INTRODUCTION
This article is Part II of a three-part series on AI Data Centers. These articles are a response to a reader who asked these three questions:
1. Which public issues are impacting AI Data Centers?
2. A few of us believe their expansion is limited by physics. Is that so?
3. And how long will this last? Is an AI bubble forming?
In response, I am answering in three parts, as follows:
· Part II: Physical Limits
· Part III: AI Bubble
Listen to it first and then read it afterward!
COMPLIANCE SOLUTIONS
AI POLICY PROGRAM FOR MORTGAGE BANKING™
Policies and procedures for artificial intelligence related to mortgage banking compliance administration, mortgage loan origination compliance, mortgage servicing compliance, and industry best practices—including, but not limited to, applicable federal and state banking and consumer lending laws. Currently, it consists of nine policies, with more planned.
AI FOR MORTGAGE LOAN ORIGINATION - SALES MANUAL
The Sales Manual provides 9 chapters covering the full AI-powered origination funnel in a 90-day implementation roadmap, with clear team roles and fair lending, FCRA, and advertising compliance guidance built into every chapter. Sample AI prompts, an ROI tracking framework, and a compliance glossary.
RESPONSE
The physics: why you can't just build faster
I concentrated in math and science in college, specializing in symbolic mathematical logic, and I have peer-reviewed papers in that field for scholarly journals. Computer science is not my specialty, but I studied it, and for over fifty years I have taken part in science and physics forums, including on artificial intelligence (AI). Today, I contribute regularly to two math groups whose members include leading AI experts and academics.
Here, I want to give a layman's view of the physics of AI Data Centers, because the physical limits shape everything else. Parts of this article are technical. If that's not for you, stay tuned to Part III, coming soon, where I discuss whether there is an AI Bubble.
In my view, the binding limit on AI Data Centers is no longer silicon. It is the physics of moving energy in and heat out, plus the slow-to-build infrastructure that does both. The International Energy Agency (IEA) projects that global data center electricity use will more than double, from 415 terawatt-hours (TWh) in 2024 to 945 TWh by 2030. One TWh is a trillion watts running for one hour, or roughly what 100,000 American homes use in a year.
Every watt becomes heat
Let's begin with the law of conservation of energy: energy is never destroyed, only converted. It is unforgiving! Nearly all the electricity fed to a Graphics Processing Unit (GPU) leaves the building as heat.
A GPU is a chip that performs thousands of calculations at the same time. It was built to draw graphics, images, and video, but that same ability to process huge amounts of data at once now powers AI, machine learning, and scientific research.
The largest AI Data Centers are gigawatt-scale facilities, called a GW Campus in AI Speak. Each uses one or more gigawatts (1,000+ megawatts) of electricity to train and run frontier models, the most capable AI models available at any given time.
Now, please follow me, as I bullet the physics:
