Zhitong Finance App learned that Zheshang Securities released a research report saying that recently, major overseas companies have released financial reports, represented by Microsoft, Google, Meta, and Amazon. Overseas AI giants are actively embracing the wave of AI commercialization in big models, cloud computing, search and advertising, productivity tools, etc., and are making efforts to lay out large-scale AI model applications and product services, and are leading a new round of AI industry transformation. The bank believes that with the continuous advancement of artificial intelligence infrastructure, model algorithms, and application product layouts by major overseas manufacturers, AI+ industry applications are expected to explode. Domestic AI applications are expected to resonate with policy and demand in the second half of the year, presenting a “blossoming” situation.
The main views of Zheshang Securities are as follows:
Cloud Computing: The commercialization of AI drives the cloud computing market to accelerate growth, with Amazon, Microsoft, and Google competing for supremacy
According to T4.ai's forecast, the global cloud computing market is expected to exceed 600 billion US dollars by 2024. Meanwhile, ForresterConsulting's research on Azure cloud services shows that enterprise users can achieve a return on investment of 284% in three-year projects using Azure AI services. AI+ cloud services can provide significant economic benefits to enterprises in the fields of model reasoning, data management, and business analysis. Currently, the competitive pattern in the global cloud computing market is stable. Amazon AWS, Microsoft Azure, and Google GCP compete for dominance. Each company already has a layout in AI models, vector databases, code assistants, etc., and the concentration of the industry is expected to continue to increase.
Search engines and advertising: Google has a strong leading position, and Microsoft takes the lead in deploying enterprise-level services
The AI model combined with search engines is expected to change user usage habits and disrupt the user ecosystem. Microsoft was the first to deploy the NewBing search engine combined with the GPT-4 model, while Google followed suit and launched the BARD and Google GenerativeExperience (SearchGenerativeExperience) search engines. The bank believes that the competitive landscape of the search engine market will not change in the short term, while Google is expected to rely on the AI model to achieve high-quality growth in advertising business. Microsoft's commercialization based on enterprise-level search services is expected to contribute revenue growth. According to estimates, under a 10% penetration rate, Microsoft's enterprise-level search engine BingChatEnterprise can contribute 2,968 billion US dollars in revenue growth every year.
Productivity tools: AIGC can greatly improve productivity, and scenarios such as office and code development are expected to be implemented first
According to McKinsey's research report, the potential economic value of AIGC technology fully penetrating all productivity links can reach 6.1 to 7.9 trillion US dollars. In marketing scenarios, the application of AIGC technology can increase user productivity by more than 30%. Major companies such as Microsoft and Salesforce have successively launched productivity tools based on AI big model technology. Currently, charging models for some applications (OfficeCopilot, GitHubCopilot, Salesforcesalescloud, etc.) have been announced. The AIGC business is expected to open up room for growth based on the original business.
Big model ecosystem: Big manufacturers are firmly advancing into multi-modal fields and continuously consolidating model competitiveness through investment and financing
Major AI companies, such as Microsoft, Google, and Meta, continue to lay out large multi-modal AI models, and continue to publish research results in the fields of video generation, processing, and robotics. At the same time, by investing in AI startup teams such as Inflection and Anthropic, major overseas manufacturers have continuously accumulated model algorithm foundations and consolidated their core competitiveness. The “big fish eats small fish” pattern in the big model field has taken shape.
Domestic AI applications are expected to resonate with policy and demand in the second half of the year, presenting a “blossoming” situation
On the one hand, the big model supervision policy is constantly being improved, and the time for full commercialization is getting closer. On August 15, the “Interim Measures for the Administration of Generative Artificial Intelligence Services” was officially implemented. The policy promotion is expected to promote the healthy development and standardized application of generative artificial intelligence, safeguard national security and social public interests, and protect the legitimate rights and interests of citizens, legal persons and other organizations.
On the other hand, the bank expects large-scale application products from domestic software vendors to be intensively released in the second half of the year. The bank saw that a number of listed companies recently held AI product launches and revealed the latest progress and results in big model iteration, indicating that the software company has explored the needs of government or enterprise customers related to the big model and has begun preparations and plans, and is expected to enter the commercialization implementation stage in the second half of the year.
It is recommended to focus on the target:
AI+ Office: Jinshan Office (688111.SH), Zhiyuan Internet (688369.SH);
AI+ Education: iFLYTEX (002230.SZ);
AI+ Government: Xindian Software (688232.SH), Torus (300229.SZ), Tongda Sea (301378.SZ);
AI+ Finance: Yuxin Technology (300674.SZ), China Science and Technology Soft (603927.SH), Hang Seng Electronics (600570.SH);
AI+ Security: Meiya Perco (300188.SZ);
AI+ remote sensing: Space Vision (688066.SH), Zhongke Star Map (688568.SH);
Risk warning:1) Risks of AI technology iteration falling short of expectations: 2) AI commercial product launches falling short of expectations; 3) risks posed by policy uncertainty; 4) risks posed by uncertainty in downstream markets.