Generative AI's Sociotechnical Evolution: Scaling Limits, Governance Gaps, and Sustainable Pathways

Gabriel Silva Atencio

doi:10.4108/airo.10075

Authors

Gabriel Silva Atencio Latin American University of Science and Technology https://orcid.org/0000-0002-4881-181X

DOI:

https://doi.org/10.4108/airo.10075

Keywords:

Artificial intelligence, energy efficiency, generative models, governance latency, scaling laws, sociotechnical systems

Abstract

This study provides a comprehensive sociotechnical analysis of the development of generative artificial intelligence (GenAI) by analysing 50 systems (2014–2023) and interviewing 25 global experts in the area. Three separate architectural epochs are identified by the research, and each is distinguished by unique scale patterns. Additionally, it demonstrates that performance peaks at 200B parameters, when a 1% increase in Fréchet Inception Distance (FID) scores corresponds to an 8× increase in processing power. There are non-linear trade-offs between increasing skills and conserving energy, according to quantitative studies. According to qualitative study, there are significant disparities in the speed at which different industries adopt new technologies. Global South nations are more affected than others (88% lack frameworks), with implementation delays of 2.3 years and governance delays of 4.2 years. A validated optimization matrix showing that new building designs can make things 3.8 times more efficient but are hard to put into practice, (1) extended scaling laws that include energy and adoption metrics, and (3) sector-specific policy tools to close the 72% policy gaps in education and the 92% accuracy-adoption paradox in healthcare. The results indicate that institutional readiness, rather than mere technical expertise, affects real-world outcomes, challenging deterministic narratives of progress. They also provide us helpful ways to develop artificial intelligence (AI) that follow the rules of Green AI.

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References

[1] Y. Bahri, E. Dyer, J. Kaplan, J. Lee, and U. Sharma, "Explaining neural scaling laws," Proceedings of the National Academy of Sciences, vol. 121, no. 27, p. e2311878121, 2024, doi: https://doi.org/10.1073/pnas.2311878121.

[2] W. Lu, R. K. Luu, and M. J. Buehler, "Fine-tuning large language models for domain adaptation: exploration of training strategies, scaling, model merging and synergistic capabilities," npj Computational Materials, vol. 11, no. 1, p. 84, 2025/03/28 2025, doi: https://doi.org/10.1038/s41524-025-01564-y.

[3] M. Krichen, "Generative Adversarial Networks," 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), pp. 1–7, 6–8 July 2023 2023, doi: https://doi.org/10.1109/ICCCNT56998.2023.10306417.

[4] P. Purwono, A. N. E. Wulandari, A. Ma'arif, and W. A. Salah, "Understanding Generative Adversarial Networks (GANs): A Review," Control Systems and Optimization Letters, vol. 3, no. 1, pp. 36–45, 2025, doi: https://doi.org/10.59247/csol.v3i1.170.

[5] T. Ma, W. Wang, and Y. Chen, "Attention is all you need: An interpretable transformer-based asset allocation approach," International Review of Financial Analysis, vol. 90, p. 102876, 2023/11/01/ 2023, doi: https://doi.org/10.1016/j.irfa.2023.102876.

[6] P. G. de la Torre, M. Pérez-Verdugo, and X. E. Barandiaran, "Attention is all they need: cognitive science and the (techno)political economy of attention in humans and machines," AI & SOCIETY, 2025/06/28 2025, doi: https://doi.org/10.1007/s00146-025-02400-z.

[7] A. Hussain, S. Ali, U. E. Farwa, M. A. I. Mozumder, and H. C. Kim, "Foundation Models: From Current Developments, Challenges, and Risks to Future Opportunities," 2025 27th International Conference on Advanced Communications Technology (ICACT), pp. 51–58, 16–19 Feb. 2025 2025, doi: https://doi.org/10.23919/ICACT63878.2025.10936649.

[8] A. Thomas, "Digitally transforming the organization through knowledge management: A socio-technical system (STS) perspective," European Journal of Innovation Management, vol. 27, no. 9, pp. 437–460, 2024, doi: https://doi.org/10.1108/EJIM-02-2024-0114.

[9] C. M. van Leersum and C. Maathuis, "Human centred explainable AI decision-making in healthcare," Journal of Responsible Technology, vol. 21, p. 100108, 2025/03/01/ 2025, doi: https://doi.org/10.1016/j.jrt.2025.100108.

[10] S. M. Hamdy and Z. Zaazou, "“Universities Devoid of Teaching Staff” Can AI Replace the Role of Professors in the Near Future?," Arab Journal of Management, vol. 45, no. 2, pp. 451–464, 2025, doi: https://dx.doi.org/10.21608/aja.2024.308915.1687.

[11] T. Kohn, "From Imitation Games to Robot‐Teachers: A Review and Discussion of the Role of LLMs in Computing Education," Journal of Computer Assisted Learning, vol. 41, no. 3, p. e70043, 2025, doi: https://doi.org/10.1111/jcal.70043.

[12] D. Catlin, M. Blamires, and J.-J. Cabibihan, "Educational Robots: Past, Present, Future," Social Robots in Education: How to Effectively Introduce Social Robots into Classrooms, pp. 399–426, 2025, doi: https://doi.org/10.1007/978-3-031-82915-4_16.

[13] O. Kudina and I. van de Poel, "A sociotechnical system perspective on AI," Minds and Machines, vol. 34, no. 3, p. 21, 2024/06/12 2024, doi: https://doi.org/10.1007/s11023-024-09680-2.

[14] S. K. Parker et al., "Quality work in the future: New directions via a co-evolving sociotechnical systems perspective," Australian Journal of Management, vol. 0, no. 0, p. 03128962251331813, 2025, doi: https://doi.org/10.1177/03128962251331813.

[15] R. Verdecchia, J. Sallou, and L. Cruz, "A systematic review of Green AI," Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 13, no. 4, p. e1507, 2023, doi: https://doi.org/10.1002/widm.1507.

[16] V. Bolón-Canedo, L. Morán-Fernández, B. Cancela, and A. Alonso-Betanzos, "A review of green artificial intelligence: Towards a more sustainable future," Neurocomputing, vol. 599, p. 128096, 2024/09/28/ 2024, doi: https://doi.org/10.1016/j.neucom.2024.128096.

[17] A. Blok and C. B. Jensen, "What Next for Actor Network Theory? Inventing around Latour on a planet in distress," Dialogues in Sociology, vol. 1, no. 1, pp. 63–74, 2025, doi: https://doi.org/10.1177/29768667241285397.

[18] Y. I. Alzoubi and A. Mishra, "Green artificial intelligence initiatives: Potentials and challenges," Journal of Cleaner Production, vol. 468, p. 143090, 2024/08/25/ 2024, doi: https://doi.org/10.1016/j.jclepro.2024.143090.

[19] S. Lambiase et al., "A Mixed-Method Empirical Investigation into the Influence of Software Communities Cultural and Geographical Dispersion Over Productivity," Available at SSRN 4397227, 2023, doi: https://doi.org/10.1016/j.jss.2023.111878.

[20] M. Baran, "Mixed methods research design," Research Anthology on Innovative Research Methodologies and Utilization Across Multiple Disciplines, vol. 1, pp. 312–333, 2022, doi: https://doi.org/10.4018/978-1-6684-3881-7.

[21] L. N. Kawar, G. B. Dunbar, E. M. Aquino-Maneja, S. L. Flores, V. R. Squier, and K. R. Failla, "Quantitative, qualitative, mixed methods, and triangulation research simplified," The Journal of Continuing Education in Nursing, vol. 55, no. 7, pp. 338–344, 2024, doi: https://doi.org/10.3928/00220124-20240328-03.

[22] R. Hunter, T. Gorely, M. Beattie, and K. Harris, "Realist review," International Review of Sport and Exercise Psychology, vol. 15, no. 1, pp. 242–265, 2022/12/31 2022, doi: https://doi.org/10.1080/1750984X.2021.1969674.

[23] S. Issar, "The Social Construction of Algorithms in Everyday Life: Examining TikTok Users’ Understanding of the Platform’s Algorithm," International Journal of Human–Computer Interaction, vol. 40, no. 18, pp. 5384–5398, 2024/09/16 2024, doi: https://doi.org/10.1080/10447318.2023.2233138.

[24] D. Alemayehu Tegegn, "The role of science and technology in reconstructing human social history: effect of technology change on society," Cogent Social Sciences, vol. 10, no. 1, p. 2356916, 2024/12/31 2024, doi: https://doi.org/10.1080/23311886.2024.2356916.

[25] J. Zheng et al., "Empowering radical innovation: how digital technologies drive knowledge transfer and co‐creation in innovation ecosystems," R&D Management, 2025, doi: https://doi.org/10.1111/radm.12764.

[26] J. Peters et al., "Emergent language: a survey and taxonomy," Autonomous Agents and Multi-Agent Systems, vol. 39, no. 1, p. 18, 2025/03/07 2025, doi: https://doi.org/10.1007/s10458-025-09691-y.

[27] M. Liu, F. Wu, B. Li, Z. Lu, Y. Yu, and X. Li, "Envisioning class entity reasoning by large language models for few-shot learning," Proceedings of the AAAI Conference on Artificial Intelligence, vol. 39, no. 18, pp. 18906–18914, 2025, doi: https://doi.org/10.1609/aaai.v39i18.34081.

[28] O. Krüger, "The Mechanical Turk: a human-centered approach to the transcendence narrative of artificial intelligence," Religion, vol. 55, no. 3, pp. 699–714, 2025/07/03 2025, doi: https://doi.org/10.1080/0048721X.2025.2502291.

[29] S. Feng, R. Zhang, and G. Li, "Environmental decentralization, digital finance and green technology innovation," Structural Change and Economic Dynamics, vol. 61, pp. 70–83, 2022/06/01/ 2022, doi: https://doi.org/10.1016/j.strueco.2022.02.008.

[30] E. Brynjolfsson, D. Rock, and C. Syverson, "The productivity J-curve: How intangibles complement general purpose technologies," American Economic Journal: Macroeconomics, vol. 13, no. 1, pp. 333–372, 2021, doi: https://doi.org/10.1257/mac.20180386.

[31] M. K. Scheuerman, A. Hanna, and R. Denton, "Do datasets have politics? Disciplinary values in computer vision dataset development," Proceedings of the ACM on Human-Computer Interaction, vol. 5, no. CSCW2, pp. 1–37, 2021, doi: https://doi.org/10.1145/3476058.

[32] M. Matthews, S. Matthews, and T. Kelemen, "The Alignment Problem: Machine Learning And Human Values," Personnel Psychology, vol. 75, no. 1, 2022, doi: https://doi.org/10.1111/peps.12500.

[33] J. Bandola-Gill, M. Arthur, and R. I. Leng, "What is co-production? Conceptualising and understanding co-production of knowledge and policy across different theoretical perspectives," Evidence & Policy, vol. 19, no. 2, pp. 275–298, 2023, doi: https://doi.org/10.1332/174426421X16420955772641.

[34] Y. Zhao, "Book Review: The Atlas of AI: Power, politics, and the planetary costs of artificial intelligence," Global Media and China, vol. 0, no. 0, p. 20594364251325469, 2025, doi: https://doi.org/10.1177/20594364251325469.

[35] S. Chikkudu and S. Annamalai, "Fundamentals of AI and NLP in Environmental Analysis," Environmental Monitoring Using Artificial Intelligence, pp. 29–44, 2025, doi: https://doi.org/10.1002/9781394270392.ch2.

[36] R. Różycki, D. A. Solarska, and G. Waligóra, "Energy-Aware Machine Learning Models—A Review of Recent Techniques and Perspectives," Energies, vol. 18, no. 11, p. 2810, 2025, doi: https://doi.org/10.3390/en18112810.

[37] R. Pasupuleti, R. Vadapalli, C. Mader, and N. Timothy, "Popular LLM-Large Language Models in Enterprise Applications," 2024 2nd International Conference on Foundation and Large Language Models (FLLM), pp. 125–131, 26–29 Nov. 2024 2024, doi: https://doi.org/10.1109/FLLM63129.2024.10852443.

[38] M. Azam, Y. Chen, M. O. Arowolo, H. Liu, M. Popescu, and D. Xu, "A comprehensive evaluation of large language models in mining gene relations and pathway knowledge," Quantitative Biology, vol. 12, no. 4, pp. 360–374, 2024, doi: https://doi.org/10.1002/qub2.57.

[39] S. Chen, Z. Pan, J. Cai, P. Fang, M. Harandi, and D. Phung, "Hierarchical Prompt-Enhanced Image Generation Using Hyperbolic Space," Neural Information Processing pp. 121–136, 2025, doi: https://doi.org/10.1007/978-981-96-6688-1_9.

[40] E. K. Hong et al., "Diagnostic accuracy and clinical value of a domain-specific multimodal generative AI model for chest radiograph report generation," Radiology, vol. 314, no. 3, p. e241476, 2025, doi: https://doi.org/10.1148/radiol.241476.

[41] F. S. Orim et al., "Implementation of automated classroom assessment in higher education using the technology acceptance model," Discover Education, vol. 4, no. 1, p. 87, 2025/04/17 2025, doi: https://doi.org/10.1007/s44217-025-00481-y.

[42] A. Hertzmann, "Generative Models for the Psychology of Art and Aesthetics," Empirical Studies of the Arts, vol. 43, no. 1, pp. 23–43, 2025, doi: https://doi.org/10.1177/02762374241288696.

[43] Y. Knight and M. P. Eladhari, "Artificial intelligence in an artistic practice: a journey through surrealism and generative arts," Media Practice and Education, pp. 1–18, 2025, doi: https://doi.org/10.1080/25741136.2024.2443865.

[44] W. Jon, "Prompting Creativity: Tiered Approach to Copyright Protection for AI-Generated Content in the Digital Age," Media and Communication, vol. 13, 2025, doi: https://doi.org/10.17645/mac.9420.

[45] K. Soares Seto, "AI From the South: artificial intelligence in Latin America through the sociotechnical imaginaries of Brazilian tech workers," Globalizations, pp. 1–16, 2025, doi: https://doi.org/10.1080/14747731.2025.2465166.

[46] U. Baresi, "Neo-colonial intelligence: How AI risks reinforcing spatial injustices in a digitally divided world," Cities, vol. 166, p. 106232, 2025/11/01/ 2025, doi: https://doi.org/10.1016/j.cities.2025.106232.

[47] M. Bartl, A. Mandal, S. Leavy, and S. Little, "Gender Bias in Natural Language Processing and Computer Vision: A Comparative Survey," ACM Comput. Surv., vol. 57, no. 6, p. Article 139, 2025, doi: https://doi.org/10.1145/3700438.

[48] D. Thakur, A. Guzzo, G. Fortino, and F. Piccialli, "Green Federated Learning: A New Era of Green Aware AI," ACM Comput. Surv., vol. 57, no. 8, p. Article 194, 2025, doi: https://doi.org/10.1145/3718363.

[49] S. Dash, "Green AI: Enhancing Sustainability and Energy Efficiency in AI-Integrated Enterprise Systems," IEEE Access, vol. 13, pp. 21216–21228, 2025, doi: https://doi.org/10.1109/ACCESS.2025.3532838.

[50] R. Boccio, "Race After Technology: Abolitionist Tools for the New Jim Code by Ruha Benjamin," Configurations, vol. 30, no. 2, pp. 236–238, 2022, doi: https://doi.org/10.1353/con.2022.0013.

[51] S. Lin, M. Wang, C. Jing, S. Zhang, J. Chen, and R. Liu, "The influence of AI on the economic growth of different regions in China," Scientific Reports, vol. 14, no. 1, p. 9169, 2024/04/22 2024, doi: https://doi.org/10.1038/s41598-024-59968-7.

[52] M. L. Navarro-Ligero and J. A. Soria-Lara, "Reassembling Collaborative Planning for Socio-Technical Transitions: An Actor-Network Theory Approach," Planning Theory & Practice, vol. 26, no. 1, pp. 66–84, 2025/01/01 2025, doi: https://doi.org/10.1080/14649357.2025.2472618.

[53] M. Hodak, D. Ellison, and A. Dholakia, "Everyone is a Winner: Interpreting MLPerf Inference Benchmark Results," Performance Evaluation and Benchmarking, pp. 50–61, 2022, doi: https://doi.org/10.1007/978-3-030-94437-7_4.

[54] S. M. Hasan, T. Islam, M. Saifuzzaman, K. R. Ahmed, C. H. Huang, and A. R. Shahid, "Carbon Emission Quantification of Machine Learning: A Review," IEEE Transactions on Sustainable Computing, pp. 1–19, 2025, doi: https://doi.org/10.1109/TSUSC.2025.3578834.

[55] C. Makri and A. Neely, "Grounded Theory: A Guide for Exploratory Studies in Management Research," International Journal of Qualitative Methods, vol. 20, p. 16094069211013654, 2021, doi: https://doi.org/10.1177/16094069211013654.

[56] C. Turner and F. Astin, "Grounded theory: what makes a grounded theory study?," European Journal of Cardiovascular Nursing, vol. 20, no. 3, pp. 285–289, 2021, doi: https://doi.org/10.1093/eurjcn/zvaa034.

[57] D. Mortelmans, "Thematic Coding," Doing Qualitative Data Analysis with NVivo, pp. 57–87, 2025, doi: https://doi.org/10.1007/978-3-031-66014-6_8.

[58] V. Braun and V. Clarke, "Conceptual and design thinking for thematic analysis," Qualitative psychology, vol. 9, no. 1, p. 3, 2022, doi: https://psycnet.apa.org/doi/10.1037/qup0000196.

[59] A. Tschand et al., "MLPerf Power: Benchmarking the Energy Efficiency of Machine Learning Systems from μWatts to MWatts for Sustainable AI," 2025 IEEE International Symposium on High Performance Computer Architecture (HPCA), pp. 1201–1216, 1–5 March 2025 2025, doi: https://doi.org/10.1109/HPCA61900.2025.00092.

[60] G. Ayana et al., "Decolonizing global AI governance: assessment of the state of decolonized AI governance in Sub-Saharan Africa," Royal Society Open Science, vol. 11, no. 8, p. 231994, 2024, doi: https://doi.org/10.1098/rsos.231994.