Abstract
The arrival of the era of artificial intelligence (AI) impacts a country’s economic growth. This work was aimed at helping a country achieve high-quality economic growth through AI. First, the penetration effect, boundary extension effect, knowledge creation effect, and self-deepening effect of AI in the process of penetration into the economy and society are analyzed. Then, the labor factors, capital factors, and production technology factors affecting economic growth are discussed. Furthermore, three channels through which AI affects economic growth are proposed: the labor channel, capital channel, and productivity channel. Finally, relevant verifications are carried out. The verification results demonstrate that AI will promote an increase in economic growth rate in the long run but have a specific inhibitory effect in a short time. According to the research results, sound policy suggestions are put forward for the positive impact of AI technology on economic growth, the negative effect on labor employment and income distribution, capital accumulation and capital structure, and the effect on production efficiency. This work has certain reference significance for the research on the economic growth rate of the national manufacturing industry in the era of AI.
1. Introduction
Since the 21st century, artificial intelligence (AI) technology has made breakthrough progress worldwide with the rapid progress of the Internet and information technology. It has accelerated penetration and integration into economic society and promotes human culture to quickly enter the intelligence era. AI is the most significant innovation in a new generation of technology. AI products are extensively used in all fields of production and life of human society, including intelligent robots, smart homes, AI doctors, unmanned factories, and autonomous vehicles. They have a profound impact on the economic community and have gradually become the focus of major countries [1]. Based on this, governments worldwide have launched a new round of competition around AI. The United States, Japan, Germany, France, and other countries have successively promulgated a series of policies and measures to support the development of the AI industry to seize the dominant position in international competition in the new period [2].
At present, countries have taken measures to seize the development opportunities of AI to accelerate the penetration of AI into the economy and society. In this context, it is essential to systematically analyze the impact channels of AI on economic growth and explore the potential problems and risks of AI in the process of infiltrating into the economy and society. These measurements are of great significance for effectively preventing and responding to the potential dangers of AI and seizing the development opportunities of AI. Acemoglu and Restrepo argue that AI affects the wages of low-skilled workers, causing their wages to stagnate [3]. Polusmakova and Glushchenko believe that although AI can bring sustained returns to capital, it does not have the potential to enable long-term economic growth because income is the only source of investment, and the automation brought about by AI inhibits income [4]. Yee and You found that AI can significantly improve productivity; however, the economic growth rate has slowed down significantly. The authors explain it with four possible explanations: estimation error, measurement error, reallocation, and execution lag [5]. Scholars usually consider the substitution effect of AI on labor, the complementary effect, and the productivity effect of creating new tasks when studying the impact of AI on the economy. Nevertheless, they generally only introduce a single effect into the constructed model for analysis and demonstration and cannot fully reflect the impact mechanism of AI on the economy. Therefore, this work introduces multiple effects into the model for research to help countries achieve high-quality economic growth driven by innovation.
The impact of the robot-assisted industry based on deep learning on economic growth is studied here to help the countries achieve high-quality economic development. Firstly, this work analyzes the four primary effects of AI in infiltrating into the economy and society and explores three factors affecting economic growth. Then, an analysis framework of the impact of AI on economic growth is proposed, which is composed of labor channels, capital channels, and productivity channels. Then, the influence of AI on economic development through three media is examined. The test results indicate that AI has a tremendous impact on economic growth. Finally, policy recommendations are put forward for the effects. This work has particular reference significance for the research on the effects of the robot-assisted industry on the manufacturing economy in the context of AI.
2. Impact of AI on the Economic Growth Rate of the Manufacturing Industry
2.1. Economic Effects of AI
As the most representative technological innovation in the new round of technological progress, AI has a wide range of penetration, almost into all industries and links of economic society. Moreover, with the continuous expansion of AI applications, the emergence of new industries, departments, and occupations has accelerated the adjustment and upgrading of the industry [6]. In addition, AI can realize self-learning and self-renewal and upgrading through machine learning and DL to realize the self-deepening of capital and trigger all-around changes from natural science to social science. AI has four economic effects: penetration effect, boundary extension, knowledge creation, and self-deepening effect [7].
Penetration effect refers to the potential of innovative technology to integrate and penetrate all sectors of economic society and all links of production and life and to change the economic operation mode. As a new universal purpose technology, AI technology can directly affect the production activities of human society, presenting strong permeability. Unlike traditional technological innovation, AI penetration shows the characteristics of intelligence and can penetrate almost all industries and links of economic society. Firstly, AI is broadly used in the industrial sector, which directly affects the production and management of the industrial sector. Secondly, AI has the characteristics of intelligence, accelerating its penetration and integration with the service industry. Finally, unlike modern technological innovation, AI also directly affects the agricultural sector [8]. Figure 1 displays the penetration scope of AI technology in the economic society.
According to the China Robot Industry Alliance statistics, the application scope of domestic industrial robots in 2017 had affected 37 big industrial categories and 102 middle industrial categories of the national economy, covering three major industries, and the scope of influence was still expanding. AI and machine learning have penetrated into almost any field. The intelligent penetration process of AI is reflected in the substitution of AI capital for traditional capital and the direct substitution of AI capital for labor factors [9].
Boundary extension refers to the potential of expanding the boundaries of social work tasks and improving the work tasks through some innovative technologies and economic and social integration. It is another primary effect of universal purpose technology. In terms of the whole economic and social scope, the continuous penetration and integration of AI into the economic society will continue to give birth to new products, technologies, business forms, and industries. In addition, some traditional backward industries will be eliminated from the market to realize the transformation and upgrading of traditional industries. For the enterprises or industry, AI can expand into new businesses, new markets, and new products and eliminate obsolete products and traditional production jobs. In general, the boundary extension effect of AI is as follows: (1) AI dramatically extends and supplements human physical and mental strength, expanding the range of tasks that humans can complete and the upper limit of work tasks; (2) AI gradually eliminates the old low-end production tasks for old products; (3) AI raises the lower limit of work tasks, adjusting and improving the production tasks of the entire society or industry [10, 11].
AI is a representative of creative technology in the novel era. Its knowledge creation effect is far greater than traditional technological innovation, opening a new stage of knowledge production from natural science to social science. First, it promotes progress in natural science and improves the research efficiency of natural science such as physics and medicine. AI has innovated the method of natural science knowledge production and created a substantial driving force for the development of natural science. Moreover, while expanding the research content of social sciences, it has changed the traditional knowledge production methods, means, and tools of social sciences, providing a massive impetus for the development of social sciences [12, 13].
In recent years, significant breakthroughs have been made in the new generation of information technologies, such as mobile Internet and cloud computing. This progress guarantees the computing power of machine learning and deep learning. The rapid development of big data has offered massive learning data for machine learning and deep learning. Consequently, machine learning has made significant progress, breaks through the dependence of AI on human programmers to a certain extent, and realizes self-learning and self-renewal. Then, the self-deepening effect of AI gradually plays a role. The self-deepening effect is mainly manifested in two aspects. First, through machine learning and deep learning, various computer technologies have achieved breakthroughs as “learning results,” giving AI more functions and expanding the range of tasks that AI products can accomplish, such as intelligent machines, algorithms, or software. Second, the intelligent, robotized efficiency of work tasks is improved through self-learning [14].
2.2. Factors Affecting Economic Growth
The analysis of economic growth theory suggests that both the classical Cobb-Douglas (C-D) and various economic growth models regard labor, capital investment, and production technology level as important variables affecting economic growth. Therefore, discussing a country’s economic growth is inseparable from analyzing production factors, let alone the technical analysis [15]. Equation (1) indicates the economic output described in the model studied by predecessors.
In Equation (1), denotes the industry; stands for the year; represents the output level; refers to the production technology level; signifies the labor factor input; represents the capital factor input. The production function of C-D can be written as
Equation (3) can be obtained by sorting out Equation (2).
In Equation (3), , , , and are the growth rate of output, labor, capital input, and production technology, respectively. and represent the share of labor and capital factors, respectively. Equation (3) shows that economic growth results from the comprehensive action of various factors, such as labor factors, capital factors, and production technology levels.
2.3. Channels of AI Affecting the Economy
The theories related to AI and economic growth suggest that the intelligent penetration effect of AI is the substitution of intelligent machines for labor and the substitution of traditional capital. Therefore, the intelligent penetration effect will directly affect labor and capital [16]. The boundary extension effect manifests in the continuous birth of new production tasks in society or industry and the gradual demise of low-end, backward production. These changes will affect labor employment and capital investment. Knowledge creation and self-deepening effects affect production efficiency by affecting the level of production technology but do not immediately affect labor employment and capital investment. Hence, this work does not consider the impact of knowledge creation and self-deepening on labor and capital. Figure 2 shows the overall framework of AI and economic growth:
Among the four basic effects of AI, intelligent penetration and boundary extension can affect the labor factors in the production process, which are indispensable for economic growth. AI affects labor employment and labor income by exerting the effect of intelligent penetration. On the one hand, AI penetrates and integrates into the economy and society. On the other hand, AI affects the quantity and structure of labor employment by increasing intelligent automation and the income effect of intelligent penetration. In addition, AI affects labor employment by creating new industries, products, and tasks, called the boundary extension employment effect. The effect of boundary extension on labor income level is called the income effect of boundary extension [17, 18]. Figure 3 illustrates the influence on economic growth due to AI’s intelligent penetration effect and boundary extension effect on labor employment and income.
Capital is the key factor to achieving economic growth. AI usually affects economic growth through intelligent penetration and boundary extension effects acting on capital accumulation and capital structure [19, 20]. Figure 4 is a visual display.
Increased productivity can generate additional resources. Production efficiency contains two parts: technological progress and technological efficiency. Based on this, the productivity channels of AI affecting economic growth can be divided into two impact paths: affecting technological progress and affecting technological efficiency. First, under the influence of the intelligent penetration effect, AI may affect production efficiency by replacing the labor force and traditional capital or by affecting the fit of connection and cooperation, among other factors. Second, the boundary extension effect creates new jobs for the economy and society, along with the disappearance of traditional backward production capacity and the escalation of social tasks, affecting production efficiency. The role of knowledge creation refers to the influence of intelligent machines on scientific knowledge. The intelligent automation of production has been realized with the progress of scientific knowledge. Knowledge production efficiency will increase exponentially and create a large amount of scientific knowledge for society. Scientific knowledge may affect production efficiency through guiding research and development practice or knowledge transformation and application. Finally, the self-deepening effect is manifested in the continuous self-renewal and upgrading of intelligent machines through machine learning, especially DL. It can further improve the production efficiency of intelligent machines and promote the continuous reform of the management mode of micro subjects, society, and economic and social organizations [21, 22]. Figure 5 presents the four economic effects of AI on economic growth by affecting technological progress and efficiency.
3. Research Methodology and Framework
Figure 6 displays the main framework of this research.
This work investigated labor force education in various industries in recent years. Table 1 shows the industry and corresponding serial numbers.
Figure 7 shows the education situation of workers in various industries.
(a)
(b)
The following primary hypotheses are made based on the survey data:
Hypothesis 1. In a closed economy, there are two sectors: manufacturers and households. The production department is in a completely competitive market, and only one manufacturer is producing the final product in the whole society. The elasticity of substitution of factor is . Equation (4) indicates the production function: In Equation (4), represents the work task, and represents the number of work tasks standardized to 1 in economic society. It is even more difficult to produce from production task to . represents the output of task . If the price of the final product is standardized as 1, Equation (5) describes the utility preference of the family under the static model. In Equation (5), represents consumption, and indicates the labor supply of the household sector. The labor supply meets the conditions and . The consumption level is . represents the capital provided by the household sector. If capital is inelastic to changes in intelligent automation, the equilibrium labor supply satisfies Suppose that , indicating the salary level after standardization.
Hypothesis 2. Based on the impact of AI on labor employment structure and referring to existing studies, labor heterogeneity is divided into the high-skilled labor force and low-skilled labor force. The high-skilled labor force is engaged in work tasks with low repeatability and relatively high creativity. The low-skilled labor force is engaged in tasks with high repeatability and relatively low creativity [23].
Hypothesis 3. In terms of the intelligent penetration effect, AI involves both intelligent penetration of low-end work tasks and intelligent penetration of high-end work tasks when integrating with the economic society. is the upper bound of low-end task intelligent penetration. When , the work task can be completed by an intelligent machine or labor force. is set as the upper bound of low-skilled work tasks. When , the work task can be completed by both low-skilled and high-skilled labor force. is set as the upper bound of intelligent penetration of high-end tasks. When , the work task can be completed by both intelligent machines and highly skilled labor. When , work tasks can only be completed by highly skilled labor [24]. In terms of the boundary extension effect, the development of AI will give birth to new work tasks. The industry’s work task boundary will be expanded, the upper bound will be improved, and the industrial structure will be optimized and upgraded.
Hypothesis 4. The improvement difficulty of intelligent penetration of low-end work tasks is greater than that of high-end work tasks due to the difficulty of work tasks. Hence, , and .
Hypothesis 5. Production efficiency of low-skilled labor on is equal to production efficiency of the highly skilled labor force on , and the labor force has comparative advantages in complex production tasks. Then, production efficiency of intelligent machines is constant at 1.
Hypothesis 6. Since the penetration of AI in the economic society is still in its infancy, AI will improve labor productivity [25].
Based on the basic theoretical assumptions, a model is established for analysis to further explore the impact of AI on economic growth through the number of employed workforce and employment structure. Equation (7) is derived by sorting out and solving the objective function equation of the final output.
In Equation (7), and are the numbers of high-skilled and low-skilled labor forces, respectively. Equation (8) can be obtained according to Equation (7).
Capital income is used to standardize the wage income of two types of the labor force to analyze the impact of AI on labor income. Then, Equation (9) holds.
Equation (9) shows that because and , . The intelligent penetration of AI in work tasks makes the standardized labor wage level show a downward trend. It is because intelligent penetration brings intelligent machines to replace skilled labor, the return on capital increases, and the relative income level of skilled labor shows a downward trend. The impact of intelligent penetration on the income gap between high- and low-skilled labor is explored and analyzed. Equation (10) describes the labor skill premium.
The labor force with high education level has higher working time elasticity and labor participation elasticity, that is, . Therefore, Equation (11) can be obtained by sorting Equation (10).
Equation (11) indicates the impact of intelligent penetration on the income gap between high- and low-skilled labor forces. and represent the wage level of low-skilled and high-skilled labor force, respectively. The equation shows that intelligent penetration will expand the income gap between high- and low-skilled labor forces. On the impact of the level of intelligent penetration on the return of capital relative factors, after the objective function equation is sorted and solved, Equation (12) can be obtained:
Equation (12) suggests that as intelligent penetration deepens, the relative return of capital increases. While attracting AI capital investment, it will crowd out traditional capital investment. AI capital has cost advantages over labor and traditional capital, resulting in a decline in the growth rate of relative return of capital. When the increased AI capital is difficult to make up for the squeezed out traditional capital, the capital accumulation shows a downward trend. Equation (13) indicates the effect of the extension effect on capital accumulation by affecting the relative factor price.
Equation (13) shows the rise in the relative return of capital and the capital accumulation under the boundary extension effect of AI. Equations (12) and (13) illustrate that the impact of AI on capital accumulation is lagging, which makes capital accumulation decline first and then rise.
4. Research Results and Policy Recommendations
4.1. Research Results
It comes to the following conclusions through the basic assumptions based on survey data and analysis, as well as the support of theory and equations.
Conclusion 1. AI affects economic growth through labor employment and labor income. (1) The impact of AI on labor employment has a lag, making the labor employment level first fall and then rise. It is primarily manifested in the decline of the employment level of the low-skilled labor force and the rise of the employment level of the high-skilled labor force, driving the economy to decline first and then rise. (2) The impact of AI on the level of labor income lags. The level of labor income decreases first and then rises, promoting the economy to decline first and then rise. (3) The impact of AI on the skill income gap also lags. The skill income gap first expands and then narrows in time, making the economic growth decline first and then rise by acting on the labor income gap. Figure 8 shows the details.
Conclusion 2. AI affects economic growth through capital accumulation and capital structure. (1) AI has a lagging impact on capital accumulation, making capital accumulation decline first and then rise; consequently, economic growth declines first and then rises. (2) AI promotes economic growth by acting on capital structure. Figure 9 presents the details.
Conclusion 3. AI affects economic growth through technological progress and technological efficiency. (1) AI can boost economic growth by promoting the progress of cutting-edge technology. (2) AI can stimulate economic growth by improving technical efficiency. Figure 10 shows the specific contents.
4.2. Policy Proposals
Based on the above conclusions, the specific policy recommendations are as follows.
Recommendation 1. It is necessary to increase support for basic research on AI-related technologies to give full play to its positive role in economic; it is essential to adhere to the AI technology innovation as the breakthrough of economic growth, accelerate the improvement of the top-level design and industry norms for the development of AI industry, and expand the scale of AI industry and AI industry chain. In addition, AI, employment, and income distribution need to be incorporated into the statistical monitoring system to promote the deep integration of AI and industry in stages and with emphasis.
Recommendation 2. Given the negative effect of AI technology on labor employment and income distribution, it is necessary to expand the coverage of social security policies, sincerely implement the unemployment insurance and assistance policies, and reduce the unstable factors caused by unemployment and wage decline. In addition, it is essential to deepen the reform of AI-oriented curriculum and skills in higher education and accelerate the improvement of labor market-oriented education. The scale of automation in low-skilled sectors needs to be appropriately controlled to prevent large-scale unemployment in low-skilled sectors due to AI penetration.
Recommendation 3. The supply-side structural reform shall advance for the effect of AI technology on capital accumulation and capital structure; it is also necessary to accelerate the improvement of the market exit mechanism to remove inefficient and backward production capacity from the market as soon as possible. According to the characteristics of different industries, the penetration and integration of AI into industries should be promoted in a focused and targeted manner. Besides, capital allocation and production efficiency in the industry need to be improved. It is essential to improve the construction of high-skilled talents, such as optimizing the talent training mechanism based on key colleges, strengthening the construction of innovative talent teams, and introducing more high-level overseas talents.
Recommendation 4. Regarding the effect of AI technology on production efficiency, it is essential to promote the continuous development of strategic emerging industries such as new materials and new energy. Besides, active exploration is required for the possible penetration fields of AI and promotes the deep integration of AI with industries such as new materials and new energy. The production and research and development process of enterprises related to new materials and new energy can be used as a testing ground for AI to promote the continuous upgrading of AI and give full play to its efficiency. Attention should be paid to the application of AI technology such as machine learning in scientific research. Besides, enterprises and scientific research institutions in the field of AI should be encouraged to strengthen exchanges and cooperation with overseas advanced technical teams to realize the deep application of AI technology in the research process of various fields.
5. Conclusion
This work studies the impact of the DL-based robot-assisted industry on the economic growth rate. First, an analysis is conducted on the intelligent penetration effect, boundary extension effect, knowledge creation effect, and self-deepening effect of AI in economic and social penetration. Besides, the labor factors, capital factors, and production technology factors affecting economic growth are discussed. Then, the analysis framework of AI’s impact on economic growth is proposed, consisting of the labor channel, capital channel, and productivity channel. Finally, AI’s impact on economic growth through three channels is tested. The test results show that AI technology can promote long-term economic growth and even exponential growth. Meanwhile, the labor force was liberated from mechanized, low-knowledge, and creative work and turned into programmed, open-ended mental work. New jobs have continuously increased the demand for high-skilled labor, raised real wages, and promoted high-quality economic growth. Moreover, the rapid penetration of AI in the economic society will attract more capital accumulation, increase capital investment, improve capital production efficiency, and support macroeconomic growth. In addition, it can significantly improve technical efficiency, reflected in the growth of total factor productivity, and provide more power for economic growth. Finally, the research conclusions are summarized, and corresponding policy recommendations are put forward.
Compared with previous studies, this work more comprehensively demonstrates the influence mechanism of AI on the economy by introducing four effects into the model for analysis. Due to limited capacity, this work does not consider the impact of AI on economic growth under the background of the declining supply of the right-age labor force and the aging population simultaneously. The follow-up research will be optimized from this aspect. This work has particular reference significance for the research on the impact of the robot-assisted industry on the manufacturing economy in the AI era.
Data Availability
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Ethical Approval
This article does not contain any studies with human participants or animals performed by any of the authors.
Consent
Informed consent was obtained from all individual participants included in the study.
Conflicts of Interest
The author declares no conflict of interest.
Acknowledgments
This work is supported by the fund of Department of Education of Guangdong Province in China (ID: 2019WQNCX156).