Decentralized training distributes computation across independent nodes to utilize existing energy sources and curb the carbon footprint of frontier model development. The IEEE Spectrum article outlines hardware pooling via Akash Network's GPU marketplace and algorithmic fixes such as Google DeepMind's DiLoCo for low-communication distributed optimization, yet it understates the compounding energy growth rate documented in Strubell et al. (arXiv:1906.02243), which projected training large NLP models could emit CO2 equivalent to five cars' lifetimes and missed how decentralization directly mitigates this by routing workloads to idle capacity near renewables rather than forcing grid expansions. Primary coverage also glosses over synchronization failures in non-fault-tolerant training steps cited by Akash CEO Osuri, which related efforts like Nvidia Spectrum-XGS and Cisco's dispersed-cluster routers only partially resolve through bandwidth increases instead of true energy-aware scheduling. Synthesizing the Spectrum report, DeepMind's DiLoCo paper (arXiv:2403.14679) showing 500x communication reduction, and IEA's 2024 electricity forecast projecting data centers at 8% of global power by 2030 reveals decentralized methods as an immediate lever on the under-solved power constraint, enabling model scaling by tapping the roughly 25% of worldwide compute sitting dormant without awaiting nuclear plant timelines.