Category Errors In the Analysis of the Economics of AI
Much of the hysteria and euphoria about AI is implicitly based on the idea that AI will be free, or alternatively, that the marginal cost of AI will be close to zero. This is the basis for the belief that AI will replace everybody, rendering humans worthless, with all the benefits accruing to the creators of AI. (This is based on incomplete, not to say idiotic, economic reasoning, but I leave that for a later post). It is also the basis for the Always Look on the Bright Side of Life way of looking at the same prediction:
But these predictions for ill or bliss are obviously, metaphysically wrong. AI is incredibly resource intensive. I just posted on power and water. But there is also the huge physical capital requirement–GPUs, with all the attendant capital costs they entail, buildings, wire, electronics, elaborate cooling systems, and on and on.
And as I’ve also posted on recently, businesses have been rudely awakened from their AI revery to realize that it is anything but free. It is damned expensive, actually, and will therefore require them to make discriminating choices on using it. “Fire everybody” is not a discriminating choice.
It seems there has been a ubiquitous category error in analyses of AI. It has been analyzed as if it is software. Software, once created, has effectively zero marginal cost. Hell, at one time the marginal cost was a floppy disk or CD: now it can just be downloaded for virtually nothing. Using software is also effectively costless. The cost of software is in its creation, and is largely fixed.
In contrast, although the creation of an advanced AI model is highly costly–that is, involves high fixed costs–using an AI model is also very costly. That is, the marginal cost of use is positive, because it involves consumption of scarce resources like power, water and GPUs.
Moreover, it is highly likely that AI is an increasing cost industry because the supply curves for AI inputs are upward sloping.
Software: high fixed cost, effectively zero marginal cost. AI: high fixed cost, positive and arguably high marginal cost.
All meaning that whereas people like Elon and other AI “visionaries” apparently believe that AI will invalidate the laws of economics–which are predicated on scarcity–this is a category error. Scarce resources are necessary to produce the AI that firms and individuals actually consume. The costs of these resources will drive the costs of such consumption, which will in turn lead to economizing decisions regarding where, how, and how much AI is used.
Almost all of those forecasting the future of AI (e.g., Elon) have ignored the constraining effects of this scarcity. Therefore, their prognistications–both dire and utopian–are worthless. Ignore anyone who does not place such scarcity front and center when attempting to discern how AI will reshape the world.
I, like everyone else, have been thinking about this a lot and have a bit of a corollary to your thesis. I believe that Anthropic’s pivot to code in Dec 2024 was inspired. Essentially, they realized that people would first take advantage of AI to create things that subsequently have near zero marginal cost. So you use AI to create code. It’s expensive but less expensive than the standard fixed costs of code. But now you don’t need the AI as much anymore. You just run the code at standard low marginal costs. Same analysis applies to maintenance of the code.
But, as you point out, this model doesn’t generalize. It only applies to processes that can have a software type cost model at the end. So the question is, are there enough of these problems that justify the staggering fixed costs?
Comment by Highgamma — July 22, 2026 @ 12:58 pm
The category error here is conflating AI with data centers. That’s like saying cars and roads are the same thing.
Comment by Michael van der Riet — July 24, 2026 @ 3:38 am