# Environmental Slow AI: Design Principles for Generative Systems

arXiv: https://arxiv.org/abs/2608.20398 · HTML: https://mlsanity.com/paper/2608.20398/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 37 references that are in the map.
- First posted on arXiv 2 Jul 2026.

## Builds on (most cited first, 8 of 8)

- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [CLIPScore: A Reference-free Evaluation Metric for Image Captioning](https://mlsanity.com/paper/2104.08718/) (2021)
- [Green AI](https://mlsanity.com/paper/1907.10597/) (2019)
- [Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learning](https://mlsanity.com/paper/2302.08476/) (2023)
- [From Efficiency Gains to Rebound Effects: The Problem of Jevons' Paradox in AI's Polarized Environmental Debate](https://mlsanity.com/paper/2501.16548/) (2025)
- [More than Carbon: Cradle-to-Grave environmental impacts of GenAI training on the Nvidia A100 GPU](https://mlsanity.com/paper/2509.00093/) (2025)
- [Climate Implications of Diffusion-based Generative Visual AI Systems and their Mass Adoption](https://mlsanity.com/paper/2505.18892/) (2025)
- [Cognitive Agency Surrender: Defending Epistemic Sovereignty via Scaffolded AI Friction](https://mlsanity.com/paper/2603.21735/) (2026)

## Abstract (arXiv)

Generative AI (genAI) systems produce cultural artefacts at scale, but they also reflect embedded cultural values through their design. Once identified, these values become open to deliberate reshaping. This position paper examines the maximalist values of current generative AI through an environmental humanities tradition and proposes design principles in which environmental sustainability serves as the core value instead. The principles are developed under the umbrella of Slow AI, a term that already circulates across several distinct research and practice programs. Five design principles are articulated (restraint, sufficiency, selectivity over retention, material visibility, and friction as affordance), each of them illustrated against the current design of widely deployed systems. Each principle operates at two levels: a design implementation, and an interpretive layer at which users and developers are prompted toward reflective engagement with the system. Together these principles extend human agency by restoring decisions that frictionless defaults have silently removed and do so by building interpretive reflection into design.