The release of the ChatGPT model by OpenAI has catalyzed an explosion of applications in the field of AI-generated content (AIGC), igniting a renewed pursuit of capital and innovation in the AI landscape.

AIGC finds applications across numerous industries, including chatting, writing, video and audio production, cartography, design, and even in generating game strategies and code.

This proliferation of AIGC applications marks the prelude to an era where AI plays an indispensable role in content creation and automation. The concept of AIGC dates back to the 1950s, but it wasn't until the 1990s to 2011 that scientific research findings were transformed into practical products. Despite facing algorithmic bottlenecks, significant breakthroughs emerged around 2010 and 2014. The introduction of Generative Adversarial Networks (GANs) in 2014 and the release of the ChatGPT model in 2022 by OpenAI triggered a series of events propelling AI development to new heights, leading to the gradual perfection of its capabilities.

The evolution from PGC (Program-Generated Content) in WEB 1.0 to UGC (User-Generated Content) in WEB 2.0, and now to the era of AIGC in WEB 3.0, has been made possible by technological innovations, particularly in deep learning models. While AI-assisted projects faced challenges and setbacks in 2014, today's AIGC systems can effectively replace manual labor, achieving almost 100% autonomous production. This explosion in AIGC applications has led to its massive growth, with the industry reaching $17 billion by 2023 and projected to reach $1 trillion by 2030, highlighting its significance and potential.

ChatGPT's future industrialization spans various domains, including induction, code generation, image processing, and intelligent customer service. Originating from GPT, based on the Transformer model, which debuted in June 2018, subsequent iterations like GPT-2 and GPT-3 have further expanded its capabilities. The introduction of Human Feedback Reinforcement Learning in March 2022 enhanced AIGC's abilities, allowing it to be trained and iterated upon with human feedback, leading to continuous improvement. By November 2022, reinforcement learning further optimized AIGC's performance, enabling more thoughtful and logical reasoning.

The development of AI relies not only on advanced models and algorithms but also on the computational power of chips. While CPUs excel in numerical calculations and complex logical reasoning, they are relatively slower and lack parallel processing capabilities. GPUs and FPGAs, on the other hand, act as accelerators, assisting in parallel task execution. In the context of training large AI models like ChatGPT, powerful computational support is essential. For instance, ChatGPT requires approximately 27.5 PFlop/s-day for single training, while a single NVIDIA V100 chip provides 125 TFlops of deep learning power. Consequently, training the ChatGPT model necessitates at least one V100 chip computing for 220 days (27.5*1000/125=220) to complete the training process.

The rise of AIGC marks a transformative phase in the realm of artificial intelligence, revolutionizing various industries and paving the way for unprecedented innovation and automation. With the convergence of advanced deep learning models and powerful computational resources, the AIGC industry is poised for continued growth and disruption in the years to come. The impact of AIGC extends beyond mere content creation, permeating into areas such as personalized advertising, medical diagnostics, and financial forecasting. The integration of AI-generated insights into decision-making processes has the potential to streamline operations, enhance efficiency, and drive business growth.