The United States is in the midst of a massive investment surge in artificial intelligence, with spending on software, computers, and data centers surpassing $1 trillion annually. Since the launch of ChatGPT in late 2022, data center construction has skyrocketed to over $42 billion a year, a 300% increase. Overall investment in computers and related equipment has reached $270 billion, a nearly 50% rise in just one year. Software development spending is even more significant, exceeding $750 billion, much of it directed at AI system development. Together, these expenditures account for about 3.5% of the U.S. GDP. Major tech companies are driving this boom, with projected physical capital expenditures among giants like Meta, Alphabet, Amazon, and Microsoft expected to exceed $600 billion this year. Alphabet predicts its own spending could hit $185 billion by 2026. In stark contrast, computer investment in Canada, the UK, and the EU has not seen similar growth, with even China lagging behind, registering only a 20% increase over the last year. A noteworthy aspect of this investment is the heavy reliance on imported technology. Nearly $400 billion in computers and parts are imported, primarily from Taiwan, Mexico, and Malaysia. Imports from Taiwan alone have surged by 670% in three years. As a result, the U.S. has exempted these imports from tariffs, making them a significant part of the trade landscape. The demand for electricity to power these data centers is also rising sharply, with commercial power consumption increasing significantly and expected to continue growing. This investment surge is altering the tech industry's landscape, shifting companies from a rapid-hiring model to one focused on capital intensity. The total physical assets held by these companies have more than doubled since ChatGPT’s launch, despite cutting around 20,000 jobs. This trend highlights a broader strategy: investing heavily in infrastructure to support the anticipated growth and capabilities of AI, even as it challenges historical norms of computing costs.