Are Biological Systems More Intelligent Than Artificial Intelligence?

TL;DR

This paper introduces Stack Theory, a formal framework that treats intelligence as adaptability across nested abstraction layers and examines how delegation of adaptation across levels shapes robustness. It formalizes multilayer architectures (MLA) and proves The Law of the Stack, which bounds higher-layer utility by lower-layer policy weakness via the inequality $\epsilon(\gamma_{i+1}) + |O_{\gamma_{i+1}}| \le 2^{|E_{\pi_i}|}$. The core claim is that biological self-organization achieves greater adaptability by delegating adaptation to lower levels, whereas AI systems with static stacks are less capable of flexible cross-scale adaptation; this insight informs boundary-condition design for robust hybrids (e.g., organoids and human-AI teams) and offers a design lens to mitigate cancer-like, over-constrained failures. The work also outlines concrete future research directions, including agent-based simulations, stack-depth benchmarking, and hybrid boundary-condition experiments, to empirically validate the proposed framework and extend it to cyberphysical and socio-economic systems.

Abstract

Are biological self-organising systems more `intelligent' than artificial intelligence (AI)? If so, why? I explore this through a mathematical lens which frames intelligence in terms of adaptability. I model systems as stacks of abstraction layers (\emph{Stack Theory}) and compare them by how they delegate agentic control down their stacks, illustrating with examples of computational, biological, human military, governmental and economic systems. Contemporary AI rests on a static, human-engineered stack in which lower layers are static during deployment. Put provocatively, static stacks resemble inflexible bureaucracies, adapting only top-down. Biological stacks are more `intelligent' because they delegate adaptation. Formally, I prove a theorem (\emph{The Law of the Stack}) showing adaptability in higher layers requires sufficient adaptability in lower layers. Generalising bio-electric explanations of cancer as isolation from collective informational structures, I explore how cancer-like failures occur in non-biological systems when delegation is inadequate. This helps explain how to build more robust systems, by delegating control like the military doctrine of mission command. It also provides a design perspective on hybrid agents (e.g. organoids, systems involving both humans and AI): hybrid creation is a boundary-condition design problem in which human-imposed constraints prune low-level policy spaces to yield desired collective behaviour while preserving collective identity.

Figures (2)

• Figure 1: Control or agency and thus adaptation in a given system can take place at a particular level of abstraction. For example, I can program in Python, or I can go down a level and program in C. If I program in C it doesn't matter whether I program one computer or fifty. Either way my control is at the same level of abstraction (figure from bennett2025thesis).
• Figure 2: Work, resources and so on can be distributed across a system. Distribution is distinct from delegation of control. For example, both food stamps and universal basic income might distribute the same amount of resources to the same people within an economic system, but the former more tightly constrains the recipients in how they use those resources (figure from bennett2025thesis).

Theorems & Definitions (2)

• proof : Proof sketch
• proof