What If Life Is Just Another Kind of Computer

Alan Turing and John von Neumann saw something long before most people did a connection so deep it still feels unsettling: the logic of life might be the same as the logic of code.

When Machines Started to Reproduce

In 1994, something strange flickered to life on a computer screen not a creature exactly, but it behaved like one. It read a list of digital instructions, copied them, and built another version of itself. Watching it felt oddly biological. It was a living demonstration of an idea John von Neumann had imagined fifty years earlier: that life, at its core, might simply be computation.

Von Neumann’s insight was that reproduction could be described as a coded process. His theoretical “self replicating machine” was inspired by Alan Turing’s Universal Machine a model capable of reading, interpreting, and executing symbolic instructions. DNA, in this sense, functions the same way. If a strand of DNA says, “when you see the codon CGA, attach an arginine,” that’s not poetic language. It’s literal programming.

It’s unsettling when you think about it: we’re made of instructions, not metaphors.

Life as Code But Messy Code

Of course, biological computing is nothing like the tidy, binary logic of your laptop. DNA is multilayered and noisy, a tangle of overlapping systems where even proximity between genes matters. Then there’s the ecosystem within us trillions of bacteria and viruses, each carrying their own code, mingling, swapping, rewriting the script of our bodies in real time.

If you want to imagine “life as computation,” picture chaos. Roughly 300 quintillion ribosomes inside you are all running their own molecular assembly lines at once. Each one is a microscopic computer, though not the predictable, clockwork kind. Instead, their actions are stochastic shaped by random thermal motion, chemical chance, and probability. Molecules drift, bump, bind, and break apart in what looks like noise but somehow resolves into order.

In a normal computer, logic gates process bits with near perfect precision a 1 or a 0, no ambiguity. Biology doesn’t work that way. It’s sloppy, redundant, constantly correcting itself, and yet it works beautifully. Randomness, rather than being a problem, seems to be part of the trick.

Why Turing Added a “Random Number” Button

Turing understood this before most people. When he helped design one of the first computers the Ferranti Mark I he insisted it include a “random number” instruction. To him, randomness wasn’t a nuisance; it was an essential ingredient of intelligence and adaptability.

Modern computer science ended up agreeing with him. Many powerful algorithms today depend on randomness from cryptography to machine learning. Randomness helps computers escape dead ends, find creative solutions, and mimic the unpredictable nature of the real world.

And then there’s parallelism multiple processes happening at once. Biological life has always been parallel. Modern computing, on the other hand, only recently began catching up. Artificial intelligence now runs on vast arrays of parallel processors called GPUs. The algorithm that trains most neural networks “stochastic gradient descent” even includes the word stochastic, meaning random. Turing and von Neumann would probably grin at how familiar it all looks.

Why Computers Started Out So Linear

You might wonder, if life’s computation is so wildly parallel, why did we ever build computers that think one step at a time The answer is pretty simple: hardware. The first computers used vacuum tubes that were fragile, hot, and expensive. To make them practical, designers kept things minimal a single “Central Processing Unit” that did all the thinking, one instruction at a time. That’s what became known as the “von Neumann architecture.”

It was never the only way to compute, though. Turing and von Neumann both suspected there were others. In the last years of his life, Turing studied how animal patterns the stripes on a zebra or the spots on a leopard could emerge from simple chemical rules. He called this morphogenesis, and it was, in essence, a form of distributed computation happening inside living tissue. Around the same time, he also dreamed up “unorganized machines” networks of randomly connected nodes that could learn, roughly like a baby’s brain.

These were early sketches of what we now call neural networks.

Von Neumann’s Dream: Life on a Grid

Von Neumann took the idea in a slightly different direction. While working at Los Alamos in the 1940s, he and mathematician Stanisław Ulam developed the concept of cellular automata grids of digital cells that all follow the same simple rule: look at your neighbors and change your state accordingly. If you’ve ever seen Conway’s Game of Life, that’s a direct descendant.

Von Neumann even designed a theoretical automaton capable of building a copy of itself complete with an instruction “tape” and machinery for reading and executing it. On paper, it was astonishing: a machine that could copy its own blueprint, pixel by pixel. But designing such a thing in practice turned out to be brutally hard. Every cell in the grid affects every other, and once you add randomness as biology does the system becomes almost impossible to predict.

Still, his intuition was right: computation doesn’t need a central processor or clean binary logic. There are infinite ways to compute, and this is the wild part they’re all equivalent. In principle, any computer can simulate any other. The only difference is speed.

The First Digital “Life” Form

That’s why it took until 1994 for von Neumann’s self reproducing automaton to finally come to life on a screen. It needed serious processing power 6,329 digital “cells” running for 63 billion time steps. Watching it was mesmerizing: a glowing, two dimensional Rube Goldberg machine slowly unrolling a tape of instructions and printing a new copy of itself beside the original. Tedious, yes but undeniably alive, in its own digital way.

The irony is that computers had to evolve, in a sense, to model evolution itself.

When Cells Became Neural Networks

Fast forward to 2020, when researcher Alex Mordvintsev blended von Neumann’s automata, Turing’s morphogenesis, and modern AI into something new: the Neural Cellular Automaton (NCA). Instead of simple if then rules, each cell contains a neural network a little brain of its own. These digital organisms can “grow” images or patterns organically, pixel by pixel, the way real cells form tissues.

Of course, biological cells don’t literally have neural nets inside them. But they do make decisions when to divide, when to die, what to absorb or release all based on internal programs honed by billions of years of trial and error. The NCA isn’t a simulation of life; it’s a mirror of how life computes.

So, Are We Just Machines

Maybe the line between “alive” and “computational” isn’t as solid as we think. The more we learn, the blurrier it gets. Our cells follow algorithms, but they also improvise. Computers imitate thought, but they don’t yet feel.

Still, the resemblance keeps growing. Turing and von Neumann didn’t just predict computers they glimpsed something deeper: that the same mathematical fabric might run through both silicon and flesh.

And that raises a haunting question not whether life is a computer, but whether computers might someday become alive.

Open Your Mind !!!

Source: ZME

Open Your Mind