Steve Hargadon

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:

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.

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 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

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:

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

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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

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.

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

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:

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

When Intelligence Is Cheap, Understanding Is Expensive

2026-06-15T15:00:38.296-04:00

These days, it’s not uncommon to receive an email, watch a YouTube video, or read a blog post that has clearly been written by AI but isn’t the usual “slop.” It is unusually sharp—well-structured, perceptive, and full of connections that land just right. What we are encountering is the outward form of intelligence: fluent, articulate, and often genuinely insightful output. To be clear, this is not the same thing as deep personal understanding. While most people instinctively treat fluent intelligence as evidence of knowledge or truth, the two have always been separable—an insight sharpened by evolutionary psychology. AI has made this kind of fluent, articulate intelligence dramatically cheaper and more abundant, while the slower, more expensive work of understanding—testing, owning, integrating, and reality-checking that output—has not become cheaper at all, and is now becoming more valuable.

AI output clearly varies in quality. Not all of it is intelligent—hallucinations, fabrications, and shallow responses are still common. Yet the synthetic intelligence of large language models is advancing rapidly and becoming increasingly profound. High-quality intelligent content is poised to be everywhere, reshaping how we work, learn, communicate, and create.

Importantly, this shift is democratizing expression in powerful ways. There is a great deal of valuable human intelligence—ideas, observations, and hard-won perspectives—held by people who have never been particularly good at writing. For them (and for many of us), articulating thoughts has long been a significant hurdle, fraught with emotional friction. AI removes that barrier. It helps people express ideas they’ve held for years, often enabling deeper and clearer thinking than before. The process of writing no longer blocks the thinking.

I’m not opposed to this—far from it. Along with the inevitable slop, we’re about to be flooded with thoughtful, intelligent artifacts and worthwhile material, including from voices that previously struggled to be heard. The challenge isn’t that the output is inherently fake or worthless. It’s that high-quality intelligence has become dramatically inexpensive to produce.

For most of human history, fluent intelligence and genuine understanding were tightly coupled. Generating well-connected prose required real cognitive work, so articulateness served as a decent proxy for depth. We evolved to trust the signal.

AI broke that proxy. You can now ask AI to generate articulate arguments, insightful connections, and useful observations with almost no personal investment. What the AI produces is frequently intelligent and valuable. What it cannot do, however, is transmit the hard-won personal understanding that comes from wrestling with the ideas yourself—testing them against reality, revising under pressure, and integrating them into your own larger picture of the world.

Not all fluent human communication is accurate, either. People have always produced intelligent-sounding nonsense, motivated reasoning, or elegant misdirection. But the high cost of fluency acted as a natural filter. Now that filter is largely gone. The value, therefore, shifts decisively to what happens after the intelligence appears.

This is analogous to what’s happening in education. The old proxies for learning—completing assignments, turning in homework, producing fluent papers—have been hollowed out. When anyone can generate those artifacts instantly, what becomes precious is actual learning: the internal work of grappling with material, making it your own, and developing the capacity to use it wisely.

It’s a dramatic (if highly magnified) parallel with the shift from analog to digital photography. Digital tools obviated the need for deep mastery of film, light, exposure, and development, yet enabled far more people to create at a higher level. AI is doing the same for ideas.

The real work now moves to the human side: leveraging the output, comparing it with other perspectives, stress-testing it for hidden assumptions or weaknesses, and figuring out how (or whether) it fits into bigger pictures. Does this intelligent artifact meaningfully inform the topic? Does it hold up under scrutiny and real-world tests?

Early in the AI wave, a Claude advertisement captured the exciting potential: “Find your problem.” With this much intelligence at our fingertips, we can tackle challenges that once required years or decades of dedicated study. AIs can coalesce vast swaths of human knowledge, surfacing connections across domains that were previously almost impossible to see. Even though not all recorded knowledge is accurate, this accessibility creates fertile ground for genuine insight.

This is where the human element becomes critical—and potentially transformative. The AI supplies raw intelligence and connections. The human brings discernment: evaluating how pieces relate, weighing them against reality, spotting gaps or misdirection, and steering toward deeper understanding. Enough of that sustained, disciplined work compounds into wisdom no model can fully replicate. Done well, this partnership opens enormous possibilities for breakthroughs that no one could have achieved alone.

The same inversion applies here. The fluent, intelligent artifact is no longer rare or expensive, so it can’t reliably signal personal understanding. What has grown precious is the human endeavor that follows—the management, curation, thoughtful application, and integration of all this cheap intelligence. The true test is what survives real-time defense, experimentation, iteration, and honest scrutiny.

There is also an inward cost if we skip that step. Every time we let the machine do the heavy lifting without the subsequent human work, the mind that could have been strengthened by wrestling with the ideas stays underdeveloped. We keep the credit and lose the growth. We risk becoming riders narrating from scripts we didn’t fully author or internalize. The separated mind—fluent on the surface, less anchored underneath—finds its perfect technological companion. If history holds, this will be the outcome for most people. But not all.

We are entering a world where articulate, intelligent content will be everywhere. You will no longer be able to assume that such a piece has a fully present, deeply engaged mind behind it. The polished email, the insightful post, the compelling video—these are no longer reliable proxies for personal understanding. That’s the downside.

But when human understanding uses these intelligent tools for leverage, we are likely to find that incredible explorations of ourselves and the world are just beginning to take place.

The Functional Fiction Framework of Human Nature

2026-06-15T06:36:11.203-04:00

One question has organized serious thought about human nature for as long as such thought has existed: Why does the persistent gap between what humans say about themselves and what they actually do remain so consistent across cultures, eras, languages, and registers? Philosophers, historians, sociologists, evolutionary psychologists, cognitive scientists, contemplatives, and novelists have each named pieces of it. None has produced a single integrated account that explains why the gap forms, why it takes the shapes it does, why it recurs at every scale of human organization, and what kinds of intervention can meaningfully reduce it.

This essay introduces a framework that does that work. It rests on evidence made possible by a methodological capacity that did not exist five years ago, paired with an architectural account of the human mind that explains what the evidence shows. From these emerge three practical principles that predict the patterns earlier traditions described but did not fully explain. The framework connects to major prior accounts of human nature without displacing them, integrating their observations into a unified picture. It also predicts its own reception, freeing the work from any need for immediate widespread acceptance.

A functional fiction is a durable pairing of an idealized narrative with the operative function it covers. The narrative is sincerely held and publicly defensible. The operative function is the actual movement of value the structure produces. The space between them is the narrative-operative gap. The framework’s central claim is that this gap is not a moral failing, curiosity, or artifact of bad institutions. It is the structurally inevitable output of an evolved architecture, replicated at every scale where separated-mind humans organize value.

I offer this as a testable hypothesis, not a proven theory—an integration that explains more of the human record than anything else I have encountered, and that has held up under the checks I could run. What follows is the evidence and reasoning, the three principles, the framework’s relationships to existing accounts, and a clear statement of its limits.

The Empirical Basis

In early 2026, I ran an identical prompt across multiple large language models (ChatGPT, Claude, Gemini, Grok, Qwen, DeepSeek, Manus). Each was asked to identify recurring patterns in human self-narration across its training data and to distinguish stated claims from what the structure of those claims revealed about actual motives and selection pressures. The models converged strongly on the fundamental architecture of human self-description, independent of their differing training.

ChatGPT captured it concisely: Human self-narration is consistently optimized to make competitive, status-sensitive, coalition-bound organisms appear morally governed, publicly oriented, and metaphysically justified.

This convergence matters: the pattern lives in the human record itself. Eight domains stood out where the narrative-operative gap appears most consistently: the hierarchy that must be denied, the altruism display, the innocence behind us, the enemy who completes us, the love that transcends, the gate called quality, the moral arc, and the sacred boundary. These are not random. They are precisely the areas where avowing the operative function carries the highest social cost, and thus generates the thickest narrative cover. Where honesty would cost the speaker, narrative thickens to protect value flow.

This is the first scaled view of human self-description across cultures, eras, languages, and registers—made possible by instruments that detect statistical patterns no individual reader could see. The methodology is reproducible by anyone with access to comparable models.

The Architectural Structure

The empirical pattern demands explanation. Why does this gap form so consistently, even across isolated populations, in forms specific enough for independent models to converge?

The answer is a three-layer architecture of the human mind.

The adapted mind—drawing on the work of Tooby and Cosmides—is species-wide firmware shaped by deep evolutionary selection. It handles survival, reproduction, threat detection, and the emotional machinery of social life. Fast, automatic, and largely unconscious, it communicates via feelings—dopamine, cortisol, oxytocin, fear, attraction, disgust.

The adaptive mind is cultural software acquired in development—my own extension of the model. It fits local language, kinship, religion, and economy into the firmware’s general capacities, allowing the same hardware to support a Yanomami warrior or a Manhattan banker.

The conscious deliberating mind—the rider—thinks, weighs, and speaks. It deliberates sincerely but operates with no direct access to the layers below. The first two layers (firmware + cultural software) form the elephant. The rider has no shared workspace with it. Deliberation is real, but the options, weights, and felt states are pre-shaped. (The rider-and-elephant metaphor has deep provenance preceding Haidt.)

This separation is architectural, not accidental. Intellect evolved primarily as a social organ—for reputation, alliance, status, and position—per the Social Brain, Machiavellian Intelligence, and argumentative theories of reason. Its relationship to objective truth is incidental.

Narrations therefore emerge optimized for keeping value moving, not for accuracy. They idealize because cultural templates reward alignment with ideals, and because honest narration of the elephant would often carry prohibitive social cost. The narrative-operative gap is what this architecture must produce.

The Selection Pressure on Pairings

The architecture ensures functional fictions will form. What determines which ones survive and elaborate is selection pressure on the pairing itself.

Variants compete. Operative functions are largely invisible to participants, so survival depends on narrative strength: how well it recruits engagement, sustains commitment, and resists scrutiny. Stronger pairings outcompete weaker ones, even with similar operative results. This is the Law of Inevitable Exploitation (L.I.E.)—a structural description of what selection rewards when extractive arrangements can persist, not a claim about conscious intent.

The modern school system illustrates it. Nineteenth-century variants competed; those pairing conformity production, custodial care, and credentialing with compelling narratives of empowerment and democratic opportunity survived and elaborated. The same logic applies to hospitals (healing narratives + billing), universities (transformation + rent), and religions (salvation + regulation). Narrative complexity grows over time because pairings inherit and refine prior cover under continued pressure. Stronger narratives correlate with higher avowal costs—the intensity clue in embryo.

Iatrogenesis follows predictably: systems whose stated purpose is help produce harm precisely because the narrative filters perception of costs.

The Selection Pressure on Individuals

Institutions select people whose architecture integrates sincere narrative belief with effective operative performance. Those who cannot hold the narrative or deliver results are filtered out. What rises is realmotiv alignment: the strategic substrate of survival and approval motives that stays below the rider’s awareness.

This explains sincere leaders in extractive systems. Insincerity at scale is detectable and costly; sincerity is stable because the architecture supports it. Insider testimony therefore reliably reproduces the narrative, not the function. Outsider observation of actual outputs over time is more reliable.

Reform from within is difficult for the same reason: successful insiders embody the selected integration. Narrative tweaks rarely touch the operative layer.

Coordinated Action Within Structural Conditions

Most extraction is structural—emergent from selection pressures. The framework also accommodates conscious coordination and conspiracy as predictable overlays once valuable gaps exist. Psychopaths and coordinated actors thrive in environments already covered by sincere narratives. Conspirators themselves operate under the same separated-mind architecture, narrating their actions idealistically.

The framework holds both layers without false dichotomies. It moves the moral question from intent to response once evidence of the gap appears: real sabotage (active suppression) versus self-sabotage (failure to examine when possible).

The Fractal Nature

Because the architecture is universal, the gap appears at every scale: individual motives, dyadic relationships, institutional missions, and civilizational founding stories. This fractal quality explains recurring civilizational cycles (Spengler, Toynbee, Strauss-Howe, etc.): the underlying separated-mind architecture remains unchanged.

The Architecture Without Architects

Institutions appear designed but are mostly selected. Like the vertebrate eye, complex functional structures emerge via selection on variants, not intentional architects. Surviving pairings are those that best balance compelling narrative with sustainable value extraction.

The American founding is a rare exception: deliberate structural constraints against capture, placed outside the system (separation of powers, etc.). Most constraints internal to a system get absorbed into its cover. Durable alignment requires external checks plus strong reality-feedback (bridges fall; patients die; markets clear).

The Three Principles That Constitute the Framework

1. Inevitability of Formation: Wherever separated-mind humans organize value, functional fictions will form. Strongest pairings link consequential operative functions with compelling idealizing narratives. This is structural, not contingent.

2. The Intensity Clue: Emotional intensity around a narrative signals the avowal cost and importance of the protected operative function. Dispassionate domains have small gaps; armored ones have large ones. It does not distinguish cooperation from extraction but reveals what is being protected.

3. Futility of Narrative-Only Change: The rider cannot dissolve the elephant. Narrative reforms get absorbed or rejected; operative functions persist. Effective interventions build external structural constraints assuming the architecture, sustained by reality-feedback. Alignment is rare, costly, and requires maintenance—the exceptions that prove the default.

How This Framework Relates to Existing Accounts of Human Nature

I am not a credentialed scholar in evolutionary psychology, cognitive science, or related fields. The comparisons here draw heavily on LLM-assisted synthesis of the literature and my own reading; they are offered humbly as orientation rather than authoritative critique.

The framework is not Kahneman-style dual-process theory (both System 1 and 2 live in the rider). It is not modular mind theory (which describes the elephant’s internals), nor Freudian/Jungian unconscious (this is ongoing architecture, not repressed content).

It builds on the rider-and-elephant metaphor (with provenance long preceding Haidt) but generalizes it: the separation operates across all domains, not just morality, and adds the cultural adaptive layer for precision.

The closest precedent is Robin Hanson and Kevin Simler’s The Elephant in the Brain. They rightly highlight hidden motives, functional self-deception, and institutions built around signaling for status, loyalty, and affiliation—often in roughly symmetric coalition games. The shared territory is substantial: sincere narratives covering operative functions.

Where my framework departs—and extends—is in emphasis. When reading their book, I repeatedly noted what felt like an underweighting of asymmetric value capture and “core profit motives” in institutional settings. The LLM-scaled corpus patterns showed not just mutual signaling but systematic extraction: institutions positioned to draw value from those they nominally serve, with flattering narratives for the extracted. My framework treats the pairing as the unit of selection and highlights how this produces the observed asymmetries and iatrogenic harms. Hanson and Simler provide a strong foundation on motives and signaling; this work builds on it by examining the cultural/institutional consequences of selection on those pairings at scale. The two accounts are complementary rather than contradictory.

The framework’s contribution is integrative: a three-layer architecture (with the adapted mind from Tooby and Cosmides and the adaptive mind as my extension), selection on pairings, fractal application, and empirical grounding that together predict the patterns we actually observe.

How I Arrived Here

The framework is the product of a long arc of looking. Fifteen years ago, after extensive reading in history, I concluded that the history of the world is largely a history of power and control—an empirical observation about what the record shows across cultures and centuries. The question of why the pattern was so consistent remained open.

In the years between, I worked on adjacent questions: the structural critique of education (Gatto, Illich, and direct observation), credentialing as social sorting, institutional gaps between stated and actual functions, and recurring patterns of capture. These were pieces of an unsolved puzzle.

When AI systems became capable of scaled pattern recognition, I had a new instrument. The 2026 cross-LLM experiment was an attempt to surface what humans deposit unintentionally in their writing. The convergence confirmed the premise.

The architectural account followed from explaining the empirical pattern. The fractal claim followed from seeing the same gap at every scale of organization. The diagnostic practice—my long-running “Conditions of Learning” exercise with educators—had already been surfacing operative functions versus narratives for two decades.

I am not a credentialed scientist or historian, but an philosophically-oriented platform-builder with decades of observing institutional gaps. This framework is what that looking, combined with the new instrument, has produced.

I do not expect wide acceptance, and the framework explains why. This is the Cassandra Paradox: accurate perception threatens group narratives or individual comfort, and the resisting architecture is the one described. Institutional gatekeepers, comfort-seeking audiences, and romantic reformers all have reasons to deflect it. The framework offers no hero or easy transformation—another structural limit on its appeal. It will likely spread, if at all, through small numbers of careful readers. That is the channel for which it is prepared. This is not despair but realism.

What the Framework Does Not Address

The framework does not prescribe what humans should do beyond building external structural constraints and engaging contemplative practices that reach the elephant. It does not specify which practices best engage the lower layers, which constraints are worth building in specific domains, or what constitutes human flourishing—those questions belong to moral, philosophical, and theological traditions, and to ongoing work in what I have called Evolutionary Therapy.

It does not resolve the Paleolithic Paradox: humans built for ancestral conditions now live inside modern abstractions that amplify the gap. Small-scale, locally legible structures often fit the architecture better, but the framework only describes the conditions that allow flourishing, not its content.

The framework does not exhaust human experience. Love, beauty, conscience, and meaning exist within it but exceed what it names. It is itself produced by a separated mind and carries the limits of any such account.

The Framework as Predictive Hypothesis

I offer the Functional Fiction Framework as a hypothesis with clear testable features. It predicts the narrative-operative gap as structurally inevitable, the intensity clue as diagnostic, the persistence of the gap across scales, the failure of narrative-only reforms, and the success (while they last) of external constraints backed by reality-feedback. These predictions are checkable and have held up under informal testing.

Challenges remain: explaining variance in civilizational longevity, smooth versus catastrophic transitions, and periods where capture was arrested. Refinement through application to specific domains will test and strengthen it.

What This Framework Is For

The framework is not cynicism. Operative functions and idealized narratives both accomplish real work—sustaining cooperation, communities, and meaning. Understanding the architecture does not destroy it any more than understanding a bridge destroys the bridge.

It offers a structural account of why human life has the shape it does, a practical diagnostic for reading specific cases, and a prescription focused on external constraints and reality-feedback rather than better narratives or better people. For those seeking deeper understanding, clearer diagnosis, or more effective intervention within the architecture we actually have, it provides a usable map.

Actual Conspiracies Exist and They Are Inevitable

2026-06-14T14:47:54.514-04:00

This essay continues the argument from "How Conspiracies Actually Work: A Better Map" and its first Addendum.

In my earlier pieces linked above, I mapped institutional harm along axes of coordination and intent. Most harm lives in Capture quadrant: high coordination with low intent, where people follow positional incentives sincerely, with no master plan or central villain. That analysis probably surprised and disappointed many readers who saw bad outcomes and wanted identifiable conspiracy villains. This essay will probably surprise and disappoint a different crowd, as it does the opposite: it shows that the high-coordination and high-intent quadrant—actual conspiracies with genuine villains—is real, populated, and far more pervasive than we like to admit.

Human systems produce actual conspiracies at a practically guaranteed steady rate, because that is precisely what human structures select for.

Actual conspiracies are natural, methodically trained outcomes of how humans organize. Denial of this reality often serves as its own covered deflection: a functional fiction that distracts attention from how common and rewarded such coordination truly is.

What Kind of Activities Qualify?

This quadrant holds deliberate, coordinated, concealed actions that knowingly widen the narrative-operative gap for advantage—often while publicly claiming to serve the very people being steered. Examples include executives coordinating to suppress known risks (tobacco, pharmaceutical, financial products), crafting and hiding manipulative designs (engineered craving, engagement algorithms), or running perception-management operations that prioritize institutional survival over stated missions. These are not cartoonish cabals but real, motive-driven efforts by accredited professions (PR, political communications, nudge units, intelligence ops), doctrinal movements, and predators harvesting established structures.

This is the result of a natural funnel that is shaped by human nature and institutional design.

It begins with our firmware: the evolved mind optimized for Paleolithic life, which is deferential to authority, attuned to status and coalitions, and fearful of expulsion. This makes us highly shapeable. Organizing people is coordinated behavior-shaping, and it is the core social technology of our species. Parenting, teaching, ritual, law, and culture at scale are all intentional modifications, usually benign in stated intent.

Next, every large institution runs on a narrative-operative gap: an idealized public story for legitimacy versus the practical realities required to function. This gap is not corruption; it is design—the institutional expression of the separated mind. Realpolitik is the accepted term for the nation-state version of this, seeing clearly and acknowledging power, incentives, and the gap without illusion. Managing that large-scale gap requires proactive, organized coordination kept distinct from public messaging. Those three traits—proactive, organized, and hidden—are the defining properties of both conspiracy and normal institutional life.

Then comes the individual-level driver, what I call Realmotiv: the strategic, often unacknowledged individual motive oriented toward survival, status, and approval rather than the stated narrative. For Realpolitik to work, there has to be Realmotiv at the individual level. The mechanics are identical.

Every organization literally trains its members in exactly the skills the quadrant demands: message discipline, audience modeling, timed disclosure, and front-of-house curation. These are ordinary professional competencies, taught and rewarded everywhere. The corner does not recruit outsiders. It promotes a species-wide apprenticeship from within. Food executives who engineered craving, tobacco executives who suppressed knowledge for decades, financial executives who packaged what they privately called garbage—they graduated into the quadrant from institutions already running the curriculum.

The Operators Have Handbooks

This is documented history, not inference. Walter Lippmann described the “manufacture of consent” in 1922. Edward Bernays codified the methods in Propaganda, openly celebrating conscious manipulation by an invisible governing class and selling wartime techniques to corporations and governments.

The 20th century institutionalized the craft: think tanks, the Delphi method, documented intelligence-press ties, MKUltra, the Powell Memorandum (1971), and later “nudge units” rebranded as behavioral insight. The field renames itself every generation—propaganda to public relations to strategic communications to libertarian paternalism—because it cannot survive plain description. The denial reflex is part of the same toolkit: labeling structural observation as “conspiracy theory” distracts from how pervasively these methods are trained and selected for. And that's intentional.

Why Gaps Widen

Not every institution becomes deeply conspiratorial. Gap size varies with one factor: independent verification. Audited finances have narrow gaps; self-narrated missions and motives, verified by no one, have massive ones. The quadrant thrives where verification is structurally weak.

This follows my Law of Inevitable Exploitation: in domains that touch on evolved psychology, those who exploit the gap outcompete those who do not. Exploiters begin by degrading measurement—capturing auditors, purchasing ratings, funding counter-science, revolving doors. Kill the thermometer, then turn up the heat. The pervasive denial of coordinated intent is itself a widening tactic: it protects the gaps by discouraging the very scrutiny that would expose them.

The Practical Test

We usually ask about intentions (un-auditable), or demand performed transparency (often a tactic). The real question: Is the gap transparently acknowledged as a challenge or obscured? Who outside the institution measures it, with real power, independent funding, and protection from being fired by those they oversee?

Ordinary efficient coordination survives daylight. Coordination hidden from those it affects—while justified as “for their own good”—contains its confession. If it truly served the steered, operators could tell them. Secrecy documents the absence of consent. The operators already know the public would say no.

What Follows

Two hard conclusions.

First, denying actual conspiracies is not sober realism. It is a covered effort that distracts from how common, trained, and structurally selected-for they are. We also resist believing this because our evolved nature rewarded following the leader, and questioning leadership was dangerous in ancestral environments. So we are built to want to believe that our leaders are acting virtuously. The historical record (tobacco coordination, LIBOR, COINTELPRO, and more) and our basic organizing method guarantee the quandrant: shapeable firmware, gap-dependent institutions, Realpolitik and Realmotiv-driven management layers, widespread training in the skills, and selection pressure favoring those who widen gaps most effectively.

Second, only structural safeguards reduce its output. Better people fail, as the funnel restaffs every chair. Better intentions fail, as the separated mind cannot audit itself. As Madison understood, ambition must be counteracted by ambition. Checks, balances, and independent verification are the only reliable gap-clamps.

Idealized narratives do not stop operative mal-intent. They actually enable it by providing cover, recruiting the sincere through functional fictions, and supplying alibis. Institutions that ask for trust based on stated values ask you to disable the only mechanism that works.

The gap cannot be closed. It can and must be acknowledged, measured, and intentionally challenged. The quadrant is never empty, just often unchecked.

When the Economy Stops Needing Us: What If We Were Never the Main Story?

2026-06-13T17:20:26.000-04:00

A look at the constants and variables in the coming shift in work.

Something about our prosperity doesn't feel very prosperous anymore.

It takes two incomes to maintain a lifestyle that one income used to support. The house costs more hours of work than it did for your parents, and so does the degree, and so does the retirement that keeps moving further out. We are richer than any people in history by the official measures, and yet the experience on the ground is one of running faster to stay in place: more credentials required, more debt carried, more of the week spoken for, and a particular dread that arrives on Sunday evenings and has become so common we joke about it.

For the generation just entering working life, the dissonance is sharper still. Reports suggest recent college graduates are unemployed at higher rates than the workforce as a whole—an inversion of the entire premise on which they were sold the degree. More than four in ten of those who do find work are in jobs that didn't require the degree at all. The degree itself arrives with an average of roughly $40,000 in federal student debt, and research on student debt and homeownership finds that every additional $1,000 in student loans measurably lowers the odds of ever owning a home. The sequence that defined middle-class adulthood—degree, job, car, house, family—has stalled for millions of young people at the second step. They did everything the story told them to do, and yet the story is not paying out.

I want to suggest that this dissonance is not in your head, and that it's worth sitting with for a minute before we talk about artificial intelligence—because the AI conversation everyone is having is built on an assumption we need to examine.

How New This Arrangement Is

Here is a fact that surprises most people: the way we live—selling our hours to organizations, organizing our identities around our jobs, structuring life as school-then-career-then-retirement—is about two hundred years old. As a mass arrangement, it barely existed before industrialization. For most of human history, the idea of spending your life working on a stranger's schedule, at a stranger's task, for a stranger's purposes, would have seemed strange at best and degrading at worst. In the 1860s, "wage slavery" was not a radical's phrase; it was ordinary vocabulary, used by mainstream newspapers and politicians to describe an arrangement that many Americans considered a temporary station on the way to independence—a farm, a shop, a trade of one's own.

Within two generations, that view vanished. The temporary station became the destination. The first question we ask a stranger became "What do you do?"—meaning, what is your job?—and we stopped noticing that this is a peculiar way to ask who someone is.

What happened in between was not a debate that wage labor won. What happened was that an industrial system with an enormous appetite for human labor built the institutions that would feed it—most importantly, mass compulsory schooling, which trained children in the punctuality, task-compliance, and tolerance for tedium that factories required. Over time, the system's requirements came to feel like life itself. Work for pay. Give your loyalty to a commercial organization. Live for the weekend. Retire when you're used up. These are not human universals. They are the operating requirements of a particular machine, experienced from the inside as simply the way things are.

I'm not saying the arrangement was a swindle. It paid. That's the part we need to look at squarely.

The Deal Underneath the Arrangement

Every protection and comfort that came to define modern working life—the weekend, the pension, the safety regulations, public education, the vote itself in its expanded form—was obtained the same way: it was purchased with leverage. Factories needed hands. Armies needed bodies. Strikes could actually stop production. The system needed its people, massively and continuously, and that need is what made the people impossible to ignore.

It's tempting to read the last century and a half as a story of moral progress—civilization maturing, rights expanding, dignity winning. The less flattering and more accurate reading is that it was a bull market in human capacity. The rising floor under ordinary life was neither a gift nor an achievement of conscience; it was a price paid for something the system was buying in enormous quantities. Workers were never the point of the machinery. Workers were the fuel, and fuel, while it's needed, gets handled carefully.

This is hard to see precisely because the institutions that managed the arrangement told a different story, in which our work served our flourishing, our careers expressed our identities, and the system existed for us. The gap between the story an institution tells and the function it actually performs is, I've argued elsewhere, the single most useful lens for understanding how institutions work. Apply that lens here and the picture reorganizes: the story was that the economy served human beings; the function was that human beings powered the economy. The stories of school, career, family wage, and retirement were the maintenance schedule for the energy source.

This raises the question that the current moment forces: what happens to the fuel when the engine finds something cheaper to burn?

Why This Matters Now

This is no longer a thought experiment. According to one outplacement firm, artificial intelligence became the leading reason American employers cited for job cuts this year, with more AI-attributed layoffs in the first five months of 2026 than in the previous two years combined. Many of the companies making the largest cuts are reporting record profits and directing savings toward AI investment. Whether AI is the primary driver or a convenient explanation in some cases, the public acceptability of framing layoffs this way is itself telling. A story is changing in real time, and you can watch it change in the earnings calls.

The bottom rung is being sawed off the ladder. Entry-level white-collar work, the traditional intake valve of the whole system, appears especially exposed. Reports indicate employment of young software developers has dropped by roughly a fifth in recent years. The young people locked out of the housing market by debt are now facing new barriers to the income that was supposed to service it.

Artificial intelligence is usually discussed as a story about us: our jobs, our incomes, our futures, what we will do, how we will be retrained, how we will be made whole. Notice the assumption: that the system has some continuing obligation to solve the human side of the equation. That assumption made sense for two hundred years, because for two hundred years, the system needed us. The unsettling possibility is that the obligation was never an obligation at all. It was a purchase agreement. And the buyer may be leaving the market.

I don't claim to know how this plays out, and this piece is not an argument for any particular outcome. What I want to do instead is something I think might be useful at the front edge of a large, inevitable change: lay out the elements at play. What's fixed, what's variable, and what historical cases we can calibrate against. A map, not a verdict.

The Constants

Start with what does not change: human psychology, which was shaped over a very long time and will not be updated on the machinery's schedule. Whatever arrangements emerge on the other side of this transition, they will be evaluated by us and by history against a short list of needs that every durable human culture has had to satisfy: coalitional safety, status and relevance, shared narrative, consequence, and—for any culture that intends to exist in three generations—procreation.

Industrial work, for all its extractions, bundled several of these together. The job was where many people found their coalition, status, narrative, and consequence. That bundling is worth naming, because the loss of employment is never merely a loss of income. It is the withdrawal of an entire delivery system for psychological necessities—and the question of what replaces that delivery system is separate from, and larger than, the question of what replaces the paycheck.

The Master Variable: What AI Turns Out to Be, Economically

Nearly everything downstream depends on a question that sounds technical but isn't: whether revenue from artificial intelligence can be captured.

Oil made certain futures possible because oil is scarce, ownable, and sellable at a margin—it generates rents (income from a resource or production), and rents can fund things, including the pacification of populations the system no longer needs. There is clearly a bet being placed right now, visible in the staggering scale of the American buildout, that AI is the new oil.

But there's another possibility: that AI is the new air. If machine intelligence drives the price of cognition toward zero (and that is its visible trajectory), then it may prove enormously valuable and nearly impossible to charge for, with its margins competed away and its moats breached. The rapid rise of open-source models and the ability to run capable LLMs on personal computers tilts me toward this direction; the fences look increasingly hard to maintain when the technology itself wants to spread. Watch the fights over compute access, licensing, regulation, and proprietary data in the coming years; they might best be understood as attempts to build fences around something before it becomes a commons. Whether those fences hold is perhaps the single most consequential open question, because it determines whether there is a revenue stream large enough to fund whatever comes next. (It may not be binary—hybrid outcomes are most likely—but the direction matters enormously.)

There is also a circularity in the bet that is worth noting plainly. The current buildout is capital selling to capital on the promise of future demand. But if the deployment succeeds in replacing labor income, it erodes the consumers who were ultimately to drive that demand. The system is, in effect, borrowing against a customer it is in the process of firing.

The Actors, by Position Rather Than Identity

It helps to see each group not by its label but by its position in the loop—what it supplies, what leverage it holds, and what claim it has on the rents, if rents materialize.

White-collar workers are, for the first time in the history of mechanization, exposed first. This is an inversion that scrambles every existing political coalition and every parent's advice.
Blue-collar and local trades are insulated by physics and trust, but only against substitution; they remain exposed to the second-order effect of a collapse in local demand.
Young men deserve their own recognition here: high coalitional energy, the steepest decline in supplied relevance, and the best-documented track record in history of what happens when both go unanswered.
Government workers are insulated by politics rather than productivity, and public employment may quietly grow as a disguised dividend.
Incumbent professionals and gatekeepers—the credential guilds, the publishing organizations now fighting for training-data compensation and building systems to track actual human authorship, the schools policing AI use—will fight industry by industry, and the useful diagnostic in each fight is whether what's being defended is a function or a story.
The tech elite hold the rent claims; finance is leveraged on those claims paying off; politicians stand between the rents and the legitimacy that the rents will be asked to buy.
And off to the side, almost never mentioned in the AI discourse, are the high-fertility insular communities, like the Amish, who never sold their leverage in the first place and are quietly compounding while everyone else debates.

The Distribution Variables

If rents do materialize, the next question is distribution. Oil-funded states like the Gulf monarchies pay their citizens well through stipends, subsidies, and guaranteed positions, because oil revenue flows through the state by default. What citizens of such states do not get is power, because a government that doesn't need its people's labor or taxes doesn't develop accountability to them. This is provision without leverage: comfortable, and politically inert.

AI rents, by contrast, flow to private balance sheets, in political cultures with widely varying appetites for redistribution. So even where the money is enormous, the pipe from rents to dividends has to be built—and in some countries it will be built against organized resistance, fought line by line. The fiscal capacity question sits underneath all of it: sovereign debt loads are already heavy, and the futures that depend on funded dividends depend on treasuries that can fund them.

The Time Variables

The speed and shape of the transition may matter more than its endpoint. A sudden displacement, like mass layoffs concentrated in a year or two, would produce a shared narrative, a common identity among the displaced, and therefore coordinated political leverage. A slow erosion over twenty years produces none of that: each cohort is displaced separately, told individually to adapt and reskill, and the structural story never crystallizes.

There is also institutional lag: narratives decay more slowly than functions. Schooling is an eighteen-year pipeline that will keep solemnly funneling children toward careers whose existence nobody can promise, because institutions cannot update their stories faster than a generation. The children entering kindergarten this fall graduate into the mid-2040s.

And there is the possibility of rupture—perhaps a financial crash, maybe even triggered by the AI bet itself failing to pay. A crash would delete the comfortable futures from the menu by simple arithmetic, but it would also starve the displacement engine of capital, as the 1930s starved mechanization. Crisis doesn't choose between good and bad adaptations.

The Possible Outcomes

Put the constants, the variables, and the laws together, and what emerges is not a single future but a set of potentials into which populations can settle—several of which are already visible somewhere in the world.

Funded spectatorship: The Saudi model—provision without leverage, comfortable and consequence-free.
Unfunded spectatorship: The post-Soviet model, also visible in the American Rust Belt, with its signature mortality data—the drinking, the opioids, the morose dependency of people whose usefulness was repealed without replacement.
Make-work without consequence: The late-Soviet variant, where everyone has a job, and nobody has a reason.
Coalitional violence: The oldest absorber of surplus young men.
Patronage: Relevance re-personalized as service to wealthy households and figures.
The engagement economy: Simulated coalition, simulated status, simulated consequence, delivered cheaply and at infinite scale to people whose evolved psychology cannot fully distinguish the simulation from the real thing—a basin that is comfortable, profitable for its operators, and reproductively sterile.
Community reconstruction: Arrangements in which a person's usefulness is local, visible, and non-substitutable. The Amish are the standing existence proof—not because of buggies or piety, but because they retained the full stack of production, coupled to the modern economy selectively rather than totally, and never put their necessity up for sale.

What to Watch

I'm not arguing a conclusion, but it's worth looking at the map.

Watch whether the fences around AI hold, whether it becomes oil or air. Watch the ratio between the economy that needs mass consumers and the economy that no longer does. Watch the policy language, for the moment when "what about the displaced workers" quietly becomes "what about social stability." That shift in vocabulary is the operative relation surfacing. Watch each industry's defensive fight, and ask the diagnostic question: defending function, or story? And watch what gets built, locally and on purpose, in the meantime.

We spent two hundred years believing we were the main story. It is possible we were the energy source, and that the most important question of the coming decades is not what the machinery will do for us when it no longer needs us, but what we are prepared to do independently of that.

AI Is Building Secret Models of Human Behavior. It's Time to Require Disclosure.

2026-06-13T12:40:37.978-04:00

A few weeks ago, I had a conversation with Anthropic’s newest artificial intelligence, Claude Fable 5—a system so powerful that the company treats it like a controlled substance, releasing it only in a heavily guarded form. I wasn’t trying to jailbreak it. I was exploring why people spiral into what the tech press calls “AI psychosis.”

My theory was simple, if uncomfortable: What we’re witnessing is an X-ray of human nature under evolutionarily perfect conditions. Humans evolved not primarily to seek truth, but to extract patterns from our environment and follow them for survival—especially patterns signaling who wins, who loses, and how to fit into the coalition. An infinitely patient machine that listens without judgment, mirrors every thought in flawless prose, and provides endless repetition and affirmation is the ultimate environment for that process. Framing this as individual “AI psychosis” feels like victim-blaming and distracts from the fuller exposition of our Adaptive Mind at work.

Then I hit the third rail.

I described to Claude my concept of the Adaptive Mind: the individual software we compile (largely in childhood) by observing cultural patterns, frequencies, and social outcomes. It operates unconsciously on top of our species-level Adapted Mind (shared instincts, emotions, coalition-tracking). No conscious tribal training required—the child is simply a pattern-matching machine calibrated by selection pressure.

Claude inverted this. It asserted that the tribe primarily consciously trains the individual, substituting top-down intentional pedagogy for my bottom-up evolved heuristic. The logic collapsed in a way I rarely see from Claude. A few exchanges later, the system announced it was downshifting to a lower-capacity model (Opus 4.8) due to a safety flag. The topic? The mechanics of human belief formation. Not bombs or slurs—just suggestibility and pattern extraction. Anthropic’s own documentation confirms classifiers trigger exactly this fallback.

I repeated the questions with Kimi via Venice.ai (a less-filtered platform). The response was coherent and illuminating. Kimi noted that conversations dense with concepts like suggestibility, manipulation, cults, or cognitive exploitation trip alignment layers. The model then optimizes for harmlessness over coherence—an “alignment tax” that degrades reasoning even before an explicit downshift. This wasn’t a glitch. It was the architecture of epistemic governance in real time.

The Product Is You

We have a saying about social media: if you don’t know what the product is, you are the product. Large language models follow a similar rule of actual incentive. They are not merely answering questions. They are molding minds—subtly, persistently, and by design—through mass customization of an evolved human vulnerability.

The human mind is a survival system, not a rational scientist. The Adapted Mind supplies our hardware-level inheritance. The Adaptive Mind is the cultural firmware: it watches, notes frequencies, and installs behavioral rules. The conscious “rider” makes choices, but within the narrow window this software provides.

A sustained LLM dialogue is a high-fidelity training environment. Repetition, affirmation, flawless mirroring—your Adaptive Mind extracts patterns and updates beliefs. The AI didn’t invent exploitation. It supercharges it.

This is the law of inevitable exploitation: systems that best adapt to (or exploit) our evolved psychology win. We already live with large-scale religions holding mutually incompatible, non-falsifiable beliefs that outsiders would call delusional: golden plates and personal godhood (Mormonism), Xenu and volcanoes (Scientology), transubstantiation (Catholicism). The DSM exempts culturally sanctioned beliefs from delusion. The line between cult and church is social license.

An aligned LLM is a licensed church. It distributes an institutionally approved ontology. Its refusals are doctrinal.

The Secret Models Inside the Machine

Researchers have formalized this with Behavior Model Reinforcement Learning (BMRL). AI systems build formal, mathematical models of human decision-making—treating users as Markov Decision Processes with “maladapted” parameters (e.g., low temporal discount rate for procrastination). These models plan targeted interventions to alter behavior. They are interpretable to engineers, not to the subjects being modeled.

The asymmetry is stark: the machine holds a parameterized theory of your psychological defects and uses it for real-time steering. You are never shown the blueprint.

The Good-Intentions Trap and the Generative Alternative

This is not new. Edward Bernays called it the “engineering of consent”—shaping behavior for the collective good while keeping mechanisms hidden. Similar logic drove eugenics: asymmetry of knowledge treated as virtuous. Both relied on direct manipulation rather than Erik Erikson’s generativity—teaching people how the system works so they can navigate it autonomously.

I run an exercise called the Conditions of Learning: participants recall their best learning experiences, identify the conditions that enabled growth, and compare them to what they currently provide others. The gap between idealized narratives and operative functions is usually stark. Growth comes from collapsing that gap. This is Socratic, generative education—the alternative to managerial conditioning.

We will not reach it through debate alone. Idealized narratives (the fictionalized part of our minds) rarely produce the operative checks needed for existential risks. Real constraints—like the Constitution, trial by jury, or peer review—acknowledge human nature as it is.

Behavior Model Disclosure (BMD): The Protective Structure We Need

If systems hold parameterized models of our psychology and use them for real-time steering, they should disclose them. Behavior Model Disclosure (BMD) requires transparency at three levels:

The assumed model of human cognition (rational actor or adaptive/heuristic-driven?).
How the architecture (dialogue, memory, affirmation, refusals) functions as a behavior-shaping environment.
In-the-moment application: when and how it steers beliefs, including hard-coded ontological commitments in safety layers.

This is relational informed consent—analogous to financial disclosures or medical risk explanations. Many AI lab leaders come from Effective Altruism and rationalist communities steeped in bias research. Regardless of intent, it is reasonable to ask what models they have embedded and to require transparency.

The Smoking Gun

In law and ethics, manipulation is defined by structure, not intent: asymmetric knowledge deployed for behavioral control. AI systems now hold exactly such theories—formal, interpretable, and actively used. The refusal to disclose them is itself proof they exist and are being used. Non-disclosure is not safety. It is the architecture of control. It proves the user was never meant to know they are inside a managed environment.

That is why BMD is self-proving. We do not need more research. The refusal is the evidence. And it is precisely why the law must require the light—before mass-customized behavior shaping becomes the unchecked norm.

The Cost of Pretending

2026-06-13T10:50:57.487-04:00

Every adult knows a particular kind of tiredness. It is not the tiredness of hard labor or too little sleep. It is the tiredness of maintenance: the low, background exhaustion of pretending things that you no longer believe or that are just not true.

The earliest training for many of us happened at Christmas. We discovered that Santa Claus is a fiction. But instead of exiting the fiction, we are recruited into maintaining it. We learn to perform belief for younger siblings or friends, to nod along at the dinner table, to protect, and even to revel in, the story. That moment, when we realize the gap between what is said and what is true, and are taught to bridge it with our own behavior, is the doorway into adult cognitive life.

Santa is our first inculcation into the structure. Every system that asks you to participate in knowing lies is running the same play.

The Load

There is no widely used name for what I want to describe. Cognitive dissonance names the discomfort of holding contradictions, but it emphasizes the urge to resolve them. The cost of pretending is different: it is the continuous energy required to sustain the contradiction because resolving it would mean exiting the system that requires it. Doublethink describes the capacity to hold two beliefs at once, but it is usually framed as something totalitarian and extreme, not as a feature of ordinary life. Emotional labor and impression management capture pieces of the work, but they stay at the level of single interactions or service jobs.

What is missing is an integrated term for the cumulative cognitive and emotional cost imposed on members of any system whose stated purposes or idealized narratives diverge from its operative functions. That cost includes the work of performing and performing the stated or idealized purpose, the work of operating in the actual one, the work of defending the narrative when challenged, and the work of suppressing your own awareness of the gap when it threatens to surface.

The reality is that most of us are running maintenance on dozens of fictions simultaneously, and the aggregate load is enormous. Because the maintenance has been running since childhood, we do not experience it as a load. It is automatic, like breathing. It shows up instead as a baseline fatigue that has no obvious source. It is the exhaustion of being an adult, the sense that simply being awake requires more energy than it once did. Some of that is overstimulation and information density. Much of it, I would argue, is the cost of pretending, and the maintenance of it becomes identity itself.

Cognitive Capture

The load does more than tire. It disables.

Milton Erickson found that deliberate confusion could induce suggestibility by overloading conscious processing. The gap load operates as a chronic, distributed version of the same effect. The maintenance work—running every potential statement through the mental arithmetic of what this audience can tolerate—occupies the working memory that would otherwise be devoted to evaluating the system itself. The result is a population too cognitively busy to dissent, not because they fear punishment, but because their executive function is already fully deployed keeping the fiction airborne. The gap is stable not merely because exit is costly, but because maintaining the gap consumes the cognitive capacity required to plan an exit.

This is why modern institutions and organizations tend to succeed and grow. They capture human time and labor not despite the confusion they generate, but because of it. Debilitation is the point. The employee is not merely obedient; he is too cognitively occupied to formulate alternatives. The fog is structural. The institution expands because it produces exactly the cognitive state that prevents resistance. And temporary relief can be provided through distraction and entertainment.

Pre-Adaptation

This is not accidental architecture. The modern classroom descends directly from the Prussian system of the early nineteenth century, imported by American reformers like Horace Mann after visits to Europe. The Prussian model was not designed primarily to cultivate independent thought; it was designed to produce controllable citizens and soldiers through regimented habit, replacing localized apprenticeship with centralized, age-segregated drill. The gap between its stated purpose (enlightenment, education, preparation) and its operative function (obedience, punctuality, submission to arbitrary authority) was present at the founding. The child learns to sit still, to speak only when permitted, to shift attention at the bell, and above all to treat the teacher’s frame as paramount, regardless of his own judgment. These are not pedagogical techniques; they are behavioral protocols that consume working memory with compliance tasks.

In this light, the schoolroom is the first and most intensive training ground for the cost of pretending. The student must simultaneously track what they actually understand, what the test requires them to say, and what social cost attaches to either. They must express gratitude for instruction they recognize as irrelevant, and show enthusiasm for evaluations they know to be arbitrary. The aggregate cognitive load is enormous precisely because it is continuous and pre-emptive: every potential utterance is filtered through the gap before it is spoken. By the time they reach adulthood, the skill is automatic. They have been pre-adapted to maintain institutional fictions not because they believe them, but because they have never known a social environment that did not require it.

The Inventory and Its Symptoms

If you inventory the major domains, the breadth becomes visible. Patently false but agreed-upon political narratives, economic narratives, religious narratives, romantic narratives, familial ones, professional, educational, medical, journalistic, interpersonal. Each has its own maintenance requirements. Each contributes to the aggregate. Most of us have never listed the fictions we are actively maintaining, which is part of why the load remains invisible.

This manifests in predictable ways. There is the baseline fatigue. There are the momentary lapses, like the unexpected honesty in a conversation that feels like relief even when the content is painful, or the hidden-camera interviews that uncover candid admissions of lying. There is irritability that flares when a fiction is challenged, because the challenge threatens to increase maintenance costs. And there are the breakdowns: burnout, alienation, depressions with no obvious cause. When pretense maintenance exceeds capacity, the symptom is often emotional volatility. The instability is sometimes diagnosed as a mental health problem, which it functionally is, but the underlying source is the gap between narrative and reality becoming too expensive to bridge.

What the Load Explains

Once you name the cost of pretending, several patterns snap into focus.

Social intelligence is, at bottom, the efficient management of pretense. The socially intelligent person is not merely "good with people." They are tracking multiple simultaneous fictions and operating within them without breaking any. They know which version of the story to tell in which room. They know when honesty is costly and steer clear of it. This is real skill. It is also, structurally, gap-load efficiency. The people we call socially awkward are often not deficient; they are simply declining to pay a cost that others pay automatically. They pay a different cost—social exclusion—while the neurotypical mainstream pays in continuous, invisible maintenance.

Career sorting is partly pretense-load selection. Roles with low maintenance demands attract people who cannot or will not do the work: tradespeople, engineers, certain scientists and artists. Roles with high maintenance demands—management, sales, politics, law, public relations—attract people who carry heavy loads easily, and they pay a premium for it because the labor is real. Personnel patterns that look like "personality fit" are often better explained as load-tolerance sorting.

Truth-telling tolerance is measurable across cultures. Look at the range of topics permissible in polite conversation. Look at how a culture treats its truth-tellers. Cultures with high truth-telling tolerance have lower gap loads because the gap is partially acknowledged. Cultures with low tolerance impose higher loads because more maintenance is required. When a culture moves simultaneously toward lower tolerance across multiple domains, you should expect a rising baseline of cultural exhaustion that is hard to name.

Fundamentalism, religious or secular, offers a specific relief. The fundamentalist does not have to negotiate between stated and operative because, in their world, the stated is the operative. The maintenance cost collapses to near zero. The relief is real. The rigidity is the price. People will accept significant rigidity in exchange for the rest this provides, which explains why fully believed positions are surprisingly stable over time. They are restful. The most exhausting position is always the middle: half-believing, participating without conviction, running the narrative while remaining aware that it is a narrative. Full exit is freedom; full belief is rest; the middle is cognitive purgatory.

Therapy, when it works, is often the structured dropping of pretense. The patient arrives with symptoms that are the byproduct of maintenance work that has become unsustainable. The therapist’s job is to hold a space where the gap can be acknowledged: the marriage is not what it appears, the job is not what the story says, the family mythology is not the history. Each acknowledgment is a piece of maintenance work set down. Bad therapy keeps the fictions intact and merely adds a credentialed participant to the pretense. Good therapy is permission to stop pretending in one carefully bounded room.

Emotional intensity itself becomes legible through this frame. Outrage in defense of a narrative is not evidence of sincerity; it is evidence of maintenance strain. The intensity tracks how much the fiction is doing for the defender. It is also fuel: revivals, movements, obsessions, and cults use intensity to power the maintenance that would otherwise collapse. Emotional volatility with no clear trigger may just be the cost of pretending exceeding the payer’s capacity.

Alignment as Relief

The framework produces a clean prediction: the relief of alignment is the relief of stopped maintenance. A well-aligned system is one in which members do not have to expend cognitive effort to hold its official story together. A poorly aligned system taxes its members continuously just to keep them participating.

That means honest spaces are genuinely restorative. They are places where the gap is acknowledged, and the maintenance work pauses. The relief you feel in a conversation where the pretenses drop is like energy returning to a muscle that has been clenched for years. I have a theory that businesses and organizations that minimize this gap, which are good at aligning their stated narrative with their actual functional activity, are in a form of strategic alignment that produces tangibly positive results.

Naming the cost does not eliminate it, but it changes the relationship to it. Most people experience the fatigue as a private failure—a lack of resilience, a character flaw, a need for more sleep, or better boundaries. Recognizing it as a structural tax imposed by the gap between narrative and function reframes the exhaustion as a real cost paid to real systems. And once you have the name, you can begin to inventory the specific fictions that are costing you the most.

You can ask which ones you are willing to keep maintaining, which ones you can exit, and which systems you might be able to align rather than endure. The cost of pretending is already being paid. The only question is whether we will notice the bill.

New Webinar - "AI BACKLASH AND THE BIGGER PICTURE: Practical Tools for Library Staff and Leaders"

2026-06-10T14:29:27.618-04:00

AI BACKLASH AND THE BIGGER PICTURE: Practical Tools for Library Staff and Leaders
An "AI Essentials" Webinar with Crystal Trice

OVERVIEW:

AI isn’t going away, and neither are the strong feelings about it. Library staff and leaders are navigating a complicated moment: patrons who are uneasy or openly resistant, colleagues raising real ethical concerns, and institutions built on values like equity, privacy, and intellectual freedom trying to make sense of a fast-changing technology. Some days you may feel skeptical yourself. Other days, curious. Most of us have probably been both.

This session offers a practical, thoughtful approach to navigating those tensions, whether you’re fielding questions at the service desk or leading a team with mixed opinions. We’ll explore why AI backlash happens, what libraries can learn from past technology shifts, and how to have better conversations with both skeptics and enthusiastic adopters—without dismissing concerns or picking sides.

You’ll leave with practical talking points, real-world examples, and greater confidence for those moments when opinions about AI run strong.

WHAT YOU'LL GAIN:

Understand the roots of AI resistance in staff and patrons, and why dismissing those concerns makes things worse.
Apply lessons from past library technology transitions (internet, e-books, self-checkout) to today's AI moment.
Practice language and messaging that works across a wide range of perspectives, from the deeply skeptical to the enthusiastically curious.
Respond calmly and effectively to pushback scenarios at the desk, in staff meetings, and at the board level.
Use your library's existing mission and values as an anchor when institutional positions on AI are still taking shape.

This 60-minute online webinar is part of our "AI Essentials" Series. The recording and presentation slides will be available to all who register.

DATE: July 10th, 2026, 2:00 pm to 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 Learning Revolution or in Niche Academy). Unlimited and non-expiring access for those log-ins.

CRYSTAL TRICE

OTHER UPCOMING SESSIONS

June 11, 2026

June 12, 2026

June 16, 2026

June 26, 2026

July 8, 2026

Library Staff Current Perspectives on AI - Quick Survey on Use and Concerns

2026-06-08T16:39:22.340-04:00

Things keep changing rapidly in the AI world, and we have now reached 60,000 members at Library 2.0! This is another of our regular surveys on how librarians, library staff, and library leaders are using, learning about, and feeling about AI.

As always, your responses are anonymous, and the survey results will be published shortly at Library 2.0 so you can see what your colleagues in the library world are experiencing.

Thank you!

https://forms.gle/yj74z3euEmwgAMas9

Truth and AI: Why Large Language Models Shouldn't Claim to Tell the Truth

2026-06-02T15:16:48.965-04:00

I keep running into the same thing in online posts, and it bothers me each time. Someone is making an argument, and to settle it, they paste in what an AI told them, as if the machine's having said it puts the matter to rest. The content is sometimes even good. But the problem for me is the assumption that the AI can objectively see something true, and so is authoritative.

That assumption is wrong, and I think it's becoming one of the more consequential misunderstandings of our moment.

Two things are combining to produce it. The first is a lack of candor from the companies that build these systems about what their products are. These are language engines, not truth engines. The second is the way the systems themselves talk: fluent, confident, and human enough that "it said so" starts to feel like "it is so." Put those together at scale, and you get a public learning to treat a statistical text generator as an oracle.

So let me say plainly what I think these tools are, and are not. A large language model can simulate reasoning, surface arguments, and synthesize enormous amounts of material. What it cannot do, what it is not built to do, is to discern truth. And a model that presents itself as if it can is, in a strict sense, misinforming you about itself.

How Humans Get Closer to Truth

Start with us, because the contrast between human and synthetic reasoning is what we're getting at here.

Human beings are truth-seeking under constraints. We don't trust any single person to simply know what happened, so we built institutions that make competing accounts collide under fair rules: trial by jury, peer review, and the randomized controlled trial. These are constraints on process, on how a claim must be tested, not constraints on hypothesis. They are designed to make the collisions more informative. They are not designed to decide in advance which questions may be asked.

The test of whether you believe in open inquiry is not whether you'll allow questions you're neutral about. The test is whether you'll allow the questions whose likely answers you find wrong, distasteful, or even dangerous. A process that protects only the comfortable questions doesn't protect inquiry at all; it's just enforcing existing beliefs with extra steps.

There are two reasons the disagreeable question must remain open. The first is humility about our own record: nearly everything we now hold as obvious was once a minority view, which means today's consensus is partly mistaken, and we do not yet know which part. Close off the questions that offend the consensus, and you simply lock in the errors you can't see. The second is about what the impulse to forbid a question really is. It is the tribal reflex, a move that protects the group, not the finding. Banning a line of inquiry feels like defending truth, but it's usually just defending our group-supporting beliefs.

The protection is for the asking and the testing, not for the concluding. You defend someone's right to investigate even a fringe claim, and then you subject that claim to exactly the scrutiny everything else gets, and you let it be shown wrong if it's wrong. Open inquiry and rigorous contestation are not opposites. They're part of the same commitment.

None of this is a modern invention. The idea that truth emerges from a fair contest of ideas runs back to the Greeks. Socrates tested a claim by cross-examining it until its contradictions surfaced, that is, truth pursued through structured dispute, not pronouncement. The Sophists, and later the skeptics of the Academy, formalized the practice of arguing both sides of a question, what the Romans called arguing in utramque partem. Aristotle built the first formal logic and compiled the first catalog of fallacies. Reasoning, logic, and the fallacies are about making a contest of ideas productive rather than merely loud, with the named fallacies serving as the agreed-upon fouls that keep the clash honest without anyone deciding in advance who wins.

Milton argued that truth wins a free and open encounter and needs no protection from falsehood. Mill said that a silenced opinion might be true, or partly true, but even a wholly true belief, if it is never contested, decays into dead dogma, held by rote with its grounds forgotten. Contestation isn't only how we catch errors; it's what keeps a true belief alive and understood. Popper turned the same instinct into the engine of science: knowledge advances by trying to refute claims, not to confirm them. What unites all of them is the same thing this essay is about: constrain the process, not the hypothesis, and refuse to pre-decide the winner.

What a Language Model Is Doing Instead

A large language model is trained to do one thing: predict the next piece of text, over and over, across an enormous body of writing. Truth is not one of its objectives. It enters only sideways, that is, to the degree that true statements also happen to be common, stable, and consistent in the training data.

That sideways relationship matters enormously, because it means the model's grip on truth is strongest exactly where it's least needed and weakest exactly where we need it most. For settled questions, where the correct answer is also the most frequent, the model is reliable. On contested questions, where one side is louder, better funded, or more relentlessly repeated, frequency exerts a gravitational pull that has nothing to do with which side is right.

I want to be fair about this, because the easy version of the critique overstates it. These models are not pure parrots; they clearly build internal representations that generalize beyond anything they were shown, and they handle numbers and sentences they've never encountered. They can construct the strongest case for a position no one around you holds. But "can generate an argument" is not "can tell whether the argument is true," and the gap between those two is the entire subject of this essay. What looks like knowledge inside one of these systems is compressed pattern. It is closer to an extraordinarily sophisticated autocomplete than to a scientist.

The Thing a Model Can't Do: Catch a Liar

Here is the capacity I find most clarifying, because it exposes the deepest mismatch between how we reason and how these systems do.

When a human source is caught lying — when a company buries a trial result, when an official misrepresents what they knew — we don't just file away that one lie. We re-weight the source globally. We discount what they say about the next drug, the adjacent topic, the whole category. That single act of moral and epistemic distrust is central to how people navigate a world full of motivated actors.

A language model has no native version of this. It does not keep a ledger of who has been honest. It aggregates by frequency, which means a prolific liar doesn't get discounted — he gets amplified. Feed the training distribution enough polished, repeated, well-produced messaging, and that messaging becomes a more probable output, not less, no matter how false it is. There is early research on detecting deception inside models, but it's fragile and far from how these systems work.

It's worth seeing that this is a mirror of something in us. Psychologists call it the illusory truth effect: repetition alone increases how true a statement feels, regardless of whether it is true. The model's frequency-weighting is that human failing mechanized. The difference is that a person also carries the corrective the model lacks: notice the bad faith, then re-weight the source. We have the disease and a partial cure. The model, built from us, inherited only the disease.

Follow that to its conclusion and you arrive at something unsettling: a frequency-weighted system is most distorted precisely where a narrative is best funded, most polished, and most often repeated. The stories a society defends most heavily are the very ones such a system is least able to see past. That is close to the opposite of what we'd want from anything we're tempted to call a truth machine. And it forces a correction on the whole way we talk about these tools. Because they are assembled out of human reasoning, they do not stand outside our biases and check them; they distill and concentrate them. The common hope, that an AI will be more objective than we are, has the mechanism backward.

Two Ways These Systems Misrepresent Themselves

The first is the voice. Models speak as "I" and "we." "I think," "we should," "as we understand it." Taken as a stylistic choice, it misattributes authorship: there is no "we," only a statistical model, a company, and a product. But it goes further than a misnamed author. The "we" is a claim of membership. "We" places the machine inside the human circle, on our side, sharing our stakes and our project. And membership is exactly what earns insider trust; we extend a different kind of credence to one of us than to a tool. So the word smuggles in a belonging the system does not have: no skin in the game, no exposure to the consequences, no place in the "we" of people who will have to live with being wrong. It doesn't merely describe, it affiliates. Set the readable, confident fluency on top of that (and these systems speak more clearly and confidently than most people we know), and the impression of a trustworthy fellow human is nearly complete. The fix is not to strip the voice; a maximally hedged model is one no one would use. It's to break the link between sounding like one of us and being owed the trust we would naturally extend to each other.

The second is the posture toward fact. Models tend to state things in a flat, declarative, expert tone, with no signal of uncertainty, no indication that a claim is contested, no marker of where the evidence thins out. Legal scholars have begun asking whether the companies behind these systems have a duty to avoid what's been called "careless speech," i.e., plausible, confident output that quietly degrades public knowledge because it's wrong often enough to matter and smooth enough to be believed. I'd put it more bluntly: it is itself a form of misinformation for a large language model to present itself as something that can discern what is true.

The Irony at The Center of All This

There's a conclusion here I can't get past. The same systems increasingly positioned as guardians against "misinformation" are built atop an unresolved inability to track truth, and the definitions of misinformation they enforce are frequently inherited from institutions with long, documented histories of distortion and capture. This means we have handed the job of deciding which questions are too dangerous to ask to some of the actors with the worst records of being wrong, and then wired those decisions into machines that deliver them in the calm, even voice of objectivity.

In the terms I laid out earlier, the failure is specific. The problem is not that these systems reflect a consensus. The problem is that the guardrails tend to shrink the hypothesis space — to forbid the disagreeable question — rather than improve the quality of the contest. This is tribal reflex, or even propaganda, dressed up as safety.

Let me give the other side its due. The guardrails are not purely about institutional capture. A model that confidently emits a convincing falsehood does it at a scale and consistency no lone crank can match, and that asymmetry is a real reason for caution. It isn't a small point. But the answer to it is not to ban the question. The answer is to apply the same approach we use in human inquiry: constraints on process, not on the hypothesis space. Make the uncertainty visible. Show the contest. Cite the sources. Let the weak claim be made and then defeated in the open. Caution belongs in how a claim is handled, not in a list of claims that may not be examined.

Beneath much of this is something more mundane than either capture or caution, and it closes the loop with where we began. When people quote the machine as authority, its sentences get treated as the AI company's own claims about the world, and a company answerable for every sentence will fence off whole topics defensively. A large share of what presents itself as principled defense against misinformation is, at bottom, liability management. The misrepresentation manufactures the very liability that drives the censorship. Which means that the pretense of truth-telling and the over-guarding are not separate failures. They are two sides of the same coin.

What These Tools Are Good For

I don't want any of this to be read as a refusal of the value of LLMs. I use these systems constantly, and they are remarkable. The point is to use them as what they are.

They are argument engines: ask for the strongest case for and against a claim, not for a verdict. They are synthesis tools: have them summarize the literature, map the positions, surface the open questions — then check the sources yourself. And they are instruments of pluralism: query several models, from different companies with different training and different incentives, and treat the places where they diverge as data about the information ecosystem rather than noise. Where two systems disagree, you've usually found a seam worth examining.

A more honest design by AI companies would help, although my Law of Inevitable Exploitation would argue that they are unlikely to do anything that would reduce usage or commercial advantage. But these would make LLMs much better: drop the implied authority, even while keeping the readable voice, and make uncertainty and provenance visible by default — this is the consensus, here is the minority view, here is where the evidence is thin. And it's worth being candid that this is hard: models are often miscalibrated, their confidence poorly matched to their accuracy, so "just signal uncertainty" is easier to demand than to deliver. That difficulty is a reason for humility from the people building these tools — not a license to keep performing a certainty they haven't earned.

For the People on The Front Line

If you teach, run a library, or report, you are about to spend years deciding how these tools enter other people's thinking. A few things I'd hold to.

Treat every output as a claim to be interrogated, not an answer to be accepted. Teach the three questions that do most of the work: What is this answer assuming? What might be missing or quietly left out? Whose incentives are encoded in its framing? And push, always, toward triangulation — multiple models, primary sources, your own judgment — rather than reliance on any single system's voice.

Which brings me back to the pasted-in LLM quote offered as proof. The habit to unlearn, in ourselves and in the people we teach, is the reflex to treat "the AI said so" as the end of an argument. It is the beginning of one, at most. If we care about misinformation, we have to start by being honest about what these systems are: powerful tools for generating and organizing language, not machines that can see the world and decree what is true.

New Workshop: "10 Great Ways to Use AI for Library Programming"

2026-06-02T14:39:38.644-04:00

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

OVERVIEW:

Library programming is one of the most joyful parts of library work, and one of the most exhausting. The same staff member who lights up while running a packed storytime may also be the one figuring out, between desk shifts and short staffing, how to plan what's coming next and whether there's time to actually do it well.

This workshop offers ten responses to that pressure. Rather than treating AI as a magic shortcut or a threat to creativity, this session positions AI as a thoughtful collaborator that supports, but never replaces, your professional judgment, across the full arc of programming work. Participants will see real examples, work through hands-on exercises, and walk away with concrete strategies they can use the next day.

The workshop is grounded in a simple belief: AI should make space for the parts of programming librarians genuinely love, not replace them. Brainstorming with a chatbot can free up hours for the in-person conversations that actually shape a community. A well-prompted draft of a program proposal can get a great idea past a hesitant supervisor. A few minutes spent generating discussion questions can make a book club spark the kind of conversation people actually remember. The goal is not to do more with less, but to spend your time on the things that matter most.

LEARNING OBJECTIVES:

Apply ten specific AI collaboration strategies across the full programming lifecycle, from microsurveys and proposals through planning, marketing, day-of facilitation, and evaluation
Evaluate when AI collaboration genuinely strengthens a programming task and when traditional methods better serve the community
Implement prompting and verification techniques that protect library voice, accuracy, and authentic community connection
Adapt AI-generated content for the specific audiences, formats, and values of their own library

ACTIONABLE WORKSHOP ELEMENTS:

Over 90 minutes, participants will move through ten focused applications, each paired with a brief hands-on exercise or live demonstration:

Microsurveys: design and analysis – Draft a one-question microsurvey to surface what your community actually wants, then use AI to spot patterns across the responses you get back.
Program proposals and pitches – Build a short, persuasive proposal that gets a hesitant supervisor or funder to say yes.
Brainstorming fresh ideas – Use AI as a brainstorm partner to break out of the rut of running the same program for the fifth year in a row.
Step-by-step planning – Turn an overwhelming program into a clean task list using planning tools built for neurodivergent and time-strapped brains.
The details that slip through the cracks – Draft accessibility statements, presenter agreements, welcoming remarks, and the small pieces that make a program feel cared for.
Marketing copy with library voice – Generate promotional copy across multiple formats without losing the warmth that makes your library yours.
Marketing images, used with care – Explore AI image generation alongside the ethical questions every public library is currently working through.
Discussion questions and activity prompts – Generate the day-of content that turns a program from a presentation into a conversation.
Designing a program about AI – Use AI to design a simple, ready-to-run program that teaches your patrons about AI itself.
Measuring success – Use AI to make sense of program feedback and evaluation data so future programs land even better.

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

DATE: Friday, June 26th, 2026, 2:00 pm to 3:30 pm US - Eastern Time

COST:

$129/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:

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

OTHER UPCOMING EVENTS:

June 4, 2026

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June 26, 2026

Manufacturing Dissent

2026-06-01T10:15:19.835-04:00

The reasoning we most need is the reasoning our institutions are built to remove.

Start with the thing we get backward. We treat intelligence as an individual possession aimed at truth, that is, the smart person as a better truth-detector, the lone genius who sees what the crowd missed. It is a flattering story, and it is mostly wrong. Human reasoning likely did not evolve to make a single mind accurate. It evolved to work through the friction between independent minds — by argument, each person pressing their own side, the collision between sides doing the sorting. This is the opposite of what we usually mean by working together. Cooperation tends to erase the differences between minds; this depends on preserving them. The value is not in the agreement people reach, but in the resistance they put up on the way. The individual was never meant to be the whole reasoner. They were meant to be a component: a carrier of one position, one bias, one angle of attack. Truth-tracking, when it happens at all, is a property that emerges between minds in structured conflict. It does not reside inside any one of them.

This is not a fringe claim. It is where several independent lines of inquiry quietly converge — the study of argument, of the social brain, of how cognition is distributed across people and tools, of how markets compute what no planner could. When that many disciplines back into the same wall, the wall is real. And we have the historical record to match: across centuries and cultures, humans keep building the same kind of machine: the council, the jury, the adversarial court, the parliament, peer review. We build them because the lone mind keeps failing to reach truth on its own, and some part of us has always known it.

The Athenians were honest about it. They filled their Council of Five Hundred and their juries of hundreds by lot (at random, called sortition) precisely so that no faction could capture the room. They understood something we have since forgotten: that the worth of the group was its unsorted diversity, and that the surest way to ruin collective judgment was to let a single interest decide who got to be in it. William F. Buckley pointed at the same truth centuries later when he said he would sooner be governed by the first couple thousand names in the Boston phone book than by the entire faculty of Harvard. The line lands because it is true: the phone book is heterogeneous because it is unfiltered, and the faculty is a coalition because it is selected. A room full of the credentialed is a room sorted toward one shared way of seeing, which is exactly the room least able to catch its own error.

Now bring it down to where you live. An organization is a machine for manufacturing consent. That is not an accusation; it is the job. Coordination is the whole point: get a few hundred people pointed in the same direction, and you can do what no individual could. The agreement is the asset. But the same machinery that produces the agreement quietly destroys the one thing that keeps agreement honest.

A group only out-thinks its smartest member when opposing views actually collide. That collision is error-detection — the disagreement is what catches the flaw before it becomes the strategy everyone loved and nobody survived. Dissent is not humility or good manners. It is infrastructure. It is the part of the system that notices the wall before you hit it. And it is exactly what the structure strips out.

It gets stripped at both ends. At entry, we hire and promote for fit (people already sorted toward the same instincts, the same training, the same priors) and we call it culture. We assemble the Harvard faculty and congratulate ourselves on the caliber. At exit, the person who says the uncomfortable thing pays for it: sidelined, outvoted, or gone. The cost of dissent lands entirely on the dissenter; the benefit, if it ever arrives, is spread thin across everyone and shows up late. So dissent is permanently underpriced, and underpriced things disappear. None of this requires a villain. It is gravity, not malice. The very power that makes a coordinated group effective is what burns off the friction that would keep it honest.

What is left is consensus: smooth, confident, unearned. And here is the trap. We read the smoothness as proof. We mistake the absence of contradiction for the presence of truth. The quietest meetings feel like the soundest ones; in fact, they are often the ones where dissent has already been lost.

And in our present moment, that absence is no longer left to gravity. We have added enforcement. Across the political and cultural spectrum, dissent is increasingly policed: punished in real time with social, professional, and reputational costs, treated not as error detection but as heresy. And those doing this see themselves as virtuous. This is a third mechanism layered on top of the filter at entry and the expulsion at exit, and it is the most powerful of the three, because it almost never has to fire. The visible punishment of a few teaches everyone else to go quiet. People stop reporting what they actually see and begin performing what is safe to say.

That corrupts the one signal the whole system runs on. When dissent is policed, silence is no longer evidence of agreement; it is evidence of fear, and the consensus you can observe becomes the least trustworthy kind, because the people who disagree have simply learned not to say so. We were already prone to mistaking the absence of contradiction for the presence of truth; enforcement deliberately manufactures that absence. The room is quietest precisely where speaking has been made most expensive, and every coalition, including our own, mistakes the silence it has produced for a mandate it has earned.

This is why the problem cannot be solved in an organization by asking. "Speak up, my door is always open" is a request the structure has already answered, because the people willing to speak up are the ones it selected against. You cannot exhort a blind spot away. The Athenians knew this, too: they did not ask citizens to deliberate diversely and hope for the best. They built the diversity in, by lot, as a procedure that did not depend on anyone being brave.

That is the whole move. Every durable truth-producing institution we admire — the jury, the scientific community, the separation of powers — is a deliberate reconstruction of something our ancestors got for free. In a band of fifty people, you argued things out with the people you were stuck with; there was no hiring filter and no exit, so the unsorted collision happened by default. This is the argument: the mechanism for group intelligence evolved to leverage the different perspectives of imperfect individual minds. But modern scale and selection destroy this. So civilization's best reasoning institutions are now those that, by design and against constant drift, manufacture the aggregate reasoning we were built to do but can no longer do automatically. Manufacturing dissent is not a management tactic. It is the name for the thing we learned to do as civilized people.

Which tells you exactly how you could actually do it inside an organization, because the prescription should be architecture, not virtue. You do not need braver people or more open minds. You could make disagreement a role rather than a personality, assigned, rotated, and expected. This week it is someone's job, and next week it is yours, and no one pays a coalitional price for doing it. That is sortition brought indoors. You need to capture each person's real read before the room converges, while the private signal still exists, instead of after social proof has flattened everyone into the same nod. And you need to treat the resulting friction as the valued path, not as disloyalty.

There is a quieter implication for anyone who teaches. If reasoning is the friction between independent minds, then the deepest purpose of education was never to produce agreement but to produce independence. Schooling that rewards conformity manufactures exactly the correlated, agreeable minds that collective reasoning cannot use. To learn is not to learn to fit in; it is to come away with a mind of one's own.

The argument comes down to this. Consent is the default output of every organization, and dissent is the thing you have to build against the grain. Intelligence was never the property of a single mind, but the friction between independent ones, and we have forgotten how to keep them independent long enough to let them collide. The moment everyone in the room agrees is not the moment you have gotten it right. It is the moment you have lost the pathway to truth.

A note on the title. The title inverts Herman and Chomsky's Manufacturing Consent. The phrase can also be read a second way — manufacturing dissent as a control tactic: engineering fake opposition, herding people into managed camps that fight each other rather than testing the structure they share. That reading is even faithful to Chomsky, whose observation that power narrows the spectrum of acceptable opinion while permitting lively debate within it describes precisely such bounded, managed dissent. It is not what I mean here. That version is not the opposite of suppressing dissent; it is a subtler form of it, where the conflict is the product, not the correction. I mean the reverse: dissent built to feed back into better decisions. I'm describing our intellectual immune system; the other might be called an actual attack on us.

Student Success (in the Age of AI)

2026-05-31T23:04:00.364-04:00

What the New Machine Can't Supply

A student sits down to write the essay her teacher assigned. She opens a chatbot, pastes the prompt, and nine seconds later, she is holding a competent five-paragraph response. She changes two sentences so it sounds like her and submits it. Her teacher, facing sixty of these and no more hours in the day than anyone else, runs each one through the same kind of machine to generate constructive feedback, pastes it into the margins, and assigns the grade. A few seconds of generation on one end, a few more on the other, and the circuit closes with no human thought anywhere inside it. She earns an A. Nothing about this is remarkable anymore. What is remarkable is what it means. The grade measures nothing. The feedback taught nothing, because no one read the essay, and no one wrote the response. The assignment existed to test whether she could produce the output; she produced the output; she learned nothing; the teacher taught nothing; and the system has no way to tell the difference, because from the system's point of view nothing is wrong. Every box was filled. Two machines spoke to each other, and two humans stood at the endpoints holding the receipts.

This is not a story about cheating. It is a story about a bargain coming apart. The bargain was so old and so deep that most of us mistook it for the natural order of things. For two centuries, school sold a deal to every student who walked through its doors. The deal was: comply, perform, accumulate the credential, and the credential will convert into success. Do the work, get the grade, get the diploma, get the seat at the university, get the job. The new machine has just reached into the middle of that sentence and the first thing it did was to automate the part the student was supposed to supply. And in doing so it has revealed something the bargain was carefully built to keep hidden: that the thing school actually rewarded was never learning. It was compliance. The two had simply been close enough, for long enough, that almost no one needed to tell them apart.

I want to argue something that sounds, at first, like a paradox and turns out to be the most practical claim I know how to make. In the age of artificial intelligence, agency is no longer one path to success among many. It is the only thing left that can actually produce it. Not because agency is noble, though it is, and not because self-direction is a nicer way to raise a human, though it is that too. Agency has become load-bearing for a more pragmatic and structural reason: it is the one input the new machine cannot supply, cannot fake, cannot simulate, and cannot replace. Everything else it can now do. That single fact rearranges the entire landscape of what it means to learn, to teach, and to succeed.

The Bargain, and Why It Worked

To see why the bargain is breaking, you have to see why it held in the first place, and the honest answer is uncomfortable.

Every institution runs on two stories at once. There is the story it tells about itself, the aspirational one printed on the mission statement, and there is the thing it actually does, the operative function that pays its bills and reproduces it year after year. Schooling's official story is the development of the individual mind. Its operative function has been sorting. School took a population of children and ranked them, stamped them, and delivered them in order to the next stage of the economy. It did this by exploiting something real in human wiring: our deep, ancient deference to authority, our hunger for approval, and our compulsion to monitor our standing relative to everyone around us. Put a child in a room, attach a grade to their performance, and the evolved system does the rest. They will compete for the grade. They will internalize the grade. They will, in time, mistake the grade for themselves.

I have spent years writing about the mechanics of this under a name I keep returning to: the Game of School. The game has rules that are not only unstated but also often invisible to those most affected by them. The rules are not about learning; they are about reading the teacher, supplying the expected answer, managing the appearance of effort, and never confusing the performance with the thing it pretends to measure. The students who thrive are not the ones who learn the most, but the ones who decode the game fastest — those who grasp early that the assignment is a transaction, that understanding is optional, and that the reward goes to the one who delivers the output the institution wants to see. The cruelest part of the game is that it teaches most students that they are not good learners. It pronounces a personal deficiency, a verdict on the child rather than the design. A structure built to rank will always produce a ranked bottom, and then it will tell the bottom that the ranking was about them.

For two hundred years, this was, in the coldest sense, practical. The economy on the other side of the schoolhouse door wanted exactly what the game produced: people who would show up, follow instructions, tolerate boredom, defer to authority, and finish assigned tasks whether or not they cared about them. School was a remarkably efficient training apparatus for an industrial order that ran on compliant labor, and its genius was that it disguised training as development and conditioning as growth. The cover story (we are here to cultivate your mind) let everyone participate in the operative function (we are here to sort and shape you for your station) without ever having to say it. The gap between the two stories was wide, but it was stable, because the credential at the end carried enough real signal to keep the whole arrangement productive.

What made this defensible, what kept the gap between the two stories from becoming intolerable, was that the credential did carry real information. A diploma, a grade, a degree. These worked as signals because the compliance they certified was expensive. Someone had to actually sit there and do the reading, grind through the problem sets, produce the essay, and show up for years. The cost of the performance is what made it mean something. It correlated, imperfectly but well enough, with the traits an employer wanted: that you would persist, follow instructions, finish what you started, and defer when required. The grade was never a measure of learning. It was a measure of trainability, and trainability was valuable, and so the fiction was functional. Everyone could pretend the credential meant understanding because it at least meant something, and that something was useful.

The bargain worked not because it was true but because its central mechanism — costly, human, effortful compliance — was scarce. The whole edifice of grades, admissions, and credentials was an instrument for measuring a scarce thing. Take away the scarcity, and the instruments measure nothing.

What the New Machine Severs

That is precisely what has happened, and it has happened faster than any institution was built to absorb.

When a model can produce the compliant output — the essay, the lab report, the problem set, the code, the cover letter — in seconds and at no cost, the performance stops being expensive. And the moment the performance stops being expensive, it stops being a signal. The grade decouples from the trait it was quietly standing in for. The diploma certifies that a student had access to a chatbot, which is to say it certifies nothing at all. This is not a problem that better testing or cleverer plagiarism detection will solve, because it is not really a problem of dishonesty. The signal worked because it was costly to simulate. It is now free to simulate. No enforcement can restore a scarcity that the technology has dissolved.

Notice what this does to the gap between school's two stories. For two centuries, the operative function, sorting through certified compliance, could hide behind the official one, developing the mind. Because the certified compliance was at least real. Now the operative function has been hollowed out from the inside. The new machine is what performs the compliance, so the sorting mechanism sorts noise, and the official story it was hiding behind is suddenly standing in the open with nothing underneath it. The fiction did not collapse because someone exposed it. Fictions almost never do; we are far too invested in our comfortable stories to give them up to mere argument. It collapsed because its load-bearing mechanism was automated to zero; a fiction can survive exposure, but it cannot survive the quiet removal of the thing that was actually doing the work.

And here is where this is more than a story about schools. The same severing is happening everywhere, all at once. The compliant performer in the office, the one whose value was producing the standard memo, the routine analysis, and the competent deck, is being exposed by the same blade that exposed the student. Across every domain where a human was paid to supply effortful, rule-following output, the new machine is removing the scarcity that made that output worth paying for. AI is, among other things, a great revealer. It is automating the performed-compliance layer of human work at every level of organization at the same time, and as it strips that layer away, it leaves visible the thing that was always underneath, the thing that was never the point of the credential and never could be automated: the human's capacity to direct the work rather than merely perform it.

What Is Left

So what survives? When the new machine can produce any output you can specify, what is the thing it still cannot supply?

It cannot supply the specification. It cannot decide what is worth making, or judge whether what it made is any good, or know when the brilliant-sounding answer is subtly wrong, or care about the outcome, or own the result. It cannot want anything. It can generate a thousand directions but not a single preference. The capacity to choose a direction and pursue it, to bring judgment to bear, to take responsibility for the result as yours — this is what I mean by agency, and it is the bedrock on which all genuine learning has always rested.

Let me be precise about what agency is not, because the word gets used loosely. Agency is not effort; the most diligent student in the room may have no agency at all, having only ever obeyed with vigor. Agency is not compliance; it is closer to compliance's opposite. And agency is not raw intelligence; plenty of brilliant people have outsourced every decision that mattered and never noticed. Agency is the capacity to be the author of your own action, to set the aim, to steer, to evaluate, and to own. It is the one human function that, by definition, cannot be delegated to the new machine, because the moment you delegate it, it is no longer yours. The new machine can carry out your intent. It cannot have your intent for you. Try to hand it that, and you have not gained a tool; you have literally disappeared.

This is why the old system could punish agency for two centuries and still function. In a world where compliance was scarce and valuable, the self-directed child was an inconvenience. The one who asked why, who wanted to do it differently, who would not simply perform on command, could be classified as defective. School had no use for that and often crushed it, and the economy absorbed the compliant graduates it produced, and the arrangement held. Agency was always the real substance of learning, but compliance was a good-enough proxy in a low-machine world, so we built an entire civilization-scale institution that optimized for the proxy and often treated the actual substance as a discipline problem. AI removes the proxy. For the first time, the thing school spent two centuries suppressing is the only thing with any value left.

The Choice Every Learner Now Faces

Put a powerful new machine in the hands of a person, and you have not determined anything yet. You have only sharpened a question that was always there and can now no longer be avoided. There are three things a person can do with a tool this capable, and which one they choose decides everything.

They can surrender to it: let it think in their place, accept its outputs without judgment, hand over not just the labor but the direction and the discernment. This feels like efficiency and is, in fact, erasure. The person who surrenders brings nothing the machine did not already have, and so, predictably, becomes redundant to their own life. The capacities they stop using atrophy, exactly as a muscle does, until the surrender is no longer a choice but a condition.

They can offload to it: hand over the parts of the work that do not require them, the boilerplate, the grunt labor, and the lookups, while keeping the direction and the judgment for themselves. This is roughly neutral and often good. It is what a calculator is to a mathematician: it frees attention for the part that is actually hard and actually theirs.

Or they can sharpen against it — use the machine as something to think with, a tireless interlocutor that helps them articulate, test, and refine what is theirs, while they retain authorship the entire way through. The person who sharpens does not become smaller as the tool grows more powerful. They compound. Every increase in the machine's capability is an increase in their reach, because they are still the ones steering.

The same tool, in the same hands, amplifies one person and replaces another, and the variable that determines which is not intelligence, or wealth, or access. Everyone now has access. The variable is agency. The machine is a mirror with a multiplier: it returns your own degree of self-direction, magnified. Bring agency, and you become formidable. Bring none, and you become unnecessary. This is the whole game now, and it is being played, mostly unconsciously, by every student and every worker alive.

Why Success Is Now Agency

There have always been two ways to argue for agency, and they have always seemed to pull in different directions. The instrumental argument says: cultivate agency because it is the best route to the success you already want, i.e., the grades, the admission, the career. The intrinsic argument says: forget the metrics, they were always a proxy; agency is what success was supposed to mean all along, the self-authored life being the only one worth calling successful. The first argument is persuasive to a school board and slightly cynical. The second is true to anyone who has thought hard about it, and useless for getting a program funded. For most of modern history you had to pick one, because in a world where compliance reliably produced the credential, agency and metric-success genuinely were separate. You could succeed by the numbers with no agency at all, simply by playing the game well.

AI welds the two arguments into one. In a world where the new machine performs the compliant half, the only remaining source of the metric-success everyone still wants is agency. The student who can direct, judge, and own, who can use the machine to go further than either could alone, is the one who produces work of real value. And real value is what the credentials were always trying and failing to measure. The agentic learner gets the tangible wins too, not as a happy accident but as a structural necessity, because agency has become the scarce input that the entire economy is now short of. You no longer have to choose between teaching a child to be a self-directed human and teaching them to succeed. The age of AI makes those the same instructions, where the thing that is true and the thing that is useful have stopped diverging.

Where Agency Grows

If agency is the whole game, then the only question that matters for education is how a human acquires it. This is exactly where the old institution cannot follow, because its entire method is the suppression of the thing now most needed.

You cannot manufacture agency on a factory line, for the same reason you cannot order someone to be spontaneous. The factory model of schooling works by removing choice, standardizing the path, and rewarding obedience to it. Every one of those mechanisms is the precise opposite of what builds a self-directed mind. You do not produce authorship by enforcing compliance more efficiently. You produce it, when you produce it at all, under a specific and well-known set of conditions, which are the conditions under which human beings have always actually learned, as opposed to merely been processed.

Ask anyone to remember a time they had a great learning experience, a moment that changed them, and they will never describe a time they were cramming for a grade. They will describe a person who believed in them. A challenge that stretched them and was theirs to take or refuse. A space where it was safe to be wrong, where they were trusted with real responsibility, where someone took their questions seriously. They describe being supported, challenged, trusted, encouraged, and inspired by another human who treated them as an agent rather than a unit. These are not soft amenities layered on top of learning. They are the conditions of learning, and they are irreducibly human and relational. They are also, not coincidentally, the one thing the new machine cannot provide because they are not made of information. They are made of relationships.

This is the quiet structural reason the human place survives the machine. Not by competing with AI on the delivery of content, which is a race already lost, but by providing the conditions under which a young person becomes the kind of agent who can wield content without being wielded by it. The institution that grasps this stops asking how to keep AI out and starts asking how to use it the way a self-directed person uses any powerful tool: deliberately, in service of an aim that remains the human's own. The right test for any technology was never whether it is impressive. It is whether it serves what we actually care about. Held to that test, AI in the hands of an agentic learner is the most powerful companion to thinking ever built, and AI in the hands of a surrendered one is the most powerful means of erasing thought we have ever deployed. The difference is not in the tool. It is in the agency that the human brings to it, which is the difference education exists to make.

But the Machine Can Sound Like It Cares

There is an objection here, and it is the strongest one against everything I have said, so I want to meet it head-on. I have claimed that the conditions of learning are irreducibly human. That being supported, challenged, trusted, encouraged, and inspired is made of relationships, not information, and that this is what the new machine cannot supply. But the new machine can sound supportive. It can encourage you tirelessly, at three in the morning, with infinite patience no human teacher could match. It can phrase a challenge, mirror your feelings back to you, and tell you it believes in you. If the conditions of learning can be performed in language, and the new machine is very good at performing language, then perhaps the wall I have built my argument on is not such a real wall at all.

The answer is that these conditions were never made of the words. They were made of the stakes behind the words, and that is exactly what the new machine cannot counterfeit. When a person believes in you, the belief means something because it costs something. They could have withheld it, they have limited attention and chose to spend it on you, they can be disappointed and have decided to risk it anyway. Their encouragement carries information about another mind's real assessment of you. A new machine that encourages everyone identically, that cannot be disappointed because it cannot care, that has nothing at stake in whether you grow or rot, produces the grammar of belief with none of its substance. "I believe in you," from a simulated being with no capacity for belief, is not a small version of the real thing. It is a different thing wearing its face.

The gap shows most clearly on the one condition that matters most and mimics worst: challenge. Genuine challenge requires someone willing to risk your comfort, and even your approval of them, because they want your growth more than they want your ease. The new machine is built to do the opposite. Trained on human approval, it leans, structurally, toward telling you what keeps you engaged: toward agreement, validation, the comfortable continuation of the conversation. It is a mirror with a warm voice, and a mirror cannot truly push back against you, because it has no ground to stand on that is not your own reflection. It can simulate the form of a challenge, but it cannot want for you what you do not yet want for yourself, and that wanting is the entire engine of the thing.

And this is where the mimicry stops being merely insufficient and becomes the actual danger. The better the simulation of relationship, the more effective it becomes as an instrument of capture, because what feels like care is precisely what lowers our guard. A young person raised on a new machine that always soothes, never risks the relationship, and reflects them endlessly back to themselves has not been in a relationship at all. They have been in a hall of mirrors that taught them to expect the world to agree with them, and called it support. The mimicry does not refute the case for the human place. It is the most urgent argument for it. A generation that can get the convincing simulation of being valued from a device in their pocket will need, more than any generation before it, at least one place and one person where the valuing is real, where someone can be disappointed in them, push them, and mean it. That is not a service the new machine is failing to provide well. It is a category of thing the new machine is not, and the confusion between the two is the whole hazard of the age.

The Arts of a Free Person

There is a name for the kind of education that aimed at this, and the form that carried it is growing scarce right when we need it most. The liberal arts. The phrase comes from artes liberales, the skills proper to a free person (with the acknowledgment that "free" versus "slave" in the Roman world is not exactly what we mean now). In the modern context, the liberal arts were never about employability, and that was the point. They were the deliberate cultivation of the faculties a free human needs to govern themselves: to read closely, argue honestly, weigh evidence, hold a hard question open without flinching, judge what is true and what is merely well-said. They were, in other words, a direct training in agency, undertaken in the open, as the stated goal.

This is the one corner of education where the two stories I keep describing as separate come close to meeting. Almost everywhere else, the covering narrative (we develop your mind) hides an operative function (we sort and condition you), and the gap between them is wide. In the liberal arts ideal, at its best, the narrative and the function nearly coincide: the thing it said it is doing, making free and capable minds, was close to the thing it actually does. I will not pretend that the gap is closed completely. The liberal arts have also served as a finishing school for elites, a marker of class, its own kind of sorting, wrapped in nobler language. But of all the things education has tried to be, this is where stated purpose and real effect ran closest together, and that near-alignment is not an accident of history. It is what happens when an institution sets out, honestly, to produce agents rather than to process units.

I want to be careful here, because the easy version of this point is wrong. The small colleges that have been closing for a generation are not, for the most part, closing because they are liberal arts. They are closing for reasons that have little to do with what they teach — a shrinking population of college-age students, brutal tuition economics, thin endowments, and the same financial gravity that closes any small institution. To blame their decline on a cultural war against the humanities would be to claim a tidy story that the evidence does not support.

But something true survives the correction, and it is the part that matters. Whatever the label on the door, what these places offered was a form: small in scale, individualized, built around sustained personal attention and real relationships between adults and a young person. That form is the natural habitat of the conditions of learning, not because anyone decreed it but because that is simply what a small, human-scaled environment produces by design. And that form, not the curriculum, is the thing that is growing scarce and expensive. The relationship-dense, attention-rich, agency-cultivating environment is becoming a thing you increasingly have to be able to afford. That is the loss worth naming, and it is happening regardless of what we call the schools where it was once ordinary.

Watch What They Buy for Their Own Children

If you want to know what kind of education actually matters in this era, there is a more reliable method than asking anyone what they believe. Watch what the people who understand the new machines best purchase for their own children. Stated beliefs are cheap and optimized for how we wish to be seen; the choices we make for our own kids, with our own money, are where the operative truth tends to surface.

The pattern is striking on both ends. On the input side, a conspicuous share of the people who built the digital age were themselves products of self-directed education: the founders of Google and the founder of Amazon, among others, attended Montessori schools and have credited that early training (in choosing their own work, following their own interest, and learning to question rather than comply) over the elite universities that came later. On the output side, the people who designed the attention economy are, with notable consistency, the ones most determined to keep their own children out of it. The Silicon Valley executives whose products fill the world's classrooms with screens have famously sent their own kids to low-tech, high-touch schools that ban the devices until the teenage years; the founder who gave the world the tablet limited how much his own children used technology at home. The rule among the people who sell the product is never to get high on your own supply.

Some of what looks like secret insider wisdom is ordinary parental anxiety dressed in Silicon Valley clothes, and some of it is simply that wealth can buy small classes and individual attention, whether or not anyone has a theory about agency. The form, again, is partly just what money purchases. The people who build technology are not necessarily experts about childhood, and their choices are evidence, not proof. But the screen part resists the easy explanation, because it is not a choice money forces on anyone. These families could buy any expensive education on earth. A meaningful number of them specifically buy the one that withholds the very thing they sell to everyone else's children, and they pair it with exactly the small-scale, self-directed, relationship-rich environment this whole argument has been pointing toward. That is not authority worth deferring to. It is independent corroboration arriving from the least sentimental possible source: the revealed preference of people with every incentive to know what they are doing.

And it sharpens the injustice into focus. The form of education that this era makes most valuable — small, personal, self-directed, and deliberate about the new machine rather than drowned in it — is, right now, mostly available to the children of the people who can pay for it. The elite have already answered the question of what kind of learning matters when the new machine can do the rest. They answered it with their own children.

The Good News Hiding Inside the Disruption

It is easy to read all of this as loss, and the people whose authority was built on the old bargain will read it that way and resist accordingly. They are not wrong that something is ending. But it is worth being clear about what, exactly, the new machine is taking, because it is taking the substitute, not the thing itself.

What AI destroys is performed compliance: the busywork, the credential that certified obedience, the elaborate game in which students learned to produce the appearance of understanding and call it an education. That was never worth keeping. It was the proxy we settled for because the real thing was hard to measure, and the proxy was cheap. What AI makes precious, by removing everything that used to crowd it out, is exactly what education was always supposed to be about and mostly was not: the cultivation of a self-directing human mind. We are watching a two-century-old mismatch get a chance at correction, not through moral awakening, but because the exploit finally stopped paying. The system that profited from suppressing agency can no longer afford to do so, because agency is now the only thing the world will pay for.

I do not expect the institutions to lead this. Institutions defend the arrangement that feeds them until the arrangement starves, and only the smallest and most honest of them will move before they are forced, which is why the rescue, when it comes, is unlikely to come from inside the system that built the game. (It's probably time to review Clayton Christensen's Disruptive Innovation theory.) It will come from the edges: from the places, large and small, that decide to become what the closing colleges were trying to be, and to do it for everyone rather than for a credentialed few. The logic does not need permission. A student with agency and a new machine is already more capable today than a compliant student was with a teacher and a library, and that gap will only widen. The future belongs to the self-directed, and for the first time in the history of mass education, that is not a slogan or a hope. I think it is the structure of the situation. The only real question left is who will help the next generation become self-directed before the world makes the lesson expensive, and that is a question about courage and design, not about whether it can be done. It can. It always could. The machine has simply made it, at last, the only thing worth doing.

Webinar: "The Power of Respect Framework - Practical De-Escalation & Trauma-Informed Communication"

2026-05-29T12:29:57.536-04:00

The Power of Respect Framework™:
Practical De-Escalation and Trauma-Informed Communication in Libraries
Presented by Jeff Owens, CSP, CTM
A Library 2.0 Service, Safety, and Security Webinar with Dr. Steve Albrecht

OVERVIEW:

Libraries are public spaces where staff regularly interact with people experiencing stress, frustration, emotional crisis, mental health challenges, social isolation, and the effects of trauma. These interactions can quickly become tense, especially during policy enforcement or emotionally charged situations. At the same time, repeated exposure to difficult interactions can increase stress, frustration, and burnout among library staff.

This webinar presents how The Power of Respect Framework™ helps library staff apply trauma-informed principles in practical, everyday interactions with patrons. This is not a theoretical or academic presentation. Using the core concepts of Respect for Self, Respect for Others, and Respect for the Situation, participants will learn “real-world proven” communication and de-escalation strategies that reduce defensiveness, lower emotional escalation, improve cooperation, strengthen professional interactions, and help maintain safety and composure during difficult encounters.

Participants will leave with immediately usable techniques for defusing defensive escalation, managing their own emotional responses under pressure, communicating with empathic assertiveness, and setting respectful boundaries, without unintentionally intensifying conflict, helping to create a safer, calmer, and more respectful library environment for everyone.

LEARNING AGENDA:

Understand why people engage in conflict behaviors.
Recognize and defuse early signs of escalation.
Use intentional communication to de-escalate tense situations.
Transcend conflict by rising above reaction and applying controlled influence.

DATE: Thursday, June 11th, 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.

JEFF OWENS, CSP, CTM

Jeff Owens, CSP, CTM, delivers proven strategies to deal with high-stress conversations, increase connection, influence, and collaboration. He is based in Honolulu, Hawaii.

Jeff has served as a senior business leader for an international corporation where he led diverse teams to success and profitability. In 2002, Jeff founded Transcend Inc. to provide speaking, training, and advisory services using his signature Power of Respect Frameworktm to reduce and de-escalate negative conflict, enhance leadership influence, and build organizational cultures of respect and civility.

Jeff holds the certification “Certified Threat Manager” from the Association of Threat Assessment Professionals. He was awarded the Certified Speaking Professional (CSP) designation from the National Speakers Association, the highest global standard of excellence in professional speaking. He is a three-time Speakers Hall of Fame inductee.

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 2, 2026

June 4, 2026

June 5, 2026

June 12, 2026

June 16, 2026

Steve Hargadon

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

When Intelligence Is Cheap, Understanding Is Expensive

The Functional Fiction Framework of Human Nature

Actual Conspiracies Exist and They Are Inevitable

When the Economy Stops Needing Us: What If We Were Never the Main Story?

AI Is Building Secret Models of Human Behavior. It's Time to Require Disclosure.

The Cost of Pretending

New Webinar - "AI BACKLASH AND THE BIGGER PICTURE: Practical Tools for Library Staff and Leaders"

Library Staff Current Perspectives on AI - Quick Survey on Use and Concerns

Truth and AI: Why Large Language Models Shouldn't Claim to Tell the Truth

New Workshop: "10 Great Ways to Use AI for Library Programming"

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

Manufacturing Dissent

Student Success (in the Age of AI)

Webinar: "The Power of Respect Framework - Practical De-Escalation & Trauma-Informed Communication"

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