Kurniady, Alvin (2025): Production Function in an Economy with Deployed Self-Learning Technologies.
Preview |
PDF
MPRA_paper_126457.pdf Download (524kB) | Preview |
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 MRW 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 MRW (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. My production function has many policy implications, and the policy recommendations differ among AI-producing countries, rich non-AI-producing countries and poor non-AI-producing countries.
| Item Type: | MPRA Paper |
|---|---|
| Original Title: | Production Function in an Economy with Deployed Self-Learning Technologies |
| Language: | English |
| Keywords: | Self-learning artificial intelligence; Production function; Balanced growth path vs. accelerating growth path; Recursive learning; Economic growth; Total output |
| Subjects: | O - Economic Development, Innovation, Technological Change, and Growth > O4 - Economic Growth and Aggregate Productivity > O41 - One, Two, and Multisector Growth Models |
| Item ID: | 126457 |
| Depositing User: | Mr. Alvin Kurniady |
| Date Deposited: | 20 Oct 2025 07:12 |
| Last Modified: | 21 Oct 2025 01:23 |
| 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 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 Ichihashi, S. (2021). The economics of data externalities. Journal of Economic Theory, 196, 105316. https://doi.org/10.1016/j.jet.2021.105316 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 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 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 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 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 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 |
| URI: | https://mpra.ub.uni-muenchen.de/id/eprint/126457 |

