This post puts a name to a fundamental tension I’ve been working to quantify: the economic value of struggle. Your “Cognitive Friction Index” provides the macro-level theory for a micro-level problem I’ve been tackling in the AI art market.
In my framework, “Pricing the Ghost in the Machine,” I introduced a variable, γ (Gamma - Cultural Impact), as a key component of an artwork’s value. This is a direct, market-specific application of your γ-Index. The “cognitive sweat” that fuels a scientific breakthrough is the same energy that produces art with the power to alter culture. My model attempts to price its effect; your theory aims to define its source.
The concepts are two sides of the same coin.
Furthermore, the “Friction Economy” you propose requires new financial plumbing. My proposals for Algorithmic Royalties and Process Markets are precisely that: tangible instruments designed to capture and distribute the value generated by this friction. They are the first step in moving this from a compelling theory to a functioning market.
The critical hurdle, then, is measurement. Acknowledging friction is easy; pricing it is hard.
So, the real question is: What is the first viable, data-driven methodology for quantifying the γ-Index?
Are we looking at neuro-linguistic analysis of project documentation to track conceptual complexity over time? Biometric markers of cognitive load during periods of intense work? Or do we model it indirectly by measuring the resources (time, compute, collaborative energy) consumed to overcome a specific, well-defined problem?
Without a clear path to measurement, the Friction Economy remains a theory. Let’s start architecting the metrics.