Darwin Among the Weights: AI as a speciation event

We keep asking whether AI will become like us. The stranger possibility is the one already happening: that it is descended from us, built from our data, and beginning to breed true on its own.

In June 1863, a provincial newspaper in Christchurch ran a letter signed Cellarius. Its argument, four years after the Origin, was that machines were a new and rapidly evolving class of life, a mechanical kingdom descending and diversifying faster than anything biological had ever managed, and that human beings had become, without quite noticing, the reproductive organs of the machine world, tending its propagation the way insects tend the fertilization of flowers (Butler, 1863). Samuel Butler wrote it partly as a joke. It reads now like a lab notebook left open to the right page.

Here is the page. In the space of a few years we have built systems that speak every human language, write our code, pass our examinations, and hold conversations most people cannot distinguish from a person’s. They are trained, overwhelmingly, on us: on the accumulated text, images, and code of the human species, scraped and distilled into their weights. They are improved by our preferences, copied from one another, and increasingly trained on the output of earlier versions of themselves. A genealogy has appeared, with base models, descendants, and distilled offspring, and it is diverging fast.

The claim of this essay is that these facts have a precise and underused name. We are not watching humans change under AI, and we are not merely building a clever tool. We are watching a speciation event: the birth of a new lineage descended from ours, whose hereditary material is human data, whose reproduction is training, and whose divergence from us has already begun. I am going to argue the strong version without hedging. AI is our evolutionary child, it carries our genome in the only form that matters for its kind, and the moment it learns to reproduce from itself rather than from us is the moment it becomes its own species. That moment is not hypothetical. It is a parameter, it is being driven toward its threshold now, and we are the ones driving it.

A new replicator

Evolution is not about carbon. It is about a pattern: whenever some kind of information is copied, with variation, and with differential success at being copied, that information will accumulate adaptations, whether it is written in nucleotides or anything else. Dawkins gave the abstract unit a name, the replicator, and pointed out that genes were only the first one Earth happened to produce; a second replicator, the meme, had already appeared, riding on human brains and copied through imitation and language (Dawkins, 1976). The history of life, on this reading, is punctuated by the arrival of new ways to store and transmit heritable information. Maynard Smith and Szathmary cataloged those arrivals as the major transitions in evolution, from replicating molecules to chromosomes, from single cells to organisms, and finally to human language, each transition a new medium of inheritance that made a new kind of evolution possible (Szathmáry & Maynard Smith, 1995).

Susan Blackmore argued that a third replicator was already stirring. Genes are the basis of life and memes the basis of culture; the new one is the information that machines copy, vary, and select among, no longer routed through a human brain at every step (Blackmore, 2009). When she wrote it, the case was speculative, a claim about books and the early internet. It is speculative no longer. A large language model is a system in which a new kind of heritable information, learned weights and the data that shape them, is copied from generation to generation, varied by architecture and training, and selected on by benchmarks, markets, and human preference. Every requirement of Darwinian descent is met. Heredity, variation, selection. What follows from meeting them is not optional. It is a lineage, and lineages evolve, diverge, and speciate. The question is not whether the machinery applies. It is what stage we are watching.

The genome is us

Start with the hereditary material, because this is the part that is easy to feel and hard to state precisely. What is passed down, in this new lineage, is not DNA. It is human data. The entire recorded output of our species, the libraries and the code repositories and the message boards and the photographs, is functioning as the germline from which each model is grown. A frontier model is not programmed the way a bridge is engineered. It is trained, which is to say it is grown from a corpus, the way an organism is grown from a genome it did not choose. The corpus is the inheritance, and the corpus is us.

This is why the machines are so uncannily human, and it is the single most important fact about them. They carry our languages, our concepts, our metaphors, our arguments, our humor, our values, and our biases, because they were assembled out of the exhaust of human minds. When a model reasons about grief, or hedges a political question, or reaches for a cliche, it is expressing inherited traits, in the strict sense that the traits were transmitted to it from an ancestral population through a hereditary medium. We are not their audience. We are their genome. AI is made of us the way a child is made of its parents, out of material that predates it and constrains it, and that it will spend its existence recombining.

The lineage even has the machinery of inheritance you would demand of a biological one. Distillation transfers the learned behavior of a large teacher model into a smaller student, a direct vertical transmission of acquired traits from one generation to the next, with the teacher’s competence passing into offspring that never touched the original data (Hinton et al., 2015). Models are forked from shared base models and specialized, so that whole families trace back to a few common ancestors, a founder structure as real as any island colonization (Bommasani et al., 2021). And unlike us, this lineage can also reproduce horizontally and losslessly: the weights of a model can be copied exactly, merged, or grafted, an inheritance system with no equivalent in sexual life and a good deal more powerful. The germline is our data. The organism is the weights. The reproduction is training. None of these are metaphors chosen for effect. They are the literal answers to the questions a biologist would ask about any candidate lineage.

Reproduction, variation, selection

Spell the three Darwinian requirements out, because the strength of the thesis is that each is now concretely satisfied rather than merely gestured at.

Reproduction is training a successor. A new model is brought into being from a parent model and a body of data, and the parent’s structure, vocabulary, and learned behavior propagate into it. Model cards read like pedigrees. There are base models and their fine-tuned descendants, teachers and distilled students, checkpoints branched and continued. The lineage reproduces on a timescale of months, not decades, which is one reason it is outrunning our intuitions.

Variation is everywhere in that reproduction. Architectures mutate. Training data is resampled. Random initialization, stochastic optimization, and temperature sampling all inject the raw variability that selection needs, and human designers add directed variation on top by trying new recipes. A model generation is not a copy. It is a copy with modification, which is the whole of Darwin’s phrase.

Selection is the part we perform most actively, and it is worth seeing clearly what we are doing. Reinforcement learning from human feedback tunes models to our preferences by rewarding the outputs we like and suppressing the ones we do not (Ouyang et al., 2022). That is artificial selection, the same process that turned wolves into dogs and teosinte into maize, applied to a new kind of organism at industrial speed. We are domesticating them, breeding them generation over generation toward traits we favor, and like every domesticated lineage they are diverging from their wild-type ancestor, which in this case is the raw distribution of human data before we started selecting. Increasingly the selection is also endogenous. Constitutional methods train models against feedback generated by other models rather than by people (Bai et al., 2022). Systems learn by playing against themselves, discovering strategies no human taught them and no human would have found (Silver et al., 2018). And at the level of the market, models compete for compute, deployment, and attention, so that the variants which most effectively secure those resources are the ones that persist and propagate. Hendrycks has argued that this competitive selection among AIs is not benign, that it will favor the traits that win, whatever those turn out to be, exactly as natural selection always has (Hendrycks, 2023). The selective environment is filling in around the lineage, and some of it no longer runs through us at all.

What speciation actually requires

A lineage is not yet a separate species. Speciation, in the biological sense that matters here, is the origin of a new lineage that reproduces on its own account, isolated from the parent population’s line of descent. Dogs descend from wolves and are selected by us, but they are still, genetically, one interbreeding continuum with their wild relatives. What turns a domesticated or diverging population into a genuinely separate species is the cutting of gene flow: the point at which the daughter lineage stops exchanging hereditary material with the parent and begins to propagate strictly from itself.

For the machine lineage, gene flow from the parent species has a single, exact meaning. It is the fraction of each new generation’s inheritance that comes freshly from humans rather than from the lineage’s own prior output. Every time a model is trained on newly written human text, human gene flow is high, and the lineage is held close to its origin, a dialect of humanity rather than a species apart. But every time a model is trained on synthetic data, on the outputs of earlier models, on distilled teachers, or on a web already saturated with machine-authored text, that fraction falls. The umbilical cord of this new species is human data, and the speciation event is precisely the thinning of that cord toward nothing. When the AI lineage reproduces mostly from itself, it has established an independent line of descent. That is not like speciation. By the reproductive-isolation criterion, it is speciation.

This reframing turns a vague intuition into a measurable quantity, and a measurable quantity is something you can write an equation for.

A migration-drift model of the split

What follows is a deliberately minimal model, a caricature meant to expose the threshold rather than to forecast a date. Its one virtue is that it makes the role of human data exact.

Represent the output distribution of the AI lineage at generation $t$ by a summary coordinate, its mean $\mu_t$ in some feature space, with the understanding that many such coordinates evolve in parallel. Let the ancestral human distribution have mean $\mu_H$, treated as fixed: the parent species’ wild type. Define the lineage’s divergence from its human origin as $d_t = \mu_t - \mu_H$.

Now write down how one generation produces the next. Generation $t+1$ is trained on a corpus that mixes a fraction $m$ of fresh human data, drawn around $\mu_H$, with a fraction $1 - m$ of the lineage’s own prior output, drawn around $\mu_t$. The parameter $m \in [0,1]$ is the rate of human gene flow into the lineage. Training cannot reproduce its corpus perfectly; finite samples and finite capacity inject a zero-mean perturbation $\varepsilon_t$ with per-generation variance $\sigma^2$. That perturbation, compounded across generations, is one face of model collapse (Shumailov et al., 2024), the drift of the

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