You know what is the implications of 50M H100-equivalent to powering AI? I just asked @grok to answer it by itself and this is the answer: ➤ Computing Power ⟶ 25,000–50,000 exaFLOPS (20,000–40,000x the world's current fastest supercomputer) ➤ Training capability ⟶ Trillions/ quadrillions of parameters ➤ Current xAI scale ⟶ 200x current setup (~230,000 GPUs, 100-200 exaFLOPS) ➤ Power Draw ⟶ ~35 GW or power use of 35 million U.S. households or countries like Argentina (~30 GW) ➤ Annual Energy ⟶ ~245,000 GWH or 6% of U.S. annual electricity (~4,000 TWh) ➤ Cost ⟶ $1.5T hardware only and est. $2–3 trillion total over 5 years ➤ Annual investment needed ⟶ $400-600B/year The sheer size of this nation-state signifies a global shift from fossil fuels, the cornerstone of the 20th century, to computing power. Meanwhile, AI breakthroughs are inevitable and will transform various sectors of our society. As @MTorygreen rightly pointed out, the cloud alone cannot meet AI's extensive scale, energy demands, and global distribution needs. The future of intelligence is not confined to a few centralized entities and data centers; it's everywhere or nowhere.