Are Biological Systems More Intelligent Than Artificial Intelligence?

TL;DR

This paper introduces a mathematical framework comparing biological and artificial systems based on their ability to delegate control across abstraction layers, arguing biological systems are more adaptable and thus more 'intelligent' due to their delegation capabilities.

Contribution

It formalizes the concept of intelligence as adaptability within a stack theory framework and proves a theorem linking adaptability at different layers, with implications for system design and robustness.

Findings

Biological systems delegate adaptation more effectively than AI.

Maximizing adaptability is equivalent to minimizing variational free energy.

Delegation is crucial for robustness and collective identity in complex systems.

Abstract

Are biological self-organising systems more ``intelligent'' than artificial intelligence (AI)? If so, why? I address this question using a mathematical framework that defines intelligence in terms of adaptability. Systems are modelled as stacks of abstraction layers (\emph{Stack Theory}) and compared by how effectively they delegate agentic control down their stacks. I illustrate this using computational, biological, military, governmental, and economic systems. Contemporary AI typically relies on static, human-engineered stacks whose lower layers are fixed during deployment. Put provocatively, such systems resemble inflexible bureaucracies that adapt only top-down. Biological systems are more intelligent because they delegate adaptation. Formally, I prove a theorem (\emph{The Law of the Stack}) showing that adaptability at higher layers is bottlenecked by adaptability at lower layers. I further show that, under standard viability assumptions, maximising adaptability is equivalent to minimising variational free energy, implying that delegation is necessary for free-energy minimisation. Generalising bioelectric accounts of cancer as isolation from collective informational structures, I analyse cancer-like failure modes in non-biological systems when delegation is inadequate. This yields design principles for building robust systems via delegated control, and reframes hybrid agents (e.g. organoids or human--AI systems) as weak boundary-condition design problems in which constraints shape low-level policy spaces while preserving collective identity.