AE x AI

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...

With the recent release of the latest large models, the media is again full of talk about "AGI" and the end of work. But from where I sit—balancing the physical requirements of industrial facilities with the deductive logic of Austrian economics—most people are missing the real story. We are treating AI like a magic oracle. But in economic terms, an AI output is just a "Token"—a unit of processed information. The real question isn't how "smart" the model is; it’s how we allocate the massive amounts of capital (energy, silicon, and human time) required to produce those tokens. Ludwig von Mises famously argued that without price signals, central planners cannot calculate. Right now, AI is centrally planned. We have big models "serving" users. But for AI to truly integrate into our economy, it has to move from being a "service" to being an economic actor that can respond to price signals in real-time. I suspect we are nearing the end ...