Forecast and Analysis of the Generative AI Industry: 2024 Will Be the Year of Generative AI's True Implementation

[baoshi.rao]

In recent years, artificial intelligence technology has rapidly advanced in areas such as computer vision, machine learning, neural networks, natural language, and image understanding. The shift from decision-making AI, which analyzes and judges based on data, to generative AI, which summarizes and imitates creation based on data, marks a significant breakthrough.

Generative artificial intelligence refers to technology that generates text, images, sounds, videos, code, and other content based on algorithms, models, and rules. Represented by OpenAI's ChatGPT, generative AI is sparking a new wave of 'AI arms races' among global tech giants and startups, including Microsoft, Google, Meta, Baidu, and Alibaba.

Generative AI is a type of artificial intelligence technology that uses machine learning models and deep learning techniques to generate new content by studying patterns in historical data. This content can be text, images, audio, or video.

Unlike traditional AI, which generates outputs based on given rules or data, generative AI autonomously creates entirely new content, akin to human creativity. For example, the widely discussed chatbot ChatGPT, which employs the core model GPT-3, can generate high-quality natural language text for applications such as chatting, writing, and automated customer service.

With the number of parameters in deep learning models soaring from tens of thousands to hundreds of billions, deep neural network technology is continuously breaking new ground in large-scale models and multimodal directions. This has led to a qualitative leap in AI-generated content (AIGC) capabilities, enabling AI algorithms to achieve self-learning, evolutionary content creation that balances creativity and controllability. In terms of creativity, expressiveness, speed of creation, dissemination value, and personalized experiences, AI has significantly surpassed the boundaries of human content creation. AIGC-related technologies have begun to be gradually applied to various creative content fields such as language dialogue, text, images, and audio-video. They are expected to significantly improve creation quality and efficiency in areas like creative ideation, innovation of content resources, and intelligent creation of text, images, and videos. This will further lower the threshold for content production, attracting more users to engage in creative activities and greatly promoting the development of the digital creative industry.

In 2023, China's generative AI market size is expected to exceed 10 trillion yuan. Generative AI is accelerating its penetration into four major industries: manufacturing, retail, telecommunications, and healthcare. The adoption rate of generative AI technologies among Chinese enterprises has reached 15%. According to the white paper 2024 AIGC Application Layer Top Ten Trends released by IDC (International Data Corporation) in collaboration with DingTalk, as general intelligent capabilities are promoted in practice, AIGC will first achieve scenario implementation among enterprise users. Enterprises will initially consider scenarios related to productivity and office work.

Development Status of the Generative AI Industry

As a high-profile term throughout the year, generative AI has flourished since the beginning of 2023, benefiting from the emergence of ChatGPT. This has also driven rapid development in areas such as AI algorithms, large models, and big data, with major tech companies worldwide entering the AIGC arena. In China, companies like Baidu and Tencent have launched large language models, stealing the spotlight. Among them, Baidu has particularly shone in this AI revolution. Notably, in August of this year, Baidu's ERNIE Bot was opened to the public, announcing to the world China's transformative breakthroughs in the field of artificial intelligence.

After a year of competition, AIGC now presents a market landscape where various language model products and applications flourish. It is even considered a key variable and important milestone in advancing AI from 1.0 to 2.0. Its development prospects in the coming year are highly anticipated. With the continuous advancement and expanding applications of AI technology, AIGC will further achieve cross-domain integration. For instance, artificial intelligence technology combined with fields such as healthcare, education, and finance will create more practical and valuable application scenarios. It is important to note that only market-recognized application scenarios can generate value, thereby achieving a win-win situation for both industrial technological progress and market expansion.

From the perspective of protecting technological development and industrial environments, most countries regulate privacy protection related to technology based on existing legal frameworks. However, technology evolves rapidly, especially with the pervasive applications of AI and big data in socio-economic life. Establishing a more scientific, mature, and effective legal environment in the future is a crucial institutional safeguard to ensure the long-term development of AI technology.

Generative AI Industry Development Forecast

In 2021, AIGC-generated data accounted for less than 1% of global data, but this proportion is expected to rapidly increase to 10% by 2025. According to Precedence Research, the global generative AI market is projected to reach $118.1 billion by 2032, with a compound annual growth rate of 27%.

Industry experts predict that 2024 will be the year of true implementation for generative AI, with AIGC technology becoming a key driver for transforming China's tech industry, enhancing corporate efficiency, and promoting high-quality development in the real economy.

In 2024, generative AI will transform its 'magic' into commercial impact. To maximize its potential, businesses can adopt three approaches. 1. Apply generative AI technology in daily work to increase productivity by 10% to 20%. Select appropriate generative AI tools for testing, conduct large-scale skills training, develop corresponding solutions to support employees in their routine tasks, and carefully evaluate implementation costs.

2. Adjust key position functions to improve work efficiency and effectiveness by 30% to 50%. Anticipate how generative AI will impact workforce and core functions in advance, establish new positions, reallocate budgets, and promote a series of pilot projects that can be steadily scaled.

3. Create new generative AI business models to establish long-term competitive advantages. Develop customer-centric efficient working methods, fully utilize proprietary data and intellectual property, and provide customers with unique interactive experiences unavailable elsewhere.