Evo-Devo Series: Part 1 — From Single Cells to AI Systems
How biology’s deep-time lessons can reshape our thinking about artificial intelligence.
When we think about AI, most analogies come from computing itself: circuits, algorithms, networks. But what if we zoomed out — way out — to biology’s 4-billion-year history? That’s where evolutionary developmental biology (Evo-Devo) offers surprising, almost poetic, parallels.
In Evo-Devo, the study begins at the cellular level — how life forms develop from a single cell into complex organisms — and asks how evolutionary pressures shape those developmental processes over time. In AI, we might not be growing from literal cells, but we are evolving from foundational models toward systems of greater complexity.
The Cellular Beginning
Every living thing starts as a single cell, carrying all the instructions it needs in its DNA. In AI terms, think of this as the base model — a compact set of weights and parameters with the potential to grow into something far more specialized.
Just as cells differentiate into muscle, nerve, or blood cells based on environmental signals, AI models “differentiate” through fine-tuning and prompt engineering. The base model is the shared starting point; the specialization comes from interaction with the world and the data it ingests.
Scaling Complexity
In biology, complexity isn’t instant — it emerges gradually. Cells form tissues, tissues form organs, and organs form coordinated systems. Each step up in complexity requires:
- Specialization: Different parts doing different jobs.
- Coordination: Communication between parts.
- Adaptation: Adjusting to external pressures.
In AI, we see similar layering:
- Foundational models (the “cells”)
- Domain-specialized fine-tunes or APIs (the “tissues”)
- Full AI products with integrated components (the “organs and systems”)
The leap from one stage to another isn’t trivial — it involves trade-offs, bottlenecks, and, often, dead ends.
Why This Matters
This biological lens changes how we think about AI development. Instead of seeing each generation of AI as a replacement for the last, we can see it as building upon what came before — reusing and repurposing structures, much like evolution does.
It also reminds us that complexity is not free. More parts mean more points of failure, and nature’s most elegant solutions often emerge from centuries of trial, error, and survival.
Next in the series: Part 2 — Evolutionary Breakthroughs: How Tiny Adaptations Unlock Big Leaps in AI
Evo-Devo is a lens. And sometimes, to see AI’s future, you need to look 4 billion years into the past.