AE x AI

In the world of economics, we often fall into the trap of treating 'Capital' as a giant, homogenous blob—a single number on a balance sheet. But Ludwig Lachmann, one of the most profound thinkers in the Austrian tradition, argued otherwise. He insisted that capital is heterogeneous. It has a specific 'structure', and its value depends on how well it fits into a specific plan. Right now, the AI industry is obsessed with 'Generality'. Everyone wants the biggest, most general model that can write a poem, solve a math problem, and suggest a recipe all in the same breath. But if we look through the Lachmannian lens, we see why this 'Generalist' approach is economically fragile. The Problem with Homogenous Intelligence A tool that does everything is rarely the most efficient tool for any specific task. In my experience in facility operations, we don't buy 'general purpose machines'; we buy specialized equipment designed for a specific thermal loa...

We are currently in a 'Prompt Engineering' gold rush. Everyone is trying to find the magic sequence of words to make an LLM spit out the perfect essay, the perfect code, or the perfect image. But as an observer of economic cycles, I can’t help but notice a familiar pattern. We are overvaluing the input and ignoring the marginal utility of the output . The Collapse of Content Value In Austrian Economics, Carl Menger taught us that the value of a 'higher-order good' (like a prompt) is derived from the value of the "lower-order good" it produces (the content). Right now, the cost of generating a paragraph of text is trending toward zero. When the supply of 'good enough' content becomes infinite, its marginal utility collapses. If everyone can generate a 1,000-word strategy memo in three seconds, the value of that memo isn't the 1,000 words—it’s effectively zero. The Shift from 'Chat' to 'Agency' I believe we are hitting the ceiling of...

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...