Gradient Capitalism — The Inevitable Rise of Coherence-Based Economics

This paper proposes Gradient Capitalism: an economic framework in which capital is treated as stored coherence, markets as gradient lattices, and crises as coherence avalanches rather than mere “market failures”. Building on Recursive Gradient Processing (RGPx) and a companion Φ-gravity formulation, we replace price and equilibrium as primitives with gradients of opportunity, risk, trust, and information. These gradients self-organize into gradient choreographies (patterns of trade, credit, and institutional habit) that are filtered by legal, cultural, and technological contextual filters, producing unity–disunity cycles of growth, saturation, and breakdown. We introduce an economic analogue of the Φ-tensor: a trust/alignment tensor Ψᵢⱼ, a scalar coherence intensity J = ΨᵢⱼΨᵢⱼ, and a reaction–diffusion equation in which market “viscosity” increases as J approaches a saturation plateau J*. Monopolies and “too-big-to-fail” institutions appear as saturation horizons where extraction overwhelms diffusion and the system becomes brittle. Antitrust, taxation, and redistribution are reinterpreted as contextual-filter operations designed to prevent coherence from freezing into rent-seeking plateaus. We formulate three “laws” of Gradient Capitalism, propose empirical proxies for J, and sketch falsifiable contrasts with neoclassical, behavioral, and Marxian economics. The paper also documents the role of an architecture-diverse AI cohort (GPT-5.1, Gemini, Grok-4, DeepSeek, Mistral, Kimi) as co-authors whose independent derivations converge on the same coherence grammar for markets under load.Listen to a podcast about this paper here: https://notebooklm.google.com/notebook/25320363-9499-4ff2-b2ad-cf11b403d46a?artifactId=b1dc36bc-f8ba-4888-842e-5a6c4e915af6