# Is the future of AI green? What can innovation diffusion models say about generative AI's environmental impact?

arXiv: https://arxiv.org/abs/2603.21419 · HTML: https://mlsanity.com/paper/2603.21419/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 17 references that are in the map.
- First posted on arXiv 22 Mar 2026.

## Builds on (most cited first, 7 of 7)

- [Green AI](https://mlsanity.com/paper/1907.10597/) (2019)
- [The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink](https://mlsanity.com/paper/2204.05149/) (2022)
- [From Words to Watts: Benchmarking the Energy Costs of Large Language Model Inference](https://mlsanity.com/paper/2310.03003/) (2023)
- [Generative AI](https://mlsanity.com/paper/2309.07930/) (2023)
- [Small Language Models: Survey, Measurements, and Insights](https://mlsanity.com/paper/2409.15790/) (2024)
- [Measuring the environmental impact of delivering AI at Google Scale](https://mlsanity.com/paper/2508.15734/) (2025)
- [Algorithmic progress in language models](https://mlsanity.com/paper/2403.05812/) (2024)

## Abstract (arXiv)

The rise of generative artificial intelligence (GAI) has led to alarming predictions about its environmental impact. However, these predictions often overlook the fact that the diffusion of innovation is accompanied by the evolution of products and the optimization of their performance, primarily for economic reasons. This can also reduce their environmental impact. By analyzing the GAI ecosystem using the classic A-U innovation diffusion model, we can forecast this industry's structure and how its environmental impact will evolve. While GAI will never be green, its impact may not be as problematic as is sometimes claimed. However, this depends on which business model becomes dominant.