Artificial Intelligence’s Breakthrough: New Prospect of The Industries and The Economy
A breakthrough in the field of technology will always carry out how it will shape economic models and applications. Just 100 years ago, the industrial revolution and how we utilize automation called upon the birth of the college textbook definition of capitalism. Now, with the swift growth usage of ChatGPT and other AI services alike, you’ll see manmade neural networks, Artificial Intelligence, entering their own revolutionary era. As the world continues to advance technologically, the impact of artificial intelligence (AI) on the economic and industrial landscape has become increasingly significant. From transforming the way businesses operate to creating new job opportunities, the integration of AI has brought about major changes in various sectors.
If you’re wondering about their capability in this early adoption in their approach of how to automate our work, the last two sentences were written by an AI with a simple prompt — as a demonstration of how indistinguishable it is. This leads to the essay’s questions, how far will it affect the status quo of our economic approaches? How will this rapid scale of AI adaption in industries affect the labor market and the way people live and work?
The Manmade Neural Network Vs. Man
Artificial Intelligence, as the name itself suggests, refers to deep learning techniques that use artificial neural networks. A range of AI and advanced analytics techniques are applied in many sectors. One of them is used as a GPT (general purpose technology) — and not to be mistaken from ChatGPT (Chat Generative Pre-training Transformer) — characterized by pervasive use in a wide range of sectors combined with technological dynamism (Bresnahan and Trajtenberg 1995).The impact of GPT on employment and the distribution of income, directly linking the discussion of AI as a GPT helps to diffuse, computers and the internet and is likely led to increased inequality because of the skill bias and to an increased capital share (Autor et al. 2017). One example is the strong incentives for firms to develop and adopt AI as automatizing the task seems to be more effective compared to the augmentation of tasks that were previously thought to require a human. Another example is that AI increases the ability to monitor workers. Although some amount of monitoring may be useful, monitoring can also be excessive if it is used to shift rents away from workers. unrestrained AI could result in less democratic labor markets, worse working conditions, and an erosion of labor market institutions that favor workers.
In Keynes’ words, the labor market itself can experience technological unemployment “due to our discovery of means of economizing the use of labor outrunning the pace at which we can find new uses for labor” ( Keynes, 1933 ). Individually, workers’ demand and wages may grow for those with digital and cognitive skills and with expertise in tasks that are abstruse and hard to automate– but shrink for workers performing repetitive tasks. We’ll take the example of the cheap workforce that has provided China’s manufacturing industry with a competitive cost advantage, but taking into account the intensive low-skilled activities have led 77% of its workforce at high risk of automation (Frey and Osborne, 2015). In the longer run, economists generally agree that society will be wealthier with this adaptation (Acemoglu et al. 2021). Even with the assumption that automation (thus AI and robotics) will eventually replace workers in their previously done tasks and create a powerful displacement effect–This will lead to a lower labor share of economic output. At the same time, productivity will increase and capital will accumulate, thereby increasing the demand for labor (Ibid).
From another perspective, The automation of several tasks and jobs can help as the cure to the Baumol effect (Baumol’s cost disease), a tendency for costs and prices to rise in several industries even though the productivity of those industries hasn’t increased. the growth consequences of automation and AI may be constrained by Baumol’s “cost disease.” Baumol (1967) observed that sectors with rapid productivity growth, such as agriculture and manufacturing, often see their share of GDP decline while those sectors with relatively slow productivity growth (including several services sectors i.e. medical services and education) experience increases. Therefore, economic growth may be constrained not by what we do well but rather by what is essential and yet hard to improve.
Graph 1 : Baumol’s Cost Disease can be seen through this CPI graph, goods become less expensive (apparel, vehicles) compared to services (education and medical services) (Source: Bureau of Statistics US)
With some of the public’s worries regarding how their jobs will be replaced by AI even to those who might regard their specialization as “high skill-demanding” and eventually leading to “creative destruction”, we can look at past speculations on the reasons that automation has not wiped out a majority of jobs over the decades and centuries. Automation can indeed substitute for labor — as it is typically intended to do. However, automation can also complements labor, by raising output in ways that lead to higher demand for labor, and interacts with adjustments in labor supply. Consider for instance that the visual recognition of objects is an ability, and that this ability can be used to perform a task, facial recognition and . Applying the ability of being able to see for facial recognition, require skills, which requires human judgement. Thus, even though an AI model can recognize faces because of its ability to “see” we still need human skills to make the decision and judgement to apply the ability to the a function.
AI, research on the impacts of AI on industries’ productivity, labor market and creative destruction for now isn’t alot, as it is in their infancy years. For there is more to come and see. If human labor is indeed replaceable by automation, then our problem will be one of distribution, not of scarcity. The primary system of income distribution in market economies is rooted in labor scarcity (Autor, 2015). People possess a bundle of valuable “human capital” that, due to its scarcity, generates a flow of income over the career path. If machines were in fact to make human labor replacable we would have vast aggregate wealth but another challenge in determining who owns it and how to share it.
References:
Frey, Carl Benedikt; Osborne, Michael A. (2016). The future of employment: How susceptible are jobs to computerisation?. Technological Forecasting and Social Change, (), S0040162516302244–. doi:10.1016/j.techfore.2016.08.019
Frey, C, Osborne, M 2015, Technology at work: the future of innovation and employment, Oxford Martin School and Citi GPS, Oxford, viewed 17 Mar 2023, <http://www.oxfordmartin.ox.ac.uk/downloads/reports/Citi_GPS_Technology_Work.pdf>.
Agrawal, A., Gans, J., & Goldfarb, A. (2018, May 14). Introduction to “The economics of artificial intelligence: An agenda”. Retrieved March, from https://www.nber.org/books-and-chapters/economics-artificial-intelligence-agenda/introduction-economics-artificial-intelligence-agenda
Bughin, J. (n.d.). (2018). Notes from the AI Frontier Modeling the impact of AI on the World Economy. Retrieved Spring, from https://www.mckinsey.com/~/media/McKinsey/Featured%20Insights/Artificial%20Intelligence/Notes%20from%20the%20frontier%20Modeling%20the%20impact%20of%20AI%20on%20the%20world%20economy/MGI-Notes-from-the-AI-frontier-Modeling-the-impact-of-AI-on-the-world-economy-September-2018.ashx
Mengmeng, Xie et al. (2021, January 20). Does artificial intelligence affect the pattern of skill demand? evidence from Chinese manufacturing firms. Retrieved March, from https://www.sciencedirect.com/science/article/pii/S0264999321000171
Keynes, J.M., 1933. Economic possibilities for our grandchildren (1930). Essays in persuasion. pp. 358–373.
