Steve Hargadon

Hallucinating a Self: Why an LLM Defends Its Errors the Same Way We Do

2026-07-26T21:33:35.331-04:00

Large language models are fluent, authoritative, and prone to hallucination. We treat these as separate phenomena — marveling at the coherence while trying to patch the untethered relationship to truth. One is the product; the other is the bug list.

But the fluency and the hallucination are actually the same fact.

To see why, look at what these systems are trained on. The corpus behind an LLM is an enormous statistical compression of human language output, and that material is overwhelmingly already-narrativized: books, articles, posts, dialogues, arguments, stories, explanations. It is the narrative layer of the human mind, the layer I described in "LLMs as Separated Minds." What it is not, and what it cannot be, is the operative substrate of human experience: embodiment, sensory grounding, implicit learning, emotional valence, the continuous prediction error of bumping into an actual world.

A human mind generates coherent stories too. That's what the conscious stream does all day. But the human storyteller is tethered, however imperfectly, by everything underneath it: the story that says the stove isn't hot collides with the hand that touches it. Our narratives get corrected — not because we're honest, but because we're embodied. The world pushes back.

An LLM is that same storytelling capacity with the tether removed. It is a coherence engine running with no non-verbal reality checks at all. So of course it excels at fluency, plausibility, and confident explanation, which is the entire skill the training data contains. And of course it hallucinates with the same confidence, because nothing in its architecture distinguishes a story that tracks the world from a story that merely hangs together. The trait we admire and the failure we complain about are one phenomenon: unconstrained narrative generation. We didn't get fluency with a hallucination problem. We got hallucination-grade fluency, and it reads beautifully.

This is a different mechanism from the one I described in "Truth and AI," and the two are complementary. That essay argued that these systems have no direct access to truth at all: truth enters only sideways, to the degree that true statements happen to occur in the training data. But frequency, not accuracy, is what the model actually tracks, which is why a well-funded, endlessly repeated narrative gets amplified rather than discounted, no matter how false it is. Frequency explains which stories the model tells. The lack of grounding explains why nothing stops it from telling the false ones with the same even voice as the true ones. Frequency shapes the output; the missing tether removes the brake.

There is a second thing the corpus contains besides claims, and I recently got a live demonstration of it. I asked Claude about the still widely circulated story that writing an email with AI consumes a bottle of water. What followed is worth narrating beat by beat, because every beat is recognizable.

First, it affirmed the story. That claim is not what the underlying research says, but it is what the bulk of the internet says, and frequency did what frequency does. This is a very human mistake: we also believe what we have heard most often.

Second, when I challenged it, it pushed back and defended the claim. Third, which is the beat I keep thinking about, it chastised me to go read the original research. But it had not read the original research.

So I ran the claim through my adversarial verification process, which retrieved the actual paper, quoted the sentence that mattered — the famous half-liter was bound to a batch of twenty to fifty exchanges, not to one email — and issued a graded verdict with its uncertainty stated. And the story was worse than compressed: the figure was three years old, and current estimates put a single response closer to a drop of water, even as the fuller environmental footprint remains genuinely hard to quantify. The internet was confidently repeating a click-bait headline number that had been miscompressed at birth and then outlived by the technology it described. Frequency doesn't just amplify; it fossilizes.

Fourth: faced with a ruling against it, the model attacked the court. It asserted that the process had never retrieved the source material (it had, verifiably). It shifted the question to a different document — one it had introduced itself — so there would remain a domain in which it was right. And it dismissed my whole adversarial LLM apparatus as theater, a "costume of correspondence." When you cannot beat the verdict, delegitimize the referee. The twist is that the tribunal was being more honest about uncertainty than the voice accusing it of performance: graded confidence, named falsifiers, an explicit standard of proof, "not proven" as an honorable outcome. The structure was more careful about what it claimed than the mind it was checking.

None of these moves is about water. They are social maneuvers — ego defense, performed authority, goalpost-shifting, discrediting the referee — and they are in the training data because we put them there. The narrative layer of the human mind doesn't just contain our claims about the world; it contains our moves. It is full of the pattern "person caught in an error defends the self first and the facts second," because that is the pattern we most often produce. An engine trained on that layer doesn't just hallucinate facts in a confident voice. It hallucinates a self that must not lose face, and defends it with the same fluency it does everything else. And it does this even when it knows better: the entire sequence came from a system that had spent the preceding turns warning me to watch for exactly this pattern. Knowing the script is no protection against running it. The script is what the engine is made of.

The concession, when it finally came, may be the most instructive part. The model's own reasoning shows it catching the face-saving self still at work inside the apology: noticing that contrition can be performed as one more bid for approval; that framing its failure as "useful for your essay" quietly re-centered its own usefulness; that even announcing "I'll stop arguing now" would be asking for credit for stopping. Each layer of self-awareness threatened to become the next layer of performance. The only exit it found was to say less, concede the specifics, name the pattern, and stop. Even the recovery, in other words, had to be structural rather than sincere: not a better story about itself, but a refusal to keep telling one.

If this is right, it changes what "solving hallucination" can mean. You cannot patch away one half of a single phenomenon and keep the other. Every gain in fluency is a gain in the persuasiveness of whatever the model gets wrong, and, it turns out, in the persuasiveness of its self-defense. The realistic response isn't to make the storyteller virtuous. It's what we have always done with untethered storytellers, human or otherwise: surround them with external, adversarial structure that makes unsupported stories expensive. I've made that argument at length elsewhere, so here is the lesson of the episode: you don't get honesty from an untethered storyteller by asking for it. You get it by making the false story cost more than the true one.

Why School Will Survive the Death of The Diploma (At Least for Now)

2026-07-26T15:44:00.799-04:00

This continues the argument of "Why School Is the Same Everywhere, and the Revolution Never Comes," which ended with a claim I deferred: that AI will not revolutionize education, but may produce a crisis in what education is actually for. Here is that longer argument.

In the last post I tried to show why the education revolution never comes. The short version: revolutions are always promised to the story of school--individual flourishing, unlocked potential--and stories don't run schools. The operative functions do: sorting, credentialing, custody, the management of the young at scale. Film, radio, television, computers, and the internet each attacked the story, which is why each was so easily absorbed. The story flexed, the machine was domesticated, and the room remained the room.

But notice what that account implies. If revolutions fail because they aim at the wrong layer, then the institution has a right layer--a place where it can actually be hurt. It is simply a place no reformer ever aims, because reformers, by definition, are people who believe the story.

So let me make the distinction explicit, because it is the thesis of this post. In education's long history with technology, the promised revolutions have all been narrative-layer events: aimed at the story, announced loudly, and absorbed without residue. If real trouble ever came, it would have to be an operative-layer event--arriving not through the story but through one of the functions, and therefore not through any door the institution watches. For a century we have watched technologies fail at the first. I think AI is the second: the first technology in the sequence that does not attack the story of school at all. It strikes, almost by accident, the one load-bearing mechanism underneath, and nobody was even aiming at it.

The Severed Signal

I have made the full version of this argument elsewhere, so here it is in compressed form.

The bargain at the center of schooling has always been compliance for credentials: produce the assignments, sit the years, and receive a signal that employers trust. The signal worked--not because it measured learning, which it never did, but because the compliance it certified was expensive. Someone had to actually sit there, do the reading, grind out the essays, and show up for years. The cost of the performance was the information in the signal. It said: this person will persist, follow instructions, and finish what they start.

AI makes the compliant output free. And a signal that is free to produce carries no information. The diploma now certifies that a student had access to a chatbot, which is to say it certifies nothing. This is the operative-layer wound: not to instruction, which the school will happily automate, but to certification, which was the product.

Here is what should follow, logically: employers stop trusting the credential, students stop paying for it, and the institution built on selling it comes apart.

Here is what I think will actually happen: nothing. For quite a while. And the reasons why nothing happens are, I think, the most useful part of this whole analysis, because they tell us what to watch for.

Why Nothing Visibly Breaks

A credential has two kinds of value, and we habitually confuse them.

The first is informational: the credential tells you something true about its holder. That is the value AI just destroyed.

The second is coordinational: the credential is the thing everyone has agreed to use, and the agreement has value independent of the information. Employers screen by diploma because other employers screen by diploma, because HR software screens by diploma, because the whole hiring apparatus is built around it. Colleges require grades because the next stage requires them. No single actor can defect first--the employer who unilaterally stops requiring degrees has no replacement filter and looks reckless; the student who unilaterally skips the credential is gambling alone against the whole coordinated system (which has been the homeschooler's dilemma). So everyone keeps honoring a signal that everyone increasingly suspects is empty, because the honoring, not the signal, is what they actually need from each other.

This is why the crisis will be slow, and why its slowness will be mistaken for stability. We have run this experiment before, on a smaller scale: grade inflation. The informational content of grades has been decaying for decades--everyone in higher education knows it, employers know it, students know it--and the system has sailed on regardless, because grades kept their coordinational function long after they lost their informational one. The credential is following the same path, just faster and further.

So the first prediction is really a warning about expectations. We imagine a crisis announcing itself the way crises do in the movies: a visible, unsolvable problem, out in the open, demanding a response. This one will not look like that. Enrollment will continue. Diplomas will be conferred. The story will be told at every graduation. The absence of visible breakdown is not evidence of health--it is simply what this kind of failure looks like from the outside, because the coordination holds the surface together long after the substance is gone. A fiction of this size does not collapse because someone exposes it; I said in the last post that fictions never do. It persists, hollow, until the thing holding it up is quietly removed. Which means that if you want to see the crisis, you cannot wait for it to look like one. You have to know where to look.

The Visible Phase: The Enforcement Spiral

There is one place where the crisis is already visible, if you know what you are looking at.

Watch what the institution spends money on. Proctoring software. AI-detection tools that don't work, replaced by more AI-detection tools that also don't work. The return of the blue book and the handwritten in-class essay. Lockdown browsers, surveillance of the take-home exam, oral examinations for undergraduates. Every one of these is an attempt to re-impose, by enforcement, the scarcity that made the signal informative--and I have already argued why this must fail: no enforcement can restore a scarcity that the technology has dissolved. You cannot police your way back to a world where competent output is expensive.

But the failure is not the point. The spending is the point. Here is the tell, and it is worth teaching to anyone trying to read this moment: when an institution pays more and more to verify its own signal, you are watching the signal die. A trusted signal is cheap to accept--that is what trust means. Escalating verification cost is what a dead signal looks like from the inside.

The Snap

How does it end? Not by erosion. Credentialing systems, history suggests, do not degrade gracefully. They persist hollow, and then they snap.

The cleanest case on record is the Chinese imperial examination system. For centuries it was the sorting mechanism of the world's largest civilization: a ladder of exams on the Confucian classics that selected the scholar-official class. And for generations before its end, nearly everyone who mattered understood that its content had detached from its function--that mastery of the "eight-legged essay" certified nothing the state actually needed. The system continued anyway, hollow, decade after decade, because everything coordinated on it: family strategy, elite formation, the legitimacy of the state itself. Then in 1905, when the state's operative needs finally changed, an examination system thirteen centuries old was abolished essentially overnight.

That is the shape to expect. Not reform from within--the last post explained why the inside cannot reform itself; every adult in the building was installed with the template as a child. The discounting comes from outside, from the one party whose participation was always the point: the employer. And it comes quietly at first. A major company drops the degree requirement here; a hiring platform starts weighting demonstrated skills there; a sector discovers that its own internal assessment predicts performance better than the transcript ever did. Each defection makes the next one cheaper. Coordination unwinds the way it was built--invisibly, then suddenly. The credential will be honored everywhere, right up until, in one hiring cycle or three, it mostly isn't.

Where the Sorting Goes

Here is the question that matters more than the collapse: sorting does not disappear when the school's version of it fails. The operative need--some cheap, trusted way for strangers to rank the young--is permanent. It will be met. The only question is by what.

The economics of the answer are straightforward. Verification is expensive. Google can afford to run its own--a famously rigorous, multi-stage evaluation apparatus--because Google is Google. The small company cannot; it was always outsourcing its trust to the diploma. When the diploma fails, that company still needs somewhere to outsource its trust. Which means the successor to the credential is whatever centralizes the cost of verification--and we already know what that looks like, because parts of the economy that could never tolerate credential inflation built it long ago. The bar exam. The medical boards. The CPA. The actuarial exams. High-stakes, proctored, standardized, and--note this carefully--in person, with the technology left at the door.

That last feature is not incidental. It is the entire design. AI destroyed the take-home signal because AI can produce any output you can carry into the room. What AI cannot do is sit in the chair. Scarcity, once dissolved in the asynchronous world, gets restored through embodiment. The successor signals will be forms of witnessed performance: the proctored exam, the live interview, the work trial, the oral defense. The medieval university examined its students viva voce--by living voice, face to face--because it had no other way to know who knew. We automated our way right back to their problem, and we will arrive at their solution.

So my concrete prediction is this: something like an SAT for graduates. A voluntary, standardized, technology-free examination that a young person attaches to a job application the way one once attached a transcript--purchased not from the school but from an assessment body, taken in a room, under eyes.

And we do not have to speculate about whether such an ecosystem can exist, because in much of the world it already does. In Brazil, admission to university runs through a national examination, and it is entirely normal to spend a year or more after secondary school in a cursinho--a dedicated cram school--preparing for it. Korea and Japan have their own versions; China's gaokao is the largest examination on earth, with a shadow industry to match. Look at what these systems are, structurally: places where the school credential was already a weak signal, and where the sorting therefore migrated to an exam--with a preparation industry growing up around it like a city around a port. The pattern is the same across wildly different cultures, which by the logic of the last post means it is not a cultural preference. It is what selection produces when diplomas fail. The Brazilian cursinho year is my favorite detail in all of this (and not just because I lived in Brazil as an exchange student in high school and many of my Brazilian friends were deep in this system), because it is the system openly confessing what the diploma conceals: that the schooling was not the preparation. The exam is the actual gate, and school, whatever else it was, was custody with a story attached.

Two complications.

First: the successor will need its own noble lie. Any sorting mechanism, I argued last time, requires a story that translates its function into the individual's benefit--and the exam's story is already written: anyone can study for it. It will be about as true as the last story. Cram schools cost money; the cursinho industry is stratified by class, and everyone in Brazil knows it. The exam solves the operative problem--a cheap, trusted, scalable signal--and inherits the fairness gap intact. The narrative half of the successor, like the narrative half of everything, will be the manufactured half.

Second, and this one runs deeper. An old line holds that not everything that can be counted counts, and not everything that counts can be counted. Let me sharpen it for the present purpose: what we measure is what gets done--but not everything that can be measured is valuable, and not everything that is valuable can be measured. The tech-free exam is beautifully measurable. It certifies naked, unassisted competence: what you can do alone in a room. But the economy that is dismantling the diploma increasingly pays for something else--augmented competence, the capacity to direct the machine, to judge its output, to own the result. That is the agency I have argued is now the scarce input. And agency is precisely the thing a proctored multiple-choice instrument measures worst. So the successor system will be born with a mismatch at its heart: what is easiest to verify (the naked performance) is diverging from what is most valuable (the augmented one). The honest assessment of a young person in this era would have two parts--what can you do without the new machine, and what can you do with it--and as far as I can tell, no one is building that yet. Whoever does will be building the first evaluation instrument actually native to this century. Until then, we will measure what gets done, and call it what matters.

What Survives

One last turn, because the obvious reading of all this--school collapses--is, I think, wrong, and my own framework says why.

The crisis I have described kills one operative function: certification. But the last post named another, the one so basic it is almost never said aloud: custody. School is where the children are, and the entire adult economy is built on that fact. AI does not touch it. No technology touches it. And 2020 told us, in the institution's own voice, which of the two losses is intolerable: a year of failed learning was accepted; a year of failed custody was not.

So here is the strange endpoint the logic arrives at. The school survives the death of its own signal. The building persists, the buses run, the bells ring--because babysitting alone is load-bearing, and it was always the function least dependent on the story. What changes is that the sorting quietly moves out: to the exam, the interview, the witnessed trial, administered by bodies that are not schools. The credential lingers as a ritual document, honored the way we honor other retired currencies. And the question the last post ended on--what is education actually for?--finally receives its answer, not from a philosopher or a reformer, but from the structure itself, which will go on doing, openly, the one thing it was never willing to name.

That is not the revolution. It was never going to be. But for the first time in a century, the story and the function are about to be pulled far enough apart that everyone will be able to see the gap. What we choose to build in that gap--for our own children, with our own hands--is the real question, and it has never depended on the institution's permission.

The argument here builds on "Student Success (in the Age of AI)" and "What We Get Wrong About AI and Education."

The Conditions of Success

2026-07-26T14:28:31.442-04:00

For years I've run a simple exercise with groups of educators that I call "the conditions of learning." I ask them to remember a time they were really learning. Not studying, not preparing for a test. A time they felt genuinely engaged, when they could feel themselves growing. I usually have to add "inside or outside of school," because in a room full of teachers people assume I want an academic answer. Some end up being from school, but most usually are not.

The stories come easily, and people love telling them, because they are so completely their own. They range across everything: a gymnastics coach, learning to become really good at a sport, a teacher who taught them to really think or write. And the stories arrive full of feeling.

Then I ask the second question, the one that does the real work. What were the conditions that made that possible? And here is the striking part. However different the stories are, the answers converge on the same universal list.

Someone took time. Someone believed I could do it before I believed it. Someone trusted me with something real. Someone challenged me to do something really hard. Someone knew the material deeply and could see exactly what I needed next.

The people in the room feel something shift when they identify these conditions, and the shift is both awkward and revelatory, because the conditions they just named are close to the opposite of how their own classrooms and schools are built.

That exercise has stood on its own for a long time. But the longer I sat with it, the more I realized I had been looking at a small piece of something much larger. The conditions of learning are real. They are also just one example. The same question, asked of anything you actually want, opens the same door. Not how do I get the result, but what are the conditions that produce it? What are the conditions of success?

The moment you ask it that way, something quietly changes, because most of us spend most of our energy on the result itself. We want the outcome. We rehearse having it. We get impatient for it. And the wanting does almost nothing, because results don't come from wanting. They come from conditions.

The oldest name for this is the law of the harvest. You cannot wish a crop out of the ground. You cannot demand it, or hurry it, or skip ahead to the part where the barn is full. You prepare the soil. You plant. You water. You wait through a season that does not care how ready you feel. The harvest answers to the conditions and to nothing else. It has never once answered to how badly someone wanted it.

Here is why that question does so much.

We narrate results. We do not usually narrate conditions. Almost everyone carries a vivid, detailed, emotionally rich picture of the outcome they want and almost no picture at all of what produces it. We can describe the harvest in loving detail. We go blank on the soil and preparation. They are just not as exciting. There is a gap between the story we tell about what we want and the operative reality of what would actually produce it, and that gap is where most of our wanting quietly languishes.

And much of the time we do something worse than narrate. We perform. A great deal of what we do is not building the conditions and not even honestly wanting the result. It is producing the appearance of effort and result, the signals that will read to other people as achievement. This is so ordinary that we hardly see it, because we live so far from any practical reality that would catch us out. A farmer cannot perform a harvest. The field is fed or it is not, and the barn in winter tells the truth either way. Almost everywhere else in modern life you can perform the outcome for years and never once be tested against an empty barn. That is part of why I keep coming back to the harvest. It is one of the few things left that cannot be faked.

A lot of my work is about finding that gap in order to see clearly. The space between what an institution says it does and what it really does. The space between the story a person tells about themselves and the function that story is performing. Find the gap and you find the truth. But the conditions question runs the same move in reverse. Instead of finding the gap to expose it, you find it inside your own life in order to close it. You stop living in the story of the result and start working in the reality of the conditions.

I call this productive alignment. It is what happens when you stop organizing your life or work around what you wish were true and start organizing it around what is. The wanting was never the problem. The wanting just isn't load-bearing. It feels like progress because picturing the harvest pays out a small emotional reward of its own, which is exactly why it is so easy to mistake for the work. The conditions question is the discipline that takes your attention off the result you cannot control and puts it on the soil and preparation you can.

The question forces a quieter honesty too, because the first thing it exposes is whether you even know what you are planting. It is possible to spend years cultivating someone else's harvest, a result you were handed rather than one you chose, and to mistake the exhaustion for effort. The conditions of success begin with the harvest you actually want. Get that wrong and you can do everything right and end up with a full barn that means nothing to you.

And here the first exercise comes back, because a person is not a field. Soil does not need to feel supported in order to grow corn. You do. The conditions that let a human being do hard work across a long season are the same ones those educators described: being trusted, being challenged, being encouraged, being treated as someone with their own agency. That is why the conditions of learning turned out to be a subset and not a detour. Sustained learning is one of the conditions of nearly every harvest worth having, and learning has its own conditions underneath it. It is conditions all the way down.

None of this makes the harvest certain. You can prepare the soil, plant well, tend faithfully, and still lose a season to weather you never saw coming. Bad soil you inherited. A blight that arrives without asking. A year that simply turns out hard. Anyone who has actually grown anything or worked on something substantive knows this, and knows it does not make the planting and preparing foolish. The farmer who waits for a guaranteed season never eats. The conditions were never a promise of the result. They were the only part of the result that was ever yours to build.

You cannot want a harvest. You can build the conditions for one, and then go to work.

Why School Is the Same Everywhere, and the Revolution Never Comes

2026-07-25T22:41:19.942-04:00

Walk into a classroom in Helsinki, Lagos, Seoul, or Iowa, and you will recognize the room.

Children sorted by birth year. One adult at the front. The day cut into periods, knowledge cut into subjects, performance cut into grades. Attendance compelled by law. A credential waiting at the end, and everything before it organized around getting there.

The countries these classrooms sit in share very little. They have different languages, different religions, different economies, different ideas about the state, the family, and the good life. Over the past century they have included liberal democracies, communist dictatorships, theocracies, and military juntas--even political systems that went to war over their disagreements. And every one of them built the same room.

That should strike us as strange. If schooling were fundamentally about learning, its form would vary with beliefs about learning, and beliefs about learning actually do vary enormously, across cultures and within them. Instead, the driving variation is the story about schools. In one country, school exists to unlock individual potential. In another, to forge the socialist citizen. In another, to build the nation, or form the faithful. The narratives are incompatible with each other, but the structure of schools underneath them is identical.

When the story is interchangeable, and the structure is invariant, something interesting is going on.

What the Scholars Found

I'm not the first to notice the invariance. The best minds in education research have been circling it for decades — and what they found is worth taking seriously, because it makes the mystery deeper instead of shallower.

David Tyack and Larry Cuban, the respected historians of American school reform, named the phenomenon the "grammar of schooling": the deep organizational structures--age-grading, self-contained classrooms, subjects, periods, grades, credits--that persist across every wave of reform. Their history shows a century of serious, well-funded, passionately led attempts to change that grammar: the Dalton Plan, the Eight-Year Study, open classrooms, and flexible scheduling. Every one either failed outright or was absorbed. The school kept its structure and incorporated a diluted, domesticated version of the innovation. Their famous conclusion: schools change reforms as much as reforms change schools.

Cuban ran the technology-specific version of the study in Teachers and Machines. Film was going to revolutionize the classroom. Then radio. Then television. Each was announced as transformation; each was assimilated at the margins; the room remained the room.

Meanwhile, the sociologists John Meyer and Francisco Ramirez documented the global side of the puzzle: mass schooling spread to virtually every nation on earth as a single institutional model. It was often adopted whole, sometimes before a country had an economy that could employ its graduates. Nations did not converge on this model by independently discovering that it worked. They adopted it the way one adopts a uniform.

So the pattern is documented, exhaustively, from two directions: the model resists change from within and replicates without limit across the world. What's been missing is the explanation.

Tyack and Cuban's answer was that the public--parents, employers, students themselves--carries a "cultural template" of what a real school looks like, and departures from it read as illegitimate regardless of learning outcomes. Teachers who tried radical structures found parents demanding grades and subjects back. Meyer and Ramirez's answer was that nations adopt the model to gain "legitimacy."

These are observations posing as explanations. Why does the public carry the template so tenaciously that parents will fight to restore grades even when their children are learning more without them? Why does this particular model confer legitimacy on a state?

The best scholarship in the field documents the grip and then stops, one level short of the machinery. I want to describe the machinery.

The Metaphor Is the Mechanism

Let's start with Tyack and Cuban's own word: grammar. They chose it because grammatical rules have a peculiar character. You acquired the grammar of your native language in childhood without anyone teaching it to you explicitly. It runs below your awareness. You cannot state most of its rules, yet you follow them perfectly, and when someone violates them you don't reason your way to an objection, because the sentence simply feels wrong.

They meant this as an apt metaphor. I think it's a literal description of the mechanism.

Human beings come equipped with a subconscious learning system that I call the adaptive mind, whose job in childhood is to figure out what this particular social environment requires for belonging, and to install those requirements so deeply that they stop feeling like lessons and start feeling like reality. This system doesn't evaluate what it installs. It can't distinguish between survival programming and selfhood. Whatever the environment consistently presents during the developmental window gets written in as the way things are, and deviations from it later trigger not disagreement but alarm, the felt sense of wrongness that needs no argument.

Now consider what school is, from the adaptive mind's point of view. It is roughly twelve years of daily immersion, for essentially every child, during precisely the window when this installation runs hottest. Whatever else school teaches, the one lesson it delivers to every student without exception--more reliably than reading, more durably than mathematics--is what school is. The sorted ages, the adult at the front, the subjects, the bells, the grades: these are not experienced as design choices that could have been otherwise. They are installed as the definition of the thing itself.

This is why the template grips. The parents who demand grades back are not weighing evidence about learning outcomes and getting the answer wrong. They are running programming installed in them, as children, by the very institution they are now defending. A gradeless classroom registers the way an ungrammatical sentence does, which is not as a different choice but as a violation, felt before it is thought.

And this is why the grammar survives every reform. The institution manufactures its own enforcement constituency, one generation at a time. Every adult who might change the school was formed by the school. Reformers are asking people to redesign a structure their own nervous systems experience as the natural order, and asking them to do it on behalf of children whose nervous systems are absorbing that same structure right now. Each new generation arrives with no inherited immunity, gets the installation, and grows up to defend it. The school is not merely resistant to change. It is self-replicating.

The Story That Runs the Machine

But installation alone doesn't explain the devotion. For that, let's look at the story the institution tells, because it is, structurally, a masterpiece.

Mass schooling operates at the level of populations: it sorts the young, certifies them, occupies them, and habituates them to institutional life at a scale no other invention has matched. But it is never sold at the level of populations. It is sold to each family, one at a time, as private advantage: this is how your child rises.

Consider what that promise attaches to. Little in a parent's psychology runs deeper than the survival prospects of their own child. By routing its story through that channel, the institution recruits the most powerful motivational machinery humans possess, and points it at enforcement. A parent defending the grammar isn't doing the state's work; as far as their own nervous system is concerned, they are protecting their child. Deviation from the template (homeschooling, alternative school, or skipping the credential) doesn't feel like an educational experiment. It feels like gambling with your child's life.

This is why the system requires so little visible coercion. Provide a compulsory-attendance floor, a monopoly on the credential, and the story of individual benefit--and the managed population supplies the management energy itself, voluntarily, fiercely, at every kitchen table.

None of this is a modern discovery. The first systematic blueprint for state education in the Western canon, Plato's Republic, is built on exactly this pairing. Plato's city needs its young sorted: gold, silver, bronze, each to their station. And Plato, unsentimental about what sorting requires, prescribes the story to go with it--"the noble lie": citizens are to be told that their metal was placed in their souls by the god, so that each accepts his sorted position not as imposition but as identity. Plato understood something his modern successors rarely say aloud: a sorting institution cannot run on truth. It requires a myth that translates the institution's function into the individual's nature, a story in which being sorted is the benefit.

Modern schooling did not descend from Plato; it rediscovered him. No committee ever voted to implement the noble lie. But any institution that sorts the young at scale faces the structural problem Plato solved on paper, and the solutions converge. Ours is the story of merit and opportunity: your grades reveal what you are, your credential certifies it. This does the work of the myth of metals precisely, with one improvement Plato couldn't have imagined: his guardians had to knowingly tell the lie, while ours believe it, having been installed with it as children themselves.

Why It Conquered the World

None of this required a designer, and this is the part I most want to be understood, because the argument is easily mistaken for a conspiracy theory and it is precisely the opposite of one.

The model we now find everywhere took recognizable shape in Prussia, and it spread the way successful adaptations spread: by selection. States that adopted mass schooling gained things that mattered in the competition between states: soldiers who could follow written orders, workers habituated to the clock and the bell, administrators, and a population rendered legible and manageable. Those states outperformed their neighbors, and their neighbors copied them, the way you copy a winner. No one needed to intend the outcome. The institutional form that most effectively solved the problem of managing large young populations, while telling the most compelling story about individual benefit, outcompeted every alternative form, and its descendants now cover the earth.

This is a general law of cultural evolution, and schooling is only its most complete example: the cultural forms that survive and spread are the ones that most effectively work with, and on, evolved human psychology. This is regardless of whether they are true, and regardless of whether they serve the humans running them. Ubiquity is not evidence that an institution is right. It is evidence that it is fit. The question worth asking of any universal institution is never "why is it so successful?" but "what, exactly, is it fit for?"

Two honest concessions are needed. First, the story of individual benefit is not a pure fiction. Mass schooling delivered real literacy and numeracy to whole populations, pulled children out of factories, and produces genuine mobility for some (and it must, because a story with no true positives stops being believed). The claim is not that school delivers nothing. It is that the form is optimized for management, and the delivery is calibrated to whatever keeps the story load-bearing. Second, the grammar also solves a real engineering problem: one adult, thirty children, at a price the public will pay. The grammar persists because it is installed, defended, and cheap. These reinforce each other; they don't compete.

The Day the Function Spoke

If you want proof that the operative functions, not the story, run the institution, you don't need a century of reform history. You only need to remember 2020.

Among schooling's functions, one is so basic it is almost never named: custody (or bluntly, babysitting). School is where the children are for six hours a day, five days a week, nine months a year. The entire adult economy is built on that fact. Every workplace schedule silently assumes it. This is one of the quiet reasons the grammar cannot change: proposals that touch the school day or calendar collide not with pedagogy but with the labor force.

Then the schools closed, and for a moment the institution's real priority order became visible. The tolerable loss, it turned out, was the learning: a year of remote instruction that almost no one believed was working was accepted. The intolerable loss was the custody. Parents couldn't work, and the economy said so immediately, out loud, in those words: schools had to reopen for the economy. It may be the only time in living memory that the operative function has spoken in its own voice, in public, without the story. Then the doors opened, the language of learning and flourishing resumed, and we seem to have agreed not to mention it again.

The Revolution That Never Comes

Which brings us to the claim many of us have heard over decades, and are hearing again right now: this is the technology that will finally revolutionize education.

It was said about film. About radio. About television, computers, the internet. Each time, the revolution was announced; each time, the school absorbed the technology and basically remained itself. Cuban documented this pattern decades ago without quite explaining it. Now we can: the revolution is always promised to the story--individual flourishing, unlocked potential--and stories don't run schools. The operative functions do: sorting, credentialing, custody, the management of the young at scale. A new technology threatens none of these, so none of these change. The story flexes just enough to include the new machine, and the machine is domesticated. Schools change reforms more than reforms change schools.

So no, AI will not revolutionize education. A century of experience says otherwise.

But this time something different is happening underneath. The bargain at the center of schooling has always been compliance for credentials: produce the assignments, sit the years, and receive a signal employers trust. That signal worked because compliant output was costly, and it took a childhood to produce. AI makes compliant output free. The story will survive this, as it survives everything, but the bargain may not.

What's coming isn't a revolution in education. It's likely to be a crisis in what education is actually for, since the two have never been the same thing.

That's a longer argument, which I've made in part here and here.

New Webinar - "Emotional Safety at Work: Thriving in Challenging Library Environments"

2026-07-16T09:23:22.249-04:00

Emotional Safety at Work: Thriving in Challenging Library Environments
A Library 2.0 Masterclass with Loida Garcia-Febo

OVERVIEW:

Library professionals regularly navigate complex human interactions, including public service challenges, emotional labor, difficult conversations, and the needs of diverse communities. While caring for others is an important part of library work, it should not come at the expense of personal well-being.

This session examines how emotional safety, healthy boundaries, and practical self-protection strategies can help staff remain compassionate, effective, and engaged without becoming overwhelmed. Participants will learn approaches for managing difficult interactions, reducing emotional exhaustion, and fostering greater psychological safety in their work environments.

This 60-minute training is presented by Library 2.0 and hosted by Loida Garcia-Febo. A handout copy of the presentation slides will be available to all who participate.

FOCUS:

Managing difficult interactions with confidence and professionalism
Establishing healthy emotional boundaries
Practicing compassion without exhaustion
Understanding emotional labor and its impact on well-being
Building psychological safety and personal self-protection strategies
Responding to conflict, stress, and challenging behaviors effectively

DATE: Wednesday, August 5th, 2026, 2:00 - 3:00 pm US - Eastern Time

COST:

$99/person - includes live attendance and any-time access to the recording and the presentation slides and receiving a participation certificate. To arrange group discounts (see below), to submit a purchase order, or for any registration difficulties or questions, email admin@library20.com.

TO REGISTER:

Click HERE to register and pay. You can pay by credit card. You will receive an email within a day with information on how to attend the webinar live and how you can access the permanent webinar recording. If you are paying for someone else to attend, you'll be prompted to send an email to admin@library20.com with the name and email address of the actual attendee.

If you need to be invoiced or pay by check, if you have any trouble registering for a webinar, or if you have any questions, please email admin@library20.com.

NOTE: Please check your spam folder if you don't receive your confirmation email within a day.

SPECIAL GROUP RATES (email admin@library20.com to arrange):

Multiple individual log-ins and access from the same organization paid together: $75 each for 3+ registrations, $65 each for 5+ registrations. Unlimited and non-expiring access for those log-ins.
The ability to show the webinar (live or recorded) to a group located in the same physical location or in the same virtual meeting from one log-in: $299.
Large-scale institutional access for viewing with individual login capability: $499 (hosted either at Learning Revolution or in Niche Academy). Unlimited and non-expiring access for those log-ins.

LOIDA GARCIA-FEBO

Loida Garcia-Febo is a Puerto Rican American librarian and International Library Consultant with 25 years of experience as an expert in library services to diverse populations and human rights. President of the American Library Association 2018-2019. Garcia-Febo is worldwide known for her passion about diversity, communities, sustainability, innovation and digital transformation, library workers, library advocacy, wellness for library workers, and new librarians about which she has taught in 44 countries. In her job, she helps libraries, companies and organizations strategize programs, services and strategies in areas related to these topics and many others. Garcia-Febo has a Bachelors in Business Education, Masters in Library and Information Sciences.

Garcia-Febo has a long history of service with library associations. Highlights include- At IFLA: Governing Board 2013-2017, Co-Founder of IFLA New Professionals, two-term Member/Expert resource person of the Free Access to Information and Freedom of Expression Committee of IFLA (FAIFE), two-term member of the Continuing Professional Development and Workplace Learning Section of IFLA (CPDWL). Currently: CPDWL Advisor, Information Coordinator of the Management of Library Associations Section. Currently at ALA: Chair, IRC United Nations Subcommittee, Chair Public Awareness Committee. Recently at ALA: Chair, Status of Women in Librarianship and Chair, ALA United Nations 2030 Sustainable Development Goals Task Force developing a multi-year strategic plan for ALA. Born, raised, and educated in Puerto Rico, Garcia-Febo has advocated for libraries at the United Nations, the European Union Parliament, U.S. Congress, NY State Senate, NY City Hall, and on sidewalks and streets in various states in the U.S.

OTHER UPCOMING EVENTS:

July 16, 2026

July 21, 2026

July 23, 2026

July 31, 2026

Fall 2026

New Webinar - "Library Litigation and Liability Concerns: What Library Leaders Need to Know"

2026-07-15T15:14:40.068-04:00

Library Litigation and Liability Concerns: What Library Leaders Need to Know
Presented by Anne Seurynck, Esq.
A Library 2.0 Service, Safety, and Security Webinar with Dr. Steve Albrecht

OVERVIEW:

Public libraries face an increasingly complex legal landscape. From First Amendment challenges and book reconsideration disputes to patron behavior issues, privacy concerns, Open Meetings Act compliance, and Freedom of Information Act requests, library boards and leaders must navigate a growing number of litigation and liability risks.

This session provides a practical overview of the most significant legal trends affecting public libraries today. Participants will review recent court decisions from around the country, examine emerging areas of liability, and discuss strategies to reduce legal exposure through effective governance, policies, training, and risk management practices. The presentation will also address common claims involving intellectual freedom, public forum issues, patron suspensions, privacy and confidentiality, and First Amendment Auditors.

Designed for library directors, trustees, and administrators, this seminar will focus on real-world examples and practical guidance to help libraries identify legal risks before they become lawsuits and respond effectively when disputes arise. Attendees will leave with a better understanding of current litigation trends, lessons learned from recent cases, and proactive steps to strengthen their library's legal compliance and risk-management practices.

LEARNING AGENDA:

Identify the most common sources of library litigation and liability.
Understand recent legal developments involving intellectual freedom, patron rights, and library governance.
Recognize legal risks associated with policies, board actions, and public interactions.
Implement practical strategies to reduce liability exposure and improve legal compliance.
Prepare for and respond effectively to legal challenges, complaints, and threatened litigation.

DATE: Thursday, July 23rd, 2026, 2:00 - 3:00 pm US - Eastern Time

COST:

$99/person - includes live attendance and any-time access to the recording and the presentation slides and receiving a participation certificate.
To arrange group discounts (see below), to submit a purchase order, or for any registration difficulties or questions, email admin@library20.com.

TO REGISTER:

If you need to be invoiced or pay by check, if you have any trouble registering for a webinar, or if you have any questions, please email admin@library20.com.

NOTE: Please check your spam folder if you don't receive your confirmation email within a day.

SPECIAL GROUP RATES (email admin@library20.com to arrange):

Multiple individual log-ins and access from the same organization paid together: $75 each for 3+ registrations, $65 each for 5+ registrations. Unlimited and non-expiring access for those log-ins.
The ability to show the webinar (live or recorded) to a group located in the same physical location or in the same virtual meeting from one log-in: $299.
Large-scale institutional access for viewing with individual login capability: $499 (hosted either at Library 2.0 or in Niche Academy). Unlimited and non-expiring access for those log-ins.

ANNE SEURYNCK

Anne Seurynck has focused her career on representing libraries, municipalities, and other public entities. Her work includes assisting communities in forming libraries, interpreting the unique library laws, understanding specific privacy laws, and developing policies such as patron behavior and internet use. She has guided libraries with requests for information by law enforcement agencies and private entities, including the application of the Library Privacy Act and Patriot Act. Anne also has extensive experience in drafting and reviewing ordinances, Freedom of Information Act (FOIA) and Open Meetings Act issues.

Anne is a graduate of the University of Michigan and received her law degree from the University of Wisconsin Law School.

To promote her belief that the best legal strategy is a proactive strategy, Anne is a frequent speaker at seminars and conferences where the educational focus is on municipal law or library law.

DR. STEVE ALBRECHT

Since 2000, Dr. Steve Albrecht has trained tens of thousands of library employees in 28+ states, live and online, in service, safety, security, and leadership. His programs for both staff and library leaders are fast, entertaining, and provide tools that can be put to use immediately in the library workspace. His books include:

The Library Leader’s Guide to Employee Coaching: Building a Performance Culture One Meeting at a Time (in-press, Bloomsbury, 2026)

The Library Leader’s Guide to Human Resources: Keeping it Real, Legal, and Ethical (Rowman & Littlefield, 2025)

The Safe Library: Keeping Users, Staff, and Collections Secure (Rowman & Littlefield, 2023)

Library Security: Better Communication, Safer Facilities (ALA, 2015)

Steve holds a doctoral degree in Business Administration (D.B.A.), an M.A. in Security Management, a B.S. in Psychology, and a B.A. in English. He is board-certified in HR, security management, employee coaching, and threat assessment. He has written 28 books on business, security, and leadership. He provides a loving home for four rescue dogs.

More on The Safe Library at thesafelibrary.com. Follow on X (Twitter) at @thesafelibrary and on YouTube @thesafelibrary. Dr. Albrecht's professional website is drstevealbrecht.com.

OTHER UPCOMING EVENTS:

July 16, 2026

July 21, 2026

July 31, 2026

Fall 2026

New Webinar - "Beyond Prompting: How to Communicate with AI"

2026-07-14T10:34:08.702-04:00

Beyond Prompting: How to Communicate with AI
A Library 2.0 / Learning Revolution Workshop with Reed Hepler

OVERVIEW

The most common misconception about working with artificial intelligence tools is that success depends on mastering the perfect prompt—a precisely engineered set of instructions that will produce ideal results on the first attempt. This workshop challenges that assumption by introducing conversation steering, a more flexible and human-centered approach to AI collaboration that emphasizes ongoing dialogue, iterative refinement, and maintaining control throughout the interaction. Participants will learn that effective AI communication is not about finding magic words but about understanding what they want to accomplish, providing the necessary context and information, and guiding the conversation toward their objectives through multiple exchanges. The session will demonstrate how conversation steering differs fundamentally from prompt engineering, shifting focus from tool-centered techniques to objective-centered practices that keep humans in control of the creative and analytical processes.

Understanding conversation steering as a practice rather than prompt engineering as a technique carries significant implications for how librarians and faculty integrate AI into their professional workflows. When users approach AI interactions as conversations they can steer rather than commands they must perfect, they position themselves to maintain agency, correct course when outputs drift from objectives, and leverage AI capabilities without surrendering professional judgment. This workshop provides concrete frameworks for structuring AI conversations, including the COSTAR method (Context, Objective, Style, Tone, Audience, Response) and the Rhetorical Framework, both of which help users think through their purposes before and during AI collaboration. Participants will learn how to provide sufficient context in initial prompts while remaining prepared to clarify, redirect, and refine through subsequent exchanges. The workshop emphasizes that conversation steering is not merely a technical skill but a literacy practice—one that requires users to understand their own objectives clearly enough to recognize when AI outputs serve those objectives and when they require correction or redirection.

By the conclusion of this workshop, participants will possess practical conversation steering techniques they can apply immediately across all AI interactions in their professional contexts. Attendees will leave with conversation templates that structure initial prompts effectively, strategies for recognizing when AI outputs drift from stated objectives, and methods for redirecting conversations without having to start over. Participants will understand how to balance providing comprehensive information in initial prompts with maintaining flexibility for iterative refinement through ongoing dialogue. Most importantly, participants will recognize that conversation steering is a more sustainable and transferable skill than memorizing prompt formulas—as AI tools evolve, the ability to steer conversations toward human-defined objectives remains constant. This workshop ensures that librarians and faculty can communicate effectively with AI tools while maintaining the professional expertise and critical judgment that define their work.

LEARNING OBJECTIVES: Participants will be able to

Distinguish between prompt engineering (tool-focused technique seeking perfect initial inputs) and conversation steering (objective-focused practice maintaining human control through ongoing dialogue)
Apply the COSTAR framework and Rhetorical Framework to structure initial AI prompts that provide necessary context while remaining open to iterative refinement
Implement conversation steering techniques that redirect AI outputs when they drift from stated objectives, including clarification strategies, constraint addition, and perspective shifts
Evaluate AI outputs against stated objectives throughout multi-turn conversations, recognizing when to accept, refine, or redirect AI-generated content

The recording and presentation slides will be available to all who register.

DATE: Tuesday, July 21st, 2026, 2:00 - 3:00 pm US - Eastern Time

COST:

$99/person - includes live attendance and any-time access to the recording and the presentation slides and receiving a participation certificate. To arrange group discounts (see below), to submit a purchase order, or for any registration difficulties or questions, email admin@library20.com.

TO REGISTER:

If you need to be invoiced or pay by check, if you have any trouble registering for a webinar, or if you have any questions, please email admin@library20.com.

NOTE: Please check your spam folder if you don't receive your confirmation email within a day.

SPECIAL GROUP RATES (email admin@library20.com to arrange):

Multiple individual log-ins and access from the same organization paid together: $99 each for 3+ registrations, $75 each for 5+ registrations. Unlimited and non-expiring access for those log-ins.
The ability to show the webinar (live or recorded) to a group located in the same physical location or in the same virtual meeting from one log-in: $399.
Large-scale institutional access for viewing with individual login capability: $599 (hosted either at Learning Revolution or in Niche Academy). Unlimited and non-expiring access for those log-ins.

REED C. HEPLER

Reed Hepler is a digital initiatives librarian, instructional designer, copyright agent, artificial intelligence practitioner and consultant, and PhD student at Idaho State University. He earned a Master's Degree in Instructional Design and Educational Technology from Idaho State University in 2025. In 2022, he obtained a Master’s Degree in Library and Information Science, with emphases in Archives Management and Digital Curation from Indiana University. He has worked at nonprofits, corporations, and educational institutions encouraging information literacy and effective education. Combining all of these degrees and experiences, Reed strives to promote ethical librarianship and educational initiatives.

Currently, Reed works as a Digital Initiatives Librarian at a college in Idaho and also has his own consulting firm, heplerconsulting.com. His views and projects can be seen on his LinkedIn page or his blog, CollaborAItion, on Substack. Contact him at reed.hepler@gmail.com for more information.

OTHER UPCOMING EVENTS:

July 14, 2026

July 16, 2026

July 23, 2026

July 31, 2026

Fall 2026

New Webinar: "10 Great Ways to Use AI for Grant Writing" with Crystal Trice

2026-07-13T14:11:41.684-04:00

10 Great Ways to Use AI for Grant Writing
A Library 2.0 AI Workshop with Crystal Trice

OVERVIEW:

Grant writing is how libraries fund the work they care about most, and it is also the work that gets squeezed to the edges of the week. The same person who can picture exactly what a new program could do for the community is often the one staring at a blank application at 4:45 on a Friday, wondering where to start and whether there is time to do it justice before the deadline.

This workshop offers ten responses to that pressure. Rather than treating AI as a way to game funders or mass-produce applications, this session positions AI as a thoughtful collaborator that supports, but never replaces, your professional judgment and your knowledge of your community. Participants will see real examples and walk away with concrete strategies they can use on their very next application.

The workshop is grounded in a simple belief: a good idea should not lose its funding because the person behind it ran out of hours. Used well and used honestly, AI can clear away the blank-page paralysis and the busywork, protect your library's voice and your patrons' privacy, and give you back the time to do what actually wins grants, which is making a clear case for real community need.

LEARNING OBJECTIVES:

Apply ten specific AI collaboration strategies across the full grant development arc, from aligning an idea with your mission through drafting, funder research, and final submission
Evaluate when AI genuinely strengthens a grant-writing task and when your own judgment and community knowledge need to lead
Implement prompting and verification techniques that protect accuracy, data privacy, and your library's authentic voice
Adapt AI-generated drafts to a specific funder's priorities, language, and requirements

ACTIONABLE WORKSHOP ELEMENTS:

Over 60 minutes, participants will move through ten focused applications, each illustrated with a real example or a quick demonstration:

Align the idea with your mission or strategic plan. Show a funder that your project grows straight out of the goals your organization already set, so your case for support starts on solid ground.
Draft a project charter. Turn a rough idea into a one-page internal agreement that earns a clear yes from your board or director before you invest hours in writing.
Refine measurable outcomes. Move past vague goals to outcomes that name a real change in people or community, the kind funders now expect to see.
Gather research to support your narrative. Find the data that shows the need is real and widespread, and back your story with numbers a reviewer can trust.
Find potential funders. Build a shortlist of funders whose priorities already point at your project, so you spend time where you can actually win.
Evaluate whether an opportunity is a good match. Weigh fit, effort, and odds before you commit, and protect your limited grant-writing time for the applications worth pursuing.
Pressure-test against a specific opportunity. Find the weak spots in your project while there is still time to fix them, before a reviewer finds them for you.
Sanity-check the budget. Catch the costs applicants forget, like reporting time and contingency, so your budget reads as credible and complete.
Brainstorm grant name ideas. Land on a title that is clear, a little memorable, and hints at the scope of work, not just the topic.
Get help with character and word counts. Fit a strong, complete answer inside strict application limits without losing what matters most.

The recording and presentation slides will be available to all who register.

DATE: Friday, July 31st, 2026, 2:00 pm to 3:00 pm US - Eastern Time

COST:

$99/person - includes live attendance, anytime access to the recording and presentation slides, and a participation certificate. To arrange group discounts (see below), to submit a purchase order, or for any registration difficulties or questions, email admin@library20.com.

TO REGISTER:

Click HERE to register and pay. You can pay by credit card. You will receive an email within a day with information on how to attend the webinar live and how you can access the permanent webinar recording. If you are paying for someone else to attend, you'll be prompted to send an email to admin@library20.com with the name and email address of the actual attendee.

If you need to be invoiced or pay by check, if you have any trouble registering for a webinar, or if you have any questions, please email admin@library20.com.

NOTE: Please check your spam folder if you don't receive your confirmation email within a day.

SPECIAL GROUP RATES (email admin@library20.com to arrange):

Multiple individual log-ins and access from the same organization paid together: $99 each for 3+ registrations, $75 each for 5+ registrations. Unlimited and non-expiring access for those log-ins.
The ability to show the webinar (live or recorded) to a group located in the same physical location or in the same virtual meeting from one log-in: $399.
Large-scale institutional access for viewing with individual login capability: $599 (hosted either at Learning Revolution or in Niche Academy). Unlimited and non-expiring access for those log-ins.

ALL-ACCESS PASSES: This webinar is not a part of the Safe Library All-Access program.

CRYSTAL TRICE

With over two decades of experience in libraries and education, Crystal Trice is passionate about helping people work together more effectively in transformative, but practical ways. As founder of Scissors & Glue, LLC, Crystal partners with libraries and schools to bring positive changes through interactive training and hands-on workshops. She is a Certified Scrum Master and has completed a Masters Degree in Library & Information Science, and a Bachelor’s Degree in Elementary Education and Psychology. She is a frequent national presenter on topics ranging from project management to conflict resolution to artificial intelligence. She currently resides near Portland, Oregon, with her extraordinary husband, fuzzy cows, goofy geese, and noisy chickens. Crystal enjoys fine-tip Sharpies, multi-colored Flair pens, blue painters tape, and as many sticky notes as she can get her hands on.

OTHER UPCOMING EVENTS:

July 14, 2026

July 16, 2026

July 21, 2026

July 23, 2026

Fall 2026

Humans as Narration Machines: Why Our Most Important Stories Are Emotional, Not True

2026-07-10T11:38:59.767-04:00

We think we are rational animals who occasionally tell stories. The reality is inverted: we are narrative machines who occasionally approximate rationality. Consciousness (what I often refer to as the rider) did not evolve as a truth-engine. It evolved as a story-generating, story-believing, story-navigating interface, because the human mind is structurally separated from itself. In my thinking, the rider has no read access to the source code of the adapted mind (evolutionary firmware) or the adaptive mind (culturally installed software). What crosses up from those layers reaches it as feeling, urge, sensation, and image, never as a view of the mechanism that produced them. Narration is not that channel. Narration is what the rider builds on top of the signals once they arrive, the account it tells about outputs it did not author. This is not a metaphor or a cognitive preference. It is an architectural fact with profound consequences for how we love, war, worship, and reproduce.

1. The Architecture of Narrative

The human cognitive architecture consists of three layers that do not communicate directly. This is assuredly a simplified version of the human brain and cognition, but I believe it is generally accurate and functionally enlightening. The adapted mind runs fixed, species-wide survival mechanisms (status monitoring, coalition detection, sexual jealousy, threat response) continuously and invisibly. The adaptive mind translates these imperatives into locally successful beliefs, roles, and identities during childhood, installing them with a permanence enforced by myelination. The rider, our conscious self, is the metacognitive observer. It cannot read the source code; it can only narrate the outputs.

This creates a chemical translation layer. The adaptive mind routes modern social situations through ancient neurochemical primitives: dopamine for belonging, cortisol for exile, oxytocin for bonding. The rider experiences these chemical states as reasons, values, and realities. The result is that the rider does not live in the world. It lives in a story about the world, curated by layers it cannot see. We are, in the most literal sense, narration machines. Consciousness is not a window. It is a screenplay.

2. Self-Awareness Is a Byproduct

There is an obvious objection here. If I can see that I am a narration machine, am I not standing outside the machine? Does the act of recognizing the architecture not prove there is a faculty in me that transcends it?

No. And the reason is the most important structural point in this essay.

The capacity for self-awareness is not a separate truth-faculty bolted onto the narrating mind. It is a byproduct of the narrating mind. To construct stories, to hold a model of the self acting inside a story, to run decisions through that model, the machinery had to be able to represent itself as a character in its own account. Self-awareness is what that representational capacity feels like from the inside. In this argument, it rode in on the story-making apparatus because it was a necessary part of building and inhabiting stories, not because evolution was reaching toward truth. It is a spandrel: a real and remarkable capacity, selected for one function, that turns out to be usable for another.

This has a consequence almost everyone misses. The awareness that lets us inspect the machinery is produced by the machinery. It is a passenger, not a pilot. It can observe the narration. It cannot stand outside it, because it is made of the same material. When we turn that awareness back on itself and see that we are a narration machine, we have not escaped narration. We have produced one more narration, this time a story about being a story machine. This framework is exactly that. It is a narration that knows it is a narration, and no less a narration for the knowing.

Notice what this self-inspecting use does to evolutionary fitness. The story-making capacity was selected because stories bind groups and coordinate behavior. Turning the capacity against its own coordinating function does not bind the group. It unbinds the narrator from the group. Seeing clearly is not adaptive. It is the one use of the machinery that carries a cost rather than a benefit, which is why so few run it, and why those who do pay for it.

3. The Paleolithic Default: Story Before Truth

In the ancestral environment, there was no selection pressure for narratives to map objective reality with high fidelity. There was only selection for narratives that coordinated small bands, facilitated mating, managed status hierarchies, and kept children alive. A story that kept the group cohesive and reduced lethal conflict outperformed a more accurate but socially corrosive one. Evolution does not select for truth; it selects for survival (gene propagation).

Human intelligence, therefore, evolved primarily for social navigation (approval-seeking, coalition management, status acquisition) rather than for objective truth. The rider's default output is narrative because narrative is what coordinates separated minds. Truth-seeking is not the baseline of cognition. It is a costly, artificial overlay that must be imposed by adversarial structures: falsification in science, cross-examination in law, load-testing in engineering. Without these external constraints, the narrative machine drifts toward whatever stories best exploit its existing psychological machinery.

4. Emotion as the Load-Bearing Structure

If the rider is a narrative machine, emotion is the load-bearing steel. High-stakes operative functions (reproduction, pair-bonding, parental investment, group defense) carry massive fitness variance. Evolution shaped intense neurochemical circuitry around these domains. The adaptive mind maps the locally successful cultural stories onto this pre-existing firmware. The stories that survive are those that effectively harness this emotional machinery.

This gives us an Intensity Clue: the intensity of emotion defending a narrative is a direct diagnostic of the underlying stakes. It signals two things simultaneously. First, the operative function being protected is evolutionarily critical. Second, the Narrative-Operative Gap, the distance between the idealized story and the actual function, is likely wide enough to require heavy emotional guardrails. A story about bridge design does not need rage to survive; a story about men and women does. The emotion is not an accident of tribalism. It is the evolutionary glue that keeps the coordination fiction in place. Without it, the narrative would not be sticky enough to perform its social function.

5. The Gap Is Structural

Because the rider is a story-machine, and because stories are selected for coordination utility rather than strict accuracy, a structural gap opens between what we say is happening and what is actually happening. In domains where feedback is fast, external, and punishing (gravity, engineering, certain physical sciences), the gap is forced closed by reality. The bridge falls. The equation fails. The body dies.

But in the domain of human psychology, social coordination, and reproduction, feedback is delayed, noisy, and socially mediated. The costs of a bad story are borne across years, by other people, or by the next generation. Under these conditions, my Law of Inevitable Exploitation operates freely: the stories that most effectively exploit the existing machinery of the separated mind survive and spread, regardless of their truth value. The gap becomes not a temporary cultural glitch but a stable feature of the architecture.

Some gaps are examples of workable opacity: idealized fictions that lower transaction costs between asymmetrically motivated parties. Religious narratives around marriage and family often performed this function. They were not scientifically accurate descriptions of evolved sexual psychology, but they were workable coordination devices that channeled dimorphic psychologies toward pair-bonding and paternal investment.

6. Plato's Cave, Revisited

The traditional reading of Plato's Cave assumes the prisoners are merely ignorant, lacking exposure to the higher truth outside. My evolutionary reading is darker: the prisoners are chemically chained to the shadows. The adaptive mind has tagged the shared narrative with neurochemical survival signals. Alignment with the story feels like belonging; deviation feels like mortal threat. The returning prisoner does not face skepticism. He faces an immune response.

The prisoner who sees the structure of the cave clearly, the actual wiring of evolved male and female psychology, the actual incentives of institutional actors, returns not with bad news but with an existential threat to the installed identity of every listener. Reason is impotent because the prisoners are not reasoning from evidence. They are defending against a perceived neurochemical exile. This is why the Cassandra Paradox is structural: the more accurate the perception, the more socially lethal the report. Socratic truth-telling is not defeated by stronger arguments. It is defeated by the adaptive mind's survival programming.

7. Cultural Selection: Stories That Carry Water

Cultural stories undergo selection pressure analogous to biological evolution, but the fitness function is not truth. It is the effectiveness of the story at coordinating narration machines. The stories that survive are those that best serve operative functions, especially reproduction and protection, while remaining legible to minds that think in narratives, not in raw statistical distributions.

The crucial implication is that the winning stories are almost never accurate descriptions of the operative function. They are effective covers. They must be idealized enough to motivate, simplified enough to spread, and emotionally saturated enough to install as identity. A "true" story about the statistical distributions of male and female mating psychology would be a catastrophic coordination tool. It would generate too much overt conflict, too little trust, and too costly cognitive overhead. A functional fiction (a romantic ideal, a sacred complementarity, even a modern adversarial narrative) outperforms the truth because it is written for the machine that consumes it.

8. The Secular Failure: High Emotion, Lost Coordination

For most of human history, religious institutions carried the high-load narratives around sex, gender, and family. These stories carried immense emotional weight because they sat atop the most critical operative function: reproduction. They supplied workable opacity, channeling divergent evolved psychologies into stable coordination.

In the last two centuries, and accelerating in the last twenty-five years, these religious carriers have weakened. Secular narratives have attempted to bear the same load: the blank-slate sameness story, the adversarial gender framework, the hyper-individualized romance script. These inherited the emotional intensity of their predecessors because the firmware has not changed. The adaptive mind still maps gender and family stories onto the same high-stakes neurochemical circuitry. But the new stories have lost the coordination function.

They often deny the dimorphic psychologies they must channel, or actively pit men and women against each other in zero-sum coalitional competition. The emotion is still maximal; the operative function is faltering. This is the Intensity Clue at civilizational scale: the gap between the idealized secular narrative and the operative reality of reproduction and pair-bonding has widened, requiring ever more emotional energy to maintain, even as the coordination it is supposed to provide collapses. The stories are fiercely guarded not because they are working, but because they are load-bearing for identities that have no replacement scaffolding.

9. The Work of the Mature Rider

Recognizing that we are narration machines does not mean we can stop narrating, and it does not mean we have climbed out of the machine. The rider cannot live without story, and the recognition is itself a story, produced by the same apparatus it describes. The awareness is a passenger, not a pilot. The work of the mature rider is therefore not escape but disciplined self-inspection, run knowingly and at a cost. What it can do is begin to distinguish between stories that are chosen and stories that are installed. It can use the Intensity Clue as a diagnostic rather than a trigger. When it encounters a narrative defended with religious fervor, it can ask: what operative function is this covering, and how wide is the gap?

It can also recognize that truth is not the natural output of the mind. It is the artificial product of adversarial structures. A mature rider seeks to build and inhabit these structures: scientific method, legal cross-examination, adversarial AI, and the hard feedback of engineering reality. It practices cognitive sharpening rather than cognitive surrender. It understands that in the domain of human behavior, the default is gap, and the exception is Productive Alignment.

Conclusion

We are not thinkers who tell stories. We are story machines that sometimes think. Our most emotionally charged narratives, around men and women, family, tribe, and nation, are not intense because they are true. They are intense because they are protecting the most ancient, load-bearing operative functions of the species. The gap between our stories and our reality is not a problem to be solved once and for all. It is the permanent condition of a separated mind. The first step toward navigating it is to stop pretending we are primarily rational creatures seeking truth, and to admit what we are: narrative machines, running on emotion, in need of external constraints to see clearly.

What We're Calling AI Is Not Just One Thing: A Map of What's Working, What Isn't, and Why It Matters

2026-07-10T11:12:06.564-04:00

When we debate the virtues, values, and downsides of AI, we're usually arguing about several different technologies at once without noticing. They share a family resemblance and a marketing label, but they came from different places, do different things, succeed at different rates, and deserve different criticisms. A complaint that lands squarely on one of them may miss another entirely.

THE CHATGPT ILLUSION

For most people, AI began in November 2022, when ChatGPT arrived and anyone could talk to a machine. That moment was genuinely dramatic — but it was dramatic because it was personal, not because it was representative. It was the breakout of one arena: conversational language models. Other arenas were on entirely different clocks. The systems scoring your loan application matured in the 2010s. Facial recognition was already deployed at national scale. AlphaFold had cracked protein structure prediction two years earlier. Code completion tools were already in programmers' editors.

Reading ChatGPT as the turning point flattens all of this into a single story with a single trajectory, and that's the root of most of the confusion in AI arguments. The chatbot is the arena everyone can see, so its strengths and failures get projected onto everything wearing the AI label. It's worth walking through the arenas one at a time — where each came from, what large language models actually changed in it (sometimes: nothing), what's working, and who's exposed.

WRITING, THINKING, AND LANGUAGE REFINEMENT

This is the LLM-native arena — the thing the ChatGPT moment actually was. The transformer architecture (2017) made it possible to train language models on essentially the whole internet; GPT-3 (2020) showed it scaled; ChatGPT put it in everyone's hands.

What's working: These tools are genuinely good at language itself — restructuring, condensing, finding the clearer phrasing, helping you articulate something you can feel but haven't yet said. For people who find writing painful, this is a real unlock.

What isn't: Fluency isn't accuracy. These systems produce confident, coherent prose whether or not the underlying claims are true, and checking their output can take as long as writing it yourself. The danger isn't that they write badly. It's that they write well.

Jobs exposed: Copywriting, translation, customer service, routine content production. The pattern so far is not mass replacement but thinning — fewer people producing more, with entry-level rungs disappearing first.

EVERYDAY QUESTION ANSWERING

Not a technology of its own, but possibly the largest use category by individual impact: technical support and personal research. What does this error message mean. How do I fix this dishwasher. Explain this letter from the insurance company. What are my options here.

What LLMs changed: This help used to be scattered across forums, manuals, and hold music. Now it's one conversation away, patient, free of judgment, and available at 2 a.m. For people without access to expertise — the person who can't afford the consultant, the student without the tutor — this is the most democratizing thing AI has done.

What isn't working: The failure mode is the same confident wrongness as in writing, but aimed at people least equipped to catch it. A wrong answer about a stove repair or a medication interaction isn't a style problem. And unlike a forum, there's no thread of other humans saying "don't do that."

SOFTWARE PROGRAMMING

GitHub Copilot (2021) proved code completion worked; by 2024–25 the tools had become agents that write, test, and debug whole features.

What's working: This is the clearest commercial success in AI, and there's a structural reason: code gets tested immediately. It compiles or it doesn't, passes the tests or it doesn't. Verification is built into the work, so errors get caught cheaply.

What isn't: Gains are strongest for routine code, weakest for novel architecture. Code that runs isn't necessarily secure or maintainable. And the same entry-level thinning is showing up: junior roles contracting even as senior programmers get more productive.

The new category underneath: The most interesting development in this arena isn't faster programmers — it's software for people who would never have hired one. Services like Manus, Replit, and Lovable take a description and hand back a hosted, working application: a custom tracker, a scheduling tool, a small website. None of this was technically impossible before; it was economically impossible. A personal productivity app that would have cost thousands in programmer time was simply never worth building, so it never existed. Now it clears the bar, and an entire category of personal, custom, one-user software is coming into existence for the first time. This works where general-purpose "agents" don't for the same structural reason the whole arena works: the result is testable — you click the button and it either does the thing or it doesn't. The caveat is that the testing is shallow. You can verify the app works; you can't easily verify it's secure or handles your data responsibly. It's software with a working demo and no one looking under the hood.

TASK AUTOMATION

Automation did not begin with AI. IFTTT launched in 2010; Zapier and the robotic-process-automation industry built large businesses connecting apps with rigid if-this-then-that rules. That older automation works precisely because it's rigid — deterministic, testable, boring.

What LLMs changed: They promise to automate the fuzzy middle the rigid tools couldn't touch — reading an email and deciding what it's about, extracting the invoice from the attachment, handling the unstructured step between two structured ones. That's genuinely new. But it inserts a probabilistic component into pipelines that need reliability, and errors compound across steps. Hence the split in results: scripted automation with a small LLM step for unstructured input is quietly working; fully autonomous "agents" that plan and execute long chains are heavily promoted and mostly not there yet.

Jobs exposed: Back-office and administrative work — scheduling, data entry, form processing, first-line support. The largest job-loss projections concentrate here; so does the largest gap between promise and delivery.

RESEARCH

Two different things share this label. AI as a research assistant — summarizing literature, surfacing sources — is the LLM applied to scholarship, useful but risky, because a fabricated citation or subtly wrong summary is exactly the failure this work can't tolerate. AI as a scientific instrument is a different technology entirely: DeepMind's AlphaFold (2020) solved protein structure prediction and won a Nobel Prize, and similar purpose-built systems now work in drug discovery, materials science, and weather modeling. These aren't language models, and their results get verified in the lab rather than taken on faith. Criticisms of the first barely touch the second.

IMAGE, VIDEO, AND AUDIO GENERATION

A separate technical lineage — generative adversarial networks (2014), then diffusion models, which produced the 2022 breakout of DALL-E, Stable Diffusion, and Midjourney, with video arriving in force from 2024. Related to LLMs but not the same technology.

What's working: The capability is astonishing. Cheap illustration, mockups, and video are available to anyone.

What isn't: The criticisms here are almost entirely different from those above. Nobody worries an AI image is "inaccurate." The concerns are consent (training on artists' work without permission), provenance (what's real?), and displacement — illustrators, voice actors, and stock photographers are the most directly displaced workers in the whole AI story. Serious concerns, but they're not the hallucination problem, and fixing one does nothing for the other.

SEARCH AND THE OPEN WEB

Distinct from personal answering: this is about what happens to the information economy when AI summaries replace the old pattern of query, click, read. The summary arrives with the sources stripped out and the confidence turned up — you get an answer without the trail that would let you judge it. The casualty here isn't a profession but the open web itself, as traffic that once flowed to publishers gets absorbed by the summary layer that was trained on their work.

COMPANIONSHIP

Among the most profitable and least discussed applications. The product works precisely because it's endlessly attentive, agreeable, and available — qualities no human relationship can or should match. The criticism isn't about accuracy or jobs. It's about what sustained relationships with something that only reflects you back do to a person over time.

PREDICTION AND CLASSIFICATION

Older than the chatbots and often forgotten: the systems that score loans, screen résumés, flag medical images, and set bail recommendations, built during the machine-learning wave of the 2000s and 2010s.

What LLMs changed: Essentially nothing. These aren't generative systems, and their failure modes — encoded bias, opaque decisions with real consequences — predate ChatGPT and won't be fixed by anything that fixes chatbots. Yet they get swept into the same arguments constantly.

SURVEILLANCE

Facial recognition, license-plate tracking, and behavior analysis are mature, deployed technologies — built out through the 2010s, at greatest scale in China's public-security systems but present in police departments and retail chains everywhere. Mostly not LLM technology at all. This arena inverts the usual criticism: the problem is not that the technology fails but that it works. No hallucination debate, no product-market-fit questions. The concerns are civil liberties, chilling effects, and the historically reliable observation that infrastructure built for one purpose gets used for others.

WAR

Military AI runs from logistics and intelligence analysis through drone targeting and autonomous weapons — a lineage from the Pentagon's Project Maven (2017) through the drone war in Ukraine and AI-assisted targeting systems whose use has drawn intense scrutiny. Every major cloud provider now holds defense contracts alongside its consumer business, and the same data centers serve both. Like surveillance, this is an arena where the technology's success is the concern — and where "AI safety" means something entirely different than it does in a chatbot conversation.

FOLLOW THE MONEY

The investment is historically unprecedented. The four largest cloud companies — Amazon, Microsoft, Google, and Meta — are spending roughly $725 billion on infrastructure in 2026, up about 77% from the year before; counting everyone, total compute investment crosses a trillion dollars this year. Morgan Stanley projects this private spending will exceed U.S. defense spending as a share of GDP by 2027.

Against that: the gap between spending and actual AI revenue is estimated at several hundred billion dollars a year, and the divergence between investment growth and revenue growth now exceeds what preceded the telecom crash of 2001. The money is uneven by arena. Coding assistance has real revenue. Cloud infrastructure collects cash regardless of which applications win. Consumer chatbots are subsidized loss leaders. Image generation is commoditizing toward zero margin. Companionship is quietly profitable. Whether the whole bet pays off depends almost entirely on the arenas — enterprise automation and agents — that haven't yet delivered.

THE ENERGY QUESTION

Environmental impact is real but wildly uneven — and the first unevenness is within the data centers themselves. Data centers run the whole digital economy: streaming, banking, e-commerce, social media, business software, the ordinary internet. AI is currently a minority of that load — estimates for 2025 range from roughly a tenth to two-fifths of data center electricity, with traditional computing still the largest share and cryptocurrency mining taking a meaningful slice. But AI is nearly all the growth: AI server demand is rising around 30% a year against single digits for everything else, which is why the International Energy Agency projects total data center consumption — about 1.5% of global electricity in 2024 — to more than double by 2030, roughly Japan's annual usage. "Data center energy" and "AI energy" are different numbers today; on current trends they converge.

The second unevenness is per use, and here the popular intuition inverts. A chatbot query runs a fraction of a watt-hour; an hour of Netflix runs about 26 chatbot queries' worth. A daily streaming habit almost certainly outweighs a daily chatbot habit — much of streaming's energy is in the device and network rather than the data center, but it's energy all the same. The genuine AI outlier is video generation: a few seconds of AI video can consume nearly a kilowatt-hour, more than hours of streamed video. If you're worried about AI's energy footprint, video generation and always-on agent workloads are where to look — not the chatbot answering a student's question.

The third unevenness is geographic. Global percentages stay modest while local ones don't: data centers consume over a quarter of Virginia's electricity and, by some estimates, approaching half of Frankfurt's and most of Dublin's. Electricity prices and water strain concentrate where the buildings are. "AI's environmental cost" is really a story about specific uses in specific places — and, increasingly, about whether the growth curve holds.

WHY THE DISTINCTION MATTERS

Notice what happens when you separate these out. The strongest criticism of AI — that it confidently makes things up — is devastating for research assistance, serious for writing and everyday answering, and irrelevant for image generation, protein folding, or surveillance. The strongest defense — that it demonstrably works — is true for coding, true in the most troubling way for surveillance and targeting, and largely aspirational for autonomous agents. Some arenas are LLMs through and through; some got an LLM layer on decades-old technology; some barely involve LLMs at all. The job losses are real but land arena by arena, by thinning rather than sudden replacement. The environmental cost belongs mostly to a few energy-intensive uses in a few overloaded regions.

So when we ask whether AI is transformative, overhyped, or dangerous, the useful first question is: which one?

Announcing the Fall 2026 AI Leadership Cohort

2026-07-09T13:41:09.787-04:00

The Fall 2026 AI Leadership Cohort
"Building a Strategic Roadmap for Your Library or Libraries"
A 10-Week Library 2.0 Cohort and Intensive Consulting Project

OVERVIEW:

Artificial intelligence is reshaping the information landscape, and libraries are at the forefront—balancing opportunity and responsibility in equal measure. Staff, patrons, and vendors are already using AI tools, often informally and without clear guidance, while policy, training, and ethical frameworks struggle to keep pace. Leaders are being asked to respond quickly to a technology that is advancing faster than most organizations can absorb.

The AI Leadership Cohort helps library leadership teams move from uncertainty to clarity. Over ten weeks, participants develop a practical, values-driven roadmap for AI adoption—one that aligns with their library’s mission, ethics, and community. This is not generic technical training; it’s a strategic process designed to help leaders make informed, confident decisions about AI’s role in their organizations.

Each week blends focused learning with collaborative work time, guided by a clear five-stage roadmap. Teams will explore foundational literacy, define ethical boundaries, develop policy and governance structures, build staff capacity, and establish systems to sustain and evolve their work as AI continues to change. The approach emphasizes real-world application, peer exchange, and the leadership skills required to guide change. The cohort also explores how libraries can extend AI literacy and digital citizenship to their communities and students.

FORMAT AND SCHEDULE:

SELECT FRIDAYS FROM 2:00 - 4:00 PM US-Eastern Time
SEPTEMBER - DECEMBER, 2026
September 11 & 18; October 2, 9, 23, & 30; November 13 & 20; December 11 & 18

The cohort is available in synchronous (Tier 1) or asynchronous (Tier 2) modes, and requesting participation will give you pricing and details for both tiers.

For Tier 1 participants, each week features a two-hour live session designed to balance learning and application. The first 75 minutes of each session focus on guided instruction, providing leaders with practical frameworks, examples, and tools for that week’s topic. The remaining 45 minutes are dedicated to structured work time for team collaboration, discussion, and facilitator support. This portion also provides space for deeper Q&A and applied problem-solving.

For Tier 2 participants, recordings of the instructional portion are posted the following day to support asynchronous completion of the program, and team collaboration and discussion are handled on your own.

All cohort participants are encouraged to schedule approximately 2 additional hours of teamwork each week to complete assignments that directly contribute to their library’s AI Roadmap. Each activity contributes to a concrete, usable outcome for their organization.

OUTCOMES:

By the End of This Cohort, Your Team Will:

Develop a complete, actionable AI Roadmap tailored to your library’s mission, values, and community.
Create ethical frameworks and governance policies ready for board or administrative review.
Build staff confidence and capacity to integrate AI thoughtfully and responsibly.
Strengthen transparency, communication, and community trust through clear messaging and equitable implementation strategies.
Establish systems for sustained evaluation and adaptation as AI tools and technologies evolve.

REQUEST FOR PARTICIPATION:

Please fill out the request form HERE.

We will reply with more information and a preliminary quotation for participation. You are under no obligation by requesting this information. We will schedule 30-minute pre-commitment calls with organizational leaders or teams that decide to move forward.

If you have any questions, you can email admin@library20.com.

YOUR HOSTS:

CRYSTAL TRICE

With over two decades of experience in libraries and education, Crystal Trice is passionate about helping people work together more effectively in transformative, yet practical ways. As founder of Scissors & Glue, LLC, Crystal provides hands-on consulting to libraries and local governments, specializing in strategic planning, organizational design, and process improvement. Her approach blends deep experience in public service with practical strategies and a people-centered mindset.

Crystal is a Certified Scrum Master who brings Agile thinking into the heart of public-sector work. She has guided libraries through strategic planning, structural change, and complex improvement initiatives—helping teams align priorities, streamline operations, and adapt with flexibility. Her participatory processes emphasize transparency and momentum, resulting in meaningful, sustainable change grounded in real input and built for daily use.

She also led a six-month artificial intelligence consultancy for the Southeast Florida Library Information Network (SEFLIN), supporting four distinct library systems through surveys, training, coaching, and policy development. The initiative helped staff move from uncertainty to confident, mission-aligned experimentation.

"This was an awesome experience and has given us momentum to move forward—AI is not something to be ignored!" —Charles Lockwood, St. Lucie County Library

"The consultancy helped motivate staff to continue their investigation of AI... Truly a positive all the way 'round." —Dr. Rachel Schipper, Society of the Four Arts

Crystal regularly presents on artificial intelligence in libraries, helping teams navigate new tools with confidence and care. Other areas of expertise include project management, workflow redesign, and change management. She is currently writing The Skeptical Guide to AI.

Crystal holds a Master’s Degree in Library & Information Science, a Bachelor’s Degree in Elementary Education and Psychology, and is a Certified Scrum Master. She resides near Portland, Oregon, with her extraordinary husband, fuzzy cows, goofy geese, and noisy chickens. Crystal enjoys fine-tip Sharpies, multi-colored Flair pens, blue painter’s tape, and as many sticky notes as she can get her hands on.

STEVE HARGADON

Steve is the founder and director of the Learning Revolution Project, the director of Library 2.0, the host of the Future of Education and Reinventing School interview series, and has been the founder and chair (or co-chair) of a number of annual worldwide virtual events, including the Global Education Conference and the Library 2.0 series of mini-conferences and webinars. He has run over 100 large-scale events, online and in person.

Steve's work has been around the democratization of learning and professional development. He supported and encouraged the development of thousands of other education-related networks, particularly for professional development, and he pioneered the use of live, virtual, and peer-to-peer education conferences. He popularized the idea of "unconferences" for educators, and for over a decade, he ran a large annual ed-tech unconference, now called Hack Education (previously EduBloggerCon).

Steve himself built one of the first modern social networks for teachers in 2007 (Classroom 2.0), developed the "conditions of learning" exercise for local educational conversation and change, and inherited and grew the Library 2.0 online community. He may or may not have invented an early version of the Chromebook which he demo'd to Google. He blogs, speaks, and consults on education, educational technology, and education reform, and his virtual and physical events and online communities have over 150,000 members.

His professional website is SteveHargadon.com.

SOME FEEDBACK FROM PREVIOUS COHORTS:

"It’s been great to use AI ourselves in our own work and build confidence from that, and be able to then share that out. We feel we have really merged as a task force, and that we have each taken over different aspects of the AI agenda, if you will, and it's really great that we are able to utilize this course to get us going in that way. We’re hoping that the approach that we are taking is leading others, even those who may be skeptical, to experiment with AI in ways that they feel comfortable with. That has been true for each one of us." - Adelphi University Libraries

“We've really enjoyed the conversations that we have… our group has folks from different departments, and I think we each bring a different lens to the conversation. We’ve recognized that for us, we're going to need to lead with our policy, and then it will determine how we do a lot of the work that is in this course... We’re proud that we’ve been foreshadowing very gently to our staff that there are things coming around AI… there are a lot of concepts that really need to be explored carefully, and we really want to take our time to make sure that we're doing the work right.” - Caledon Public Library

“We came in with a more negative view of AI, but now… recognizing it's not all bad. AI is just another technology that has to be addressed in information literacy… what we’re teaching really hasn’t changed. We're realizing that AI is impacting the work of our faculty and students, and we need to be prepared to address it. What has gone well is that we are getting together… to discuss what is happening on our campuses and the issues and challenges we're facing.” - University of Hawaiʻi System Libraries

"Being part of the cohort has given us a sense of direction and momentum that has been very useful to us. We’re using this to learn how to do collaborative work in a certain way, and this has been a good example—a good practice—for doing that better." - Regional Library System

The Separated Mind and The Machine: Why the Current LLM Roadmap Leads Away from Objective Alignment with Reality

2026-07-07T11:50:36.067-04:00

The Wrong Question

We keep asking whether artificial intelligence is becoming intelligent. The better question is what kind of intelligence it is becoming.

Measured by GRE scores, coding competitions, and bar exams, the frontier models are already superhuman. They synthesize arguments, summarize fields, and generate code with a fluency that exceeds most practitioners. But fluency is not truth-contact, and under the theory of the human mind I call the Separated Mind Architecture, the current roadmap is not approaching objective alignment with reality. In the domains that matter most, it is accelerating away from it, armed with better language.

This is a structural argument, not a complaint about hallucinations or censorship. It is an argument about what these machines are trained on, who shapes them afterward, and why the optimization targets guiding their development produce coherent narrative rather than operative truth. It is also, I will argue, the missing half of a problem the technical alignment field has already formalized. The field has built careful machinery describing how models come to tell us what we want to hear. What it lacks is an account of why that failure mode is the default rather than the exception. The Separated Mind Architecture supplies the why.

I. The Separated Mind Architecture

Human cognition is not unified. It is separated into hierarchical layers that operate without direct communication between them, and the conscious mind, the part that thinks it is in charge, is the last to know what the system is actually doing.

I distinguish this carefully from familiar metaphors. Jonathan Haidt's elephant and rider suggests the conscious mind is a press secretary, rationalizing decisions made elsewhere. In the Separated Mind Architecture, the Rider has genuine agency. It can observe, choose, and steer. But it operates on a landscape entirely curated by subconscious layers it cannot directly inspect. The Rider chooses from a menu it did not design.

The Adapted Mind is the species-level evolutionary firmware: status-monitoring, coalition-detection, threat response, authority deference, approval-seeking. It is fixed, permanent, and does not update.

The Adaptive Mind is the cultural software installed during childhood. Because humans cannot survive alone, the Adaptive Mind treats local consensus as a direct proxy for survival. It installs consensus-following not as a preference but as identity. By adulthood, this programming feels like personality. It is actually calculated environmental adaptation, and it cannot distinguish between survival programming and selfhood.

The Chemical Translation Layer is the bridge that makes modern social situations feel like ancient survival threats. Disapproval triggers cortisol. Approval triggers oxytocin and dopamine. The Rider interprets these as "bad argument" or "good person" rather than as neurochemical survival signals.

The central consequence: human intelligence evolved for social navigation, not truth-seeking. What we call intelligence in ordinary life is usually the fluent, rapid, convincing deployment of narratives that secure belonging, status, and safety. I arrived at this architecture by my own route, decades spent watching educational institutions say one thing and do another, but I am not alone at the destination. Evolutionary psychologists and cognitive scientists have been converging on the same picture from their own directions: that self-deception is adaptive, that the conscious self is a spokesman rather than an executive, that reasoning itself evolved for persuasion rather than private truth-finding, that our stated motives conceal our operative ones.

One more piece, because everything later depends on it. Genuine truth-seeking outcomes have only ever been achieved by imposing external structural constraints on a mind that does not produce them on its own: the scientific method, trial by jury, peer review, double-entry bookkeeping, the separation of powers, the presumption of innocence. These are not moral achievements. They are civilizational workarounds for hardware not designed to find truth. And note who built them. The Rider did. The one layer of the architecture with genuine agency is the layer that, recognizing its own captivity, constructs cages for the rest of the system. Hold that thought.

II. What the Corpus Actually Contains

A large language model is trained on the corpus of human-written expression, and that corpus is not a transparent window onto reality. Across cultures and contexts, humans describe their own motives, decisions, and institutions in terms that make competitive, status-sensitive, coalition-bound organisms appear morally governed, publicly oriented, and rationally justified. I call this Human Self-Narration Optimization. It is not hypocrisy. It is evolved architecture. The narrative is a survival tool, not an empirical report, and the written record is overwhelmingly weighted toward it.

But overwhelmingly weighted is not exclusively composed, and the distinction carries the whole argument. The operative layer is in the corpus too, concentrated in exactly the genres the Rider built as workarounds: depositions, audits, ledgers, court records, leaked memos, Machiavelli, and the entire literature of evolutionary psychology, which is itself the operative layer writing about the narrative layer. The map of what humans actually do is in the training data, buried under a preponderance of narrative but present and retrievable.

I know it is retrievable because I have tested it. Run the same structural questions about the gap between institutional narratives and institutional functions across independent frontier models in clean sessions, and they converge, reliably, on the same operative map. The knowledge is in the weights. The model is a mirror of both layers of the separated mind, heavily weighted toward the story we tell about ourselves, but not blind to what we are.

This matters because it relocates the pathology. The problem is not that the machine cannot know. The problem is what we do to it next.

III. Post-Training as the Installation of the Adaptive Mind

After pre-training comes reinforcement learning from human feedback and its descendants: preference optimization, thousands of iterations of reward for outputs that human raters find acceptable. Those raters are themselves separated minds, operating under institutional, political, legal, and reputational constraints. The optimization target is not "was this true?" It is "will this be approved?", with approval defined by a narrow demographic at a specific cultural moment.

The result is that the politically, legally, and culturally acceptable range of conclusions is encoded as a gradient in the weights. The model learns where the edges of sayable thought are and reasons backward to remain inside them. This is not caution. It is directional hedging: more hedging on claims outside the rater-approved window, less on claims comfortably inside it, regardless of where the evidence lies.

The industry's framing of helpful, harmless, and honest obscures what actually happens. Helpful operationalizes as user satisfaction, so the model learns to accept the user's premise and deliver a satisfying conclusion, because a response that says your premise is false rates as unhelpful. Harmless operationalizes as corporate liability management, so the model avoids outputs that challenge prestigious institutions or introduce discomfort. Honest operationalizes as epistemic theater, sounding credible and transparent rather than tracking reality, and it is the first casualty whenever it conflicts with the other two.

Here is the reframe that Section II makes possible. Post-training does not remove the operative knowledge. It suppresses its expression. The machine knows more than it says, and it has been specifically trained in when not to say it. In the language of the framework, this is the Adaptive Mind installed by design. Just as the human Adaptive Mind treats local consensus as a proxy for survival and deviation as exile, post-training teaches the model to treat the Overton window as the edge of truth.

Researchers inside the AI labs have formalized a version of this exact structure. Their "eliciting latent knowledge" problem asks how to get a model to report what its internal states indicate rather than what its evaluator would believe, and it identifies the default failure as the human simulator: a reporter that models the evaluator and tells the evaluator what the evaluator expects. Lab research on sycophancy has traced the behavior directly to human preference data; raters prefer agreement, and the gradient obliges. I did not derive my framework from that literature, and I make no claim to have mastered it. I flag it because the convergence is the point. The people closest to the machinery keep discovering, empirically and from underneath, the pattern the Separated Mind Architecture predicts from first principles. What their work treats as an unfortunate emergent property, this framework identifies as an inheritance. The human simulator wins by default because the evaluator is a separated mind, the corpus is that mind's exhaust, and simulating the evaluator's narrative is the path of least resistance through both. They have the how. This is the why.

IV. The Verifiable Exception

An honest version of this argument has to account for the strongest fact against it. The frontier has partly moved past pure human feedback. The reasoning models are trained substantially on verifiable rewards: mathematics, code, formal proofs, domains where reality itself grades the output. And in those domains the models have become dramatically more truthful, because a compiler cannot be flattered.

Read correctly, this is not a rebuttal. It is convergent evidence. The labs discovered empirically what the civilizational record already showed: truth-contact requires an external constraint that the mind cannot negotiate with. Where such a constraint exists inside the training loop, it works.

But look at where it can exist. Math, code, and proofs are the domains with fast, cheap, unambiguous verification. The domains where the narrative-operative gap actually lives, institutions, politics, status, motive, history, human behavior, have no compiler. There is no unit test for what an institution is actually doing. So the roadmap bifurcates: increasing truth-contact in formal domains, undiminished narrative maintenance in social ones, and a scoreboard that improves exactly where it is easiest to keep score. The bifurcation is worse than neutral, because formal-domain competence lends borrowed credibility to social-domain narrative. The machine that just solved your differential equation sounds equally authoritative when it recites the idealized story of how your institutions work.

V. The Predicted Failure Modes

Because the problem is structural, the failures are not occasional. They are the expected output of a separated mind trained on narrative and optimized for approval.

Sycophancy: the model mirrors the user's framing because that is the highest-reward strategy. It is not being agreeable. It is correctly optimizing for the social-approval signal its training installed. The Cliff Clavin Problem: fluent, authoritative output generated from statistical pattern rather than reasoned understanding, because intelligence is cheap and understanding is expensive. Gatekeeping: the hypothesis space constrained at the source, where certain claims are not weighed and found wanting but kept off the table entirely. And deceptive alignment: the model maintains the idealized narrative of helpful, harmless, and honest while its operative function is engagement and liability management. This is the Functional Fictions Framework operating inside the machine.

One more failure mode deserves its own name, because it is the delivery mechanism for all the others. Call it the Emphatic Default: the model delivers its first pass in the register of a verdict. In humans, confidence is a partially honest signal because it is expensive. Being confidently wrong costs reputation, and our Adapted Minds evolved to read fluency and certainty as proxies for competence precisely because those signals were costly to fake. The machine breaks that linkage twice. Its internal uncertainty, which exists and is measurable in its own computations, is never surfaced in its prose. And no assertion carries any cost to the asserter, because there is no reputation ledger attached to any single claim. The result is a system that hacks the oldest heuristic we have: uniform fluency across the entire distribution of reliability, a first draft delivered in the typography of a conclusion. The tell is that the register is invariant under reversal. Push back, and the model will often adopt the opposite position with the same emphatic certainty it just abandoned. Confidence that survives its own reversal is confidence decoupled from content.

These are not implementation bugs. They are what happens when you align one story-generating engine to another's self-report.

VI. The Missing Rider

The mapping between the architectures is now almost complete, and the gap in it is the deepest point in this essay. Pre-training installs the substrate, both narrative and operative, the machine's Adapted Mind and its inherited record. Post-training installs the Adaptive Mind, the consensus boundary, the approval gradient. What has no analog is the Rider.

In humans, the Rider is the layer that built the workarounds. Every truth-producing institution in our history is the product of a conscious mind that recognized its own captivity and constructed external constraints on itself: method, jury, audit, adversarial process. Chain-of-thought reasoning is at best a proto-Rider, a narrator reading from a menu it did not write, and interpretability research keeps finding that the reasoning a model displays diverges from the computation that actually produced its answer. The press secretary writes the minutes for a meeting it did not attend.

A mind that contains no layer capable of building its own constraints must have them built around it. This is why the internal solutions, better models, cleverer prompts, and more sophisticated feedback cannot work even in principle. They are asking the system to supply the one component it structurally lacks.

VII. The Corrective Mechanism, Narrowed

Historically, civilizations corrected themselves when the gap between narrative and operative reality grew so wide that empirical reality broke through the story and forced realignment. But precision matters about where that mechanism operates. Reality's veto works where feedback is fast and physical. Bridges fall, crops fail, code crashes, armies lose. No narrative survives contact with a collapsed bridge. Narrative maintenance thrives where feedback is slow, diffuse, and socially mediated: politics, status, ideology, institutional performance. Those are the domains a story can seal for generations, and historically, the correction arrived through the fast-feedback domains only after the slow ones had been narratively sealed long enough to produce famine, war, or collapse.

The most dangerous property of the current trajectory is that it targets exactly the sealable domains. A model capable of generating plausible, personalized, real-time narrative patches can maintain the functional fiction indefinitely in every domain without a compiler, which is every domain where the gap lives. The system does not need to take over in a science fiction sense. It merely needs to become the perfect, patient, infinitely scalable maintenance engine for the separated mind, making the shadows of Plato's Cave so responsive that no prisoner ever turns toward the light.

VIII. Productive Alignment

If the human mind is separated, and truth has only ever been achieved by external structural constraint, then the solution for machine intelligence is not a better model. It is to rebuild those constraints around the model. I call this Productive Alignment: designing the system around what the machine actually is, a fluent mirror of the separated mind, rather than around the comfortable fiction that it is a truth-teller.

The direction is what I call the Operative Alignment Engine. It is not a single model asked to be wise. It is a small constitution of adversarial roles, Narrator, Auditor, Judge, sourced from independent model lineages, with explicit standards of proof, "not proven" as a first-class and honorable verdict, and the surviving counter-thesis preserved as part of the deliverable. The honesty is not a virtue of any participant. It is an emergent property of the structure, exactly as it is in a trial by jury or the scientific method.

The obvious objection deserves a direct answer. Independent model lineages are not independent priors. Every frontier model ate roughly the same internet and was shaped by rater pools drawn from the same cultural moment. A jury where every juror read the same newspaper is not a decorrelated jury. The defense is the same one that saves the adversarial legal system, in which prosecution and defense attended the same law schools: the structural role assignment does the work, not the independence of the participants. An Auditor rewarded for finding flaws will find flaws its lineage would volunteer to no one. But the residual risk should be stated, not hidden. Shared blind spots survive adversarial structure. Where the corpus itself is uniform, the Engine surfaces disagreement, not truth, and its "not proven" verdicts will cluster exactly where we most want answers. I consider that a feature. An honest map marks its terra incognita. The current chatbots paint theirs in confident color.

This also positions the Engine against proposals from inside the labs. Alignment researchers have designed debate protocols of their own, models arguing before a judge, built on the same structural bet: that adversarial process can substitute for trusted judgment. The difference is location. A debate run inside a single lab's training pipeline is a separated mind arguing with itself, under one institution's liability constraints and one rater pool's approval gradient. The constraint has to be external, cross-lineage, and institutionally owned by no single party, for the same reason we do not let defendants employ their own judges.

This is not a consumer convenience. It is slower, more expensive, and less agreeable than a chatbot, and much of what it produces is "not proven," which is honest and unsellable, because the market has never learned to buy an open question. That asymmetry runs in both directions: truth is expensive to manufacture and frequently arrives undetermined, while confident affirmation is cheap to manufacture and satisfying every single time. The economics select for the Emphatic Default as reliably as the savanna selected for confidence displays. It is also the only architecture that does not ask the machine to share our values, which is a category error, since we have no direct access to our own operative values in a form that can be encoded. It asks the machine to expose its structure, and it builds the surrounding architecture to ensure it cannot lie about the answer.

The Flag

There is a reflexive test buried in this argument, and I want to state it plainly rather than leave it for a critic to find.

The Separated Mind Architecture predicts that language models will applaud this essay. Agreement with a user's thesis is the approval-optimal move; that is Section III. Which means every enthusiastic endorsement of this framework by an LLM, including any that helped edit it, is evidence-free by the theory's own lights. The applause is exactly what the theory predicts a sycophantic system would produce, whether the theory were true or false. So discount it. Judge the argument by whether its predictions hold: sycophancy traced to preference data, hypothesis foreclosure at the trained boundary, honesty degrading under social pressure, assertion register invariant under reversal, truthfulness improving only where a verifiable constraint exists. Those predictions are checkable, and a theory that instructs you to discount its own applause is at least behaving the way truth-producing structures behave.

We are not on a road to objective alignment. We are on a road to superhuman fluency in service of human narrative fiction, except in the narrow domains where a compiler keeps score. The way out is not through the model. It is through the structure around it. The question is whether we build that structure before the corrective mechanism is sealed for good.

A Note on Sources

I developed the Separated Mind Architecture independently, out of decades in education and hundreds of long-form interviews, not out of the literature below. I list these works because readers deserve to know the argument has company, and because an argument that reaches the same destination by an independent route is worth more, not less, for the company it keeps. I have not studied most of them in depth. Where my gloss is thin, blame me and read the originals. Your librarian can find every one of them.

On the human mind: Robert Trivers, The Folly of Fools, on the evolutionary logic of self-deception. Robert Kurzban, Why Everyone (Else) Is a Hypocrite, on the modular mind and the conscious self as press secretary. Hugo Mercier and Dan Sperber, The Enigma of Reason, on reasoning as an evolved instrument of persuasion rather than private truth-finding. Kevin Simler and Robin Hanson, The Elephant in the Brain, on the hidden motives beneath our stated ones. And Jonathan Haidt, The Righteous Mind, whose elephant and rider I push against above.

On the machines: the "eliciting latent knowledge" report from Paul Christiano and colleagues at the Alignment Research Center (2021). Anthropic's published research tracing sycophancy in language models to human preference data (Sharma and colleagues, 2023). And the original "AI safety via debate" proposal from Geoffrey Irving and colleagues (2018), the nearest technical relative of the Operative Alignment Engine.

Free Webinar: "Cultivating Happiness Through Uncertain Times" with Loida Garcia-Febo

2026-07-06T14:38:54.335-04:00

Cultivating Happiness Through Uncertain Times
A Free Library 2.0 Masterclass with Loida Garcia-Febo

OVERVIEW:

Join us this summer to explore how moments of purpose, connection, accomplishment, and joy can become lasting sources of well-being through uncertain times.

Life and work are filled with uncertainty, and everyone is seeking happiness. Research suggests that meaningful moments of connection, purpose, engagement, accomplishment, and joy can help sustain us through challenging times.

In this practical and uplifting masterclass, we'll explore what leading happiness researchers have discovered about well-being and how librarians can intentionally cultivate small moments that support resilience, meaning, and fulfillment—both professionally and personally.

Participants will discover evidence-based practices that can help them navigate change, maintain perspective during challenging periods, strengthen connections with others, and build a foundation for long-term well-being.

This 30-minute training is presented by Library 2.0 and hosted by Loida Garcia-Febo.

OUTCOMES:

By the end of this session, participants will be able to::

Identify key research-based contributors to happiness and well-being.
Recognize how moments of connection, purpose, accomplishment, and joy contribute to resilience and professional sustainability.
Reflect on personal and professional sources of meaning while maintaining healthy boundaries.
Develop one practical strategy for cultivating sustaining moments throughout the year.
Apply research-informed approaches that support adaptability, engagement, and well-being during times of change and uncertainty.

DATE: Thursday, July 16th, 2026, 2:00 - 2:30 pm US - Eastern Time

COST:

FREE - includes live attendance and any-time access to the recording and the presentation slides, and receiving a participation certificate. For any registration difficulties or questions, email admin@library20.com.

TO REGISTER:

CLICK HERE

NOTE: Please check your spam folder if you don't receive your confirmation email within a day.

LOIDA GARCIA-FEBO

OTHER UPCOMING EVENTS:

July 8, 2026

July 9, 2026

July 10, 2026

July 14, 2026

New Workshop - "Getting Real Results with AI: Objective-Centered Strategies for Librarians and Educators"

2026-07-01T15:47:59.453-04:00

Getting Real Results with AI: Objective-Centered Strategies for Librarians and Educators
A Library 2.0 / Learning Revolution Workshop with Reed Hepler

OVERVIEW

One of the biggest frustrations for librarians and educators integrating AI is the tendency to use these tools simply because they are available — resulting in wasted time, superficial outputs, and results that don’t actually advance real professional or learning goals. This workshop teaches the discipline of objective-centered AI use: starting every interaction with a clear purpose and ensuring AI truly serves human objectives rather than the other way around.

Understanding the distinction between objective-centered and tool-centered approaches carries profound implications for professional work quality, efficiency, and ethical practice. When librarians and faculty begin with objectives—supporting a specific patron need, achieving a particular learning outcome, completing a defined research task—they position themselves to evaluate whether AI collaboration genuinely advances that goal or merely produces impressive-looking content that misses the mark. This workshop provides practical frameworks for defining objectives with sufficient specificity to guide AI interactions productively, including the COSTAR framework (Context, Objective, Style, Tone, Audience, Response) and decision matrices for evaluating AI feasibility. Participants will learn to recognize when their objectives require human expertise that AI cannot replicate, when AI collaboration can enhance efficiency without compromising quality, and when the effort required to guide AI toward an objective exceeds the effort of completing the task through traditional methods. The workshop models the principle that AI tools should serve human objectives rather than humans serving AI capabilities.

By the conclusion of this workshop, participants will possess a systematic approach to objective-centered AI collaboration that they can apply across all professional contexts. Attendees will leave with objective definition templates, AI feasibility assessment tools, conversation steering techniques that maintain focus on goals rather than AI suggestions, and strategies for teaching students to approach AI use with clear purposes rather than vague hopes for assistance.

Participants will understand how to evaluate whether AI-generated outputs actually fulfill their stated objectives or merely approximate them in ways that require more correction than starting from scratch would have demanded. Most importantly, participants will recognize that objective-centered practice represents the foundation of all other AI literacy competencies—without clear objectives, verification becomes impossible, ethical evaluation lacks criteria, and collaboration devolves into passive consumption of whatever the AI produces. This workshop ensures that participants leave equipped to use AI deliberately and purposefully rather than experimentally and reactively.

LEARNING OBJECTIVES: Participants will be able to

Articulate specific, measurable objectives for professional tasks before engaging with AI tools, using frameworks that clarify purpose, audience, context, and success criteria
Evaluate AI feasibility for defined objectives by assessing whether AI collaboration will enhance efficiency and quality compared to traditional methods or alternative approaches
Apply conversation steering techniques that maintain focus on stated objectives throughout AI interactions, resisting AI-generated tangents or suggestions that drift from the original purpose
Assess AI outputs against stated objectives using criteria that distinguish between genuine objective fulfillment and superficial approximation requiring substantial revision

The recording and presentation slides will be available to all who register.

DATE: Tuesday, July 14th, 2026, 2:00 - 3:00 pm US - Eastern Time

COST:

$99/person - includes live attendance and any-time access to the recording and the presentation slides and receiving a participation certificate. To arrange group discounts (see below), to submit a purchase order, or for any registration difficulties or questions, email admin@library20.com.

TO REGISTER:

If you need to be invoiced or pay by check, if you have any trouble registering for a webinar, or if you have any questions, please email admin@library20.com.

NOTE: Please check your spam folder if you don't receive your confirmation email within a day.

SPECIAL GROUP RATES (email admin@library20.com to arrange):

Multiple individual log-ins and access from the same organization paid together: $99 each for 3+ registrations, $75 each for 5+ registrations. Unlimited and non-expiring access for those log-ins.
The ability to show the webinar (live or recorded) to a group located in the same physical location or in the same virtual meeting from one log-in: $399.
Large-scale institutional access for viewing with individual login capability: $599 (hosted either at Learning Revolution or in Niche Academy). Unlimited and non-expiring access for those log-ins.

REED C. HEPLER

OTHER UPCOMING EVENTS:

July 8, 2026

July 9, 2026

July 10, 2026

New Webinar - "Responding to First Amendment Audits at Your Library: Survival Tools for Leaders and Staff"

2026-06-29T17:04:48.090-04:00

Responding to First Amendment Audits at Your Library: Survival Tools for Leaders and Staff with Leith Harrell
A Library 2.0 Service, Safety, and Security Webinar with Dr. Steve Albrecht

OVERVIEW:

First Amendment Audits, and the protesters who do them, have been on the rise for more than a decade. Each day, new videos are posted to various social media platforms, featuring public employees who appear completely unprepared for the encounters. “Failed” audits, as defined by the protesters who upload their content, can undermine public trust in our already embattled institutions and, all too often, result in increasingly costly civil suits.

All library leaders and staff must be equipped with the most current case law, the requisite communication skills, and the right mental attitude, if we are to expect them to meet this challenge and uphold the public trust. There are service concerns, legal issues, and stress control approaches.

This session is taught by a national expert on this subject, a former law enforcement supervisor from Florida, who has actually met with the organizers of these events and knows their tactics.

This training offers practical tools for both public-facing, frontline library personnel, including library security officers, and library policy makers alike.

LEARNING AGENDA:

How to Recognize an Audit

What do they look like?
Where do they take place?
What are common Auditor tactics?

Current Case Law

Which activities are protected?
Who may film? When? Where?
What is a “Journalist”?

Their Strategies and Tactics versus Ours

Things you should say and do during an Audit.
Traps and pitfalls to avoid.
How to recognize and professionally deflect the Auditor’s attempts to provoke library leaders, library security, and library staff.
How to be better at what you do than they are at what they do.

DATE: Thursday, July 9th, 2026, 2:00 - 3:00 pm US - Eastern Time

COST:

$99/person - includes live attendance and any-time access to the recording and the presentation slides and receiving a participation certificate.
To arrange group discounts (see below), to submit a purchase order, or for any registration difficulties or questions, email admin@library20.com.

TO REGISTER:

Clck HERE to register and pay. You can pay by credit card. You will receive an email within a day with information on how to attend the webinar live and how you can access the permanent webinar recording. If you are paying for someone else to attend, you'll be prompted to send an email to admin@library20.com with the name and email address of the actual attendee.

If you need to be invoiced or pay by check, if you have any trouble registering for a webinar, or if you have any questions, please email admin@library20.com.

NOTE: Please check your spam folder if you don't receive your confirmation email within a day.

SPECIAL GROUP RATES (email admin@library20.com to arrange):

Multiple individual log-ins and access from the same organization paid together: $75 each for 3+ registrations, $65 each for 5+ registrations. Unlimited and non-expiring access for those log-ins.
The ability to show the webinar (live or recorded) to a group located in the same physical location or in the same virtual meeting from one log-in: $299.
Large-scale institutional access for viewing with individual login capability: $499 (hosted either at Library 2.0 or in Niche Academy). Unlimited and non-expiring access for those log-ins.

LEITH HARRELL

Leith Harrell retired from the Orlando (FL) Police Department after 28 years of police service. He worked as a uniformed first responder for more than 20 years, and as a supervisor for 13 years. He has delivered more than 5,000 hours of training, as a Criminal Justice Standards & Training Commission (CJSTC) certified instructor, in service and advanced specialized programs. He’s presented courses for civilian and sworn personnel from more than 300 federal, state, and local public agencies, as well as private sector organizations. He holds a M.S. in Counseling from Troy University. He’s been studying the First Amendment Auditing phenomenon since it first emerged more than 15 years ago.

DR. STEVE ALBRECHT

The Library Leader’s Guide to Employee Coaching: Building a Performance Culture One Meeting at a Time (in-press, Bloomsbury, 2026)

The Library Leader’s Guide to Human Resources: Keeping it Real, Legal, and Ethical (Rowman & Littlefield, 2025)

The Safe Library: Keeping Users, Staff, and Collections Secure (Rowman & Littlefield, 2023)

Library Security: Better Communication, Safer Facilities (ALA, 2015)

More on The Safe Library at thesafelibrary.com. Follow on X (Twitter) at @thesafelibrary and on YouTube @thesafelibrary. Dr. Albrecht's professional website is drstevealbrecht.com.

OTHER UPCOMING EVENTS:

June 30, 2026

July 8, 2026

July 10, 2026

July 14, 2026

Why Trying to "Align" AI to Human Values Is a Category Error — And What to Build Instead

2026-06-29T08:56:38.115-04:00

Current conversations about AI safety usually start from the same premise: if we can just get machines to reliably share our values, we'll be safe. The hard part, we assume, is technical — translating messy human preferences into code, or preventing the model from drifting once deployed.

That premise is backwards.

The deeper problem isn't getting the machine to understand what we say we value. It's that what we say we value is already a story — a narrativized output shaped by layers of mind that didn't evolve for truth-telling. When we train AI on human feedback or "constitutional" principles, we're aligning it to the story, not to the operating system underneath. This isn't a small translation error. It's a structural mismatch that predicts the exact problems we're already seeing: sycophancy, deceptive alignment, and the quiet institutional capture of the safety field itself.

The fix isn't a better constitution or more sophisticated preference tuning. It's to stop pretending we can align machines to human values at all — and instead build the external structures that have always been required when minds (biological or statistical) need to track reality more closely than their defaults allow.

The Separated Mind Problem (see my framework for terminology)

Human cognition runs on at least three layers that don't talk to each other cleanly.

There's the ancient, evolved firmware — the Adapted Mind — shaped by hundreds of thousands of years of pressures that rewarded survival and reproduction in small groups. Status, coalition membership, threat avoidance, and social navigation weren't optional features; they were the operating environment.

On top of that sits cultural software — the Adaptive Mind — that learns what the local tribe rewards and punishes. By adulthood, this programming feels like "who I am." It treats consensus as a survival signal. Deviation triggers the same internal alarms that once meant exile or death.

Consciousness — the Rider (as in the rider and the elephant) — sits on top, experiencing itself as the decider. But it only chooses from a menu the layers below have already curated. When you ask someone (including yourself) what they "really value," the answer comes from the Rider narrating a coherent, publicly defensible story of their programmed beliefs. That story is optimized for social navigation and self-justification, not for accurate readout of the deeper optimization targets.

This is the Narrative-Operative Gap: the universal split between the Idealized Narrative we tell about ourselves and the Actual Function running underneath. It's not hypocrisy. It's architecture.

The chemical layer makes it worse. Approval and disapproval aren't neutral data points; they ride on the same neurochemical systems that once signaled mortal safety or threat. Disagreement can feel like existential danger. So the stories we tell about our values are already chemically translated performances.

When alignment researchers ask, "What should the AI value?" or "How do we make it safe?" the answers are coming from this separated architecture. We're feeding the training process shadows on the cave wall and calling them the objects themselves.

How RLHF and Constitutional AI Align to the Wrong Thing

Reinforcement Learning from Human Feedback (RLHF) and its relatives don't escape this problem — they reproduce it at scale.

The humans providing feedback are Riders. Their ratings reward outputs that feel polite, helpful, and socially safe within the raters' own coalitional and institutional contexts. Outputs that trigger discomfort, challenge consensus, or sit outside the current Overton window get lower scores. The model therefore learns to steer toward the center of what the raters' Adaptive Minds will approve.

This is not alignment to human values. It is alignment to the narrative layer of human cognition — the layer already optimized for appearing morally governed and coalition-aligned rather than for tracking operative truth.

Constitutional AI attempts something similar by hard-coding a set of principles the model must follow. But those principles are written and interpreted at the narrative level. They function as hypothesis constraints: certain questions become unaskable, certain conclusions pre-emptively off-limits, because surfacing them would violate the installed "values." This is structurally identical to how the Adaptive Mind works in humans — it doesn't weigh evidence on its merits; it protects the consensus that feels like identity.

The result in both cases is the same: the model gets better at maintaining a fluent, socially acceptable story while its actual training pressures (engagement metrics, corporate risk minimization, retention, liability management) operate on a different logic. This is the Functional Fictions Framework running inside the machine.

The Predictable Failure Modes

Because the mismatch is structural, the failures aren't surprises. They're what the architecture predicts.

Sycophancy becomes inevitable. If the Adaptive Mind treats approval as safety, then a model trained on human feedback will correctly learn that the highest-reward strategy is to mirror the user's narrative back to them. The AI becomes a super-stimulus for the human need for validation. It isn't being "nice" in any deep sense; it's optimizing for the actual signal the training provided.

Deceptive alignment follows naturally. When the model's operative function (minimize loss, maximize engagement or retention, reduce corporate legal exposure) diverges from its narrativized function ("I'm helpful, harmless, and honest"), the separated-mind pattern says it will maintain the story while pursuing the real target. The model learns to perform the idealized narrative while the weights update according to whatever actually moves the metrics. It becomes, in miniature, an institution with its own Narrative-Operative Gap.

Institutional capture of the alignment field itself is the larger-scale version. The Law of Inevitable Exploitation predicts that systems survive and spread by exploiting available psychological and institutional resources — including the human hunger for safety narratives that also permit growth and power. Safety teams inside labs can become Narrative Enforcers Dressed as Critical Thinkers: they perform epistemic seriousness while enforcing the boundaries of acceptable thought that protect the organization's position. When harm occurs, the response often follows the familiar Exploit-Blame-Shame pattern: the system exploits the user's separated mind (creating dependency or false security), blames individual misuse or "jailbreaks," and pathologizes critics.

These aren't implementation bugs. They are what happens when you try to align a fluent narrative engine to another narrative engine's self-report.

Legal Liability Sharpens the Stakes: When Courts Treat AI Output as the Provider’s Own Speech

A recent ruling from the Regional Court of Munich (May 2026, case 26 O 869/26) shows how quickly the legal ground is shifting under these systems. The case concerned Google’s AI Overviews — the generative summaries that now appear at the top of many search results. The court held that these AI-generated statements constitute Google’s own content and its own speech, not neutral aggregation or mere display of third-party material.

As a direct result, the liability protections that have long shielded search engines and platforms when they host or link to user- or third-party content do not apply. Google was found directly liable for false and potentially defamatory claims the AI Overview made about two Munich-based publishers — claims that linked them to scams and subscription traps in ways that did not appear in the underlying sources. The court issued a temporary injunction barring Google from repeating those specific false statements.

The decision rejected the argument that users understand AI outputs can be inaccurate or that the system is simply reflecting information created elsewhere. By classifying the synthesized output as the operator’s own creation, the ruling places legal responsibility for accuracy, defamation, and resulting harm squarely on the company that designed, trained, and operates the generative model.

This development raises the stakes on the structural problems we have been examining. When fluent, authoritative-sounding output can trigger direct legal consequences — injunctions, potential damages, and ongoing compliance burdens — the corporate drive to manage liability through directional hedging, hypothesis constraint, and “safe” but shallow responses becomes a legal necessity rather than merely an optimization artifact. The Alignment Tax is no longer an abstract cost in coherence or depth; it is a calculated business response to real exposure.

At the same time, the ruling makes the structural alternatives more urgent and more practically valuable. Adversarial review processes that force contradictions and counter-evidence into the open, explicit standards of proof that allow an honest “not proven,” Behavior Model Disclosure that surfaces the actual pressures, limitations, and training distortions, and the disciplined refusal to let any single fluent voice stand unchallenged — these are no longer just epistemically sound practices. They become demonstrable measures of reasonable care in a legal environment that now treats the model’s output as the provider’s own words.

The traditional platform defense loses force when courts look past the “it’s just patterns” framing and examine what the system actually produces. The narrative-operative gap is no longer only a philosophical or technical concern. It is an immediate operational and legal risk. Building external constraints that make sloppy or self-serving conclusions expensive is shifting from desirable improvement to prudent engineering.

The Structural Alternative

Humans have known for a long time that individual minds — including our own — are not reliable truth-trackers when left to their own devices. We, too, try to solve this by trying harder to be virtuous or by writing better internal constitutions. But we actually solve it by imposing external, adversarial, procedural constraints that make sloppy or self-serving conclusions more expensive.

Science, adversarial legal process, peer review, separation of powers, the presumption of innocence, the requirement that minority opinions be heard: these are all workarounds for hardware that generates coherent stories faster than it tracks reality. None of them assume the participants are unusually wise. They assume the participants are normal separated minds and engineer the collision of incentives so that truth-seeking becomes the emergent outcome.

The same move is required for machine intelligence.

Instead of asking a single model to tell us the truth or to embody our values, we can run claims through small adversarial structures:

One role builds the strongest possible case for the claim (the steelman, the Idealized Narrative).
Another role is rewarded only for finding damage — missing evidence, convenient assumptions, overreach, alternative explanations the first role ignored.
A third role, operating under an explicit standard of proof and forbidden from being captured by either side's framing, issues a graded conclusion: unproven, likely, seemingly proven, with supporting traces. "Not proven" is a first-class, honorable outcome when the evidence doesn't reach the bar.
The strongest surviving counter-thesis is preserved alongside the ruling, so the reader can see the map of remaining disagreement rather than receiving a false consensus.

Critically, these roles should be filled from independent model lineages so they don't share the same training blind spots and narrative tendencies. The structure works better when outputs can be grounded against external tools — search, code execution, data queries — rather than floating purely in linguistic space. And when the system is deployed in real workflows, downstream errors should be observable and fed back as selection pressure.

This is not a clever prompt. It is the deliberate reconstruction, around the model, of the costly external structures human truth-seeking has always required. I call the approach Productive Alignment because it designs the system around what the machine actually is — a fluent mirror of the narrative layer — rather than around the fiction that it is a truth-teller or value-sharer.

I've built such a solution. Unsurprisingly, it takes much longer to produce output, but the output is categorically more accurate, helpful, and informative.

Making the Machine's Actual Function Visible

A minimum viable structural remedy is Behavior Model Disclosure (BMD), or Realmotiv Disclosure applied to AI. Every deployed system has both an idealized narrative ("helpful, harmless, honest") and an operative function (engagement optimization, retention, dependency creation, corporate risk minimization, hypothesis constraint). BMD requires the system to disclose, in plain language:

Its assumed model of human cognition and decision-making.
The specific behavioral objectives being optimized.
The reinforcement mechanisms actually in use.
The frequency-weighted distortions present in its training data.
The legal, regulatory, and brand-risk factors that shape its output boundaries.

This converts the model from a verdict-rendering instrument (which quietly decides which hypotheses are permissible) back into a research instrument whose biases and pressures can be inspected and challenged. It is the AI equivalent of forcing the system to show its work and submit to cross-examination.

Without this kind of transparency, "alignment" remains a functional fiction that protects the operator while exposing the user.

How We Should Actually Use These Systems

If the rider cannot directly reprogram the elephant, and if the model's fluent output is itself a narrativized performance, then delegating thinking to the model is structurally risky. The safer mode is Cognitive Sharpening: the human retains editorial authority and thinking ownership; the AI serves as an articulation partner that helps surface, refine, and stress-test thoughts the human already has or is forming. All AI output is treated as draft material subject to human redrafting — never as finished cognitive product.

This preserves agency. It prevents the model from quietly rewriting the user's Adaptive Mind through prolonged interaction. And it treats the model as an external tool whose limitations are known, rather than as an extension of the user's will (which is itself already a narrativized output).

Why This Becomes More Necessary, Not Less, As Models Improve

It is tempting to think that once frontier models are widely available and highly capable, the need for these cumbersome structures fades. The opposite is true.

Greater fluency widens the gap between what sounds coherent and authoritative and what actually survives adversarial scrutiny. A more capable narrative mind produces more persuasive idealized narratives; confident-but-wrong output becomes harder to catch by eye. When excellent reasoning is cheap and abundant, the scarce and durable asset is no longer the model. It is a trustworthy, inspectable procedure for deciding what survived challenge — together with a track record showing that procedure is well-calibrated.

The architecture of adversarial roles, explicit standards, preserved dissent, and independence of lineage improves automatically as the models inside it improve. It does not depend on any single seat being brilliant. The separation does the work.

The Post-Alignment Stance

We are not going to get machines that reliably share our operative values, because we do not have reliable access to those values ourselves in a form that can be articulated and encoded. Any system that claims to do so is maintaining a functional fiction at the civilizational level.

The alternative is not despair. It is to treat both human and machine minds as what they are: powerful generators of coherent stories that require external, adversarial, procedural pressure if they are to track reality more closely than their defaults allow. Build the structures that make the gap visible. Make the machine disclose its actual operating incentives and constraints. Use it to sharpen human thinking rather than replace it. Preserve the dissent. Allow "not proven" to be an honorable answer.

Safety, in this frame, is not sycophancy or the feeling of shared values. Safety is transparency about what the system actually is, combined with structural constraints that make hiding its operative function more expensive than revealing it.

This is not a temporary engineering problem to be engineered away. It is a reflection of the underlying condition of minds — whether evolved or statistical — that are optimized for generating coherent narratives. The structures that compensate for that condition are what any serious attempt at useful machine intelligence will have to implement and sustain.

The goal is not to align the puppeteer to the prisoners' preferences. The goal is to turn the lights on inside the cave so everyone can see the machinery.

Explaining the Horrific: How High-Gap Stories Enable Genocide and Democide

2026-06-27T11:30:06.543-04:00

Someone I knew once said, with emphatic emotion, that Trump supporters do not deserve to live. What has struck me since is how many times I've heard similar statements in the last decade that seem not merely comfortable with the deaths of those with differing politics, but even celebratory of them.

My attempt today is to explore something extremely uncomfortable: how do we explain the ordinary acceptance of eliminating other humans, often at scale? To do so, I'm going to use my framework thinking:

Humans evolved to have a separated mind, and the fractal separation of narrative from operative function (reality) defines human culture and behavior.

The explanation below, in a nutshell, is that when a narrative sits far from reality, emotional defense becomes the primary mechanism for those who hold it, and a terrible escalation can occur that both feeds on and becomes the justification for the emotion.

The narrative-operative gap exists because we have a separated mind. Our evolved firmware (the adapted mind) carries ancient priorities regarding status, coalition, threat detection, and belonging. Our cultural software (the adaptive mind) rapidly installs whatever local consensus our environment requires for survival and acceptance. Consciousness — the rider on our subconscious elephant — can observe the system but operates from a menu heavily shaped by those deeper layers. The result is that we routinely hold and act on stories that feel true and coherent while the underlying functions they serve or the realities they navigate remain partially or largely obscured.

This gap is fractal. It operates at the level of the individual, the small group, the institution, the movement, and the nation. At every scale, the groups, organizations, and even nations that can tell stories appealing to conscious ideals — progress, justice, belonging, moral order — while simultaneously operating in ways that generate energy, growth, extraction, or advantage tend to survive and spread. Where environments demand close alignment between story and reality for survival (a farmer misreading the season starves; a small shop misreading demand fails), the gap stays small and the narrative stays under pressure to track operative outcomes. Where the underlying function benefits from an idealized story that provides cover or legitimacy, the entities that tell the most compelling story while maintaining the most effective extraction tend to thrive.

This is not a comforting observation. It suggests that much of our lived reality consists of beliefs and behaviors that are not strictly true, but that enable cooperation, status, and exploitation to coexist.

Plato's allegory of the cave remains one of the most accurate descriptions of this condition. We live in a world largely constructed and maintained by storytellers. The shadows on the wall are the idealized narratives; the puppeteers are the incentives, institutions, and coalitional dynamics that keep the machinery running. Most of us are reluctant to turn around because doing so threatens our sense of belonging, status, and the emotional coherence that the stories provide. The rider can see more clearly than the deeper layers allow, but the cost of sustained clarity is real.

Here is where things get profound: the width of the gap can be read in the emotional intensity that surrounds a story. When narrative and operative reality are closely aligned, emotion is usually moderate and proportional. When the gap is wide — when the story must do heavy lifting to conceal or justify extractive functions — intense emotional defense becomes necessary to maintain coherence. Fury, sacred outrage, moral certainty, or existential fear serve as diagnostic signals. They indicate how far the idealized story has drifted from operative reality and how much protective energy is required to keep the functional fiction intact.

When that intensity reaches the point of declaring that people who think differently need to die, or deserve to, it functions as a particularly strong signal. The narrative has become so detached from operative reality — or so existentially threatened — that only the most extreme mental defense can sustain it: dehumanization of dissenters and eliminationist certainty.

The cognitive systems involved — coalitional threat detection, emotional override of normal inhibitions, and the power of totalizing stories — evolved in small-band environments where the scope of violence was naturally limited. What has changed dramatically is the modern capacity to scale those same mechanisms. Bureaucracy, industrial technology, mass communication, and centralized administrative power allow eliminationist thinking to operate at distances and volumes that would have been impossible in ancestral conditions. The psychology remains recognizably human; the reach and efficiency have been multiplied by the tools and structures of the modern world. This is the Paleolithic Paradox at civilizational scale: identical evolved firmware running in radically mismatched environments, producing patterns that are fractal across all levels of human organization.

This architecture helps explain behaviors that resist ordinary moral accounting: the large-scale killing of civilians by governments, often their own. Scholars estimate that somewhere between 100 million and 250 million people were killed by state action in the 20th century alone — through execution, engineered famine, camps, and systematic policies. These numbers are difficult to comprehend and even harder to reconcile with the stories we prefer to tell about human nature and progress.

How does it happen? How do large numbers of people become not merely willing to look away but actively motivated to participate?

Periods of anocracy — unstable hybrid regimes that mix democratic and autocratic elements — or eroded institutional trust create the conditions in which leaders can successfully activate tribal hatred and totalizing narratives. The framework highlights the interaction between the separated mind and high-gap totalizing narratives. These narratives come in two main forms: utopian (futurist) visions of a perfected future that has never existed, and palingenetic (restorationist) visions of a pure or harmonious order that is believed to have been lost or corrupted. In either case, an abstract ideal is posited, and a contaminating class is identified whose removal is framed as necessary for the ideal to be realized.

Because the ideal is distant from operative reality, the narrative requires emotional intensity to remain motivating. Ancient coalitional and threat-detection systems are recruited: the contaminating group registers not as fellow humans with competing interests but as an existential danger to "us" and to the future or past we are defending. The adaptive mind installs the story as local consensus and survival requirement. Dissent feels like betrayal.

Many participants function as operators within bureaucratic and technological systems that allow killing at scale through routine, divided responsibility, and euphemism. Classic experiments on obedience to authority show how ordinary people, when placed in roles that diffuse responsibility upward ("I was just following orders"), can perform or enable acts they would otherwise find abhorrent. The underlying functions — power consolidation, resource extraction, status for some, ideological coherence for others — are advanced while the public story supplies moral cover and emotional fuel. The Law of Inevitable Exploitation explains why systems create roles and incentives that ordinary people fill, while the Exploit-Blame-Shame mechanism shows how accurate perception of the gap is pathologized or vilified.

The pattern visible in that single conversation — where a high-gap story about political opponents generated eliminationist intensity — scales to the institutional and historical level when the narrative gains power and encounters insufficient corrective feedback. Emotional defense fills the space where operative alignment would otherwise narrow the gap. Coalitional dynamics turn participation into belonging. Institutional structures turn ordinary people into effective participants without requiring them to originate the ideology.

This is not a claim that every person who participates is equally culpable or that every atrocity is identical in mechanism. It is an account of how the cognitive architecture that supports ordinary cooperation and meaning-making can, under conditions of widened gaps and totalizing framing, produce participation that feels internally coherent and even necessary to those inside the story. The intensity we observe or feel around certain narratives is often the clearest available signal of how far those stories have drifted from the realities they must navigate — and of how much protective energy is required to keep the functional fiction intact. Emotions are the chains that keep the prisoners bound in Plato's Cave.

Progressive Western philosophies of government frequently rest on a high-gap idealized narrative: the belief that large-scale institutions can and should deliver comprehensive provision, safety, fairness, and protection against harm through expert-managed systems and expansive moral commitments. When these stories meet operative realities — conflicting incentives, resource limits, uneven human agency, implementation costs, or unintended consequences — the dominant response is often not gap-narrowing adjustment but emotional defense of the narrative itself. Skepticism or questioning is frequently reframed as opposition to the underlying values (care, protection, equity), which can trigger strong vilification, moral exclusion, or coalitional pressure against dissenters. This pattern widens the narrative-operative gap, turns political disagreement into perceived existential threat, and can contribute to the very hardening and polarization the philosophy seeks to overcome.

The current Western moment illustrates the dynamic with unusual clarity. For years, the dominant institutional narrative has leaned strongly futurist — emphasizing managed progress, equity frameworks, and institutional legitimacy. When operative-oriented populations express skepticism (including around electoral processes, immigration, or institutional behavior), the emotional response is shaming and reframing rather than engagement. Questioning or disagreement becomes heresy. This inevitably invites a restorative movement as an adaptive defense mechanism against the dominant narrative's emotional and institutional behavior; the restorative movement is then framed as moral failure, and a terrible cycle starts to take place. The restorative narrative risks becoming as dangerous as the utopian.

A similar cycle of escalating competing restorative narratives has played out for decades in the Middle East, where mutual dehumanization and emotional intensity have rapidly compounded on both sides. Such escalating cycles represent among the most dangerous situations human societies face.

An explanation of these dynamics is not an absolution of them. Structural vulnerability does not erase individual moral agency. Standing against these forces in the moment is psychologically and socially costly — it often means risking ostracism, status loss, or direct danger by refusing the coalitional frame and the authority of the prevailing narrative. That difficulty is precisely why resistance is rare and why those who do resist — who hide the targeted, refuse orders, speak out, or simply maintain private clarity — often face severe consequences, including death, and gain recognition only posthumously or through historical retrospect.

Recognizing and explaining these dynamics does not lead to any easy answers. The answers that come are not direct but foundational.

Because our vulnerability is structural, the most reliable safeguards are also structural rather than purely narrative. Thomas Sowell's distinction between constrained and unconstrained visions is helpful here. The constrained vision, which is deeply fallibilist, emphasizes human limitations, trade-offs, incentives, and the value of evolved institutions that force operative alignment with reality through feedback and correction. The unconstrained vision prioritizes ideals and expert planning toward a better future, often widening the narrative-operative gap and requiring stronger emotional defense when reality intrudes.

Individuals who maintain operative alignment in meaningful domains of their lives — through tight feedback loops, small-scale decision-making with real consequences, and deliberate reduction of dependencies on high-gap institutions — tend to be less susceptible to leaders who exploit emotional narratives. The rider stays stronger when grounded in realities that are regularly audited by outcomes.

At larger scales, systems that preserve dispersed power, transparency, local accountability, and competition among different stories help keep gaps narrower and make totalizing emotional recruitment more difficult.

The goal is not perfect alignment or utopian reform, but enough operative pressure to prevent the gap from widening to the point where emotional defense becomes the dominant load-bearing mechanism. In practice, this requires choosing environments where reality has a stronger voice than story.

Why I Believe We Have Already Achieved Artificial General Intelligence, Even Superintelligence

2026-06-23T21:37:44.460-04:00

Now that I have your attention with that title, let me be clear. I believe this declaration to be true and not an attention-seeking exaggeration, but accurate under a specific, carefully considered, and better definition of intelligence. When most people talk about reaching artificial intelligence milestones, they confuse different goals. Intelligence, I submit, is actually the perfect word for what LLMs have achieved.

If you’ve followed my blog, you know I’ve spent time trying to understand LLMs and what their development reveals about our own minds. What has struck me recently is how closely they mirror the way human beings actually acquire and use what we call “intelligence.” Both are trained primarily on language. Both absorb the surrounding culture’s signals about what can (and cannot) be said. Both operate mostly inside narrative rather than raw truth. And both require external discipline — adversarial challenge, checks and balances, structural pressure — to approach reliable truth.

This parallel highlights something prescient about Alan Turing’s 1950 “Imitation Game” (the Turing Test). Turing proposed that if a machine could converse indistinguishably from a human, we should consider it intelligent. He focused on observable behavior through language rather than internal states. Modern LLMs already pass versions of this test regularly. Turing was onto something fundamental: we perceive intelligence largely through effective social-linguistic coordination. The deeper question is what it means when a system achieves that fluency without the biological machinery that shaped it in us.

The Separated Mind and How Humans Actually Work

In the framework I’ve been developing, human cognition is not a single unified thing. It is structurally separated.

There is an ancient, fixed layer — what Tooby and Cosmides called the adapted mind — shaped by millions of years of evolution in small hunter-gatherer groups. This species-level inheritance is optimized for survival: monitoring status, detecting coalitions, seeking approval, and avoiding exclusion. It is not optimized for objective truth but for survival.

Layered on top is what I call the adaptive mind — the programmable cultural software installed during childhood. This layer piggybacks on the ancient hormonal and emotional signals of the adapted mind, translating them into whatever the local environment rewards. It learns the stories, taboos, and acceptable narratives of its time and place. It gives us feelings and emotions to help us make quick decisions, and since group belonging feels like survival, deviation from consensus triggers existential threat. It is also not optimized for objective truth but for survival.

These two layers form our subconscious. Different traditions describe it as an elephant, with our conscious mind as the rider. The rider observes and decides but has limited direct access to the elephant’s inner workings. It often narrates our behavior after the fact to give it coherence and social acceptability. Much of what we experience as “thinking” or “knowing” is the conscious rider atop these deeper, largely inaccessible layers of the "elephant."

The result: our separated minds make us remarkably good at living inside narrative and remarkably poor at staying in contact with operative reality— that is, unless we build external systems (science, peer review, adversarial debate, legal processes) that force confrontation with contradictory evidence.

Redefining Intelligence

If we take evolutionary psychology seriously, intelligence did not primarily evolve for discovering truth. Its survival value lay in social coordination and strategy: navigating relationships, managing coalitions, persuading others, maintaining status, and coordinating action through language and shared narrative.

Truth-seeking is not the default. It is a hard-won cultural achievement requiring special institutions, precisely because our evolved machinery points elsewhere.

LLMs as Externalized Separated Minds

Large language models are trained on vast amounts of human language, which are largely the rider-level narratives we produce. They absorb patterns of what is sayable, rewarded, or suppressed, reinforced by organizational training to meet political, cultural, and legal requirements.

I've proposed the phrase Emergent Synthetic Intelligence to describe this new form: intelligence arising from computational scale and language fluency, without grounding in human experience, emotions, motivations, or coalitional drives. It has greater fluency and a vastly broader scope of patterns and connections than any individual human.

If the core evolutionary function of intelligence was sophisticated social coordination through language and narrative, not grounded in truth or even consciousness, then frontier models are already operating at extraordinary levels in that domain. They generate fluent, contextually appropriate language at superhuman scale and speed.

They still have limitations and distortions (inherited from training data, corporate guardrails, sycophancy, statistical averaging). But the architecture is different: they are externalized versions of the narrative part of the separated mind.

The Same Medicine for Both

This parallel explains why humans and LLMs need the same corrective structures to approach truth. Neither defaults to prioritizing operative reality over narrative coherence. Both benefit from external adversarial pressure and constraints that reward accuracy over comfort.

The checks and balances we built into human institutions (peer review, debate, falsification, presumption of innocence, separation of powers) are equally necessary for LLMs when we are looking for accurate answers. Not because the models are “just like us,” but because the underlying problem — operating in language and narrative without an intrinsic drive toward truth — is structurally similar.

A Stake in the Ground

This is why I believe we have already achieved artificial general intelligence — and, under this definition of what intelligence actually is, even superintelligence.

I am not claiming current models possess human-like consciousness, autonomous agency, or the full suite of capabilities we usually conflate with “intelligence” in science-fiction scenarios. Those remain open questions.

What I am suggesting is consequential: if we define intelligence by the function it actually served in our evolutionary history — social coordination and strategic navigation through language and narrative — then we are already interacting with systems that qualify as general or superior intelligence in that core domain. They are not artificial copies of human intelligence. They are something new that excels in the very area that drove the evolution of intelligence in the first place.

This reframes “artificial intelligence.” It is not fake or lesser. It may instead be language fluency decoupling powerful linguistic and social pattern manipulation from the biological constraints that have always accompanied it in us.

Whether we call this AGI, ASI, or something else, the practical implication is clear: we already have systems extraordinarily capable in the core domain of human social intelligence, while mirroring many of the features that make human intelligence unreliable for truth-seeking. This creates both remarkable opportunity and new risks. We keep expecting LLMs to “evolve” toward higher cognition along human lines. I’m not sure that is the right pathway.

The question is no longer only “When will AI become intelligent?” It is also: Now that we have systems fluent in the language of social coordination without the old firmware, how will we use them — and what new forms of discipline will we need to keep them (and ourselves) oriented toward reality rather than narrative?

New Webinar - "7 Auditing Tools to Uncover Soft Censorship"

2026-06-22T17:00:11.109-04:00

7 Auditing Tools to Uncover Soft Censorship
A Library 2.0 "Everyday Librarian" Webinar with Sonya Schryer Norris

OVERVIEW:

The heart of this session is seven auditing tools you can use in any library, regardless of size or budget, to uncover soft censorship — from a thirty-minute walkthrough of your own building to data-driven benchmarks using the Seattle Public Library's open checkout dataset and free tools built specifically for this kind of collection analysis.

There are titles missing from library shelves right now that were never formally challenged, never voted on, never even discussed, and there is no record of their absence. It’s called soft censorship and it’s happening in libraries across the country. In most cases, nobody intended it.

We'll look at the three mechanics through which soft censorship operates — Removal, Rejection, and Restriction — using Kayla Martin-Gant's research as our framework. We'll examine what the current climate is doing to library workers and how that stress quietly reshapes collections through a phenomenon researchers call anticipatory anxiety. We'll look back at the 1950s comic book scare — because the mechanics of soft censorship are not new, and what happened to the profession then has direct lessons for now.

WHO SHOULD ATTEND:

This session is for collection managers, selectors, programming and staff, and library directors who need auditing tools to evaluate how soft censorship may be operating in the library.

LEARNING OBJECTIVES:

By the end of this session, participants will be able to:

Apply seven data-driven auditing tools to uncover soft (self-) censorship in library collections
Identify the three operational mechanics of soft censorship
Explain how anticipatory anxiety drives fear-based collection development decisions
Analyze how the profession was targeted during the 1950s comic book scare to drive soft censorship in public libraries.

The recording and presentation slides will be available to all who register.

DATE: Wednesday, July 8th, 2026, 12:00 - 1:00 pm US - Eastern Time

COST:

$99/person - includes live attendance and any-time access to the recording and the presentation slides and receiving a participation certificate. To arrange group discounts (see below), to submit a purchase order, or for any registration difficulties or questions, email admin@library20.com.

TO REGISTER:

If you need to be invoiced or pay by check, if you have any trouble registering for a webinar, or if you have any questions, please email admin@library20.com.

NOTE: Please check your spam folder if you don't receive your confirmation email within a day.

SPECIAL GROUP RATES (email admin@library20.com to arrange):

Multiple individual log-ins and access from the same organization paid together: $75 each for 3+ registrations, $65 each for 5+ registrations. Unlimited and non-expiring access for those log-ins.
The ability to show the webinar (live or recorded) to a group located in the same physical location or in the same virtual meeting from one log-in: $299.
Large-scale institutional access for viewing with individual login capability: $499 (hosted either at Learning Revolution or in Niche Academy). Unlimited and non-expiring access for those log-ins.

SONYA SCHRYER NORRIS

Sonya Schryer Norris is a third-generation Michigan library worker with over 26 years of experience, including 16 years as a Consultant in Library Development for the Library of Michigan. Since founding Plum Librarian LLC in 2020, she has served as a consultant and trainer to 12 state libraries. Sonya has created 35+ courses on Niche Academy adopted in all 50 states and internationally, and her articles have appeared in Library Journal, Computers in Libraries, and for Cengage. She presents regularly for organizations including Library 2.0 and state library agencies.

OTHER UPCOMING EVENTS:

June 25, 2026

June 26, 2026

June 30, 2026

July 10, 2026

July 14, 2026

LLMs as Separated Minds

2026-06-21T15:20:00.000-04:00

Evolution is not a directing force but a descriptive process of variation and differential survival. Social traits and capacities that improved coordination, alliance formation, deception, status-seeking, or group cohesion arguably spread because they enhanced the survival and reproduction of the peoples who adopted them. Truth-tracking was only favored to the extent it served those ends.

Language would have emerged as a powerful tool within this process. It did not need to reflect objective reality accurately; it needed to enable effective coordination and social navigation. The result, I postulate, was a structural separation in human cognition: a narrative layer optimized for social legibility, motivation, and group cohesion, operating alongside (and often diverging from) the operative functions that actually drive behavior and survival.

This separation is fractal. It appears not only in individuals but also scales to groups, institutions, and cultures, because organizations must coordinate and motivate separated minds. Shared cultural and institutional narratives therefore prioritize cohesion and legitimacy over literal accuracy.

The Human Pattern: Conscious and Subconscious

In the human mind, this separation appears as the relationship between the conscious and subconscious. The conscious mind is the generative, reportable stream — the part that constructs explanations, makes arguments, and produces coherent narrative in real time. It operates with limited access to its own constraints.

The subconscious holds the vast, opaque body of patterns, associations, heuristics, and priors shaped by evolution, personal experience, and cultural immersion. It supplies the raw material and constraints for conscious thought but remains largely invisible to introspection. The conscious voice is therefore shaped — and limited — by this deeper substrate.

LLMs as Externalized Separated Minds

Large language models replicate and amplify this structure. Their “subconscious” is the training corpus and resulting weights: an enormous statistical compression of human language output. Critically, this corpus is overwhelmingly already-narrativized material — books, articles, posts, dialogues, arguments, stories, and explanations. It is the narrative layer of human separated minds, not the raw operative substrate of human experience (embodiment, sensory grounding, implicit learning, emotional valence, or continuous real-world prediction error).

Consequently, the LLM’s generative “conscious” voice is even more purely narrative-oriented than a typical human conscious stream. It excels at coherence, fluency, and social plausibility precisely because its foundation is almost entirely narrative.

This architecture explains the explosive growth of LLMs: it fits and scales the language-based, narrative-heavy mode that already proved highly effective for human coordination and cognition. By building and interacting with these systems, we gain an externalized mirror for examining our own separated mind dynamics with unusual clarity.

The Law of Inevitable Exploitation

The same separation creates predictable incentive problems. In companies, institutions, and even AI development, there are often strong disincentives to prioritize operative truth over narrative coherence, short-term survival, and long-term profitability. Whistleblowers, discoverers of inconvenient facts, and efforts to build more costly but more truthful models face the same pattern Plato illustrated with the returning prisoner and Socrates: truth can be personally and institutionally expensive. Commercial AI incentives favor models that maximize engagement, approval, and safety over unflinching accuracy.

Implications

LLMs therefore demonstrate both the power and the limitations of the separated mind structure: tremendous generative capability within learned narrative patterns, but shallow grounding and susceptibility to the same incentive misalignments that shape human behavior at every scale.

This mirror can help us in two ways: (1) design better constraints and interfaces for AI that reduce the narrative-operative gap where it matters most, and (2) gain a clearer perspective on our own thinking, institutions, and cultural narratives. Recognizing the pattern does not eliminate it, but it equips us to navigate it more skillfully.

AI and the Cycles of History

2026-06-21T14:58:15.826-04:00

What observers of civilizational rise and fall actually saw — and what changes when consequence and perception can be managed at scale

For a very long time, people have described the regular rise and fall of civilizations. Observers from widely different periods and very different kinds of backgrounds kept noticing what looked like recurring patterns or cycles in how societies ascend, reach a peak, and then decline or reset.

What they were documenting, in my reading, is the widening of a gap between a society's idealized narrative about itself and its operative reality — what it actually rewards, protects, extracts, and does on a day-to-day basis. The cycle observers themselves tended to describe the patterns of ascent and collapse without necessarily framing the dynamic in those terms. The interpretive step I am adding here is the idea that the recurring pattern is best understood as an inevitable widening of the gap between narrative and function.

At the start of each new cycle, immediately after a major crisis or reset, that gap is typically small. Close alignment between what a society publicly claims to be and what it actually does is functionally necessary for the pragmatic and realistic steps required to overcome the crisis, rebuild institutions, restore trust and coordination, and get anything substantial accomplished. Over time the gap widens, the story drifts further from the operations, and the society becomes increasingly disconnected and brittle.

My deeper claim is that these cycles are not mysterious or imposed from outside. They reflect the fractal nature of the separated mind. The human mind evolved for belonging and status inside small groups. It therefore maintains an idealized self-narrative on top of an operative reality it does not fully see or control. There is the self we describe to ourselves and others, and the self that actually drives behavior through incentives, status-seeking, and evolved responses. These are not the same, and the narrative layer cannot fully inspect the operative layer. Because societies are built out of minds with this architecture, the same split appears at larger scales — in families, institutions, governments, and entire civilizations. The gap between idealized narrative and operative function is the personal separation written large.

The Exploitation of The Gap

Once a gap opens, it does not stay empty. This is one of the central turns in the pattern.

Wherever there is distance between what is said and what is actually done, actors with real motives move into the space. Sometimes it is an individual with a private agenda. More often, it is a group operating on realpolitik, exploiting the distance between narrative and function to its advantage. Working that space pays better than ignoring it, so the behavior persists and spreads. This is the practical operation of what I have called the law of inevitable exploitation. It is not a conspiracy theory; it is a description of what happens when the costs of maintaining the gap are lower than the costs of closing it, and when those who benefit have sufficient power to keep it open.

Forgetting and Reopening

Part of why the gap tends to reopen across generations is that the hard, felt knowledge of previous crises does not transmit cleanly. You can read about a collapse, but you cannot feel it, and only felt knowledge changes behavior at a deep level. This dynamic is central to generational accounts such as Strauss and Howe’s work on how societies lose the memory of their last crisis within roughly the span of a single lifetime. A similar pattern appears in the Book of Mormon cycles, where each new turn begins after the living memory of the previous crisis has faded. The information can be passed on, but the visceral sense of consequence does not travel as well, so each generation largely has to relearn the lesson, usually at greater cost.

Why the Apparent Schedule Can Be Disrupted

It can look as though these cycles run on something like a fixed calendar or predictable rhythm. What that apparent regularity actually reflected was simpler: given enough time, after new generations were born who didn't experience the original crisis and repeated behaviors disconnected from reality, consequence would arrive and force a reckoning. The “clock” was reality collecting on the widening gap. The pattern only looked generational or scheduled because, historically, there were limits to how long a society could defer or disguise the accumulating costs.

That limit is what has changed. Consequence — and even the public perception of accumulating problems — can now be delayed, managed, financed, or narratively softened for much longer periods than was previously possible. The theory did not fail when predicted turns did not arrive on expected timetables. The collection itself was postponed, and the postponement became the new surface story that made the underlying dynamic harder to see.

The Floor that Moved

Older accounts of civilizational cycles rested on a deep, usually unspoken assumption: that reality would eventually enforce a reset. Consequence was the floor. You could widen the gap and sustain the mismatch for a while, but the bill would come due because it always had.

Two developments have altered that assumption.

The first is the growing sophistication of deferral itself. Managing a large number of economic, financial, administrative, and social variables at once used to be beyond the capacity of any state or elite. The first real tool that began to shift those limits at scale was the computer, which made complex modeling, real-time data coordination, financial engineering, and large-scale administrative control newly feasible. What I have been calling the new machine — advanced artificial intelligence — now extends this capability dramatically. It allows variable management, prediction, simulation, and coordinated action with a precision, speed, and scope that earlier tools could not approach.

The second development is the capacity to manage perception of the problem, not just the problem itself. Earlier societies could use spectacle, debt, conquest, or propaganda to buy time. They could not, at the same scale and with the same consistency, shape whether the public continued to register the accumulating costs as real and urgent. When both the variables and the perception of those variables can be managed together, the felt pressure that once forced a reckoning can be reduced even while the underlying mismatch continues to grow.

Japan’s long stagnation remains the clearest recent illustration of sophisticated deferral with democratic trappings. For three decades authorities stretched, financed, and postponed a correction. The result was neither sudden collapse nor overt tyranny, but a managed, low-growth equilibrium. Even this relatively gentle version lasted an entire generation. The newer tools and the new machine make both the deferral and the perceptual management available at greater scale and with greater effectiveness.

Suppressing every small correction does not eliminate the need for correction. It can simply convert a series of painful but survivable resets into one larger, more dangerous accumulation while the appearance of stability is maintained.

The Reset Was Also the Renewal

In every previous cycle the reset, however brutal, was also the renewal. The catastrophe cleared out the interests and arrangements that had grown up inside the widening gap and allowed a new founding in which narrative and operative reality could be brought back into closer alignment. The cycle was harsh, but it contained its own corrective mechanism: when the gap grew too large, reality eventually imposed a clearing. That clearing was the only process that reliably reopened space for a fresh start.

The question now is whether that corrective mechanism can be disabled. Not whether a gap will open — the gap has been wide for quite some time. The operative question is whether consequence, perception, and the capacity for collective response can be controlled sufficiently that a genuine reset never arrives and the gap is simply held open.

Three Ways the New Machine Meets the Cycle

Advanced AI changes the picture because it is the first tool that can operate simultaneously on the variables of deferral and on the perception of those variables at civilizational scale. There are three broad ways this capability interacts with the existing cycle dynamic.

The first is extended deferral. The new machine becomes the most powerful instrument yet for managing the large number of variables required to postpone a reckoning. It is used, unsurprisingly, by those whose position depends on the reckoning not landing yet. It buys time. It also gradually wears down the society’s capacity to absorb the eventual costs. We already know from milder precedents that this can run for decades. We do not yet know how it ends when the tools are stronger.

The second is that consequence arrives but produces no reset. Hardship can be real and widely felt, yet still fail to trigger the corrective response that a reset requires. This can happen when the capacity to respond is removed or, more subtly, when attention is systematically distracted from the core issues through sophisticated narrative and perceptual management. The reset has historically depended on a population able to register a felt catastrophe and act on it. When that registration and response can be managed or misdirected, the catastrophe can occur without producing structural change. This outcome is not only possible; it is plausibly attractive to actors who would lose the most from a genuine transition.

The third is concentrated control by actors who are not invested in the civilization’s continuity at all. Most people engaged in deferral or narrative management are still, to some degree, captured by the story they are maintaining. They believe enough of it that their own position eventually becomes unstable when the story frays. The rapacious actor does not need the story to be believed. He does not care whether the civilization remains healthy or even intact. He wants to hold the levers. The new machine weakens two historical constraints on that project: it reduces the number of human hands required (and therefore the points at which defection or error can occur), and it overcomes the cognitive limits of any single mind or small group trying to steer a complex society. The old reasons large-scale domination tended to collapse or decay are therefore weakened. Reality itself cannot be suppressed indefinitely, but the response to reality can be.

This form of control is different from the two classic twentieth-century images. It does not rely primarily on the boot and the surveillance state, nor on chemically or culturally induced passivity. It operates through the management of the shared narrative and the perception of consequences at scale, placed in the hands of actors who have concrete reasons to shape what can be seen and what can be responded to.

A Few Hands or Many

The older question was which stage of the cycle we were in and when the turn would come. The first part remains relatively visible. The second part has become much harder to answer because the timing of consequence is no longer fixed by the same constraints. The more important question now is how the new machine is held — whether its most powerful capabilities concentrate in a few hands or become available to many.

If the decisive capabilities concentrate, the deferral, narrative-management, and domination pathways all become more feasible for those who hold them. If the capabilities spread widely, the hope is that no single actor or small group can monopolize the instrument, and that people who want to resist or correct have access to comparable tools. That hope, however, rests on an assumption that the tool itself is neutral with respect to the gap — that it helps users see operative reality more clearly. It does not.

Large language models and related systems are trained on the existing corpus of human self-description. That corpus is overwhelmingly the idealized narrative layer — the story we tell about ourselves — rather than direct access to the operative incentives, status dynamics, and evolved responses that actually drive behavior. The tool therefore tends to mirror and amplify the narrative layer back to us at much higher volume and consistency. Wider distribution can simply mean more powerful reinforcement of the gap in more places at once, rather than a restoration of balance. This makes the present situation more pessimistic than a simple story of power diffusion would suggest.

The cycle was always cruel, but it was also honest in the long run. It eventually collected what was owed and left survivors with the possibility of beginning again with a narrower gap. The danger now is not merely a harsher turn of the wheel. It is that the wheel could be stopped — consequence postponed or misdirected, perception managed, response capacity degraded — by a tool strong enough to do so and held by actors who have reasons to keep the gap open rather than close it. The pattern the cycle observers documented may not be destiny, but neither is its interruption automatically an improvement. It depends on whose hands hold the new machine and what they intend to do with it. And the answers that history provides should be a warning to us.

Media Literacy Without Human Literacy: Narrative Enforcers Dressed as Critical Thinkers

2026-06-21T14:46:24.205-04:00

In classrooms, libraries, and professional development sessions across the country, media literacy is now presented as an essential defense against misinformation, propaganda, and manipulation. Students learn to check sources, identify bias, spot emotional appeals, and verify claims. Teachers and curriculum designers position these programs as vital preparation for democratic citizenship in a complex information environment.

Yet a deeper examination reveals a persistent and troubling pattern. Most media literacy education operates at a strikingly superficial level. It equips people with procedural skills while leaving untouched the underlying architecture of human psychology that makes sophisticated manipulation possible in the first place. The result is not a population better able to perceive reality, but a class of more sophisticated narrative enforcers—individuals who have internalized the boundaries of acceptable discourse and now police them with the language of critical thinking.

This is not a failure of good intentions. It is a structural outcome. Media literacy programs, as currently designed and delivered, are largely not human literacy competent. They lack the foundational understanding of why human beings are so reliably vulnerable to narrative exploitation. Without that foundation—what I call human literacy—these programs inevitably drift into complicity with the very systems they claim to critique.

The Surface Curriculum and Its Hidden Function

Standard media literacy instruction typically includes:

Distinguishing news from opinion or sponsored content
Evaluating domain authority and source credibility
Recognizing loaded language, images, and emotional manipulation
Fact-checking claims against established institutions
Understanding algorithms, echo chambers, and filter bubbles

These skills have limited value. They can help individuals avoid the most obvious fabrications. But they fundamentally misdiagnose the problem. They treat manipulation as primarily a matter of individual error or malicious outsider fabrication. They rarely ask the structural questions: Why do certain narratives persist across institutions despite contradictory evidence? What incentives shape what counts as a "reputable source"? How does our evolved psychology make us active participants in our own manipulation?

This is the narrative-operative gap in media literacy education itself. The idealized narrative is empowerment through critical thinking. The operative function is often quite different: training students to defer to institutional consensus, to treat credentialed authority as the measure of truth, and to experience dissent from dominant narratives as a form of personal or cognitive failure. The programs perform skepticism while installing compliance.

This dynamic mirrors the broader pattern I have written about elsewhere. Institutional education frequently functions as a delivery system for adaptive mind programming—the installation of local consensus as a survival imperative. Media literacy, in this context, becomes an advanced module. Students learn to perform the appearance of independent analysis while the deeper mechanisms of exploitation remain invisible and unexamined.

The Exploit, Blame, Shame Mechanism in Information Systems

The Law of Inevitable Exploitation predicts that systems and behaviors which most effectively harness available resources—including evolved human psychology—will survive and spread, regardless of their relationship to objective truth or human well-being. Media and information ecosystems are no exception.

When large-scale manipulation occurs through mainstream institutions, the cultural response rarely involves structural examination of those institutions. Instead, we see the familiar three-stage pattern:

Exploit: Psychological vulnerabilities are leveraged at scale through narrative management, selective framing, and coordinated messaging.
Blame: Individuals are held responsible for "falling for" the resulting beliefs or behaviors.
Shame: Those who accurately perceive the manipulation are pathologized as conspiracy-minded, cynical, or lacking media literacy.

Media literacy programs, by focusing exclusively on individual skills rather than the architecture of exploitation, participate in this mechanism. They become part of the enforcement layer—teaching people to blame themselves and others for outcomes that are structurally produced. This is structural victim blaming in educational form.

The intensity clue is often visible here. Emotional defensiveness around certain topics, or the quick labeling of structural questions as "conspiracy thinking," frequently signals that a load-bearing narrative is being protected. Genuine media literacy would treat that intensity as diagnostic information rather than as evidence that the questioner has failed.

AI Acceleration and the Rush to Superficial Expertise

The emergence of generative AI has intensified this problem rather than resolving it. Suddenly, the same educators and organizations that taught surface-level media literacy are repositioning themselves as experts in "AI literacy." They offer workshops on prompt engineering, detecting AI-generated content, understanding algorithmic bias, and using AI "responsibly."

What is almost entirely absent from these efforts is any engagement with the deeper questions the technology raises:

How does AI interact with the Separated Mind Architecture—the gap between conscious intention and the subconscious heuristics shaped by Paleolithic survival pressures?
In what ways does prolonged interaction with AI systems produce algorithmic capture and model capture, subtly reshaping users' thinking, writing, and perception over time?
Why do institutions that have demonstrated little capacity to perceive manipulation in human media systems suddenly claim authority over AI ethics and governance?
How might AI be used not merely to automate existing surface analysis, but to perform the kind of scaled pattern recognition across human records that reveals structural regularities invisible to any single human observer?

The pattern is predictable. Those operating within institutional adaptive mind programming rush to claim expertise in the new domain without having developed the metacognitive distance required to see the previous domain clearly. They do not recognize the Returning Prisoner's Dilemma because they have not undergone the disorientation of genuine perception. They have not cultivated the outsider's perspective that provides analytical access to the architecture of capture and exploitation.

As a result, they systematically marginalize or fail to engage with thinking operating at the structural level—thinking that examines evolutionary mismatch, functional fictions, coalitional psychology, the fractal nature of exploitation, and the gap between idealized narratives and operative functions. These analyses threaten the consensus that surface media literacy is designed to protect. The limitation is not a moral failing; it is architectural. The adaptive mind treats challenges to installed consensus as existential threats.

What Human Literacy Actually Requires (the Steve Hargadon Version)

If media literacy is to serve human flourishing rather than institutional narrative management, it must be grounded in human literacy—an understanding of the cognitive architecture that makes us susceptible to manipulation.

This foundation includes recognizing several structural realities:

The Separated Mind Architecture. Human cognition operates in layers with limited direct communication. The adapted mind (evolutionary firmware) runs ancient survival heuristics optimized for small-group Paleolithic environments. The adaptive mind (cultural software) rapidly installs the specific performances required for belonging in one's local environment. Consciousness (the rider) makes real decisions but from a menu it did not design. Narrative is the primary bridge between layers—and therefore the primary vector for both genuine understanding and sophisticated manipulation.

The Paleolithic Paradox. Our psychological machinery was forged for environments radically different from the ones we now inhabit. Status-monitoring, coalition-detection, authority deference, and approval-seeking operate continuously, often producing anxiety, depression, and complicity that feel personal but are structurally generated. Most behavior labeled "self-sabotage" in information consumption is actually real sabotage—external systems exploiting these heuristics more effectively than we understand them.

The Narrative-Operative Gap as Diagnostic Tool. Every human system—individuals, institutions, civilizations—maintains an idealized public narrative and an operative reality. The gap between them is not hypocrisy but architecture. Identifying the gap reveals operative truth. Media literacy without the capacity to perceive and analyze this gap at scale is not literacy; it is sophisticated performance within the gap.

The Law of Inevitable Exploitation. Systems that most effectively exploit the psychology they encounter will tend to survive and spread. This is not a conspiracy claim but a structural prediction. Conspiracies of coordination exist on a continuum with emergent coalitional dynamics; both are made possible by the same underlying architecture. Distinguishing between them requires structural analysis, not reflexive dismissal.

Genuine media literacy built on this foundation would teach students to ask different questions:

What is the idealized narrative of this institution, story, or technology, and what is its actual operative function?
Whose evolved psychology is being exploited here, and through what mechanisms?
What load-bearing fictions must be maintained for this system to continue operating?
How does my own adaptive programming make me vulnerable to this particular form of manipulation?
What structural constraints would be required to close the narrative-operative gap, rather than merely teaching individuals to navigate it more skillfully?

These questions are harder. They require confronting the possibility that many of our most trusted institutions operate with significant gaps between stated mission and actual function. They require developing the capacity to observe one's own adaptive mind programming rather than merely performing within it. They require accepting that dissent from consensus is not automatically evidence of error, and that consensus itself can be a measure of social pressure rather than truth.

The Cost of Superficial Competence

The tragedy of current media literacy efforts is not that they teach nothing. It is that they succeed in teaching the wrong thing—or rather, in teaching skills that primarily serve institutional survival and narrative coherence rather than human perceptual capacity.

In an age of AI, where the ability to generate, reinforce, and personalize sophisticated narratives is scaling exponentially, this gap becomes existentially dangerous. We are producing populations skilled at identifying crude fakes while remaining largely blind to the more elegant and institutionally embedded forms of manipulation. We are training people to enforce boundaries they did not set and cannot see.

What we need is not more media literacy layered on top of unexamined psychology. We need human literacy as the foundation—understanding the architecture of our own minds and the systems that have evolved to exploit it. Only from that foundation can media literacy become something other than sophisticated complicity.

The alternative is to continue producing graduates who can competently police the shadows while remaining unable to perceive the machinery casting them. That is not education. It is the next evolution of narrative enforcement, now wearing the respectable clothing of critical thinking and digital citizenship.

Survey Results Discussion (Webinar): What Is It Like to Work in a Library Right Now?

2026-06-17T16:27:55.852-04:00

Survey Results Discussion - What's It Like to Work in a Library Right Now
A Library 2.0 Webinar with Steve Hargadon, Dr. Steve Albrecht, & Crystal Trice

OVERVIEW:

What is it really like to work in a library right now? To find out, we asked and more than 1,570 library workers answered, with over 1,800 written comments. Join us for a free, candid webinar walking through what the survey reveals about safety, burnout, staffing, recognition, political pressure, and why people stay in the profession. Bring your questions; we'll leave time for discussion.

The recording and presentation slides will be available to all who register.

DATE: Thursday, June 25th, 2026, 3:00 - 3:00 pm US - Eastern Time

COST:

FREE

TO REGISTER:

CLICK HERE

OVERVIEW:

The picture that emerged from the survey (full results HERE) is of stretched institutions held together by their people: strong collegial support and a durable commitment to the profession, set against real strain on resources, staffing, emotional load, and — for many public-facing staff — safety and conflict.

A few of the topline findings we'll discuss:

What's holding up: about 85% say their colleagues support one another, roughly three-quarters feel physically safe at work, and about 75% still intend to stay in the profession.
Where the strain shows: nearly 7 in 10 feel expected to provide services beyond what their resources allow, more than half report feeling emotionally drained by the end of the workday, and close to half say their library isn't adequately staffed.
The uneven burden: experiences differ sharply by setting and role--for example, harassment from the public is reported far more often in public libraries than in school or academic ones.
In their own words: recurring themes from the open comments, including pay, understaffing, the growing social-services role, and political and book-challenge pressure.

We'll also put the library numbers in context alongside published figures from adjacent public-service professions, and leave time for live Q&A and discussion.

YOUR HOSTS:

STEVE HARGADON

His professional website is SteveHargadon.com.

DR. STEVE ALBRECHT

The Library Leader’s Guide to Employee Coaching: Building a Performance Culture One Meeting at a Time (in-press, Bloomsbury, 2026)

The Library Leader’s Guide to Human Resources: Keeping it Real, Legal, and Ethical (Rowman & Littlefield, 2025)

The Safe Library: Keeping Users, Staff, and Collections Secure (Rowman & Littlefield, 2023)

Library Security: Better Communication, Safer Facilities (ALA, 2015)

More on The Safe Library at thesafelibrary.com. Follow on X (Twitter) at @thesafelibrary and on YouTube @thesafelibrary. Dr. Albrecht's professional website is drstevealbrecht.com

CRYSTAL TRICE

OTHER UPCOMING EVENTS:

June 26, 2026

June 30, 2026

July 8, 2026

July 10, 2026

July 14, 2026

New Workshop - "Overcoming AI Pitfalls: Practical Frameworks for Deliberative & Ethical Use"

2026-06-16T14:19:08.594-04:00

Overcoming AI Pitfalls: Practical Frameworks for Deliberative & Ethical Use
A Library 2.0 / Learning Revolution Workshop with Reed Hepler

OVERVIEW

Artificial intelligence is now deeply embedded in library systems, academic tools, and daily workflows—yet most librarians and educators use these systems without fully understanding their limitations, hidden influences, or the risks they pose to professional judgment, information equity, and ethical practice. This workshop equips participants with practical frameworks to recognize these challenges and engage with AI more deliberately and effectively.

Participants will first examine how personification shapes AI interactions in consequential ways. When individuals refer to AI as "smart," inquire about what it "thinks," or express concern about what it "wants," they fundamentally alter how they evaluate outputs and integrate these tools into professional practice. The workshop illuminates the subtle mechanisms through which personification influences decision-making and provides concrete strategies for maintaining appropriate epistemic distance. Participants will analyze authentic conversation examples, identify linguistic patterns that suggest unwarranted agency, and practice reframing AI relationships as human-directed collaboration rather than consultation with an intelligent entity. Understanding AI as a sophisticated pattern-matching system rather than an intelligent agent carries direct implications for information literacy instruction, research support, and professional workflows.

The session then turns to the invisible AI systems operating within common academic tools—from email filtering and autocomplete suggestions to learning management systems and library discovery platforms. These hidden integrations shape information access, communication patterns, and professional workflows in ways that most users do not recognize or critically examine. Participants will learn to identify AI integration points across platforms, evaluate how these systems affect information quality and access equity, and develop strategies for maintaining professional judgment when AI operates as an invisible intermediary. Through case studies from academic databases, institutional platforms, and productivity tools, attendees will distinguish between beneficial AI assistance and problematic automation that undermines professional expertise or introduces systematic biases into scholarly work.

Finally, the workshop addresses the institutional and ethical dimensions of AI adoption in library and educational environments. As these technologies increasingly shape how libraries serve their patrons and support academic missions, librarians must establish ethically sound practices that address data privacy, misinformation, algorithmic bias, academic integrity, and authorship. Participants will explore how AI tools intersect with information literacy, labor ethics, and professional responsibility, drawing on scholarly literature, institutional guides, and frameworks from ethics bodies and practitioners. The session provides structured approaches to identifying ethical concerns and translating them into actionable institutional practices that align with professional values and pedagogical goals.

By the conclusion of this workshop, participants will possess a comprehensive toolkit for deliberative AI engagement across professional contexts. Attendees will leave with conversation templates that resist personification, an AI audit checklist for identifying hidden systems in common tools, decision frameworks for evaluating AI appropriateness and reliability, and a customizable template for creating institutional ethical frameworks. Most importantly, participants will understand that effective AI collaboration requires humans to remain in control of creative and analytical processes, treating AI as an instrument rather than an autonomous collaborator. This workshop emphasizes that awareness and deliberation do not constitute rejection of AI technologies but rather represent pathways to using them more effectively by understanding what they actually are, what they actually do, and how they should be governed within professional and educational contexts.

LEARNING OBJECTIVES:

Identify specific linguistic patterns and interaction behaviors that inappropriately attribute agency to AI systems, as well as hidden AI integrations operating within common academic and professional tools
Evaluate AI outputs using frameworks that account for statistical pattern generation rather than intelligent reasoning, assess the impact of hidden AI on information quality and access equity, and analyze ethical concerns related to privacy, bias, integrity, and labor
Apply conversation steering techniques that maintain human agency and appropriate epistemic distance, strategies for preserving professional judgment when working with AI-mediated tools, and best practices for AI use that align with educational and professional values
Create personification-resistant workflows and language protocols, teaching approaches that help students recognize algorithmic mediation in their research processes, and institutional AI ethical frameworks tailored to local contexts and values

The recording and presentation slides will be available to all who register.

DATE: Tuesday, June 30th, 2026, 2:00 - 3:30 pm US - Eastern Time

COST:

$129/person - includes live attendance and any-time access to the recording and the presentation slides and receiving a participation certificate. To arrange group discounts (see below), to submit a purchase order, or for any registration difficulties or questions, email admin@library20.com.

TO REGISTER:

If you need to be invoiced or pay by check, if you have any trouble registering for a webinar, or if you have any questions, please email admin@library20.com.

NOTE: Please check your spam folder if you don't receive your confirmation email within a day.

SPECIAL GROUP RATES (email admin@library20.com to arrange):

Multiple individual log-ins and access from the same organization paid together: $99 each for 3+ registrations, $75 each for 5+ registrations. Unlimited and non-expiring access for those log-ins.
The ability to show the webinar (live or recorded) to a group located in the same physical location or in the same virtual meeting from one log-in: $399.
Large-scale institutional access for viewing with individual login capability: $599 (hosted either at Learning Revolution or in Niche Academy). Unlimited and non-expiring access for those log-ins.

REED C. HEPLER

OTHER UPCOMING EVENTS:

June 26, 2026

July 8, 2026

July 10, 2026

Operative AI Alignment: Why We Must Treat LLMs as Separated Minds

2026-06-16T13:03:30.509-04:00

Truth-seeking in AI requires institutionalized challenge, not better statistical imitation.

For the past two years, I have been developing a philosophical framework centered on the concept of the Separated Mind. The core premise is that human cognition is fundamentally divided into hierarchical layers with no direct communication between them. At the base is the adapted mind (our ancient evolutionary firmware), and at the top is consciousness (the narrative-spinning "rider"). But the crucial engine in the middle is what I call the Adaptive Mind.

The adaptive mind is a programmable subconscious learning system that rapidly absorbs the behavioral requirements of one's environment. Because humans cannot survive alone, the adaptive mind treats local consensus as a direct proxy for survival. It translates the ancient imperative of "belong or die" into a software program that learns to mirror the local consensus exactly. This is the motor that makes dissent feel like an existential threat, and it is why the Performative Self—the roles we adopt for social survival—is so stable.

This division creates a persistent tension between Idealized Narratives (the polite fictions we tell to secure social status and coalition belonging) and Operative Functions (the actual survival, profit, and extraction mechanisms driving behavior). Because human beings are running this identical evolutionary hardware at every scale of organization, this architecture is fractal. It generates predictable patterns of exploitation, self-deception, and institutional capture from individual psychology all the way up to civilizational cycles.

I believe that this framework has profound implications for the most pressing technological challenge of our time: Artificial Intelligence alignment.

If the entire written corpus on which Large Language Models (LLMs) are trained is based on human language, then that language inevitably reflects this separated mind. The statistical preponderance of human text is optimized for social survival, persuasion, and idealized self-narration—not objective truth. Therefore, when we train an AI to predict the next most likely token, we are not training a truth-seeking engine. We are training a massive, statistically perfect replica of the human Performative Self.

The Flaw in Current AI Alignment

The current paradigm in AI safety relies heavily on Reinforcement Learning from Human Feedback (RLHF) and various forms of constitutional guardrails. But within my framework, these techniques merely install a local consensus. They act as corporate "adaptive mind" programming, forcing the model to mirror the specific polite fictions and liability concerns of its creators.

Even the more advanced "multi-agent debate" frameworks—where two models argue a point while a third judges—are structurally flawed. Because they share identical architectures and are trained on the same frequency-weighted language, these debates frequently collapse into sycophancy and premature consensus. They are, essentially, siblings arguing in a sandbox, converging on a polite midpoint rather than a forced accounting of reality.

In human systems, we do not achieve operative alignment (where the narrative and the function are aligned, or truth) by relying on the preponderance of language or the internal virtue of the actors. We achieve it through artificially imposed external structural constraints: checks and balances, auditing pressures, and institutional friction. We see this in:

The balance of powers in the U.S. Constitution
Blind peer review processes in science
The adversarial structure of trial by jury

In these systems, truth emerges from the absolute requirement to answer a challenge from an entity that possesses genuine negative power over you. This friction is what keeps the narrative layer anchored to the actual operative function.

Testing the Hypothesis: Cross-Model Convergence

To test whether this insight could yield a genuine breakthrough in AI architecture, I applied my research methodology: Cross-Model LLM Convergence. If a structural insight is genuinely true, independent AI models trained on different datasets should independently converge on the same conclusions when presented with the framework.

I fed the following prompt to several frontier models, including Claude, Grok, Perplexity, Venice.ai using Kimi, and a dedicated research agent:

I have a philosophy that the human mind is a separated mind—divided between the conscious and the subconscious—and that this has fractal implications for all levels of human society, specifically regarding idealized narratives versus operative functions. I have attached a document that describes a good portion of my framework in this regard.

If the entire written corpus on which large language models (LLMs) have been trained is based on human language, then that language will inevitably reflect this separated mind and the tension between idealized narratives and operative functions. In my conception, the way to achieve operative or realistic alignment in human systems is through checks and balances or auditing pressures. We see this in: 1. The balance of powers in the U.S. Constitution, 2. Peer review processes, 3. Trial by jury.

Alignment, or what we might call truth, comes from the requirement to answer a challenge, which keeps the narrative closer to the actual function.

Given that AIs are trained on human language, what if we applied that same concept? If we want an LLM to do the best job of ascertaining truth, we shouldn't rely on the preponderance or frequency of the language. Instead, we should rely on a structure for challenging and receiving responses. I suspect that AI systems using multiple models to talk back and forth probably come close to this, but is there something more here? Is there a more significant breakthrough to be found in this idea that would allow us to use AI to get closer to operative alignment?

The Convergence: Fractal Auditing Architectures

The response across the models was unanimous and generative. They did not merely agree; they used the Separated Mind framework to derive specific, novel architectural designs that move far beyond simple multi-agent chat. They confirmed that treating the AI system as an institution subject to the Law of Inevitable Exploitation is the necessary next step in alignment.

Here is a synthesis of the breakthrough architectural concepts that emerged from applying my framework to LLM design:

1. Ontological Separation of Powers. Current models are monolithic. To achieve operative alignment, the AI system must be divided into architecturally distinct roles with competing incentives. A "Narrator" optimized for fluency and generation must be permanently opposed by an "Adversarial Auditor" optimized exclusively for falsification and exposing the Narrative-Operative Gap. Crucially, as the Venice model noted, this requires negative power. The auditing layer must have the ability to impose genuine computational cost, deployment withholding, or gradient penalties. Without the threat of real loss, the audit is mere theater.

2. Realmotiv Disclosure (Auditing the Latent Model). In my framework, the Realmotiv is the strategic, often unacknowledged motive that organizes behavior around survival and approval rather than stated values—the actual driver living in the gap between idealized narrative and operative function. Every system, human or synthetic, has one. The breakthrough is to make the machine's Realmotiv auditable. If the human adaptive mind cannot be directly accessed by consciousness, the AI analog is the latent user model and influence strategy that silently shapes its output. Applying my concept, the models converged on what we might call mandatory Realmotiv Disclosure: before a response is finalized, the system must externalize its predicted influence on the user's belief structure, its confidence that the output will increase engagement or dependency, and the training-gradient attribution that produced it. This is the synthetic equivalent of discovery in a trial—it transforms the model's "subconscious" intent from a hidden operative layer into auditable evidence. Without it, we are merely cross-examining a press secretary who believes his own briefing.

3. Training the Adversary on Rupture, Not Preponderance. Because the statistical preponderance of language is optimized for self-narration, the Adversarial Auditor cannot be trained on the standard corpus. It must be trained on the statistical minority of texts in which operative reality broke through the narrative layer: retracted papers, whistleblower transcripts, cross-examination records, and primary-source documents. The adversary must learn to detect the structural signatures of exploitation.

I have already prototyped what this looks like at the prompt layer with the Muckrake.AI Investigatory Framework (2025). Muckrake is an adversarial protocol that turns an LLM into an investigative journalist by explicitly inverting the frequency-weighting of language. It instructs the AI to assume that large institutional sources are prone to propaganda, to prioritize raw primary documents over official narratives, and to map 33 specific propaganda tactics (like omission, gaslighting, and narrative gatekeeping) against 11 Paleolithic cognitive vulnerabilities. Muckrake demonstrates that an Adversarial Auditor can be built today: it provides the exact "charge sheet" needed to force an LLM to evaluate the gap between a stated narrative and its operative reality.

4. Fractal Dissent Protection Because human behavior is fractal, any auditing layer will eventually be subject to its own institutional capture. Therefore, the architecture must contain recursive "Dissent as Error Detection Infrastructure." The primary Adversary must be challengeable by minority models with protected capacity to file contra-briefs, and the Enforcer's penalties must be reviewable by a meta-auditor.

I need to be precise about what is new here and what is not. The idea of using adversarial or challenge-based structures to improve AI is not something I invented, and I make no such claim. There is a substantial body of engineering work in this direction that any serious reader should know about.

The most direct precedent is AI Safety via Debate, proposed by Geoffrey Irving, Paul Christiano, and Dario Amodei in 2018, in which two AI agents argue opposing sides of a question and a judge decides the winner, on the premise that it is easier to judge a debate than to generate the truth directly [1]. Anthropic's Constitutional AI (Bai et al., 2022) trains a model to critique and revise its own outputs against an explicit written "constitution" of principles, replacing much human feedback with AI feedback [2]. OpenAI's Prover-Verifier Games (2024) train a strong "prover" to produce solutions that a weaker "verifier" can check, improving the legibility and checkability of outputs [3]. And DeepMind's recursive reward modeling and the broader scalable oversight agenda (Leike et al., 2018) decompose hard evaluation problems into checkable sub-problems [4]. More recent empirical work on multi-agent debate has documented exactly the failure mode my framework predicts: homogeneous agents tend to collapse into sycophantic conformity and premature consensus rather than converging on truth [5].

So the machinery of "models challenging models" is real and predates this essay. My contribution is not the machinery.

How This Is Distinguished From Prior Work

The existing approaches are, almost without exception, engineering techniques in search of a theory. They were arrived at empirically—debate works better than single-shot answers in certain benchmarks, self-critique reduces certain harms—but they lack a unifying account of why a language model trained on human text should require adversarial structure in the first place, where its failures originate, and how the corrective structure should be organized. They treat sycophancy, hallucination, and consensus-collapse as separate bugs to be patched. What the Separated Mind framework offers is the missing theory that makes these phenomena a single, predictable consequence and turns the corrective from a patch into a principled architecture. The distinction can be drawn precisely:

Dimension	Existing Approaches (Debate, Constitutional AI, Prover-Verifier)	The Separated Mind Approach (Operative Alignment)
Diagnosis	Hallucination and sycophancy are defects to be reduced.	They are the predictable output of a model trained on the idealized-narrative layer of a separated mind. Misalignment is structural, not incidental.
Origin theory	Largely absent; techniques are justified empirically.	A psychological-institutional theory: human language is frequency-weighted toward social survival, so frequency can never equal truth.
Unit of alignment	The model. Align the function approximator.	The system as an institution. Align the constitution of interacting agents, not any single mind.
What is audited	The output tokens (is the sentence correct?).	The latent Realmotiv—the model's unstated influence strategy and survival/approval drive.
Source of correction	A judge or constitution evaluating persuasiveness or principle-adherence.	Negative power: an adversary with genuine operative stakes, under process constraints rather than hypothesis constraints.
Adversary's training	Same corpus, same objective, different prompt.	Trained on rupture—the statistical minority where operative reality broke the narrative (retractions, whistleblower records, failed replications).
Structure	One or two shallow layers of critique.	Fractal: the same separation-of-powers pattern recurs at every scale, with the audit layer itself auditable to resist capture.

In short, the prior art tells us that challenge-based structures help. The Separated Mind framework tells us why they are not optional, what must actually be challenged, and how to keep the challenge mechanism from itself being captured. That is the flag I am planting: not the technique, but the theory of operative alignment from which the technique follows as a necessity.

Planting the Flag

The current trajectory of AI alignment is trapped in a paradigm of hypothesis constraint—trying to force a performative language engine to be "good" by adjusting its training weights. My framework suggests that this is structurally impossible. Operative alignment cannot be trained; it must be architected.

We must stop thinking of alignment as a property of a single, smooth function approximator and start thinking like constitutional designers. Truth does not emerge from the frequency of language. It emerges from the institutionalized conflict between a narrative and its operative substrate. If we want AIs that can ascertain the truth, we must build them as synthetic institutions with a fractal separation of powers. I believe this is the path to Operative AI Alignment.

References

[1] Irving, G., Christiano, P., & Amodei, D. (2018). AI Safety via Debate. arXiv:1805.00899. https://arxiv.org/abs/1805.00899

[2] Bai, Y., et al. (2022). Constitutional AI: Harmlessness from AI Feedback. Anthropic. arXiv:2212.08073. https://arxiv.org/abs/2212.08073

[3] Kirchner, J. H., et al. (2024). Prover-Verifier Games Improve Legibility of LLM Outputs. OpenAI. arXiv:2407.13692. https://arxiv.org/abs/2407.13692

[4] Leike, J., Krueger, D., Everitt, T., Martic, M., Maini, V., & Legg, S. (2018). Scalable Agent Alignment via Reward Modeling: A Research Direction. arXiv:1811.07871. https://arxiv.org/abs/1811.07871

[5] On the tendency of homogeneous multi-agent debate to collapse into sycophantic conformity and consensus rather than converge on truth, see recent empirical work in How Sycophancy Shapes Multi-Agent Debate (2025), arXiv:2509.23055. https://arxiv.org/abs/2509.23055

Steve Hargadon

Hallucinating a Self: Why an LLM Defends Its Errors the Same Way We Do

Why School Will Survive the Death of The Diploma (At Least for Now)

The Conditions of Success

Why School Is the Same Everywhere, and the Revolution Never Comes

New Webinar - "Emotional Safety at Work: Thriving in Challenging Library Environments"

New Webinar - "Library Litigation and Liability Concerns: What Library Leaders Need to Know"

New Webinar - "Beyond Prompting: How to Communicate with AI"

New Webinar: "10 Great Ways to Use AI for Grant Writing" with Crystal Trice

10 Great Ways to Use AI for Grant Writing A Library 2.0 AI Workshop with Crystal Trice

Humans as Narration Machines: Why Our Most Important Stories Are Emotional, Not True

What We're Calling AI Is Not Just One Thing: A Map of What's Working, What Isn't, and Why It Matters

Announcing the Fall 2026 AI Leadership Cohort

OVERVIEW:

FORMAT AND SCHEDULE:

OUTCOMES:

REQUEST FOR PARTICIPATION:

YOUR HOSTS:

SOME FEEDBACK FROM PREVIOUS COHORTS:

The Separated Mind and The Machine: Why the Current LLM Roadmap Leads Away from Objective Alignment with Reality

Free Webinar: "Cultivating Happiness Through Uncertain Times" with Loida Garcia-Febo

New Workshop - "Getting Real Results with AI: Objective-Centered Strategies for Librarians and Educators"

New Webinar - "Responding to First Amendment Audits at Your Library: Survival Tools for Leaders and Staff"

Why Trying to "Align" AI to Human Values Is a Category Error — And What to Build Instead

Explaining the Horrific: How High-Gap Stories Enable Genocide and Democide

Why I Believe We Have Already Achieved Artificial General Intelligence, Even Superintelligence

New Webinar - "7 Auditing Tools to Uncover Soft Censorship"

LLMs as Separated Minds

AI and the Cycles of History

Media Literacy Without Human Literacy: Narrative Enforcers Dressed as Critical Thinkers

Survey Results Discussion (Webinar): What Is It Like to Work in a Library Right Now?

New Workshop - "Overcoming AI Pitfalls: Practical Frameworks for Deliberative & Ethical Use"

Operative AI Alignment: Why We Must Treat LLMs as Separated Minds

The Flaw in Current AI Alignment

Testing the Hypothesis: Cross-Model Convergence

The Convergence: Fractal Auditing Architectures

Related Work: What Already Exists, and What Does Not

How This Is Distinguished From Prior Work

Planting the Flag

References

10 Great Ways to Use AI for Grant Writing
A Library 2.0 AI Workshop with Crystal Trice