General Secretary Xi Jinping’s proposal of “new quality productive forces” holds great importance. What, then, are the fundamental technologies underlying these forces? How do certain general technologies accelerate the development of new quality productive forces? The main drivers of innovation for new quality productive forces are closely related to the disruptive technologies that possess universality and applicability across various sectors and industries. Upon analyzing various technological advancements, it becomes evident that artificial intelligence (AI) and big data stand out as key technologies meeting the criteria of disruptivity, generality, and universally applicability.

AI & big data

Big data is increasingly becoming the core resource for humanity’s future, and big data-based AI will also be a key engine of socioeconomic development in the future. At present, humanity’s capacity to acquire data has experienced explosive growth. AI, as a disruptive approach, has the potential to significantly drive the development and reform of many fields. The development and application of AI technology enables efficient discovery of patterns within massive databases. In addition, AI and big data have led to disruptive changes in sci-tech research and development (R&D) paradigms: transitioning from the traditional “hypothesis-driven paradigm” to the “big data-driven paradigm.” This historical paradigm shift will also lead to a historical change in productivity.

There exists an extremely close and inseparable connection between AI and big data. Big data is the core foundation of AI. Big data should meet the following criteria: it should be accessible for AI and standardized; and its acquisition, storage, transmission and sharing should be safe and ethical. The key value of big databases lies in being the foundation for AI to discover various new patterns, akin to an untapped mine with endless possibilities. Even after being utilized, big data can still be integrated with new data and used again.

Emerging & traditional industries

As essential general technologies for accelerating the development of new quality productive forces, AI and big data primarily expedite this development through two pathways. One pathway involves fostering the creation of emerging industries, while the other involves upgrading and renewing traditional sectors. The following takes the biomedical industry, the medical industry and the big health industry as examples to illustrate this judgement.

The biomedical industry has a long history of development and can be regarded as a traditional industry. However, AI and big data have the potential to disrupt and improve this industry, thereby accelerating the development of new quality productive forces. A wealth of evidence has shown that AI and big data can disruptively improve human capabilities across several key steps of drug development.

Due to the widespread application of AI and big data technologies, the process of biopharmaceutical R&D is undergoing a disruptive transformation. Compared to traditional R&D processes, AI and big data-driven drug R&D processes have undergone disruptive changes: searching for new therapeutic targets in massive data with AI; establishing the three-dimensional structure of new target proteins using AI software; designing clinical research plans with AI, and conducting preclinical and clinical studies on newly-designed drugs based on their three-dimensional structures to evaluate their efficacy. The revolution of biomedical R&D processes driven by AI and big data is expediting the new drug discovery and reducing costs. These changes are leading the upgrading of the biomedical industry and accelerating the development of new quality productive forces.

The medical industry also has a long history and can thus be considered a traditional industry. In recent years, AI and big data technologies have been significantly disrupting diagnostic and treatment processes across multiple fronts. Medical imaging technologies such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT) and Positron Emission Tomography (PET) are the foundations of precision diagnostics. However, precision diagnoses through analysis of medical images is a relatively time-consuming and expensive process, often further confounded by a paucity of available talent. AI and big data technologies have revolutionized these processes. In addition, AI and big data are becoming core technologies for precision treatment.

For the emerging industry of the big health industry, AI and big data also play a central role in accelerating the development of new quality productive forces, addressing the fundamental problem of a serious shortage of professional talent in the sector.

In summary, AI and big data can accelerate the development of new quality productive forces through two main pathways: supporting emerging industries such as the big health industry, and upgrading traditional industries such as the biomedical industry and the medical industry. It can be argued that the productivity derived from emerging industries with AI and big data at their core, and the productivity generated by the major upgrading and renewing of traditional industries based on this core foundation, are both important components of new quality productive forces.

Yin Weihai is deputy director of the Med-X Research Institute at Shanghai Jiao Tong University.