Human civilisation is now so accustomed to advanced technology that we forget how astonishing it truly is. A smartphone contains billions of transistors. A modern processor performs billions of operations every second. Artificial intelligence platforms analyse language, recognise patterns, and predict outcomes at scales that exceed human capacity. Yet if you placed humanity’s most brilliant engineer on an island with only raw natural resources, they would never build a laptop. They would not create a transistor. They would not create a functioning integrated circuit. They would not even create the pure silicon wafer needed for the first step.
This is not a criticism of intelligence. It is a demonstration of the limits of individual design. Modern computing exists only because of a long chain of incremental advances made over thousands of years by millions of people. No one had the full blueprint. No one foresaw the endpoint. Each step happened because it solved a local problem, and each step opened new possibilities for the next.
This is the logic of evolution. It is the logic of cumulative complexity. It is the logic of emergence. And it is the logic that many people struggle to accept when applied to biology, even though they intuitively accept it in every other domain of human achievement.
The evolution of computing provides a real world, modern, observable model that mirrors the central principles of biological evolution. It is not a metaphor. It is an example of how complexity naturally arises through incremental improvements, selection pressures, functional survival, path dependence, and long chains of workable solutions that no single mind could ever design from scratch.
This long form analysis explores that parallel in depth. It addresses the similarities, the limitations, and the challenges. It also integrates the most common creationist or design based objections directly into the narrative. The intention is not to mock or belittle. The intention is to show why these objections collapse when viewed against the actual history of technological development.
The story of computing is the story of evolution played out in human culture. It is the clearest living demonstration of how complexity arises from simplicity, one workable step at a time.
1. Why Complexity Confuses Us
Human intuition is poorly adapted to understanding long chains of cumulative changes. We excel at understanding single causes and immediate results. We struggle with systems shaped by many small steps over long periods of time. As the historian Daniel Boorstin observed, “The greatest obstacle to discovery is not ignorance; it is the illusion of knowledge.”
We are quick to believe we understand the world because we see the finished forms. A modern tree. A modern smartphone. A modern eye. A modern CPU. We forget the history behind them. The thousands of discarded ideas. The lost prototypes. The slow accumulation of small refinements that build into something extraordinary.
Complexity looks designed. The illusion of design emerges from the fact that we only see the finished product, not the path that led there. That same illusion underpins much of creationist thought. The assumption is that complexity must be the result of a mind because the pathway is difficult to imagine.
Yet computing offers an accessible way to pierce that illusion. The path is recorded. The steps exist. The dead ends are documented. The incremental evolution is visible. Even the mistakes, missteps, and accidental breakthroughs are known.
Computing makes the unseen mechanism of evolution visible. It shows how complexity accumulates without a master architect. It shows how systems evolve beyond the understanding of any individual, even though each step is small and understandable.
As Stephen Jay Gould once wrote, “Great complexity evolves from simple beginnings, but not in simple ways.”
Computing proves this point.
2. The Evolution of Tools: The First Steps Toward Computation
The earliest computational tools were not machines, algorithms, or electronics. They were marks on bones. Archaeologists have found tally sticks as old as 35,000 years. These were simple counting tools. Not calculators. Not programmable devices. But the concept of externalised counting was already present.
This is the first principle of cumulative complexity: new capabilities do not arise fully formed. They begin as crude, minimal functions that work just well enough to persist.
From tally sticks came stones arranged in grooves. From stones came the abacus. The abacus is a transformative device because it externalised numerical logic. As the philosopher Alfred North Whitehead noted, “Civilisation advances by extending the number of important operations which we can perform without thinking of them.”
The abacus did exactly that. It took the cognitive burden of arithmetic and shifted it into a stable physical system.
Creationists sometimes argue that evolution cannot create new functions because mutations are errors. Yet the abacus shows how transformation begins from crude, low resolution tools that succeed simply because they work well enough. They do not need to be perfect. They only need to be functional.
Once tools enable new behaviour, they create selection pressures for more refined tools. This is as true for technology as it is for biology.
3. Babbage: The First Glimpse of Programmable Evolution
Charles Babbage’s Analytical Engine was a conceptual giant. It was never fully built, but it introduced the core idea of general purpose computation. This was a monumental conceptual leap.
Yet even here, the leap was not magic. Babbage’s ideas were built on previous mechanical calculators. They were shaped by advances in metalworking, machining, and textile automation. The punched cards of the Jacquard loom directly inspired the concept of machine instructions.
Every idea Babbage had was made possible by previous workable ideas. Ada Lovelace, who wrote the first algorithm for the Analytical Engine, observed: “The Analytical Engine weaves algebraic patterns just as the Jacquard loom weaves flowers and leaves.”
What appears as genius is actually evolution. Ideas do not appear from nowhere. They emerge from the accumulated landscape of previous ideas.
This mirrors evolution, where biological functions emerge from modifications of older structures. Feathers did not arise for flight. They arose for insulation and display. Only later were they repurposed for aerodynamics. Similarly, Babbage’s ideas arose from earlier machinery and were repurposed for abstraction.
Creationist objection: “Babbage designed the Analytical Engine. Design requires a designer.”
This objection misunderstands the point. Yes, individual steps have designers. But no individual created computation as a whole. No individual designed the modern computer. Every designer built upon the work of countless others. No one wrote the full blueprint.
This mirrors evolution. Local actions. Global consequences. Incremental accumulation.
4. From Mechanical to Electronic: The Threshold of New Possibility
The move from mechanical machines to electronic ones is similar to the evolutionary leap from single celled to multi celled life. It unlocked an explosion of new forms, each building upon what already worked.
Vacuum tubes offered speed and reliability far beyond gears and levers. They created new constraints, but also new possibilities. Early machines like ENIAC and Colossus were monumental, fragile, power hungry, and unreliable. Yet they were good enough to survive as a workable idea.
Biology works the same way. Eyes did not begin as complex organs. They began as simple light sensitive patches. The key principle is that evolution only needs one thing: something that works slightly better than the alternative.
Technological evolution follows this law precisely. Vacuum tube machines succeeded because they outperformed mechanical machines in specific tasks, not because they were perfect.
Creationist objection: “Computers improve because humans guide them. Evolution cannot guide itself.”
This misunderstands the mechanism. Improvement is not guided by omniscience. It is guided by selection pressures. Early electronic machines succeeded because they worked better in given contexts. Engineers did not foresee the destination. They only responded to what worked.
Evolution operates on the same rule: what survives, survives.
5. The Transistor Revolution: The First True Analogy to Natural Selection
When the transistor arrived, everything changed. It was smaller, faster, more reliable, and used far less power. It outcompeted vacuum tubes in almost every environment.
This is selection pressure. Not intelligent design on a cosmic scale. The environment provided the filter. Transistors survived because they worked better.
Integrated circuits were the next step. Not because anyone had a vision of modern CPUs, but because grouping transistors reduced cost and increased reliability. This incremental improvement created exponential potential.
Richard Feynman’s famous line captures the principle: “What I cannot create, I do not understand.” If applied to computing today, no one understands the entire system. No one created the whole thing. Yet the system exists. This demonstrates that complex systems do not require complete understanding at any step.
Biology is the same. No gene understands the organism. No cell understands the body. No organism knows its evolutionary future.
Creationist objection: “Transistors were designed. Evolution cannot copy that.”
The point is not who designed the components. The point is that no one designed the system as a whole. Complexity arises through cumulative steps that each solve immediate problems.
6. No One Can Build a Computer From Scratch: The Strongest Parallel
If a modern engineer had to build a computer from raw natural resources, the project would fail. They would lack:
• the mining equipment to obtain the minerals
• the factories to refine the ores
• the purity level required for semiconductors
• the chemical processes for doping silicon
• the machinery for photolithography
• the cleanrooms needed to prevent contamination
• the robotic systems required for assembly
• the global networks required for research and production
• the software foundation for programming anything
This is not a matter of intelligence. It is a matter of cumulative complexity. A single human mind, even a brilliant one, cannot recreate the historical pathway.
This is exactly the point evolution makes about life. No single cell can recreate an organism from scratch. No single genetic step can rebuild complexity. Complexity arises only from previous complexity.
Systems evolve. They do not appear fully formed.
Creationist objection: “But humans still designed it.”
Humans designed steps. Not the full system. No one designed the entire modern computer any more than any organism designed the entire evolutionary tree.
Complexity builds itself through accumulated workable structures.
7. The Biological Parallels: Why Computation Mirrors Life
Both computing and evolution show the same core principles.
1. Incrementalism
Small workable steps accumulate into complex systems.
2. Path dependence
New capabilities arise only because earlier steps existed.
3. No full blueprint
No one sees the full process. No one needs to.
4. Selection pressures
Better solutions survive. Worse ones do not.
5. Functional layering
Systems build layers upon layers of previous structures.
6. Emergent complexity
The whole is greater than the sum of its parts.
These are not metaphorical similarities. They are structural. They show how complexity grows when the only requirement is that each new step works better than what came before.
As Carl Sagan wrote, “The universe is not required to be in perfect harmony with human ambition.” Complexity does not need intention. It needs opportunity.
8. Where the Analogy Stops, and Where It Becomes Strongest
Computing and evolution are not identical. Evolution does not rely on conscious engineering, and computing does not rely on genetic mutation. But the analogy becomes strongest in the mechanisms of complexity accumulation.
Both systems build astonishing outcomes without a master architect.
Both systems demonstrate that complexity is a result of incremental refinement, not sudden invention.
Both systems reveal that no single mind can grasp the entire system.
If someone rejects evolution on the grounds that complexity cannot arise without a designer, they also reject the reality of computing, because no designer ever created computing as a whole.
The evolution of computing proves that human intuition about complexity is poor. It proves that stepwise accumulation can produce outcomes that no single mind could conceive or design.
9. The Philosophical Implications
The story of computing forces us to confront a central fact: complexity does not require foresight. It requires only variation, retention, and time.
In both nature and technology, systems evolve because change piggybacks on previous success. The abacus did not aim to create the smartphone. Feathers did not aim to create flight. Both emerged from cumulative processes that rewarded what worked.
As David Deutsch observed, “Everything that is not forbidden by the laws of nature is achievable, given the right knowledge.” That knowledge accumulates. It composes. It evolves.
The same principle applies to DNA. The same principle applies to computing.
Complexity does not need a designer. It needs a pathway.
Computing gives us that pathway in visible, documented form.
10. FAQ “People Also Ask”
Q: Is the evolution of computing similar to biological evolution?
Yes. Both systems develop through incremental improvements. Computing evolves through engineering selection and technical refinement, while biological evolution is shaped by natural selection. Both processes accumulate small workable changes into complex outcomes.
Q: What does the history of computing teach us about complexity?
It shows that complexity does not emerge fully formed. It grows through many small steps, each building on previous solutions. This mirrors the way natural systems evolve over long periods.
Q: Why is the evolution of computers used as an analogy for evolution?
Because computing is a visible example of how small changes accumulate into advanced systems. No single person designed the modern computer. It arose from centuries of incremental progress, similar to how biological complexity emerges from genetic variation and selection.
Q: How do creationists argue against this analogy?
They claim that computers require designers. However, no one designed computing as a complete system. Each step was designed independently but evolved collectively into complexity that no individual mind could plan or foresee.
Q: Does complexity require a designer?
No. Both technology and biology demonstrate that complexity can arise from incremental steps that each solve a simple problem. These steps accumulate into sophisticated systems without a master blueprint.
Conclusion
The evolution of computing is the clearest living demonstration of how complexity arises without a master architect. It shows that small, workable improvements accumulate into astonishing systems. It shows that no one understands the full system, yet the system still exists. It shows that complexity builds itself from the bottom up.
Computers evolved because the steps that worked were kept.
Life evolved because the steps that survived were kept.
The mechanisms differ. The logic does not.
Computing is evolution we can hold in our hands.