The popularity of ChatGPT has seen various industries start paying closer attention to the new production mode of AI Generated Content (AIGC). Broadly speaking, AIGC refers to applying generative AI technology to effect changes in many fields involving digital content. It's important to note that 'content' in this context represents all forms of digital content generation, not simply conventional media content.

Now, as more AIGC models are developed, exploration of general artificial intelligence appears inevitable. This article will examine how historically influential inventions in the field of science and technology transitioned from specific applications to general usage. Insights that surface from this examination might be instructive for enterprise management and the autonomous driving industry in their exploration of artificial intelligence.

01. Historical Revisiting: The Transition from "Dedicated" to "Universal" Usage

In retrospect, significant scientific and technological inventions tend to transition from a "dedicated purpose" to "universal use", invariably catalysing social productivity at each stage.

The First Industrial Revolution is emblemised by the steam engine, initially built with a fixed and one-way function primarily used for pumping in coal mines. It was only decades later, after being improved upon by Watt, that this invention saw geographical limitations lifted. The resulting "universal steam engine" found application in trains, ships, and factories for high-efficiency, low-cost, large-scale production, significantly amplifying human energy utilization efficiency.

The Second Industrial Revolution holds the automobile as its icon. Although the independent invention of automobiles occurred in the 1880s and 1890s in Europe and the US, it took several more decades before Ford launched the universal "Model T" produced on an assembly line, revolutionizing the automobile into a "national means of transportation."

The Third Industrial Revolution is marked by the advent of the integrated circuit (IC). Initially, the 1960s and 1970s saw the development of clunky and wasteful purpose-specific chips with high customization costs used mainly in military and scientific applications. Only after Hoff designed the microprocessor did the era of large-scale personal computers arrive with the birth of the "universal chip."

Steam engines, vehicles, and ICs – these inventions are renowned as the "General Purpose Technologies" (GPTs) of their respective eras. Their common characteristics can be summarized into four main points:

• Wide-scale application: GPTs can be utilized extensively across many fields. For instance, the steam engine powered trucks and ships, and found application in factory machinery.
• Increased productivity and cost reduction for users: With the invention of transistors, for example, computer processor performances have amplified by hundreds of times, power consumption has been reduced by a similar margin, and yet prices fell to as low as a cup of coffee.
• The promotion of technological innovation and new product creation: The universal chip, for example, enabled the production of mobile phones, computers, and other electronic devices.
• The continuous enhancement and optimization of production, distribution, and organization: The invention of the steam and internal combustion engines set the foundation for assembly line production, which went on to spur the growth of various industries.

02. AI Marches Towards Universal Integration Across Industries

Marx once stated that "the differences among economic eras lie not in what is produced but in how it's produced and with what means of labor." A glance at history reflects the evolution of significant scientific and technological inventions transitioning from a "specific purpose" to "general use". Analogously, the artificial intelligence (AI) industry is following a similar path.

Since its inception in the 1950s, AI has undergone decades of technological accumulation, delivering remarkable advancements in areas like computer vision, phonological and semantic recognition, and deep learning. Thus, AI qualifies as the General Purpose Technology (GPT) of its era. Furthermore, AI potentially holds the power to catalyze a technological revolution on par with past industrial revolutions, altering both physical and mental labor production methods.

In terms of optimizing production efficiency, businesses can employ AI technology to analyze a large amount of historical data dynamically adapting operations and cost-saving measures. This ensures that enterprises can continually deliver high-quality production services.

In promoting technological innovation and new product production, AI continues to develop alongside other technologies like big data, cloud computing, and 5G, thereby enhancing the cumulative impact of these technologies.

In the context of fine-tuning and optimizing production, distribution, and organizational management, AI has restructured labor force distribution (caused by changes in production time) and resource allocation (energy consumption and technology) in the operational phase. The subsequent progress in productivity encourages the emergence of new production relations to solidify each organizational process, ultimately enhancing overall operational efficiency.

Artificial Intelligence Generated Content (AIGC) symbolizes a critical stride in AI's journey to universal use, offering opportunities for choice, innovation, and transformation across numerous industries.

In essence, AIGC facilitates an upgrade and iteration of AI technology across various industries, driving industry value. Its direct worth lies in reducing content creation costs and enhancing the output; the indirect value lies in boosting interaction efficiency between people and between people and machines. Ultimately, its final value lies in fostering productivity to gradually instigate changes in production relationships.

In the autonomous driving industry, AIGC could potentially resolve data constraints impeding autonomous driving development, paving the way for advances in algorithmic models and synthetic data generation based on AIGC.

Firstly, synthetic data can enhance the quality of benchmark data, remedying issues like data shortage and quality by enabling data enhancement and simulation.

Secondly, training AI models with synthetic data can effectively evade privacy concerns.

Lastly, synthetic data can automatically construct and generate data scenarios that are either challenging or impossible to collect in real scenarios, effectively addressing long-tailed and edge cases to improve the accuracy and reliability of model algorithms.

Looking back in history, the advent of the "universal steam engines" two hundred years ago catapulted the world into the mechanical age. Common people surpassed the physical strength limits, gaining the power to perform Herculean tasks. A century ago, the introduction of the assembly line allowed mass-produced commodities to enter households worldwide, enabling ordinary people to live lives comparable to ancient monarchs. Fifty years ago, the birth of universal chips heralded the "PC age", creating a "flatter" world where ordinary people could engage with the world without leaving their homes. In this new era, a new generation of information technologies led by AI will reshape production patterns. Therefore, enterprises need to understand the scope of AI and how it can assist humans in empowering industries.