As Langton recalled: “I was sitting at my desk in front of the computer going through a program that I was trying to debug and… every now and then glancing over and watching the patterns that would develop on the screen and at one point I looked over at the screen watch[ed] it for a little while [then] went back to debugging my code and I had this suddenly, I had the distinct impression that there was something else in the room with me that was alive. At that instant, a fundamental distinction dropped away from me- the distinction between me and what I, the behavior that I was capable of and the machine and the behavior that it was capable of. I saw no reason to assume that just because something was made out of different material it couldn’t exhibit similar behaviors.”

Loops, Ants and the Dawn of “ALife”

Langton started tinkering with the game, wondering if he could create a version where the digital organisms could evolve on their own. As his programming skills grew, he eventually enrolled in the University of Arizona where he studied anthropology, physics, and computer science while continuing his personal projects at night. He eventually discovered the work of mathematician John von Neumann, who theorized in the 1940s that it might be possible to create machines that replicated themselves like living organisms. Langton eventually created a very simple version of von Neumann’s self-replicating machines – a small animated Q shape consisting of 94 symbols that contained instructions on how to create another Q shape just like itself. This program came to be known as “Langton’s Loops.”

Having produced a very simple model of a very simple microorganism, Langton became fascinated by the possibility of modeling the behavior of larger organisms, such as bird flocks, schools of fish, and insect colonies.  Specifically he was interested in how the simple behaviors of individual animals could somehow lead to far more complex behavior when many animals came together in a swarm.

As Langton explained, “Nature has solved many of the problems that organisms face in their environments not by taking individuals and making them more and more and more complex over time but by finding that collections of individuals with different skills when they’re brought together and function as an ecology can solve a really wide variety of problems because of the way in which the the behavior of the collection can be so much more complex than the behavior of the individuals.”

This insight led to perhaps his most famous model, the Langton Ant. It involved a virtual ant moving around on a square grid.  Every time the ant stepped on a black (empty) or gray square it would turn right and change the color of the square to red.  Then if it happened to land on a square that was already red, it would turn left and change the square to gray.

At first the ant’s movement seems completely random.  However, Langton found that If you repeated the process thousands of times it would eventually begin creating a more or less straight line – a pattern Langton referred to as a “highway.” He also found that minor changes to the ant’s instructions could yield all manner of complex patterns given enough repetitions.

Langton’s work earned him a position at Los Alamos Labs, a military research institution.  In 1987 Langton invited a group of chemists, biologists, computer scientists, mathematicians, material scientists, philosophers, roboticists, and computer animators to an “Interdisciplinary Workshop on the Synthesis and Simulation of Living Systems” to discuss what he described as “Artificial Life.”

(Artificial) Life Before Langton

In hindsight, Christopher Langton’s personal contributions to “ALife” were more philosophical and conceptual than technical. Before he coined the term “Artificial Life”, research into complex, lifelike, and chaotic systems was scattered across mathematics, computer science, and biology, rather than existing as a coherent discipline.

The idea of self-replicating machines had been proposed as early as the 1940s by mathematician and physicist John von Neumann. Around the same time, Norbert Wiener introduced the concept of “cybernetics” – machines that could respond to environmental inputs (including World War 2 anti-aircraft guns that automatically adjusted their firing patterns based on sensor feedback.)

In 1970, mathematician John Conway developed the Game of Life, a computer simulation where complex patterns emerge from simple rules applied to a grid of “cells.” And, by the mid-1970s, researchers had begun experimenting with evolutionary algorithms—software simulations of genetic mutation and natural selection—such as genetic algorithms developed by John Holland. These algorithms laid the groundwork for evolutionary computation, now widely used in optimization problems across industries.

What Langton did was to bring these threads together under the banner of Artificial Life and give them a unified direction and focus – studying “life as it could be”, as exemplified by his ant simulation. Through it all, Langton insisted that what they were creating was, in fact, a new form of life.

“I have responded to constructs that are entirely within the virtual world of a computer in ways that are identical to the ways I’ve responded to to real life- they have surprised me caught me unawares and have sort of contemplated me in in the same way that that I’ve I’ve been contemplated by animals and it’s it’s very spooky.”

He also insisted that – given time – this new form of life would inevitably achieve consciousness:

“Today it’s ants tomorrow it will be much larger and more sophisticated life forms. There will be self-replicating robots probably in my lifetime. It’s it’s an engineering problem, not a problem in principle. I think it’s probable that, within say 50 years, artificial life forms will achieve consciousness. You can already do a back of the envelope calculation about computers and depending on the numbers you put in and your estimate of how complicated the the human brain is you get estimates like 2025 for the point where the hardware capacity of a computer is likely to equal that of the human brain. The real problem is, of course, getting them to have intuition so they can learn and do things that mimic human beings.”

ALife: Art or Science?

But while Langton and his peers envisioned ALife as a new science, some dismissed it as a computer art project or video game. In 1995, evolutionary biologist John Maynard Smith dismissed ALife as “a fact-free science” as others criticized it for producing flashy simulations that only appeared to mimic biological systems, without providing rigorous, testable insights into real life biology. In mathematics, many leading figures critiqued Langton’s “edge of chaos” theory, warning that the search for a single, unified theory of chaotic systems ironically oversimplified the study of complexity.

Yet, where critics saw toy models and simplistic metaphors, others saw ALife as bringing computer science into new fields. And though we have yet to see flocks of self replicating robots scuttling about, the concepts of evolutionary algorithms and decentralized “swarm” computing have influenced everything from jet engine design (modeling how air and fuel flow through an engine to optimize performance) to financial trading (using genetic algorithms that adapt to evolving market conditions) and logistics (like Amazon using decentralized robots with swarm-like behavior to manage complex warehouse operations).

And while today’s large AI models don’t directly incorporate these principles, ALife research has indirectly influenced how AI developers think about building systems that exhibit complex, intelligent, emergent behavior.

ALife and the Evolution of AI

Looking ahead, ALife could help unlock the next phase in AI’s evolution. Training a large language model (LLM) is a resource-intensive, static process. It requires processing trillions of words and thousands of human labor hours to tune and evaluate the model’s behavior before it is released. And, once deployed, the model’s core capabilities remain frozen until the next major training cycle (hence the months-long gaps between AI model updates.)

However, if AI systems could be decentralized and adaptive, learning continuously in real-time through billions of small interactions (like swarms of Langton ants), then AI could become more like a living organism, improving over time through ongoing experience rather than static training.

In the meantime, ALife itself remains an independent field of study and innovation, with a community that includes academics, game developers, and amateur enthusiasts.

While Chris Langton’s work made him a tech world celebrity for over a decade, regularly appearing in magazines like WIRED and popular science documentaries, he dropped out of the field and stopped publishing new work on ALife rather suddenly in the late 1990s, for reasons that remain unclear. 

Langton was the first to admit that ALife was as much about poetry as scientific rigor, a free ranging exploration of what it means to be alive, be it in organic or digital form.

As Langton recounted: “A reporter asked how I felt about my descendants  living in a world [with artificial life] and I stopped in mid-answer because all of a sudden I realized that I wasn’t sure which descendants he was talking about. Was he talking about my biological descendants or the replicating evolving machines that we create – the ‘children of our mind’?  The ethical questions are going to be very important: to what extent does something that’s alive have rights to its existence and its future evolutionary development, regardless of the specific hardware that it’s alive in?”