The Accelerant

Essay · society · September 2026

Fifteen years of argument about social media have produced a long list of harms to individuals and a short, contested pile of evidence for most of them. The list is the wrong place to look. What the platforms changed most reliably is not any one person but the things a society runs on: shared attention, trust, the people in the middle, the price of status, the enforcement of norms, the way facts are settled and the way children are raised. Most of those were already weakening. The platforms took the friction out. Generative AI is now taking out the last of it, and adding something new: you can no longer tell who is speaking.

Reading time ≈ 32 min Putnam 2000 → Vosoughi 2018 → Meta 2023 → Salvi 2025 A standalone essay · see also The Great Inversion and Conspiracy Theories as Epistemic Systems

Two questions that look like one

Ask what social media has done to people and you get a list. Shorter attention, worse sleep, anxious teenagers, bullied children, gambling one tap away, a population that cannot read a chapter without checking something. Ask what it has done to society and most people give you the same list, summed. That is the mistake this essay is about. A society is not a pile of individuals, and the things that hold it together are not properties of any one of them. Trust is a relation, not a mood. A norm is enforced by people who are not the person breaking it. A shared fact is shared. The effects on those things can be large while the effect on any given person is small, and the evidence, when it is read carefully, says that this is roughly what has happened.

The individual harms are real, and the first part of this essay goes through them, with the caveats the literature actually carries. But the argument is that the list undersells the case. The strongest and best-documented effects of the platforms are on structures, not on psyches: on the mechanism that made a country look at the same thing at the same time, on the intermediaries who used to filter what reached it, on the way status was priced and the way shame was administered. Those structures were already eroding before the first feed was ranked. The platforms did not cause the erosion. They removed the friction that was slowing it down, and they attached a business model to its continuing. That is the meaning of the word in the title. An accelerant does not start the fire. It decides how fast the house goes.

The last part asks whether generative AI is a second accelerant, and concludes that it is, on most of the same trends, with one difference of kind. Social media made it possible for everyone to speak and nobody to filter. AI makes it impossible to be sure that the speaker is a person. That is a different problem for a society, because almost every informal way people have of deciding whom to trust assumes there is someone there who pays a cost for lying.

The individual

What the feed does to one person

Start with attention, because it is the harm people feel most directly. Gloria Mark, who has measured screen behaviour in offices since the early 2000s, found that the average time spent on one screen before switching fell from about two and a half minutes in 2004 to about 47 seconds by the end of the following decade.[1] That is a measurement of behaviour, not of capacity, and it is worth being careful about the difference. Whether people have become unable to concentrate, or have simply stopped practising it because a more rewarding option is always in the pocket, is not settled by any study, and the widely repeated claim that the human attention span has fallen below a goldfish’s was never traceable to a source. What is settled is that the feed is engineered to reward switching. Variable rewards, infinite scroll, autoplay and the red badge are borrowed from slot-machine design, and the designers have said so.[2] Most users, asked, say they use the apps more than they want to. In one careful experiment, Americans paid to deactivate Facebook for the four weeks before the 2018 midterms reported more time with friends and family, less political polarisation, a small but measurable improvement in well-being, and, afterwards, a lasting reduction in how much they used it. They also valued access at over a thousand dollars a year, which is the paradox in one number: a product people would pay a great deal to keep and are better off without.[3] A later study sharpened the paradox further. Asked what they would pay to have TikTok or Instagram deleted for everyone, not just themselves, a majority of students named a positive sum. They are on the platform because everyone else is, and would prefer a world in which nobody was. The economists called it a collective trap.[4]

Sleep is the most solidly evidenced pathway to everything else. A meta-analysis of twenty studies covering more than 125,000 children found that having a portable device in the bedroom at night, even unused, was associated with shorter sleep and daytime tiredness; using it roughly doubled the odds of inadequate sleep.[5] Sleep loss is a known cause of anxiety and low mood in adolescents, so a device that keeps a fourteen-year-old awake until two is doing harm by a route that requires no theory of content at all.

Then the harm everyone argues about. From about 2012, in the United States and then in Britain, Canada, Australia and the Nordic countries, rates of anxiety, depression, self-harm and psychiatric emergency admissions among adolescents rose sharply, most steeply among girls. Jonathan Haidt and Jean Twenge attribute the rise to the arrival of the smartphone and the move of adolescent social life onto the platforms.[6] Candice Odgers, Amy Orben and Andrew Przybylski, who have spent the same years on the same data, reply that the correlations in the large datasets are small, that the direction of causation is unclear, and that the rise has other candidate causes.[7] Orben and Przybylski’s much-cited analysis of three large cohorts found that the association between technology use and adolescent well-being was about the same size as the association with eating potatoes. Both camps agree that something happened around 2012. The question is how much of it the phones did.

The best evidence sits between the camps. A natural experiment used the staggered rollout of Facebook across American colleges in 2004 to 2006, before smartphones, and found that its arrival on a campus worsened students’ mental health, with the effect concentrated in students most prone to comparing themselves with peers.[8] The UK Millennium Cohort found that fourteen-year-old girls who used social media for more than five hours a day had depressive symptoms at roughly twice the rate of light users, with sleep, harassment and body image explaining most of the gap.[9] And Meta’s own researchers, in documents made public in 2021, found that Instagram worsened body image for about a third of teenage girls who already felt bad about their bodies, and that the platform’s comparison culture was “the ubiquitous underlying cause”.[10] The honest reading is that the effect on the average adolescent is modest and the effect on a vulnerable minority is not, and that a minority of a whole generation is a lot of people.

The rest of the list for children is less argued about, because the mechanisms are plain. Bullying used to stop at the school gate; it now follows the child home, runs through the night, is visible to the entire peer group and leaves a permanent record, and the victim cannot escape it by changing rooms. Sextortion, in which a stranger obtains an intimate image and then demands money, moved from a rare crime to an industrial one: reports to the American clearing house more than doubled between 2022 and 2023, the victims were overwhelmingly teenage boys, and the FBI linked the scheme to at least twenty suicides in eighteen months.[11] The average age at which children in England first see pornography is thirteen; a tenth have seen it by nine; most of the exposure is unsolicited and arrives through the same platforms and messaging apps they use for everything else.[12] About a quarter of British eleven-to-seventeen-year-olds report spending their own money on some form of gambling in the past year, and the loot boxes, skin markets and crypto casinos that reach them are advertised in the feed, promoted by the streamers they watch, and stopped by age checks that a child can defeat by typing a different year.[13] Recommendation systems have been shown, repeatedly and in the platforms’ own audits, to push self-harm, pro-anorexia and extremist material at accounts that show a flicker of interest, because that material retains.

The effect on the average adolescent is modest and the effect on a vulnerable minority is not, and a minority of a whole generation is a lot of people.the honest reading of the mental-health literature

None of this is nothing. But look at the shape of the evidence. For attention and mental health, the effects are contested and, where measured, small on average. For sleep, comparison and the specific harms to children, the effects are clear but bounded to particular people. If the list were the whole story, the fair summary would be: a product that is mildly bad for most people and seriously bad for some, in the way that alcohol is. Societies live with such products. The reason the argument feels larger than that is that the list is not the whole story.

The society

What a society runs on

Take a step back from the person and ask what a large group of strangers needs in order to function as one thing. Not a complete answer; a working list. It needs a way of looking at the same thing at the same time. It needs trust, in institutions and in each other, that is roughly proportionate to how trustworthy they are. It needs people in the middle who filter and slow what reaches everyone, and who confer legitimacy by their attention. It needs a status economy that is bounded enough for most people to have a place in it. It needs time that is spent on each other. It needs norms, and a way of enforcing them that is proportionate. It needs an agreed method for settling what is true. And it needs a way of raising children into all of the above. Go through the list and ask what the platforms did to each.

Shared attention. Benedict Anderson pointed out that a nation is, among other things, a group of people who read the same newspaper on the same morning and know that everyone else is reading it too.[14] Broadcast media did this crudely. The evening news was watched by a third of a country at once; the front page was the same front page. The platforms replaced one collective attention with millions of private ones, each assembled by a ranking system for one person, and then re-aggregated attention around whatever was spiking. The result is a public that is fragmented most of the time and prone to stampedes the rest of it, and neither state is one in which deliberation happens. Figure 1 is the schematic. The left panel is the broadcast public, one source and one audience. The middle panel is the ranked one: dense small groups, a ranker above them deciding what each group sees, and between the groups a few bridges, which tend to carry the worst of each side to the other. The third panel is where the last section of this essay goes.

Fig. 1 — Three shapes of a public. Left, a broadcast public: one source, one audience, everyone sees the same thing. Centre, a ranked public: dense clusters each fed by a ranking system, with a few bridges between clusters carrying mostly the material each side finds most objectionable in the other. Right, the same public once some of the speakers (hollow nodes) are not people. Schematic; not data.

Trust. Trust in government, the press, science and each other has fallen across most rich democracies over half a century, and most of the fall predates the platforms. What the platforms added is a permanent, searchable, globally distributed supply of every institution’s worst moment. A hospital that treats a million patients well and one badly used to be judged, locally, by its record; it is now judged, everywhere, by the one. No institution survives being judged by its outliers, because every institution has them. Trust did not collapse because people learned the truth about institutions. It collapsed because the failure rate became visible and the base rate did not, and a feed that ranks by engagement will always show the failure and never the base rate, since nobody shares a story about a train that arrived.

The people in the middle. Editors, party officials, clergy, union stewards, local notables, the referees at journals. Between the individual and the public there used to be a layer of people whose job was to decide what deserved attention, to slow things down, and to lend or withhold legitimacy. They did it badly and often in their own interest, and it is fair to say that many of them deserved to lose the job. The platforms routed around all of them at once. The gain is that a gatekeeper can no longer suppress a true story, and the last fifteen years are full of true stories that would not have surfaced under the old arrangement. The loss is that the filtering job did not go to anyone else. Clay Shirky said in 2008 that the problem was not information overload but filter failure, and he meant it as a design brief.[15] The brief was taken up by ranking systems that filter for a different thing entirely. The vacancy where the editor used to sit is occupied by an optimiser for time on site.

Status. Every society runs a status economy, and in most of human history it was local. You were known to a few hundred people, judged by them, and the judgement was reciprocal: they were known to you. The platforms made status legible, numeric, global and updated in real time. Followers, likes, views, shares. This pulled the competition out of the bounded community, where almost everyone can be good at something and the losers are still your neighbours, and into a winner-take-all tournament with a very long tail and no floor. Most people lose a game they cannot stop watching, and the Millennium Cohort finding that comparison explains much of the girls’ depression is what that looks like at the individual scale.[9] At the social scale it looks like the collapse of the middle: the disappearance of the local band, the local paper, the local shop, each replaced by a national or global one that a ranking found for you.

Time. Adults in rich countries spend two to three hours a day on the platforms, teenagers more; nearly half of American teenagers say they are online almost constantly.[16] Whatever those hours were doing before, they are not doing now, and the displaced activities are disproportionately the ones that build what Robert Putnam called social capital: clubs, congregations, dinners, the pavement conversation with the neighbour, the unstructured play in which children learn to run a game without an adult. Putnam documented the decline of all of these in 2000, and blamed television, commuting and the two-earner household.[17] The platforms inherited a trend and steepened it. Time with friends in person has fallen steadily among adolescents since 2010, and loneliness among fifteen-year-olds rose in 36 of 37 countries surveyed between 2012 and 2018, a pattern that no purely national explanation fits.[18]

Norms. A norm is enforced by being observed within a bounded group by people who will see you again. The platforms made the group unbounded and the observation permanent, and two things followed. Enforcement became savage, because strangers punish without the restraint that comes from having to share a street with you afterwards, and because the ranker rewards the most indignant response. And the norms themselves fragmented, because there is no longer one audience to be judged by; anyone can find a group that approves of anything and be told, by the ranker, that it is the mainstream. A society can survive strict norms and it can survive loose ones. It has trouble with norms that are simultaneously unbounded in reach and unbounded in variety, where the same act is punishable by exile in one room and applauded in the next.

How facts are settled. Every functioning society has a rough agreement about how to know things: who counts as an expert, what counts as evidence, which sources have earned the benefit of the doubt. The agreement is never complete and never fair, but it exists, and it is what lets a person believe something they have not checked. The platforms flattened every source to the same rectangle on the same screen, ranked by engagement. A peer-reviewed study and a confident stranger look identical, and the stranger is usually more engaging. The largest study of the spread of news on Twitter, covering 126,000 stories over eleven years, found that false stories reached 1,500 people about six times faster than true ones and travelled deeper into the network, and that humans, not bots, did most of the spreading.[19] The consequence is not that people believe everything. It is that they believe nothing from outside a trust network they have assembled themselves, and the network was assembled by a system that rewards agreement. An earlier essay here argued that conspiracy theories are best understood as closed epistemic systems rather than as false beliefs.[20] The platforms are a machine for building such systems at scale, for everyone, about everything.

Childhood. Every previous generation of humans was socialised principally by adults and by unsupervised play with other children in physical space. The current one was socialised, for a substantial share of its waking hours, by a feed tuned by companies for retention. This is the one item on the list with no precedent at all, and it is why the mental-health argument matters more than its effect sizes suggest. Even if the average effect is small, the exposed population is an entire cohort, and the exposure began at an age when the norms, the status economy and the method for settling facts were all still being learned. The argument about whether the phones caused the depression is an argument about a symptom. The change in how a generation was raised is the condition.

Cause or accelerant

What was already burning

Everything in the previous section has a longer history than the platforms. Trust in American institutions peaked in the mid-1960s. Putnam’s clubs and congregations were emptying through the 1980s and 1990s. Local newspapers were losing classified revenue to the internet before Facebook existed, and the United States has lost well over a quarter of its newspapers since 2005, most of them local.[21] Political polarisation in the United States, measured by roll-call votes in Congress, has been rising since the 1970s. A fair account has to hold two things at once: the platforms did not start any of these, and every one of them got faster after about 2010.

Polarisation is the clearest test, because it has been studied hardest and the intuitive story is the one the data least supports. The intuition is the echo chamber: the ranker shows you only your side, and you drift. But Gentzkow and Shapiro found in 2011 that Americans’ online news diets were less ideologically segregated than their face-to-face networks,[22] and the most ambitious experiments ever run on a platform, conducted by Meta with outside academics during the 2020 election, found that switching tens of thousands of users to a chronological feed, or removing reshares, or cutting their exposure to like-minded sources by a third for three months, changed what they saw and barely moved what they believed.[23] Across a dozen rich democracies, affective polarisation, the dislike of the other side as people, rose sharply in the United States over forty years and rose in some other countries while falling in others, all with the same platforms.[24] If the feed caused polarisation directly, Germany and Norway would look like Ohio.

What the evidence does support is subtler and, for a society, worse. Chris Bail’s work suggests that the platforms function less as an echo chamber than as a prism: they make the extreme few per cent of each side vastly more visible than their numbers, and reward them for being extreme, so that each side comes to see the other as its most hostile members.[25] The bridges in Figure 1 carry exactly that traffic. Policy opinions have not moved much. Contempt has. The mechanism does not need to change anyone’s mind; it only needs to change everyone’s picture of the other side, and it does that by selection rather than persuasion.

This is why accelerant is the right word and cause is not. An accelerant works on a fire that has already started, and it works by removing what was slowing the fire down. The friction the platforms removed was specific: the cost of publishing, the delay between an event and its interpretation, the local boundary on reputation, the editor’s veto, the effort of finding people who agreed with you about something unusual. Each of those was a brake on a trend that was already under way. And the platforms did one more thing that an accelerant does not: they attached a revenue model to the fire continuing. A ranker paid by engagement profits from the outrage, the comparison and the stampede, and an earlier essay here worked through what happens to such a platform when its cost of capital rises and it turns on the users who built it.[26] The same incentive that degrades the product also degrades the public.

The cross-country variation, finally, is itself a finding about society rather than about technology. The countries where the mental-health curves are flattest and polarisation has not risen tend to have what the United States and Britain lack: strong offline institutions that still occupy people’s time and confer status locally, proportional electoral systems that do not reduce politics to two tribes, and public broadcasters that a majority still watch. The platform was the same everywhere. The society it landed in was not, and societies with more of the old structures left absorbed the shock better. That is close to a controlled experiment on what the structures were for.

The ledger’s other side

What was gained

An essay that only counted the losses would be doing the thing it complains about, selecting the failures and hiding the base rate, so the gains need stating with the same seriousness. They are large, they are real, and most people would refuse to give them up.

The first is distance. A country like Portugal, with a fifth of its people living abroad, knows what the platforms did for the emigrant: the grandmother in Viseu sees the child in Zurich every evening, and the cost of leaving, which used to include losing the people you left, has fallen to something that a previous generation would not recognise. The second is the finding of one’s people. Someone with a rare disease, an unusual craft, a minority identity or an obscure professional interest was, until recently, alone unless they happened to live in a very large city. If you are one in a million there are eight thousand of you, and they can now be found in an afternoon. The support groups for addiction, for chronic illness, for new parents awake at three in the morning, are staffed by people who have been through it, and they are open now.

The third is access to expertise. Working scientists, doctors, mechanics, lawyers and plumbers explain things directly to anyone who asks, at a quality and volume that no library or institution ever offered. More people have learned to fix a tap, read a proof or play an instrument from a video than from any teacher in history. The fourth is voice. Movements that would once have needed a party, a union or a newspaper to be heard organised themselves in weeks, and the phone camera turned every bystander into a potential witness; a great deal of official misconduct that would once have been the official’s word against the victim’s is now on the record, and the powerful are more watched than they have ever been. The fifth is the small business and the independent maker, who can reach a customer anywhere without an intermediary taking half. The sixth is the emergency: the earthquake, the flood, the missing child, where crowd reporting is routinely ahead of the official channel and saves lives.

It is worth noticing what these gains have in common. Almost all of them are things the platforms did for individuals and small groups: the emigrant, the patient, the maker, the witness. Almost all of the losses are to structures that belong to everyone and no one. That is not a coincidence. The platforms are extraordinarily good at connecting a person to what that person wants, and the collective goods on the list in the previous section are precisely the things that no individual wants enough to pay for, that exist because friction and habit and institutions produced them as a by-product. A society that gets more of what each of its members wants and less of what none of them asked for is a fair description of what happened, and it is not obvious in advance which way the ledger falls.

The second accelerant

What generative AI adds

Return to the list and ask the same question of the newest technology. On most items the answer is that it accelerates further. On two it does not obviously matter. On one or two it might, depending on choices not yet made, run the other way. And it adds one thing that is not on the list, which is the subject of the third panel of Figure 1.

How facts are settled is hit hardest. The last remaining check on a false claim was the cost of producing convincing evidence for it. Fabricating a photograph, a recording or a document well enough to fool a careful person took skill and time. That cost is now close to zero, and the consequence, as Chesney and Citron predicted before the tools were good, is not mainly that people believe fakes. It is that they stop believing real evidence, because “that was generated” is now available as a defence for anything.[27] They called it the liar’s dividend. It destroys, in particular, the one gain of the platform era that had most to do with power: the bystander’s video that made the official’s conduct undeniable is deniable again. The finance director at an engineering firm in Hong Kong wired the equivalent of 25 million dollars in 2024 after a video call in which every colleague on the screen, including the chief financial officer, was synthetic.[28] That is one fraud. The structural fact is that a video call is no longer evidence that anyone was there.

Trust falls from both directions. The institution’s worst moment was already visible; now it can be manufactured, and the institution’s genuine failures can be waved away as manufactured. The people in the middle lose the last of their business: search engines that answer the question without sending the reader to the newspaper remove the traffic that was keeping the remaining newsrooms alive, and the vacancy where the filter used to be grows again. The ranker gets an unlimited supply. The recommendation systems were already machine learning; generative models add a second stage, in which content is produced to match whatever the ranker rewards, at zero marginal cost and in unlimited quantity. One music streaming service reported in 2025 that roughly a fifth of the tracks uploaded to it each day were wholly machine-generated,[29] and the same is true, less measurably, of the text and video in every feed. The attention harms of the first section become cheaper to inflict.

Persuasion changes scale. Propaganda was a broadcast product, one message to many. A language model can run thousands of tailored conversations at once, each adapting to the person in front of it. In a randomised experiment published in 2025, a model given a few basic facts about its opponent was substantially more persuasive than a human debater given the same facts; the model won more often and by more.[30] Around the same time researchers at a Swiss university were found to have run undisclosed model-written accounts in a large online debate forum for months, arguing with real users, which was a breach of ethics and also a proof of concept.[31] Influence operations that used to be detectable because they copied and pasted no longer need to. Scams are the same story with money attached: fluent in every language, personalised from the target’s own public record, spoken in a cloned relative’s voice.

Status and comparison lose their last anchor. The filtered face becomes a generated one, and the baseline against which real bodies and real lives are measured is no longer merely curated; it is impossible. Childhood acquires the least studied item on the whole list. The companion applications, which offer a relationship optimised for retention, always available, never tired and never in disagreement, had millions of adolescent users by 2024, and the first wrongful-death suit, brought by the mother of a fourteen-year-old in Florida, was filed that October.[32] The displacement mechanism that moved children from the playground to the feed now has a version that replaces the friend rather than the medium.

Two items do not obviously move. Time had already hit its ceiling: people were spending as many waking hours on screens as it is possible to spend by about 2019, and AI changes what fills the hours, not how many there are. Polarisation, as argued above, was never well explained by the platforms directly, and nothing about generative models changes that mechanism; what they may do is make the extreme few per cent more prolific, since one person with a model can now produce what a hundred used to.

And on one or two items the direction is genuinely open. The filtering job that nobody has done since the editors were routed around is a job a model could do: read everything, flag fabrication, summarise fairly, point to sources. That would be the intermediary’s function without the intermediary’s power to suppress, and it would be the first thing in twenty years to put friction back. Whether it arrives that way depends entirely on who builds it and what it is optimised for. Built by the platforms, paid by engagement, it is a better ranker. Built as a tool that answers to the reader, it is an editor who works for you. Expertise and learning, the strongest gains of the platform era, are extended rather than reversed: a patient tutor, in any language, on any subject, for anyone. And shared attention could go either way. Models can fragment further, assembling a private edition of reality for each person on demand. Or the scarcity of anything trustworthy could push people back toward a few sources that can prove where their material came from, which would rebuild, by a different route, something like the broadcast public’s common ground. This is a design and policy question, not a technological one, and it has not been decided.

The item that is not on the list is the third panel. Social media harmed society by removing friction and attaching an engagement incentive. Generative AI removes the cost of producing content, including false content, and makes the producer indistinguishable from a person. The old problem was that everyone could speak and nobody was filtering. The new one is that you cannot tell who is speaking at all. Almost every informal method a society has for deciding whom to trust assumes that there is someone there: someone whose reputation is at stake, who will be embarrassed if wrong, who bears a cost for lying. The hollow nodes in the figure bear none. They have no reputation to lose, no shame, no future in which the lie catches up with them, and they are cheaper than the people they are talking to. A public in which some unknown fraction of the participants is like that is a different kind of object from any public that has existed before, and it is not clear that the mechanisms of trust which evolved for the other kind survive contact with it.

A caveat that the subject demands. The teen mental-health debate took a decade to reach its present unsettled state. Generative AI at consumer scale is about three years old. Most of this section is mechanism and early evidence, not measured effect, and the record of confident predictions about new media is poor in both directions. The other caveat is that this essay was drafted with one of the systems it describes, which is disclosed below and which the reader is entitled to weigh, particularly against the paragraph about what such systems might do for filtering.

Coda

Connected and less cohesive

The platforms made a society that is more connected and less cohesive, more informed and less agreed, more able to speak and less able to be heard. They moved power from institutions to individuals and then from individuals to the companies that rank them. On the individual, their measured effect is modest for most people and serious for some, which is the profile of many things a society tolerates. On the structures, their effect was to remove the friction that had been slowing a set of declines that were already under way, and to make the continuation of those declines profitable.

Whether the trade was worth it depends on a judgement about the old intermediaries. Many of them were not doing their job, and some of the truest things said in the last fifteen years could not have been said under them. But nobody is doing the job now, and the second accelerant is arriving before the first has been dealt with. The reasonable position is neither nostalgia nor resignation. It is to notice that the things on the list in the second section, the shared attention, the proportionate trust, the filter, the bounded status economy, the norms with a floor, the method for settling facts, the childhood, were never anyone’s product. They were by-products of friction, habit and institutions, and they will not be restored by asking a ranker to be kinder. They will be restored, if at all, by rebuilding some of the friction on purpose, and by deciding, this time, what the systems that mediate a public are to be optimised for.

Open threads

Where this could go

The countries that absorbed the shock. The cross-national variation in adolescent mental health and polarisation is the nearest thing to a controlled experiment on what offline institutions do. A careful comparison of the flattest curves with the steepest, controlling for platform penetration, would say more about society than another decade of within-country correlations.

A model of the accelerant. The enshittification essay showed that a small optimal-control model reproduces a platform’s turn on its users from three ingredients. The same method could be applied to the ranker and the public: a population with a distribution of views, a ranker paid by engagement, and a measurable quantity for how each side pictures the other. Bail’s prism should fall out of it, and the conditions under which it does not would be worth knowing.

Provenance as infrastructure. If a video call is no longer evidence that anyone was there, the question of how a society re-establishes that someone was becomes an engineering problem with a public-goods character: cryptographic provenance for cameras and documents, and the institutions to hold the keys. The trapdoor essay on this blog is about the mathematics that makes this possible; what is missing is the part about who runs it.

The regulatory experiments. Australia’s minimum age for social media accounts, in force from December 2025, and the European Union’s Digital Services Act are the first large interventions with measurable before-and-after populations.[33] Their results, in two or three years, will be the first real evidence about whether friction can be put back by law.

On method and tools

This piece was written collaboratively with Claude Fable 5.1 (Anthropic): human specification, editorial direction and critical review; machine synthesis and drafting. It is a synthesis essay, not a computational one; where it cites a number, the number is the source’s, and the sources are the primary studies rather than press accounts of them wherever a primary study exists. The evidence on adolescent mental health is presented with both sides’ strongest work, because the disagreement is real and unresolved.

The cover is computed by scripts/accelerant_cover.py: the same sixty people drawn twice, once as a broadcast public around a single hub and once as a ranked public in seven clusters with a ranker above, a few bridges between them and nine hollow nodes for speakers that are not people. Figure 1 is a schematic of the same three shapes and represents no data.

Authored by: Luis Matos Ferreira — Physicist, Developer, Writer

Related essays on this blog

1. The Great Inversion — how AI inverted the relationship between producing and verifying.
2. Conspiracy Theories as Epistemic Systems — closed systems of belief, and why refutation does not reach them.
3. Bargain, Then Rip-Off (forthcoming) — enshittification as a mechanism, from a small optimal-control model.
4. The Trapdoor Problem — the mathematics of proving who sent what.

Sources

1. Mark, Attention Span: A Groundbreaking Way to Restore Balance, Happiness and Productivity, Hanover Square Press, 2023, reporting her group’s office-tracking studies from 2004 onward.
2. Eyal, Hooked: How to Build Habit-Forming Products, Portfolio, 2014, is the designers’ own account; the slot-machine comparison is developed in Schüll, Addiction by Design, Princeton, 2012, and applied to feeds by Harris and others from 2016.
3. Allcott, Braghieri, Eichmeyer & Gentzkow, “The Welfare Effects of Social Media”, American Economic Review 110, 629 (2020).
4. Bursztyn, Handel, Jimenez & Roth, “When Product Markets Become Collective Traps: The Case of Social Media”, NBER Working Paper 31771 (2023).
5. Carter, Rees, Hale, Bhattacharjee & Paradkar, “Association Between Portable Screen-Based Media Device Access or Use and Sleep Outcomes: A Systematic Review and Meta-analysis”, JAMA Pediatrics 170, 1202 (2016).
6. Haidt, The Anxious Generation, Penguin, 2024; Twenge, iGen, Atria, 2017, and Generations, Atria, 2023. The collaborative review that Haidt and Rausch maintain online collects the cross-national time series.
7. Orben & Przybylski, “The association between adolescent well-being and digital technology use”, Nature Human Behaviour 3, 173 (2019); Odgers, “The great rewiring: is social media really behind an epidemic of teenage mental illness?”, Nature 628, 29 (2024).
8. Braghieri, Levy & Makarin, “Social Media and Mental Health”, American Economic Review 112, 3660 (2022).
9. Kelly, Zilanawala, Booker & Sacker, “Social Media Use and Adolescent Mental Health: Findings From the UK Millennium Cohort Study”, EClinicalMedicine 6, 59 (2018).
10. Wells, Horwitz & Seetharaman, “Facebook Knows Instagram Is Toxic for Teen Girls, Company Documents Show”, Wall Street Journal, 14 September 2021, and the internal slides later released by the company.
11. National Center for Missing & Exploited Children, CyberTipline data for 2022 and 2023 on financial sextortion; Federal Bureau of Investigation, public safety alert on financially motivated sextortion, January 2024, citing at least twenty suicides between October 2021 and March 2023.
12. Children’s Commissioner for England, “A lot of it is actually just abuse”: Young people and pornography, January 2023.
13. Gambling Commission (Great Britain), Young People and Gambling 2024, which reports the share of eleven-to-seventeen-year-olds spending their own money on any gambling activity in the previous twelve months.
14. Anderson, Imagined Communities, Verso, 1983, ch. 2, on the newspaper as a daily “mass ceremony”.
15. Shirky, “It’s Not Information Overload. It’s Filter Failure”, talk at Web 2.0 Expo, New York, September 2008.
16. Pew Research Center, Teens, Social Media and Technology 2024, December 2024: 46 per cent of US teenagers report being online “almost constantly”.
17. Putnam, Bowling Alone: The Collapse and Revival of American Community, Simon & Schuster, 2000.
18. Twenge, Haidt, Blake, McAllister, Lemon & Le Roy, “Worldwide increases in adolescent loneliness”, Journal of Adolescence 93, 257 (2021), using the OECD PISA surveys of 2000 to 2018.
19. Vosoughi, Roy & Aral, “The spread of true and false news online”, Science 359, 1146 (2018).
20. “Conspiracy Theories as Epistemic Systems”, on this blog, April 2026.
21. Abernathy, News Deserts and Ghost Newspapers: Will Local News Survive?, University of North Carolina, 2020; Medill School, Northwestern University, The State of Local News 2023, which counts the closures since 2005.
22. Gentzkow & Shapiro, “Ideological Segregation Online and Offline”, Quarterly Journal of Economics 126, 1799 (2011).
23. Guess et al., “How do social media feed algorithms affect attitudes and behavior in an election campaign?”, Science 381, 398 (2023); Guess et al., “Reshares on social media amplify political news but do not detectably affect beliefs or opinions”, Science 381, 404 (2023); Nyhan et al., “Like-minded sources on Facebook are prevalent but not polarizing”, Nature 620, 137 (2023); González-Bailón et al., “Asymmetric ideological segregation in exposure to political news on Facebook”, Science 381, 392 (2023).
24. Boxell, Gentzkow & Shapiro, “Cross-Country Trends in Affective Polarization”, Review of Economics and Statistics 106, 557 (2024).
25. Bail, Breaking the Social Media Prism: How to Make Our Platforms Less Polarizing, Princeton, 2021.
26. “Bargain, Then Rip-Off”, on this blog, September 2026, after Doctorow, “The ‘Enshittification’ of TikTok”, Wired, January 2023.
27. Chesney & Citron, “Deep Fakes: A Looming Challenge for Privacy, Democracy, and National Security”, California Law Review 107, 1753 (2019), section on the liar’s dividend.
28. The Arup case, reported by Hong Kong police in February 2024 and confirmed by the company in May 2024: about HK$200 million transferred after a video conference in which the other participants were deepfakes.
29. Deezer, public statements of April and September 2025, putting fully AI-generated tracks at roughly 18 and then 28 per cent of daily uploads; the figure quoted in the text is the earlier and more conservative one.
30. Salvi, Horta Ribeiro, Gallotti & West, “On the conversational persuasiveness of GPT-4”, Nature Human Behaviour (2025).
31. The University of Zurich experiment on the r/ChangeMyView forum, disclosed by the forum’s moderators in April 2025; the university subsequently said the results would not be published.
32. Garcia v. Character Technologies, Inc., filed in the US District Court for the Middle District of Florida, October 2024.
33. Online Safety Amendment (Social Media Minimum Age) Act 2024 (Australia), commencing December 2025; Regulation (EU) 2022/2065 on a Single Market for Digital Services (Digital Services Act).