This paper introduces Entangled Time — a novel economic variable representing the simultaneous production-consumption state characterizing human engagement with algorithmic digital interfaces. We develop a formal equilibrium model in which rational agents allocate time to zero-price digital platforms, where their behavioral data constitutes unpriced cognitive labor driving AI capital formation. We demonstrate three principal results. First, under a non-stationary algorithmic resonance state formalized through a Preference Expansion Function, the marginal utility of interface time can be non-decreasing, violating Gossen’s First Law and generating a corner solution (Proposition~1). Second, the firm operating as an algorithmic monopsony facing perfectly inelastic labor supply optimally sets the fiat wage for digital labor equal to zero, substituting monetary compensation with endogenous digital utility (Proposition~2). Third, we define and calibrate Dark GDP — the aggregate value of uncompensated cognitive labor invisible to the System of National Accounts—and show it accounts for a measurable fraction of the secular decline in global labor share (Propositions~7–9). We establish equilibrium existence via Brouwer’s Fixed Point Theorem and propose an empirical identification strategy using privacy-mandate shocks as instruments for data extraction. Three institutional redesigns are proposed: an Algorithmic Monopsony Standard, a Pigouvian Algorithmic Severance Tax, and a Cognitive Depreciation Allowance.

That is all from Nav Vaidhyanathan, who estimates the value of these unpriced services may be in the range of $1.3 trillion.  Here is the easier to follow Substack version.  Speculative, but worth a ponder.

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