Core Insights - The article discusses the evolution of artificial intelligence (AI) from a rapid initial phase to a more mature stage characterized by cognitive enhancement, collaborative clusters, and deep industry integration, outlining ten core trends that shape the new blueprint of the intelligent era [1]. Group 1: AI Model Evolution - The evolution of foundational models is described as machines approaching human cognitive limits, with the "pre-training + post-training" paradigm validated by the industry since late 2024 [1]. - Breakthroughs in the multimodal field hinge on the transition from "Next Token Prediction" to "Next-State Prediction (NSP)," enabling AI to learn physical dynamics, temporal continuity, and causal relationships like humans [1]. Group 2: Industry Trends and Developments - By 2025, the industry is expected to enter a "clearing" phase, with over 230 embodied intelligence companies in China, including more than 100 humanoid robot firms, facing significant technical challenges and funding requirements [2]. - The commercial focus has shifted from laboratory validation to mass production, with humanoid robot sales surpassing 10,000 units and large-scale orders becoming common [2]. Group 3: Multi-Agent Systems (MAS) - AI applications are evolving from single-agent systems (SAS) to multi-agent systems (MAS), with SAS applications currently accounting for 63% in areas like customer service and code generation [3]. - A report indicates that 57% of organizations have deployed agents to handle multi-stage workflows, with this figure projected to rise to 81% by 2026 [3]. Group 4: Communication Protocols and AI for Science - The core breakthrough in MAS is the unification of communication protocols, with MCP and A2A protocols being integrated into the Linux Foundation, supporting complex applications [4]. - AI for Science (AI4S) has evolved from a supportive tool to an AI Scientist capable of executing a complete research workflow, marking a significant shift in scientific research methodologies [4]. Group 5: Global Competition and Infrastructure - The international competition is intensifying, with the U.S. launching the "Genesis Project" in November 2025 to accelerate the large-scale implementation of AI4S [5]. - China exhibits strengths in application but lacks in foundational infrastructure such as computing power, data, and models, with the national data center holding 4.6PB of data as of 2025 [5]. Group 6: Consumer AI and Vertical Markets - Consumer AI competition is focusing on "Super Apps," which integrate various functionalities into a single platform, with apps like ChatGPT and Gemini achieving over 100 million daily active users [5]. - Vertical markets show significant potential, with multimodal models demonstrating high value despite low usage frequency, as seen in the success of health management apps like Ant Financial's Aifeng [6]. Group 7: Challenges and Future Outlook - Many ToB AI applications remain in the proof of concept (PoC) stage, with 95% of GenAI pilot projects failing to produce measurable impacts due to data quality and integration challenges [6]. - The second half of 2026 is anticipated to be a critical period for the MVP rollout of ToB applications, with a clear implementation path for data governance and API connections [7]. Group 8: Synthetic Data and Cost Reduction - Synthetic data is emerging as a crucial resource for the AI 2.0 era, addressing the shortage of real data, with companies like NVIDIA optimizing 3D detection using synthetic datasets [8]. - The cost of inference has significantly decreased, with the cost per million tokens dropping from $20 to $0.07 between November 2022 and October 2024, reflecting a 280-fold reduction in 18 months [8].

Investment Rating - The industry investment rating is "Increase" [5] Core Insights - The era of AI for Science (AI4S) is transforming scientific research, particularly in materials development, which has become increasingly complex due to multi-objective optimization requirements. AI4S utilizes AI algorithms to enhance molecular structure insights through quantum physics calculations and integrates real-world data from high-throughput robotic laboratories, significantly shortening research cycles [1] - The potential market size for AI4S in the pharmaceutical sector is estimated at approximately $108.2 billion, based on a 33% value share of the preclinical research market within the global pharmaceutical market of $1.64 trillion. Additionally, assuming a 25% penetration rate in sectors such as chemicals, pharmaceuticals, new energy, alloys, displays, and semiconductors, the total AI4S market demand could reach around $148.6 billion [2] - Key application areas for AI4S include innovative drug development, where the complexity of drug research aligns well with AI capabilities, and space photovoltaics, particularly with perovskite materials that can significantly enhance satellite energy efficiency [3] Summary by Sections AI4S Empowerment in Scientific Research - AI4S capabilities encompass "reading, computing, and doing." For instance, the company Tai Holdings has developed a patent data mining platform that can extract literature and patent data in one hour with a 95% accuracy rate, and over 200 AI models that enhance research speed and precision [1] Market Size and Potential - The pharmaceutical sector's AI4S market potential is approximately $108.2 billion, while the overall market demand across six sectors could reach about $148.6 billion under a 25% penetration assumption [2] Notable Application Areas - Innovative drug development is a primary focus for AI4S due to the high investment and complexity involved. Additionally, perovskite materials in space photovoltaics present a promising area for AI optimization, addressing technical challenges related to stability and efficiency [3][4]

Core Viewpoint - Jingtai Holdings (02228) has seen a significant increase in stock price, rising nearly 7% in early trading and currently at HKD 12.95, with a trading volume of HKD 2.236 billion. The company has announced a strategic cooperation agreement with a subsidiary of JinkoSolar to develop AI and automation technologies for high-throughput tandem solar cells, establishing a joint venture to create a closed-loop manufacturing line for solar cells [1][1][1]. Group 1 - Jingtai Technology is transitioning from a "0 to 1" phase to a "1 to N" phase, indicating a critical growth stage where its business model has been validated and is entering a high-speed growth trajectory [1]. - The continuous realization of large orders and revenue recognition are identified as the core drivers of stock price in the short term [1]. - The long-term value of the company is attributed to its technological barriers in the "AI + robotics" platform and the potential for expansion into the "AI for Science" domain [1].

Core Viewpoint - Jingtai Holdings (02228) has seen a significant increase in stock price, rising nearly 7% in early trading and currently up 3.27% at HKD 12.95, with a trading volume of HKD 2.236 billion [1] Group 1: Company Developments - Jingtai Technology has announced a strategic cooperation agreement with a subsidiary of JinkoSolar to develop AI and automation high-throughput stacked solar cell technology [1] - The partnership will establish a joint venture to create the world's first "AI decision-making - robotic execution - data feedback" closed-loop manufacturing line for stacked solar cells, aimed at developing high-efficiency and high-stability solar cell products for various applications [1] Group 2: Market Analysis - Zhongyou Securities has released a research report indicating that Jingtai Technology is at a critical transition phase from "0 to 1" to "1 to N," with its business model validated and entering a high-growth phase [1] - In the short term, the continuous landing of large orders and revenue recognition are the core drivers of the stock price; in the long term, the technological barriers of its "AI + robotics" platform and the potential for expansion into "AI for Science" across various fields are central to its value [1]

Core Insights - The company is a rare player in the "AI for Science" sector, founded by three MIT physicists in 2015, focusing on quantum physics first-principles computing combined with AI and robotics to provide drug and materials science R&D solutions [1][2] - The company achieved a revenue of RMB 517 million in H1 2025, representing a year-over-year increase of 404%, with a net profit of RMB 83 million, a significant turnaround from a loss of RMB 362 million in the same period last year [1] Drug Discovery Solutions - The drug discovery solutions segment generated revenue of RMB 435 million in H1 2025, a year-over-year increase of 616%, accounting for 84% of total revenue [1] - The company signed a record-breaking collaboration with DoveTree worth HKD 47 billion (USD 5.99 billion), with an initial payment of USD 51 million and potential milestone payments totaling USD 58.9 billion [2] Pipeline Progress - The company’s incubated pipeline, RTX-117, received approval for clinical trials in China and has also been granted orphan drug designation by the FDA, allowing for expedited review and extended market exclusivity [3] - The global market for CMT treatment is projected to grow from USD 1.013 billion in 2024 to USD 11.394 billion by 2035, indicating significant clinical demand and market growth potential [3] Hair Growth Products - The company developed two innovative hair growth products, Remeanagen (XTP-118) and AquaKine (XTP-016), which have received FDA approval and are set to be launched in overseas markets [4] - The global hair loss treatment market is expected to reach USD 5.044 billion by 2025 and expand to USD 6.146 billion by 2033, with over 2.5 billion people affected by hair loss globally [4][5] Intelligent Robotics Solutions - The company established a scalable standardized intelligent robotics wet laboratory, automating over 80% of chemical experiments to enhance data accumulation and AI model iteration [6] - A strategic partnership with JinkoSolar aims to develop a fully closed-loop solar cell manufacturing line, integrating AI decision-making and robotic execution [6] Investment Recommendations and Profit Forecast - Revenue projections for 2025-2027 are RMB 787 million, RMB 998 million, and RMB 1.465 billion, with year-over-year growth rates of 195.3%, 26.8%, and 46.7% respectively [6] - The company is expected to transition from a "0 to 1" phase to a "1 to N" growth stage, with a validated business model and a high growth trajectory [6][7] Recommendation Logic - The company's foundation in quantum physics provides reusable capabilities in drug discovery and chemical raw materials, positioning it as a leader in the AI for Science infrastructure [7] - Collaborations with major pharmaceutical companies are anticipated to enhance R&D success rates and shorten development cycles, leading to stable cash flow contributions [7] - The partnership with JinkoSolar is expected to open broader application spaces in physical and chemical industries, enhancing the company's reputation and experience [7]

Core Insights - The article highlights a growing interest among companies in utilizing AI for various functions, including production, operations, marketing, and research and development [1] Group 1 - Many companies are reaching out to discuss their AI initiatives, indicating a trend towards integrating AI into their business models [1] - The term "AI for science" is mentioned, suggesting a focus on using AI to enhance scientific research and applications [1] - Companies are expected to provide detailed accounts of how they are leveraging AI to create value and their future investment plans in this area [1]

Core Viewpoint - The article discusses the advancements and future plans of the company Zhipu AI, emphasizing its focus on innovation and coding capabilities in the context of AGI development and competition with U.S. models [2][9]. Group 1: Company Developments - Zhipu AI's GLM-4.5 model integrates reasoning, coding, and agent capabilities, marking a significant step in AI model development [5]. - The GLM-4.7 model, launched in December 2025, achieved top rankings in various coding assessments, outperforming competitors like GPT-5.2 and Claude Sonnet 4.5 [7]. - Zhipu AI's AutoGLM model gained rapid popularity, reaching 10,000 stars on GitHub within three days, indicating strong community interest [7]. Group 2: Market Position and Competition - Despite the success of Chinese models in open-source rankings, there is a recognition that the gap between Chinese and U.S. models may still be widening due to the latter's closed-source developments [9]. - The company aims to enhance its cloud revenue through high-performance coding tools like GLM CodingPlan and AutoGLM, which are expected to have a significant impact in 2026 [8]. Group 3: Future Focus Areas - Zhipu AI plans to concentrate on scaling known and unknown paradigms, technical innovations, and multi-modal capabilities to enhance AI's functionality in real-world applications [11]. - The company anticipates 2026 to be a pivotal year for AI in scientific applications, driven by improved capabilities and the potential for AI to perform long-term tasks in human environments [11].

Investment Rating - The report assigns an "Add" rating for the company, marking its first coverage [2]. Core Insights - The company is a rare player in the "AI for Science" sector, founded by three MIT physicists in 2015, focusing on drug and material science R&D solutions using quantum physics principles, AI, and robotics. In H1 2025, the company achieved revenue of RMB 517 million, a year-on-year increase of 404%, with a net profit of RMB 83 million [6][10]. - The company signed the largest AI pharmaceutical order in the industry with DoveTree, valued at HKD 47 billion (USD 5.99 billion), which reflects its technological strength and potential for future business development [7]. - The company has made significant progress in drug discovery, with its AI platforms being applied across various fields and partnerships with major pharmaceutical companies like Johnson & Johnson and Pfizer [6][7]. Summary by Sections Company Overview - Latest closing price: HKD 11.35 - Total shares: 4.303 billion - Total market capitalization: HKD 48.8 billion - 52-week high/low: HKD 3.85 / HKD 15.12 - Debt-to-asset ratio: 8.9% - Price-to-earnings ratio: -226.34 [4]. Financial Performance - Revenue forecast for 2025-2027: RMB 787 million, RMB 998 million, RMB 1.465 billion, with growth rates of 195.3%, 26.8%, and 46.7% respectively. The net profit is projected to improve from -RMB 156 million in 2025 to RMB 196 million in 2027 [13][16]. Product Development - The company has developed two innovative topical ingredients for hair growth, Remeanagen™ and AquaKine™, which have received FDA approval and are expected to generate significant revenue in the consumer market [10][11]. - The global market for hair loss treatments is projected to grow from USD 5.04 billion in 2025 to USD 6.15 billion by 2033, indicating strong demand and growth potential [11]. Strategic Partnerships - The company has established a strategic partnership with JinkoSolar to develop a new generation of solar cells using AI and automation, enhancing its capabilities in the chemical raw materials sector [12][14].

Core Viewpoint - The article emphasizes that AI in healthcare is just a part of a broader trend known as "AI for Science," which is driving scientific innovation across various fields, particularly in molecular materials and drug discovery [1][2]. Group 1: AI in Healthcare - OpenAI's new ChatGPT feature is positioned as a "trusted medical advisor," capable of analyzing medical records to enhance patient care and influence retail healthcare products [1]. - The introduction of ChatGPT Health is seen as a watershed moment that could reshape how patients access medical information and the products they choose for treatment [1]. Group 2: Data as the Foundation - The article argues that the most critical foundation for AI is data, with coding and medicine being the two fields that possess the most comprehensive datasets [2]. - Major AI companies are developing vertical models specifically for the medical sector, indicating the industry's potential for innovation driven by data [2]. Group 3: AI for Science - The term "AI for Science" is highlighted as a fitting description for AI's role in driving scientific advancements, particularly in natural sciences like chemistry and physics [2][3]. - AI projects in molecular materials, such as those for electrolyte solutions, are demonstrating rapid advancements and precision in finding optimal configurations for different environments [2]. Group 4: Drug Discovery Innovations - Tsinghua University's AI-driven platform, DrugCLIP, has significantly accelerated drug virtual screening, achieving a speed increase of one million times and covering the human genome scale [3][4]. - The AuroBind system, developed by Shanghai Jiao Tong University, acts as a sophisticated tool for drug discovery, efficiently identifying promising drug candidates among millions of compounds [4][5]. - Traditional drug discovery methods are inefficient and costly, while AuroBind enhances the process by predicting protein-ligand interactions and their therapeutic effects, akin to using GPS for navigation [5].

Core Insights - The future development direction and planning of AGI (Artificial General Intelligence) includes achieving bidirectional scaling, continuously exploring the upper limits of known domains while uncovering new paradigms [1] - In terms of practical applications, the focus is on advancing multimodal sensory technology to support AI's integration into the physical world and work scenarios, thereby realizing embodied intelligence [1] - The initiative aims to facilitate a breakthrough in AI for Science [1]