AI, Games, and Sustainability

Productive uses of AI in games are evident, particularly in coding agents. The environmental and social impacts of AI are also increasingly evident. For game developers to make informed decisions about the use of AI, in order to maximize the benefits and minimize negative impacts, more information is needed from AI providers.

AI and Sustainability in Game Development

Read this jointly prepared summary by Manchester Metropolitan University, the Sustainable Games Alliance, and STRATEGIES to explore takeaways, risk management considerations, and recommendations based on the limited data currently available. Sources are provided at the end of the document.

AI & Sustainability in Game Development Takeaways

The IEA has been tracking the growth of energy demand for data centres and AI. In its April 2026 update it sees

“electricity consumption from data centres roughly doubling from 485 TWh in 2025 to 950TWh in 2030, accounting for around 3% of global electricity demand by that date. Electricity consumption from AI-focused data centres grows much faster than overall data centre electricity consumption, tripling in this period.“ (IEA 2026)

Games & AI: Meetup Recordings

AI & Games: Sustainability Challenges and Opportunities

A discussion on the findings of two research projects on the impact of AI on sustainability goals, aiming to equip developers with the knowledge that can help align the use of these technologies with sustainability.

Sustainability, games & AI: working group session 1

GenAI and LLMs are popping up all over the games ecosystem, and data center emissions are rapidly rising as a result of the AI boom. If you are interested in how these new AI tools are being used in our industry & their impact on the environment, please watch this recording of the first meetup of our AI working group chaired by none other than Nic Walker.

Coordinated advocacy

Coordinated advocacy through the SGA and similar organisations is essential to secure greater transparency and ensure that the impacts of AI training and inference are minimised. 

We advocate for:

  • Mandatory lifecycle emissions disclosure
  • Standardised reporting for training and inference
  • Transparency about energy procurement and data‐labour practices
  • Guidance that protects employees and the climate
SGA Statement on ai

AI environmental impacts standards are emerging which the SGA encourages AI trainers and inference providers to adopt for disclosures: