Getting the AI story right

HONG KONG – For the past two years, the dominant narrative about AI has been one of boundless possibility. Larger models, trillion-token training runs, and record-breaking capex (capital expenditure) cycles have reinforced a sense of uninterrupted acceleration. But technological change is rarely so straightforward, and this time is no exception. As AI moves from experimentation to real-world applications, the limits imposed by the physical world, capital markets, and political systems clearly matter more than its theoretical potential. The most immediate constraint is electricity. Nowhere is this more evident than in the United States, where data-center power demand is expected to rise from roughly 35 gigawatts to 78 GW by 2035. Northern Virginia, the world’s largest cloud-infrastructure cluster, has already effectively exhausted its available grid capacity. Utilities in Arizona, Georgia, and Ohio warn that new substations may take almost a decade to build. A single campus can require 300-500 MW, enough to power an entire city. Silicon can be manufactured quickly; high-voltage tra