AI Data Centers Projected to Reach $200 Billion Cost and Nuclear-Scale Power Needs by 2030

Skynet Chance (+0.04%): The massive scaling of computational infrastructure enables training increasingly powerful models whose behaviors and capabilities may become more difficult to predict and control, especially if deployment outpaces safety research due to economic pressures.

Skynet Date (-1 days): The projected doubling of computational resources annually represents a significant acceleration factor that could compress timelines for developing systems with potentially uncontrollable capabilities, especially given potential pressure to recoup enormous infrastructure investments.

AGI Progress (+0.05%): The dramatic increase in computational resources directly enables training larger and more capable AI models, which has historically been one of the most reliable drivers of progress toward AGI capabilities.

AGI Date (-1 days): The projected sustained doubling of AI compute resources annually through 2030 significantly accelerates AGI timelines, as compute scaling has been consistently linked to breakthrough capabilities in AI systems.