1. AI and Economics
In this document, it is recorded that contribution to the modeling of Artificial Intelligence (AI) in the field of economics by adapting the task-approach to labor markets. By creating an ability-sensitive specification of the task-approach, we were able to model the labor market consequences of AI progress in a more detailed and nuanced manner. One key insight from the reformulation is that the use of AI technologies in an economy will be more widespread (large NIT) if (i) the economy has an abundance of sophisticated programs and machine abilities compared to human skills, (ii) there are a significant number of AI-providing businesses and experts in the economy, and (iii) the task-specific productivity of AI services is higher compared to the task-specific productivity of general labor and labor skills. In addition, access to data is crucial for task-specific AI in the model, so its relative abundance will also play a role in the diffusion of AI. Therefore, the modification of the task-approach to labor markets provides a more detailed and nuanced description of AI automation. (Gries, et al. 2021)
2. Economics of AI
The advancement of AI technology presents fresh challenges not only for the economy but also for economics research. Firstly, it is explored how AI is introduced and addressed in economic models. The first inquiry opens up an intriguing area to explore; nevertheless, it is also the most demanding. Almost all of the current economic models addressing AI assume that AI is, to some extent and on various levels, a substitute for human beings. Even though these models make different assumptions and setups, they anticipate that the economy could have distinct growth paths under specific parameters and conditions, among which the evolving relative price of capital to labor is a crucial factor. To stimulate further thoughts on modeling, it is highlighted that some fundamental issues should be addressed before integrating AI into the model. Nevertheless, we need to establish this “value” judgment before proceeding to modeling. Regarding the second query, it has been observed through empirical studies that the Solow Paradox remains unresolved, which implies that AI, like other technological advancements, may also fall under this paradox. Empirical studies on historical trends have established some generally agreed-upon stylized facts, such as: (1) labor productivity growth has slowed down in recent decades, especially after the GFC; (2) productivity gains have a negative correlation with employment within individual industries but are likely to have a positive effect on the overall economy; (3) productivity growth could lead to employment redistribution and an increase in inequality. These under-addressed aspects are crucial because policy implications from these areas could help offset the negative effects on employment and inequality while also preparing people for future AI development.(Yingying Lu,et al, 2019.)
3. The Effects of Artificial Intelligence on the World as a Whole from an Economic Perspective
Consequently, it is evident that the utilization of AI in the future is likely to have a significant influence on the business models and approaches of companies worldwide due to its potential to revolutionize business models. When its viewed AI solely as a prediction tool for decision-making, it may not be apparent how it will affect pure strategy. This implies that if we only consider AI as a prediction tool, we may not realize how its efficiency will impact the direction of our pure strategy in the future. However, if we step back and examine it from a different perspective, we may realize that AI’s accuracy in forecasting can significantly influence the strategy itself. Despite being perceived as a conservative discipline, AI is gradually penetrating the field of academic economics. Economists may need to combine all the ingredients in their minds and taste them before they can formulate and solve their traditional mathematical models that incorporate all the assumptions and complexity they entail.(Sharma, et al ,2021)
4. AI and Economic Growth
Initially, AI introduced into the production function of goods and services and attempted to reconcile the evolving automation with the stable capital share and per capita GDP growth observed in the past century. The model combined Baumol’s “cost disease” perception with Zeira’s automation model to generate several potential outcomes. It established sufficient conditions for achieving balanced growth with a constant capital share that remains below 100%, even with almost complete automation. Baumol’s cost disease leads to a decrease in the share of GDP associated with manufacturing or agriculture (once automated), which is counterbalanced by the increasing portion of the economy that is automated over time. It is possible that ongoing automation could eliminate the necessity for population growth in generating exponential growth as A.I. increasingly replaces people in generating ideas. Notably, we assumed automation to be exogenous, and the incentives for introducing A.I. in different contexts can significantly affect the outcomes. However, with Cobb-Douglas production, a singularity could occur with less than complete automation due to the non-rivalry of knowledge resulting in increasing returns. Even if numerous tasks are automated, growth may remain limited due to essential areas that are hard to improve. In the Appendix, we demonstrated that if some stages of the innovation process necessitate human R&D, then super A.I. may decelerate or even terminate growth by exacerbating business-stealing, which, in turn, discourages human investments in innovation. Such prospects, as well as other implications of “super-A.I.” (such as cross-country convergence and property right protection), are promising avenues for future research. Similarly, rapid creative destruction may impose its own limit on the growth process by restricting the returns to an innovation. Lastly, it was analyzed sectoral-level evidence regarding the evolution of capital shares in conjunction with automation. However, evidence connecting these trends to specific measures of automation at the sectoral level is weak, and there are many economic forces at play in the capital share trends. Developing more precise measures of automation and investigating the role of automation in the dynamics of the capital share are additional crucial avenues for further research. (Philippe Aghion , et al. 2017.).
5. AI and Gobalisation and strategies for Economic development
The increasing automation in manufacturing could potentially lead to the downfall of the export-led developmental model in manufacturing, which has had remarkable positive effects on numerous developing economies. The concern is that while past technological advancements resulted in shared prosperity and equality between nations, as predicted by the convergence hypothesis in standard neoclassical theory, new advancements may halt the convergence of living standards between wealthy and developing nations. They may instead lead to increased inequality both within and among countries, unless we implement policies that offset them. We need to adapt and update our economic frameworks to reflect the models that will describe the next fifty years, just as the production functions used by Ricardo to evaluate agrarian and rural economies were different from those in the manufacturing models that predominated in the mid-twentieth century. Economic analysis, based on models that are appropriate for this new era, has the potential to help create policies at both the global and national levels that can mitigate these harmful effects, ensuring that this new era of innovation leads to increased living standards for everyone, including the billions residing in developing countries.(Anton Korinek, et al., 2021.)
6. The macroeconomic consequences of artificial intelligence
However, if artificial intelligence capital and labor substitute for each other, the competition between humans and machines increases, leading to more jobs being replaced by machines and a decrease in labor share. Furthermore, improving the technology of artificial intelligence capital-augmenting or labor-augmenting production is another consideration of this model. If artificial intelligence capital is supplementary to labor, the Baumol’s cost disease may occur, meaning that an increase in one sector’s productivity results in a further decline in its share. In this scenario, technological advancements in artificial intelligence capital-augmenting assist humans, decrease workload, and improve wages. However, if artificial intelligence capital and labor substitute for each other, humans will compete with machines. Therefore, the technological advancements in artificial intelligence must displace more workers from their jobs, leading to lower wages and labor share. In the absence of external technology, the creation of any artificial intelligence technology can result in a stable economic growth equilibrium in the long term, and all per capita output, per capita traditional capital, and per capita artificial intelligence capital growth rates are equally positive. When external technology is considered, the development of technology that enhances labor can result in a sustainable economic growth equilibrium in the long run, while the development of artificial intelligence can achieve lasting economic growth.(Huang, et al, 2019.)
7. AI, Income Distribution and Economic growth
AI has emerged as a major form of automation, giving rise to concerns about the potential for increased technological unemployment and inequality. If AI technologies only automate routine tasks and displace medium-skilled workers in the process, this could lead to higher levels of unemployment and inequality. The net impact of AI on jobs and inequality will depend on the relative strengths of the displacement effect and the countervailing reinstatement effect. While the task approach has provided valuable insights, it has a fundamental shortcoming in that it cannot model the uncertain jobs impact of the reinstatement effect. As the task approach is not an economic growth model, it cannot capture these dynamic aspects of the labor market. However, this assumption ignores differences in intertemporal decisions of rich and poor households and their respective effects on aggregate consumption and savings, which is not adequate when considering an automation technology that has asymmetries in factor rewards and potential changes in income distribution as key features of interest. As a result, both the task approach and endogenous growth models are currently limited in their theoretical modeling of key economic impacts of AI and automation. First, by integrating the task-based approach in our growth model, we show that AI automation can decrease the share of labor income and increase the income share of financial wealth owners and technology owners, regardless of the size of the elasticity of substitution between AI and labor. To maintain employment levels, wages may remain stagnant in line with slower GDP and productivity growth. Therefore, the model presented explains why advanced countries experience high employment rates with stagnant wages, productivity, and GDP, despite the hype surrounding AI.( Gries, et al, 2020.)
8. Artificial intelligence and covid-19: an analysis in business and economics
The transition to the digital economy necessitates the restructuring of industries, transportation, and agriculture, opening up new opportunities for utilizing Big Data and streamlining processes through intelligent environments, cryptocurrencies, and hybrid forms of human-AI interaction .”Contemporary technologies are now accessible to furnish appropriate information and enhanced services in healthcare delivery, and during the COVID-19 pandemic, these technologies have played a crucial role in providing advanced and digital solutions” . Within the field of accounting, it is recommended that audit firms invest in digital programs such as artificial intelligence, blockchain, network security, and data function development due to the effects of social distancing and the implementation of work-from-home strategies. This investment would enable audit firms to adapt to the remote work experience and ultimately enhance communication effectiveness and flexibility between auditors and their clients. (Sorin-Ciprian Teiusan,et al, 2021)..
9. Application Of Artificial Intelligence In Control Systems Of Economic Activity
Artificial intelligence differs from most other information systems in that it relies on probabilistic processes instead of clear rules and algorithms to generate outcomes. Unlike auditors who verify information based on expected results, artificial intelligence does not expect any specific outcome, but instead calculates the probabilities of each potential outcome. However, the unpredictability of these results creates a risk that may lead to potential problems for auditors who rely on traditional methods.Furthermore, the algorithms of artificial intelligence systems used in control systems for economic activity may change depending on the processed data, unlike the approach taken by auditors who follow a compiled program. With automated audit support systems, the results are determined by a programmed algorithm rather than by data that does not alter the algorithm. In contrast, in control systems that use artificial intelligence, data is an integral component of the algorithm.This article explores the concept of applying artificial intelligence in business control systems, outlining its goals, principles, objectives, and functions.(Oleksandr Melnychenko, 2019.)
10. Sustainable Economic Development And Post-Economy Of Artificial Intelligence
The primary economic and societal implications of the emergence of the post-artificial intelligence economy can be broadly summarized as follows. However, in the socially distributive economy, where the focus is on employment and access to goods and services in high demand, economic growth is only possible if it generates jobs. In the post-artificial intelligence economy, the criteria for measuring economic progress will also change. In the post-artificial intelligence economy, the conventional quantitative indicators of economic growth may not accurately reflect the extent of intellectual and social advancement in public relations. Previously, employees of these companies could find alternative employment, but this is no longer an option in the artificial intelligence economy. It is important to note that artificial intelligence is not a homogeneous field, and certain technological directions within it may function as new sub-industries or independent entities while also serving various sectors of the economy. In addition, two directions of post-economy artificial intelligence development can be identified. The second direction involves creating an artificial intelligence “mind” that would encompass all existing AI systems and be capable of solving numerous economic and social problems faced by humanity.(Oktay Mamedov ,et al2018.)
CONCLUSION
In conclusion, our adaptation of the task-approach to labor markets provides a more comprehensive understanding of the consequences of AI progress. We found that widespread use of AI technologies in an economy is more likely when there is an abundance of sophisticated programs and machine abilities compared to human skills, a significant number of AI-providing businesses and experts, higher task-specific productivity of AI services, and access to data. This modified approach enhances our ability to describe the effects of AI automation in labor markets with greater detail and nuance. The advancement of AI technology poses significant challenges for economics research. Empirical studies have established some stylized facts, including a slowdown in labor productivity growth and its correlation with employment and inequality. These under-addressed aspects of AI development are crucial to consider for policymakers and researchers to prepare for future AI development and mitigate its negative effects on employment and inequality. The impact of AI on the business models and approaches of companies worldwide is significant and has the potential to revolutionize them. Analysis suggests that the introduction of AI into the production function of goods and services has the potential to revolutionize the economy and generate balanced growth with a constant capital share. While it has the potential to revolutionize the way we produce goods and services, it also poses a threat to the convergence of living standards between wealthy and developing nations. The relationship between artificial intelligence and labor is complex and depends on whether they substitute for or complement each other. The impact of technological advancements in artificial intelligence on the economy is significant, and policies need to be implemented to ensure that it leads to sustainable economic growth and improved wages for workers. While AI has the potential to greatly impact the labor market and income distribution, its net impact on jobs and inequality is complex and depends on multiple factors. Current economic models are limited in their ability to fully capture these impacts, but integrating the task-based approach in growth models can provide valuable insights into the potential effects of AI on labor income and wealth distribution. The digital economy presents numerous opportunities for industries, including healthcare and accounting, to improve their processes and enhance their services through the use of advanced technologies such as Big Data, AI, blockchain, and network security. The COVID-19 pandemic has highlighted the importance of digital solutions in providing remote and flexible work experiences, and businesses that invest in these technologies will be better positioned to adapt to future challenges and opportunities. The article highlights the differences between traditional audit methods and the use of artificial intelligence in business control systems. While the probabilistic nature of artificial intelligence offers new opportunities, it also presents new challenges and risks for auditors. Nonetheless, the potential benefits of AI in control systems make it a promising area for future research and development. The post-artificial intelligence economy will bring significant changes to our economic and societal structures, including the need to redefine measures of economic progress. The development of AI technologies presents both opportunities and challenges, and it is important to ensure equitable distribution and ethical standards are maintained.
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