QCM (Quantitative Complexity Management) technology has been around for twenty years. The applications span numerous fields, such as engineering, medicine, air traffic, economics, finance, society, or pharma. QCM can measure the complexity of things and explain its nature and constitution. This allows to “select the simpler option” on scientific grounds, not based on sensations or feelings. Selecting options is a crucial aspect of decision making. It is obvious that with all things equal, the simplest option is the best one. Complexity acts as the final filter and is a crucial decision-triggering mechanism in heuristics and intuition.
The common theme has so far been this: QCM has been restricted to processing only two types of input:
- Data
- Images
What has always been missing from the list is text. However, today this is possible. Today we can add a filter to AI-generated text and, whenever needed, to select “the simpler option”. This is an example why applying a complexity filter to text would be something useful:
AI without a complexity filter: “Here are 5 different strategic plans for your business. They all have a similar probability of success based on the training data.”
AI with a complexity filter: “Here are 5 plans. While all are plausible, this one has the lowest complexity score. It achieves the goal with the fewest moving parts, making it the most robust, easiest to implement, and least likely to fail due to unforeseen interactions. This is the one we recommend.”
The second response is undeniably more intelligent and useful.
Coupling AI with a complexity quantification technology is a crucial step toward creating more intelligent, reliable, and human-aligned systems. It addresses the “last mile” problem in AI reasoning: moving from generation to discerning selection.
This isn’t just about making AI “smarter” in an academic sense; it’s about making it more functional, trustworthy, and deployable in the complex, real world. It bridges, at least partially, the gap between artificial pattern matching and genuine, pragmatic intelligence. This approach moves AI’s output from being merely plausible to being actionably optimal in a human context.
The bottom line is that:
a methodology for quantifying the complexity of text has been established
and may be applied to AI-generated text. It will make Artificial Intelligence less artificial and more intelligent.
Artificial Intuition 
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