Foresight Hallucinations

In the late eighties, Fortune magazine put "trophy wife" into wide use, in a story whose reporter noted that the woman she was describing works hard. This spring, a survey offered young American women four dream lives: tradwife, girlboss, digital nomad or trophy wife. Nearly half chose the option first described as a loving marriage, a stable job and a home full of kids, and headlines reported a generation obsessed with becoming trad wives. Tim Stock and Marie Lena Tupot of scenarioDNA follow how a portrait of a few people flattens into a caricature, how a survey turns the caricature into a percentage, and what happens when employers, cities and forecasters plan around the result.

This fall, two randomized trials of AI tutors pointed in opposite directions. Maryland undergraduates who were offered a tutor in their course finished with lower grades, while secondary students in England who revised with a tutor scored higher. Both results are real, and both come with details a headline drops: the Maryland tutor gave direct explanations by default, and only about one in seven students offered it ever used it. This episode follows a finding like that as it loses its conditions on the way into a scenario, and gives the result a name: a foresight hallucination. Joseph Voros's futures cone and Nele Fischer and Sascha Dannenberg's work on plausibility show where a team's assumptions about the present go unrecorded. The 2024 Wharton and Harvard tutoring studies show how two well-built experiments became opposite headlines. Fabricated citations get caught when someone looks them up, but a foresight hallucination cites real studies, so the only defense is keeping each finding attached to the conditions that produced it.

Cultural research has more signal than structure, and the loudest signal usually sets the story. Knolling, the studio habit of squaring every tool on one surface so each can be seen at once, gives analysts a way to slow that stream down. Laid flat, signals show what they share and what is missing.Charles Sanders Peirce, a mathematician and logician, treated meaning as a relation that shows up in what people do. His logic of abduction starts from the item that fits no group. The pertinent negative, a term borrowed from emergency medicine, trains the eye to record what should be in a scene and is not. Together they turn choices that look irrational into evidence about what a group values.AI tools sort everything into equal rows. An analyst lays that output on the same table, checks what it repeats and what it leaves out, and knolls again next season to see whether the outlier has found neighbors.scenarioDNA Cultural Intelligence Workshop SeriesStory Systems and Cultural Research

Donald Trump told the UN General Assembly that artificial intelligence would now be called super intelligence, saying the word artificial makes AI sound fake. The word artificial describes where the technology came from, and the word super describes where it ranks. We trace the adjective's history from Gulf War smart bombs to smartphones and smart homes, and look at what happens when a machine borrows the words we use to praise people. Research on algorithm appreciation and algorithm aversion shows how a label changes the weight people give to machine advice. Pew Research Center data on smartphone overuse and an experiment in blocking mobile internet point toward a different ideal. Stanford's AI Index calls the current state of AI jagged intelligence. Models that reach gold-medal level in Olympiad mathematics misread an analog clock about half the time. The counter-ideal to smart technology is bounded technology: devices that do a defined job and can be switched off. scenarioDNA Cultural intelligence Semiotics | Foresight | Narrative Intelligence

Cultural change shows up in the words people use long before it shows up in sales figures or policy. People describe a new experience to each other, try out phrases, and argue over what to call it. Reading those words closely shows what people are starting to tell apart, and what they still leave unsaid. Anaïs Nin knew this from the inside. As a girl learning English in New York, she decided that "you" was the most beautiful thing she had ever heard. She grew up speaking French, which makes a speaker choose between a familiar you and a formal one. English folds both into one small word, and most listeners never notice the choice inside it. We follow how people talk about being tired, from burnout at work to brain rot after a night on the phone. Each word puts the cause somewhere and steers around words people would rather not say. The same reading works on what designers make. A fashion caption states a season's choice as fact, and Starbucks calls its staff partners while their union calls them workers. Sorting words by who says them, and what they leave out, gives designers something they can act on. scenarioDNA Cultural Intelligence Workshop Series Semiotics | Foresight | Narrative Intelligence

Lately it feels like every organization has built a bigger pair of binoculars. There are dashboards for everything, weekly drops of “emerging signals,” and now AI sitting on top of it all, stitching neat summaries of what is supposed to be happening next. But in the rooms where decisions get made, the conversations still stall in the same place: everyone can point to the spike, nobody agrees on what it means. We keep upgrading the signal machine while leaving the thinking machine more or less where it was.That tension sits at the heart of our new book Story Systems and Cultural Research. If machines are going to handle more of the scanning and clustering, then foresight has to mature on the interpretation side: how we read contested words, map the stories people are rehearsing, and see the codes that make new behaviors feel allowed. It quietly changes the procurement question for organizations. It is no longer only “Which tool listens best?” but “What kinds of meaning-making are our tools smuggling in by default, and what kind of cultural reading practice do we want to build on top of them?”

When Prada draped the Chelsea Hotel in silver and cleared Katz’s tables for a dance floor this June, the filmmaker it hired to design the takeover described the project as satellites invading Manhattan. The experience economy has morphed into an invasion economy, in which a brand re‑skins mythologized addresses, broadcasts the takeover, and lifts off five days later.

Hollywood still talks about “discoveries,” even as the hard work now happens in public, long before anyone calls it a film. Worlds are tested in comment sections, passed around as memes, and adjusted in the small edits that keep a clip in people’s feeds for one more loop. By the time a feature arrives, it is one more format a world moves through, not its origin. If you only look at the finished movie, you are looking at the afterimage, not the process that made people care.That process is easy to miss if you only track box office or subscribers. The more useful signal is what people return to: which scenes they quote, redraw, argue about, or quietly maintain. Those are the places where story behaves less like a product and more like a shared habit. Paying attention there is a kind of foresight. It treats shorts, fan edits, and low-budget experiments as evidence that a world already exists, and that the “breakout hit” is simply the moment everyone can see it at once.

Most of what passes as “trend” right now is really about how we route memory. Feeds and platforms teach us to click what we already recognize, so nostalgia quietly becomes a default strategy. It feels safe, but if every brand keeps raiding its own archive, the past stops feeling special and starts feeling like inventory.The next phase of brand work has to focus less on throwbacks and more on continuity: building products, rituals, and experiences people want to return to, not just recognize once. We discuss this needed shift in strategy in our new book Story Systems, which looks at how cultural research can show where people use references for comfort, status, and belonging, and how brands can respond by adding new chapters to culture rather than replaying old scenes.

California’s SB 343 “Truth in Recycling” law is interesting partly because it exposes how much the recycling symbol was never really about recycling. The chasing arrows became a civic ritual of the post-recession urban era: rinse the jar, sort the cardboard, participate in the system. In many cities, the blue bin became a quiet social signal tied to sustainability, institutional trust, and the belief that the system underneath everyday life was basically working.What’s changing now is not happening evenly across the country. Some places are moving toward stricter forms of systems accountability while others are drifting toward a politics shaped more by nostalgia, deregulation, and distrust of institutional complexity. After years of supply-chain failures, infrastructure strain, and visible institutional weakness, many people increasingly want visibility into the system beneath the symbol. SB 343 quietly shifts the recycling label from an aspirational gesture into something closer to an audited claim. The arrows now have to answer to infrastructure, not intention.