The Multidimensional Impacts of Artificial Intelligence on Economic Growth
Artificial Intelligence (AI) is often applied in key sectors like healthcare, finance, manufacturing, and transport. We are observing a shift in production from traditional inputs to more information and communications technology (ICT)-based, capital-intensive tools. The introduction of AI is a recent phenomenon, led to changes in the techniques of production. Initially, there were shortages of capital and labor replacement in its earlier adoption of new technologies. Yet, in recent years we have found potential sources of economies of scale through the AI environment. AI is changing global economic activities. It is replacing traditional factors of production, like physical capital and labor. This replacement aims at achieving rapid economic growth. AI is not only replacing human and animal labor with machines. It is also accumulating human capital through new ways of learning and generating knowledge. AI is considered an innovative capital which is different from physical and human capital for its non-rival contribution. AI is an integral part of our daily lives. It assists in areas ranging from industrial applications to personal assistants. AI is influencing economic growth and income by acting as an important factor of production.
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AI as a Driver of Economic Growth
AI symbolizes a driver of productivity and economic growth. It can increase efficiency by analyzing large amounts of data. This significantly improves the decision-making process. At the same time, it creates serious risks. These include job market polarization, rising inequality, structural unemployment, and the emergence of new undesirable industrial structures. It is suggested that AI can stimulate growth by replacing labor. Labor is a limited resource. AI replaces it with capital, an unlimited resource, for the production of goods, services, and ideas.
Artificial Intelligence is fast transforming the global economies and opening new horizons for innovation and productivity. AI technologies can offer potential improvements in operational efficiency, resource management, and service delivery. Yet, several obstacles stand against their widespread adoption. These include inadequate infrastructure, regulatory uncertainties, and a gap in skills about AI technology. Despite these challenges, integrating AI significantly boost economic growth. This can be achieved through better use of resources. AI also improves operational efficiencies. Additionally, it creates new business models.
The growing adoption of Artificial Intelligence (AI) has sparked ubiquitous concerns worldwide. Artificial intelligence can affect economic growth and employment. The influence is assumed to be significant. Adopting AI technology will lead to increased productivity. Labor-abundant countries should adopt labor-augmenting technology, while countries with an aging population can adopt capital-augmenting technology.
Wright and Schultz (2018) find that advances in technological automation increase productivity significantly. We adopt the perspective of the economic theory of a firm. This helps us understand how technological changes drive growth. We explore the changes that AI brings to the firm. The underlying proposition of growth theory is that a sustained growth rate of output per capita depends on advances in technological knowledge. This is true in the long run. These advances can be in the form of new goods, new markets, or new processes. The driver of growth, thus, is technological change. The source of rising productivity growth by new technologies dates back to the 1960s.
Implications of AI innovation on economic growth
The application of artificial intelligence (AI) across firms and industries warrants a line of research. This research should focus on determining AI’s overall effect on economic variables. AI serves as a general-purpose technology (GPT). For example, it aids in the production, marketing, and customer acquisition of firms. This results in increased productivity and consumer reach. Aside from these, AI leads to an enhanced quality of services. It improves work accuracy and efficiency. It also increases customer satisfaction.
Effects of AI on consumption
AI’s effects on consumption are reflected in enhanced customization and convenience. It also improves search and discovery. Optimized pricing and predictive analytics are other impacts. AI creates immersive experiences and increases security. It is also seen as a driver for creating new business models in many industries. The “Sizing the Prize” study was published by the audit company PwC in 2017. It identifies eight main sectors directly affected by AI.
(1) Health: this includes assistance with diagnosis enabled by data, identification of pandemics, and diagnosis by imaging. It also involves the prediction of diseases by the human genome and the use of surgical robots.
(2) Automotive: autonomous fleets for carpooling, smart cars and driver assistance, predictive and autonomous maintenance, etc.
(3) Financial services: (banking and insurance): automation of customer relations and transactions. This is achieved in particular thanks to robo-advisors. Customized financial offers are provided. Detection of fraud and the fight against money laundering are also important aspects, etc. ;
(4) Retailing: customized product design, customer data list, automated inventory and delivery management, etc. ;
(5) Communication and entertainment: media archiving and research, content creation (films, music, etc.), personal assistants, etc.;
(6) Manufacturing and production: reinforced control and self-correction of processes. There is improvement of the supply chain and manufacturing. Production on demand occurs, etc.;
(7) Energy: smart meters, optimized networks and storage operations, smart infrastructure maintenance, etc.;
(8) Logistics: autonomous deliveries (by trucks, drones, etc.), traffic control and reduction of traffic jam, enhanced road safety, etc.
Socio-economic impacts of AI
AI covers a set of models and techniques whose fields and modes of application are heterogeneous. 3D image recognition is, for example, used to make medical diagnoses and to steer self-driving cars. Recently, Yong, Zeshui, XinXin, and Marinko (2023) show that the proliferation of AI in the economy has been unprecedented. In particular, the advent of the post-pandemic era has intensified the reliance on and wish for AI for economic development.
According to the economist Philippe Aghion (2023), AI should, nonetheless, also have negative effects on economic growth. This is due to the brakes (or hidden costs) weighing on the development of AI. These obstacles are mainly of the following nature:
(1) Technological: some technologies have reached an insufficient stage of maturity to assess their economic benefits, like the autonomous vehicle which is at an experimental stage or the quantum computer which is at an exploratory stage;
(2) Legal: The protection of personal data remains insufficiently regulated. Cybersecurity also lacks sufficient regulation. Furthermore, the competitive environment of the AI ecosystem is inadequately regulated.
(3) Socio-professional: the effects of skills deficits and resistance to organizational change are difficult to measure.
(4) Organizational: AI models in current business and government management systems are still insufficiently integrated.
(5) Institutional: public action on training in AI is not equally promoted. This includes both initial and continuous training. The aim is to reduce the digital divide and promote the retraining of professions. It depends on the country and region. The potential brakes can also be of a competitive nature. AI markets are controlled by “fringed oligopolies”. A few leaders monopolize their market segments (GAFAs – Google, Amazon, Facebook, and Apple). A galaxy of small players, like start-ups, also exists.
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