The intense power requirements for AI infrastructure have created a seller's market for equipment, forcing customers to plan much further ahead. Utilities and data centers are now contracting for gas turbines and other hardware for delivery as far out as 2031-2032, a timeline described as a new and surprisingly long for the industry.

Despite significant community and political opposition, the underlying demand for AI compute, proxied by token usage, continues to rise. The primary business risk isn't a reduction in demand for AI services, but rather a critical bottleneck in the physical supply of data center capacity.

The primary bottleneck for new data centers has shifted from power generation capacity to the physical supply chain. Long lead times for critical components like transformers and turbines, with some manufacturers sold out until 2031, and a severe shortage of skilled electricians are the new binding constraints.

Contrary to the common focus on chip manufacturing, the immediate bottleneck for building new AI data centers is energy. Factors like power availability, grid interconnects, and high-voltage equipment are the true constraints, forcing companies to explore solutions like on-site power generation.

The scale of the AI buildout is staggering, with data center construction starts representing one out of every four dollars spent on new non-residential building projects. This makes the entire construction sector's performance highly dependent on the continued growth of data centers.

Pushback against new data centers is driven by practical local concerns like water usage, environmental impact, and quality of life, not abstract fears about AI. Hyperscalers are successfully mitigating these on a case-by-case basis with concessions, suggesting the overall buildout will continue.

The primary constraint on building new AI data centers isn't acquiring land or power, but securing "powered shells"—fully energized buildings with cooling and components. Supply chains for transformers and a severe shortage of accredited electricians are the true limiting factors.

The primary constraint on powering new AI data centers over the next 2-3 years isn't the energy source itself (like natural gas), but a physical hardware bottleneck. There is a multi-year manufacturing backlog for the specialized gas turbines required to generate power on-site, with only a few global suppliers.

The primary obstacle to AI's growth is not semiconductor supply but physical power infrastructure. Data centers face a massive power deficit, needing more than double the contracted grid capacity by 2028, with long delays for connections, labor shortages, and local opposition acting as major hurdles.