How will AI affect long run economic growth in developed countries?

Aliyah Koujan-Patel – Notting Hill and Ealing High School

Artificial Intelligence (AI) is becoming increasingly prevalent within our society, and has evolved into a specialised tool capable of reshaping economies through enhanced production and innovation. Consequently, AI has a variety of effects on long-run economic growth within developed countries. For instance, increased investment in technology and innovation allows markets within these countries to achieve technological economies of scale, reducing long-run average costs and allowing investment to be further stimulated into generating long-run economic growth. However, history suggests otherwise, outlining how technological breakthroughs do not automatically translate into long-term economic growth. For example, the widespread adoption of electricity and information technology required decades of investment in infrastructure, organisational change and human capital before their full economic benefits were established (Brynjolfsson, Rock and Syverson, 2018). These conflicts create a greater debate on AI’s influence in a developed economy as a technological breakthrough, posing the question - How will AI affect long-run economic growth in developed countries?

AI may influence long-run economic growth through its potential to raise productivity - especially in developed countries that have greater access to these technological resources. Highlighted by the Solow Growth Model, long-run economic growth in developed economies is primarily driven by technological progress rather than factors such as capital accumulation, meaning sustained growth through increases in output depend on innovations that increase efficiency in labour and capital. Therefore, AI can be utilised as a general-purpose technology that can enhance productivity across a range of sectors - allowing long-run economic growth.

Figure 1: Solow Growth Model (Team, 2023)

This is furthered by firms achieving economies of scale through AI adoption. As developed firms are able to invest in AI to increase automated labour production, unit labour costs fall as AI’s fixed costs are spread over more units, lowering long-run average costs. These lower costs increase profits which firms can invest in further innovation and expansion, raising productivity and productive capacity - increasing long-run aggregate supply and generating long-run economic growth in developed countries.

However, economic benefits of these technologies often have significant delay. Described as the “productivity paradox” (Brynjolfsson, Rock and Syverson, 2018), new technologies like computing and electricity did not immediately translate into measurable productivity gains as firms had to reorganise their production processes and invest in complementary technologies to allow them to be integrated more efficiently. Brynjolfsson et al. state “there are likely to be significant lags between technological progress and productivity growth,” - showing adoption alone is insufficient for a wide economic impact like long-run economic growth. Applying this to AI, productivity growth will therefore depend on the speed and breadth of its diffusion across the economy. This is supported by Acemoglu who argues AI’s macroeconomic impact is likely to be “nontrivial but modest” (Acemoglu, 2024) unless its adoption extends beyond frontier firms and becomes more integrated within the economy - meaning that AI’s influence remains uncertain.

AI’s impact on productivity therefore may be variable but its influence on innovation can provide a more significant source of sustained economic growth. Unlike the Solow Growth Model that considers technological processes as external influences, Romer’s Endogenous Growth Theory (Romer, 1989) counters this. Romer argues that technological change results from deliberate investment in research, development, and accumulation of knowledge, highlighting that AI not only has the potential to improve productivity, but also create new ideas, products and industries that allow deeper economic growth.

Figure 2: Endogenous Growth Model (Merish, 2023)

This can be seen in sectors such as pharmaceuticals, where AI is reducing the time required to identify potential drug compounds. AlphaFold (AI) has predicted the structures of over 200 million proteins and dramatically accelerated biotechnology and biological research that previously required years of experimentation (Jumper et al., 2021). It is estimated that between $2.6 trillion and $4.4 trillion annually will be contributed by generative AI to the global economy through gains in productivity and innovation (Chui et al., 2023). As AI reduces the time and cost associated with these factors, firms enter a self-reinforcing cycle of innovation and economic growth, again reflecting Romer’s argument.

Nevertheless, these benefits remain conditional. If AI becomes concentrated within a small number of dominant firms, markets will begin to reflect oligopolistic characteristics as barriers to entry increase, limiting the diffusion of its benefits throughout the wider economy. AI’s productivity gains will therefore depend on competitive markets and the spread of innovation rather than frontier firm adaptation alone (OECD, 2024).

Overall, AI is likely to influence long-run economic growth through productivity gains and accelerated innovation. The Solow Growth Model and Endogenous Growth Theory both highlight this and the importance of technological progress in driving sustained growth. However, economic history suggests these benefits are not immediate, and so in developed countries AI’s impact will depend less on innovation and more on diffusion across sectors. Consequently, AI is best seen as a catalyst for growth whose long-run influence is dependent on how effectively developed countries adapt and implement it.

References:

Acemoglu, D. (2024). The Simple Macroeconomics of AI. [online] National Bureau of Economic Research. doi:https://doi.org/10.3386/w32487.

Brynjolfsson, E., Rock, D. and Syverson, C. (2018). Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics. [online] NBER. Available at: https://www.nber.org/books-and-chapters/economics-artificial-intelligence-agenda/artificial-intel ligence-and-modern-productivity-paradox-clash-expectations-and-statistics? [Accessed 28 Jun. 2026].

Chui, M., Hazan, E., Roberts, R., Singla, A., Smaje, K., Sukharevsky, A., Yee, L. and Zemmel, R. (2023). The economic potential of generative AI: The next productivity frontier. [online] McKinsey & Company. Available at: https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-economic-potential-of-ge nerative-ai-the-next-productivity-frontier.

Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S.A.A., Ballard, A.J., Cowie, A., Romera-Paredes, B., Nikolov, S., Jain, R., Adler, J. and Back, T. (2021). Highly Accurate Protein Structure Prediction with Alphafold. Nature, [online] 596(7873), pp.583–589. doi:https://doi.org/10.1038/s41586-021-03819-2.

Merish, N. (2023). Henry George and Poverty Perpetuation: Dynamics, Mechanisms and Reforms. [online] Available at: https://www.researchgate.net/publication/377437832_Henry-George-and-Poverty-Perpetuation Dynamics-Mechanisms-and-Reforms-Nishu-Mehrish.

OECD (2024). The impact of Artificial Intelligence on productivity, distribution and growth. [online] OECD. Available at: https://www.oecd.org/en/publications/the-impact-of-artificial-intelligence-on-productivity-distrib ution-and-growth_8d900037-en.html.

Romer, P. (1989). Endogenous Technological Change. [online] National Bureau of Economic Research Working Paper Series. Available at: https://www.nber.org/papers/w3210.

Team, P.F. (2023). What Is the Solow Growth Model (Definition and Key Assumptions)? [online] Peak Frameworks. Available at: https://www.peakframeworks.com/post/solow-growth-model.