2023 AIGC Industry Market In-depth Research Analysis: Reshaping the Productivity Scenario Design Ecosystem
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Generative AI (AIGC) is a significant milestone marking the transition from AI 1.0 to AI 2.0. The convergence of technologies such as GAN, CLIP, Transformer, Diffusion, pre-trained models, multimodal techniques, and generative algorithms has spurred the explosive growth of AIGC. Continuous algorithmic iteration and innovation, along with the qualitative leap in AIGC capabilities driven by pre-trained models and multimodal advancements, have endowed AIGC with more versatile and robust foundational capabilities.
From the progressive development of computational intelligence, perceptual intelligence, to cognitive intelligence, AIGC has opened the door to cognitive intelligence for human society. By training on large-scale datasets, AI can acquire knowledge across multiple domains. With appropriate model fine-tuning, it can perform tasks in real-world scenarios. The core idea of AIGC technology is to use AI algorithms to generate creative and high-quality content. Through model training and learning from vast datasets, AIGC can produce contextually relevant content based on input conditions or guidance. For example, given keywords, descriptions, or samples, AIGC can generate matching articles, images, audio, and more.
AIGC Industry Development Status and Market Size Analysis
AIGC holds milestone significance for human society and artificial intelligence. In the short term, AIGC transforms foundational productivity tools; in the medium term, it alters social production relations; and in the long term, it drives qualitative breakthroughs in overall societal productivity. Within this transformation of productivity tools, production relations, and productivity itself, the value of data—as a production element—is vastly amplified. AIGC elevates data to the status of a core resource of our era, accelerating the digital transformation of society to some extent.
The AIGC industry chain primarily consists of three segments: the upstream segment involves the formulation of underlying logic and the provision of relevant algorithms and data, including data suppliers, creator ecosystems, foundational support tools, and open-source algorithms; the midstream segment focuses on content generation, encompassing text generation, image generation, digital humans, and related content; the downstream segment mainly involves content application and the utilization of relevant model algorithms.
To some extent, artificial intelligence has been expected to be used for content creation since its inception. After more than half a century of development, with rapid data accumulation, enhanced computing power, and improved algorithm efficiency, today's AI can not only interact with humans but also perform creative tasks such as writing, composing, painting, and video production. In 2018, an AI-generated painting sold for $432,500 at Christie's, becoming the world's first AI artwork to be auctioned, drawing widespread attention. As AI is increasingly applied to content creation, the concept of AI-generated content (AIGC) has quietly emerged.
Deep learning models continue to iterate, and AIGC has achieved breakthrough progress. Particularly in 2022, algorithms experienced explosive development, and breakthroughs in underlying technologies made the commercial application of AIGC feasible. This progress has been concentrated in the field of AI painting: in June 2014, generative adversarial networks (GANs) were proposed. In February 2021, OpenAI introduced the CLIP multimodal pre-training model. By 2022, the diffusion model gradually replaced GANs.
Amid the ChatGPT frenzy, various industries are actively exploring more possibilities of integrating AIGC (AI-Generated Content) technology. The financial sector is continuously investigating the application of AIGC to meet personalized customer demands and achieve innovation in financial services. The AI industry chain primarily consists of three layers: foundational, technical, and application. The foundational layer focuses on building support platforms, including sensors, AI chips, data services, and computing platforms. The technical layer emphasizes core technology R&D, covering algorithm models, basic frameworks, and general technologies. The application layer concentrates on industrial applications, encompassing industry-specific solution services, hardware products, and software products. Research indicates that China's AIGC industry chain structure comprises five main components: foundational large models, industry/scenario-specific medium models, business/domain-specific small models, AI infrastructure, and AIGC support services, forming a robust industrial ecosystem.
Before 2021, AIGC primarily generated text. However, the new generation of models can now process various formats, including text, speech, code, images, videos, and robotic actions. Post-2022, AIGC has experienced rapid development, driven by continuous improvements in deep learning models, the push for open-source models, and the exploration of commercialization possibilities for large models—factors that have accelerated AIGC's growth. With favorable national policies and advancements in foundational technologies like 5G, China's AI industry has entered an explosive growth phase, showcasing immense market potential. Data shows that China's core AI industry scale reached 150 billion yuan in 2020 and is projected to hit 400 billion yuan by 2025.
AIGC witnessed a breakthrough at the end of 2022. With the synergistic enhancement of data, algorithms, and computing power, AI models' intelligence levels have continuously risen, gradually awakening AI's "creative intelligence." As AI technology upgrades and breakthroughs continue, generative AI is rapidly penetrating fields like text, images, and audio-video content. Currently, "deep synthesis + computation-driven" virtual humans leverage text, image, and audio generation technologies to create fully anthropomorphic digital content, including appearance, facial expressions, and speech patterns, representing a key area within AIGC. This multimodal generation technology is widely applied in virtual idols, virtual hosts, and similar domains.
Since the second half of 2023, the AIGC-related concept sector has been on the rise. Tech giants like Microsoft, Google, and Amazon are investing heavily, while gaming companies are also announcing new AI initiatives. This has raised concerns among some about 'AI replacing humans.'
In February 2023, Meta CEO Mark Zuckerberg announced that Meta would form a top-tier product team focused on AIGC. YouTube, owned by Google, announced it is developing AIGC content creation tools. Previously, overseas giants like OpenAI, Microsoft, Google, and Buzzfeed have also launched related services. Domestic companies have responded swiftly. After the popularity of AI-generated art in 2022, leading enterprises like BAT, ByteDance, Wanxing Technology, and BlueFocus have accelerated their investments in the AIGC sector.
AIGC Industry Future Development Trends and Opportunities
In the AIGC field, ChatGPT currently represents the highest standard, but China is also at the forefront of global AIGC development. China's AI industry is rapidly expanding. According to IDC data, China's AI software and application market was valued at $5.1 billion in 2021 and is expected to reach $21.1 billion by 2026.
AIGC has rich applications in intelligent compliance. For example, after new regulations are released, AIGC can automatically generate interpretations and update internal policies accordingly. It can also be used for compliance Q&A, where business personnel describe scenarios to the AI model, which then identifies violations and suggests solutions. Financial innovation driven by AI and other technologies is gradually penetrating areas like financial products, business models, and workflows, creating new opportunities.