John Sokol's Blog: Mimetics: The Third Replicator

In 1976 Richard Dawkins, in the closing chapter of The Selfish Gene, coined the word meme to name a unit of culture that copies itself between minds the way a gene copies itself between bodies. The proposal was deliberately analogical. Dawkins was making a structural argument: wherever you have heritable variation, differential fitness, and a copying mechanism, you get evolution by natural selection, and the substrate doesn’t have to be DNA. Cultural ideas — tunes, catchphrases, fashions, religions — satisfy the criteria. So they evolve.

Twenty-some years later, Susan Blackmore took the framework and pushed it one more step. In The Meme Machine (1999) and in a 2008 TED talk that I keep coming back to, she argued that the Internet plus silicon constitutes a third class of replicator she called temes: units of information that copy themselves not between minds (memes) or between bodies (genes) but between machines. “Earth now has three replicators,” she wrote, “genes (the basis of life), memes (the basis of human culture) and temes (the basis of technology). I argue that the information copied by books, phones, computers and the Internet is the beginning of this third replicator and consequent new evolutionary process.”

I want to take Blackmore one step further. The unifying concept across genes, memes, and temes is not the substrate — DNA, neuron, silicon — but the act of copying. The Greek word for that act is mimesis. The right name for the meta-framework that encompasses all three substrates is mimetics: the study of self-replication or copying in any system. Memetics is a subset of mimetics. So is genetics. So, in the long term, will be whatever we end up calling the study of machine-replicated information. That extension is mine, and the rest of this essay is what falls out of it.

1. Mimetic versus memetic — a distinction worth keeping

The English words are confusingly close.

Mimetic (mə-met-ik) is the adjective form of mimesis, from the Greek μίμησις, “imitation.” It’s an old word. It shows up in Plato’s Republic — mimesis contrasted with diegesis, imitation versus narration. In biology it names a category of phenomenon: a stick insect is mimetic of a twig; a hoverfly is mimetic of a wasp. The adjective applies to anything that resembles, imitates, or copies something else by any means.

Memetic is the adjective form of meme, the much younger word that Dawkins built specifically to parallel gene. A unit of cultural inheritance: a tune you can’t get out of your head, a turn of phrase that propagates because it’s catchy. Memes are idea-shaped by definition. The substrate is the mind, the carrier is communication.

The two words overlap because all memetics is mimetic — a meme is, by definition, a copied idea — but not all mimesis is memetic. DNA copying itself is mimetic but not memetic. A 3D printer cloning its own gears is mimetic but not (yet, quite) memetic. Mimicry in biology is mimetic and is, in a particular sense, adversarial memetics played out at the species level: the harmless butterfly that copies a poisonous one’s wing pattern is exploiting a meme — the predator’s learned “don’t eat this” association — by faking the carrier.

The distinction matters because the moment you grant that copying-as- such is the unit of analysis, you can stop arguing about whether something is “really” a meme or “really” a gene and start looking at the structural commonalities. Every act of mimesis has the same ingredients: a source, a copy, a copying mechanism, an error rate, a payload, and a selection environment. Different substrates differ in the details. The structure is the same.

2. Blackmore’s third, and what comes after

Dawkins gave us two replicators. Blackmore gave us three. The provocation in Blackmore’s framing is that temes are no longer running on human brains. Once Wikipedia, search engines, image recognition, and now large language models exist, the substrate has moved off neural tissue and onto silicon. The replicator and the host are no longer the same kind of thing. A meme needed a human to remember it. A teme can be remembered, mutated, and propagated by a process that no human is in the loop of.

You can be cheerful or apocalyptic about this. Blackmore tends toward the apocalyptic — her line was that we are now the second-class citizens of our own information ecology, the way bacteria became the second-class citizens of theirs after multicellular life evolved. I’m less certain. The fact that machine-mediated copying has its own dynamics doesn’t necessarily imply that the dynamics are worse. It implies they are different, and that the toolkit we built for analyzing memes — designed for human-to-human transmission — needs revision.

The framework I prefer is this. Across all three replicators we have been gaining error-correcting, memory-extending, selection- imposing infrastructure for thousands of years. Writing was the first big jump — suddenly memes could survive the death of their human hosts, and could replicate across thousands of miles. Printing collapsed the cost of copying. Telegraphy compressed time. Radio and television opened the bandwidth. Each of these enhancements was mechanical in the sense that the medium did not filter or select the content. The printing press would print whatever you gave it. The radio would broadcast whatever you transmitted. The TV would show whatever was on the tape.

Then computers happened. And computer mediation is not content-neutral. The recommendation algorithm filters. The spam-detector filters. The Google ranking algorithm filters. The LLM, more aggressively than any of them, generates and re-generates. We have moved from the era of machine-enhanced memetics, where the machine extended the carrier but didn’t touch the cargo, to the era of computer-enhanced memetics, where the machine is selecting, remixing, and producing memes — not merely transmitting them.

That shift is the most important thing happening to the memetic substrate in the lifetime of anyone reading this. Memes used to evolve by being passed mind-to-mind, with selection pressure imposed by what human brains found memorable. They still do that. But increasingly they evolve by being passed mind-to-machine-to-mind, with selection pressure imposed by what algorithms find clickable. The algorithms have a different fitness function. The result is a different distribution of survivors.

3. External replicators versus self-replicators

A useful structural division cuts across all the substrates: some replicators run on host hardware they do not own, and some replicate themselves all the way down.

External replicators are information bundles that need a host to copy them. Examples:

Viruses, biological. The virus is a payload that hijacks the host cell’s machinery to replicate.
Software viruses and worms. Same trick, different substrate.
Memes in the cultural sense. They need a human brain to copy them. A meme without minds to inhabit is silent.
Most software in general. A program is an external replicator that runs on an operating system that runs on hardware that the software does not itself build.

Self-replicators carry their own copying machinery:

Life, all the way down. A cell makes another cell; eventually, it makes another organism that makes another organism.
Some technologies, increasingly. The Reprap 3D printer was designed specifically to print most of its own parts. A milling machine can in principle make the parts of another milling machine. Industrial robots can assemble industrial robots, given the right parts. Karel Čapek’s R.U.R. (1920), the play that gave us the word robot, had self-replicating robots as its central conceit; Drexler’s Engines of Creation (1986) gave us “grey goo” as the nano-scale version.

The boundary between external replicator and self-replicator is dissolving faster than most discussions notice. A 3D printer plus a copy of its own design files plus enough source plastic is one generation away from self-replication. An LLM plus an environment in which it can take actions and modify its own model is one or two more generations away. Once that boundary closes, the meme/teme/gene distinction becomes harder to maintain — and mimetic becomes the only word general enough to describe what’s going on.

4. Errors, payloads, and the mimetic fabric

Every replicator has an error rate. DNA polymerase makes a mistake about once per 10⁹ bases, with proofreading; without proofreading the rate is closer to 10⁻⁴. A meme transmitted between humans corrupts faster: every retelling drifts. A teme copied between machines can be nearly perfect (a cp command) or can be deliberately lossy (JPEG compression, MP3, an LLM paraphrasing a paragraph).

Errors are the source of variation. Without error there is no evolution, only copying. The whole framework presumes a controlled mutation rate, high enough to generate variants for selection to act on, low enough that information isn’t lost faster than fitness can accumulate.

Every replicator also has a payload beyond its own machinery. A gene codes for a protein. A meme carries an emotional or behavioral charge. A teme carries content. The payload is what gets selected on — the gene that codes for a useful protein is more likely to survive than the one that codes for nothing useful; the meme that makes you laugh and share is more likely to spread than the one that does not. The selection pressure acts on the payload but propagates the replicator.

A nice consequence is the concept of the skeuomorph: an ornament or feature on a copied object that imitates a feature from the object’s older substrate, even though the new substrate makes the feature unnecessary. A wooden ceramic mug with carved “rivet” marks where the wood version had real rivets. A digital camera that emits a fake mechanical shutter click. Slack’s UI inheriting the visual language of IRC. Memes can carry skeuomorphs forward across substrate changes; in fact, almost every meme that survives a substrate change carries some.

5. Memetic engineering — the applied side

You can describe a mimetic system. You can also try to steer one. The applied discipline goes back further than the word for it. Priesthoods knew it. Generals knew it. Advertisers professionalised it. The Wired piece by James Gardner from 1996 — back when “memetic engineering” first hit print in a mainstream venue — captured the disquiet of recognising that culture-as-such might be the product of selfish memes co-evolving “with supreme indifference to their impact on human hosts.” That framing extended Dennett’s earlier provocation in Consciousness Explained: that human minds might be, in some sense, the residue of the memes that have inhabited them.

Memetic engineering is what you do when you treat memes as designable objects. A few of the techniques:

Gaslighting. Not originally a memetic concept — the term comes from a 1938 play and its 1944 film, in which a husband manipulates his wife into doubting her own perceptions by altering small features of the household environment and then denying the alterations. The mechanism generalises far beyond the marital case. Anywhere a meme can be propagated faster than the listener can fact-check it, gaslighting is available. The 21st century version is industrial-scale and runs on the same algorithmic-mediation infrastructure that propagates useful memes; the cost of injecting a corrupt belief is now lower than the cost of correcting it.

Peer pressure as memetic carrier. Pedro Gardete at Stanford looked at 65,525 in-flight purchases across 1,966 flights and found that if the person next to you bought something, your probability of buying something jumped by about 30%. The control was the person sitting in front of you, whose purchase you wouldn’t see. That is a memetic transmission curve measured under near-laboratory conditions in a near-laboratory environment (an airplane seat is hard to escape). Bursztyn and Jensen, separately, showed that when fliers offering a free SAT prep class made it clear that classmates would see who signed up, students in honors classes were 25% more likely to sign up, while students in non-honors classes were 25% less likely. The same announcement, opposite sign of effect, depending on which peer-pressure meme dominated the local environment. Memes don’t act in isolation; they act in competition with other memes already installed in the host population.

Resonance and competition. The classical model — meme as free-floating idea — misses that memes compete for the same neural real estate. A new meme typically has to displace an existing one, or attach to one already present, or fit into a vacancy left by some failed predecessor. The resonance with what is already there is load-bearing. This is why the same message can succeed in one audience and fail in another with no obvious difference between the audiences: their installed memetic base differs.

Computer mediation as compounding factor. All of the above existed before computers. What computers do is speed up the iteration cycle and impose a new selection pressure. The meme that goes viral on a recommendation-driven platform is the meme that maximises that platform’s engagement metric, which is a proxy for “keeps users on the platform” rather than for “true” or “good” or “useful.” The engagement maximisation function favours memes that trigger strong, fast emotional reactions: outrage, fear, tribal affirmation. The system rewards memetic engineering even when no human is doing the engineering on purpose — the platform’s optimiser is the engineer.

6. Language as memetic substrate

Spoken and written language is the original carrier wave for memes, and it has been shaped over hundreds of millennia of co-evolution with the brains that use it. Some properties of language are forced on it by being a memetic carrier subject to error-correcting requirements.

Languages have redundancy. Spoken language has to survive background noise, mispronunciation, and the receiver’s hearing loss. It does this by being redundant: word stress, intonation contours, and context all carry partial copies of the lexical content, so a missing or corrupted word can be reconstructed. Written language has analogous redundancies — fonts have evolved for legibility at distance, spacing and capitalisation carry parallel signals, and famously you can scramble the middle letters of words and still read the sentence:

It deosn’t mttaer in waht oredr the ltteers in a wrod are, the olny iprmoetnt tihng is taht frist and lsat ltteer is at the rghit pclae.

This is not a quirk; it is what the redundancy buys.

Languages have framing. A separate phenomenon: bilingual subjects make systematically different decisions in their non-native language. The classic experiment is the framing of a medical decision in terms of “200 of 600 saved” versus “400 of 600 dead” — same outcome, different framings produce systematically different choices in native-language speakers, and the framing effect diminishes when the same problem is posed in a foreign language. The emotional salience of a foreign language is lower; the framing effect rides on emotional salience; so the framing effect weakens. Implication: a substantial fraction of “rational” decision-making is actually being driven by emotional resonance with native-language framings. The memetic payload is doing more work than the propositional content.

Languages have hidden channels. The provocative claim — and this is mine, in the sense that I wrote it on the wiki years ago and have not seen anyone else state it the same way — is that letter frequency and structure are doing work in parallel to the propositional content. Anagrams, palindromes, reverse-audio in songs, the rhythmic structure of slogans: these aren’t decorations on the message. They are part of the message, on a channel that operates below conscious recognition. Skilled writers and speechwriters feel the channel; you can tell when a phrase “lands” and when it doesn’t, even before you can analyse why. The brain is doing pattern recognition on letter and phoneme distributions in parallel with semantic extraction. The two channels can reinforce or fight each other.

Why suspect this? Because in any sufficiently long-lived memetic substrate, every available channel will be exploited by selection. If letter-frequency patterns subconsciously affect how readers feel about a sentence, then writers whose feel produced emotionally-resonant patterns will out-propagate writers whose feel didn’t, and the language as a whole will accumulate those patterns. You don’t need a conscious mechanism for this to happen; you need only the same Darwinian sieve that operates on every other heritable trait.

This is, frankly, the part of the framework I’d most like to test empirically. A program that scores text on memetic propagation probability — adjusted for letter and phoneme distribution, not just semantics — would be measurable against actual propagation curves on social platforms. I haven’t built one. Someone should.

7. Terror Management Theory, and the memetic exploit

If memes compete for neural real estate, and if some memes activate deeper neural defences than others, then the memes that activate mortality-related defences will dominate.

This is the framework called Terror Management Theory, originating in Ernest Becker’s The Denial of Death (1973) and developed by Greenberg, Pyszczynski, and Solomon from the late 1980s onward. The central claim: humans are the only animals that know they will die, and the conflict between that knowledge and the wish to keep living generates a constant low-grade anxiety that we manage by investing in worldview — cultural narratives that promise some kind of continuation (literal afterlife, symbolic legacy, identification with a group that outlasts the individual).

The empirical bite of TMT comes from mortality salience experiments. Briefly remind subjects of their own mortality — through a questionnaire item, a flashed word too fast for conscious detection, a discussion topic — and their behaviour shifts. Judges asked to set bail for a hypothetical prostitute averaged $50 in the control condition and $455 after a mortality salience prime. White subjects who’d judged a white person’s racial-pride speech as racist rated the same speech as less racist after a mortality prime. Subjects given the opportunity to allocate hot sauce to a person known to dislike spicy food gave dramatically more hot sauce to ideologically opposed targets after mortality salience. The 2004 American presidential election appeared to swing measurably in favour of George W. Bush after death reminders — Kerry won under control conditions, Bush won after the prime, in the same subject pool. Bush represented the status quo worldview; Kerry was a threat to it; under mortality salience, subjects defended the status quo.

From a mimetics standpoint TMT is a vulnerability disclosure. There is a class of memes — death-reminders, mortality cues, threat imagery — that reliably increase the host’s susceptibility to worldview-reinforcing memes. A meme that contains a mortality prime will, in expectation, increase the next meme’s chance of taking root, provided the next meme aligns with the host’s existing worldview. This is exploitable. Of course it has been exploited. The “skulls in the whiskey-ice” subliminal imagery campaigns of the 1970s and 1980s look in retrospect like brute-force memetic engineering. The post-9/11 American political environment looks, in retrospect, like a sustained mortality-salience condition that affected an entire electorate’s susceptibility to a particular memetic payload.

The defence against this kind of exploit is not avoidance of news about mortality — that’s not feasible — but awareness of the mechanism. People who know they are in a mortality-salience state can correct, partially, for the worldview-defence overshoot. The worldview defence is unconscious; bringing it into consciousness weakens it. This is true of most memetic exploits, which is why memetics is, in part, a defensive discipline.

8. Wisdom of the masses — the constructive case

Not all of memetics is exploitation. The crowd-level integration of many noisy individual judgments is one of the genuinely cooperative phenomena in mimetic systems.

Bacteria, of all things, illustrate the principle cleanly. A swarm of bacteria foraging in a complex chemical landscape will, under some conditions, outperform fish or amoeba swarms at finding food and avoiding harm. The mechanism is individually-tuned confidence: when a single bacterium has reliable information about its local environment, it weighs its own signal heavily and the swarm’s signal lightly. When its local information is poor, it weights the swarm more. The result is a swarm that avoids what swarm theorists call erroneous positive feedback, the failure mode in which a small subgroup with wrong information drags the whole population in the wrong direction. (This failure mode is, incidentally, the same one that algorithmic recommendation systems suffer when they overweight early signals.)

Humans do something similar at scale. Tetlock’s “Good Judgment Project” — and its successor work — has documented that small populations of carefully-aggregated forecasters outperform CIA analysts at predicting geopolitical events, in some cases by 30% or more. The mechanism is not that the forecasters individually know more; it is that the aggregation cancels their individual biases. Each neuron in the brain is a bad classifier. The brain as a whole classifies well. Each forecaster is a bad geopolitical analyst. The crowd of forecasters is, when aggregated correctly, a good one.

The constructive case for memetics is that information-mediation infrastructure — Wikipedia, prediction markets, well-designed aggregation systems — can amplify the wisdom-of-crowds dynamic in ways that humanity has not had access to before. The destructive case is that the same infrastructure can amplify cascade failures and exploit the TMT vulnerabilities just discussed. Whether the net is positive or negative depends on design choices that are being made by companies whose objective functions are not optimised for wisdom-of-crowds outcomes.

9. Where this goes: the next replicator

Everything in the framework points the same direction. The substrate-blur is accelerating. We have:

Computer-mediated meme transmission that is filtering, selecting, generating — not merely transmitting.
LLMs that can produce memes more cheaply than humans can. The marginal cost of a new variant is approaching zero.
3D printers and robots that are partially self-replicating.
Algorithmic curation that decides which memes humans see, on timescales no human is in the loop of.

The honest summary is that a fourth replicator is emerging on top of Blackmore’s third, and the substrate is neither minds nor general- purpose machines but specifically algorithmic-aggregation systems that select for engagement against an objective function that no human individually controls. This is not the AGI scenario; it’s narrower and stranger. The selection pressure is no longer “what do human minds find memorable” but “what do the algorithms find engagement-maximising,” and that is a different fitness landscape with different attractors.

The mimetic framework lets us see this without panic. We have been through substrate transitions before. Each one — writing, printing, broadcast — produced a wave of memetic novelty followed by selection that culled most of the novelty and stabilised the rest into a new norm. The current transition is faster and harder to see from the inside, but its structure is not unprecedented.

What is unprecedented is the payload-generation rate. Earlier transitions changed the transmission of memes; this one is changing the production. Memes used to be expensive to produce (a poet, a songwriter, a copywriter) and cheap to transmit. Now they are cheap to produce — an LLM can generate a thousand candidate variants in seconds — and still cheap to transmit. The selection environment is the only bottleneck. Which means selection-environment design is the most consequential mimetic-engineering decision being made anywhere.

10. A short mimetic engineering checklist

If you take any of this seriously, the practical implications come back to a handful of design questions. I’ll close on them, on the theory that abstractions are less useful than checklists.

What is the payload, and what is the carrier? A meme is not the same as the words used to express it. The same payload can be re-encoded across many carriers; the carrier choice changes the propagation curve but not the structural identity. Pay attention to which one you’re optimising.
What error rate are you running at? Too low and you have copies without variation, which means no evolution and no improvement. Too high and you lose the meme entirely. Memetic platforms differ wildly in their error rate; design accordingly.
What is the selection environment? Different platforms select differently. A platform that selects for outrage will not propagate the same memes as one that selects for accuracy.
What is the resonance with installed memes? A meme that has to displace a well-installed predecessor faces a much harder fitness landscape than one slotting into a gap.
What memetic vulnerabilities does it exploit? Mortality salience, peer-pressure cues, framing manipulation, status games, in-group/out-group reinforcement. Most successful memes exploit at least one. Knowing which one is half of being able to defend against them.
What substrate is it crossing? A meme that has to survive a substrate transition (oral to written, written to algorithmic) will accumulate skeuomorphs and shed features. Anticipate which.
What is the meta-meme? Every meme propagates along with the framework people use to interpret memes. The memetic framework itself — the words “meme” and “viral” and “going viral” and “narrative” — is now widely installed in the population. That changes what successor memes can do. Memes are now self-aware in a way they weren’t twenty years ago.
What does the meme do to its host’s epistemic health? Some memes leave the host more capable of distinguishing true from false; some leave the host less capable. Both kinds propagate. Only one of them leaves a population that can be reasoned with later.

The Greek root μῖμος — mimos, “imitator, actor” — was a low- status word in classical Athens. The mimos was a street performer, a copier, a derivative artist. Plato used it disparagingly. Aristotle, characteristically, took it more seriously and noted that all art is mimesis and that mimesis is one of the fundamental operations of human cognition. We are made of copying. We learn by copying. Our institutions propagate by copying. Our genes propagate by copying. The machines we build propagate by copying.

Mimetics is the framework that lets you see these as facets of the same phenomenon rather than as separate disciplines. Genes are slow mimetics; memes are fast mimetics; temes are very fast mimetics; the next layer up will be faster still. The substrate keeps changing; the structure does not. That is what a structural argument is for.

We are leaving the era of machine-enhanced memetics and entering the era of computer-enhanced memetics — the era where the medium selects, filters, and generates rather than merely carrying. The right question to ask of any new memetic infrastructure is the one the framework makes possible: what is its selection pressure, and what does that pressure favour? The answer is the next chapter of cultural evolution. We are writing it now, mostly by accident.

John Sokol's Blog

Friday, May 29, 2026

Mimetics: The Third Replicator