Analysis of the Competitive Landscape and Development Trends of China's AIGC Industry in 2023

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Generative artificial intelligence (AIGC), large models, AGI, and MaaS have become the key buzzwords of 2023. Their popularity evokes memories of the 'metaverse' era, but unlike the metaverse, the industrial implications behind large models and AIGC are not only shaking the entire To B sector but also revolutionizing the underlying information technology infrastructure.

AI will not only be the theme of 2023 and 2024 but also an unavoidable topic for the next decade. By the end of August, the first batch of large models received regulatory approval, with Baidu's ERNIE Bot being the first to fully open to the public, followed by Zhipu AI, Baichuan Intelligence, ByteDance, SenseTime, and others. Fast-forward two months to early November, and the second batch of AI large models also passed regulatory approval, including applications from Meituan, Kunlun Wanwei, Ant Group, FaceWall Intelligence, Zhihu, and 11 others.

Current State of AI Development

Combined, the two batches of approved large model applications total 22 companies. From March 27, when Baidu's ERNIE Bot—China's first large model—officially launched, to November 4, when the second batch of large models received approval, the past 222 days have seen domestic large models transition from conceptual hype to practical applications, moving from consumer-facing (C-end) to business-facing (B-end) and industrial implementation. Currently, large models are in the stage of commercial exploration.

The approval of these 22 large model applications not only marks a perfect conclusion for all enterprises preparing large models in 2023 but also sets a promising start for the entire To B and cloud computing industries in 2024.

According to the China Center for Information Industry Development (CCID) under the Ministry of Industry and Information Technology, China's generative AI market size is expected to exceed 10 trillion yuan this year. Generative AI is rapidly penetrating four major industries: manufacturing, retail, telecommunications, and healthcare.

Data shows that this year, China added 368 AI enterprises, and the adoption rate of generative AI among domestic companies has reached 15%, with a market size of approximately 14.4 trillion yuan. The adoption rate of generative AI technology in the four major industries—manufacturing, retail, telecommunications, and healthcare—has seen rapid growth.

Experts predict that by 2035, generative AI could contribute nearly 90 trillion yuan in global economic value, with China accounting for over 30 trillion yuan, representing more than 40% of the total.

China's AI Applications Accelerate Penetration in Finance, Telecom Manufacturing, and Healthcare Industries

Currently, as the integration of the digital economy and the real economy deepens, and digital scenarios of internet platforms transition to the metaverse, the overall demand for digital content volume and richness continues to rise. AIGC, as a new form of content production, has already achieved significant innovative developments in industries with high digitalization and rich content demands, such as media, e-commerce, film, and entertainment. Its market potential is gradually becoming evident. Meanwhile, in the process of promoting the integration of digital and real economies and accelerating industrial upgrades, AIGC applications in finance, healthcare, manufacturing, and other industries are also developing rapidly.

According to IDC, the top five industries in China with the highest AI application penetration in 2022 were the internet, finance, government, telecom, and manufacturing. Additionally, the value AI brings to autonomous driving and transportation logistics cannot be overlooked. McKinsey estimates that AI will generate 380 billion yuan in economic value for the transportation sector.

AIGC Market Scale to Continue Expanding

According to forecasts by the Qianzhan Industry Research Institute, China's AIGC market size is expected to reach 17 billion yuan in 2023. The period from 2023 to 2025 marks the first phase of market growth, with a steady growth rate of around 25%, reaching approximately 26 billion yuan by 2025. Starting in 2025, as the industry ecosystem matures (particularly with the open availability of foundational large models), the flourishing application layer will drive rapid industry growth, with an annual compound growth rate exceeding 70%. By 2027, China's AIGC industry is projected to surpass 60 billion yuan. From 2028 onward, the AIGC industry will extend into a complete industrial chain, continuously broadening and deepening commercial scenarios, revolutionizing the sector. From 2028, China's AIGC industry will maintain high-speed growth, with the market size exceeding one trillion yuan by 2030.

Analysis of the Competitive Landscape in the Generative AI Industry

China's generative AI industry can be broadly categorized into three types of players:

1. "Full-Stack Chain Leaders" Dominated by Tech Giants: These players possess comprehensive technical capabilities spanning computing power, algorithms, and applications. With exceptional overall strength, they are potential monopolists of foundational large models, equipped with the financial and technical resources to integrate various vertical experts.
2. Specialized Players Represented by Application Product Providers, Model Algorithm Developers, and Infrastructure Providers: These players typically leverage their niche advantages to extend their presence into other value chain segments.
3. "Technology Lighthouses" Represented by Research Institutions: Focused on cutting-edge research and innovation in model algorithms, these players prioritize long-term technological progress over commercialization.

Analysis of Generative AI Development Trends

Advances in Natural Language Generation

With the continuous advancement of deep learning technology, generative AI has made significant progress in natural language generation. From simple text generation to complex dialogue systems, generative AI is gradually improving the fluency and accuracy of natural language generation.

Multimodal Input and Output

In the future, generative AI will not be limited to processing text data. Multimodal input and output, such as images, audio, and video, will become new development directions. This will enable generative AI to have broader application prospects in fields such as artistic creation, voice assistants, and virtual reality.

Cross-Disciplinary Integration and Innovation

Generative AI will integrate with technologies from many other fields, such as computer vision, reinforcement learning, and bioinformatics. This interdisciplinary exchange and innovation will provide strong momentum for the development of generative AI.

Personalized Customization and Optimization

As the demand for personalized experiences continues to grow, generative AI will place greater emphasis on personalized customization and optimization. Through deep learning technology, generative AI can provide more accurate and personalized services based on user preferences and habits.

Intelligent Content Creation

In fields such as news media, advertising, and entertainment, generative AI will become an important tool for intelligent content creation. It can automatically generate articles, advertising slogans, scripts, and more that align with themes and styles based on client needs, significantly improving the efficiency and quality of content creation.

Interaction and Intelligent Recommendations

In areas such as e-commerce, search engines, and social media, generative AI will achieve more precise interaction and intelligent recommendations by analyzing user behavior and interests. This will provide users with a better service experience while enhancing the content quality and user engagement of platforms.

Security and Privacy Protection

With the proliferation of generative AI applications, security and privacy protection issues will receive significant attention. In the future, researchers will dedicate efforts to developing more secure and privacy-preserving generative AI systems to ensure the safety of user data.

Explainability and Transparency

As the demand for AI explainability and transparency continues to grow, generative AI will undergo continuous improvements in enhancing model interpretability and transparency. This will help users better understand AI decision-making processes while increasing the reliability and trustworthiness of AI applications.

High-Performance Computing and Storage

To meet the substantial data and computational resource requirements of generative AI, high-performance computing and storage technologies will become crucial development directions. By improving computational efficiency and reducing storage costs, these advancements will promote the widespread application of generative AI.

Ethics and Moral Standards

During the rapid development of AI technology, ethics and moral standards will play an increasingly important role. In the future, the development of generative AI will place greater emphasis on ethical norms and social responsibility, ensuring that AI applications comply with ethical and legal regulations while creating more value for humanity.