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The IKEA effect in the age of AI

3 June 2026 at 19:00

I have a drawer at home full of leather offcuts I cannot bring myself to throw away. They are scraps, most of them, the edges trimmed off wallets and card holders I made by hand back when I was teaching myself leathercraft and slowly turning it into a small side business. Objectively they are waste. To me they are the evidence of something. I worked for those scraps, and that work changed how I see them.

If you have ever kept a wonky mug a child made, or refused to bin a piece of furniture you built badly, you already know the feeling I am describing.

And there is a name for it.

A quick note before I go further. I am not a psychologist or a neuroscientist, just a writer who makes things and reads widely. What follows is reflection on a handful of studies, most of them observational or small, not advice about how you should work or think.

The IKEA effect is the tendency to place a disproportionately high value on things you helped make. It was named and documented by Michael Norton, Daniel Mochon and Dan Ariely in a 2012 paper with the lovely title “The IKEA Effect: When Labor Leads to Love”. Across four studies using IKEA boxes, origami and Lego, people consistently rated their own clumsy creations as “similar in value to experts’ creations”. 

The number that tends to get repeated comes from the first experiment in the paper. Builders bid an average of $0.78 for the plain storage boxes they had assembled themselves, while non-builders bid an average of $0.48 for the identical pre-built box, a premium of roughly 63 percent. 

The authors are blunt about it. They write that “labor alone can be sufficient to induce greater liking for the fruits of one’s labor: even constructing a standardized bureau, an arduous, solitary task, can lead people to overvalue their (often poorly constructed) creations”. Note the “can.” This is a tendency, not a law.

And there is one important catch, and it matters for where I am going. The effect only showed up when the labor succeeded. When people failed to finish, or built something and then took it apart, the extra value vanished. Effort that goes nowhere does not buy attachment. Completed effort does.

Now sit that next to the thing reshaping how a lot of us work. Generative AI is, broadly, a labor remover. It takes much of the cognitive grind out of writing, coding, summarising, drafting. Or at least it’s supposed to. The output arrives, often decent, and the effort that used to produce it simply did not happen.

If the IKEA effect is right that we love what we labor over, the obvious question is what we feel about what we did not labor over at all.

A 2025 study looked at this. In “Your Brain on ChatGPT”, researchers had 54 people write essays using an LLM, a search engine, or nothing but their own heads, while measuring brain activity. The people who leaned on the LLM reported the lowest sense of ownership over what they had written, and showed the weakest brain connectivity of the three groups. Many of them struggled to quote back the essay they had just produced.

And this seems to show up on the other side too: not just in how creators feel about AI-assisted work, but in how audiences judge it. In a 2023 study, “Humans versus AI: whether and why we prefer human-created compared to AI-created artwork”, researchers found that people tended to prefer artworks they believed were human-made over artworks they believed were AI-made. Part of that preference came down to the qualities people associated with human creation: intention, emotion, effort, and a sense that someone was actually behind the work.

That matters because the Ikea effect is not really about furniture. It is about ownership. We do not only value the finished object. We value the effort we believe went into it. And when that effort feels absent — whether we are the maker or the audience — something in the value seems to drop.

I notice this in my own days. I use AI for parts of my writing work: the research, the lookup, the first-pass structuring, the awkward sentence I cannot quite untangle. But the pieces I feel most attached to are still the ones where I had to wrestle with the idea myself. The ones where I got annoyed, deleted half of it, walked away, came back, and finally found the line I was looking for.

The other risk here is not just that AI-made things feel less ours. It is that the effort we skip might have been doing something for us beyond producing the object.

A 2025 study by Michael Gerlich found a significant negative correlation between frequent AI use and critical thinking. This one study so but put beside the IKEA finding about failed labor and a pattern suggests itself. The value, in both the products study and our own heads, seems to live in the completed effort, not the finished result. Skip the effort and you may keep the result while losing what the effort was quietly building.

I do not buy the idea that AI is something to refuse. I use it. It is genuinely useful. What the leather drawer and the writing work both tell me is that the value was never really in the object. It was in the doing.

So the practical move, for me at least, is not to do everything the hard way out of nostalgia. It is to be deliberate about which labor I keep. Let the machine carry the parts that are pure friction with no learning in them. Hold onto the parts where the effort is the point, where struggling through is what builds the skill or the judgment or the sense that the thing is mine.

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The Great Wall of China is not actually visible from space with the naked eye — astronauts from Apollo to the ISS have confirmed it — and the popular myth that it is predates space travel itself, with the best-known version coming from a 1932 Ripley’s Believe It or Not cartoon

“The Great Wall of China is not visible from orbit with the naked eye. It’s too narrow, and it follows the natural contours and colours of the landscape.” So wrote the Canadian astronaut Chris Hadfield from the International Space Station during his five-month tour in 2012-2013. Hadfield’s posting was one of dozens of public statements from astronauts who have attempted, and failed, to see the Great Wall from orbit. The consensus among people who have been to space is unambiguous. The wall is not visible from the International Space Station, was not visible from any Apollo mission, was not visible from the Soviet Salyut and Mir stations, and was not visible to China’s first astronaut, Yang Liwei, who spent 21 hours in orbit on the Shenzhou V mission in October 2003.

The result has produced a small but durable embarrassment for the popular fact that “the Great Wall of China is the only human-made object visible from space.” The claim is one of the most widely-repeated pieces of geographical trivia in modern circulation, taught in textbooks, repeated in documentaries, and frequently invoked in casual conversation. It is wrong on at least three counts. The wall is not visible. It is not the only human-made object. And the claim itself predates the technology that would have been needed to verify it.

What astronauts actually report

According to BBC Sky at Night Magazine’s review of the question, the issue with the Great Wall is straightforward: it is too narrow and too poorly differentiated from its surroundings to be visible at orbital distances. The wall averages roughly 5 to 9 metres in width along most of its length. The International Space Station orbits at approximately 400 kilometres altitude. At that scale, the wall is far below the resolving power of the unaided human eye. The wall is also constructed largely of local materials — stone, rammed earth, brick — that share the colour of the surrounding landscape, eliminating the contrast that would be necessary to pick out a narrow feature against its background.

Yang Liwei was direct about the matter when he returned from orbit in 2003. According to Al Jazeera’s coverage of his post-flight interview, Yang told China Central Television that “the scenery was very beautiful, but I didn’t see the Great Wall.” The statement was politically inconvenient enough that Chinese state media reported it carefully, and the country’s geography textbooks were subsequently revised to remove the claim that the Great Wall was visible from space. The American astronaut Leroy Chiao, then commander of the International Space Station, took what is generally considered the first verifiable photograph of the wall from orbit on 24 November 2004, using a digital camera with a 180mm telephoto lens. A second, more famous Chiao photograph followed on 20 February 2005, taken with a 400mm lens in favourable conditions with snow cover and shadows helping to identify the position of the wall against the landscape. Even with the better lens, only short sections of the wall could be identified, and only after extensive comparison with maps.

The Apollo astronauts addressed an even stronger version of the claim. The original popular framing held that the Great Wall was visible from the Moon, a claim several Apollo crews had the opportunity to test directly. Alan Bean of Apollo 12 famously said: “The only thing you can see from the Moon is a beautiful sphere, mostly white, some blue and patches of yellow, and every once in a while some green vegetation. No man-made object is visible at this scale.” Neil Armstrong, Buzz Aldrin, Michael Collins, Jim Lovell, and Jim Irwin all confirmed the same observation. From lunar distance, Earth is a marble. No surface features of any kind, natural or artificial, can be distinguished.

What is visible from orbit

The interesting part of the story, often lost in the popular framing, is what astronauts can in fact see. According to NBC News’s coverage of the question, which interviewed astronaut Ed Lu of Expedition Seven aboard the ISS, astronauts in low Earth orbit can readily see cities, highways, airports, bridges, large dams, ships at sea, and the wakes of large vessels. At night, the artificial lighting of major cities is visible from orbit as bright patterns against the dark sides of continents. Sufficiently large vehicles — aircraft on runways, container ships — can be made out with the naked eye. The list of human-made objects visible from the International Space Station with no optical aid runs to dozens of categories.

The reason these things are visible and the Great Wall is not has to do with size and contrast, not with the impressive scale or fame of the structure in question. The Great Pyramid of Giza, much shorter than the Great Wall but far wider — about 230 metres on each side — is closer to the threshold of orbital visibility, particularly at low sun angles when the play of light and shadow distinguishes it against the surrounding desert. Astronauts have attempted to see the Pyramid with the naked eye and have produced inconsistent reports. The Great Wall, despite being orders of magnitude longer, is at least an order of magnitude too narrow to compete.

Where the myth came from

The most widely-circulated modern version of the claim is generally traced to a Ripley’s Believe It or Not cartoon published in 1932, which stated that the Great Wall of China was “the mightiest work of man, the only one that would be visible to the human eye from the Moon.” Ripley’s was, at the time, one of the most popular newspaper features in the United States, syndicated to hundreds of papers with a combined readership in the tens of millions. The cartoon planted the claim firmly in mid-20th-century popular consciousness, and from there it propagated through textbooks, encyclopaedias, and casual conversation for the next several decades.

The 1932 cartoon was not the earliest version of the idea. The English antiquarian William Stukeley, in a letter dated 1754 about Hadrian’s Wall and later published in his Family Memoirs (1887), wrote that “this mighty wall of four score miles in length is only exceeded by the Chinese Wall, which makes a considerable figure upon the terrestrial globe, and may be discerned at the Moon.” Stukeley’s remark, written more than two centuries before any human had been to space, is the earliest documented version of the claim. The English journalist and travel writer Henry Norman repeated a similar assertion in his 1895 book on the Far East, calling the wall “the only work of human hands on the globe visible from the Moon.” Both of these earlier sources existed in obscurity until historians traced the popular myth backward. Ripley’s, in 1932, brought the claim out of antiquarian obscurity and into the mass cultural mainstream.

The Ripley’s organisation has, in the decades since, hosted on its own website a careful debunking of the claim that originated in one of its cartoons. The Great Wall, the modern Ripley’s article notes, cannot be seen from the Moon, cannot reliably be seen with the naked eye from the International Space Station, and required favourable conditions and a long telephoto lens for even the most successful orbital photograph of it. The claim that survived in popular culture for seven decades was never tested against orbital observation until orbital observation became possible, and the moment it was tested, it failed.

The post The Great Wall of China is not actually visible from space with the naked eye — astronauts from Apollo to the ISS have confirmed it — and the popular myth that it is predates space travel itself, with the best-known version coming from a 1932 Ripley’s Believe It or Not cartoon appeared first on Space Daily.

I’ve been studying emotion regulation for 6 years, and I think the most practical skill you can learn is to notice your nervous system before your mind starts writing tragic fiction.

3 June 2026 at 16:00

Six years of studying emotion regulation has not given me what people tend to assume it would.

I am not unflappable. I don’t move through difficult days with particular grace. I still get activated by things that are, in the cold light of later, not as catastrophic as they felt in the moment. I still spiral sometimes. And I’ve made peace with the fact that the academic literature — as dense and illuminating as it is — doesn’t deliver anything resembling immunity from the ordinary turbulence of being a person.

What it has given me is something smaller and, I’ve come to think, considerably more useful: a particular kind of noticing. Not the dramatic insight that reorganises your inner life but the unglamorous, repeatable skill of catching something a fraction of a second earlier than you used to. That fraction of a second turns out to matter more than I would have predicted when I started this work.

The insight that keeps recurring across the research, across my own practice, and across everything I’ve read and studied is this: there is a gap between what your body does first and what your mind does with it. And most of us spend our lives living almost entirely in the second half of that sequence — in the story the mind has already written by the time we arrive — without ever clearly registering that the sequence has two distinct parts.

What the body does before the story begins

Here is what happens, physiologically, when you perceive a threat. Your nervous system registers something — a shift in tone, an unexpected message, a door that closes too firmly — and it responds before you have consciously processed what you’ve encountered. Heart rate changes. The chest tightens. Breath becomes shallower. These are not symptoms of a problem. They are the nervous system doing its job, providing information in the form of sensation.

The problem is not the signal. The problem is what the mind immediately does with it.

Given a physiological cue it cannot yet explain, the mind does not sit with the sensation and wait. It begins writing. It reaches for a narrative — quickly, efficiently, with remarkable confidence — and the narrative it reaches for tends toward worst-case. It assumes the threat is as large as the feeling suggests. It assumes permanence. It reads a single data point as evidence of a pattern. It extrapolates. And because the mind is very good at its job, the story it writes is coherent and internally consistent and feels, in the moment, like perception rather than interpretation.

By the time the spiral is well underway — by the time you’re three or four chapters into the tragedy the mind has constructed — the nervous system is no longer responding to the original cue. It is responding to the story. The story has become the signal. And so the physiological activation intensifies, which gives the mind more material to work with, which deepens the narrative, which intensifies the activation.

This is not pathology, though. This is the mind doing precisely what it evolved to do in environments where threat assessment needed to be fast and errors in the direction of danger were cheaper than errors in the direction of safety. But in contemporary life, the fictional elaboration often becomes more frightening than the initial cue ever was.

The science of intervening early

James Gross, whose process model of emotion regulation is among the most replicated and cited frameworks in the field, identified something that sounds obvious in retrospect but has profound practical implications: the earlier in the emotion-generative sequence you intervene, the less effort the intervention requires and the more effective it tends to be.

Gross distinguishes between antecedent-focused strategies — things you do before the emotional response has fully unfurled — and response-focused strategies, which are efforts to manage an emotion that is already in full expression. His research consistently shows that cognitive reappraisal, which involves changing how you interpret a situation and is largely antecedent-focused, is both more effective at reducing distress and less taxing to deploy than suppression, which attempts to manage the emotional response after it has already arrived.

Suppression works, after a fashion, but it costs more — physiologically, cognitively, over time.

The implication of this model is not complicated, but it is demanding: if you want to regulate emotion more effectively, you need to catch the process earlier. And you cannot reappraise something you haven’t yet noticed.

You cannot reappraise something you haven’t yet noticed. The gap between sensation and story is where the leverage lives — and most of us skip it entirely.

What state is the nervous system in?

Stephen Porges’s polyvagal theory — a framework that remains the subject of active scientific debate around its neurophysiological foundations, though its clinical applications are widely used — adds another layer to this that I find practically useful. Porges proposed that the autonomic nervous system operates in distinct states — not simply a binary of calm and aroused, but a more nuanced hierarchy. Ventral vagal activation is the state of felt safety, social engagement, openness. Sympathetic activation is the mobilised state: fight or flight, high energy, urgency. Dorsal vagal activation is the collapse state: freeze, shutdown, disconnection. These states are not chosen. They arise. But they are also not fixed — movement between them is possible, and specific practices can facilitate that movement.

What matters for the skill I’m describing is this: you cannot move deliberately between nervous system states if you don’t know which one you’re in. Noticing which state has been activated — and recognising it as a state, a physiological condition with a duration, rather than a permanent truth about your situation — is a prerequisite for everything else. It doesn’t resolve the difficulty. But it opens the possibility of a different relationship to it.

The body as the place to begin

Interoception — the capacity to notice and interpret internal bodily signals — is the underlying mechanism that makes any of this possible. Research has shown that interoceptive awareness is trainable, and that for many people, higher interoceptive accuracy is associated with better emotional regulation outcomes, including greater emotional clarity — though the research also notes that for those prone to anxiety, increased attention to bodily sensation requires care and is not straightforwardly beneficial. The ability to notice that the chest is tight, that the breath has changed, that the jaw is held — these are not trivial observations. They are, in a real sense, the data.

What the research also shows is that many people have spent decades being more attuned to what is happening around them than what is happening in them. The orientation outward — toward other people’s states, toward environmental cues, toward what is needed or expected — often develops at the expense of attunement inward.

The result is that the body’s signals arrive, but they arrive without being clearly received. They get interpreted directly as emotion, or as evidence of a problem, rather than as sensation that the mind is then working with. The sequence collapses into a single event, and the gap — the few seconds between physiological response and narrative elaboration — gets bypassed entirely.

The practical skill, specifically

The skill is not to stop the narrative. Stopping the narrative is hard, and it is largely unnecessary. The mind will write its stories. That is what minds do. The skill is to notice, in that brief window before the story has fully taken hold, that the nervous system fired first — and that what comes next is interpretation, not raw perception.

This window is small. A few seconds, sometimes less. It requires a kind of attention that has to be built, because it runs counter to the natural momentum of emotional activation, which pulls awareness into the content of the story rather than its origins. But the window exists. And locating yourself in it, even imperfectly, changes something about your relationship to both the sensation and the narrative that follows.

You are not trying to be unmoved. You are not trying to assess whether the threat is real. You are simply noting the sequence: the body fired first, and the story is subsequent. That noting — which sounds minor and possibly is — has the effect of creating a small distance from the narrative. Not dissociation. Not detachment. But enough space to recognise that what you are experiencing is a nervous system response plus a story the mind has constructed around it, and that these are two different things that can be considered separately.

How I came to know this in my body, not just my head

I want to be honest about something, because I think it matters. I understood this framework intellectually for a long time before it became practically useful to me. I could have explained Gross’s process model to you with accuracy and reasonable fluency well before I had any reliable ability to catch myself in the window he describes. Academic understanding and embodied practice are not the same thing, and in this area the gap between them is particularly wide.

What changed it for me was treating this as a body practice rather than a cognitive one. Not analysis during the activation — I was already doing that, and it wasn’t landing — but something slower and more physical: breath-based practices, body scanning, the deliberate cultivation of the habit of checking in with physical sensation at neutral moments throughout the day, so that the recognition of a bodily state became available as a skill when activation made it harder to access. The academic framing gave me the map. The practice gave me some ability to actually navigate.

I can now often catch the nervous system firing before the story has fully begun. Not always. There are days when I am well into the tragic fiction before I realise that’s what’s happening, and the best I can do is notice it mid-chapter rather than before the first line. But often enough that it changed something real about my relationship to difficult emotional experiences. The storms don’t pass faster, necessarily. But I am less confused about what I’m in the middle of, and that confusion, it turns out, was doing a significant amount of the damage.

The data and the interpretation

I want to close with this, because I think it is the part that matters most. The tragic fiction the mind writes in the wake of a threat signal is not necessarily wrong. The threat might be real. The fear might be warranted. The relationship might be in trouble, the situation might be genuinely precarious, the worst case might arrive. I’m not arguing for optimism as a regulatory strategy, and the research doesn’t support that either.

What I’m arguing for is a clearer relationship to the sequence. The nervous system gives you data. It tells you something registered as significant, something that warranted mobilisation, something that your body assessed as requiring a response. That is real information. But the mind gives you a narrative — an interpretation, a story built from the data and from memory and from pattern and from fear, woven together with remarkable speed and presented as obvious truth.

Both of these things matter. Neither should be dismissed. But they are not the same thing, and conflating them — treating the mind’s story as if it were the raw sensation — is where much of the unnecessary suffering lives.

Not all of it. But enough that the distinction seems worth making. The body told you something.

What the mind makes of that is a second step. And in between those two steps, for a few seconds that are easy to miss, there is a window that is worth learning to find.

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The more I work with AI, the less interested I am in whether it’s conscious and the more interested I am in what happens to human consciousness around it

3 June 2026 at 15:00

Today, I came across a note on Substack by Karly V Studio that stopped me mid-scroll.

It was a single sentence: The more I work with AI, the less interested I am in whether it’s conscious and the more interested I am in what happens to human consciousness around it. 

That was it. No elaboration. Just the sentence sitting there. I read it three times, put my phone down, and spent the next hour thinking. This piece is the result.

There was a period when the question of AI consciousness felt genuinely live to me.

I have a background in psychology, I’ve spent years thinking about cognition and inner experience, and the question — does anything like experience accompany what these systems do? — seemed like one of the most interesting open problems of our moment.

I read the papers. I followed the debates. I found myself, occasionally, genuinely unsure.

At some point, without quite deciding to, I stopped. Not because the question got answered — it didn’t, and it may not for a very long time. But because a different question had started to feel more urgent, more observable, more real in my day-to-day life. Less philosophical, more immediate. The question I couldn’t stop turning over wasn’t about what’s happening inside AI. It was about what’s happening inside us when we’re around it constantly.

The question that displaced the other one

What happens to human consciousness when it operates alongside AI — not in the speculative sense, not the sci-fi sense, but in the specific, textured, daily sense? What happens to attention? What happens to the capacity to sit with uncertainty long enough to let it resolve into something? What happens to the experience of thinking something through, fully, from start to finish, when you know that a machine can generate fifty variations of your half-formed idea in the time it takes you to finish a sentence?

These aren’t rhetorical questions. I notice things now that I didn’t notice three years ago. A faint impatience when my own thinking feels slow. A slight deflation when I’ve worked something out and find that the AI had already gone there. A recalibration — gradual, unannounced — in what I expect thinking to feel like, and how long it should take.

Nicholas Carr documented something adjacent to this in The Shallows, his examination of how internet use rewires the neural pathways involved in reading and sustained attention. His argument, drawing on neuroscience and media theory, was that the medium isn’t just a vessel for content — it actively reshapes how the brain processes information. We adapted to search engines. We adapted to hyperlinks. The adaptation happened quietly, at the level of habit and expectation, and most of us noticed the change only in retrospect, if at all. AI is a different order of tool, but the principle holds — and may hold more sharply.

Cognitive offloading, turbocharged

There’s a well-established phenomenon in cognitive science called cognitive offloading — the tendency to stop retaining information you know you can retrieve later. We’ve done this with phone numbers for twenty years. We do it with dates, addresses, facts that used to live in memory and now live in a search bar. The research on this has been building for years, examining how external memory storage affects internal cognition and what we lose (and gain) when we outsource recall to devices.

What AI introduces is something more radical than retrieval offloading. It’s what I’d call reasoning offloading. You can now hand off not just “what is the capital of Portugal” but “work through the implications of this argument for me” or “tell me what I’m probably missing here.”

The cognitive steps between question and answer — the searching, the synthesizing, the holding of multiple possibilities in tension — can be skipped. The result arrives. The journey doesn’t happen.

I don’t think this is simply bad. There are genuinely liberating things about having a capable thinking partner available at all times. But I’d be lying if I said I hadn’t noticed a change in the texture of my own reasoning on the days I lean into AI heavily versus the days I work without it. There’s something different about the feel of an idea you arrived at slowly, on your own, compared to one you arrived at quickly, with assistance. I can’t prove that difference matters. But I notice it, and I think the noticing is worth something.

AI as an unexpected mirror

Here is the thing that has surprised me most, working with these tools as extensively as I do: being around AI has made me more aware of my own cognition, not less. The consciousness debate about AI centers almost entirely on whether the machine has inner experience. But there’s an underexplored symmetry at play. Being around something that processes, generates, retrieves, and responds at speed — without (apparently) any of the friction of genuine uncertainty, any of the experience of reaching for a word and not quite finding it — throws your own processing into relief.

I have started to notice the seams in my own cognition in ways I didn’t before. The moments where I’m genuinely generating something versus where I’m retrieving a cached response I’ve given a hundred times. The difference between thinking through a problem and pattern-matching to a solution I already hold. I had, before this, a vague sense that these were different activities. Working with AI has made the distinction feel specific and detectable. The tool, unexpectedly, became a mirror.

The observer the tool created

There’s something else specific that I’ve noticed, and it’s difficult to articulate without sounding either precious or alarmed, when really it’s neither. It’s more like: a thing worth paying attention to.

When you use AI for thinking tasks regularly, you start to notice the moment just before you think — the moment when you’re about to engage with a problem — and you catch yourself reaching for the AI instead. That pause, that noticing, is a form of metacognitive awareness that many people didn’t have access to before. The friction created the observer.

Metacognition — thinking about thinking — has a substantial research base linking it to better learning outcomes, improved self-regulation, and stronger decision-making, particularly when explicitly developed. What’s interesting about AI as a metacognitive prompt is that it’s not deliberate at all. It’s incidental. You reach for the tool. You notice yourself reaching. You get a brief, clear view of what you were about to do and why. That view is new. It wasn’t forced by a therapist or a mindfulness practice. It was forced by the availability of an alternative.

I don’t want to romanticize this. The pause doesn’t always lead anywhere useful. Plenty of times I notice it, ignore it, and hand the task over anyway — because that’s the right call, because the AI will do it better, because I have seventeen other things competing for the same attention. But sometimes the pause leads to a realization that I actually want to think this one through myself. That I’d lose something by not doing so. That the thinking is the point, not just the output.

What I’m watching

I’m not worried, exactly. I find all of this more interesting than alarming. The relationship between humans and cognitive tools has always been generative and strange — writing changed memory, printing changed authority, the internet changed attention, and we’re still sorting out what those changes mean. AI is the next chapter of that story, not a rupture from it.

But I’d rather pay attention to it than not. Because if the tool is changing the nature of thinking — changing what it feels like to have an idea, what it means to understand something, what we expect from our own minds — and we’re not watching that happen, we’ll notice the change only after it’s already settled in. Only after the new baseline has become invisible, the way all baselines eventually do.

The question of whether AI is conscious is still genuinely open. Smart people are still working on it, and I don’t dismiss it. But it has, for me, become the less pressing question. The pressing one is what’s happening in here — in the human mind that now has, available to it at all times, something that thinks alongside it, faster and without fatigue. What that does to attention, to patience, to the felt sense of cognition. What it makes visible that was always there. What it quietly changes that we won’t see clearly for years.

I’d rather be watching now.

And I’m grateful to the author of the Subtstack note for putting it into one sentence so cleanly that I had no choice but to think it through.

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Thought of the day from French philosopher Blaise Pascal: “The sole cause of man’s unhappiness is that he does not know how to stay quietly in his room”

3 June 2026 at 14:00

I came across Pascal’s line again the other day and did the thing you do with a good quote: nodded, felt a little seen, moved on. Then it followed me out the door.

I went for a walk that evening, the way I usually do, and somewhere along the way I noticed the phone in my pocket. I wasn’t looking at it. But it was there, the way it’s always there, and it occurred to me that I couldn’t remember the last time I sat in a room with nothing in it. No screen, no book, no podcast, no plan. Just me, staying put.

What Pascal wrote, in his Pensées, felt relevant, modern even: “The sole cause of man’s unhappiness is that he does not know how to stay quietly in his room.” But he was writing in the 1600s, long before notifications and screens.

I suppose, the uncomfortable thing about silence is that it does not feel productive while it is happening. Nothing is being consumed. Nothing is being answered. Nothing is being crossed off a list.

But that does not mean nothing is happening. 

When the mind is not being fed by a screen, a podcast, a book, or another little hit of instruction, it starts doing something we rarely give it time to do: wander, sort, connect, and return to whatever has been sitting underneath the noise. Research on mind-wandering, by Akina Yamaoka and Shintaro Yukaw, has suggested that this kind of mental drifting can help with creative problem-solving, especially when we are doing something simple enough to leave part of the mind free.

Walking seems to do something similar. A 2014 Stanford study titled “Give Your Ideas Some Legs: The Positive Effect of Walking on Creative Thinking” found that people produced more creative ideas while walking and shortly afterward.

So maybe silence is not just the absence of distraction. Maybe it is one of the few conditions in which the mind gets to catch up with itself.

I would like to tell you I have this one figured out. I don’t. The closest I get is the walk, and the walk has a phone in it. The other thing I reach for is coffee in the morning before I touch a screen, which I manage some days and lose on others. It comes and goes. There are mornings I am halfway through an email before the kettle has boiled. So when I read Pascal, my honest reaction isn’t agreement so much as recognition. He is describing me.

It turns out I am not unusual. University of Virginia psychologist Timothy Wilson ran a series of studies that asked people to sit alone in a bare room for a few minutes with nothing to do but think.

They did not enjoy it.

Given the option, many chose to give themselves a small electric shock rather than sit there quietly: twelve of eighteen men in one version, and one of them pressed the button 190 times. People would rather be jolted than be left alone with their own heads.

You would think the lesson is to march yourself into the empty room and stay until you get good at it. But the part that stuck with me came from Wilson himself. He was careful to say he did not yet have the evidence but admitted he remained convinced that “the mind may be freed up if it’s mildly engaged in the world, such as going for a walk or looking out a window.”

That stopped me, because it is more or less the only version of this I actually do. Not the empty room. The walk. The window. The cup of coffee where the only thing happening is the coffee. I had been filing these under cheap substitute, the thing you settle for when you can’t manage the real, monkish article. Maybe they are not the substitute. Maybe, for a mind that was never trained to sit in a void, a little motion is the way in rather than a way around.

What the phone takes from me isn’t really the grand stillness Pascal had in mind. I was never going to sit cross-legged in an empty room anyway. What it takes is the small stuff: the walk where my thoughts get to wander instead of being handed something to look at, the ten minutes with a coffee before the day starts talking. Those are the gaps where whatever I have been avoiding tends to surface, and I notice I have gotten very good at filling them before they can.

I am not going to pretend I am about to become a person who sits in silent rooms. But I have started leaving the phone in the pocket on the walk on purpose now, instead of by accident, and trying to win the morning coffee more often than I lose it. Pascal would probably consider this a low bar. He might be right. But at least it is a bar I can actually reach.. 

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Neuroscientists found a region of the brain specialized for recognising faces that activates in as little as 50 milliseconds and it develops even in people who have been blind since birth

brain decision making

Somewhere on the ventral surface of the temporal lobe, in a region called the fusiform gyrus, there is a small area of cortex that responds far more strongly to faces than to almost anything else. The area is known as the fusiform face area, and two papers published roughly six years apart have each added something precise and somewhat counterintuitive to what we know about it.

The first finding, from a 2014 paper in Nature Communications, concerns timing. Using electrodes placed directly on the fusiform face area of four patients undergoing epilepsy monitoring, neuroscientist Avniel Singh Ghuman and colleagues at the University of Pittsburgh recorded electrical activity while participants viewed images of faces, bodies, houses, hammers, shoes, and phase-scrambled faces. What Ghuman’s team found was that the region was responding selectively to faces within 50 to 75 milliseconds of a face appearing on screen. That is faster than this region had previously been shown to respond to any visual category in humans, and faster than most other categories reach the temporal cortex at all.

The second finding, from a 2020 paper in the Proceedings of the National Academy of Sciences by N. Apurva Ratan Murty, Nancy Kanwisher, and colleagues at MIT, concerns development. When people who have been blind since birth handle three-dimensional printed models of faces, the fusiform face area becomes active. Visual experience with faces, it turns out, is not what builds the area’s preference for them.

These are findings from two separate papers, each with its own design, sample, and limits. Neither should be read as a settled account of how face perception works. But together they describe something worth sitting with.

What the Ghuman paper actually measured

The electrode method Ghuman’s team used is called intracranial electrocorticography, or ECoG. It involves recording directly from the brain’s surface at very high temporal resolution, far finer than what fMRI allows. The four participants in the study were epilepsy patients who already had electrodes implanted as part of their clinical care. The researchers used a machine learning algorithm to decode, on a trial-by-trial basis, whether the brain signal from the fusiform face area at any given moment was consistent with the participant viewing a face.

Face-selective activity appeared in the 50-to-75-millisecond window after stimulus onset and remained distinguishable from responses to other categories through to about 350 milliseconds. Crucially, phase-scrambled faces, which preserve the spatial frequency structure of a face but destroy its recognisable shape, did not produce the same early signal. This argues against the early response being driven simply by the visual statistics of a face-shaped image rather than by something more specific to face recognition.

The study was conducted on four participants. That is a small sample by the standards of most research, though the intracranial recording method compensates partly for sample size with signal precision that non-invasive imaging cannot approach. The authors are careful about what they claim: the early activity shows that face-specific information is present in the fusiform face area at 50 to 75 milliseconds, and they argue this is consistent with the region playing a role in initial face detection. They do not claim to have resolved all debates about the temporal architecture of face perception. There is ongoing disagreement in the field about when and where in the brain face selectivity first arises, and this paper contributes to that debate rather than closing it.

Beyond face detection, the same paper also found that the fusiform face area encodes which specific face someone is viewing, but this individuation happened considerably later, between 200 and 500 milliseconds, and was stable across changes in facial expression. And a late-sustained signal, broadband gamma activity lasting more than 500 milliseconds, tracked how long it took participants to respond in a gender-classification task. Longer gamma activity corresponded to slower responses. The area appears to be doing several different things at different moments, and not all at once.

A feature the area may not need to acquire

The MIT study took a different approach to a different question. Kanwisher and her colleagues wanted to know whether the fusiform face area develops its preference for faces because people spend years looking at faces, or whether the region has something more like a predetermined role that does not depend on that visual history.

To test this, they recruited people who had been blind from birth and had therefore never had visual experience with faces or anything else. Using fMRI, they scanned participants while they handled 3D-printed objects including faces, hands, chairs, and mazes. The fusiform face area was active during face handling, in roughly the same location it occupies in sighted people, and the selectivity for faces over other objects was comparable.

The finding does not mean visual experience is irrelevant to how the area functions in sighted people. Kanwisher has been quoted in MIT News as saying precisely that: visual input probably does play a role in sighted subjects. What the study shows is that visual experience is not necessary for the area to develop face selectivity in the first place. The researchers propose that long-range connectivity, the area’s structural relationships to other parts of the brain, may be what positions it to become selective for faces regardless of the sensory route through which face information arrives.

This finding builds on earlier work. A 2017 study from researchers in Belgium, published in the Proceedings of the National Academy of Sciences, scanned congenitally blind participants while they listened to face-related sounds such as laughing or chewing, and found elevated activity in the vicinity of the fusiform face area compared to non-face sounds. The MIT paper extended this with the more direct test of haptic face recognition.

What these two findings put together

Reading these papers alongside each other draws out something the standard account of the fusiform face area tends to flatten. It is easy to describe the area as a face-recognition module and leave it there. But Ghuman’s data show it operating at least three distinct processing stages, on three different timescales, doing different things with face information at different moments. And Murty and Kanwisher’s data show the area claiming its face selectivity without any visual faces ever having been seen.

What the area appears to have is something like a structural commitment to faces as a category, one that exists prior to, and independent of, a lifetime of looking. That does not mean it is a rigid or fixed processor. The late gamma activity Ghuman’s team found appears tied to working memory and task demands, suggesting the area is also responsive to what someone is trying to do with a face, not only to the presence of one.

The question of what this means for people with face recognition difficulties, or for understanding how face perception varies across individuals, is not something either paper directly addresses. Neither is clinical in that sense. Both are asking about the fundamental architecture of a region, not about what goes wrong when it malfunctions.

The limits worth naming

Ghuman et al. worked with four participants. That is small. The electrode placement was determined by clinical need, not experimental design, which means the precise location varied across subjects. The authors acknowledge their method is more sensitive to information encoded in temporal patterns than to information encoded spatially, so absence of a signal in their analysis does not necessarily mean absence of processing in the region.

The MIT haptic study relied on fMRI, which captures activity integrated over seconds rather than milliseconds. It tells us that the fusiform face area is involved when blind people handle face-shaped objects; it does not tell us precisely what computations are occurring or at what speed. The researchers are also working with a small group of congenitally blind participants, a population that is difficult to recruit in large numbers. The finding is worth taking seriously, but replication and extension will matter.

The broader question about what specifies the location and function of cortical areas is active and not resolved. The connectivity hypothesis is plausible and consistent with the data; it is not yet a confirmed account of development. Future work may complicate or qualify it.

What the research does not do

Neither paper suggests that the 50-millisecond finding represents the full story of how quickly the brain begins to recognise a face. Other regions contribute to face perception, and the signal Ghuman’s team recorded is from one area of the processing network. The fusiform face area appears to be unusually fast compared to how quickly the temporal cortex responds to non-face categories, but the authors are careful to frame this as a finding from this dataset with this method, not a universal statement about the speed of human face recognition.

Similarly, the MIT result about blindness does not mean the fusiform face area functions identically in people who have and have not had visual experience. Kanwisher says explicitly that visual input probably does shape the area in sighted people; the study only shows that such input is not required for face selectivity to emerge. These are different claims, and the difference matters.

The region continues to be studied, and the picture that has accumulated is more layered than a simple face-recognition box in the temporal lobe. It is active very early, it handles individual faces later, it maintains information in support of decisions, and it manages to do all of this without requiring the owner ever to have seen a face. How those capacities fit together is still being worked out.

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The case for jotting down a few things we are grateful for

3 June 2026 at 11:00

The image most of us have of a gratitude journal is a little precious. A leather notebook, a quiet corner, a candle maybe, and a person carefully composing several lines about the sunset and the smell of coffee. It looks like a ritual you have to earn the time for.

This was the image I used to have of it at least, and I think because it looks like that for some, many of us never start, or start once and quietly let it go.

When I went looking at the actual research on this, I expected it to be flimsier than the hype. It was, in fact, sturdier than I thought, and also much smaller and less precious than the candle version suggests.

A quick note before we go further: I am a curious generalist, not a psychologist. What follows is my reading of the research, not advice for your situation. The studies here are observational or short experimental trials, and population-level patterns are not promises about what any one person will feel.

The modern science of this traces back to a 2003 paper by Robert Emmons and Michael McCullough, “Counting Blessings Versus Burdens.” As put by the researchers, across three experiments, “gratitude-outlook groups exhibited heightened well-being across several, though not all, of the outcome measures across the 3 studies, relative to the comparison group.” The findings suggested that taking account of what we have in life has emotional and interpersonal benefits. 

The benefits are also well backed up by experts like those at UCLA Health who not that gratitude can help to reduce depression and anxiety, relieve stress and even improve heart health. 

But here’s the twist. Doing it more often does not always appear to be better. A frequency study led by Sonja Lyubomirsky, reported by the Greater Good Science Center, found that people who journaled once a week for six weeks felt happier afterward, while people who did it three times a week did not. The likely reason is the thing that quietly undermines most good feelings. As Emmons puts it, “We adapt to positive events quickly, especially if we constantly focus on them. It seems counterintuitive, but it is how the mind works.” That single line reframes the whole thing for me. The instinct, if you believe something is good for you, is to do it harder and more often.

The writing is something I think we should touch on, too. It’s not just a way of recording the gratitude, it seems. It might be where a lot of the work happens. Emmons describes it this way: “Writing helps to organize thoughts, facilitate integration, and helps you accept your own experiences and put them in context.”

I think most of us already feel grateful for things in a vague, passing way. The dog is fine, the work email got sorted, a friend texted back. These thoughts float by and dissolve. Putting one of them into a sentence forces you to decide what it actually was and why it mattered, and that small act of naming is what seems to give it weight. The guidance that has settled out of this body of work leans toward depth over breadth, one thing properly felt rather than ten things listed flat.

The reassuring thing is that the experts do not ask for the candle. Emmons is blunt about it: “You don’t need to buy a fancy personal journal to record your entries in, or worry about spelling or grammar.” And against all the tidy tips, he keeps one honest caveat in play, that “there is no one right way to do it.” That line matters more than any of the prescriptions around it, because it takes the pressure off getting it right.

So the version I would actually defend is almost embarrassingly small. A few lines, once or twice a week, on whatever is at hand. Not a ritual, not a system, just the act of jotting down a few things we are grateful for. 

If the reason you are reading about gratitude is that things have felt heavy lately, that is worth taking seriously. A journal is a fine thing, but a good therapist is a better one when the weight is real.

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The first fax machine was invented in 1843 — more than thirty years before the telephone — which means that for a 22-year window in the mid-1800s, a Japanese samurai could have theoretically sent a fax to Abraham Lincoln, since the samurai class was not formally abolished until 1867 and Lincoln died in 1865

On 27 May 1843, the Scottish clockmaker Alexander Bain was granted British Patent No. 9745 for what he called an electric printing telegraph: a device that used synchronised pendulums to scan a flat metal surface and reproduce its markings, line by line, at a receiving station over a telegraph wire. The patent describes the basic working principle of a fax machine. Bain had invented one. The same year, Charles Dickens was publishing A Christmas Carol, Queen Victoria was six years into her reign, and Alexander Graham Bell, the eventual inventor of the telephone, was minus 4 years old. The fax machine predates the telephone by more than three decades.

The corresponding window of chronological overlap produces one of the more peculiar facts about nineteenth-century technology. The fax existed from 1843. Abraham Lincoln lived until 1865. The Japanese samurai class, despite the popular impression of belonging to a distant feudal past, was still a recognised legal caste in Japanese society throughout Lincoln’s lifetime and was not formally dismantled until well into the 1870s. There was, therefore, a 22-year window — from 1843, the patent of Bain’s machine, until Lincoln’s assassination in April 1865 — during which a samurai could, in theory, have sent a fax to the sitting president of the United States. As a Truth or Fiction analysis of the popular version of this claim documents, the fact has circulated as an internet meme since July 2021 and the underlying chronology checks out, with the kind of practical qualifications that the word “theoretically” exists to cover.

Alexander Bain’s machine

According to Britannica’s biography of Alexander Bain, Bain’s invention came less than seven years after Samuel Morse had patented the electric telegraph. Bain was a clockmaker who had already patented the world’s first electric clock in 1841, and his fax machine grew directly out of his clockwork expertise. The patent describes a system in which two pendulums, one at the transmitting station and one at the receiving station, are synchronised by an electric clock and made to scan their respective metal surfaces line by line. According to HowStuffWorks’s history of the fax machine, the transmitting station’s metal surface was covered with raised metal type. As the pendulum’s stylus passed over the type, it closed an electrical circuit, sending a pulse down the telegraph wire. At the receiving station, the pulse caused a corresponding stylus to mark a piece of chemically treated paper that had been impregnated with a solution. The marks built up, line by line, into a reproduction of the original document.

Bain’s machine was rudimentary by modern standards. The pendulums drifted out of synchronisation, the printed images were faint, and the transmission speed was slow. But it worked, and the principle was sound. The Englishman Frederick Bakewell improved on it in 1848 with a rotating cylinder, and the Italian Giovanni Caselli took the technology to commercial maturity in 1865 with his pantelegraph, which operated a regular commercial fax service between Paris and Lyon between 1865 and 1870. Several thousand documents were transmitted over Caselli’s system, including business correspondence and the signatures on financial instruments. By the time Lincoln was inaugurated as president in 1861, fax technology was no longer experimental. It was a working communications medium in commercial use in Europe.

Why the samurai had not yet been abolished

The popular image of the samurai as a figure from medieval Japan obscures the fact that the samurai class survived until well into the modern industrial era. The transition began with the Meiji Restoration of 1868, which overthrew the Tokugawa shogunate and restored imperial rule, but the dismantling of the samurai class itself was a gradual process that played out over the following decade. According to KCP International’s overview of the abolition of the samurai class, the relevant landmarks were the 1871 abolition of the han domain system, the 1873 Conscription Ordinance that ended the samurai’s military monopoly by establishing a Western-style national army, and the 1876 Haitōrei Edict that prohibited the samurai from carrying their characteristic two-sword set in public. The 1877 Satsuma Rebellion, led by the disaffected samurai Saigō Takamori, was the last armed resistance to the new order, and its defeat marks the practical end of the samurai as a political force.

For the full duration of Lincoln’s life and presidency, none of this had yet happened. When Lincoln was assassinated at Ford’s Theatre on 14 April 1865, the Tokugawa shogunate was still nominally in power, the samurai were still the legally recognised warrior class, and the Boshin War that would dismantle the old regime was still three years away. The viral meme’s specific date of 1867 is an approximation of when the Meiji process began, rather than when the samurai class actually ended, but the broader point holds: the samurai class as a feudal institution was still intact throughout the Lincoln presidency, and the chronological overlap with the fax-machine patent is real.

What “theoretically” is doing

The word “theoretically” in the popular framing of the fact is doing important work. The fax machine existed. The samurai existed. Lincoln existed. All three overlapped in time. What did not exist, during this same window, was the infrastructure that would have been required for an actual transmission. Bain’s machine, like every 19th-century fax system, required a continuous telegraph wire connecting the sending and receiving stations.

Japan did not have a working domestic telegraph network until 1869, four years after Lincoln’s death. The first transpacific telegraph cable connecting Japan to North America was not completed until 1906. A samurai in Japan attempting to fax Washington in 1864 would have faced the immediate problem that there was no wire connecting his country to anything beyond its shores. The technology of the fax existed. The wires necessary to use it across an ocean did not.

The qualification extends further than people who have not looked into it tend to assume. According to a History.com account of the first transatlantic telegraph cable, the first cable was completed in August 1858, with Queen Victoria sending the inaugural message to President James Buchanan on 16 August, but the cable failed within weeks and went silent. A second attempt in 1865 broke during the laying and was abandoned. The first reliable, continuously-operating transatlantic telegraph cable was not in service until 27 July 1866 — more than a year after Lincoln’s assassination. For the full duration of Lincoln’s presidency, from March 1861 to April 1865, no electric signal could be transmitted across the Atlantic Ocean by any means. All communication between Europe and North America during the American Civil War travelled by ship, taking roughly two weeks each way. A samurai in Paris with full access to Caselli’s pantelegraph and the entire European telegraph network of 1864 would still have been unable to send a fax to Washington. The wire to do so did not exist.

The chronology, in other words, is more concrete than it sounds, and the impossibility of the actual transmission is more concrete still. Bain’s 1843 patent is a real document. The samurai’s continued legal existence through the 1860s is a real fact. Lincoln’s presidency overlapped both. The 22-year window in which all three coexisted is the kind of fact that resists easy mental categorisation, because the popular images of “samurai” and “fax machine” sit in mental boxes that do not normally touch. The boxes touched, briefly, in the middle of the nineteenth century. They just happened to do so on opposite sides of an ocean that, for the duration of the overlap, no electric signal could yet cross.

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In 1938, Harvard researchers began following a group of young men to learn what makes a good life. Almost nine decades on, the strongest finding in their data is not wealth or achievement, but something quieter.

What follows is reflection on a long-running piece of research, not advice. We are writers and editors reading the literature, not clinicians, psychologists, or therapists. The study at the centre of this piece is observational, and patterns drawn from one cohort are not prescriptions for any single reader’s life.

In 1938, what became the Harvard Study of Adult Development began with the Grant Study, which followed 268 Harvard sophomores. It was later combined with the Glueck Study, which followed 456 boys from Boston. The study not now includes these mens’ offspring. 

The aim was modest and a little vague: to watch ordinary lives unfold and learn what kept people healthy. Nearly nine decades later, it is still running, now with a broader participant base. 

When the researchers pooled what they had collected and looked for what predicted a good old age, the obvious candidates underperformed.  The study director, Robert Waldinger has put it this way:  “When we gathered together everything we knew about them about at age 50, it wasn’t their middle-age cholesterol levels that predicted how they were going to grow old. It was how satisfied they were in their relationships.”  This is a finding from one cohort, not a universal law of medicine, but within this group the relationship measure carried more predictive weight than the markers people tend to worry about.

The pattern appeared elsewhere in the data too. Participants who reported good relationships were, according to summaries of the work, associated with less heart disease, diabetes, and arthritis.

Still, the direction was consistent enough that Waldinger has summed up the headline plainly: good relationships, in his telling, keep us happier and healthier. That is a confident line, drawn from a largely white, male sample, and worth reading as one researcher’s framing of a correlation rather than a settled verdict on everyone.

Waldinger has described the result as a “surprising finding,” that our relationships and how happy we are in them appear to have “a powerful influence on our health.” Influence, not cause. The study cannot prove the arrow runs only one way, and it doesn’t claim to.

Perhaps the strangest thing about the result is how badly it travels. Waldinger’s 2015 TED talk on the study has been viewed more than 29 million times on Youtube alone, and the message is not complicated: tend your close relationships. Anyone who has let a relationship lapse because work felt more urgent has run the small experiment the study runs at scale. The advice is easy to nod at and hard to act on, partly because it competes with everything louder, salaries, titles, the next achievement.

The study’s limits are worth stating plainly, even while taking it seriously. The original cohorts were men, mostly white, and both groups were unusually narrow slices of mid-century America. The headline is a correlation built on that sample, expanded now to spouses and offspring who now number  well over a thousand, but still rooted in one long, specific thread of lives. What it offers is a clue with unusual staying power, not a formula.

If the question of who you are close to, and how that is going, lands somewhere tender, a qualified counsellor or therapist is a good person to talk it through with.

The quiet implication of the study’s own length is the part that stays. It took the better part of a century, four directors, and decades of near-precarious funding to arrive at an answer many people could have guessed at the start. The difficulty was never in finding it. The difficulty is in believing something that ordinary could be the thing that matters most.

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Almost all the gold on Earth — every wedding ring, every coin, every gram in every bank vault — was forged in the collision of dead stars billions of years ago, in events so violent that a single one can produce hundreds of Earth-masses of gold, and the heavy elements were then scattered across the galaxy before our sun was even born

On 17 August 2017, two neutron stars in a galaxy called NGC 4993, about 130 million light-years from Earth, completed a spiral inward that had taken them millions of years and ended in a collision lasting fractions of a second. The gravitational waves from that collision reached Earth and were detected by the LIGO and Virgo observatories. Within hours, telescopes around the world had identified the afterglow of the event across the electromagnetic spectrum, from gamma rays to radio waves. The event, designated GW170817, was the first confirmed observation of a neutron-star merger. It also resolved one of the longest-standing open questions in astrophysics: where the universe’s gold comes from.

According to the 2017 paper in Nature by Daniel Kasen of UC Berkeley, Brian Metzger of Columbia, and colleagues, the GW170817 event produced and ejected heavy elements totalling approximately 6 percent of a solar mass — roughly 20,000 Earth-masses of material — including about 200 Earth-masses of gold and nearly 500 Earth-masses of platinum, plus comparable quantities of uranium and other elements heavier than iron. The team’s models, developed over the preceding decade in anticipation of exactly this kind of observation, matched the optical and infrared afterglow of the event with sufficient precision to characterise the composition of the ejected material. The colliding neutron stars had assembled, in the violence of their merger, hundreds of times more gold than exists in the entire mass of Earth.

Why ordinary stars cannot make gold

The elements heavier than iron — including silver, gold, platinum, lead, mercury, and uranium — present a problem for the standard theory of stellar nucleosynthesis. Ordinary stars fuse hydrogen into helium, then helium into carbon, and continue fusing progressively heavier elements all the way up to iron. The process releases energy, which is what makes stars shine. But fusion stops working at iron. Combining iron nuclei into heavier elements requires an input of energy rather than producing one, so ordinary stellar fusion cannot proceed past iron. Some heavier elements form slowly in giant stars via a process called slow neutron capture, or s-process, which can build elements up to bismuth. The heaviest elements, including gold, require something else.

The “something else” turned out to be a process called rapid neutron capture, or r-process. In r-process nucleosynthesis, atomic nuclei are bombarded with so many free neutrons in such a short time that they absorb neutrons faster than they can decay, building up to extremely heavy and neutron-rich isotopes which then beta-decay into stable heavy elements. The conditions required — enormously high neutron densities, sustained for fractions of a second — exist almost nowhere in the universe. For decades, the leading candidates were rare types of supernovae and the collisions of compact stellar remnants. The 2017 detection settled at least part of the question. Neutron-star mergers really do produce heavy elements via r-process nucleosynthesis, and they do so in quantities sufficient to enrich entire galaxies.

What a neutron-star merger looks like

A neutron star is what remains when a massive star runs out of fuel, collapses, and explodes as a supernova, leaving behind a dense core. A typical neutron star contains roughly 1.4 times the mass of the Sun, compressed into a sphere about 10 kilometres across. A teaspoon of neutron-star material weighs roughly a billion tonnes. The density is comparable to that of an atomic nucleus, because the star is essentially a single giant nucleus made of densely-packed neutrons. When two neutron stars exist in a binary system, they slowly lose energy through gravitational wave emission and spiral inward over geological timescales. The final phase of the inspiral, when the two stars are within a few kilometres of each other, can complete in fractions of a second. The GW170817 neutron stars were spinning around each other more than 300 times per second in the final moments before merger.

The collision itself is described in the literature as a kilonova — a term coined by Brian Metzger and colleagues in 2010, who calculated that the light from a neutron-star merger would be approximately one thousand times brighter than a typical nova explosion but much fainter than a typical supernova. According to Lawrence Berkeley National Laboratory’s coverage of the GW170817 detection, the radioactive decay of the freshly synthesised heavy elements in the ejected debris is what makes a kilonova glow. The team’s models had predicted that this glow would be “tinged red if heavy elements were produced,” distinctive enough to identify the kilonova by its colour signature. The 2017 observations matched the predictions in detail, providing the first direct spectroscopic evidence of r-process nucleosynthesis as it happens.

What this means for the gold on Earth

The gold in any wedding ring, any coin, any bar of bullion sitting in any bank vault on Earth, was produced in events like GW170817 that occurred long before the Solar System existed. The Sun and its planets formed approximately 4.6 billion years ago, from a cloud of interstellar gas and dust that had been enriched, over previous billions of years, by the ejecta of supernovae and kilonovae from the prior generations of stars in the Milky Way. The heavy elements in that cloud, including all the gold, had been scattered across hundreds of light-years by the violence of their original production. The cloud collapsed under its own gravity, the Sun formed at the centre, and the remaining material accreted into the planets. Earth inherited its share of pre-existing heavy elements from this enriched cloud.

The Earth as a whole contains roughly 1.6 × 10²¹ grams of gold, most of it in the planet’s core, where it sank during the molten phase of Earth’s early history. The gold accessible at Earth’s surface, and therefore the gold that has been mined throughout human history, represents a small fraction of the total — itself the result of a late veneer of asteroid impacts that delivered fresh heavy elements to the crust after the core had finished forming. Every gold atom in human possession spent billions of years in interstellar space before it became part of Earth, and was produced billions of years before that in the collision of two dead stars somewhere in the early Milky Way or one of its progenitor galaxies.

The 2017 confirmation also left an open question, which is still under active investigation. According to a 2024 analysis by the astrophysicist Ethan Siegel, the rate of observed neutron-star mergers may be too low to fully account for the abundance of gold and other heavy elements in the present-day universe. Other mechanisms — including a rare type of supernova called a collapsar, in which a massive star’s core collapses directly to a black hole, and magnetar giant flares, in which the magnetic fields of highly magnetised neutron stars rearrange catastrophically — may contribute additional r-process production. The 2017 event confirmed that neutron-star mergers produce gold. It did not settle whether they produce all of it. What is settled is that most of the gold on Earth was forged in events of cosmic violence whose like has not been seen near our solar system since long before our solar system existed.

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The conversation women aren’t having with their doctors about menopause and memory loss isn’t just overdue — it may be one of the most important health decisions of their fifties

Most conversations about menopause, to the extent they happen in a clinical setting at all, start and end at the same set of symptoms. Hot flashes. Night sweats. Sleep disruption. Mood changes. These are real, they are common, and for many women they are severe enough to significantly affect quality of life. But they are also, in an important sense, the surface of a much deeper physiological story — one that involves the brain directly, in structural and functional terms, and one that most women are not hearing from the people who are supposed to be helping them navigate this transition.

The cognitive dimension of menopause — the memory changes, the concentration difficulties, the particular kind of mental fatigue that many women in their late forties and fifties describe — has been systematically underrepresented in clinical guidance and research funding for decades. That is beginning to change, but the change is arriving slowly, and the practical consequence is that women are frequently left to interpret their own symptoms without context, without a framework, and without information about interventions whose effectiveness is, at this point, reasonably well supported by evidence — provided the timing is right. The timing, it turns out, is everything.

What the brain actually goes through

A 2026 review published in The Lancet titled “Advances in understanding of cognitive symptoms during menopause” brought together the current state of evidence on what happens neurologically during this transition, and the picture it presents is more specific and more structural than the popular understanding of menopause typically includes. Estrogen is not merely a reproductive hormone. It has well-documented neuroprotective effects — it supports synaptic plasticity, promotes the production of acetylcholine (a neurotransmitter central to memory and attention), and appears to modulate the brain’s inflammatory response. When estrogen levels decline during the menopausal transition, the brain is not simply losing a hormone. It is losing a system of support it has relied on throughout adulthood.

The structural consequences are measurable. Research cited by the Menopause Society has documented reductions in gray matter volume in the frontal and temporal cortex and in the hippocampus — precisely the regions involved in memory formation, executive function, and the ability to hold and manipulate information in working memory. These reductions are not subtle on a population level. They are consistent enough across studies to be considered a feature of the menopausal transition rather than an incidental variation. What this means, practically, is that the brain fog many women report during perimenopause is not psychosomatic, not a side effect of stress or poor sleep alone, and not a symptom that politely awaits acknowledgment before making itself felt in daily life.

The cognitive symptoms women are experiencing but not naming

There is a particular kind of suffering that comes from experiencing symptoms you cannot name, in a domain where your reports have historically been met with skepticism or normalization. Many women going through perimenopause describe a cognitive texture that is difficult to articulate precisely because it is diffuse — not a single dramatic deficit but a constellation of subtle difficulties that compound over time. Forgetfulness that feels qualitatively different from ordinary absentmindedness. Difficulty holding a thread of thought through a complex task. A kind of mental friction that wasn’t there before, an extra effort required to do things that previously felt automatic.

The research vocabulary for this cluster of experiences covers attention, working memory, verbal memory, and executive function — all the cognitive capacities associated with the prefrontal and hippocampal regions where gray matter reductions have been documented. The SWAN (Study of Women’s Health Across the Nation) cohort, which has followed women longitudinally through the menopausal transition for over two decades, found that cognitive performance declines measurably during perimenopause. Crucially, the SWAN data also suggests that this decline may not be permanent — there is evidence of possible reversal, or at least stabilization, in the postmenopausal phase as the brain adapts to its new hormonal environment.

That potential reversal is important information. It means that what women experience during perimenopause is not necessarily a preview of permanent cognitive decline but a transition period with its own arc — one that the brain navigates, imperfectly and with varying degrees of difficulty, toward a new equilibrium. The problem is that understanding this arc, and making informed decisions about whether and how to intervene, requires a conversation that is not yet happening routinely in clinical settings.

The timing problem with hormone therapy

The most consequential piece of information in the current evidence base — and the one most likely to remain unshared in a routine clinical visit — is that the effectiveness of hormone therapy for cognitive outcomes is not uniform across time. It depends critically on when treatment is initiated, and the window during which initiation appears most beneficial is the same window during which most women are still actively navigating the transition and most actively need support.

An observational study published in Neurology found that estrogen therapy initiated in midlife — during or shortly after the menopausal transition — was associated with improved verbal memory. The same intervention initiated later in life showed no such association. This is not a minor calibration note. It is a fundamental characteristic of how the intervention works, and it means that a woman who waits until her sixties to discuss hormone therapy with a doctor, perhaps because the cognitive conversation never happened in her fifties, may have missed the window during which that therapy could have meaningfully supported brain health.

This timing dependence is sometimes described as the “critical window hypothesis” — the idea that the neuroprotective effects of estrogen are most available when the brain’s estrogen receptors are still responsive and the menopausal transition is still underway. The research supporting this hypothesis is actively contested. A 2025 meta-analysis in The Lancet Healthy Longevity, applying stricter risk-of-bias criteria, found no evidence for a cognitive benefit tied to the timing of hormone therapy. Other analyses, including a Weill Cornell meta-analysis of 34 randomised trials, found timing-dependent effects on verbal memory for certain formulations. The broad signal is present in parts of the literature, but it is not yet settled science. Individual variation, hormonal formulation, and interaction with other risk factors all affect outcomes in ways the research has not fully resolved.

But the broad signal — that earlier intervention is more effective than later intervention for cognitive outcomes — is consistent enough that leading researchers have begun calling explicitly for earlier, more routine discussion of these options with patients.

The UK Royal College of Obstetricians and Gynaecologists identified the cognitive effects of menopause as one of its top ten research priorities — a designation that reflects both the seriousness of the issue and the relative thinness of the clinical infrastructure currently built around it.

Why the conversation isn’t happening

The reasons the conversation isn’t happening are multiple, and none of them are particularly flattering to the systems involved. Menopause has historically been undertreated and under-researched relative to its prevalence and impact. The WHI study of the early 2000s, which raised concerns about hormone therapy and was widely interpreted as a broad warning against it, cast a long shadow over the field — even though subsequent analysis substantially revised that picture, particularly for younger women and for the specific question of cognitive outcomes. That shadow has been slow to lift from clinical practice.

There is also the matter of consultation time. A standard appointment is not well structured for a conversation that requires explaining neurological mechanisms, walking through evidence about timing and formulation, discussing individual risk factors, and arriving at a genuinely informed decision. Many women do not bring the cognitive symptoms up, partly because they are uncertain whether they are real or significant, partly because they have absorbed the cultural message that menopause is something to be endured rather than managed. And many clinicians, even those who are receptive, do not ask — either because it falls outside their training, because they are uncertain of the evidence, or simply because the appointment ends before the topic arises.

What changes if the conversation does happen — earlier, more routinely, and with better information on both sides — is that women can make decisions about their own brain health during the window in which those decisions carry the most weight. Not all women will want or be appropriate candidates for hormone therapy. There are legitimate individual differences in risk profile, personal preference, and clinical judgment that should shape those decisions. But the decision cannot be made well if the information never arrives. The current situation, in which timing matters enormously and most women are not told that timing matters, is not an acceptable equilibrium — and the evidence base is strong enough that calling for more routine clinical discussion is not premature.

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Laughter activates many of the same brain reward circuits as food and sex, and a 2025 study finds it measurably lowers cortisol and may restructure how the developing brain builds resilience to stress

We tend to think of laughter as a social performance — the audible signal that something is funny, the punctuation on a joke well received. Even people who study emotion professionally can drift into treating laughter as essentially expressive, as the outward visible surface of an inner state. But a growing body of research is pushing back against that framing, and the pushback is coming from neuroscience rather than philosophy.

Laughter, it turns out, is a biological event with measurable consequences for the hormonal environment, the neural reward system, and — in the case of children — the actual architecture of the developing brain. It is not merely the sign of a good mood. It is, in important respects, a driver of one.

A 2026 book, “The Brain Loves to Laugh” by Dr. Jacqueline Harding, an early childhood researcher at Middlesex University, published by Routledge, brought a degree of biological specificity to this question that has rarely been attempted at the developmental level. Harding’s analysis synthesized research across neuroscience, developmental psychology, and endocrinology to ask what laughter does to the brain — not in the abstract, but in the physiological and structural sense, and particularly during the period when the brain is most susceptible to experience-dependent shaping. The findings complicate the idea that laughter is something that happens to children. They suggest it is something that happens inside them, at a level that shapes who they become.

The reward circuit connection

One of the more striking findings in Harding’s analysis is the mapping of laughter onto the brain’s mesolimbic reward system — the same distributed network activated by food, sex, social bonding, and other stimuli that evolution has decided are worth pursuing. This is not a metaphor about how laughter feels good. It is a description of neural architecture. The experience of genuine laughter recruits the ventral tegmental area, the nucleus accumbens, and the prefrontal cortex in patterns that overlap substantially with other primary rewards. Dopamine is released. So are serotonin, endorphins, and oxytocin.

What this means, from a developmental standpoint, is that laughter is not a secondary or incidental feature of a child’s emotional life. It is wired into the same motivational circuitry that drives learning, attachment, and the pursuit of pleasure more broadly. The child who laughs is not simply reacting — their brain is generating the same neurochemical conditions associated with reward and approach behavior that are foundational to motivated engagement with the world.

This helps explain something that developmental researchers have noted for decades but struggled to fully account for: the surprising intensity with which young children seek out the experiences and people that make them laugh, long before they have language to explain why.

It also reframes laughter’s developmental timeline. Laughter precedes speech — children laugh reliably before they produce words, and the emergence of shared laughter between caregiver and infant is one of the earliest markers of social bonding. The fact that this emerges so early, and that it maps onto the same reward circuitry as other primary biological drives, is not coincidental. It appears to be how the social brain bootstraps itself into function before language is available to do the same work.

What the cortisol data shows

Beyond the reward system, Harding’s analysis is specific about what laughter does to the hormonal environment — and the finding that has attracted the most attention is the effect on cortisol. Cortisol is the primary stress hormone in humans, produced by the adrenal glands in response to perceived threat or demand. It is not inherently harmful — cortisol plays important roles in metabolism, immune function, and alertness — but chronically elevated cortisol is associated with a wide range of negative outcomes, and in developing children, sustained cortisol elevation has particular consequences for neural development that research has tracked with increasing precision.

Laughter, Harding’s analysis found, physically lowers circulating cortisol. This is not a claim about mood or subjective wellbeing. It is a measurable change in the hormonal environment, and it comes paired with a reduction in epinephrine — the other major stress-response neurochemical — while simultaneously raising the neurochemicals associated with positive affect and social connection. A systematic review and meta-analysis of interventional studies on spontaneous laughter and cortisol levels provides convergent evidence for this effect across populations, and a 2025 meta-analysis of laughter interventions in children found large effect sizes for anxiety reduction in pediatric patients — specifically in hospital settings using structured clown-therapy interventions. This suggests the hormonal mechanism has meaningful real-world consequences, not just lab-based correlates.

The phrase “physically lowers cortisol” is worth pausing on. It is not unusual, in popular writing about emotional states, to describe psychological experiences in language that implies biological reality without committing to it. The research here does commit. When a person laughs — genuinely laughs, not a performed social laugh but the involuntary kind — the body produces less of the hormone associated with threat-response and more of the hormones associated with approach, bonding, and reward. That is a biological event. Its consequences are biological consequences.

How this restructures the developing brain

The most significant dimension of Harding’s analysis, from a developmental perspective, is the argument about what repeated emotional experiences do to the architecture of a young brain. Early emotional states, she argues, do not merely pass through a child — they become embedded in its neural structure. The brain develops in the context of its dominant emotional environment, and the circuits that are most frequently activated during early childhood are the circuits that develop most robustly. This is a version of the Hebbian principle — neurons that fire together wire together — applied to affective experience at scale.

The implication is that a child who experiences frequent shared laughter is not simply having more pleasant moments than a child who does not. They are developing, gradually and through repetition, a brain that has built stronger infrastructure around the states associated with those moments: reward, safety, approach, connection, the resolution of playful tension. The prefrontal network that laughter activates — and that humor, as a cognitively demanding activity requiring the resolution of conflicting ideas, exercises with particular intensity — is the same network involved in executive function, emotional regulation, and the management of stress.

This last point about humor as cognitive work is underappreciated. Harding’s analysis notes that humor is genuinely demanding — understanding a joke requires holding two incompatible frameworks simultaneously and resolving the incongruity between them. That is not a trivial cognitive task, and doing it repeatedly appears to exercise the neural machinery of flexible thinking in ways that have downstream effects on cognitive and emotional resilience. The child who laughs a lot is, in this account, also a child whose brain is being worked in particular ways that matter for development.

The co-regulation dimension of this is equally important. When an adult and child share laughter — when the adult’s face and voice and body communicate delight, and the child’s nervous system responds to that signal — what is happening is not merely bonding in the social sense. Research into parent-child co-regulation during positive shared experiences — including play and laughter — has found measurable physiological coordination between caregiver and child, including heart rate alignment and coordinated brain activity, suggesting their nervous systems are actively attuned during these moments.

The child’s limbic system is, through that alignment, acquiring a working model for what regulated emotional states look like and feel like — a model it can eventually deploy independently. Co-regulation through shared joy is, in this sense, a form of instruction in self-regulation that requires no words and no deliberate teaching.

What remains when the laughter fades

There is a temptation, when encountering research like this, to reach immediately for prescriptions — to convert findings about laughter and neural development into a program, a set of recommendations, a checklist of things parents should do more often. That is probably not the most useful response to what the science is showing. The research does not describe a deficit to be corrected. It describes a mechanism that is already operating in most children’s lives, in the ordinary texture of play and silliness and shared delight that tends to happen naturally when adults and children spend time together without too much pressure on either side.

What the neuroscience adds is a more accurate description of what is actually happening during those moments. The child who collapses in giggles is not simply expressing happiness. Their hypothalamic-pituitary-adrenal axis is producing less cortisol. Their reward network is receiving a signal that the present moment is safe and worth approaching. Their prefrontal circuitry is being exercised in ways that contribute to cognitive flexibility and emotional regulation.

Their nervous system is synchronizing with the nervous system of the person laughing with them, and that synchrony is building a model they will carry forward. None of this requires anything more complicated than what most adults, at their best, already bring to the children in their lives. The science is not an instruction manual. It is an explanation for something that was already working.

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Tomatoes were considered poisonous in much of Europe for nearly two centuries after they were introduced from the Americas — not because of anything dangerous in the fruit itself, but because the plant belongs to the same botanical family as deadly nightshade, with leaves and stems that really do contain toxic alkaloids, and the misunderstanding kept the fruit out of European cuisine until the late 1700s

The tomato is generally believed to have arrived in Europe in the early 16th century, brought by Spanish explorers — most likely Hernán Cortés after the conquest of Tenochtitlán in 1521. For the next two and a half centuries, Europeans grew the plant in their gardens and refused to eat it. Tomatoes appeared in herbals, in botanical illustrations, in ornamental displays, and in private correspondence between curious naturalists. They did not appear on European dinner plates in any meaningful way until the late 1700s. The reason was not pewter plates, lead poisoning, or any specific incident. The reason was botanical resemblance. The tomato is a member of the nightshade family, Solanaceae, and Europeans correctly identified that the plant was related to species they already knew to be dangerous.

What Europeans actually saw

The earliest documented European reference to the tomato as food comes from the Italian herbalist Pietro Andrea Mattioli, who described the fruit in 1544 and classified it as a type of mandrake. According to a 2025 Smithsonian Magazine update on the cultural history of the tomato, originally published in 2013 by K. Annabelle Smith and updated by Kayla Randall, Mattioli’s classification of the tomato as a mandrake “had later ramifications.” The classification was not arbitrary. Mandrake (Mandragora officinarum) is a real plant, used in European folk medicine and witchcraft traditions for centuries, and it is genuinely poisonous. It is also a member of the same family, Solanaceae, as the tomato. So is deadly nightshade (Atropa belladonna), the bittersweet nightshade (Solanum dulcamara), the black nightshade (Solanum nigrum), henbane (Hyoscyamus niger), and a number of other plants well known in early modern Europe to be variously poisonous, narcotic, or hallucinogenic.

From a 16th-century botanist’s perspective, the tomato fitted neatly into a known family of dangerous plants. The leaves were similar in shape to those of black nightshade. The flowers were similar to those of belladonna. The fruit was small and round, in the early imported varieties yellow or pale red rather than the large red tomatoes of modern cultivation. Mattioli’s classification was reasonable. The tomato was related to plants that killed people. The natural assumption, in the absence of any direct experimental evidence to the contrary, was that the tomato also killed people.

The leaves and stems are actually toxic

The assumption was not entirely wrong. The leaves and stems of the tomato plant do contain alkaloids that would, in sufficient quantity, make a person sick. The principal compound is tomatine, a glycoalkaloid related to solanine, the better-known toxic compound in green potatoes. According to a peer-reviewed review on tomato glycoalkaloid toxicology published in Food Chemistry in 2024, tomatine concentrations are highest in the stems, roots and leaves of young tomato plants, and progressively diminish as the fruits ripen. The compound can cause vomiting, diarrhoea, abdominal pain and lethargy in mammals at sufficient dose, and acts as a natural fungicide and insecticide protecting the plant from microbial and herbivore attack.

An adult would have to consume a substantial quantity of tomato leaves to experience symptoms, but the toxic compounds are real. Livestock occasionally poisons itself by overgrazing on tomato plants. Children who chew on the leaves can become mildly unwell. The plant defends itself against insects and grazers with the same chemistry that makes the rest of the family a poor choice for casual experimentation. Europeans observing the tomato as a new species, examining its leaves, smelling its pungent foliage, and noting its membership in a recognised family of poisonous plants were not making a foolish judgement. They were making a botanically defensible one. The error was specifically in extending that judgement to the ripe fruit.

Two hundred years of hesitation

The tomato’s reputation in Europe was therefore a botanical reputation rather than an experiential one. The fruit was not actually killing anyone. No physician of the 1600s could point to a chain of confirmed tomato-related deaths. The plant was simply assumed dangerous on the grounds that its relatives were dangerous, and the assumption persisted in the absence of widespread tests of the contrary.

In the warmer climates of southern Europe, where tomatoes grew more readily, this reluctance eroded first. Italian cookbooks began including tomato recipes in the late 17th century. The earliest known recipe for tomato sauce was published in Naples in 1692, in Antonio Latini’s two-volume cookbook Lo Scalco alla Moderna (“The Modern Steward”). Latini, a former orphan who had risen to become steward to the first minister of the Spanish viceroy of Naples, included a recipe for “Salsa di Pomodoro alla Spagnuola” — tomato sauce in the Spanish style — calling for roasted and peeled tomatoes mixed with chopped onion, chilli, thyme, salt, oil and vinegar. The sauce was intended for meat and fish rather than pasta, but it marked the formal entry of the tomato into recorded European cooking. By the 18th century, tomatoes were a regular feature of Mediterranean cuisine, particularly in southern Italy and Spain. In the colder regions of northern Europe — England, the Netherlands, Germany, the Scandinavian countries — the suspicion lasted longer. English gardeners grew tomatoes ornamentally well into the 1800s, and the fruit only entered British and American kitchens widely in the second half of the 19th century.

The popular story that the tomato’s bad reputation was driven by lead leaching from pewter plates has been widely repeated in food-history journalism, but the explanation has been challenged by chemists. As cited in the Smithsonian Magazine update, Dr. Joe Schwarcz, director of the Office for Science and Society at McGill University in Montreal, has dismissed the lead-leaching story directly, noting in a 2023 video for the Montreal Gazette that “the amount that would be leached out would be trivial, and you’d never get sick from it.” Wine and vinegar — both more acidic than tomatoes — were extensively consumed from pewter vessels throughout the medieval period without producing any analogous panic. The pewter story, in the form that has circulated in recent decades, appears to be a colourful retrospective explanation rather than a documented historical cause.

How the fruit eventually won

The rehabilitation of the tomato in northern Europe and North America happened slowly through the 18th and 19th centuries, driven by gradually accumulating evidence that the fruit was, in fact, safe to eat. According to the Thomas Jefferson Foundation’s Monticello research, Jefferson was an early American adopter of the tomato. His butler Étienne Lemaire purchased tomatoes for Presidential dinners during Jefferson’s time in office, and Jefferson’s garden book records the planting of tomatoes at Monticello from 1809 until 1824. Jefferson had referred to tomatoes as a common Virginia garden plant in his 1781 Notes on the State of Virginia, suggesting they were already familiar in some American gardens before the wider rehabilitation got underway. In Salem, New Jersey, in 1820, a local gentleman named Robert Gibbon Johnson is said to have eaten a basket of tomatoes on the steps of the courthouse in front of a curious crowd, with no ill effects. The story is colourful and probably embellished, but it captures the social process accurately. The tomato was a fruit Europeans and Americans had to decide, collectively and gradually, to start eating.

By the end of the 19th century, the rehabilitation was complete. The development of Pizza Margherita in Naples in 1889, the spread of Italian immigration to the United States in the late 19th and early 20th centuries, and the rise of tin canning all helped the tomato establish itself as a staple ingredient in Western cuisine. The original botanical worry — that the tomato belonged to a family of poisonous plants — remained literally true. It was just that the fruit itself, of this one member of that family, turned out to be harmless. Two centuries of European caution had been based on a reasonable inference that happened to be wrong about which parts of the plant were dangerous.

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Saudi Arabia imports sand — despite being a country dominated by desert, the sand of the Arabian Peninsula is too smooth and round for use in construction concrete, because thousands of years of wind erosion have polished the grains to a shape that does not bond well with cement, and most construction sand must be imported from countries like Australia

In 2023, Saudi Arabia paid approximately $140,000 to import construction-grade sand from Australia. The figure is small in dollar terms, but the trade flow it represents is the visible part of a much larger phenomenon. Saudi Arabia, a country dominated by some of the largest sand deserts on Earth, cannot use the sand in its own deserts for most construction work. The grains are the wrong shape. They have been polished smooth by thousands of years of wind erosion, and smooth round grains do not bond with cement in the way that concrete requires. Construction-grade sand has to come from somewhere else — from rivers, lakes, seabeds, or quarries, where the grains have been broken sharp by water rather than rounded by air. For a country building cities the size of NEOM and the Red Sea Project, the result is that even abundant sand is the wrong sand, and imports become a structural feature of the economy.

Why desert sand fails

The materials science is straightforward and well understood. Sand grains come in a range of shapes, depending on how they were produced. Grains that have been carried by rivers, tumbled in streams, ground by glaciers, or pulverised in quarries tend to have rough, angular surfaces. They lock together when packed, like irregular puzzle pieces, and they bond mechanically with the cement paste in concrete to produce a strong composite material. Grains that have been carried by wind, in contrast, behave very differently. Each collision between airborne grains, repeated countless times across the dunes of a desert, slightly rounds off the corners and edges. After thousands of years of this process, the grains end up smooth, spherical, and uniform in size. They are beautiful under a microscope. They are nearly useless in concrete.

The behaviour of desert sand in a wet concrete mix is sometimes described in industry literature as resembling ball bearings. Smooth round grains slide past one another rather than interlocking. They fail to engage mechanically with the cement paste, leaving microscopic voids and weak interfaces throughout the cured material. Concrete made primarily with desert sand cracks more easily, weighs more for the same strength, and ages worse than concrete made with angular sand. For a small structure, the difference might be tolerable. For a 200-storey skyscraper or a kilometre-long bridge, it is not. The structural engineers responsible for Saudi Arabia’s mega-projects need sand whose grains can do their structural job, and the grains in the Empty Quarter cannot.

What gets imported, and from where

The construction sand reaching Saudi Arabia comes from a small number of countries with abundant water-eroded sand reserves and the export infrastructure to ship it economically. Australia is among the most important suppliers. According to the Observatory of Economic Complexity, Australia exported approximately $273 million worth of sand in 2023, making it the second-largest sand exporter in the world. Saudi Arabia, the United Arab Emirates, and other Gulf states are among the regular destinations. Australia’s geological history, with its rivers, quarries, and glacial deposits, has produced the sharp-grained sand that concrete production requires. Australia ships it. The Gulf buys it.

The asymmetry is sometimes treated as an absurdity, the kind of fact that seems to contradict common sense, but it is the logical outcome of how sand actually forms. According to a 2026 analysis by Gulf Good News referencing UN Environment Programme research, Dubai’s Burj Khalifa required approximately 330,000 cubic metres of concrete, with most of the sand component imported from overseas because local desert sand could not provide adequate structural strength. The Palm Jumeirah artificial island in the UAE consumed 94 million cubic metres of marine sand, dredged from specific locations in the Persian Gulf where the grain size was suitable, and even that supply could not be drawn from the surrounding desert. The pattern repeats across the Gulf. Mega-construction demands angular sand. Local deserts cannot supply it. Foreign rivers, quarries, and seabeds do.

The wider problem

Saudi Arabia’s situation is the most counterintuitive example of a global pattern. According to the May 2026 United Nations Environment Programme report Sand and Sustainability: An Essential Resource for Nature and Development, the world extracts approximately 50 billion tonnes of sand and gravel every year — a fivefold increase since 1970, when annual extraction stood at 9.6 billion tonnes. Sand is now the second most consumed natural resource on Earth, after fresh water. Demand has grown at an average annual rate of 3.2 percent over the past half-century, and UNEP projects that demand for sand used in buildings alone could rise by up to 45 percent by 2060. The volume of sand humanity already uses each year is enough to construct a wall 27 metres high and 27 metres thick around planet Earth.

The UNEP report emphasises that most of this sand cannot come from deserts. The reserves usable in construction are concentrated in river systems, coastal areas, and continental shelves, and they are being extracted faster than geological processes can replace them. The result is what UNEP describes as the “sand gap,” in which unregulated sand mining is causing riverbed erosion, the destruction of marine habitats, the collapse of beach ecosystems, and the disappearance of small islands. The countries that supply construction sand are paying environmental costs to do so. The countries that import it are insulated from those costs only because their geography happens to produce the wrong kind of sand.

The longer-term response is shifting toward alternatives. Manufactured sand, produced by mechanically crushing rocks into the angular grain shapes that concrete requires, is becoming an increasing share of construction supply in countries that have started to take the problem seriously. Recycled concrete, in which old buildings are crushed and re-incorporated into new ones, is another partial solution. Saudi Arabia itself is investing in both approaches, and is considering domestic manufactured-sand production as part of its Vision 2030 infrastructure plans. The total amount of imported sand the country actually requires for any given year is therefore a moving figure, dependent on how quickly alternatives scale. What stays constant is the underlying physics. The grains in the Arabian deserts have been rounded by the wind for thousands of years. They will not bond with cement. The country that has more sand than almost any other still has to buy the sand it can actually use.

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Sequoia and redwood trees alive today were already mature when the Roman Empire was at its peak — the oldest living giant sequoias are over 3,000 years old, which means they were standing in California before the Parthenon was built in Athens, before Julius Caesar was born, and before the Roman Empire even existed

In Sequoia National Park in California, a giant sequoia named the President stands 247 feet tall, carries roughly 2 billion leaves, and has been alive for about 3,200 years. It germinated as a small seed in the late twelfth century BC. At the time, the New Kingdom of Egypt was building additions to the temples at Karnak. The Trojan War, if it actually happened, was still within living memory. The first Olympic Games would not be held for another four centuries. The founding of Rome, in the traditional reckoning, would not occur for another 425 years. The Parthenon would not be built for another seven centuries. Most of recorded human history is younger than the President tree.

The President is not even the oldest known giant sequoia. According to a National Park Service history of sequoia age estimates, the Grizzly Giant in Yosemite’s Mariposa Grove was estimated at 3,800 years of age by early 20th-century researchers, and was considered by many to be the oldest of all living giant sequoias. Other historic estimates pushed individual trees as old as 4,000 years. The most carefully verified figures, based on ring counts of stumps in the early 20th century, identified a small number of giant sequoias more than 3,000 years old, with the oldest at about 3,200 years.

Why sequoias live so long

The biological mechanism behind sequoia longevity is a combination of features that together produce an organism unusually resistant to the ordinary causes of tree death. The bark of a mature giant sequoia is up to three feet thick, fibrous, and rich in tannins that deter insects and fungi. The wood itself contains compounds that resist decay, so even fallen sequoia logs remain intact for centuries. The trees are notably fire-adapted: their thick bark insulates the living cambium beneath, and surface fires that would kill most species sweep through a sequoia grove without ending the trees’ lives. In fact, periodic fires are required for sequoia reproduction. Sequoia cones release their seeds in response to the heat of a passing fire, exposing the freshly cleared mineral soil that seedlings need.

The biggest threat to a mature giant sequoia is not age but mechanical failure. The trees grow so large that root systems eventually become unable to anchor them against high winds, particularly in saturated soil after heavy storms. Most sequoias that die in old age do so by falling over rather than by senescence in any biological sense. There is no indication that giant sequoias have a maximum natural lifespan in the way most species do. They simply continue to grow, slowly, for as long as they remain upright. According to Atlas Obscura’s profile of the General Sherman tree, the largest sequoia by volume is “adding volume faster than ever, overturning previous theories that trees grow more slowly as they get bigger.”

Two species, often confused

The popular phrase “sequoia and redwood” conflates two distinct species. The giant sequoia (Sequoiadendron giganteum), confined to about 75 groves on the western slope of the Sierra Nevada in California, is the species producing the famous 3,000-year-old specimens. The coast redwood (Sequoia sempervirens), found along the Pacific coast from southern Oregon to central California, is a closely related but separate species. Coast redwoods are the tallest trees on Earth, with the current record-holder, Hyperion, measuring 380 feet, but they typically do not live as long as giant sequoias. The oldest documented coast redwoods are around 2,200 years old. The world’s oldest individual tree of any kind is neither a sequoia nor a redwood, but a bristlecone pine (Pinus longaeva) named Methuselah, growing in California’s White Mountains, with a verified age of approximately 4,855 years.

What makes the giant sequoias famous is the combination of their longevity with their sheer mass. The General Sherman tree, the largest single-stem tree in the world by volume, contains roughly 1,487 cubic metres of wood and weighs over 2,000 tons. The National Park Service’s current age estimate places it at about 2,200 years, with other published figures ranging from 2,200 to 2,700 years depending on the dating method. Earlier 20th-century estimates were considerably higher, reaching 3,500 years or more, but successive scientific revisions have brought the figure down. The uncertainty reflects the practical difficulty of dating a living tree without coring it deeply enough to count its innermost rings, which for the oldest sequoias would require boring through metres of dense, often partially-rotten heartwood.

What 3,200 years actually looks like

The historical context becomes more striking the more carefully you spell it out. A tree that germinated in 1,175 BC was already a sapling when the Phoenician alphabet was being developed. It was approximately 425 years old when Rome was founded in 753 BC by the traditional reckoning. It was approximately 728 years old when work began on the Parthenon in 447 BC. It was approximately 1,075 years old when Julius Caesar was born in 100 BC. It was approximately 1,200 years old when the Roman Empire was founded under Augustus in 27 BC. It was approximately 1,290 years old when the Roman Empire reached its territorial peak under Trajan around 117 AD.

The tree was already ancient by every meaningful measure when the Western Roman Empire fell in 476 AD. It had been alive for 2,460 years when the Norman Conquest of England occurred in 1066. It had been alive for 2,975 years when the United States declared independence in 1776. It has now lived through the entire span of recorded human history of California’s indigenous Yokuts, Tubatulabal, and Mono peoples, who lived in proximity to the giant sequoia groves for the last several thousand years and who, by oral tradition, regarded the trees with religious significance. The first widely-documented European sighting of a giant sequoia, by Augustus T. Dowd, occurred only in 1852. From the tree’s perspective, that encounter happened less than 6 percent of its life ago.

The General Sherman, the President, the Grizzly Giant, and the other named individuals of the giant sequoia groves are quietly older than nearly every cultural reference humans use to anchor the deep past. They were there when those reference points happened. They are still there now.

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A single cumulus cloud — the kind that looks like a fluffy white pillow drifting across a summer sky — typically contains hundreds of tons of water by weight, suspended in the air because the water droplets are small enough that air resistance keeps them aloft against the pull of gravity

A typical fair-weather cumulus cloud, the kind that drifts across a summer sky looking like a fluffy white pillow, contains roughly half a million kilograms of water suspended in the air. Translated into more familiar units, that is about 1.1 million pounds, or 550 short tons (about 500 metric tonnes). Comparable weights include 100 adult African elephants, five adult blue whales, or rather more than a fully loaded Boeing 747. The cloud weighs all of this, and yet it does not fall. The reasons it does not fall combine three distinct physical mechanisms, working together rather than in order of importance.

The calculation is straightforward, and the United States Geological Survey’s Water Science School publishes the standard version of it. A typical cumulus cloud occupies roughly one cubic kilometre of atmosphere, or one billion cubic metres. Atmospheric scientists estimate the average liquid-water density of a cumulus cloud at about half a gram per cubic metre. Multiplying these two figures together gives 500 million grams, or 500,000 kilograms. The cloud is mostly empty air, with a small mass of water droplets distributed through a very large volume. The water is the heavy part. The air is the support.

Why a cloud floats: three contributing factors

The popular explanation for why clouds float treats them as a kind of mist of tiny droplets held aloft by air resistance against gravity. This is part of the story, but not the whole story. The full picture combines three physical effects, which together account for the cloud’s quiet suspension above the ground.

One factor, and the one most often missed in popular explanations, is buoyancy. The reason a cloud sits on top of the air below it is in some ways the same reason oil floats on water: the cloud-laden air is, on average, less dense than its surroundings, and the denser surrounding medium supports it from below. This sounds counterintuitive, because the cloud contains water and the air around it does not. The resolution is that water vapour, the gaseous form of water, is actually lighter than air. A water molecule (H₂O, molecular weight 18) is less massive than a nitrogen molecule (N₂, weight 28) or an oxygen molecule (O₂, weight 32), and a parcel of warm, moisture-laden air is therefore less dense than a parcel of cool, dry air at the same pressure. Once water has condensed into liquid droplets, the situation is more complicated, but the parent air parcel that produced the cloud is still typically warmer and less dense than the dry air around it.

A second factor is the active one. Cumulus clouds do not just hover; they are also held up by rising columns of warm air, called updrafts. According to NASA’s reference on convective cloud formation, cumulus clouds form when surface air warmed by the sun-heated ground rises, expands and cools as it ascends, and reaches the dew point at which water vapour begins to condense around airborne aerosol particles such as dust, sea salt or pollen. The condensation releases latent heat, which further warms the parcel and accelerates its rise. The result is a continuous updraft beneath and within the cloud, typically moving upward at metres per second. The water droplets are held aloft not by stillness but by motion: they are riding an active column of rising air.

The third factor is the one most often cited in popular accounts. The droplets themselves are extraordinarily small, with typical cumulus cloud droplets measuring roughly 20 micrometres in diameter, or 0.02 millimetres. A droplet of this size, falling through still air, has a terminal velocity of around 1 to 2 centimetres per second. Updrafts in even a modest cumulus cloud move air upward an order of magnitude faster than this. A droplet trying to fall at one centimetre per second through air rising at, say, two metres per second simply does not fall. It is suspended in a fluid whose net motion is upward, with only a small downward component contributed by its own weight. By comparison, a typical raindrop is about 2 millimetres across, a hundred times the diameter of a cloud droplet, and falls at roughly 9 metres per second, fast enough to fall through even strong updrafts.

What changes when it rains

The transition from a floating cloud to a raining one is a transition in droplet size. Cloud droplets grow primarily by collision and coalescence: small droplets, jostled by turbulence within the cloud, bump into and merge with their neighbours, gradually accumulating mass. A droplet that grows from 20 micrometres to 200 micrometres has increased its terminal velocity from about 1 centimetre per second to about 70 centimetres per second. A droplet that grows to 2 millimetres falls at 9 metres per second. At some point in this size progression, the falling speed exceeds the updraft speed, and the droplet begins to fall through the cloud rather than being carried along by it. The droplets that emerge from the bottom of the cloud as rain have grown roughly a thousand times in mass during their time within the cloud.

A thunderstorm cloud, technically a cumulonimbus rather than a cumulus, contains far more water than a fair-weather cumulus. A typical thunderstorm cloud can have a mass on the order of a million tons of water rather than five hundred tons, with a corresponding requirement for far more vigorous updrafts to keep it aloft. Updrafts in severe thunderstorms can exceed 30 metres per second, enough to hold hailstones the size of golf balls suspended for many minutes while they grow by accreting layer after layer of supercooled water. When the updraft finally weakens, all of that mass falls at once, which is why thunderstorms produce heavy precipitation in short bursts.

A fair-weather cumulus cloud, by contrast, is in a quiet equilibrium. It is roughly a kilometre across, contains roughly half a million kilograms of suspended water, and is held aloft by a combination of buoyancy from being warmer and less dense than its surroundings, an active updraft from the convection that produced it, and the very small terminal velocity of its constituent droplets. Take any of these three away and the cloud changes character: cool the parcel and it begins to sink; remove the updraft and it begins to settle and dissipate; let the droplets grow and it starts to rain. The cloud as it appears, suspended quietly above a summer field, is the product of three physical processes in balance.

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On a sunny day, the top of the Eiffel Tower slowly drifts in a small circle about six inches wide — it isn’t the wind, it’s the sun, heating one side of the iron at a time and making the whole tower lean a little away from whichever side is warmest

On a clear afternoon in Paris, the very top of the Eiffel Tower, 330 metres above the ground, is not exactly where it was at sunrise. It has moved a few centimetres to the west. By midday it has moved a few more centimetres to the south. By late afternoon it is several centimetres east of where it began. By the time the sun sets and the iron cools back to the surrounding air temperature, the top of the tower returns to its starting position. Over the course of a sunny day, the summit traces a slow, irregular curve roughly 15 centimetres in diameter, or just under six inches. The cause is not wind. Wind makes the tower sway and shudder on its own faster timescale. The cause of the slow daily circle is the sun.

According to the official Eiffel Tower website maintained by the Société d’Exploitation de la Tour Eiffel, “the sun only hits one of the 4 sides of the Tower creating an imbalance with the other 3 sides, that remain stable, thus causing the Eiffel Tower to lean. In this way, the sun’s movement over the course of a clear day can cause the top of the Tower to move in a more or less circular curve measuring approximately 15 centimetres in diameter.”

The physics, in three lines of arithmetic

The mechanism is the simplest thermal physics there is. Solids expand when they get warmer, because the atoms in them vibrate more vigorously and on average sit slightly further apart. The amount of expansion is described by the material’s coefficient of linear thermal expansion. According to a 2025 explainer in The Conversation by architecture professor Federico de Isidro Gordejuela, the puddled iron and steel components used in the Eiffel Tower have a coefficient of approximately 12 × 10⁻⁶ per degree Celsius. That figure means a one-metre iron bar grows by 12 micrometres for each degree Celsius of warming, which is roughly the width of a human hair.

The Eiffel Tower is not a one-metre bar. It is 330 metres tall after the installation of a new digital radio antenna in March 2022, which added 6 metres to the previous 324-metre height. The top of the original 1889 iron lattice structure, before any antennas, sits at 300 metres. Multiply 300 metres by 12 micrometres per metre per degree, and you get 3.6 millimetres of expansion per degree Celsius along the tower’s vertical axis. A 40-degree temperature change between a cold Paris winter and a sun-heated summer surface gives 14 centimetres of vertical expansion. The Eiffel Tower’s seasonal height range, as monitored by engineers with continuous strain-gauge readings, is between 12 and 15 centimetres, comfortably within what the simple calculation predicts.

Why the tower leans

The seasonal vertical expansion is uniform across the whole tower, because cold winters and hot summers heat all four faces about equally. The daily circular drift at the top is different. On a sunny day, the sun is in the east in the morning, the south at noon, the west in the afternoon. Each of these positions illuminates a different face of the tower’s four-sided lattice, while the other three faces remain in shade. The illuminated face heats up several degrees above the shaded faces, and that face expands more than the others. The result is that the tower bends, a few millimetres per metre of height, away from the sun.

Because the sun moves across the sky over the course of the day, the warmest face changes. The lean changes with it. In the morning, when the east face is illuminated, the top of the tower leans west. By noon, with the south face illuminated, the top leans north. In the afternoon, with the west face illuminated, the top leans east. After sunset, with no differential heating, the top returns to its starting position above the centre of the base. The trace of the top’s position over a full day is a roughly circular curve, with the irregularities reflecting variations in cloud cover and wind. The amplitude of the daily drift is about 7 centimetres in each horizontal direction, for a circle approximately 15 centimetres in diameter.

What Gustave Eiffel knew

The behaviour is not a surprise to the engineers who built the tower. Gustave Eiffel and his team, including the chief engineers Maurice Koechlin and Émile Nouguier, were well aware of thermal expansion when they designed the structure for the 1889 Exposition Universelle. Late-nineteenth-century iron-and-steel construction was already routine for railway viaducts and large bridges, and any competent engineer of the period understood that 300-metre iron structures would change shape with temperature. The tower’s design includes the lattice geometry and riveted joints that allow thermal movement to be distributed across thousands of small connections rather than concentrated in a few stressed points. The seasonal 15-centimetre rise and the daily lean are deliberately accommodated by the structure, not problems to be solved.

The same physics governs nearly every large engineered structure on Earth. The Garabit Viaduct, also designed by Eiffel and completed in 1884, is 565 metres long; the Forth Bridge in Scotland is 2.5 kilometres long; modern long-span bridges and tall buildings are all engineered with expansion joints, sliding bearings, or flexible connections to accommodate thermal movement. Railway tracks have expansion gaps welded into them at calculated intervals. The Eiffel Tower is the most visible of these examples because it is tall, slender, and isolated against the Paris sky, with its movement directly observable to anyone with a precise enough measurement system.

What you would see if you could see it

The drift is too slow to perceive in real time. A 15-centimetre circle traced over the course of a 10-hour day means the top is moving at an average speed of about half a centimetre per hour, far below the threshold of human visual detection. The Eiffel Tower has been continuously instrumented for structural monitoring since 2021, with GPS sensors, accelerometers, inclinometers, and strain gauges tracking the spire’s inclination, the deformations of its four pillars, and ambient temperature and humidity across the structure. The data shows the tower’s response to temperature, sunlight, wind, and the cycle of seasons with millimetre precision.

From the perspective of a tourist standing at the base, the tower looks as still as any building. From the perspective of the iron itself, the tower is in continuous quiet motion. The sun warms one face, that face stretches, the tower leans. The sun moves, the next face warms, the lean shifts. The structure that has dominated the Paris skyline since 1889 is, on any clear day, also a 7,300-tonne sundial, with the position of its summit telling you, within a few centimetres, where the sun is.

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Sound travels about four times faster underwater than it does through air — which is why whale songs can travel hundreds of miles across the ocean, and why early submarine sonar operators sometimes picked up the calls of distant whales communicating from hundreds of miles away

Sound in air travels at about 343 metres per second, the figure that fighter pilots cross when they break the sound barrier. Sound in seawater travels at about 1,500 metres per second. The ratio is roughly 4.3 to 1. The reason is straightforward physics: sound waves propagate through a medium by transferring vibrational energy between adjacent molecules, and the denser and stiffer the medium, the faster that transfer happens. Water is roughly 800 times denser than air, with much smaller intermolecular distances, and it transmits acoustic energy with much less loss per metre than air does. The same whale call that would fade to nothing within a kilometre in air can carry for hundreds of kilometres through the deep ocean.

According to NOAA’s Ocean Service reference on underwater sound, the speed of sound in seawater varies between roughly 1,450 and 1,540 metres per second depending on temperature, salinity and pressure. The first reasonably accurate measurement was made in 1826 on Lake Geneva by the Swiss physicist Jean-Daniel Colladon and the French mathematician Charles-François Sturm, who used an underwater bell, a flash of gunpowder for synchronisation, and two boats positioned ten miles apart. Their figure was within a few percent of the modern measurement, despite the relatively crude instruments available.

Why whale songs cross oceans

The speed of sound in water is only part of what makes long-distance whale communication possible. The other part is the way sound bends in the ocean. Sound speed in seawater depends on temperature and pressure, and these vary with depth. Near the surface, the water is warm and the sound speed is high. Below the warm surface layer, the temperature drops sharply through the thermocline, and the sound speed drops with it. Below the thermocline, the temperature is nearly constant but pressure continues to rise, and the sound speed begins to climb again.

The result is a minimum in the sound speed at roughly 1,000 metres depth in mid-latitudes, with faster speeds both above and below. According to the Discovery of Sound in the Sea reference, an academic resource maintained by oceanographers at the University of Rhode Island and elsewhere, this minimum creates a natural waveguide. A sound wave emitted at or near 1,000 metres depth that strays upward into faster water gets bent back down. A sound wave that strays downward into faster water gets bent back up. The wave is trapped, channelled by the gradients of temperature and pressure, and propagates horizontally with almost no energy loss to absorption.

This is the SOFAR channel — Sound Fixing and Ranging — discovered toward the end of the Second World War by Maurice Ewing and Joseph Worzel at the Woods Hole Oceanographic Institution. A low-frequency sound emitted into the SOFAR channel can travel thousands of kilometres before its energy dissipates. Blue whales, fin whales and other large baleen whales produce calls below 20 hertz, on the edge of human hearing or below it, and these low-frequency calls couple efficiently into the SOFAR channel. Researchers led by Christopher Clark at Cornell University have tracked individual whales across entire ocean basins using the long-distance propagation that the SOFAR channel makes possible.

The “Jezebel Monster”

The Cold War history of underwater sound is where the popular framing of “sonar operators filtering out whale noise” comes from. Beginning in 1950, the US Navy developed a global underwater listening network called the Sound Surveillance System, or SOSUS, designed to detect Soviet submarines by their low-frequency acoustic signatures travelling through the SOFAR channel. According to the Discovery of Sound in the Sea account of SOSUS history, the system was very successful at detecting noisy diesel and nuclear Soviet submarines. It was also picking up sounds that no one initially knew how to classify.

One particularly persistent unknown source was labelled the “Jezebel Monster” by SOSUS analysts. The sounds were low-frequency, came from no known submarine, and could be heard from enormous distances. They turned out to be the calls of blue and fin whales, propagating through the SOFAR channel exactly as Soviet submarines would have been, and detected by SOSUS arrays exactly as Soviet submarines would have been. The whales were not being filtered out in the everyday sense of being a routine nuisance. They were being identified as a category of acoustic source that the Navy had not previously known to look for. Once identified, the calls became valuable rather than confusing: they confirmed that the SOFAR channel was working as predicted, and they later became the basis of the most comprehensive long-distance whale-tracking research ever conducted.

At the end of the Cold War, the Navy declassified the technical operation of SOSUS sufficiently to allow civilian researchers with security clearances to use the system. The result has been a generation of marine biology research that would have been impossible without the Cold War infrastructure. Whales communicating across thousands of kilometres of ocean are now routinely tracked by hydrophone arrays that were originally listening for Soviet ballistic-missile submarines. The same physics, the same channel, the same long-distance propagation.

How loud is loud, under the ocean

Whale calls below 20 hertz are produced at source levels of up to 188 decibels relative to one microPascal at one metre — a figure that does not directly map onto the decibel scale used for air-based sound, because the reference pressures are different, but that corresponds to one of the loudest sustained biological sounds on Earth. A blue whale’s call at source can be heard at a level above background ocean noise for hundreds of kilometres in unmodified ocean conditions. The 2012 paper by Denise Risch and colleagues in PLOS One documented the inverse case: humpback whales in the Stellwagen Bank National Marine Sanctuary off Cape Cod measurably reduced their singing during an Ocean Acoustic Waveguide Remote Sensing experiment 200 kilometres away. The whales could detect the experiment, and modify their behaviour in response, from a distance comparable to the route between London and Paris.

The modern problem is anthropogenic noise. Shipping traffic, oil-and-gas exploration, military sonar, and underwater construction now produce so much low-frequency noise that the effective range over which whales can communicate with each other has shrunk substantially. Christopher Clark has estimated that the acoustic environment in which whales operate has been reduced to a small fraction of what it was a century ago, because human noise occupies the same frequency band as whale calls. The physics of underwater sound has not changed. What has changed is the level of competing noise sharing the channel. The whales are still singing. The ocean is just no longer quiet enough for the songs to travel as far as they once did.

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Oxford University is older than the Aztec Empire — teaching at Oxford began in 1096, while the Aztec capital Tenochtitlán was founded in 1325, which means Oxford was already more than two centuries old by the time the civilization that built one of the most sophisticated cities of the medieval world had even begun

In 1096, scholars were already teaching in the streets of Oxford, in the kingdom of William II, the son of William the Conqueror. England had been under Norman rule for thirty years, the Domesday Book had been compiled ten years earlier, and the First Crusade had just been declared by Pope Urban II at Clermont. Across the Atlantic, in the central highlands of what is now Mexico, the Aztec people would not arrive in the Valley of Mexico for another two hundred years. They would not see the omen of the eagle, the cactus and the snake that, according to tradition, told them where to build their capital, until 1325. By the time the first stones of Tenochtitlán were laid on a small island in Lake Texcoco, Oxford had been a centre of teaching for 229 years.

The comparison is one of those facts that everyone half-remembers as plausibly true and few people stop to verify. A 2024 Smithsonian Magazine piece by Meilan Solly documented the contrast in some detail. The dates check out. The cultural intuition about which institution feels older does not.

Oxford does not actually have a founding date

The 1096 date for Oxford is the earliest documented evidence of teaching activity in the town, not the formal foundation of the university. According to Oxford’s own history page, “there is no clear date of foundation but teaching existed at Oxford in some form in 1096.” The university grew rapidly from 1167, when Henry II banned English students from attending the University of Paris during his ongoing feud with Thomas Becket, sending a generation of English scholars home to study in Oxford instead. By 1188, the historian and royal clerk Gerald of Wales was giving public readings to assembled Oxford dons. By around 1190, the first known overseas student, Emo of Friesland, had arrived. By 1248, Henry III had issued the university a royal charter. By 1264, three original colleges — University, Balliol and Merton — were operating as residential institutions for students. The institution emerged over the course of a century and a half, not in any single founding moment.

The other claimants for “oldest university” are older still. The University of Bologna, generally regarded as the oldest in continuous operation, dates to 1088. The University of Paris emerged around 1150. These three institutions, together with a small handful of others, laid the foundations for the European university system that the rest of the world eventually adopted. Oxford is the second-oldest university in continuous operation in the world, and the oldest in the English-speaking world.

Tenochtitlán: the city founded on an omen

The Aztec people, who called themselves the Mexica, arrived in the Valley of Mexico in the early 14th century after a long migration from a homeland in the northwest of Mexico called Aztlán. According to Britannica’s account of the city, the founding of Tenochtitlán in 1325 followed a long pilgrimage during which the Mexica’s patron god Huitzilopochtli instructed them to settle wherever they saw an eagle perched on a prickly pear cactus, eating a snake. They saw the omen on a small island in Lake Texcoco, in the Valley of Mexico. The image is preserved on the modern Mexican flag.

The city the Mexica built on that island grew rapidly. Originally confined to two small islands, Tenochtitlán was extended through the construction of artificial islands, called chinampas, until the city covered more than five square miles of the lake. It was connected to the mainland by causeways, supplied with fresh water by aqueducts, and traversed by a network of canals. Estimates of its peak population vary considerably. The Britannica entry gives a figure of about 400,000 people in 1519, “the largest residential concentration in Mesoamerican history” and larger than any contemporary European city, including Paris, London, or Rome. Other scholarly sources give more conservative figures in the range of 200,000 to 300,000. By any measure, it was among the most populous cities in the world at the time of Spanish contact.

The Aztec Empire as a political entity dates not from the founding of the city but from the formation of the Triple Alliance in 1428, when Tenochtitlán joined with the neighbouring cities of Texcoco and Tlacopán to dominate central Mexico. By that point, Oxford had been operating as a university for over three centuries, had survived multiple plague outbreaks, had produced Roger Bacon and John Wycliffe, and had been teaching the Latin classics to clerics, lawyers and merchants’ sons for so long that the institution was already considered ancient by its own English contemporaries.

What the comparison actually changes

The intuition that gets disrupted is not about Oxford. Most people are aware that the university is old. The intuition that gets disrupted is about the Aztecs. Aztec civilization tends to feel anchored in the distant past, in the same general mental category as Egyptian pharaohs, Mesopotamian temples, or ancient Greece. The actual historical position of Tenochtitlán is much closer to the modern present. The city was founded 229 years after Oxford began teaching, conquered by Cortés in 1521, and razed within the lifetime of people who were born in the same year as William Shakespeare’s grandparents. Aztec civilization, in the sense of Tenochtitlán-centered Mexica society, existed for less than two hundred years before its destruction. The empire proper, dating from the Triple Alliance, existed for less than a century.

Oxford has, by contrast, existed continuously through every European event from the Crusades through the Reformation, the Industrial Revolution, two world wars, and the digital age. The same town, the same general institution, the same broad teaching tradition has been operating without interruption for over 900 years. The Aztecs were latecomers to history. Oxford was already producing graduates when the first Mexica arrived in the Valley of Mexico, and was already a chartered university when the first stones of the Templo Mayor were laid. The mental image of which civilization belongs to the deeper past, in this comparison, is almost exactly reversed.

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We talk about anxiety as if it starts in the mind — but for some people, the eyes may be the first place it shows up

1 June 2026 at 21:00

The first sign was never a thought.

It was visual. Something in the way the room looked. The walls would seem slightly farther away than they had been a moment before. Colours stayed, shapes stayed, the furniture stayed in exactly the right places — and yet something about the scene lost a quality I can only describe as immediacy. The world was still there. It just stopped feeling available.

This would happen before I felt afraid. Before I could name what was coming. Before any thought had formed that I could call anxious. Something changed in the way I was receiving the world through my eyes, and only later — sometimes much later — would the rest of the experience catch up.

I spent years not knowing what to call this.

I tried “dizzy,” which wasn’t right. I tried “tired,” which was too soft. I tried “dissociating,” which felt too clinical for something that happened in quiet moments, not only in crises. What I was looking for was a word for the way the world could go slightly flat. Not dark. Not frightening in any obvious way. Just — less textured. Less arrived. As if someone had turned the resolution of reality down just slightly, and I was the only one in the room who noticed.

The world went flat before I had words for it

What I was experiencing had a name. Derealization — the sense that the external world has become unreal, distant, or visually altered — is a well-documented symptom that occurs frequently in anxiety and panic, and in the broader condition known as depersonalization-derealization disorder. It affects a surprising number of people, though most, like me, spend a long time describing it badly before they discover it has a name at all.

What I did not expect, once I found the name, was to realize how early in the anxiety sequence it was arriving for me.

Most descriptions of anxiety lead with thought. The worry, the spiral, the catastrophizing. The racing mind. And for many people that may be accurate — the cognitive element comes first, and the body follows. But for me, the sequence ran differently. The visual alteration came before the worry. My eyes created distance before my mind could explain why. By the time I was consciously afraid, I had already been looking at the world through a kind of filter for several minutes. Sometimes longer.

The world went flat before I had words for what was happening.

Once I recognized this, I started paying attention to it differently. Not as a malfunction, but as a signal. Something my system was doing before it had time to speak.

Before anxiety had language, it had a way of altering sight

The neuroscience here is not fully settled, but the broad shape of it makes sense.

The brain does not passively receive visual information and then decide what it means. It actively constructs perception, using prior experience, expectation, and internal state to shape what we experience as seeing. When the nervous system is in a state of hyperarousal — even before that state is consciously registered — the way the brain builds the visual world can shift. Attention narrows. Certain details flatten. The sense of depth and richness that makes the world feel real can diminish, because the system is already doing something else with its resources.

The amygdala, which processes emotional and threat-relevant stimuli, is thought to receive threat-relevant signals very rapidly — in some models, before the slower analytical pathways that give us conscious perception have fully resolved what we’re seeing. This means the body’s threat response can activate before the thinking mind has noticed anything. The alarm goes off, the nervous system reorganizes, and the first sign you have — if you are paying attention to your body rather than your thoughts — might be something as subtle as the way the room looks.

That was my experience. I didn’t first think anxiety. I saw it.

The first thing anxiety stole, reliably, was the texture of the world.

Learning to read the signal

For years, the visual shift frightened me in its own right. The unreality was unsettling before any worry arrived to explain it. There were moments when I genuinely questioned whether I was losing something — my grip on reality, my trust in my own perception, something I couldn’t name. The derealization felt like a symptom without a cause, which is one of the lonelier things you can experience.

It is also disorienting in a specific way: when perception itself becomes the thing you can’t trust, you lose the ground you’d normally stand on to figure out what is wrong. You can’t think your way out of a problem that is currently happening in your thinking. You can’t look clearly at something when it is your looking that has shifted.

What changed was noticing the pattern.

Not during the episode, but afterward. Tracing the sequence: where had I been, what had I been carrying before I noticed the flatness, what came before the flatness itself. And what I found, slowly, was that the visual shift was not random. It was a leading indicator. Something had already been building in my nervous system — a stress response, a low-grade overwhelm I hadn’t consciously registered — and my eyes were the first thing that showed it. Before my thoughts caught up. Before my chest tightened. Before I would have said, if anyone had asked, that anything was wrong.

My eyes were filtering the world before I knew I needed a filter.

Maybe it was never malfunction

I am careful about what I claim here. I am not saying anxiety lives in the eyes, or that this is how it works for everyone. What I am saying is something smaller and, to me, more useful: for some people, the first felt experience of anxiety may be visual. Perceptual. Something that shows up in how the world looks before it shows up in what the mind thinks.

And if that is true — even sometimes, even for some people — then it changes where you learn to look for the early signs.

I used to search for the anxious thought. The belief I could challenge, the worry I could reframe, the cognitive distortion I could name and dispute. These have their place. But I kept arriving at them too late, after the nervous system had already been organizing itself around something I hadn’t consciously noticed. I was looking for the fire after the smoke had already been there for a while.

Now I know to check in with what I’m seeing. Whether the room feels arrived. Whether the world has its texture. Whether reality is still emotionally available, or whether it has quietly started to step back — a little flatter, a little more distant, a little less like itself — without explanation.

Those were never signs that something was wrong with my eyes.

They were signs that something in me was trying to protect itself before I understood what from. The nervous system, doing what nervous systems do — adjusting the aperture, reducing the input, creating a small buffer between me and a world it had decided, for some reason, was temporarily too much.

That is not a disorder. That is a system trying to survive.

It just took me a long time to recognize the signal for what it was, instead of fearing it as one more thing that was wrong.

This article reflects personal experience and is for informational purposes only. It is not a substitute for professional mental health advice. If you are experiencing symptoms of derealization or anxiety, consider speaking with a qualified professional.

The post We talk about anxiety as if it starts in the mind — but for some people, the eyes may be the first place it shows up appeared first on Space Daily.

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