Wednesday, June 17, 2026

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

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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: 

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:

11002877698?profile=RESIZE_180x180STEVE 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.

12255199694?profile=RESIZE_180x180DR. 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

12435796494?profile=RESIZE_180x180CRYSTAL 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

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 June 30, 2026

 July 8, 2026

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 July 10, 2026

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 July 14, 2026

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Tuesday, June 16, 2026

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

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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: 

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.

12420251095?profile=RESIZE_180x180REED 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:

 June 26, 2026

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 July 8, 2026

31175875677?profile=RESIZE_710x

 July 10, 2026

31176280701?profile=RESIZE_710x

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

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:

  1. The balance of powers in the U.S. Constitution
  2. Blind peer review processes in science
  3. 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

Monday, June 15, 2026

When Intelligence Is Cheap, Understanding Is Expensive

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

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.

Sunday, June 14, 2026

Actual Conspiracies Exist and They Are Inevitable

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.

Saturday, June 13, 2026

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

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.

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:

  1. The assumed model of human cognition (rational actor or adaptive/heuristic-driven?).
  2. How the architecture (dialogue, memory, affirmation, refusals) functions as a behavior-shaping environment.
  3. 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.