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 Insights - The article discusses the transformative impact of AI on workplaces and daily life, highlighting the shift from AI as a tool to AI as an autonomous colleague [6][14] - Gartner's 2026 strategic technology trends report indicates that organizations and individuals are at a crossroads of transformation due to rapid technological advancements [6][12] Group 1: AI Evolution - AI is evolving from a passive tool to an intelligent colleague capable of making autonomous decisions and actions [6][7] - Multi-agent systems (MAS) are expected to become digital employees, collaborating to complete complex tasks, with organizations automating 80% of customer-facing processes by 2028 [7][10] - Physical AI is emerging, with robots and drones performing tasks in real-world environments, enhancing human capabilities in dangerous or repetitive jobs [7][10] Group 2: Domain-Specific AI - The rise of domain-specific language models (DSLM) is driven by the need for AI that understands industry-specific knowledge and terminology [8][9] - By 2028, over half of generative AI models in enterprises are expected to be domain-specific, utilizing the expertise of professionals to train AI [9] Group 3: Security and Trust - AI security is becoming increasingly critical, with over 50% of enterprises projected to adopt dedicated AI security platforms by 2028 [10][11] - Technologies like confidential computing and digital provenance are essential for protecting sensitive information and ensuring transparency in AI systems [10][11] Group 4: Geopolitical Factors - Geopolitical considerations are influencing technology choices, with a trend towards data and service localization, particularly in Europe and the Middle East [12] - By 2030, over 75% of enterprises in these regions are expected to migrate workloads back to local jurisdictions, impacting cloud strategies and service accessibility [12] Group 5: Organizational and Personal Adaptation - Companies must view AI as integral to their operations, with 80% expected to be enhanced by AI-driven small teams by 2030 [13] - Individuals will need to develop skills to work alongside AI, with a focus on maintaining critical thinking and creativity while leveraging AI capabilities [13][14]

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].

Group 1 - The revised regulations by the China Securities Regulatory Commission (CSRC) aim to enhance the enthusiasm for mergers and acquisitions among listed companies by simplifying review processes and increasing regulatory inclusiveness [1] - Shichuang Yihui's recent capital operations, including the sale of its subsidiary to a state-owned enterprise, have attracted market attention and are seen as a model for regional industrial transformation [1][2] - Cangnan County, where Shichuang Yihui operates, has a projected GDP of 54.32 billion and a tertiary industry proportion exceeding 55%, indicating a strong economic foundation for business intelligence [1][2] Group 2 - The establishment of the Shichuang IoT production base in Cangnan is a key part of the company's strategic adjustment and aims to optimize its industrial structure and operational efficiency [2] - The collaboration between Shichuang Yihui and Cangnan state-owned assets will focus on industrial synergy, talent and innovation platform development, financial services, and investment attraction [2] - The initiative aims to create a comprehensive IoT industry cluster by attracting high-quality RFID industry enterprises and providing customized financial solutions to overcome funding challenges [2][3]