arXiv:2602.19718v2 Announce Type: replace-cross Abstract: The rapid adoption of Generative AI (GenAI) in the software development life cycle (SDLC) increases computational demand, which can raise the carbon footprint of development activities. At the same time, organizations are increasingly embedding governance mechanisms into GenAI-assisted development to support trust, transparency, and accountability. However, these governance mechanisms introduce additional computational workloads, including repeated inference, regeneration cycles, and expanded validation pipelines, increasing energy use
The rapid and widespread adoption of Generative AI (GenAI) is exposing its significant computational requirements and subsequent energy consumption, bringing sustainability to the forefront of development practices.
This highlights the growing tension between AI advancement, operational governance, and environmental impact, pushing organizations to integrate carbon awareness into their development pipelines.
The focus shifts from purely performance-driven GenAI development to one that incorporates environmental impact as a critical governance metric, influencing design choices and deployment strategies.
- · Energy-efficient AI hardware providers
- · Carbon accounting software for AI
- · Cloud providers with green energy commitments
- · AI developers ignoring energy consumption
- · Organizations with high-carbon data centers
- · AI models with inefficient architectures
Increased demand for tools and frameworks that measure and minimize the carbon footprint of AI development and operation.
Regulatory bodies may introduce mandates or incentives for sustainable AI practices, influencing industry standards and investment.
The development of 'eco-conscious' AI models and architectures becomes a competitive differentiator, driving innovation in efficient computation.
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Read at arXiv cs.AI