I am sure most of you are familiar with the story of Charles Steinmetz, or one of its many variations. Steinmetz was a brilliant engineer at General Electric, and the story goes like this.
Henry Ford was having trouble with a massive generator and called in Steinmetz. After listening to the machine for a few moments, Steinmetz took out a piece of chalk and made a small ‘X’ on a specific metal casing. Ford’s engineers opened it up and found the defect exactly where the mark was. When Steinmetz sent a bill for $1,000, Ford, ever the businessman, asked for an itemised invoice. Steinmetz replied:
- Making chalk mark: $1
- Knowing where to mark: $999
Ford paid the bill without further question. He understood that the physical act was trivial; the value lay in the decades of experience required to know exactly where that one-dollar mark belonged.
We are living through a remarkably similar moment, yet we may be heading in the opposite direction. Many AI companies are attempting to persuade us that the act of generation is more valuable than what is generated. They are moving away from simple subscription models towards pay-per-token billing. Even those that have not fully transitioned are clearly pivoting that way. In doing so, they are, perhaps unintentionally, asking us to pay for the weight of the chalk.
A token is a mathematical fragment, the raw material of a response. Billing by the token reflects real computational costs, but it also participates in a market that can prize volume over validity. Intelligence starts to resemble a metered utility, much like water or electricity. But intelligence is not a liquid; it is a coordinate. In software and engineering, the most elegant solution is rarely the longest. A thousand lines of generated code may solve a problem, but they are often a liability, a burden of technical debt. Ten lines of precise logic can be a masterpiece.
Under the current model, however, the thousand-line mess can end up being priced as though it were a hundred times more valuable than the ten-line stroke of genius. We can find ourselves paying for the stuttering of the machine, the computational friction it incurs while searching for an answer, rather than for the answer itself. We are, quite literally, paying for the ink and ignoring the idea.
This push towards ever greater output is accelerating beyond the limits of human review. Where an engineer might once have produced a single page of clear, concrete documentation, a model now generates thousands. This is not necessarily progress; it can become a flood. It creates an artificial demand for even more powerful and expensive models, just to process the noise produced by earlier ones. We can end up in a loop in which we need AI to summarise the verbosity of other AI.
This begins to reveal a structural tension in the current AI arms race. The incentives do not always point towards efficiency; they often reward scale. By flooding the ecosystem with information, the need for larger contexts and more powerful reasoning becomes easier to justify. These, in turn, support higher price points and more ambitious positioning. The result is not a map to the ‘X’, but an ever-growing supply of chalk, along with the tools to manage it.
This creates a perverse incentive for the future of technology. If we measure the value of AI by the number of tokens it produces, we encourage a digital world of bloat. We risk being buried under a mountain of cheap, generated noise, where quantity is mistaken for quality. It is a system that can reward the machine for being chatty rather than correct.
The true revolution of artificial intelligence should not be that it makes the chalk mark easier to produce. The revolution is that it should help us find the ‘X’ faster. But as long as the price remains closely tied to the token, the industry will tend to focus on the tool rather than the result.
We should eventually demand a different kind of invoice from the architects of these models. We should stop subsidising the cost of digital ink and start valuing the precision of knowledge. Until we shift our perspective, we are not fully purchasing intelligence; we are still largely paying for the act of writing. Steinmetz’s ‘X’ was valuable because it was singular and precise. If he had covered the entire generator in chalk, his bill would not have been worth a penny, regardless of how much chalk he used.
A deeper question follows. What if AI does not consistently deliver what is promised, not because it cannot, but because the incentives are misaligned? At present, many incentives favour the production of large volumes of content that require millions of tokens and iterations to process. They do not always favour the creation of systems that can deal with complexity in a genuinely intelligent way. It is worth asking whether the pursuit of revenue might, at times, be steering us away from the outcome we actually want.
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