# Are Biological Systems More Intelligent Than Artificial Intelligence?

arXiv: https://arxiv.org/abs/2405.02325 · HTML: https://mlsanity.com/paper/2405.02325/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 112 references that are in the map.
- First posted on arXiv 23 Apr 2024.

## Builds on (most cited first, 12 of 12)

- [Robust agents learn causal world models](https://mlsanity.com/paper/2402.10877/) (2024)
- [The Neural Coding Framework for Learning Generative Models](https://mlsanity.com/paper/2012.03405/) (2020)
- [Path integrals, particular kinds, and strange things](https://mlsanity.com/paper/2210.12761/) (2022)
- [Bad Universal Priors and Notions of Optimality](https://mlsanity.com/paper/1510.04931/) (2015)
- [Mortal Computation: A Foundation for Biomimetic Intelligence](https://mlsanity.com/paper/2311.09589/) (2023)
- [The Optimal Choice of Hypothesis Is the Weakest, Not the Shortest](https://mlsanity.com/paper/2301.12987/) (2023)
- [Symbol Emergence and The Solutions to Any Task](https://mlsanity.com/paper/2109.01281/) (2021)
- [Emergent Causality and the Foundation of Consciousness](https://mlsanity.com/paper/2302.03189/) (2023)
- [OpenCog Hyperon: A Framework for AGI at the Human Level and Beyond](https://mlsanity.com/paper/2310.18318/) (2023)
- [Philosophical Specification of Empathetic Ethical Artificial Intelligence](https://mlsanity.com/paper/2107.10715/) (2021)
- [Maze Learning using a Hyperdimensional Predictive Processing Cognitive Architecture](https://mlsanity.com/paper/2204.00619/) (2022)
- [Enlarging the scope: grasping brain complexity](https://mlsanity.com/paper/1310.7277/) (2013)

## Built on by (most cited first, 5 of 5)

- [Agent Identity Evals: Measuring Agentic Identity](https://mlsanity.com/paper/2507.17257/) (2025)
- [Why Is Anything Conscious?](https://mlsanity.com/paper/2409.14545/) (2024)
- [What the F*ck Is Artificial General Intelligence?](https://mlsanity.com/paper/2503.23923/) (2025)
- [Why the Third Axis Is Freedom](https://mlsanity.com/paper/2608.05423/) (2026)
- [From cacophony to hierarchy: a principled framework for assessing AI consciousness](https://mlsanity.com/paper/2609.35618/) (2026)

## Abstract (arXiv)

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 (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 (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.