Sustainability in the Loop: AI Model Development Should Be Multi-Objective

This position paper argues that environmental cost should be part of the objectives used to train, select, and deploy AI models. Today, sustainability is mainly measured after design choices have been made, while architectures, data, hyperparameters, and serving strategies are chosen through accuracy, latency, and other proxies. This separation weakens model development. In training, newer models can move toward higher emissions without a comparable gain in performance. In inference, small differences in energy per query can become large lifecycle costs at scale. FLOPs, parameter counts, and latency do not capture these effects, because the real cost depends on hardware, datacenter efficiency, energy mix, water demand, and deployment volume. We call for multiobjective model development where performance and lifecycle environmental cost guide decisions together. The paper explains how to distinguish sustainable progress from costlier forms of progress, addresses alternative views, and outlines research directions for objectives, benchmarks, and model selection practices that make sustainability part of optimization.