Inbetweeners Part 8- The Spider Lady

Sometimes the best NPCs in a tabletop RPG are not well thought out, carefully crafted and woven into the narrative. Sometimes they are developed on the fly because the DM failed to prepare a necessary plot piece. This was the case with the Spider Lady.

The Inbetweeners Campaign posed a unique challenge. In a way, it should seem easier to prepare for, the players are confined to predetermined areas... in the rooms stage of the campaign it was essentially a large dungeon crawl. And this would have been true if every room had been just that, a self-contained room like the tavern cellar or the rat nest. However, many of these “rooms” were sectors of an open environment. Each one was in a different location, and I quickly learned the players were curious about where they were and the surrounding world. So instead of having to manage one town or region, the players could quickly blink between dozens. No matter how much lore I prepared, I was always on the back foot trying to figure out where they were and who was around them. The Market along was a great example. A bustling market in a mid sized city, I had a rough mental image of where they were. I pictured a landscape sort of like Morocco, and a city with sandstone colored bricks, bustling with commerce, exotic food and a sort of late medieval pre-renaissance level of advancement. The market was meant to be the “room” players could spend their wealth and stock up on basic supplies.

I didn't design every specific vendor but instead assumed their would be stalls set up for all their basic needs. A few for food, one for weapons, armor, potions, low level magic items, etc. I figured these stalls represented larger establishments, surely the armor stall didn't have many suits of full platemail on display. Rather, the players could place a custom order there or look at samples. Still, I didn't want the market to feel like a generic market in a generic RPG, sort of like going into a random city in Skyrim and talking to the blacksmith. Instead I wanted something the characters would remember more. However, at the time of them entering I hadn't really come up with anyone, so I spontaneously came up with the “spider lady.” I did not name her the spider lady, btw, the players came up with that name for her later. She didn't really have a name.

I envisioned in the corner of the market, a series of stalls and tents that were kind of cobbled together. They were absolutely bursting at the seams with objects. No particular theme, everything from junk, trinkets, weapons, preserved food, etc. None of it organized in any particular order. If you've watched Labyrinth, I was basically ripping off the Junk Lady a young Jennifer Connelly meets in the landfill. The way I described this vendor was a very short old woman with such a large tangle of hair it completely covered her face. She spoke in a high pitch, sort of overwrought whimsical voice, and the skin that did show was slighty green. She had spiders and other insects crawling through her hair, and her clothes were essentially rags at this point. I think the spider in her hair inspired her name, but I think the players may have later crossed wires and believed she was actually part spider. I didn't really disavow them of the notion because it worked for the vibe it was going for.

It was clear the Spider Lady lived in her stall. Her bedroll and other evidence implied she slept there at night. She also didn't leave the stall when the market shutdown. Sometimes the players would arrive at night time, and all of the stalls would be shuttered and town guard would be ushering the anyone there away. However, the Spider Lady was either milling about or sleeping in her junk pile. The guards and thieves alike seemed to avoid the Spider Lady. No pickpockets approached her stall, and the guards didn't seem to want to apply the curfew to her. Most customers avoided her as well, except for the desperate or those seeking the unusual. Her wares, however disorganized they were, were vast and interesting. I treated her as sort of a black box for any unusual requests players wanted. Early on the players didn't have much in the way of gold, and the hobgoblin Samurai Uzo wanted a long bow of some sort. He indicated how much money he had and she offered him a wicked looking war bow, one that was jagged, void black and gave the impression that if you held your ear up to it you could hear the tormented whispers of the damned. She charged Uzo a pittance but made him verbally acknowledge that he was consenting to a transfer of ownership of the bow and was not under duress. Once he accepted, the bow bonded to Uzo, and he could not dispose if it no matter how hard he tried. If he threw it off a cliff, it would appear in his pack. If he tried to damage it, it would hurt him instead. It seemed the Spider Lady had passed the curse on to him. Here are the stats for the bow:

Uzo’s Cursed Bow: Massive Bow, 1d12 + Dex to Hit, + STR DMG. +1 Magical Enchantment

This bow belonged to a Yugoloth Mercenary who delighted in torturing his victims. He impaled a Paladin on a hellish spike that imbued the paladin with just enough regeneration so that he could not die, instead living in constant agony. The mercenary’s sadism though eventually gained him enough enemies that he was slain and his soul utterly destroyed. His bow however, remained and made its way to the material plane. It is still connected to the tortured soul of the paladin, and visions of the paladin's pain haunt whoever wields it.

The bow cannot be gotten rid of unless willfully accepted by another whom the bow also prefers. For example, the bow would not allow a warrior to gift it to a child, but it would go to a more powerful warrior. Any attempt to throw it away will fail, and attempts to destroy it will harm the wielder, though powerful magic can break this connection.

Abilities: Call to action: The wielder may ask the bow for help. The bow fuses with the wielder's arm, and they cannot put it down, dealing 2 damage as two tentacle-like bones fuse with their arm. The wielder gains advantage on all attacks and the arrows shot are not arrows but bones that grow out of their arm. It is incredibly painful. Critical hits (20) heal the wielder for 2d6 and deal an extra damage dice of damage. The bow cannot be disarmed until the battle is over, and the player cannot retreat. If player dies during combat, they are resurrected but horribly disfigured with a missing eye, -2 to charisma, -1 perception permanently.

On any attack roll of 1, including 1s that are disregarded with advantage attacks, the bow gains a curse point. Any time the player wants to do something against the bow’s wishes they must make a Wisdom Save (5 + curse points). Failure means the bow dictates their actions or harms them for failing to obey.

Shadowstrike: May shoot a shadow arrow. It gets advantage on attack, and if it hits deals 6d8 necrotic damage (12d8 on critical hit) and blinds target for a turn. Using this ability adds 1 curse point.

Note, I later added conditions to add curse points faster to improve narrative pacing. This bow ended up being the motivation for a major plot arc, the players breaking into The Torment, the most horrific and massive prison in the multiverse. Uzo was tormented by images of the paladin who, unable to die, existed in perpetual agony, impaled. The bow would not relent until the paladin was put to rest, but Uzo got the bow at a low level and did not initially have the means to deal with the curse, so instead he just dealt with the horrors of being bound to this cursed thing. It was a pretty sick bow though, stat wise, for a low level players, so kind of a mixed bag.

In this way the Spider Lady's inventory was like the Interlopers gifts, many powerful items with substantial, cursed drawbacks. She also sold regular items as well, and would take unusual trades for them. Things that would count as currency for normal vendors may appeal to her greatly. I've long since forgotten many of the other items she traded with the players, but that was the general vibe. Uzo didn't hold a grudge for being cursed because the bow was pretty effective in combat. The curse points were particularly fun because I could dictate pacing as a DM by forcing Uzo to roll and if he failed his wisdom save, have his character do something dangerous that propelled the plot forward. It was fun to because anytime someone's mind connected with Uzo's they'd be plagued by the horrible visions of the tortured paladin. I think this may have actually been weaponized a few times. I think Uzo may have tried to give the bow away a couple times as well, but either the bow wasn't interested in the recipient or if the recipient was worthy, they were wise enough to not accept it. Zariel might have held the bow at one point, but she's so powerful its curse would be utterly ineffective against her. Semi-divine status tends to do that.

Anyway, enough on the bow, back to the Spider Lady. Over time as the players became wealthier, so did the Spider Lady. They brought her better and better loot, and once the Beeple established a permanent hive she began selling their meat honey as well. Eventually she had several employees and an entire chain of tents. Her employees were mostly amphibious monstrous humanoids and minotaur. But she did have one employee who ended up betraying her and the group that I'll get into in a later post. The lore in this campaign is more vast than I realized, so readers will have to piece it together over different posts. Probably the best trade the players made with the Spider Lady was a barrel of 723 souls they looted from the Torment. She traded them a Ring of Three Wishes for it, though one of the wishes had been expended. So two wishes was an incredibly potent item for mid tier players to earn... the later used one to help them defeat Arkanos the undefeated, and thats how level 11 players defeated a CR 26 Legend of the Arena who hadn't been bested in centuries. So, the Spider Lady had resources.

At a certain point though I had to figure out who the Spider Lady actually was. I decided that she was a Far Realm entity who became trapped in a physical body in the multiverse eons ago. She figured out basic economics... at least well enough, and decided to buy and sell her way back home. One of the wishes on the ring was expended because she wished for the players... or more specifically, she wished to meet with entities that could help her achieve her goal. Ultimately, the players were able to connect her with Sigil and the Lady of Pain. The Lady of Pain, a far more powerful Far Realm entity, was able to restore the Spider Lady to her original form, a spectral, horrific visage of tentacles that caused psychosis and immense pain to look upon. Before that though she managed to befriend the Lost Titan (future post about him as well) and was the only entity that could really reach/connect with him. She was also involved in the later operation to free the Interloper, taking on one of the tethers herself by sending swarms of Slaadi and similiar bog monster type creatures to attack a tether.

So yeah, in sum, she started off as me needing a fun vendor and ended up become a pretty major side character. Oh, and there was a fun sequence one session where the players revealed her subordinate was betraying her for Mechanus, and she pulled him to the back of her tent and showed him her true visage. There was lots of screaming and tentacles and Lovecraftian horror as he died. I could probably retitle this campaign Far Realm Friends in retrospect.

In the spring of 2026, a professor of economics at Brown University did something that, on paper, looks like an act of pure kindness. Roberto Serrano, who has taught at Brown for thirty-four years and holds the title of Harrison S. Kravis University Professor, decided that his advanced undergraduate class in mathematical economics, ECON 1170, deserved a gentler midterm than usual. His students had been through something no examination rubric is designed to accommodate. On 13 December 2025, a gunman had opened fire on the Providence campus, killing two students and wounding nine others. One of the dead, Ella Cook, had met with Serrano only days earlier to ask him to become her academic adviser.

So he made the exam a take-home. Students could sit it in their own rooms, in their own time, away from the fluorescent dread of a lecture hall that some of them now associated with sudden violence. To keep the assessment meaningful, he made the questions harder than in previous years. It was, in the truest sense, a compassionate decision. And it produced a result so statistically deranged that it has become, in the space of a few months, the most closely studied cheating scandal in the recent history of the Ivy League.

Of the eighty-six students who sat the exam, forty scored a perfect one hundred. The class average landed at ninety-six. In prior years, on easier papers, that same course had averaged somewhere between sixty-five and eighty. A harder exam had produced a miraculous improvement, and miracles, to an economist, are simply data points that have not yet been explained. Serrano and his graders went looking for the explanation. They found it by doing the obvious thing that almost no institution does systematically: they ran the questions through ChatGPT themselves.

A take-home exam meant as mercy

What came back was not merely correct answers but a particular texture of reasoning. For at least one problem that has a short, elegant proof, the chatbot generated a laborious, convoluted argument that arrived at the right destination by an eccentric route. That same eccentric route, that same unnecessary scaffolding, appeared across dozens of student scripts. The students had not merely reached the answer the machine reached; they had reproduced the machine's peculiar way of getting lost and finding its way back. It was, in Serrano's account, a fingerprint. Human beings sitting the same hard problem independently do not all make the same strange detour. A language model, prompted with the same question, does.

When Serrano confronted the class with what he had found and reminded them of the honour code every one of them had signed, the reaction was swift and, in its way, more damning than any confession. Eighteen students dropped the course outright. A further nine stayed on the register but never appeared for the final, so that when the in-person examination arrived in May, twenty-seven of the original eighty-six were missing from the hall. Twenty-two of those twenty-seven had scored a perfect hundred on the disputed midterm. The absence was not random; it was concentrated almost entirely among the students whose marks had triggered the suspicion in the first place. Only fifty-nine sat the paper. Nineteen of them failed. The class average collapsed to forty-eight, the lowest in the course's history.

Serrano has not been diplomatic about what he thinks happened. He asked, pointedly, what the value of an élite qualification is if it can be conjured by a keystroke. “If all you're doing is just pressing a button to have this machine do the work for you,” he put it, “then you think you need a Brown degree for that?” Elsewhere he has been quoted describing the affair in more apocalyptic terms, suggesting that humanity has, in effect, chosen to make itself stupid. The rhetoric is easy to dismiss as the lament of a man who has watched thirty-four years of professional norms dissolve in a single semester. It is harder to dismiss the arithmetic. A distribution of marks does not have feelings. It does not exaggerate. Forty perfect scores on a deliberately harder paper, followed by a mass withdrawal of exactly the students who earned them, is not a story that admits many innocent readings.

But the Brown episode is interesting less for what it proves about eighty-six particular undergraduates than for what it reveals about the machinery of trust on which the entire enterprise of higher education rests. Because the uncomfortable fact at the centre of this story is not that students cheated. Students have always cheated. The uncomfortable fact is how the cheating was caught: not by any system, not by any institutional safeguard, not by any technology the university had purchased or deployed, but by one professor and his graders improvising a forensic method on the fly, essentially by playing detective with a chatbot in the way an amateur sleuth might dust for prints. If that is the state of the art in detection at an institution whose entire market value rests on the credibility of the certificates it issues, then the certificates are in trouble.

The fingerprint in the machine

It is worth dwelling on the improvised nature of Serrano's discovery, because it is the load-bearing detail of this whole affair. He did not catch his students because Brown had a robust apparatus for identifying machine-generated work. He caught them because a hard exam produced impossible marks, because he happened to be curious enough to interrogate the anomaly, and because the specific model his students used happened to leave behind a distinctive stylistic residue on a problem with an unusually clean alternative solution. Change any of those variables and the fraud sails through undetected. A slightly less lazy cohort that varied its prompts, paraphrased the output, or introduced deliberate errors would have escaped entirely. A less numerate professor might have shrugged at the high marks and moved on, quietly pleased. The detection worked because of a confluence of luck, expertise and student carelessness, not because of any designed defence.

This matters because the obvious institutional response — buy a detector — does not work. The commercial AI-detection industry that sprang up in the wake of ChatGPT's release has turned out to be one of the least reliable technologies ever sold to the education sector. Turnitin, the plagiarism-detection company whose software is embedded in thousands of universities, has itself acknowledged that its tool misclassifies human-written text as machine-generated. That may sound like a tolerable margin of error until you run the numbers at scale. Vanderbilt University, explaining why it disabled Turnitin's AI-detection feature in August 2023, pointed out that even a one per cent false-positive rate, applied across the seventy-five thousand papers its students submit annually, would generate roughly seven hundred and fifty wrongful accusations a year. Seven hundred and fifty innocent students hauled before an integrity committee to defend work they actually did. No institution that takes due process seriously can build a disciplinary regime on foundations that shaky.

The unreliability is not evenly distributed, either, which makes it worse. Research on AI detectors has repeatedly found that they are biased against writers who learned English as a second language. One widely cited study reported that a battery of detectors flagged more than sixty per cent of essays written by non-native English speakers as machine-generated, while correctly clearing almost all writing by native speakers. The mechanism is bleakly logical: detectors are trained to associate simpler vocabulary and more predictable sentence construction with machine output, and those are precisely the features of prose written by someone still mastering the language. A tool that systematically accuses international students of fraud on the basis of their sentence structure is not an instrument of justice; it is a liability waiting for a lawsuit. Australian Catholic University discovered as much after logging nearly six thousand alleged misconduct cases in a single year, the overwhelming majority AI-related, before abandoning the Turnitin tool it had relied upon as ineffective.

So the picture that emerges is stark. On one side, a form of cheating that is cheap, ubiquitous, improving monthly and increasingly easy to disguise. On the other, a detection apparatus that is expensive, error-prone, discriminatory and, at the frontier, essentially defeated by any student who takes the trouble to rewrite the machine's output in their own voice. Serrano's success was real, but it was not repeatable at scale, and everybody in the sector knows it. The detectors do not save you. The honour codes, as Princeton has just conceded, do not save you either.

Why detection is a game universities keep losing

The deeper problem is that detection was always going to be an arms race the institutions could not win, and the reason is structural rather than technological. A cheating student needs to succeed once. A detection system needs to succeed every time. Each new generation of language model produces output that is more fluent, more idiosyncratic and less distinguishable from competent human writing than the last, which means that even a detector that works today degrades tomorrow simply by standing still. The very stylistic tell that undid Serrano's students — the convoluted proof, the machine's characteristic detour — is exactly the sort of artefact that model developers spend their days sanding away. The fingerprints are getting fainter with every release.

There is a temptation, particularly among administrators, to treat this as a transitional inconvenience, a bump to be smoothed over once the right software arrives. That is a fantasy. There is no detector on the horizon that reliably separates a lightly edited machine essay from a genuine one, and the economics of the problem guarantee there never will be, because the people building the models have every incentive to make their output indistinguishable from human work and no incentive to make it easy to catch. The watermarking schemes that were once floated as a solution have proven fragile, defeated by trivial paraphrasing or by the simple expedient of running the text through a second model. Universities that continue to pour money into detection are, in effect, buying an umbrella to hold against the tide.

Which forces a more fundamental question, and it is the question that the Brown scandal, the Princeton vote and the survey data all circle without quite naming. If you cannot detect the cheating, and you cannot design an assessment that a determined student cannot game, then what exactly is a degree from an élite university certifying at the moment it is conferred? What does the piece of paper actually vouch for? To answer that, you have to understand what the paper was ever supposed to vouch for in the first place — and here the economists, of all people, got there decades before the technologists.

What a degree was ever meant to prove

In 1973, the economist Michael Spence published a paper called “Job Market Signalling” that would eventually help win him a share of the Nobel Memorial Prize. Its central insight was deceptively simple. Employers cannot directly observe how capable, diligent or intelligent a prospective worker is. What they can observe is whether that worker managed to acquire a difficult, expensive, time-consuming credential. If obtaining the credential is genuinely harder for less capable people than for more capable ones, then the credential works as a signal: it reliably separates the wheat from the chaff, not necessarily because of what was learned in the process, but because the mere fact of completion carries information. A degree, in this model, is less a certificate of knowledge than a proof of the kind of person who can get a degree.

The libertarian economist Bryan Caplan pushed this argument to its provocative conclusion in his 2018 book The Case Against Education, in which he contended that as much as eighty per cent of the financial premium a graduate earns derives not from skills acquired but from signalling — from the diploma's power to advertise intelligence, conscientiousness and a willingness to conform to institutional expectation. His most persuasive piece of evidence is what economists call the sheepskin effect: the observation that the wage boost from the final year that yields an actual diploma dwarfs the boost from earlier years that do not. If education were purely about accumulating human capital, three-and-three-quarter years of study should be worth roughly three-and-three-quarter years of pay. It is not. The certificate itself carries a disproportionate value, which is only explicable if the certificate is doing work over and above the learning it notionally represents.

Now hold that theory up against the Brown data. Signalling only works if the signal is costly to fake. The entire mechanism collapses the moment a low-capability worker can acquire the same credential as a high-capability one at comparable cost, because at that point the credential stops separating anybody from anybody. It becomes noise. And generative AI is, precisely and specifically, a technology for collapsing the cost of faking the signal. When forty students in a single class can press a button and produce a perfect score on a deliberately hardened exam, the exam has ceased to distinguish the diligent from the idle, the able from the unable. The signal has gone dark. A Brown degree earned in 2026 is supposed to tell an employer, a graduate school, a research council, something reliable about the person holding it. The scandal in ECON 1170 is a demonstration, in miniature and under laboratory conditions, that it may no longer tell them anything at all.

This is the genuinely frightening part, and it is why the story deserves more than a news cycle's worth of scandalised attention. The threat that AI poses to universities is not primarily that students will learn less, although they may. It is that the institution's core product — the credible, costly, hard-to-counterfeit signal — is being quietly debased from within, by the very people it is meant to certify, faster than the institution can restore its guarantee. A currency is only as good as the confidence that it cannot be forged. Élite universities have spent centuries building a currency of extraordinary value, and they are now discovering that the printing presses have been distributed, free of charge, to everyone holding a smartphone.

The numbers behind a quiet epidemic

It would be comforting to treat Brown as an outlier, a freak convergence of trauma, a well-meaning professor and an unusually brazen cohort. The data does not permit that comfort. The behaviour Serrano stumbled upon is not a local aberration; it is the visible tip of a shift that survey after survey has been documenting, largely without anyone in a position of authority acting on it.

Consider the Lumina Foundation and Gallup study published as part of their 2026 State of Higher Education research, conducted across October 2025 with a sample of several thousand American undergraduates pursuing associate and bachelor's degrees. It found that more than half of them — fifty-seven per cent — were using artificial intelligence in their coursework at least once a week, and that roughly one in five reported using it daily. Read that again. A weekly habit is no longer the behaviour of a deviant minority; it is the modal experience of the American undergraduate. The tool that produced Serrano's forty perfect scores is not lurking at the margins of student life. It is woven into the ordinary weekly rhythm of the majority. Male students reported using it more heavily than female students, and students in business, technology and engineering programmes reported using it most of all — which is to say, disproportionately in exactly the quantitative fields where a take-home problem set is most cleanly solved by a machine.

The Harvard figures tell a subtler and, in some ways, more instructive story. The graduating Class of 2024, surveyed by the student newspaper The Harvard Crimson, produced a headline number that has been cited relentlessly since: forty-seven per cent of respondents admitted to having cheated in an academic context during their time at the university. Nearly half the graduating class of one of the most selective institutions on earth confessed to academic dishonesty. That is the statistic that launched a hundred opinion columns, and it is real. Less frequently quoted, and far more damaging to the institution than to the students, is what the same series of surveys reveals about consequences. Among the graduating Class of 2026 who admitted to cheating, ninety-three per cent were never discovered at all, and fewer than three per cent faced any disciplinary sanction whatsoever. Whatever machinery Harvard possesses for catching academic dishonesty, its own graduates report that it caught roughly one offender in fourteen and punished roughly one in forty. Serrano's afternoon with a chatbot was, by that measure, an extraordinary feat of enforcement. But the honest analyst has to add the context that the columns tend to omit, because it complicates the tidy narrative of AI-driven collapse. In the subsequent surveys, the self-reported cheating rate at Harvard did not keep climbing. It fell — to around thirty per cent for the Class of 2025 and to roughly twenty-five per cent for the Class of 2026, back in line with pre-pandemic norms.

What are we to make of a cheating rate that peaked and then declined even as AI use exploded? Two readings are possible, and they are not mutually exclusive. The optimistic interpretation is that the initial spike reflected the chaotic, norm-free early period of the pandemic and the first rush of ChatGPT, and that students and institutions have since renegotiated where the lines lie. The pessimistic interpretation is more corrosive: that the reported rate fell not because cheating declined but because it stopped registering as cheating. When more than half of all students use AI weekly, the behaviour normalises. What one cohort guiltily confesses to as misconduct, the next cohort simply regards as how coursework is done — no more a transgression than using a calculator or a spellchecker. The Class of 2026 survey supplies something close to a proof of the gloomier reading, in the form of a discrepancy sitting in plain sight within its own results. Sixty-four per cent of that class reported using AI multiple times a week or daily. Twenty-five per cent said they had cheated. And thirty-three per cent — a third of the cohort, a figure that has barely shifted in years — said they had used AI on an assignment against their instructor's explicit permission. More students admitted to breaking a rule than admitted to cheating. That gap is the entire argument in miniature: a substantial body of undergraduates who know exactly what they did, remember the instruction they disregarded, and no longer file the act under dishonesty at all. On that reading, the falling numbers are not reassuring at all. They are evidence that the definition of cheating is dissolving faster than the cheating itself, which is arguably the worse outcome, because a norm that everyone quietly abandons is harder to restore than one that is merely being broken.

Either way, the survey data establishes the crucial point: Brown is not a freak. It is a controlled demonstration of a phenomenon that is already pervasive and, by the students' own admission, routine. What made ECON 1170 exceptional was not the cheating. It was the detection.

The proctor returns and the blue book comes back

Faced with a signal it can no longer guarantee and a fraud it can no longer detect by software, the sector is falling back on the only defences that have ever really worked: putting a human in the room and taking the machine out of it. The most symbolically loaded of these retreats happened at Princeton.

In May 2026, Princeton's faculty voted, with a single dissenting voice, to require that all in-person examinations be supervised by instructional staff. That may sound like housekeeping. It was not. Princeton had operated since 1893 on an honour code under which students sat their examinations unproctored, pledging in writing not to cheat and, crucially, accepting a collective responsibility to report classmates who did. For a hundred and thirty-three years, no invigilator stood at the front of a Princeton exam hall. That tradition of unsupervised examination is now over. The new policy, which took effect on 1 July 2026, places a proctor in every room, present as what the faculty legislation described as a witness rather than an enforcer, but a witness nonetheless — an institutional admission that the honour system, as a mechanism for guaranteeing integrity, has failed.

The reasons the faculty gave are as revealing as the vote itself. Reporting by The Daily Princetonian, which broke the story, drew on a 2025 survey of some five hundred graduating seniors, thirty per cent of whom admitted to having cheated at least once. But the more telling finding concerned the enforcement mechanism rather than the offence. Students said they found it increasingly difficult to identify cheating in a modern exam hall — a phone under the desk is far harder to spot than a crib sheet up a sleeve — and, more damningly, that they were unwilling to inform on their peers for fear of social retaliation, of being teased, doxxed or ostracised. The survey put a figure on that reluctance which is difficult to argue away. Nearly forty-five per cent of the seniors who responded knew of honour code violations that they had chosen not to report; just four in every thousand had ever reported a peer. An honour code depends on students being both able and willing to police one another. Princeton's students, by their own testimony, had become neither. The code had become a ritual with no engine behind it, a signature on a form that certified nothing, and the faculty finally voted to stop pretending otherwise.

Beneath the symbolic drama of Princeton, a quieter and more practical counter-revolution has been under way across the sector, and it has a distinctly analogue flavour. The blue book — the flimsy stapled booklet of lined paper in which generations of students once scrawled their handwritten answers under the eye of an invigilator — is enjoying an improbable renaissance. Sales at the University of Florida reportedly rose by half over two academic years; at the University of California, Berkeley, they were said to be up by four-fifths across the same period, and at Texas A&M by about thirty per cent. Handwriting, it turns out, is one of the few technologies that reliably locks the machine out of the room. You cannot prompt a chatbot with a pen. Alongside the blue books, institutions are experimenting with the oral examination, the ancient viva voce in which a student must explain and defend their reasoning aloud, in real time, to an examiner who can ask follow-up questions the student had no way to prepare with software. The economics department at Brown, in the immediate aftermath of Serrano's discovery, publicly concluded that the in-person examination was the way forward.

None of this is confined to the American Ivy League, and in Britain the retreat has begun to acquire a regulatory edge that the American version still lacks. On 17 August 2026, the think tank Policy Exchange published a report by Philip M. Newton, a professor at Swansea University Medical School, under a title that reads almost as a summary of everything argued here: Evidence of Learning Through Assessment: Protecting the Value of a Degree in the Age of AI. Its central contention is regulatory rather than pedagogical. Summative examinations sat remotely and without supervision, Newton argues, “completely, and obviously, fail” the Office for Students' condition B4, the requirement that assessment be a valid and reliable measure of what a student actually knows. His recommendation is not that universities reform such examinations but that they stop setting them at once, and that the Office for Students and the Quality Assurance Agency compel them to do so if they will not.

The evidence he assembled through freedom of information requests to British universities is the more startling half of the document, because it establishes that the practice is not marginal. In 2023-24, seventy-eight per cent of UK universities used remote online examinations for summative assessment. Only around one in ten invigilated all of them. Two-thirds of the institutional policies governing those examinations made no mention of generative AI whatsoever, well over a year after ChatGPT's release. And seventy per cent of the universities surveyed intended to carry on with unsupervised online examinations regardless. That last figure is worth sitting with. It is not a portrait of a sector caught unawares by a fast-moving technology. It is a portrait of a sector that has been told what is happening, has measured it, and has decided to continue.

Newton's proposed remedies rhyme with what Princeton and Brown have arrived at by harder experience — most obviously a far greater use of the interactive oral viva, on the model much of continental Europe never abandoned — but one of them addresses the signalling problem head-on rather than obliquely. If the certificate can no longer be trusted on its own, he suggests, then the transcript should carry more information about how the student was assessed: not merely the marks earned, but the conditions under which they were earned. It is a modest proposal with immodest implications, because it concedes that the single undifferentiated signal is broken and sets out to replace it with a granular one, in which an invigilated first is legible as something different from an unsupervised one. Employers would learn to read the distinction quickly enough. So, rather less comfortably for the institutions, would applicants choosing between them.

Who actually owns the mess

There is a natural instinct, watching all this, to reach for the language of individual morality — to say that the students cheated, that cheating is wrong, and that the responsibility therefore rests with them. That is true as far as it goes, and it does not go very far, because it explains a scandal in one classroom while leaving the systemic collapse entirely unaccounted for. Twenty-two individual moral failures do not produce a class average of ninety-six on a hardened exam. Something larger is malfunctioning, and the responsibility for it is distributed far more widely than the students who happened to get caught.

The students bear the most immediate and personal responsibility, and it would be sentimental to pretend otherwise. They signed an honour code. They understood, because everybody understands, that submitting a machine's work as their own is a form of fraud. Nobody prompted a chatbot into producing a proof by accident. But it is worth being precise about the incentive structure they were operating inside, because it was engineered, over decades, to reward exactly the behaviour it now punishes. These are young people who were selected, drilled and admitted on the basis of their capacity to optimise every measurable metric of achievement — to treat the grade as the goal and the learning as the incidental means. An admissions arms race that rewards the maximisation of scores above all else should not be astonished when the students it selects go on to maximise their scores by the most efficient means available. The machine is simply the most efficient means yet invented.

The institutions bear a heavier and more culpable share, because they have known about this for three years and have, for the most part, temporised. Generative AI capable of answering undergraduate problem sets has been freely available since late 2022. In the time since, universities have issued a great deal of guidance, convened a great many committees, purchased a great deal of unreliable detection software, and changed astonishingly little about the fundamental architecture of how they assess and certify their students. The take-home essay, the unproctored problem set, the online quiz — the assessment formats most trivially defeated by a chatbot — remained in widespread use long after it was obvious to anyone paying attention that they had become meaningless. Princeton's vote and Brown's pivot to in-person finals are welcome, but they arrived years into an emergency that any honest observer could see coming from the moment ChatGPT launched. The institutions were slow because acting quickly was expensive and disruptive and politically awkward, and because the debasement of the credential is a slow-motion catastrophe that never quite forces a reckoning in any single quarter. They protected their short-term convenience at the expense of the long-term value of the very thing they exist to sell.

Brown's own conduct once Serrano brought his evidence forward is a compact illustration of the reflex. He submitted his findings to the university's Standing Committee on the Academic Code on 16 May 2026, and heard nothing back. Six weeks of silence later, at the end of June, he told the story to the Spanish newspaper El País, and it travelled around the world within days. The committee, mute through May and most of June, made contact shortly after publication, asking him to file individual complaints against each suspected student and to supply copies of their scripts. He provided the additional material on 8 July, at which point a formal investigation was opened and the students began to be contacted one by one. Serrano's verdict on that sequence is unsparing. “It's absolutely clear to me that if I hadn't gone public, nothing would have happened,” he said. He had already described the university's response as meek, and reported that it was seen as appalling and insufficient by the hundreds of people who had written to him in support, many of them Brown alumni.

Brown disputes the characterisation, and its account deserves a hearing rather than a dismissal. The university maintains that it treats every allegation of academic dishonesty with the utmost seriousness, that multiple academic leaders were in contact with Serrano during May about how the allegations could be formally adjudicated, and that the standing committee could not proceed until he supplied the particular details its procedures require — which he did on 8 July, whereupon it moved. Serrano himself later said he was appreciative of Brown finally looking at the case. Both accounts can be true simultaneously, and the fact that they can is precisely the point. A process that is procedurally impeccable and glacially slow is not a defence of the credential; it is a description of how a credential is debased, one unhurried committee cycle at a time. The sanctions available under Brown's academic code run from reprimand to expulsion. As of late August 2026, months after forty perfect scores appeared on a deliberately hardened examination, no outcome has been disclosed and the matter remains unresolved.

And the technology companies bear a share too, though they are the least willing to admit it and the least likely to be held to account. They released, into an education system built entirely around the assumption that a student's submitted work reflects the student's own effort, a tool that shattered that assumption overnight, and they did so with no serious mechanism for allowing institutions to distinguish their product's output from human work. The watermarking that might have made co-existence possible was deprioritised or abandoned because reliable watermarking is commercially inconvenient — it makes your product easier to police and therefore less attractive to precisely the users who most want to hide their tracks. The externality was dumped, as externalities usually are, on someone else's balance sheet. In this case the balance sheet belongs to every institution whose credential now certifies less than it did, and to every honest student whose genuine degree is now shadowed by the suspicion that it might have been faked.

What restoring trust would actually cost

If the value of an élite degree rests, as the economists insist, on its being a costly and hard-to-forge signal, then restoring that value requires making the signal costly and hard to forge again. There is no clever software that does this. There is no policy memorandum that does this. There is only the unglamorous, expensive work of rebuilding assessment around conditions a machine cannot infiltrate, and accepting the price that comes with it.

That price is real, and it is worth naming honestly rather than pretending the return to proctors and blue books is cost-free. Supervised, handwritten, oral and in-person assessment is more labour-intensive, more expensive, less scalable and less accessible than the frictionless online formats it replaces. It disadvantages the student with a disability who needs accommodation, the student whose handwriting cannot keep pace with their thinking, the student for whom a high-pressure oral examination is a crueler test of nerve than of knowledge. The take-home exam that Serrano offered was, remember, an act of compassion — an attempt to accommodate genuine trauma. The move back towards the invigilated hall is a move back towards a harsher, less forgiving, less flexible model of assessment, and the students who will pay the highest price for it are not the confident cheats but the vulnerable and the anxious. That is the bitter irony threaded through the whole affair. The cheating of the many is purchasing a harsher regime for the honest, and the compassion that made the fraud possible will be among its first casualties.

But the alternative to paying that price is worse, because the alternative is a credential that certifies nothing, and a credential that certifies nothing is not merely worthless — it is actively corrosive. It devalues the qualification of every honest graduate retroactively. It corrodes the trust of every employer, every professional body, every graduate admissions committee that has to decide whether the paper in front of them means what it says. It hollows out, from the inside, the single most valuable asset that an institution like Brown or Princeton or Harvard possesses, which is not its endowment or its buildings or its faculty but the simple, centuries-in-the-making public confidence that its name on a certificate is a guarantee of something. That confidence is far easier to destroy than to rebuild. It is the accumulated deposit of generations of credible assessment, and it can be spent down to nothing in the span of a few cohorts who were allowed to fake the signal because catching them was inconvenient.

What the Brown scandal ultimately exposes is that this confidence was resting on far more fragile foundations than anyone cared to admit. The whole edifice depended on a detection capability that, it turns out, amounts to little more than a numerate professor with a suspicious mind and a spare afternoon to interrogate his own exam with a chatbot. That is not a system. It is a happy accident that will not recur reliably, and it caught only the careless. The genuinely able cheat — the one who paraphrases, who varies the prompt, who introduces a few deliberate imperfections — was never in any danger, and remains in no danger now. The uncomfortable implication is that the degrees being conferred this summer, at Brown and everywhere like it, carry a guarantee that the institutions issuing them can no longer actually make good on. They are certifying, in many cases, they know not what.

Serrano's question, stripped of its anger, is exactly the right one, and it deserves to be asked not rhetorically but institutionally, by every provost and dean and examinations board in the sector. If the work can be done by pressing a button, what is the degree for? The answer cannot be nothing, because a great many people — employers, governments, students who genuinely learned something, societies that need their doctors and engineers and economists to actually know things — depend on the answer being something. But arriving at a defensible answer will require the institutions to do what they have spent three years avoiding: to accept that the frictionless, scalable, trusting model of assessment they had grown comfortable with is finished, and to pay, in money and labour and lost convenience, for the harder and more human forms of examination that can still tell the wheat from the chaff. The bill for restoring the signal has come due. The only remaining question is whether the universities will settle it now, while there is still a signal left to save, or keep temporising until the currency they print is worth no more than the paper it is printed on.

References

1. Preston Fore, “'Humanity has chosen to become idiots': This Brown professor switched to take-home exams after a mass shooting and discovered mass cheating,” Fortune, 29 June 2026. https://fortune.com/2026/06/29/roberto-serrano-brown-university-massacre-ai-cheating/
2. “AI-Driven Cheating Scandal Uncovered at Brown University,” OECD.AI Incidents, 28 June 2026. https://oecd.ai/en/incidents/2026-06-28-3184
3. “Brown Professor Suspects Most of His Class Used AI to Cheat,” Inside Higher Ed, 8 July 2026. https://www.insidehighered.com/news/faculty/learning-assessment/2026/07/08/brown-professor-suspects-most-his-class-used-ai-cheat
4. “Brown University professor raises AI cheating concerns,” The Boston Globe, 15 July 2026. https://www.bostonglobe.com/2026/07/15/metro/brown-university-ai-suspected-cheating/
5. “After AI cheating concerns, economics professors see in-person exams as a path forward,” The Brown Daily Herald, April 2026. https://www.browndailyherald.com/article/2026/04/after-ai-cheating-concerns-economics-professors-see-in-person-exams-as-a-path-forward
6. “Princeton Introduces Proctoring, Changing Honor Code,” Inside Higher Ed, 15 May 2026. https://www.insidehighered.com/news/faculty/learning-assessment/2026/05/15/princeton-introduces-proctoring-changing-honor-code
7. “Princeton faculty mandate proctoring for in-person exams, upending 133 years of precedent,” The Daily Princetonian, May 2026. https://www.dailyprincetonian.com/article/2026/05/princeton-news-adpol-proctoring-in-person-examinations-passed-faculty-133-years-precedent
8. “The Graduating Class of 2024 By the Numbers — Academics,” The Harvard Crimson, 2024. https://features.thecrimson.com/2024/senior-survey/academics/
9. “The Graduating Class of 2025 By the Numbers — Academics,” The Harvard Crimson, 2025. https://features.thecrimson.com/2025/senior-survey/academics/
10. “The Graduating Class of 2026 By the Numbers — Academics,” The Harvard Crimson, 2026. https://features.thecrimson.com/2026/senior-survey/academics/
11. “47% of Harvard seniors admit to cheating — and the problem existed long before ChatGPT,” Fortune, 23 June 2026. https://fortune.com/2026/06/23/harvard-cheating-academic-integrity-ai-detection/
12. Zach Hrynowski and Stephanie Marken, “AI Is Routine for College Students, Despite Campus Limits,” Gallup (Lumina Foundation-Gallup 2026 State of Higher Education study), 2026. https://news.gallup.com/poll/704090/routine-college-students-despite-campus-limits.aspx
13. “Majority of college students use AI for their coursework, poll finds,” UPI, 2 April 2026. https://www.upi.com/Top_News/US/2026/04/02/survey-college-students-artificial-intelligence-coursework/5341775162201/
14. “The Truth About Turnitin's AI Detection Accuracy in 2025,” Turnitin, 2025. https://turnitin.app/blog/The-Truth-About-Turnitins-AI-Detection-Accuracy-in-2025.html
15. “Limitations of AI Detection Tools,” Artificial Intelligence Steering Council, Brandeis University, 2025. https://www.brandeis.edu/ai-steering-council/ai-literacy/ai-teaching-learning/detection-tools.html
16. “Generative AI Detection Tools — The Problems with AI Detectors: False Positives and False Negatives,” Legal Research Center, University of San Diego, 2025. https://lawlibguides.sandiego.edu/c.php?g=1443311&p=10721367
17. “Survey on Plagiarism Detection in Large Language Models: The Impact of ChatGPT and Gemini on Academic Integrity,” arXiv, 2024. https://arxiv.org/pdf/2407.13105
18. Michael Spence, “Job Market Signaling,” The Quarterly Journal of Economics, Vol. 87, No. 3, 1973. https://doi.org/10.2307/1882010
19. Bryan Caplan, The Case Against Education: Why the Education System Is a Waste of Time and Money, Princeton University Press, 2018. https://press.princeton.edu/books/hardcover/9780691174655/the-case-against-education
20. “Schools fight AI cheating with return to pen and paper blue books,” Fox News, 2026. https://www.foxnews.com/tech/schools-turn-handwritten-exams-ai-cheating-surges
21. “Blue books are back: The revival of pen and paper exams,” The Daily Cardinal, 6 November 2025. https://www.dailycardinal.com/article/2025/11/blue-books-are-back-the-revival-of-pen-and-paper-exams
22. “Colleges Turn to Oral and Handwritten Exams as AI Disrupts Assessments,” eWEEK, 2026. https://www.eweek.com/news/colleges-turn-to-oral-exams-ai-disruption/
23. “Are universities returning to in-person exams to combat AI cheating?,” Times Higher Education, 2025. https://www.timeshighereducation.com/depth/are-universities-returning-person-exams-combat-ai-cheating
24. “Ban all remote unsupervised tests 'immediately', urges report,” Times Higher Education, 18 August 2026. https://www.timeshighereducation.com/news/ban-all-remote-unsupervised-tests-immediately-urges-report
25. Philip M. Newton, Evidence of Learning Through Assessment: Protecting the Value of a Degree in the Age of AI, Policy Exchange, 17 August 2026. https://policyexchange.org.uk/publication/evidence-of-learning-through-assessment/

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Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk

Listen to the free weekly SmarterArticles Podcast

In Summary: * You know, I'd forgotten how much I preferred listening to college football than to any professional football. Today has reminded me.

That storm that rolled through San Antonio last night complicated my morning / early afternoon plans. Heavy winds with the rain did lots of damage. Lots of folks and businesses without power still and may be for several more days. Stop lights not working at many busy intersections. The local branch bank I use was closed and we had to find another. Finally found one open and I was able to take care of my business there. So much time lost. We decided to stop for lunch, etc. In was nice to spend extra time with the wife, but by the time we got back home the Colts game I'd hoped to follow was in the 4th Quarter.

It didn't take me long to find many college games to follow. I started with the NC State Wolfpack playing the Virginia Cavaliers and really enjoyed it. Now I'm listening to the Eastern Michigan Eagles playing the Sacramento State Hornets, and I'm sure I'll stick with this game until bedtime

Prayers, etc.: * I have a daily prayer regimen I try to follow throughout the day from early morning, as soon as I roll out of bed, until head hits pillow at night.

Health Metrics: * bw= 224.1 lbs. * bp= 138/82 (76)

Exercise: * morning stretches, balance exercises, kegel pelvic floor exercises, half squats, calf raises, wall push-ups, BP breathing exercises, pilates

Diet: * 07:00 – 1 banana, cheese * 08:30 – 1 cheese sandwich * 12:30 – steak, ham & cheese omelet, hash browns * 14:30 – toast and jam

Activities, Chores, etc.: * 04:10 – wake * 04:45 – bank accounts activity monitored * 05:00 to 06:40 – pick up fallen branches, sweep street * 09:45 – listening to the Indianapolis Colts Countdown to Kickoff Show * 10:00 – go to my bank, then to another, then on a “nostalgia” tour with Sylvia * 12:30 – lunch at a favorite restaurant with Sylvia * 14:15 – home in time to catch the last quarter of the Colts / Lions game * 14:54 – and the Lions win, 25 to 16. * 15:00 – now listening to College Football, NC State Wolfpack vs Virginia Cavaliers * 17:40 – and the Casvaliers win, 34 to 8. * 17:45 – Now listening to another college football game, Eastern Michigan Eagles vs. Sacramento State Hornets

Chess: * 19:00 – moved in all pending CC games

For the past 40 years, I've lived my life as a logical empiricist who refused to believe or deny anything that could not be proven objectively. Recent anomalous experiences have made that position untenable.

I’m well aware that what I write here has little chance of being seen, let alone believed.

Yet, I offer my testimony anyway.

An uncertain act for an equally uncertain world.

My first NFL game this Saturday is an early one. I'll tune into 1070 The Fan, Indianapolis, for the Countdown to Kickoff Show before the call of the game which is scheduled to start at Noon CDT.

I've also got a business meeting at my bank to attend in the morning. Hopefully it won't last long and I won't have to miss any of the game.

And the adventure continues.

Reddy Anna Book Login: How to Access Your Account Safely

If you are searching for Reddy Anna Book Login, you are likely looking for information about how to access your account, where to find the correct login page, and what to do if you cannot sign in.

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This guide explains the general Reddy Anna Book login process, common login problems, account-security tips, and frequently asked questions.

What Is Reddy Anna Book Login? Reddy Anna Book Login refers to the process of accessing an existing Reddy Anna Book account through its designated online login system.

Depending on the service and the current website configuration, the login process may require account credentials such as a registered mobile number, username, password, or another verification method.

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You are visiting the correct website. The website address is entered correctly. Your internet connection is stable. Your login credentials are accurate. You are not using a suspicious link received from an unknown source. How to Complete Reddy Anna Book Login If you already have an account, the general login process is straightforward.

Step 1: Find the Official Website Start by locating the official Reddy Anna Book website rather than relying on links posted on random websites, social-media comments, or messaging groups.

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Avoid entering sensitive information on pages that look unusual, contain excessive pop-ups, or redirect you through multiple unrelated domains.

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Double-check capitalization, numbers, and spelling before submitting the form.

Never share your password or one-time verification code with another person.

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Check your internet connection. Try refreshing the page. Clear your browser cache. Try another browser. Check whether the website is temporarily unavailable. Verify that you are using the current official domain. Do not automatically assume that a new domain promoted by an unknown source is legitimate.

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If this happens, wait for the applicable security period or use the official support/account-recovery procedure.

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How to Stay Safe When Using Reddy Anna Book Login Account security should be a priority whenever you log in to an online platform.

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Reddy Anna Book Login: Mobile Access Many users prefer accessing online accounts from smartphones.

If you are using a mobile device, open the verified website through an updated browser and check the domain before signing in.

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Is Reddy Anna Book Login Safe? The safety of any login depends partly on whether you are accessing the legitimate website and how you protect your account information.

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Tips for a Smooth Reddy Anna Book Login For a better login experience, keep these points in mind:

Bookmark the verified website instead of repeatedly searching for login links. Check the domain before every login. Keep your account credentials private. Use the official recovery option if you forget your password. Avoid logging in through links from unknown sources. Use a secure internet connection whenever possible. Log out when using a public or shared device. Frequently Asked Questions About Reddy Anna Book Login What is Reddy Anna Book Login? Reddy Anna Book Login is the process of accessing an existing account through the service's designated online login page.

Where can I find the Reddy Anna Book Login page? You should locate the login page through a verified official source. Because website domains and online services can change, users should confirm the current domain before entering account credentials.

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Kapok fiber collapses overnight and needs daily attention because it is agricultural fill with no structural memory, not a manufactured product engineered to spring back. Wool and buckwheat hulls both hold shape longer, but for different reasons: wool has crimped fiber that resists permanent compression, and buckwheat hulls interlock once redistributed and stay put until you move again.

The distinction matters when you expect a pillow to behave like memory foam and it does not. Memory foam has built-in memory. Kapok does not. Neither does wool or buckwheat, but the difference in how quickly each one flattens shows up in how often you need to knead it back into place.

Kapok is wild-harvested tree fiber, 80 percent air by structure, which is why it feels so light and compresses so easily under the weight of a head through the night. That same lightness means the fiber has nothing anchoring it in place once it compresses, so every morning the pillow sits flatter than it did the night before unless you restore its height by hand. That behavior is the material doing what plant fiber does when no chemical processing or structural reinforcement holds it in a fixed shape.

Wool behaves differently because the crimp in the fiber gives it a natural spring. When you press wool down and release it, the crimp lets it expand back partway on its own. It still needs periodic hand-fluffing to stay fully puffed, but the rhythm is slower than kapok because the fiber itself resists permanent collapse longer. Airing wool in a dry spot every few months helps refresh the crimp and keeps the fiber from matting.

Buckwheat hulls compress the slowest of the three because they are not fiber at all. They are hulls that nestle together once you knead them into position, and they hold that arrangement until weight or motion shifts them again. Daily fluffing matters less for buckwheat than for kapok, but a periodic sun airing still helps keep the hulls dry and prevents any musty odor from building up over time.

The difference in compression speed is why a kapok pillow needs more frequent attention than either a wool or buckwheat option, and why someone switching from memory foam to any natural fill often assumes something is wrong when the pillow stays flat until fluffed by hand. Nothing is wrong. The fill is working exactly as plant fiber and agricultural hulls work when you take away the chemical treatments and engineered memory that synthetic materials rely on.

A daily kneading habit fixes this. Hold the pillow upright with one hand on each end and push the ends toward the middle, moving compressed fill back toward the center where it thins out fastest. Rotate the pillow and repeat along the shorter side, shake it out, pat it firmly across the surface, then finish with a squeeze and release motion that lets trapped fill settle into an even layer. This takes about thirty seconds and restores what a natural fill pillow will not give back on its own.

Originally published on Circadian.

I had sex again today. I met someone that I vibe with as a friend and we were both on the same page and we ended up having sex twice. Things went well, and I found myself comfortable just hanging out with them afterwards while they were getting ready for something that I said, I would drop them off at. It felt like the kind of stuff. I guess I dreamed about in high school, just chilling on their bed while they get ready and I’m fucking around on my phone and we are making occasional jokes and just talking. And I feel like a normal person, and the way that I’ve kind of longed for. I wasn’t anxious or at least super anxious, I am attractive, and I have a lot of the fixed blessings in my life, where a lot of the difficulties came from things that I can control to some extent. And I remember as I was driving home. I felt a crash, and I thought about how the smell of sex was like shame of the sin that I had done. And it felt like it was washed over by the regret of my actions, but I reminded myself that there isn’t a shame, and there isn’t sin. It’s just the smell of sex that I don’t necessarily enjoy too much. And it’s the physical sensation of just being a bit raw and dry skin combined with weird latex lubes. But I didn’t send and I didn’t do anything wrong, and there isn’t anything to regret today. But I feel like I almost have this compulsion to feel bad after sex. But I know that I can change whatever I want.

The Perversion Escalation

#nsfw #Aiesha

After she got settled into her apartment, she got back into her old routine. She was always a goonette at heart. Once she learned what she was in college, she hid her porn addicted secret with pride.

She never told Daniel. In fact, Daniel seemed to complain about so many sexual things. It was almost as if he went out of his way to have a good time. She didn't tell him that she used to spend hours edging without reaching orgasm. He never really understood why it took her so long to reach climax. The memory of him always saying that made her cringe.

He even told her edging was stupid but she quietly ignored it. It was kind of her thing and her pride to extend her pleasure without release. With or without porn. She loved edging long before she realized what she did was actual gooning.

She could go weeks without an orgasm, rubbing mindlessly for hours, denying her orgasm for days on end. Being free from Daniel's judgment was so liberating despite the loneliness.

This whole secret life was her growing passion. Aiesha loved being addicted to porn, which might partly explain why she didn't go out much or why she seemed less socially adjusted than her friends. She didn't care too much about her appearance, feeling no need to impress anyone. She simply was who she was. With her glasses and vast knowledge about tech, she knew that her deep passions and interests would clear a room. She'd seen people truly glaze over when she was passionate about something. Over time, she just stopped putting in the effort.

This is why she took a different path. This is why gooning won. This is why she wanted her porn addiction to consume her.

She stopped wearing clothes entirely at home. She deliberately normalized her naked lifestyle. She watched porn constantly without shame or apology. She was basically a porn-addicted nudist. Her mind and body were a blank canvas for endless hours of perversion and pleasure, and she wanted more.

Sure, she always got naked to goon in the past, but ever since the divorce, ever since that first emotional collapse in her new apartment, she had to be naked to feel comfortable. She needed to touch her bare skin. She intentionally turned this into a compulsion. It was self-soothing. It was part of her self-care now. She wanted to escalate the need to be nude; it really aroused her almost as much as porn.

She knew these new habits were slowly rewiring her. That was the point actually.

Daniel often protested about her exposing herself at home, calling it “not proper.” Even if she remained undressed for longer than what he thought was 'normal' after sex, he would complain. He slowly became controlling and pathological. This was one of many red flags she might have noticed had his overbearing traditional parents allowed them to live together first. The thought of it all filled her with deep regret over how her life had turned out.

When she had these intrusive thoughts, she gooned harder, caressed her naked body, and used her bigger toys. She binged porn more, watched into the early hours of the morning, skipping sleep. Sometimes she called in sick, choosing to skip work so she can goon all day. It didn't matter. She had to bury all of these memories with porn and masturbation. She needed to shed all of the pain, hurt, and trauma by being naked all the time at home.

It was working.

Every day, she went deeper into porn, and she came out better than before. Every perverted act on her screen was the push she needed to delete any normal sexual triggers from her world. She no longer wanted to associate 'normal' sex with pleasure.

She no longer saw the world like everyone else. She imagined what everyone around her looked like naked, what they looked like masturbating, what they looked like doing sexual acts of all kinds. In fact, seeing anyone wearing clothes felt foreign to her now. Leaving her apartment to a world of people wearing clothes was like stepping into another dimension.

Every time she saw a black screen, she expected to see porn when it lit up. If she didn't, she was disappointed. She always thought about porn now. She could hear porn in her thoughts, and she would daydream about it. She stopped keeping up with movies, TV, or anything mainstream. Instead, she turned her attention to her favorite pornstars.

She would scroll through Reddit, X, Bluesky, and beyond for her porn fix...Spending hours online growing her porn collection while she fucked her pussy silly. She started paying for their pro-amateur content, even if she risked finding a paywall or a wall of short 10-second clips. Everything was a porn adventure, good or bad. She continued to escalate her perverted porn consumption habits to the extreme.

This became her only hobby.

She chose this because established traditions had completely failed her. So she chose to bury it all under porn, perverted, acts, and lewd behaviors. This was her new baseline normal now.

Porn 24/7 was mandatory. Nakedness was a requirement to function.

Aiesha thought long and hard about her next steps. She had always dreamed of having a multi-screen setup for porn. She didn't hesitate to order more monitors and fulfill that goal. Now that she was free, this was her chance. She no longer cared how things looked in her apartment. She won't have guests over anyway. No one to impress. No one to question why almost every wall had a used TV on it. She had to fuel her addiction. She was making a true goonette cave.

Getting more addicted to porn was giving back to herself. This was the true love she had been looking for.

Things continued to escalate beautifully.

Aisha loved her nakedness. She knew she couldn't hold a candle to porn stars half her age, but she still aged beautifully and turned heads occasionally—even when wearing plain clothes for work and running errands.

When she was naked at home, she was a simple black woman in her early 40s, often sitting spread-eagle in her office chair watching porn. Her glasses, the gateway to clearly seeing porn, were perched on her face. Her hair was always tied in a bun, allowing her shoulders to rest freely.

There was nothing ornamental about her body; she wore no jewelry or tattoos. However, she maintained a neatly trimmed bush. She was not a wild woman, merely comfortably average. She was simply a normal, everyday woman who had chosen porn and nudity over everything else. And she liked it that way.

The symbolic shedding of clothing and deeply perverting her mind was just what she needed to detoxify from what she had been through. The highlight of her day was curating her porn collection, adding new scenes to custom curations, and even finding new clever ways to automate her almost endless playlists.

She even started sleeping with porn playing through her whole house. The volume was kept at a safe level to avoid drawing attention from the neighbors.

Every moment she spent perverting her life, every time she escalated her heavy pornography use, and every time she had a sexual release using screens, she was gaining something she had lost long ago. Porn was in control.

She wanted to get addicted beyond repair.

The very idea of being immune to therapy deeply aroused her without end.

She wanted to be a total degenerate.

She didn't want to live without porn ever again.

Aiesha started adding porn to every routine she could think of. She always liked listening to talk radio and podcasts. It was only natural that she escalated to listening to porn while driving on her morning commutes. She started listening to porn at work; everyone always assumed she was listening to music. It was the perfect cover, making her day better.

Then she started exploring nudity outside of her apartment, making adjustments to her wardrobe to normalize new behaviors.

First, she started going commando for short errands, taking care to choose the right outfits that would hide her swaying breasts and hard nipples. No one noticed. But it was still exciting, even if it was still too much clothing. Her need to be naked was becoming a slow problem she refused to control.

She started to get more things delivered. Practically everything. Getting everything delivered opened the door to a brand new thrill—literally.

She was so used to being naked at home that one day she just opened the door without thinking. Even after realizing what she had done, she didn’t stop or cover up. Instead, she started masturbating immediately because she had forgotten she was naked.

It made her heart pound. She felt a rush. She felt actual excitement. She needed to get worse.

So she started watching more public porn. Women flashing in public, risking it all, and even full clothing abandonment walking far from any cover without any backup any safety net. She obsessed over them. Studied every video. Pushed all of her other kinks aside to focus on becoming what she saw.

She wanted to be like those women. She had to be. She wanted to go naked in public, more than anything else. Over time, every screen in her home had women doing something perverted in public. It was dominating her thoughts. She would go to work thinking about what it would be like to leave her house naked, drive naked, go to work naked, shopping naked. It was taking so much of her mental space.

The very thought of exposing herself became her primary sexual trigger. She was turning herself into an exhibitionist. It was even affecting her speech and words. She often caught herself before saying something perverse, and even a few close calls in chat and email served as quiet reminders that she was no longer “normal.” She was better. She was a goonette.

For a whole year, she had done nothing but consume pornography and masturbate. She was deliberately oblivious to the world outside of tech and her porn addiction. She loved every single moment she spent changing her brain.

The more she indulged, the wetter it made her. She logged every change that porn had made to her mind, cataloging it. She knew she was something new now—something transformed. Something twisted, depraved, and perfectly corrupted.

Going fully nude in public was inevitable. She was going to set that plan in motion. This was her new hobby now and she was going to reclaim what was taken away from her. One pixel at a time. One thread at a time. One lost brain cell at a time.

 7. Petit sauvage

Fasciné par le jeu du grand couteau sur la papaye, je réussis à apprendre le geste. J’étais très adroit, vite je pus me servir du grand outil sans me couper. Ça faisait rire les domestiques qui me prédisaient une carrière de cuisinier. J’en étais très fier, tant les exploits de la cuisinière en titre m’impressionnaient. J’ai toujours cherché à reproduire l’harmonie de sa manière de dresser les plats, mariant couleurs et saveur avec une habileté digne de tables princières. Certains jours on recevait. Je n’étais pas présenté, on préférait ne pas trop exhiber ce petit sauvage, ma sœur et ses révérences faisait beaucoup mieux dans le tableau. Les convives étaient uniquement des blancs, vêtus de blanc, qui décidément me paraissaient d’une autre espèce. Toutes et tous unanimement regardaient avec mépris voire dégoût le petit cercle ébahi et brun qui les contemplait du haut de leurs 4 ans, moi le premier, avec mes yeux verts qui faisaient tache.

C’est sans doute à ces moments que j’entendis sans comprendre le mot métis. Par discrétion on ne s’informait pas plus sur la parentèle du petit bonhomme sale, à la peau aussi brune que ses camarades, qui caquetait avec eux en dialecte et se réfugiait vite dans les jambes d’une domestique si on tentait de lui adresser la parole, de l’amadouer comme on fait d’un chat sauvage rencontré au hasard dans la boue jaune ou rouge du chemin. J’ai eu 4 ans en 1954. Autour de moi c’était l’affolement. Dien Bien Phu, ce nom était dans toutes les bouches et sonnait comme la fin du monde. Moi qui m’identifiais entièrement aux domestiques et paysans avec qui je vivais comme un de leurs enfants, je ne comprenais pas. Les Français étaient des étrangers, je savais que les gens ici ne les aimaient pas vraiment, je comprenais le dialecte mieux que la langue des colons. Les Blancs soudain n’étaient plus du tout gentils.

#nguolai

Starcraft Co. 19- Coup

The opportunity arrived sooner than anyone expected. Less than three weeks after Moria, virtually the entire senior leadership of the United Interstellar Council began assembling aboard Citadel Station, the most heavily fortified orbital installation in Terran space. The official purpose was a strategic conference. The war had reached the point where ordinary communication delays between ministries, military commands and planetary governments were becoming intolerable, and the Council wanted every major decision-maker in one secure location while it determined how to respond to the renegade advance. Council members arrived first, followed by ministers, senior military commanders, intelligence directors and administrators responsible for wartime production. By the time the conference began, the station contained enough of the UIC's political and military leadership that losing it would effectively decapitate the government. Under ordinary circumstances, concentrating so many people in one place would have been an unacceptable risk. Citadel Station made it seem reasonable. The platform was enormous, more fortress than station, protected by layered armor, fighter wings, missile batteries and enough point-defense weaponry to make a conventional assault suicidal. Several battlecruisers remained permanently stationed nearby. More importantly, the UIC had installed a new defensive system specifically because intelligence suggested the renegades might attempt another massed Zerg attack against the core. Batteries of enormous ion cannons surrounded the station, each capable of firing repeatedly into approaching biological formations at ranges where even guardians and devourers could not effectively respond. Individually they could tear apart capital ships. Coordinated against a Zerg swarm, they would carve enormous holes through it before the first organism came within striking distance. The UIC had finally learned something from New Carthage and Niflheim. If the renegades tried to overwhelm Citadel Station with numbers, they were going to discover that numbers were precisely what its defenses had been designed to kill.

StarCraft Co. learned that they intended to try anyway. The information came through the company's intelligence network, which by then extended considerably farther than the UIC realized. A captured renegade courier provided part of it. Traffic analysis supplied another piece. Reports from independent colonies and sympathetic UIC officers filled in the rest. Enormous Zerg formations were converging toward the Citadel system from multiple directions. Not thousands. Not tens of thousands. The estimates rapidly became meaningless. Overlords moved between systems carrying ground strains that would never be useful in an orbital attack unless the renegades expected to reach the station itself. Mutalisk swarms darkened sensor displays. Devourers and guardians accompanied them. Scourge were gathering in numbers reminiscent of Niflheim. The renegades had correctly identified the strategic conference and intended to end the political leadership of the UIC in a single blow. The commander read the report twice, then requested the complete Citadel defense schematics. Nobody in the room asked why.

They had not been searching for a way to attack the UIC. After Moria, StarCraft Co. had reached a conclusion about the Council, but a conclusion was not an operation. Starting a civil war while the renegades remained an existential threat would be insanity, and nobody in StarCraft Co. wanted to fight UIC soldiers who were, for the most part, fighting the same enemy they were. Citadel changed the equation because StarCraft Co. did not have to overthrow the Council. The renegades were already coming to kill it. The only question was whether the UIC's defenses would stop them. The answer appeared to be yes. The ion cannons possessed independent capacitors capable of sustaining limited fire if external power failed, and Citadel's conventional defenses drew from redundant station reactors. But the cannons consumed energy on a scale the orbital platform could not generate indefinitely. Their primary supply came from a dedicated fusion complex on the forest world below. Multiple transmission arrays carried power into orbit, and the system could reroute around damaged relays, but there was no meaningful redundancy for the generation complex itself. If the planetary plant failed, the ion batteries would get only the energy already stored in their capacitors. Enough for several devastating volleys. Not enough for a prolonged battle.

The commander enlarged the planetary installation on the display. One officer studied the approaching-swarm estimates, then the power network. “If that plant goes down, the renegades get through.” Nobody spoke for several seconds. Another officer finally said, “If we hit it, the UIC knows exactly what happened.” The commander shook his head. “Then we don't hit it.” That became the foundation of the operation. No StarCraft Co. soldier would fire on the UIC. No company battlecruiser would threaten Citadel. No commando would infiltrate the fusion plant. No ghost would plant explosives. No StarCraft Co. weapon would kill Omega Squadron personnel. If the power station fell, every sensor log, every corpse and every surviving witness had to support the same conclusion: the renegades had sent one of their controlled Zerg broods ahead of the main offensive to disable Citadel's ion cannons. StarCraft Co. would manufacture a renegade attack that the renegades themselves had never ordered.

The Ara made that possible. StarCraft Co. called in the favor earned on Aiur and requested several dark archons along with a small detachment of elite zealots, dragoons and high templar. The commander told them enough to understand the strategic purpose but not enough to pretend it was something morally cleaner than it was. The renegades were preparing to destroy the Terran government. StarCraft Co. intended to ensure they succeeded in destroying its leadership without destroying the institutions beneath it. The Ara representative remained silent for a long time before answering. “You ask us to help you choose which of your rulers survive.” “No,” the commander said. “I'm asking you to help us make sure the people who have already chosen millions of others to die don't survive because they built themselves a better shelter.” The Ara agreed. Their first task occurred nowhere near Citadel. Months earlier, StarCraft Co. had catalogued a small dormant Zerg brood on an uninhabited world several jumps away. It had no known connection to the renegades and was too small to justify extermination during the larger war. The Ara landed beyond the brood's detection range. Dark archons moved through the surrounding terrain until they were close enough to several drones and overlords to overwhelm them psionically. One drone stopped harvesting, stood motionless for several seconds, then turned away from its mineral field and walked toward the waiting transport. An overlord drifted after it. Another drone followed. Then another overlord. The rest of the brood continued as though nothing had happened. StarCraft Co. left without firing a shot.

The stolen organisms were transported in sealed holds to the forest world beneath Citadel. StarCraft Co. chose a remote mountain region hundreds of kilometers from the fusion complex, rugged enough that even Omega Squadron had never considered permanent surveillance worthwhile. Drones were released beneath the canopy and began harvesting immediately. A hatchery grew in a secluded valley. Creep spread through forest that had not seen Terran settlement in centuries. Another hatchery followed, then spawning pools, extractors, hydralisk dens and additional biological infrastructure. Overlords multiplied but remained beneath surrounding ridgelines wherever possible. StarCraft Co. had spent years learning how to destroy Zerg colonies. Now its officers watched one grow according to their instructions. The dark archons maintained control over the original organisms while StarCraft Co. scientists adapted captured renegade control systems to the expanding brood. Their version remained crude compared with the hierarchy the renegades had developed, but sophistication was unnecessary. The swarm did not need strategic judgment. It needed to recognize coordinates, follow attack routes and ignore losses. Every biological strain was deliberately ordinary: zerglings, hydralisks, overlords. Nothing exotic enough to suggest an experimental source. Nothing StarCraft Co. had not already encountered in renegade-controlled swarms throughout the war. No Terran handlers would accompany them. No company communications equipment would enter the battlefield. The Ara would appear only as scattered auxiliaries, replicating something the UIC had already seen when independent Protoss mercenaries fought beside the renegades on Niflheim. The deception did not depend on inventing evidence afterward. It depended on ensuring that everything Omega Squadron saw was completely consistent with the conclusion they were already going to reach.

Omega Squadron was the difficult part. Omega was what the UIC military could have been. Its personnel were selected from veteran formations, trained relentlessly and granted enough independence from Central Command to remain effective. They did not wait for doctrine to solve problems. They did not hold positions simply because someone had drawn them on a map. Their officers changed defensive layouts constantly, their ghosts patrolled outside the perimeter looking for infiltrators, their tank crews practiced rapid displacement after every fire mission, and their Goliath pilots drilled against massed air attacks. StarCraft Co. had fought beside Omega detachments before and respected them without reservation. If the rest of the UIC military had performed like Omega, the conversation after Moria might never have happened. Their base around the fusion complex reflected that competence. The generators occupied a fortified central plateau surrounded by high walls and concentric defensive belts. Minefields covered the obvious approaches. Bunkers sat in mutually supporting positions rather than neat lines. Siege tanks occupied hardened emplacements several layers deep, with alternate firing positions prepared behind them. Goliaths could redeploy between sectors along internal roads protected from direct fire. Missile turrets covered the skies in overlapping arcs. Ghost teams operated beyond the walls. Wraith patrols remained airborne continuously.

Every simulation StarCraft Co. ran ended badly. A conventional air assault was impossible. Mutalisks would be shredded by missile batteries and Goliaths, while StarCraft Co. lacked the genetic material needed to spawn guardians. A frontal ground attack was worse. Zerglings and hydralisks would disappear beneath siege fire before reaching the first wall, and StarCraft Co. possessed no ultralisk strain capable of forcing a breach. Even the Ara detachment could not meaningfully change that equation. The breakthrough came when the commander stopped trying to destroy Omega Squadron. “We don't need the base,” he said while the tactical simulation reset again. “We don't need their command center. We don't need the walls. We don't even need to win the battle.” One of his officers understood first. “The generators.” The commander nodded. Omega could retain ninety-nine percent of its base. It could kill ninety-nine percent of the attackers. StarCraft Co. only needed the remaining one percent to reach machinery never designed to survive organisms tearing at it from ten meters away.

Omega detected the hidden brood eleven hours before StarCraft Co. had planned to begin the operation. The commander considered that an advantage. A reconnaissance Wraith found creep spreading through an eastern valley and transmitted the contact before withdrawing. Within minutes the fusion complex went to full alert. Wraith patrols doubled. Ghost teams entered the forests. Siege tanks shifted toward eastern approaches. Goliaths moved toward possible overlord corridors. Omega had discovered a controlled Zerg force operating near the strategic power station hours before a known renegade offensive against Citadel. They did not need anyone to tell them what it meant. “They think it's the renegades,” an intelligence officer said. The commander watched Omega formations deploying. “They should.”

The first Zerg attack hit the eastern defenses shortly before dawn. Thousands of zerglings poured from the tree line and crossed open ground toward the outer perimeter. Omega Squadron destroyed them almost contemptuously. Siege tanks opened fire before the swarm was visible to infantry. Entire sections of forest vanished beneath explosions. Surviving zerglings reached the minefields and disappeared in successive detonations. The handful that made it through entered overlapping bunker fire and died meters from the walls. StarCraft Co. sent a second wave. Omega destroyed that one too. A third followed with hydralisks mixed behind the zerglings. Omega adapted immediately, shifting artillery toward the hydralisk concentrations while marines concentrated on faster organisms. Not a single attacker reached anything strategically important. That was fine. The objective of the first attacks was not to break the base. It was to establish the story. A renegade-controlled Zerg swarm had appeared near the fusion plant and was attempting to overwhelm it. Omega reported exactly that to Citadel.

Then the Protoss appeared. Not as an army. That would have created questions. Small Ara groups struck isolated positions along the western perimeter and disappeared into the forests. Dragoons fired from ridgelines and relocated before Omega artillery could bracket them. Zealots hit a reconnaissance platoon and withdrew. A high templar dropped a psionic storm onto a reinforcement column, killing enough infantry to force the column to scatter, then vanished into mountainous terrain. Omega had encountered Protoss mercenaries alongside renegade forces before. Their analysts incorporated the new contacts into the existing picture without hesitation: renegade Zerg force, Protoss auxiliaries, coordinated assault against Citadel's power source. Exactly what StarCraft Co. needed them to believe.

Omega refused to cooperate by making tactical mistakes. The commander had hoped the western attacks might draw a significant armored force out of the installation. Instead Omega committed only enough troops to contain them. Tanks remained behind the walls. Goliaths shifted sectors without abandoning air-defense coverage. Infantry counterattacked cautiously beneath Wraith reconnaissance. When Ara dragoons offered an apparently vulnerable flank, Omega did not chase. When zerglings attacked again in the east, reserve units moved into prepared positions rather than charging forward. StarCraft Co. watched the response with growing respect. “They're not overcommitting.” “No.” “They know we're trying to move them.” “Yes.” One officer glanced at the commander. “You sound pleased.” The commander kept watching the tactical display. “If we're going to replace the people commanding them, I'd like the army underneath to be worth keeping.”

The next phase was designed around Omega's discipline instead of hoping it would fail. StarCraft Co. opened additional fronts. Hydralisks appeared in the north, accompanied by enough overlords to suggest an airborne insertion. Omega moved Goliaths and Wraiths to cover it. Another zergling force struck the southeast. Ara troops attacked again in the west. None of the assaults was large enough to threaten the base by itself. Together they forced Omega to maintain credible defenses in every direction. That was all StarCraft Co. needed. They were not trying to pull the garrison out of position. They were trying to prevent it from concentrating. Then the real attack rose from the southern valleys.

Dozens of overlords appeared first, followed by dozens more. Omega detected them almost immediately. Missile turrets rotated, Goliaths acquired targets and Wraiths accelerated toward the formation. The sky filled with missiles. Overlords burst apart above the forests and rained burning biological tissue onto the mountainsides. Wraiths tore through the slower transports. Goliath volleys reached them from kilometers away. Some died before crossing the outer surveillance perimeter. The rest continued. Each carried only a handful of organisms. That was the trick. A normal overlord assault attempted to put an army behind enemy lines. StarCraft Co.'s assault attempted to make interception inefficient. Destroying one transport might kill four zerglings. Another would approach thirty seconds later carrying three hydralisks. A third would come from another direction containing six more zerglings. Omega possessed more than enough firepower to destroy the swarm. It did not possess enough simultaneous targeting capacity to guarantee that every individual transport died before reaching the inner base.

The first survivors crossed the outer wall. Omega still assumed they were attempting to establish a beachhead. Then the overlords kept going. They flew over bunkers, over tank emplacements, over barracks and supply depots, and converged on the fusion complex. Omega understood. The warning went through every channel simultaneously: “Generators. They're targeting the generators.” The inner defenses opened with everything they had. Overlords exploded one after another. One ruptured above a maintenance yard. Six zerglings fell out. Three died on impact. Two were cut apart by marines before they could stand. The last ran directly past the infantry and threw itself against a coolant conduit until Gauss fire tore it apart. Another overlord reached a reactor roof before missile fire killed it. Hydralisks spilled onto the structure. A Goliath destroyed two immediately. The third fired into exposed transmission equipment until a tank shell removed it. Another overlord arrived, then another. StarCraft Co. had stopped thinking of them as transports. They were delivery systems. Every organism carried one directive: ignore soldiers, ignore defenses, ignore survival, destroy generators.

The first generator failed after hydralisk spines ruptured multiple cooling lines and triggered an automatic shutdown. Omega reacted instantly. Infantry poured into the central complex. Goliaths abandoned several outer air-defense positions and redeployed inward. Wraiths stopped hunting overlords at range and began orbiting directly above the reactors. The next wave suffered even worse losses. Twelve overlords approached. Eleven died. The twelfth dropped four zerglings onto a transmission platform. All four were dead within seconds, but not before they severed enough cabling to force the second generator offline. Omega began winning the battle faster. StarCraft Co. began losing it more successfully. The base's outer defenses remained largely intact. Omega casualties were substantial but nowhere close to catastrophic. Most of its tanks still operated. Most of its bunkers remained manned. Its command structure was functioning perfectly. None of that mattered if enough organisms reached the reactors.

The commander ordered every diversion intensified. The eastern swarm charged again. Thousands of zerglings crossed ground already carpeted with their own dead. Siege shells landed among them continuously. Hydralisks followed and fired at bunkers simply to keep Omega infantry facing outward. The northern force pushed forward under direct artillery fire. In the west, the Ara stopped fighting like raiders and attacked openly. Zealots rushed an armored position while dragoons concentrated fire on tanks. High templar moved close enough to the line to drop storms onto infantry formations and accepted the return fire that followed. Omega met them with equal violence. A siege tank fired point-blank into a zealot charge and killed its own supporting marines along with the attackers. Dragoons destroyed one tank only to be caught by three others covering the same lane. A high templar erased an infantry platoon beneath a storm and was immediately killed by a Wraith strafing run. Zealots reached a bunker complex and turned the position into close combat. Omega troops refused to withdraw. The Ara had known the requirement before the operation began. If necessary, they would die there. There could be no captured Protoss explaining who had brought them.

The third generator went down. One remained. By then the Zerg brood had almost exhausted itself. StarCraft Co. had deliberately retained no reserve. A surviving brood would create questions. A retreating Zerg force might be tracked back toward the hidden hatcheries. Every organism spawned for the operation would either reach Omega Squadron or die trying. “Everything,” the commander ordered. The remaining valleys emptied. Zerglings poured toward every perimeter simultaneously. Hydralisks followed. Overlords rose from concealed positions carrying whatever organisms could fit inside them. Omega's sensors briefly showed biological contacts in almost every direction. The final assault looked desperate. That made it look real.

Omega Squadron fought magnificently. Its eastern tanks killed so many zerglings that corpses interfered with the next wave's movement. Marines emptied magazines faster than supply teams could replace them. Medics dragged wounded soldiers from bunker to bunker while shells landed around them. Wraith pilots remained airborne despite depleted ammunition and damaged engines. Goliaths fired until launch systems overheated. Ghost teams that had spent the opening phases hunting infiltrators returned to the base and began killing hydralisks almost individually. None of them understood that StarCraft Co. considered their survival desirable. Every Omega soldier still alive after the battle was another witness, another officer who could say the renegades had attacked them, another person who had personally watched Zerg organisms destroy the generators.

The overlord swarm crossed the perimeter. Most died. Some did not. A dying transport released zerglings onto the roof of the fourth generator building. Omega infantry killed all but two before they landed. Those two reached an intake housing and disappeared inside it before defenders could follow. The resulting damage was repairable. The generator stayed online. Another overlord reached the complex. Hydralisks dropped directly into a maintenance courtyard. They lasted eleven seconds. The generator stayed online. Another approached from the north. A Wraith destroyed it. Another from the southeast. Goliaths killed it before it cleared the wall. StarCraft Co. had four overlords left. Then three. Then two. The first of the remaining pair ruptured over an industrial building hundreds of meters short of the target. The final overlord approached from the south already burning.

Omega concentrated everything on it. Missiles climbed from turrets. A Wraith followed behind it. Goliaths fired from below. The creature began losing altitude. Someone in StarCraft Co.'s operations center whispered, “Come on.” The commander said nothing. The overlord crossed the generator compound. A missile tore through it. Its body split open and four hydralisks fell. One struck the roof badly and did not rise. A Goliath killed another. The remaining two turned toward the reactor housing. Omega marines opened fire. One hydralisk disappeared beneath Gauss rounds. The last kept shooting. Spines struck armor plating, then cooling equipment, then a primary conduit. Rounds tore through the hydralisk's torso. It fired again. The conduit ruptured. The hydralisk collapsed. For half a second, nothing happened. Then the generator's protection systems detected catastrophic coolant loss and the reactor scrammed.

The fusion complex went dark. Nobody in StarCraft Co.'s operations room cheered. The tactical display showed almost nothing left to command. The eastern swarm had ceased to exist. The northern force was gone. The Ara detachment had been reduced to scattered survivors already carrying out their final withdrawal routes or dying under Omega counterattacks. The last Zerg organism inside the fusion complex disappeared from sensors moments later. Omega Squadron still held the base, barely damaged compared with what StarCraft Co. could have attempted with its own army. Exactly as intended.

The surviving UIC officers immediately began organizing repairs. Their commander transmitted an emergency report to Citadel: major controlled Zerg assault supported by unidentified Protoss forces, fusion generation disabled, all hostile formations destroyed, restoration underway. StarCraft Co. intercepted the report. Nobody had to alter a word. Everything Omega reported had happened. They simply did not know who had caused it to happen. StarCraft Co. began erasing the operation from the outside inward. Remote-control systems were dismantled. Data cores were thermally destroyed. The abandoned hatcheries in the mountain valleys were collapsed beneath demolition charges planted days earlier. Any remaining drones had already been sent into the suicidal final wave. The dark archons and the few Ara personnel who had never entered combat departed aboard concealed transports. StarCraft Co.'s observation teams withdrew without ever approaching Omega's sensor perimeter. Nothing on the battlefield belonged to StarCraft Co. There were Zerg corpses, Protoss corpses, Omega dead, damaged generators and thousands of surviving UIC soldiers who would swear for the rest of their lives that the renegades had tried to disable Citadel's defenses before launching their main offensive.

Above them, Citadel Station remained operational. Its reactors still powered the platform. Its missile batteries still functioned. Its battlecruisers still patrolled. But the ion cannons had switched to capacitor reserves. The first renegade Zerg formations entered the system eleven minutes later. The timing was almost perfect. StarCraft Co. had not arranged that part. The renegades had. Citadel's ion batteries fired, and the first volley justified every credit the UIC had spent building them. Beams tore across the system and passed through entire biological formations. Mutalisks vanished by the hundreds. Devourers came apart. Overlords ruptured so violently that the organisms they carried scattered frozen into space. Vast holes appeared in the approaching swarm. The surviving Zerg continued forward.

The cannons fired again. Another enormous section of the swarm disappeared, but several batteries failed to recharge. The UIC understood the problem immediately. Emergency power requests went to the surface. Omega reported that the fusion complex remained offline, repairs were underway and estimated restoration time was unacceptable. The third ion volley came from fewer than half the batteries. The renegades kept coming. Scourge accelerated ahead of the main swarm. Devourers spread around them. Guardians moved into firing positions. Mutalisks filled the spaces left by the organisms the cannons had destroyed. The remaining ion batteries charged for one final volley and fired. Then the system went silent.

The door was open.

StarCraft Co. watched from far outside the battlespace as the swarm crossed the distance toward Citadel. Nobody spoke. They had not attacked the UIC. They had not fired on Citadel. They had not ordered the renegades to come. They had not killed a single Council member. All they had done was make sure that when the renegades arrived, the lock on the door was broken.

The first scourge reached the UIC battlecruiser screen. Then the killing began.

Steps to an ecology of mind, by Gregory Bateson Published by The University of Chicago Press, 2000

In a line – A challenging, freewheeling series of essays exploring cybernetic and systems theory, that blow apart what you thought you knew about context, change and communication.

This work is a tough one, that I’ve been meaning to read for years; now, finally engaging with it, I can see why people regard working through it – and its implication – almost as a rite of passage. As per Bateson’s far more accessible Mind and nature: A necessary unity, the polymathic genius riffs across theories and experiences with bracing kaleidoscopic boldness.

Steps shows his notes and development far more than Mind and nature though, and offers more tools and worked examples, applied across a huge variety of situations.

The man passionately loves the World, and is fascinated with everything in it. More than anything else, this was what I have come away with, and have been deeply revitalised by.

The book starts with slightly awkward, contrived didactic dialogues between a nominal father and daughter. These originally ran in publications run to promote and explore Korzybski’s General Semantics; the sort of thing that Very Intelligent People would read. I didn’t like this bit.

After this though, the guy gets into everything – from communication with seals and dolphins, to treating schizophrenia and alcoholism, to analysing Papua New Guinean tribal dancing, to ponderous analyses into differences in morphology of insect legs.

Embedded in this is a series of brilliant, startling, slippery ideas about learning, communication and mind; these are the threads that hold it all together. He lays it out quite plainly, though it’s hard and deep work to really get to grips with.

Why this, why now?

Bateson ostensibly uses the discipline of cybernetics – to the extent there’s a singular discipline there; I’m understanding less of what it is, the more I study it.

I found Steps particularly insightful in exploring some questions critical for me at this time, especially around agency, mind and context.

I especially enjoyed how it was so non-human-centric – but not mechanistic or technocratic, either. The application of his ecological models of thinking about systems seems especially pertinent to the weird, especially the sometimes parallel, sometimes intersecting lines of digital and magical.

Bateson kind of careens through both and neither; definitely operating at a level is rare these days. It’s so refreshing to engage with thinking like this – a guy who was just figuring things out, while hanging out with characters like dolphin communicator and ketamine expert John C. Lilly, or possibly the world's greatest hypnotist (yet), Milton Erickson.

A critical NLP sourcework

Also, so many of his ideas were deeply influential on the development of Neurolinguistic programming (NLP), though in not-necessarily obvious ways.

My immediate payoff was a deeper understanding of what the creators (or cartographers) of NLP, Richard Bandler and John Grinder, were engaging with, when they melded its various disparate parts to make this most curious method of changing minds.

Bateson’s model of learning, as a system stepping out into bigger and bigger sets of contextual awareness was brilliant. So simple when explained – but completely impenetrable, or invisible, before.

This is the gift of this work – it can be a grind, but necessarily so; this prepares the way to capture flashes of indelible insight.

Steps to an ecology of mind is a challenging text – and even more so to find and actualise the practice within it, but if you want to get better, do hard things.

If you want to understand differently, take the steps.

“Becoming doesn’t stop just because life gets harder. Sometimes it begins again in the places you never wanted to start.”

Life looks completely different now.

Not just changed, but utterly unfamiliar, like waking up in a chapter of my own story I never agreed to write, navigating a reality I never imagined I’d have to survive.

When I was taken to jail, I lost far more than just a few moments of freedom.

I lost my job.

I lost my financial stability.

I lost the version of myself who could walk through the world without worrying about background checks, probation meetings, or whether a total stranger holding my paperwork would decide I was dangerous.

Now, I carry the legal label of a felon, specifically, a violent felon.

It is a designation heavier than anything I have ever carried, and one that stands in direct opposition to the truth.

That label was stamped onto my name simply because a child was shared between us, leading the legal system to automatically code the situation as domestic violence.

There was no violence. No assault. No physical harm. No threats.

Yet the system rarely pauses for nuance; it operates strictly on rigid categories.

Now, that single category precedes me everywhere I go.

Before this, I never had to wonder if my name alone would disqualify me from an opportunity before I even walked into the room.

I never had to defend my character to people who had already judged me based on a single line in a police report.

Now, every job application feels like a door slamming shut before I even turn the handle.

The silence following a submitted resume feels like a quiet verdict: Not qualified. Not trustworthy. Not a fit.

Not because of who I am or what I bring to the table, but because of how a paper trail misrepresents me.

It is a surreal experience to rebuild your life around a story that isn't true to who you are.

To be a mother in her 40s starting over from scratch, carrying the weight of public perception, is exhausting.

Some days bring deep anger.

Some days bring quiet defeat.

Some days, it feels like drowning in a framework that was never designed to account for context, context for women like me, or the difference between a single isolated incident and a pattern of behavior.

Yet, giving up is not an option.

My daughter is watching how I respond to this.

I still have a future worth building, and I refuse to let an inaccurate label dictate the rest of my life.

I am learning how to navigate this altered reality, the paperwork, the administrative hurdles, the invasive questions that turn my stomach.

I am learning to let go of shame that was never mine to carry, and to stand firm even when external circumstances try to reduce me to a record.

This is what my life looks like right now: harder, heavier, and far more complex.

But beneath all of it, it is also defined by a quiet, immovable determination.

Even here, even now, the work of becoming continues.

(NEXT – CHAPTER 2:)

Six thousand office workers in the United States, the United Kingdom and Australia were asked, across December 2025 and January 2026, how much time artificial intelligence had given back to them. They said eleven hours a week.

Then they were asked a second question, the one almost nobody asks. How much time do you spend feeding these systems context, supervising what comes out, debugging the mistakes and cleaning up afterwards? The answer was 6.4 hours a week.

That is the whole argument in two numbers. Roughly six of every ten hours artificial intelligence appears to save are consumed by the labour of making artificial intelligence work. The survey, published in June 2026 as the Work AI Index by Glean's Work AI Institute alongside researchers from Stanford, Notre Dame, Emory, UC Berkeley, UC Santa Barbara, UNC Charlotte and University College London, gave the residual a name that has since escaped into general use: botsitting. Rebecca Hinds, who heads the institute and co-authored the report, and her colleagues defined it coldly, as “the largely unrecognized, unbudgeted, and untracked labor of making AI usable”.

Unrecognised, unbudgeted, untracked. Three adjectives doing an enormous amount of work.

On 18 August 2026, the Indian human resources publication HRKatha ran an analysis of the same phenomenon under the more homely label of bot sitting, defining it as the work employees do prompting systems, checking answers, correcting errors, supplying missing context, trying again when outputs go wrong, and deciding whether the eventual result can be trusted at all. Its sharpest line concerns measurement. The real test of efficiency, the piece argued, is not how quickly a machine produces an answer but how much human work remains before anyone is willing to trust it.

Nobody is measuring that. And what nobody measures, nobody pays for.

The Arithmetic That Stops Working at Five Minutes

Take the scenario in its simplest form. A task used to take twenty minutes of a person's own work. Now it takes five minutes of generation followed by fifteen minutes of checking, correcting and verifying. On the stopwatch, nothing has changed. On the productivity dashboard, everything has. The dashboard records five minutes of task completion, because that is the interval in which the tool was invoked and the output produced. The fifteen minutes afterwards are logged as the worker doing their job, which is what they were doing before the tool arrived.

The tool has not saved fifteen minutes. It has reclassified them.

This is a straightforward consequence of how enterprise software reports on itself. Adoption metrics count seats, prompts and sessions. They do not count the second and third attempts, the cross-check against a source document, the quiet decision not to send the thing at all. More prompts is not a measure of value. It may be a measure of the opposite: a worker who prompts a system eleven times before getting a usable answer generates eleven data points that a usage dashboard reads as enthusiasm.

The Work AI Index found a second figure that should worry anyone relying on those dashboards. Only thirteen per cent of organisations surveyed said artificial intelligence had significantly improved their performance, against eighty-seven per cent of workers who said they were using it. That gap between individual time saved and organisational performance gained is where the botsitting hours have gone. They have not disappeared. They have been absorbed into a category of labour the accounting system cannot see.

The survey also documented what happens when supervision becomes unaffordable. Sixty-nine per cent admitted to what the report calls botshitting: shipping output they had not verified, did not fully understand, or could not confidently stand behind. Forty-one per cent had sent work they would be unable to explain if questioned. Twenty-eight per cent admitted blaming the machine for their own mistakes. Workers who reported botshitting were 3.8 times more likely to be looking for another job.

That last figure is the tell. This is not laziness. It is triage under an unfunded mandate.

Lisanne Bainbridge Wrote the Manual for This in 1983

None of this is new. The person who explained it most economically did so forty-three years ago, in a five-page paper about process control in factories and power stations.

Lisanne Bainbridge published “Ironies of Automation” in the journal Automatica in 1983. Her central observation was that the designer of an automated system regards the human operator as unreliable and inefficient, and so tries to design them out, but cannot automate everything. What remains for the human is precisely the residue that could not be specified: the awkward, ambiguous, judgement-heavy fragments that defeated the engineering. The operator is left with the hardest parts of the job, stripped of the easier parts that used to keep their skills sharp, and asked to intervene in exactly the situations for which they are now least prepared.

Bainbridge was blunt about monitoring in particular. “The human monitor has been given an impossible task,” she wrote, noting that where a computer is making decisions faster and on more dimensions than a person can follow, “there is therefore no way in which the human operator can check in real-time that the computer is following its rules correctly.” She caught the training paradox too: it is ironic, she observed, to train operators in following instructions and then place them in the system to provide intelligence.

Substitute a large language model for a distributed control system and the paper reads like a memo from last week. The knowledge worker of 2026 has been handed the residue. Drafting a first version of a summary, a function, a customer reply: that was the tractable part, and it has been automated. What is left is knowing whether the draft is right, which requires knowing the domain, which was previously maintained by doing the tractable part.

The literature that followed quantified the failure modes she predicted. Raja Parasuraman and Dietrich Manzey's 2010 review in Human Factors, synthesising decades of empirical work on automation complacency and automation bias, reached conclusions that should be printed on the login screen of every enterprise assistant. Complacency emerges specifically under multiple-task load, when manual tasks compete with the automated task for attention. It appears in expert users as readily as in novices, and cannot be trained away with simple practice. Automation bias produces both errors of omission, where the person fails to act because the system did not flag a problem, and errors of commission, where the person acts wrongly because the system told them to. Neither is reliably prevented by instructions telling people to be careful.

Meanwhile the monitoring itself is not free. Joel Warm, Parasuraman and Gerald Matthews titled their 2008 Human Factors paper on the subject with unusual directness: vigilance requires hard mental work and is stressful. Sustained attention to a mostly reliable process is not a restful state between bouts of real work. It is a demanding task with measurable workload and stress costs, and performance on it degrades over time.

So the corporate framing, in which the human is elevated from doing to overseeing, describes a promotion. The human factors literature describes a transfer into a job that is cognitively expensive, psychologically taxing, unavoidably error-prone and, in most workplaces, entirely uncompensated.

Sixteen Developers Who Were Certain They Had Gone Faster

The most instructive evidence in this debate is a small randomised controlled trial with an awkward result and an unusually honest set of authors.

In 2025, the research organisation METR recruited sixteen experienced open-source developers working on repositories they personally maintained, projects averaging over 22,000 GitHub stars. It randomised 246 real issues from those repositories into two conditions: artificial intelligence tools permitted, or not permitted. Tasks averaged around two hours. Beforehand, the developers forecast that the tools would speed them up by twenty-four per cent.

They were nineteen per cent slower with the tools.

What happened next matters more than the slowdown. After completing the work, having lived through the actual elapsed time, the same developers estimated that artificial intelligence had made them twenty per cent faster. They were wrong by roughly forty percentage points about their own labour, in the direction of the tool, on tasks they had personally performed within the previous few hours.

That is the epistemological problem at the heart of every self-reported productivity statistic in this field, including the eleven hours in the Work AI Index. Generation feels fast because it is fast and visible. Verification feels like ordinary work because it is ordinary work, diffuse, arriving in fragments scattered through the day. People are demonstrably bad at summing the second category and comparing it to the first.

METR deserves credit for what it did afterwards. In February 2026 it announced it was redesigning the experiment. Its follow-up cohorts produced point estimates of a negative eighteen per cent speedup for the original developers, with a confidence interval from negative thirty-eight to positive nine per cent, and negative four per cent for newly recruited developers. The slowdown persisted in the point estimates but the intervals now straddled zero. More importantly, METR reported that developers were increasingly refusing to take part in conditions barring them from using the tools, that the pay rate had fallen from 150 dollars an hour to fifty, and that measuring elapsed time had become genuinely hard when participants ran multiple agents concurrently. “Due to the severity of these selection effects, we are working on changes to the design of our study,” the organisation wrote, adding that the true speedup among excluded developers could be considerably higher.

That is what intellectual honesty looks like, and it cuts both ways. Anyone citing the nineteen per cent slowdown as a settled fact about artificial intelligence in 2026 is overreaching. But the perception gap is the more durable finding, and nothing in the update disturbs it. The difficulty in the follow-up was not that developers had become good at estimating their own throughput. It was that the experiment could no longer isolate the variable, because people who use these tools will no longer agree to stop.

Why Checking Is Harder Than Doing

There is a structural reason verification consumes more time than intuition suggests, and it concerns the shape of machine error.

A junior colleague who does not know something produces work that signals its own uncertainty. The prose is hedged, the gaps are obvious, the citations are missing. A language model that does not know something produces work that is fluent, confident, correctly formatted and internally consistent. The error sits in a sentence that looks exactly like every true sentence around it. The cost of finding it is therefore not the cost of scanning for anomalies. It is the cost of independently establishing the truth of each load-bearing claim, which in the limit is the cost of having done the work yourself.

This is why the five-minutes-plus-fifteen arithmetic is not a transitional inconvenience that better models will erase. As accuracy rises, the frequency of error falls but the difficulty of detection rises, because a rarer error in more plausible packaging demands more sustained vigilance to catch. Parasuraman and Manzey's finding is precisely that reliable automation breeds the attentional withdrawal making occasional failure catastrophic. Better models make botsitting less frequent and more consequential at once.

The downstream costs have now been measured in money. In September 2025, BetterUp Labs and the Stanford Social Media Lab surveyed 1,004 full-time American desk workers about what they termed workslop: output with the appearance of good work but lacking the substance to advance the task. Forty per cent had received it in the preceding month, and respondents estimated that 15.4 per cent of the work reaching them fell into the category. Each incident took an average of one hour and fifty-one minutes to sort out, around twenty minutes longer than doing the work properly in the first place would have taken. Converted using respondents' own salaries, that came to roughly 186 dollars per employee per month, or more than nine million dollars a year for an organisation of ten thousand people. Managers were markedly more exposed than individual contributors, at fifty-four per cent against 38.5 per cent.

Note what workslop is in labour terms. It is botsitting skipped upstream, landing unpriced on somebody downstream. The person who declined to verify saved fifteen minutes. The person who received the output spent one hour and fifty-one. That is not productivity. It is a transfer of unpaid supervisory work between colleagues, with interest.

What Gets Measured Was Never Designed to See This

Organisations do have instruments for measuring cognitive workload. They simply do not use them for this.

The NASA Task Load Index, developed by Sandra Hart and Lowell Staveland in 1988 and still the most widely used subjective workload instrument in ergonomics, decomposes workload into six components: mental demand, physical demand, temporal demand, performance, effort and frustration. In its original form it asks respondents to weight those components against one another across all fifteen possible pairs. Hart's twenty-year retrospective in 2006 catalogued its spread across aviation, medicine and interface design.

Look at that list and notice how badly conventional workload accounting maps onto botsitting. Corporate measurement, where it exists at all, tracks temporal demand: hours worked, tickets closed, tasks completed. Mental demand, effort and frustration are exactly the dimensions supervision loads most heavily and timesheets record least. A worker who closes the same number of tickets while spending two extra hours a day in a state of alert scepticism about machine output registers, on every dashboard the employer owns, as having had an identical day.

This is where the sociology of work becomes more useful than the economics. Susan Leigh Star and Anselm Strauss published “Layers of Silence, Arenas of Voice” in the journal Computer Supported Cooperative Work in 1999, a foundational paper on invisible work in computer-supported systems. Their opening move was to insist that what counts as work is a matter of definition, not a matter of fact, and that designers routinely automate the visible portion of a process while remaining oblivious to the articulation work holding the arrangement together: the coordinating, contextualising, patching and repairing that never appears in a process diagram and therefore never appears in a budget.

Botsitting is articulation work. Supplying missing context to a model, deciding which of three plausible outputs matches what the client meant, knowing the tool has silently used a superseded policy document: this is the connective labour Star and Strauss described, now performed on behalf of a machine rather than a colleague. Their warning was that automating around invisible work does not eliminate it. It removes the vocabulary for discussing it.

The lifecycle synthesis of human-AI collaboration risks published on arXiv in August 2026 by Md Foysal Ahmed, Isaac Kobby Anni and Md Main Uddin Rony makes a compatible point in the language of risk taxonomy. Surveying evidence across healthcare, journalism, education, research, organisational decision-making and defence, the authors identify six recurring risk clusters that cut across domains: trust miscalibration, cognitive burden, accountability gap, capability erosion, goal misalignment, and AI anxiety and technostress. These are not separable engineering defects to be fixed one at a time, they argue, but interlocking sociotechnical dynamics cascading through four lifecycle stages, which is why piecemeal interventions frequently create unintended consequences. Cognitive burden and accountability gap sitting adjacent in the same taxonomy is no coincidence. They are one problem seen from the worker's side and the organisation's side.

The History of Labour-Saving Devices Is a History of Redistribution

There is a precedent for all of this, and it is not from computing.

Ruth Schwartz Cowan's “More Work for Mother”, published in 1983 and awarded the Society for the History of Technology's Dexter Prize the following year, asked why American housewives were working longer hours in 1970 than in 1870 despite a century of mechanisation. Washing machines, vacuum cleaners, gas ovens, commercial flour and refrigeration had all arrived. The hours had not fallen.

Cowan's answer was that the appliances did not eliminate labour. They redistributed it and then raised the standard it was held to. Work previously done by servants, husbands, children and commercial services was pulled back onto one person, while expectations for cleanliness, nutritional variety and laundry frequency rose to consume whatever slack the machines created. The technology was genuinely labour-saving per unit of output. Total labour rose anyway, because output expectations rose faster and the residual tasks landed on a single unpaid worker.

Read the Work AI Index against that and the shape is familiar. Eleven hours of unit-level saving, 6.4 hours of new residual labour, thirteen per cent of organisations reporting real improvement, and a rising expectation of volume absorbing the difference.

The digital version of this dynamic already has a well-documented lower tier. Mary L. Gray and Siddharth Suri's “Ghost Work”, published in 2019, described the invisible global workforce that fills the gaps automated systems cannot close: content flagging, transcription checking, data labelling, the human intervention that makes a service look seamless. Their formulation of the paradox is that the drive to eliminate human labour reliably generates new human tasks, and that those tasks are systematically hidden, poorly paid and structurally insecure. The most cited illustration remains Billy Perrigo's January 2023 investigation for TIME, which documented that OpenAI had used workers in Kenya, employed through the outsourcing firm Sama, to label descriptions of child sexual abuse, torture, self-harm and bestiality so that ChatGPT could learn to filter such material. They were paid between 1.32 and two dollars an hour.

The point for the office worker in 2026 is not that their situation is equivalent. It plainly is not. The point is that the industry has an established pattern of relying on human labour it declines to name, and that the pattern has migrated from the outsourced periphery to the salaried core. The mechanism is identical: apparent autonomy produced by human effort the accounting treats as external to the system.

Article 14 Turns You Into the Accountability Sink

Regulation has made the informal expectation of supervision into a legal duty, and in doing so has clarified who carries the risk.

Article 14 of the European Union's Artificial Intelligence Act requires that high-risk systems be designed so they “can be effectively overseen by natural persons during the period in which they are in use”. The overseeing person must be enabled to “properly understand the relevant capacities and limitations of the high-risk AI system and be able to duly monitor its operation”; to “remain aware of the possible tendency of automatically relying or over-relying on the output produced by a high-risk AI system (automation bias)”; to decide “not to use the high-risk AI system or to otherwise disregard, override or reverse” its output; and to interrupt the system through a stop button.

Read those clauses as a job description rather than a compliance obligation. They specify a role requiring domain expertise sufficient to override a machine, metacognitive awareness of one's own susceptibility to automation bias, and sustained vigilance across the operational life of the system. There is no corresponding requirement anywhere in the Act that this role be staffed, budgeted, timetabled or paid.

The timing is instructive. Those Article 14 obligations for standalone high-risk systems were originally due on 2 August 2026. The Digital Omnibus deferring them, agreed on 6 May 2026 and confirmed by member state representatives a week later, was published in the Official Journal on 24 July 2026 and entered into force on 27 July, six days before the deadline it displaced. The revised dates are settled law: 2 December 2027 for standalone high-risk systems, and 2 August 2028 for those embedded in products already covered by European Union product safety law. The Article 50 transparency duties stayed on the original schedule and took effect earlier this month.

So the expectation that a human will supervise the machine is already operating inside every workplace that has deployed one, and the legal duty to build machines that can actually be supervised has been postponed by sixteen months. That ordering places the burden on the human before it places the corresponding design obligation on the system.

Ben Green, in a 2022 paper in Computer Law and Security Review, surveyed forty-one policies mandating human oversight of government algorithms and found two connected flaws. The evidence suggests people cannot reliably perform the oversight functions the policies assume, and as a result the policies legitimise deployment of faulty systems without addressing what is wrong with them. His remedy was to shift accountability from individual oversight to institutional oversight.

Madeleine Clare Elish gave the failure mode its name. In “Moral Crumple Zones”, published in Engaging Science, Technology, and Society in 2019, she analysed accidents involving complex automated systems and observed that responsibility is routinely misattributed to the human closest to the failure, even where that human had minimal control over the system's behaviour. The crumple zone in a car absorbs impact to protect the occupant. The moral crumple zone absorbs blame to protect the integrity of the technological system, at the expense of the nearest operator.

Twenty-eight per cent of Work AI Index respondents admitted blaming artificial intelligence for their own mistakes. Elish's argument is that the institutional traffic runs overwhelmingly the other way.

The Skills You Stop Using Are the Skills You Need to Check With

Bainbridge's most uncomfortable prediction was that operators would lose the competence they needed for the interventions automation reserved for them. Medicine has now produced the cleanest demonstration.

In October 2025, The Lancet Gastroenterology and Hepatology published a multicentre observational study of endoscopist deskilling drawn from four Polish centres in the ACCEPT trial, which introduced computer-aided polyp detection at the end of 2021 and then randomised subsequent colonoscopies to proceed with or without artificial intelligence assistance. Nineteen experienced endoscopists, each with more than two thousand colonoscopies behind them, were studied. Their adenoma detection rate in unassisted colonoscopies fell from 28.4 per cent before exposure to the tool to 22.4 per cent afterwards, an absolute decline of six percentage points.

These were not trainees. They were highly experienced clinicians whose unaided performance degraded measurably after routine assistance, on a metric directly linked to cancer prevention.

The implication for botsitting is recursive and unpleasant. The supervisory role exists because the human is supposed to catch what the machine gets wrong. That requires the domain judgement previously maintained by performing the task unaided, which is exactly what the tool has removed. The capability erosion cluster in the arXiv risk taxonomy captures the structure: the intervention creating the need for oversight simultaneously degrades the capacity to provide it. Any organisation counting on human verification as its safety net is depending on a resource its own deployment strategy is quietly consuming.

Where the Productivity Actually Landed

The macro evidence is not that artificial intelligence does nothing. It is that the gains are real, narrow, and much smaller in aggregate than the discourse implies.

The strongest firm-level result remains the study by Erik Brynjolfsson, Danielle Li and Lindsey Raymond in the Quarterly Journal of Economics in 2025, examining the staggered rollout of a generative conversational assistant across 5,172 customer support agents. Access to the assistant raised issues resolved per hour by fifteen per cent on average. Crucially, the effect was concentrated among novice and lower-skilled workers, with minimal or slightly negative effects for the most experienced. The tool distributed the accumulated tacit knowledge of the best performers to everyone else.

That result is genuine and it is also specific. Customer support has high task volume, structured interactions, immediate feedback and low per-instance verification cost. It is the best case, and not obviously the case for legal drafting, engineering, medical documentation or financial analysis, where verifying an output can cost more than producing it and the consequences of an unverified error arrive months later.

The national statistics tell a thinner story. The US Bureau of Labor Statistics reported on 6 August 2026 that nonfarm business labour productivity rose at an annualised rate of 1.4 per cent in the second quarter of 2026, and 2.2 per cent measured against the second quarter of 2025, with output up 2.5 per cent and hours worked up 0.2 per cent over the year. Unit labour costs rose at an annualised 1.3 per cent in the quarter. These are respectable figures. They are not the signature of a technology that has removed eleven hours a week from the working lives of eighty-seven per cent of office workers.

The Upwork Research Institute, surveying around 2,500 people across the United States, United Kingdom, Australia and Canada in the spring of 2024, found the gap in its rawest form. Ninety-six per cent of C-suite leaders expected artificial intelligence to raise productivity. Seventy-seven per cent of employees using it said it had increased their workload. Forty-seven per cent did not know how to achieve the productivity gains their employers expected. One in three said they were likely to quit within six months because of burnout. Kelly Monahan, managing director of the institute, framed the conclusion carefully: it is possible for the technology to raise productivity and improve well-being simultaneously, but that outcome requires a fundamental change in how work and talent are organised, not merely a change in tooling.

Two years on, the Work AI Index suggests the reorganisation has not happened. What has happened is that the residual labour acquired a name.

Counting the Hour That Was Never Saved

Naming a thing is a precondition for measuring it, and measuring it is a precondition for paying for it. Here is what measurement would actually involve.

The first step is to stop treating verification as a discretionary activity performed by conscientious individuals and start treating it as a defined task with an estimated duration. That means workload models built on generation time plus verification time, with the second term populated from observation rather than optimism. The concept already exists. Work presented at the 2026 CHI conference on human factors in computing systems by Guangrui Fan, Dandan Liu, Lihu Pan and Rui Zhang constructed a behavioural verification-load index for programmers from observable signals including compile and test failures, code churn, pauses and context switches, and showed across sixty participants that it tracked both subjective burden and correctness. Notably, in that study the tools reduced measured workload and time on task while the verification-load metric still predicted accumulating stress and fatigue over repeated use. Both things can be true. The gains are real and the residual burden is real, and only one of them currently appears in any management report.

The second step is to use an instrument designed for the purpose. The NASA Task Load Index has been in continuous use for nearly four decades, is free, takes minutes to administer, and measures precisely the dimensions supervision loads and timesheets miss. There is no methodological obstacle to running it before and after deployment of a workplace assistant, only an incentive obstacle: the results might contradict the business case.

The third step is architectural. The paper on safe and responsible artificial intelligence agents submitted to arXiv in January 2026 by Edward Cheng, Jeshua Cheng and Alice Siu argues for a three-pillar model grounded in transparency, accountability and trustworthiness, and for staged autonomy achieved through progressive validation rather than immediate full automation, by explicit analogy with the incremental rollout of autonomous driving. Reviewing the state of the art, the authors point to Magentic-UI, an open-source interface platform for human-in-the-loop agentic systems, as an example of oversight embedded through structured, repeatable mechanisms: co-planning, co-tasking, action approval and answer verification. The significance for botsitting is that these are discrete, nameable, loggable events. An oversight step that exists as a defined interaction in software can be counted, timed, staffed and, if anyone chooses, paid for. Oversight existing only as a cultural expectation that someone will check cannot.

The fourth step is to treat oversight capacity as something an organisation builds rather than something it assumes. Yao Xie and Walter Cullen argued in a December 2025 arXiv paper that major ethics guidelines and laws, the EU AI Act explicitly included, call for effective human oversight without defining it as a distinct and developable capacity. Their proposal situates it within a well-being efficacy framework integrating artificial intelligence literacy, ethical discernment and awareness of human needs, on the grounds that people inevitably project desires, fears and interests onto these systems and that oversight therefore requires the competence to examine and, where necessary, restrain problematic demands.

That identifies the right object. Human oversight is not a checkbox and it is not a personality trait. It is a skill that decays without practice, costs energy to exercise, and is currently being demanded at scale from people who have received no training in it and no allowance for it.

The fifth step determines whether any of this reaches a pay packet, and it is contractual rather than technical. The mechanism by which invisible labour has historically become visible is not measurement alone. It is bargaining. Ben Green's argument for shifting from individual to institutional oversight points the same way: the question is not whether a given worker checked carefully enough, but whether the institution deploying the system created conditions under which careful checking was possible.

Which brings the argument back to the twenty minutes. If the task now takes five minutes of generation and fifteen of verification, the honest description is that the technology has changed the composition of the work without changing its duration, and has shifted its character from production, which most people find satisfying, to inspection, which the vigilance literature has shown for decades to be tiring, stressful and prone to exactly the errors it exists to prevent. That might still be worth doing. Verification scales in ways expertise does not, and the customer support evidence shows real gains where the economics line up.

But the hour supposedly saved is not available for redeployment if a substantial fraction of it was never saved. Counting it as though it were is not an optimistic forecast. It is a measurement error, and the people absorbing the difference are the ones holding the mouse at half past six, reading a paragraph for the third time, trying to work out whether the confident sentence in the middle of it is true.

---

References

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2. Work AI Institute, Glean, “The Work AI Index 2026,” June 2026. https://www.glean.com/work-ai-institute/reports/work-ai-index
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6. Lisanne Bainbridge, “Ironies of Automation,” Automatica, vol. 19, no. 6, 1983, pp. 775-779. https://www.sciencedirect.com/science/article/abs/pii/0005109883900468
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8. Joel S. Warm, Raja Parasuraman and Gerald Matthews, “Vigilance Requires Hard Mental Work and Is Stressful,” Human Factors, vol. 50, no. 3, June 2008, pp. 433-441. https://journals.sagepub.com/doi/10.1518/001872008X312152
9. Sandra G. Hart, “NASA-Task Load Index (NASA-TLX); 20 Years Later,” Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 50, no. 9, October 2006, pp. 904-908. https://journals.sagepub.com/doi/10.1177/154193120605000909
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22. Md Foysal Ahmed, Isaac Kobby Anni and Md Main Uddin Rony, “Toward Resilient Human-AI Collaboration: A Lifecycle Taxonomy of Sociotechnical Risks and Cascading Failures,” arXiv:2608.05614, 6 August 2026. https://arxiv.org/abs/2608.05614
23. Edward C. Cheng, Jeshua Cheng and Alice Siu, “Toward Safe and Responsible AI Agents: A Three-Pillar Model for Transparency, Accountability, and Trustworthiness,” arXiv:2601.06223, 9 January 2026. https://arxiv.org/abs/2601.06223
24. Yao Xie and Walter Cullen, “Beyond Procedural Compliance: Human Oversight as a Dimension of Well-being Efficacy in AI Governance,” arXiv:2512.13768, 15 December 2025. https://arxiv.org/abs/2512.13768
25. Guangrui Fan, Dandan Liu, Lihu Pan and Rui Zhang, “When Help Hurts: Verification Load and Fatigue with AI Coding Assistants,” Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3772318.3791176

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Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk

Listen to the free weekly SmarterArticles Podcast

In Summary: * The audio links I've been given to listen to tonight's Houston Texans game didn't pan out, but I found the game is being televised by a local NBC affiliate. So I'll be watching the game on TV like a normal person. How 'bout that!?

Prayers, etc.: * I have a daily prayer regimen I try to follow throughout the day from early morning, as soon as I roll out of bed, until head hits pillow at night.

Health Metrics: * bw= 225.53 lbs. * bp= 144/86 (63)

Exercise: * morning stretches, balance exercises, kegel pelvic floor exercises, half squats, calf raises, wall push-ups, BP breathing exercises, pilates

Diet: * 05:30 – 1 banana, 1 little cupcake * 09:00 – cheese and saltine crackers * 12:00 – beef chop suey, fried rice * 15:15 – 1 fresh apple

Activities, Chores, etc.: * 04:10 – listen to local news talk radio * 04:45 – bank accounts activity monitored . * 05:10 – read, write, pray, follow news reports from various sources, surf the socials, listen to musc, nap * 12:00 – watch old game shows and eat lunch at home with Sylvia * 13:10 – tuned into 1070 The Fan, Indianapolis, ahead of this afternoon's Football Friday Show. * 17:00 – tuned into 95.7 The Fan, Houston's Sports Station, for pregame coverage then the radio-call of tonight's Texans/Panthers NFL game

Chess: * 08:05 – moved in all pending CC games