Analysis examines power availability constraints as critical factor for AI infrastructure and Intel's role in compute alternatives.

Intel's leadership crisis and the broader political landscape will shape chip manufacturing this year. While Trump has a history of not following through on his threats, either he will figure out the tariff situation or someone will point out that tariffs will not fix the problem of made-in-American chips. Regardless, he will deliver the money needed for the CHIPS Act, particularly because Ohio is a big beneficiary of the funds and Vice President-elect JD Vance will not allow his home state to be denied. Intel's turnaround will require fresh leadership from outside the company—the first CEO not to have risen through the ranks. Some people are bullish on Qualcomm CEO Cristiano Amon to take on this role.

The massive investment in AI hardware is creating unsustainable pressure on infrastructure. OpenAI, Google, Microsoft, Meta, X, and more are going to start to feel pressure to deliver revenue, as right now they're not getting a return on their investment, and that will have to change or spending will slow. Google and Microsoft are already investing in power utilities because the grid is stressed to its limits and the demands of AI are enormous. The public utilities simply cannot deliver on the growing power needs of AI data centers, and the hyperscalers cannot sit around and wait for the power companies to get their act together—they're going to go into business for themselves and provide their own power.

Technical innovation will accelerate across cooling and data center operations. The heat generated by AI systems has already driven the need for liquid cooling because air cooling is simply not enough, and new CPUs from Intel and AMD are pretty toasty as well. HPE and Dell will finally do their own liquid cooling, similar to Lenovo's Project Neptune. AI will be used to optimize data center operations and improve performance, which will be reflected in the Top500 supercomputer rankings with a big jump in overall performance, and AI will improve security, especially in zero trust scenarios, to isolate questionable nodes and systems.

Intel must spin off its fabrication business just like AMD did in 2008—it was expensive, painful and necessary for long-term success. Gelsinger simply didn't have the bandwidth to manage Intel foundries and Intel products, and all three suffered for it. Maximizing GPU utilization will become the primary design goal for modern data centers, as Nvidia is showing no signs of cutting power draw. On-premises data centers will continue to thrive thanks to data privacy and integrity concerns; on-premises data centers will die when mainframes do. With almost 500 data centers in the Virginia area, that region is reaching its limit, as is Texas and Santa Clara. The demand for large-scale processing of data for AI, data analytics, and quantum computing will shift where new data centers are built to remote locations that have a lot of land and a lot of cheap power.