Energy constraints and geopolitical competition define AI infrastructure future
Energy access and cooling capacity are emerging as primary constraints on AI growth, shifting competition toward chip manufacturing and data center ownership. The market is expected to become multi-provider, similar to multi-cloud computing.
AI infrastructure is becoming increasingly power-hungry, with energy access, cooling capacity, and data center locations identified as the primary constraints on AI growth in 2026. Predictions suggest an impending energy crisis for AI, potentially driving infrastructure into space for efficiency. The competitive landscape between the US and China is shifting from model superiority to a technological Cold War focused on chip manufacturing, energy supply, and data center ownership. While Nvidia currently dominates the hardware space, neoclouds are seeking to support inference across multiple chip architectures, including AMD and potentially Intel. Older generation chips are being repurposed for smaller, distilled models to reduce compute costs. The market is expected to become multi-provider, similar to multi-cloud computing, where companies use different AI providers for different workflow stages. IPOs for major AI labs like OpenAI and Anthropic are anticipated to have significant market implications. Daniel Dominguez predicts that the biggest AI stories in 2027 will occur outside the models themselves, focusing on energy, chips, infrastructure, and geopolitics. He argues that the AI competition between the United States and China is going to increasingly look like a technological Cold War.