Industry Investment Rating - The investment rating for the computer industry is "Outperform the Market" and is maintained [1] Core Viewpoints - The report highlights the strong demand for AI computing power, driven by increased capital expenditures from major tech companies such as Alphabet, Microsoft, and Meta, indicating a robust growth trajectory for the industry [6] - The release of GPT-5 by OpenAI is expected to accelerate the commercialization of AI applications, enhancing capabilities in various sectors including software development, writing, and financial analysis [5] - The performance of overseas AI application companies has exceeded expectations, suggesting a rapid acceleration in AI commercialization [7][8] Summary by Relevant Sections Industry Basic Situation - The closing index for the computer industry is 4993.28, with a 52-week high of 5440.49 and a low of 2805.53 [1] Relative Index Performance - The relative performance of the computer industry against the CSI 300 index shows a significant upward trend, with a 40% increase observed by August 2025 [3] Recent Developments - Major tech companies have significantly increased their capital expenditures, with Alphabet raising its 2025 capital expenditure guidance from $75 billion to $85 billion, primarily for GPU/TPU servers and data center expansions [6] - Microsoft's Azure cloud service revenue grew by 39% year-on-year, reflecting strong demand for AI and cloud services [6] - Palantir's revenue reached $1 billion, a 48% increase year-on-year, driven by surging AI demand [8]

Core Viewpoint - DBS maintains a "Buy" rating on SenseTime-W (00020), raising the target price by 16.7% from HKD 1.8 to HKD 2.1 [1] Group 1: Product Development - SenseTime launched the upgraded SenseNova V6.5 at the 2025 World Artificial Intelligence Conference (WAIC), which is considered a top model globally, offering a cost-effectiveness improvement of approximately 5 times compared to its predecessor [1] - The company introduced an embodied intelligence platform, referred to as the "robot brain," to enhance its competitive advantage [1] Group 2: Market Position and Capabilities - SenseTime's leading multimodal generative AI capabilities (text, image, audio, and video processing) benefit from proprietary visual data and strong training and inference efficiency, providing the company with a significant advantage in developing AI applications [1]

Group 1 - The core viewpoint of the article is that DBS maintains a "Buy" rating for SenseTime-W (00020) and raises the target price by 16.7% from HKD 1.8 to HKD 2.1 [1] - SenseTime's upgraded SenseNova V6.5, launched at the 2025 World Artificial Intelligence Conference (WAIC), is noted as a top-tier model with a cost-effectiveness improvement of approximately 5 times compared to the previous version V6 [1] - The management is focused on commercializing artificial intelligence to create results for clients, indicating a strong commitment to innovation and market application [1] Group 2 - SenseTime introduced an embodied intelligence platform, referred to as a "robot brain," which enhances its competitive advantage in the market [1] - The company possesses leading multimodal generative AI capabilities (text, image, audio, and video processing), benefiting from proprietary visual data and strong training and inference efficiency [1]

Core Insights - The focus of the market has shifted from "what AI can do" to "how to effectively build, deliver, and commercialize AI products" as AI technology moves into deeper industrial applications [1][2] - Companies are no longer debating whether to use AI but are instead considering how to implement it effectively [2][3] Group 1: Building AI Products - Companies are evolving from traditional SaaS models to AI-driven futures, with 31% embedding AI in existing products, 37% developing standalone AI products, and 32% building their core business around AI [4] - AI-native companies are significantly ahead in product development, with 47% in the scaling phase compared to only 13% of AI-enabled companies [6][9] - Nearly 80% of AI-native companies are developing Agentic Workflows, which have become a popular product direction [10] - The focus has shifted from performance to cost, with 57% of companies now prioritizing cost considerations in model selection [18] - Companies are increasingly adopting multi-model strategies, using an average of 2.8 different model providers, while OpenAI maintains a 95% adoption rate [20] Group 2: Market Entry and Compliance - AI-driven features are rapidly becoming central to product strategies, with projections showing that by the end of 2025, AI-driven features will account for 43% of high-growth companies' product roadmaps [31] - The most common pricing model for AI products is a hybrid approach, combining traditional subscription with usage-based billing [35] - Companies are exploring new pricing models linked to ROI, with 37% actively investigating changes [43] - Transparency and explainability in AI products are becoming essential as products mature, with 25% of companies providing detailed model transparency reports at the scaling stage [48] Group 3: Organizational Structure - Establishing dedicated AI leadership roles is a sign of maturity in AI strategy, with 61% of large companies having specialized AI leaders [56] - AI/ML engineers, data scientists, and AI product managers are critical roles, but hiring challenges persist, with an average recruitment cycle of 70 days for AI/ML engineers [60][64] - High-growth companies plan to allocate 37% of their engineering teams to AI projects by 2026, significantly higher than the 28% of other companies [68] Group 4: AI Cost Structure - Companies are allocating 10-20% of their R&D budgets to AI development, with plans to increase this share by 2025 [72] - The cost structure of AI projects shifts from talent costs dominating in the pre-launch phase (57%) to machine costs becoming significant in the scaling phase (nearly 50%) [80] - API usage fees are identified as the most challenging cost to control, with 70% of respondents highlighting this issue [81] Group 5: Internal AI Utilization - Companies are expected to double their internal AI budgets by 2025, with significant investments in productivity-enhancing AI tools [94] - Despite high availability of AI tools, actual usage rates reveal a gap, with only about 50% of employees consistently using them [97] - Coding assistance is the most popular internal AI application, with a 77% adoption rate, leading to productivity improvements of 15-30% [104][108] Group 6: AI Builder Technology Stack - Traditional deep learning frameworks like PyTorch and TensorFlow remain popular among developers, while managed platforms like AWS SageMaker are gaining traction [120] - Monitoring and observability tools are still dominated by traditional solutions, but ML-native platforms are beginning to gain early traction [122] - The market for AI tools is fragmented, with many teams still unaware of the specific tools they are using, indicating a knowledge gap [126]