Modeling and Analysis of the Impacts of the Rise in AI Use on Data Center Growth, Resulting Workforce Displacement, and Environmental Impacts Through a Coupled System of Ordinary Differential Equations Within a Feedback Framework

Generative AI has widespread impacts on data center growth, environmental emissions, resource consumption, and the workforce. However, the complex, interconnected nature of these systems—and their tendency to evolve through dynamic feedback loops—makes them challenging to accurately model with static or linear methods. To address this, we model these dynamics through a coupled system of ordinary differential equations (ODEs) within a continuous-time feedback framework. Workforce dynamics are both incorporated into the ODE system and modeled explicitly through a compartmental framework—Susceptible (At Risk), Infected (Unemployed), and Recovered (Reskilled). We solve the system numerically to simulate the evolving impacts AI adoption has on variables including data center growth, energy use, carbon emissions, water consumption, and labor transitions. We then examine various scenarios by running simulations that compare increases in the AI adoption rate (r) and the intensity of policy pushback (psi_c) to the peak variable output. Over a simulated 365-day period, AI adoption stabilizes; however, the slight increases in adoption significantly elevate energy use, water consumption, and carbon emissions. We also find that unemployment rises substantially regardless of reskilling efforts, as proportionately, fewer workers reskill successfully. Policy pushback is effective at quickly decreasing the timespan released emissions, even at less intense levels. However, with pushback, emissions are elevated in this short timeframe, exacerbating unemployment. This project aims to inform future AI mitigation policies, particularly when discussing sustainable measures of regulating AI adoption so as to not further exacerbate environmental and workforce concerns. Our work supports UN Sustainable Development Goals #13 (Climate Action) and #8 (Decent Work and Economic Growth). This research also offers a foundation for future data-driven modeling from Physics-Informed Neural Networks (PINNS).