变革。 ...

Core Viewpoint - The article emphasizes that 2026 will be a pivotal year for technology leaders, with unprecedented speed in transformation, innovation, and risk driven by artificial intelligence (AI) and a highly interconnected world [2]. Group 1: AI Supercomputing Platforms - AI supercomputing platforms integrate various computing paradigms to manage complex workloads, enhancing performance and innovation potential [5]. - By 2028, over 40% of leading companies will adopt hybrid computing architectures for critical business processes, a significant increase from the current 8% [6]. - The technology is already driving innovation across industries, significantly reducing drug modeling time in biotech and lowering portfolio risks in financial services [7]. 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 [9]. - These systems allow for modular design, improving efficiency and adaptability in business processes [9]. Group 3: Domain-Specific Language Models (DSLM) - DSLMs are trained on specialized datasets for specific industries, providing higher accuracy and compliance compared to generic large language models (LLMs) [11]. - By 2028, over half of generative AI models used by enterprises will be domain-specific [12]. - Context is crucial for the success of AI agents based on DSLMs, enabling them to make informed decisions even in unfamiliar scenarios [13]. Group 4: AI Security Platforms - AI security platforms provide unified protection mechanisms for third-party and custom AI applications, helping organizations monitor AI activities and enforce usage policies [13]. - By 2028, over 50% of enterprises will utilize AI security platforms to safeguard their AI investments [15]. Group 5: AI-Native Development Platforms - AI-native development platforms enable rapid software development, allowing non-technical experts to create applications with AI assistance [17]. - By 2030, 80% of enterprises will transform large software engineering teams into smaller, more agile teams empowered by AI [17]. Group 6: Confidential Computing - Confidential computing reshapes how enterprises handle sensitive data by isolating workloads in trusted execution environments [18]. - By 2029, over 75% of business workloads processed in untrusted environments will be secured through confidential computing [18]. Group 7: Physical AI - Physical AI empowers machines and devices with perception, decision-making, and action capabilities, providing significant benefits in automation and safety-critical industries [19]. Group 8: Proactive Cybersecurity - Proactive cybersecurity is becoming a trend as organizations face increasing threats, with predictions that by 2030, proactive defense solutions will account for half of enterprise security spending [23]. Group 9: Geopolitical Data Migration - Geopolitical risks are prompting companies to migrate data and applications to sovereign or regional cloud services, enhancing control over data residency and compliance [26]. - By 2030, over 75% of enterprises in Europe and the Middle East will migrate virtual workloads to solutions that mitigate geopolitical risks, up from less than 5% in 2025 [26].