DeepSeek upends AI’s great energy race — how can the US compete? 

The AI industry is facing a moment of reckoning. While U.S. tech giants pour billions into massive data centers and ever-larger models, a small Chinese startup named DeepSeek may have quietly demonstrated that the “bigger is better” philosophy is fundamentally flawed.  Just days before DeepSeek went viral, President Trump stood alongside tech titans Sam Altman, Masayoshi Son and Larry Ellison to unveil Stargate — a $500 billion plan to maintain U.S. dominance in AI infrastructure. The timing couldn’t be more ironic.  The success of DeepSeek raises an uncomfortable question: What if we’re building tomorrow’s Rust Belt?  DeepSeek’s latest model achieves what seemed impossible: comparable capabilities to leading models while using significantly fewer resources. Their API costs $0.55 per million input tokens, compared to OpenAI’s $15 — a reduction in computing costs greater than 90 percent. That’s not just an efficiency gain — it’s a fundamental challenge to how we think about AI development. This efficiency gap becomes even more striking when we consider the open source strategies at play. As Meta’s chief AI scientist, Yann LeCun, notes, it’s not that China’s AI is “surpassing the U.S.,” but rather that “open source models are surpassing proprietary ones.”  This opens up an exciting possibility: what if massive compute spending isn’t the price of progress after all? DeepSeek’s approach suggests that when you combine transparency with efficiency, you create something powerful — a pathway to more sustainable and accessible AI development.  The parallel to American industrial history is stark: U.S. steel companies once continued…DeepSeek upends AI’s great energy race — how can the US compete?