Kurniady, Alvin (2025): New Production Function (which is Supported by Empirical Evidences) for an Economy with Deployed Self-Learning Technologies.

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Abstract
The purpose of this paper is to introduce a new production function that takes into account self-learning AI, which can improve itself and therefore productivity without any additional human capital or labor, even though it still requires physical capital. The difference between my production function and any other existing production function is that my production function separates technologies into self-learning and non-self-learning technologies. The value of exponent for the self-learning technologies depends on the value of its base and this is the unique recursive feature of my production function. Unlike in the Mankiw-Romer-Weil production function, in my production function, technology and labor force are separated and this is allowed because I make the technology endogenous. My production function leads to only two possibilities, which are an economy that is in balanced growth path (BGP), and an economy that is in accelerating growth path. The determining factor that decides whether an economy is in BGP or not is the exponent for the self-learning technologies in my production function. If the sum of all exponents is less or equal to 1, then the economy is in BGP, which is consistent with Mankiw-Romer-Weil (1992). If the sum of all exponents is greater than 1, then the economy is in accelerating growth path, which is consistent with Romer (1986). There is no steady state in my production function. I also rule out the possibility of singularity. I support my production function with empirical evidences that confirm that my production function is quite accurate and quite useful for cross-countries comparison. Furthermore, I conduct simulations that show how the U.S economy will transition from balanced growth path to accelerating growth path.
| Item Type: | MPRA Paper |
|---|---|
| Original Title: | New Production Function (which is Supported by Empirical Evidences) for an Economy with Deployed Self-Learning Technologies |
| Language: | English |
| Keywords: | Self-learning AI; Production function; Economic growth; Total output; Recursive learning |
| Subjects: | O - Economic Development, Innovation, Technological Change, and Growth > O4 - Economic Growth and Aggregate Productivity > O41 - One, Two, and Multisector Growth Models |
| Item ID: | 127275 |
| Depositing User: | Mr. Alvin Kurniady |
| Date Deposited: | 30 Dec 2025 04:27 |
| Last Modified: | 30 Dec 2025 04:27 |
| References: | 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. https://doi.org/10.1257/aer.20160696 Acemoglu, D. (2024, April 5). The simple macroeconomics of AI [Unpublished manuscript]. Massachusetts Institute of Technology. Prepared for Economic Policy. Aghion, P., Jones, B. F., & Jones, C. I. (2017). Artificial intelligence and economic growth (NBER Working Paper No. 23928). National Bureau of Economic Research. https://doi.org/10.3386/w23928 Bahri, Y., Dyer, E., Kaplan, J., Lee, J., & Evci, U. (2024). Explaining neural scaling laws. Proceedings of the National Academy of Sciences, 121(6), e2311878121. https://doi.org/10.1073/pnas.2311878121 Besiroglu, T., Emery-Xu, N., & Thompson, N. (2023). Economic impacts of AI-augmented R&D. arXiv. https://arxiv.org/abs/2212.08198v2 Brynjolfsson, E., Korinek, A., & Agrawal, A. K. (2025). A research agenda for the economics of transformative AI. NBER Working Paper No. 34256. National Bureau of Economic Research, Cambridge, MA. Available at http://www.nber.org/papers/w34256 Bureau of Economic Analysis (2025). ‘Fixed Assets Accounts, Table 6.1: Current-Cost Net Stock of Fixed Assets.’ U.S. Department of Commerce, accessed at https://apps.bea.gov/iTable/?reqid=10&step=2&isuri=1. CEIC Data (2025). United States – Gross Fixed Capital Formation (economic indicator). CEIC Global Database, accessed at https://www.ceicdata.com/en/indicator/united-states/gross-fixed-capital-formation CleanBridge (2025). CleanBridge Global Data Center Market Report 2025 – United States (Regional Market Overviews). CleanBridge, accessed at https://www.cleanbridge.co/insights/energy-transition/cleanbridge-global-data-center-market-report-2025/regional-market-overviews-gdc2025/united-states-gdc2025/ Farach, A., Cambon, A., & Spataro, J. (2025). Evolving the productivity equation: Should digital labor be considered a new factor of production? arXiv. https://arxiv.org/abs/2505.09408v1 Farboodi, M., Mihet, R., Philippon, T., & Veldkamp, L. (2019). Big data and firm dynamics. AEA Papers and Proceedings, 109, 38–42. https://doi.org/10.1257/pandp.20191001 Gollin, D. (2002). Getting income shares right. Journal of Political Economy, 110(2), 458–474. https://www.jstor.org/stable/10.1086/338747 Hestness, J., Narang, S., Ardalani, N., Diamos, G., Jun, H., Kianinejad, H., Patwary, M. A., Yang, Y., & Zhou, Y. (2017). Deep learning scaling is predictable, empirically. arXiv. https://arxiv.org/abs/1712.00409 Hoffmann, J., Borgeaud, S., Mensch, A., Buchatskaya, E., Cai, T., Rutherford, E., de Las Casas, D., et al. (2022). Training compute-optimal large language models. In Advances in Neural Information Processing Systems (Vol. 35, pp. 30016–30030). https://proceedings.neurips.cc/paper_files/paper/2022/hash/c1e2faff6f588870935f114ebe04a3e5-Abstract-Conference.html Hu, A. G. Z., & Jefferson, G. H. (2001, April 10). R&D, productivity, and profitability in Chinese industry [Unpublished manuscript]. Department of Economics, National University of Singapore, and Graduate School of International Economics and Finance, Brandeis University. Ichihashi, S. (2021). The economics of data externalities. Journal of Economic Theory, 196, 105316. https://doi.org/10.1016/j.jet.2021.105316 German Council of Economic Experts (2023). Annual Report 2023/24: Overcoming sluggish growth – Investing in the future. ‘Table 19: Potential Output, Labour, and Capital Aggregates’, Appendix, values for 2024. German Council of Economic Experts, accessed at https://www.sachverstaendigenrat-wirtschaft.de/en/annualreport-2023.html German Datacenter Association (2024). Data Center Impact Report Germany 2024. German Datacenter Association, accessed at https://www.germandatacenters.com/en/data-center-impact-report-germany-2024/ Jacobo-Romero, M., Carvalho, D. S., & Freitas, A. (2022). Estimating productivity gains in digital automation. arXiv. https://arxiv.org/abs/2210.01252v2 Jones, C. I. (1995). R&D-based models of economic growth. Journal of Political Economy, 103(4), 759–784. https://doi.org/10.1086/261995 Jones, C. I., & Tonetti, C. (2020). Nonrivalry and the economics of data. American Economic Review, 110(9), 2819–2858. https://doi.org/10.1257/aer.20191330 Jorgenson, D. W., & Stiroh, K. J. (2000). Raising the speed limit: US economic growth in the information age. OECD Economics Department Working Papers, No. 261. OECD Publishing, Paris. https://dx.doi.org/10.1787/561481176503 Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., Gray, S., et al. (2020). Scaling laws for neural language models. arXiv. https://arxiv.org/abs/2001.08361 Karabarbounis, L., & Neiman, B. (2013). The global decline of the labor share. NBER Working Paper No. 19136. National Bureau of Economic Research. http://www.nber.org/papers/w19136 Lucas, R. E. (1988). On the mechanics of economic development. Journal of Monetary Economics, 22(1), 3–42. https://doi.org/10.1016/0304-3932(88)90168-7 Mankiw, N. G., Romer, D., & Weil, D. N. (1992). A contribution to the empirics of economic growth. Quarterly Journal of Economics, 107(2), 407–437. https://doi.org/10.2307/2118477 Maslej, N., L. Fattorini, R. Perrault, Y. Gil, V. Parli, N. Kariuki, et al. (2025). The AI Index 2025 Annual Report. AI Index Steering Committee, Institute for Human-Centered Artificial Intelligence (HAI), Stanford University, Stanford, CA, Chapter 4 “Economy”. Accessed at https://hai.stanford.edu/assets/files/hai_ai_index_report_2025.pdf Matthews, D. (2023, August 31). Germany promises huge boost in artificial intelligence research funding and European coordination. Science|Business. Accessed at https://sciencebusiness.net/news/ai/germany-promises-huge-boost-artificial-intelligence-research-funding-and-european Mohammed, S., Budach, L., Feuerpfeil, M., Ihde, N., Nathansen, A., Noack, N., Patzlaff, H., Naumann, F., & Harmouch, H. (2025). The effects of data quality on machine learning performance on tabular data. arXiv. https://arxiv.org/abs/2207.14529v6 National Science and Technology Council (NSTC) (2023). The Networking & Information Technology R&D Program and the National Artificial Intelligence Initiative Office: Supplement to the President’s FY 2024 Budget. Subcommittee on Networking & Information Technology Research & Development and Machine Learning & Artificial Intelligence Subcommittee. Office of Science and Technology Policy, Washington, DC. Available at https://www.nitrd.gov/publications/ OECD (2024). OECD Artificial Intelligence Review of Germany. OECD Publishing, Paris. Available at https://doi.org/10.1787/609808d6-en Puaschunder, J. M. (2022). Extension of endogenous growth theory: Artificial intelligence as a self-learning entity. In Proceedings of the Research Association for Interdisciplinary Studies (RAIS) Conference (October 23–24, 2022). https://doi.org/10.5281/zenodo.7372430 Rathinam, S. S. (2024, January 30). The U.S. AI workforce: Analyzing current supply and growth (Data Snapshot). Center for Security and Emerging Technology (CSET), Georgetown University, Washington, DC. Accessed at https://cset.georgetown.edu/publication/the-u-s-ai-workforce-analyzing-current-supply-and-growth/ Romer, P. M. (1986). Increasing returns and long-run growth. Journal of Political Economy, 94(5), 1002–1037. http://links.jstor.org/sici?sici=0022-3808%28198610%2994%3A5%3C1002%3AIRALG%3E2.0.CO%3B2-C Romer, P. M. (1990). Endogenous technological change. Journal of Political Economy, 98(5, Part 2), S71–S102. https://doi.org/10.1086/261725 Schrittwieser, J., Antonoglou, I., Hubert, T., Simonyan, K., Sifre, L., Schmitt, S., Guez, A., et al. (2020). Mastering Atari, Go, Chess and Shogi by planning with a learned model. Nature, 588(7839), 604–609. https://doi.org/10.1038/s41586-020-03051-4 Shojaee, P., Mirzadeh, I., Alizadeh, K., Horton, M., Bengio, S., & Farajtabar, M. (2025). The illusion of thinking: Understanding the strengths and limitations of reasoning models via the lens of problem complexity. arXiv. https://arxiv.org/abs/2507.12345 Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., et al. (2017). Mastering the game of Go without human knowledge. Nature, 550(7676), 354–359. https://doi.org/10.1038/nature24270 Solow, R. M. (1956). A contribution to the theory of economic growth. Quarterly Journal of Economics, 70(1), 65–94. https://doi.org/10.2307/1884513 Strauss, H., & Samkharadze, B. (2011). ICT capital and productivity growth. EIB Papers, 16(2), 8–28. Tom, G., Schmid, S. P., Baird, S.G., Cao, Y., Darvish, K., Hao, H., Lo, S., Pablo-García, S., Rajaonson, E. M., Skreta, M., Yoshikawa, N., Corapi, S., Akkoc, G. D., Strieth-Kalthoff, F., Seifrid, M., & Aspuru-Guzik, A. (2024). Self-driving laboratories for chemistry and materials science. Chemical Reviews, 124, 9633–9732. Trammell, P., & Korinek, A. (2023). Economic growth under transformative AI (NBER Working Paper No. 31065). National Bureau of Economic Research. https://doi.org/10.3386/w31065 Wang, L., Sarker, P. K., Alam, K., & Sumon, S. (2021). Artificial intelligence and economic growth: A theoretical framework. Scientific Annals of Economics and Business, 68(4), 421–443. https://doi.org/10.47743/saeb-2021-0027 World Bank (2025). GDP (current US$) (NY.GDP.MKTP.CD) – Germany (World Development Indicators). World Bank, Washington, DC. Accessed at https://data.worldbank.org/indicator/NY.GDP.MKTP.CD?locations=DE World Bank (2025). GDP (current US$) (NY.GDP.MKTP.CD) – United States (World Development Indicators). World Bank, Washington, DC. Accessed at https://data.worldbank.org/indicator/NY.GDP.MKTP.CD?locations=US World Bank (2025). Human Capital Index (HCI), overall (HD.HCI.OVRL) – Germany (World Development Indicators). World Bank, Washington, DC. Accessed at https://data.worldbank.org/indicator/HD.HCI.OVRL?locations=DE World Bank (2025). Labor force, total (SL.TLF.TOTL.IN) – Germany (World Development Indicators). World Bank, Washington, DC. Accessed at https://data.worldbank.org/indicator/SL.TLF.TOTL.IN?locations=DE |
| URI: | https://mpra.ub.uni-muenchen.de/id/eprint/127275 |
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