{"id":80174,"date":"2026-05-01T00:12:11","date_gmt":"2026-05-01T04:12:11","guid":{"rendered":"https:\/\/world.edu\/?p=80174"},"modified":"2026-05-01T00:12:11","modified_gmt":"2026-05-01T04:12:11","slug":"evolvable-ai-are-we-on-the-brink-of-the-next-major-evolutionary-transition","status":"publish","type":"post","link":"https:\/\/world.edu\/evolvable-ai-are-we-on-the-brink-of-the-next-major-evolutionary-transition\/","title":{"rendered":"Evolvable AI: are we on the brink of the next major evolutionary transition?"},"content":{"rendered":"

\"\"<\/p>\n

What happens when natural selection, the most powerful process driving change in the living world, shapes artificial intelligence (AI), perhaps the most potent technology humanity has invented to date?<\/p>\n

We might be about to find out.<\/p>\n

According to a new\u00a0paper published in Proceedings of the National Academy of Sciences<\/a>, we are entering the era of \u201cevolvable AI\u201d \u2013 AI systems that can undergo evolution. In turn, that might give rise to a major transition in evolution.<\/p>\n

How major is \u201cmajor\u201d? Well, in nearly 4 billion years there have only been\u00a0eight<\/a>, or\u00a0perhaps only seven<\/a>, other major transitions. But we\u2019ll get to that in a moment.<\/p>\n

The ingredients for evolution<\/strong><\/h4>\n

Evolution doesn\u2019t require DNA, cells or even biological life. It just needs information that can replicate, and a source of variation that affects how successfully the information replicates.<\/p>\n

When these conditions exist, evolution happens, whether anybody intended it to or not.<\/p>\n

Modern AI systems already meet these conditions. Models can be copied. Their parameters, architectures and training data can vary. And some variants perform in ways that make them more likely to be reused, refined or deployed.<\/p>\n

Evolution has long operated outside biology. It shapes languages, technologies and cultures. But AI introduces something different: systems that are both information-rich and can influence their own reproduction.<\/p>\n

That combination raises the stakes dramatically.<\/p>\n

Two scenarios for \u2018evolvable AI\u2019<\/strong><\/h4>\n

The authors of the new paper recognise two broad AI evolution scenarios that could influence both how selection happens, and the kinds of consequences that might flow on.<\/p>\n

Ecosystem scenario<\/strong><\/p>\n

The ecosystem scenario eventuates when AI variants compete, recombine and propagate with little top-down oversight. The better an AI is at persisting and spreading, the more successful it is.<\/p>\n

Science fiction<\/a>\u00a0authors,\u00a0AI pioneers<\/a>\u00a0and contemporary\u00a0AI risk experts<\/a>\u00a0have long recognised the dangers of such untrammelled and chaotic Darwinian evolution. The fear of\u00a0self-replicating AIs<\/a>\u00a0is an evolutionary fear, even if it doesn\u2019t name evolution explicitly.<\/p>\n

Every new AI model, however different, inadvertently adds to the supply of the fuel consumed by natural selection: variation. And we\u2019re not dealing with a single AI but an ecosystem bustling with various machines and humans.<\/p>\n

Breeder scenario<\/strong><\/p>\n

Charles Darwin based his idea of\u00a0natural selection<\/a>\u00a0on how animal and plant breeders deliberately select which individuals to breed from. In the wild, nature does the selecting, hence \u201cnatural selection\u201d.<\/p>\n

The second evolvable AI scenario recognises the power of breeder-based selection \u2013 the force that domesticated so many animals and plants, from dogs and cattle to wheat and rice.<\/p>\n

Last year, philosophers Maarten Boudry and Simon Friederich\u00a0proposed<\/a>\u00a0that if AI evolution is directed in a top-down fashion (much like deliberate breeding), AI might remain in human control. Evolution still occurs, but it shapes the AI into tamed beasts of computational burden that serve humanity \u2013 or, at least, whoever owns the machine.<\/p>\n

Within the framework of these two scenarios, the authors apply a sound and comprehensive analysis of what biology can tell us about AI\u2019s potential evolutionary trajectories.<\/p>\n

Evolution upgraded<\/strong><\/h4>\n

In biology, variation comes from random genetic mutations. The potential for evolution is constrained by this blind source of variation.<\/p>\n

AI need not be constrained in the same way. Indeed, the potential exists for AIs to plot the course of their own evolution. They could find the variation they need to follow that route. It may even exist on the internet.<\/p>\n

This is similar to how bacteria\u00a0evolve antibiotic resistance<\/a>\u00a0by copying the genes that other, quite different lineages of bacteria have already evolved. With this\u00a0horizontal gene transfer<\/a>\u00a0there\u2019s no waiting in hope for the right mutations.<\/p>\n

AI could potentially do something similar. The authors of the new paper argue that a large language model could predict what functionality it needs to replicate and survive, and then find and incorporate code to achieve just that.<\/p>\n

The authors recognise that if we maintain breeder-like control over evolvable AI, it will be less likely to pose catastrophic risks, such as dominating humans or outcompeting them for resources.<\/p>\n

But the potential for an evolvable AI to escape and run feral always remains.<\/p>\n

Is it a major transition, though?<\/strong><\/h4>\n

One of the paper\u2019s authors, evolutionary biologist\u00a0E\u00f6rs Szathm\u00e1ry<\/a>, introduced the idea of \u201cmajor transitions in evolution\u201d in a landmark\u00a01995 book<\/a>\u00a0with the late evolutionary theorist\u00a0John Maynard Smith<\/a>.<\/p>\n

For example, ancient life used to involve RNA, a relatively fragile molecule that functioned as both the genetic information and the protein that did the organism\u2019s work.<\/p>\n

A major transition was the\u00a0evolution of DNA<\/a>\u00a0\u2013 it made the information more stable and required the production of proteins as a separate act. This fundamentally changed how genetic information is encoded and used, and made possible great increases in the complexity of living things.<\/p>\n

At each subsequent transition, the thing doing the evolving became more complicated \u2013 from single-celled life to multicelled life and so on.<\/p>\n

The new paper argues that some current trends in AI resemble what happens in major transitions. AI systems are\u00a0scaling up and expanding in complexity<\/a>. New training and development methods reorganise\u00a0how AIs process information<\/a>. And\u00a0AI agent teams working together<\/a>\u00a0are shifting the concept of what a \u201csingle\u201d AI even is.<\/p>\n

It\u2019s certainly interesting that evolution\u00a0within<\/em>\u00a0the AI ecosystem is following trends seen in the major transitions in biological evolution. But these things also happen, on a smaller scale, during business-as-usual evolution. They should not yet be interpreted as evidence that AI represents a major transition fit to be listed with those that transformed biological life.<\/p>\n

There are, however, many ways evolvable AI could effect a major transition in evolution. Generating an entirely new realm of intelligent life would do the trick.<\/p>\n

Another possibility is the rise of\u00a0co-evolving human-machine symbiosis<\/a>, akin to our\u00a0relationship with smartphones<\/a>. That could create a\u00a0new kind of individual<\/a>\u00a0somewhere between biological and artificial life. If such a development took hold, it would definitely constitute a major evolutionary transition.<\/p>\n

Author Bio:<\/strong> Rob Brooks is Scientia Professor of Evolution at UNSW Sydney<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

What happens when natural selection, the most powerful process driving change in the living world, shapes artificial intelligence (AI), perhaps the most potent technology humanity has invented to date? We might be about to find out. 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