AE x AI

When people talk about AI today, they talk about 'parameters' and 'datasets'. When I look at AI, I see physical infrastructure. In my years managing corporate facilities, I’ve learned that you cannot have a digital breakthrough without a physical foundation. Every 'token' produced by an LLM has a direct physical cost—specifically in the 'Fixed Capital' of high-bandwidth memory (HBM) and the massive energy loads required for cooling. Most economists are treating AI as a 'fluid' service that can scale forever. But if we apply Austrian logic, we realize that AI is actually a highly heterogeneous capital good . It is subject to the same physical bottlenecks as any other industrial process. We are entering a phase where the 'Digital-Physical Paradox' will become impossible to ignore. The companies that win won't just have the best code; they will be the ones that best manage the Inference-Energy Nexus . If AI is going to scale, it needs to...

In the mainstream tech press, Artificial Intelligence is often described as 'fluid'. We hear about it 'flowing' into every sector, scaling infinitely in the cloud, and being accessible via a simple browser window. But as an  Facilities Management (FM) guru , I see it differently. When you spend your days managing the physical lifecycle of industrial assets, you learn a hard truth: Nothing is fluid without a fixed foundation.   The Physical Reality of "Tokens Every time an LLM generates a response, it isn't 'thinking' in a vacuum. It is consuming a specific amount of electricity, requiring a precise degree of cooling, and occupying a slice of high-bandwidth memory (HBM). In economic terms, AI is not a service; it is a processed output of fixed capital. If we apply the lens of Capital Heterogeneity (a favorite topic of Austrian economist Ludwig Lachmann), we see that the hardware running these models isn't just a generic 'computer'. It is a h...