Google’s Project Suncatcher envisions moving AI computation into orbit, building constellations of solar-powered satellites equipped with TPUs and laser interlinks. By harnessing the Sun’s constant energy and future low-cost launches, the project proposes a scalable, space-based infrastructure for machine learning. It’s a blueprint for computing beyond Earth—where data centers orbit, powered by sunlight instead of fossil grids.

This article explores the environmental impact of large language models (LLMs), based on Dauner and Socher’s 2025 study. By analyzing 14 models across reasoning tasks, it reveals a trade-off between accuracy and CO₂ emissions. Larger models and reasoning modes achieve higher performance but drastically increase energy use due to verbose outputs. The findings highlight the urgent need for optimizing reasoning efficiency and integrating sustainability into AI development.