Crusoe is currently constructing about 2 gigawatts of AI infrastructure and has 20 gigawatts in the pipeline, transforming both the energy and data center landscapes to support AI.

In Abilene, Texas, we employ a substantial part of the local population in skilled trades necessary for building AI data centers, which requires importing labor due to the specialized skills needed.

Abundant energy will allow for local production at low costs, reducing the need for competitive approaches and fostering collaboration instead.

Our plan involves scaling compute by raising $500 billion to build infrastructure in Abilene, Texas, which will support the training of advanced AI models.

We decided to simplify data centers by integrating all components into a single building block, making them more reliable and easier to manage, while also trying to replicate Apple's model by providing large building blocks for customers to build their own data centers, similar to how Apple built the iPhone with integrated hardware and software.

If we can make permitting super fast, AI can support the process and help generate energy in remote areas by plugging in AI data centers next to energy sources.

The AI data center of the future differs significantly from traditional data centers, as it requires a more industrial approach to infrastructure, energy, and scale.

China's advancements in physical infrastructure and energy production pose a competitive threat to the U.S. in AI development, highlighting the need for the U.S. to streamline data center construction and energy production.

Energy constraints are a significant challenge for AI development, as AI is creating a significant increase in demand for energy, leading to a need for innovative solutions to power data centers. The U.S. energy landscape is evolving to meet these growing demands of AI infrastructure.