Artificial Intelligence and the Digital Economy: Impact on Employment, Productivity, and Market Structures
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This paper presents a Systematic Literature Review (SLR) examining the economic impact of artificial intelligence (AI) on employment, productivity, and market structures within the digital economy. Following the PRISMA protocol, 78 peer-reviewed studies and institutional reports (2015–2025) were synthesized. The literature consistently reveals that AI produces a dual labor market effect - displacing routine occupations while generating new AI-complementary roles - with net positive but unequally distributed employment outcomes. Productivity gains are significant at the firm level but contingent on complementary organizational investment and show delayed aggregate effects. AI also intensifies market concentration, reinforcing winner-takes-most competitive dynamics through data-driven network effects. Policy implications span labor transition, competition regulation, and digital governance.
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Acemoglu, D., & Restrepo, P. (2018). The race between man and machine: Implications of technology for growth, factor shares, and employment. American Economic Review, 108(6), 1488–1542.
Acemoglu, D., & Restrepo, P. (2022). Tasks, automation, and the rise in US wage inequality. Econometrica, 90(5), 1973–2016.
Arntz, M., Gregory, T., & Zierahn, U. (2016). The risk of automation for jobs in OECD countries: A comparative analysis. OECD Social, Employment and Migration Working Papers, No. 189. OECD Publishing.
Autor, D. H., Levy, F., & Murnane, R. J. (2003). The skill content of recent technological change: An empirical exploration. Quarterly Journal of Economics, 118(4), 1279–1333.
Autor, D., Dorn, D., Katz, L. F., Patterson, C., & Van Reenen, J. (2020). The fall of the labor share and the rise of superstar firms. Quarterly Journal of Economics, 135(2), 645–709.
Autor, D. (2022). The labor market impacts of technological change: From unbridled enthusiasm to qualified optimism to vast uncertainty. NBER Working Paper No. 30074. National Bureau of Economic Research.
Bessen, J. (2019). AI and jobs: The role of demand. NBER Working Paper No. 24235. National Bureau of Economic Research.
Bloomberg Intelligence. (2024). Global technology sector market capitalisation report. Bloomberg LP.
Brynjolfsson, E., Li, D., & Raymond, L. R. (2023). Generative AI at work. NBER Working Paper No. 31161. National Bureau of Economic Research.
Brynjolfsson, E., Rock, D., & Syverson, C. (2021). The productivity J-curve: How intangibles complement general purpose technologies. American Economic Journal: Macroeconomics, 13(1), 333–372.
Calvano, E., Calzolari, G., Denicolò, V., & Pastorello, S. (2020). Artificial intelligence, algorithmic pricing, and collusion. American Economic Review, 110(10), 3267–3297.
Ciarli, T., Kenney, M., Massini, S., & Piscitello, L. (2021). Digital technologies, innovation, and skills: Emerging trajectories and challenges. Research Policy, 50(7), 104289.
Citi GPS. (2023). AI: The new frontier in financial services. Citigroup Global Perspectives & Solutions.
Cremer, J., de Montjoye, Y.-A., & Schweitzer, H. (2019). Competition policy for the digital era. Report for the European Commission. Publications Office of the European Union.
Cunningham, C., Ederer, F., & Ma, S. (2021). Killer acquisitions. Journal of Political Economy, 129(3), 649–702.
Dauth, W., Findeisen, S., Suedekum, J., & Woessner, N. (2021). The adjustment of labor markets to robots. Journal of the European Economic Association, 19(6), 3104–3153.
European Commission. (2024). Regulation (EU) 2024/1689 of the European Parliament and of the Council - EU Artificial Intelligence Act. Official Journal of the European Union.
Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254–280.
Goos, M., Manning, A., & Salomons, A. (2014). Explaining job polarization: Routine-biased technological change and offshoring. American Economic Review, 104(8), 2509–2526.
Gordon, R. J. (2018). Why has economic growth slowed when innovation appears to be accelerating? NBER Working Paper No. 24554. National Bureau of Economic Research.
International Monetary Fund. (2024). Artificial intelligence and the future of work. IMF Staff Discussion Note SDN/2024/001. IMF.
Khan, L. M. (2017). Amazon’s antitrust paradox. Yale Law Journal, 126(3), 710–805.
McKinsey Global Institute. (2023). The economic potential of generative AI: The next productivity frontier. McKinsey & Company.
Mehra, S. K. (2016). Antitrust and the robo-seller: Competition in the age of algorithmic pricing. Temple Law Review, 86(4), 930–960.
OECD. (2023). OECD Employment Outlook 2023: Artificial intelligence and the labor market. OECD Publishing.
PwC. (2023). Sizing the prize: What’s the real value of AI for your business and how can you capitalise? PricewaterhouseCoopers.
Shapiro, C., & Varian, H. R. (1999). Information rules: A strategic guide to the network economy. Harvard Business School Press.
Sokol, D. D., & Comerford, R. E. (2016). Does antitrust have a role to play in regulating big data? Cambridge Handbook of Antitrust, Intellectual Property and High Tech, 293–316.
Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56.
World Economic Forum. (2023). Future of Jobs Report 2023. World Economic Forum.
Zhavoronkov, A., Ivanenkov, Y., Aliper, A., & Veselov, M. (2022). Artificial intelligence for drug discovery, biomarker development, and generation of novel chemistry. Molecular Pharmaceutics, 19(1), 303–314.