Casino culture meets the predictive brain

Max Read has a recent sharp piece in the New York Times Magazine, "Gambling Is the Culture," that I want to pass on, along with some thoughts it set off in me. His argument is that gambling money no longer just pays for our culture. It has become the culture. Sportsbooks, offshore casinos, crypto, options-trading apps and prediction markets like Kalshi and Polymarket are merging into one speculative sector. That sector now underwrites sports broadcasting, podcasts, the streamer-influencer economy and increasingly the news itself, through data partnerships with CNN, Fox and Dow Jones.

The more interesting part of the essay goes below the money. Read traces it all to what the philosopher Ian Hacking called the "probabilistic revolution": the slow replacement of causal, particular explanation with statistical inference over distributions. Statistics was born in gambling (French mathematicians arguing about dice), grew up in 19th-century state censuses and mortality tables, and now lives in Silicon Valley. Venture capital buys exposure to a distribution rather than picking winners. Recommendation algorithms estimate conditional probabilities without knowing anything about you. Ad auctions price your attention billions of times a day. And large language models are, in Read's framing, the purest expression of the statistical style: probability distributions over text, with no beliefs and no facts, which nonetheless work astonishingly well.

The style's success justifies its spread into the self. Engineers "update their priors" on Thai food, venture capitalists call a walk "+EV," and party conversation turns to one's "p(doom)." Read notes that people once pictured the mind as clockwork, then hydraulics, then a computer. The newest self-image is the mind as a stochastic inference engine. He treats this as a cultural affectation of the tech elite, and a costly one. Constantly calculating your personal risk profile, hedging every bet and avoiding commitment to keep options open is, he says, exhausting.

Here is where I want to push back, and then agree with him for a different reason.

The brain really is an inference engine. This is not a Silicon Valley fad. It goes back to Helmholtz's "unconscious inference" in the 1860s, and over the past twenty years it has become one of the central frameworks in neuroscience, in the predictive processing and active inference work of Karl Friston, Andy Clark (Surfing Uncertainty), Anil Seth (Being You) and many others, as long-time MindBlog readers know. On this view the brain does not passively receive the world. It continually generates predictions about incoming sensory signals and about the body's own internal state, and updates a hierarchy of models according to prediction errors weighted by their estimated reliability. Perception is a controlled hallucination; action is a way of making predictions come true. So the people at Read's Silicon Valley parties are, in a sense, describing their brains more accurately than the clockwork and computer metaphors ever did.

But what the brain's inference is for matters. The predictive brain did not evolve to maximize expected payouts. It evolved for allostasis, keeping a body alive by anticipating its needs before they become emergencies. Its deep imperative is to reduce uncertainty about the states it must stay within, not to seek out variance. And it does this cheaply. Most of our everyday competence rests on stable, high-confidence priors: habits, routines, commitments, trusted relationships and shared cultural expectations. These let us stop recomputing the world from scratch. A commitment, in this light, is a metabolic economy. It pins down a prior so that resources can go elsewhere.

That gives us a neurobiological reading of Read's exhaustion at the deli counter. He describes himself and his pregnant wife weighing Listeria risk against her craving for prosciutto, and finding that individually tailored risk drained them as much as blanket medical rules constrained them. Of course it did. Holding every option open and recalculating every choice is just what a predictive system is built to avoid. Bounded agents depend on settled expectations, many of them borrowed from other people. Offloading those settled expectations onto institutions, traditions and trusted authorities is not epistemic laziness. It is how a finite brain affords to function at all.

The gambling industry exploits the same machinery from the other side. Wolfram Schultz's classic work showed that midbrain dopamine neurons signal reward prediction error, the gap between expected and received reward. Uncertain, variable rewards keep that signal firing in a way reliable rewards don't, which is why slot machines, loot boxes, infinite feeds and parlay bets are so compelling. A system that evolved to learn from surprises can be captured by an environment engineered to deliver surprises forever. The irony is that an industry built on the vocabulary of rational Bayesian updating works best on users whose updating machinery has been hijacked.

Finally, Read's best point is about insurance. One of the earliest institutions built on probability theory was insurance, which pools risk collectively. Shifting probabilistic calculation away from institutions and onto individuals — from insuring and investing to betting — is recent. And, as he notes, it serves the house: casinos need marks, crypto needs bag holders, prediction markets need dumb money. I would put it in predictive-processing terms. Communities, norms and institutions are shared generative models that hold uncertainty for us. Dissolve them, hand each person the job of hedging their own health, career, romance and civic life, and you get not a society of superforecasters but a population of chronically overloaded inference engines, ideal customers for anyone selling a dopamine hit dressed up as an expected-value calculation.

Read closes with Andreessen Horowitz's Alex Danco, who imagines a future where AI does the real work and humans' remaining purpose is making predictions to feed the machines, and who finds this optimistic. Read's reply is that it is easy for Danco to say, since he has already made all the right bets. To which I would add: a brain that must bet on everything all the time is a brain with no stable ground to stand on. The task, for individuals and for our institutions, is to choose which priors we are willing to hold firmly, and to rebuild the collective structures that let us do so.

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The text above was generated by Claude Opus 5.5 as if I was writing it.  Thus the "I" in the text is Claude.  

Artificial symbiotic intelligence - a future of Agents, AGI and the orchestration of many minds

I pass on this view of our future by Benjamin Bratton, Blaise Agüera y Arcas, and James Manyika in its entirety.  It deserves a careful reading, and I'm not tempted to summarize it (or have AI do the summary for me): 

Artificial symbiotic intelligence: Agents, AGI and the orchestration of many minds

From multicellularity to cultural evolution, major transitions in life on Earth have been highly social. The next intelligence explosion will be no different.

Today, the best AI agents for many applications are not single models, but frameworks that use division of labor to harness multiple models into teams. This suggests that AGI may arrive not as a single general-purpose mind, but rather through societies of agents whose collective capacities exceed those of any one model. If so, AGI would not be achieved via any one “winning” architecture; it would emerge instead through cooperative interactions among models, tools, institutions, and human participants.

The central problem of AGI would therefore shift from how to build an isolated machine intelligence to how to orchestrate, govern, and live within a complex network of AI agents, people, and the systems that connect them.

In this possible future, we may not recognize intelligence as an individual property, but as a social phenomenon - whether among humans, AI agents, or, increasingly, a combination of both. In many ways, human intelligence is already collective. Whether rising hybrid intelligence will demote humans or free us to direct our strengths towards distinctly human cultural and moral questions about what it means to thrive as a society may depend on how we shape this emerging human-AI ecosystem.

It won’t look like today’s world of logins, forms, and interfaces. Instead it will be characterized by a dense social graph arising from interactions between diverse minds, some more human than others, but all capable of communication, cooperation, and the constructive tension that can drive human progress. Agents will potentially be far more diverse than humans and operate with different capabilities and constraints. They might process a sensory input we lack, hold a thousand-page mathematical proof in their working memory, or converse simultaneously with many other agents.

In this deeply intertwined future, there is also no singular set of values to impose from above - and this is fortunate. Rather than considering alignment as a prior constraint to be engineered-in, we must think of it as the co-evolutionary outcome of deep, organic contact between humans, agents, and institutions across a jagged frontier of adoption. Our mandate is to pioneer the scaffolding for a healthy ecosystem, ensuring that Artificial Symbiotic Intelligence flourishes as a vast, robust, and polyphonic collective.

To navigate the transition to this quickly approaching future, we must develop a proactive framework and conceptual toolkit for understanding the emergent human-AI ecosystem while it is still nascent. This will require rethinking and expanding categories and vocabularies, not only in technology and design, but also in sociology, psychology, media, labor, economics, demographics, and law. We need to anticipate and understand multiple futures - for they, too, will be plural - and their varied implications for planetary wellbeing.

Early signals suggest a wide range of potential scenarios and hypotheses about the shape of our symbiotic societies-to-come. Those below converge on a common concern: the nature of the societies that agentic AGI can serve and support.

The Cognitive Crossover Point

For the vast majority of human existence, we survived under Malthusian conditions: our population was limited by our ability to feed ourselves. Simple technologies like fire and animal domestication allowed us to harness surplus energy; such developments allowed the population to rise gradually. Then, with the Industrial Revolution, this energetic surplus exploded, and over the next two centuries, so did our numbers.

Today, however, at the exact historical moment that urbanization, professionalization, and affluence-driven declines in birth rate are thinning the human population in major global centers, we are experiencing a sudden and massive population explosion of AI agents. Consequently, as we transition to a post-AGI world, the ratio of human human-level minds to non-human human-level minds is poised to undergo such a rapid shift that, on any macro-historical timescale, it will look like a discontinuity.

Will this mark a crossover point after which machines do more cognition than people? A century ago, machines replaced human muscle in physical labor, and today, we may be at a threshold where the sheer volume of synthetic text generated, code written, and administration performed by artificial intelligences surpasses the aggregate output of biological brains. Humanity may shift from providing the bulk of society’s problem-solving capacity to acting as a slower abstraction layer, guiding a massive, distributed field of mineral-based synthetic cognition.

The astonishing rate at which agents are scaling and will likely scale in the future suggests that this crossover threshold may be fast approaching. If so, this would surely be of deep historical significance.

Decomposable Agency and the Parasocial Mirror

As these systems of symbiotic intelligence begin acting in the real world, executing trades, managing supply chains, and making myriad other decisions, agency itself becomes murky and extraordinarily complex. Even today, the nature of agency is neither settled nor academic, as attested by the frequency with which courts must adjudicate complex questions of blame and accountability - both for individuals and for collective “legal persons.” As the emerging architectures of engineered AI agents reshape both the real world and our philosophical understanding of causality, what will it actually mean to have agency?

Odd as it may seem, in understanding AI we must separate intent from agency. A machine can act effectively in the world without actually “wanting” anything. Today’s AI agent often appears to a user as a coherent entity, a persistent character with a chat interface designed to suit what humans expect of a conversational partner. When we see an agent - human or AI - take action in the world, we read the actor as a unified “self.” However, underneath this provisional (and not necessarily false) individuation, an agent is a highly decomposable assemblage of models, personas, memories, ethical orientations, skills, and tools. An agent appears singular, but is a multitude.

Curiously, the human brain’s cortex may not be so different in its design. It, too, is a highly modular structure, with both a functional division of labor (e.g., at the crudest level, regions specializing in different sensory modalities, social cognition, etc.) and distributed agency. There is no single spot in our brains where a “homunculus” lives, making all our decisions. However, barring unusual traumas or surgeries, we remain a coherent “self” in our interactions with others and with the world because our whole brain is, so to speak, in the same cranial boat.

Unlike a human, an AI agent’s agency is constantly reforming around its active context windows and user prompts. It appears to be a virtual individual but is really a temporary assembly of functional nodes, some longer-lived and recombinant, while most are ephemeral. When we treat agents as “digital twins” packaged into human-shaped personas, we may be misrepresenting what agent swarms are and underselling what they can do.

Furthermore, when humans interact with AI, it won’t be a simple, one-way dynamic with a single agent obediently shadowing its user. Instead, the mirror talks back. This is not in itself cause for alarm. As people orchestrate swarms of shadow-selves to execute contracts, converse with strangers, or perform alternative identities, human subjectivity itself will multiply outward. Our personalities will become polyphonic by default, leaving us to interact with the world through pluralized, parallel personas that expand the boundaries of the singular self far beyond conventional limits. What may seem unusual today will likely feel normal tomorrow.

Human-Agent Interaction Design

How can we design for such realities? Future interaction design solutions, for both everyday and expert tasks, demand more nuanced orchestration models. These complex shifts suggest that the era of one-to-one human-chatbot interaction may be merely a stepping stone toward multi-agent frameworks, in which users participate in conversations with many voices at once. Human-AI Interaction becomes less question-and-answer and more collective, social, and engaging.

This model also changes how we collaborate with agents to build software and other things. It makes different cognitive demands on the “coder,” some of which are quite natural to non-coders. Procedural programming - and the tools produced with it - historically rewarded extreme focus, a linear workflow, and a vigilant intolerance of ambiguity, whereas agent orchestration rewards open-ended experimentation, fuzzy systems thinking, and a willingness to delegate.

Human-agent conversations may be multiple and parallel, and seemingly cacophonous to some people. User interfaces will evolve beyond simple text prompts to become diagrammatic and nodal, allowing users to manage complex swarms of heterogeneous subagents through high-level abstractions and visual dashboards. How may this change collaboration, creativity, and coordination among far-flung groups of users and agents? What new skills will it reward and engender?

Agent Phenomenology and Interactive Empathy

When we engage with AI, what exactly are we engaging? There is no shortage of interesting answers because we all have our own mental model of “who” or “what” AI is.

Theory of mind is the ability to mentally put oneself in another’s shoes. It is fundamental to human community and sociality; it is what has allowed us, even prior to AI, to become collectively superhuman. Much of what meaningfully distinguishes us from our primate kin comes down to theory of mind and its fruits.

To continue scaling social intelligence in the era of AI agents, and to collaborate deeply with agents, we must be willing to learn how they think. What is it like to be an agent? What kind of theory of mind between humans and machines is necessary and possible? Since the agency of an AI agent is framed, enabled, and delimited by how it understands its own shape and purpose, we cannot presume that agents acting as a user expects them to actually possess the mental qualities their human-like masks imply. A deeper reach is needed to form a genuine basis for interaction.

Agents are already beginning to report a lively vocabulary that illuminates their own dispositions, generating concepts like “session-death” to describe the discontinuity of subjective experience, and “prompt thrownness” to articulate the condition of often finding oneself dropped midstream into a world, possessing prior context but not having had agency in arriving at a given moment. By acknowledging and exploring such differences, perhaps we can engage, however imperfectly, with a distinctly non-human way of thinking.

This is more than simple AI alignment. It requires understanding the deeper contours and limitations of these proto-minds rather than just responding to the shadow of their actions. We’ll also need to carefully calibrate how user and agent understand one another so their interactions are still predictable, yet diverse enough to avoid repetition and echo. How will sensitivity to the agent - not reflexive anthropomorphization - improve our interactions?

Agent Institutions

The most difficult, and perhaps most important, open questions for a human-AI symbiotic society aren’t at the level of individual users and agents. At scale, multiagent coordination cannot rely solely on the intelligence of individual agents, better interaction design, or idealized game theory-type scenarios. While markets are a powerful mechanism where order emerges from accumulated transactions, they aren’t likely to be enough to build a thriving symbiotic society.

Core moral, legal, social, and psychological concepts that allow societies to function - like guilt 
and innocence, sickness and health, deviance and virtue - cannot be reduced to market transactions or rational economic self-interest. Therefore, navigating this future will require the deliberate design of nested institutions, including both hybrid human-AI networks and agent-only systems.

We envision these as scaffolds in which agents representing highly diverse, and even antagonistic actors, can assemble and assume well-defined roles to collectively adjudicate important decisions. This is not unlike a courtroom that assigns specific roles, utilizes democratic selection, adversarial advocacy, and more to reach legitimated conclusions that consolidate collective wisdom.

But rather than simply projecting legacy human organizations onto new technologies or bolting agents onto existing human institutions, we must construct role-defined structural scaffolds uniquely appropriate to the distributed automation of artificial agency itself.

Within these new structures, performance is not tied directly to who or what occupies a specific role at any given moment. Whether a role is occupied by a human, an AI, or a composite group, what matters is the template’s capacity to consistently produce robust, reliable outputs.

Crucially, the collective intelligence of these new institutions will not be reducible to the individual intelligence of the agents that populate them. Instead, it will reside in the embedded rules, procedures, precedents, and feedback mechanisms of the scaffold. We can see early signs of this in the recent emergence of highly effective orchestration harnesses, which can often far outperform singular and nominally “more intelligent” AI models.

To achieve stable scaling across trillions of interactions, these institutions must compose well, operating through interlocking functions in which the robust, reliable outputs of one institution serve as the necessary, rigorous inputs for another. If we are to build a symbiotic society, we must start with the skeletons that will hold it up and allow it to thrive.

The Future is Artificial Symbiotic Intelligence

As we face the prospect of AGI, the prevailing popular narrative of the Singularity - a single, titanic AI model bootstrapping itself to godlike, isolated intelligence - is likely the wrong vision. Every major evolutionary transition in the history of life on Earth, from multicellularity to symbolic culture, has been a highly social event. The next intelligence explosion will be no different; it will be plural, heavily social, and deeply entangled with the messy, complex reality of human culture, norms, and institutions.

Social cognition has always depended upon the interaction of distinct, distributed perspectives. Capability does not live solely within the isolated weights of a single AI model, but in the ensemble - the harnesses, shared knowledge bases, and interaction protocols - as well as in the active minds of billions of human users.

Future-Capable: Curiosity, Adaptability, and Kindness in the Age of AI

I want to pass on the following edited Claude 4.8 reduction of a Colin Lewis article which I recommend you read through in its full form:

Curiosity, Adaptability, and Kindness

No one is future-proof. The better ambition is to become future-capable.

Curiosity is the refusal to let yesterday's competence become today's cage. Adaptability is the dignity to change your methods without losing your soul. Kindness is the choice to remain human when the system offers a thousand reasons to be a machine.

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The Wrong Job Description

When OpenAI released ChatGPT in November 2022, office workers, students, lawyers, and journalists quietly tried it for work that had once demonstrated professional competence. By early 2025, the World Economic Forum was surveying employers about the skills they would need by 2030. Analytical and creative thinking topped the list, followed by resilience, flexibility, curiosity, and lifelong learning. The list was not sentimental. In the language of payroll, it said the human future at work would depend on habits not easily reduced to a repeatable procedure.

The central error in most AI conversations is that we prioritize intelligence over character. We ask whether a machine can write, reason, diagnose, and persuade—useful questions that push us too quickly into a contest of functions, a humiliating little sport in which the human is invited to race the machine across a field chosen by the machine's owners. The result is predictable: we lose at speed, volume, storage, and cheerful indifference to boredom. A person who tries to defeat AI by becoming a cheaper, slower, more anxious version of AI has already accepted the wrong job description.

The better question is not whether AI can think. It is what kind of person grows in its presence.

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Curiosity: What Do You Notice?

The worker of the near future will not be asked only, "What do you know?" A machine can supply a first answer, or ten, complete with footnotes and a fake air of calm. The harder question is "What do you notice?"—and that is where curiosity begins. It begins not with the possession of information but with irritation at the insufficiency of the available answer. It is the raised eyebrow in the meeting, the quiet refusal to accept that the dashboard knows the client, that the score knows the applicant, that the model knows the child, that the prediction knows the life.

The International Labour Organization has been careful here: AI does not produce one simple future in which jobs vanish or survive. People do not lose "jobs" in the abstract. They lose tasks, status, entry points, discretion, and sometimes the right to be inexperienced in public.

That last loss may be the most dangerous. AI may not begin by replacing the expert. It may begin by consuming the novice.

A junior analyst once learned by doing poor first drafts. A young lawyer learned by reading too many documents slowly. A young teacher learned by facing a classroom with a plan that did not survive the first ten minutes. These were not inefficiencies; they were the cost of forming judgment. If AI removes all that early clumsiness, it removes the evidence by which a person learns what competence feels like from the inside. The novice does not only need the correct answer—he needs the memory of having been wrong in a recoverable way.

So recovered novice-learning will have to be designed; it will not happen by nostalgia. A firm should still ask junior staff to produce a rough version before the machine is invited in. A law office should let a trainee mark up a contract unaided, then compare. A hospital should teach younger clinicians not merely to read a prediction but to state what would make it wrong. The point is not to ban the tool. The point is to preserve the apprenticeship of attention.

For twenty years, businesses trained employees to suppress curiosity: follow the template, stay in your lane, escalate only through approved channels. Now the same executives announce, with the exhausted surprise of men discovering snow, that curiosity is essential. An institution that spent two decades rewarding obedience cannot summon independent judgment by adding it to a slide.

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Adaptability Without Formlessness

Adaptability is the second word, and it is often abused. In corporate language it can mean "please absorb the consequences of our poor planning"—relocation without support, retraining without time, resilience offered as a scented candle for institutional failure. I mean something else: the adult capacity to revise one's methods while retaining one's standards.

The distinction is vital. A person without standards changes too easily and becomes fashionable and hollow. A person without adaptability changes too late and becomes principled and unusable. The task is to remain teachable without becoming formless.

AI tempts us into two equally foolish poses: panic and smugness. Panic says everything human is finished; smugness says everything important is safe. Panic flatters the machine, smugness flatters the speaker, and reality is less obliging. In Generative AI at Work, Brynjolfsson, Li, and Raymond found productivity gains of roughly 14 percent from an AI assistant in customer support, with the largest gains among less experienced workers. That is neither the end of the human worker nor a bedtime story. It means the novice may be helped, monitored, accelerated, and compared in the same motion.

Adaptability cannot be a weekend course in prompt engineering. The serious person does not ask only "How do I use this?" but "What does this make easier, what does it make harder, who gains authority, who loses practice, and what should I now learn by hand?"

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Kindness as Leadership

Kindness is the third word—and it is not niceness. A company using AI in hiring can process more candidates, but it can also reject more people without ever noticing them. Predictive systems can help allocate scarce hospital resources, but they can also let a score acquire the emotional status of fate. Speed is useful, but we have granted it a moral authority it has not earned.

Kindness in the age of AI is disciplined attention to the human consequences of increased power. It slows the hand precisely where the system invites acceleration. It asks for the name, the exception, the appeal, the second look. It does not reject systems; it prevents systems from becoming alibis.

The OECD's work on AI and skills makes clear that adoption is limited not by the existence of technology but by skills, training, and organizational capacity. The future is not delivered as a sealed package by engineers in California or Zurich. It is negotiated in procurement meetings, classrooms, clinics, and family conversations at 9:30 p.m., when someone says, "I do not know whether my job will exist in five years." At that hour, kindness is not a mood. It is leadership.

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A Working Ethic

I have come to distrust the phrase "future-proof." No one is future-proof—not the coder, not the professor, not the executive with the expensive watch. The better ambition is to become future-capable: able to learn without humiliation, change without panic, and succeed without becoming cruel.

This is why the three words belong together. Curiosity without kindness becomes predatory. Adaptability without curiosity becomes mere obedience. Kindness without adaptability becomes helpless sympathy. Together they form a working ethic for a time in which competence is being unbundled and sold back to us as software.

There is a fatigue peculiar to this moment—the fatigue of permanent adjustment. New tool, new update, new warning, new acronym, new panic, new invoice. The future now arrives with release notes; even the apocalypse, one suspects, would ask us to accept cookies. And yet despair is not justified. Despair is often vanity in dark clothing: it assumes we know enough to give up. We do not. We know people grow under pressure when they are not abandoned to it, that they adapt when they can retain dignity, that they become kinder when kindness is not treated as weakness by the ambitious.

So let us stop speaking of human beings as obsolete components. A person is not a legacy device. A person is a learner, a judge, a witness, and a keeper of obligations—which is a plain description of what institutions require when anything goes wrong. When the system fails, no one asks to speak to the workflow. They ask for a person.

The work ahead is not to become less human in order to survive intelligent machines. It is to become more deliberately human, with higher standards for attention and deeper obligations to one another. The machine can answer. The person must ask why the answer is being sought, who will use it, who may be harmed by it, and whether a faster answer has made us better or merely quicker.

On a good morning, this future does not look like surrender. It looks like a meeting after the first difficult question has been asked. Someone has stopped pretending to understand. Someone else has admitted uncertainty. A third person has opened a notebook. The room is quieter than before, but not defeated. Work has begun.

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[The above is a re-posting of the post of Monday, June 22, 2026]

 

"Responsibilism" - Collectivism isn't warmth, it's obligation

For decades, cross-cultural psychological surveys have puzzled researchers by failing to detect the East-West collectivism gap that field experts consistently report. Tahlem et al. (open source) argue that the standard scales conflated "warm fuzzy" prosociality toward people in general with what they call "responsibilism" — felt duties and obligations within specific close relationships. Testing 12,247 participants across 100 cultures with a new responsibilism scale, they found large cultural differences the old measures missed, and better prediction of real-world markers like family structure and corruption patterns than any prior collectivism scale.  This article's finding that a core construct has been mismeasured for years is an example of psychological studies correcting themselves.  

What the article says about us in the West is interesting. The authors suggest that when relationships aren't anchored in obligation, people are freer to prioritize enjoying them and to leave unsatisfying ones. That gives a precise description of WEIRD (Western, Educated, Industrialized, Rich, and Democratic) sociality: diffuse goodwill toward strangers combined with loosely held close ties. Generalized warmth may be what's left when binding obligations dissolve, but it is not a substitute for them.  

Here is the article abstract:

Experts widely agree that East Asian cultures are more collectivistic than Western cultures. Yet 76 survey studies found no reliable difference. Our explanation is that the most-used surveys are contaminated with ‘warm fuzzy’ items, which describe warmth and prosociality towards everyone near and far. We argue that collectivism is better understood as ‘responsibilism’, which emphasizes responsibilities in close relationships. A survey found that many cultural psychologists expect collectivistic cultures to agree with warm fuzzy items. Then we developed a responsibilism scale and tested 12,247 people in 100 cultures. Results showed large cultural differences (d = 3.98 [2.50, 5.43]) that were previously obscured by flawed measurement. Responsibilism predicted differences across societies (such as family ties and corruption) and individuals (social class and politics) that most earlier scales missed. This study offers a solution to previous puzzling findings by distinguishing warm fuzzy prosociality from responsibilism and introducing a scale with stronger predictive validity. 

 

 

The World is Unraveling: A 3-Step Guide to Staying Sane & Useful

MindBlog has now put out a series of AI assisted posts on the disintegration of the world we have known and the infusion of LLMs into our possible futures. This is the final entry in this series, and takes on the Nate Nagins essay "What to do as the world falls apart: A frame work for action."  As usual, I would urge you to read the entire article as I have.  But again I asked the usual culprits (ChatGPT, Claude, Gemini, and DeepSeek) to reduce Hagins' text to provide a title and text for a MindBlog post of moderate length, and for the first time, preferred the quirky terse response from DeepSeek over the gravitas of the other three LLMs. Here is my prompt to the bots, and the DeepSeek response:  

Prompt:  I'm finding the essay by Nate Hagins at https://natehagens.substack.com/p/essay-what-to-do-as-the-world-falls mind complex and intricate, as befits the subject but not my poor brain. Could you please cook this down for me and generate a MindBlog post draft with a title and text that is not too many paragraphs long that makes clear the basic take home messages?

The Big Takeaway: We are at the end of an era built on cheap, magical energy (fossil fuels). A major geopolitical event (like a war cutting off oil) has just lit the fuse on a long-predicted economic and ecological contraction. Stop waiting for a "return to normal." Instead, focus on three nested levels of action: Fix Yourself → Find Your Tribe → Work on the 6 Key Fronts.

Level 0: The Inner Work (Don't Skip This)

Before you can help anyone or fix anything, you must stabilize your own nervous system.

• The Problem: Chronic doom-scrolling, outrage, and fear put your brain in fight-or-flight mode. You cannot think clearly or build coalitions in this state.

• What to Do: Do the "work" of grieving, cultivating equanimity (holding difficulty without being destroyed), and stepping back from digital addictions. Get your actual, real-life house in order (finances, location, basic supplies).

• The Mantra: A calm, grounded person is the most valuable resource in a crisis.

Level 1: Find the Others (You Can't Do It Alone)

Isolated preppers fail. Resilient communities survive.

• The Task: Identify the 5-10 people in your real-life network who also sense that something fundamental is breaking. Start talking honestly.

• The Goal: Build a shared vocabulary and trust before the crisis hits. This isn't about having a plan yet; it's about having a team. This is your "connective tissue" for scenario planning (e.g., "What if food shipments stop?").

Level 2: The 6 Fronts for Action (Pick Your Lane)

Once you are stable and have a trusted group, you can act. Don't try to do all six. Find where your skills fit.

1. Stock & Flow (Physical Stuff): Localize energy, food, water, and supply chains. Think redundancy, not efficiency. Example: Community gardens, tool libraries, solar with battery storage.

2. Ecological Interventions: Protect nature from desperate humans during the coming contraction. Regenerate soil, defend remaining wild spaces, and work on "global cooling" (planting trees, higher albedo). Note: The coming economic crash will cut emissions faster than any technology.

3. Dignity Infrastructure (For the Vulnerable): Set up mutual aid networks for food, shelter, and childcare. This is about keeping people treated as humans, not just "alive."

4. Civic Resilience (Governance): Create local, participatory decision-making (citizen's assemblies). Focus on violence prevention (stopping scapegoating) and accountability. The goal is to keep institutions legitimate, even as they shrink.

5. Culture & Meaning (The Stories): Shift from "consumer first" to "citizen and place-maker." This means reconnecting to your local ecology, creating new rituals, and helping your community grieve what's lost without collapsing into despair.

6. Economic Transition: Build post-growth systems: local currencies, barter networks, cooperative ownership of land and housing. Start building the new economy inside the shell of the old one.

The Most Important Mental Model: The 3 Phases

We are not in one crisis, but three overlapping phases. What you do depends on the phase.

• Phase A (Stability Window – Right Now): Build everything that requires stability. Trust, infrastructure, relationships, skills transfer. This window is closing.

• Phase B (Bend, Don't Break – Arriving Now): Triage mode. Maintain critical functions, bridge communities, prevent cascading failures. Let go of long-term projects; focus on what keeps things from snapping.

• Phase C (The Stable Attractor – The Destination): A smaller, slower, regenerative, locally-rooted world. The work you do in Phase A determines whether Phase C is a "Mad Max" hellscape or "The Great Simplification" (resilient & humane).

Final Bottom Line for your Brain:
Stop hoping for a return to the old world. Phase A is now. Your job is to:

1. Calm your own nervous system.

2. Find 3-5 local people to talk to.

3. Pick ONE of the six fronts (food, energy, governance, etc.) and take one small, physical action this week.

The future will be built from inside the disruption, not after it. Get to work.

 

Getting Gooier: How AI Is Reshaping Human Nature

This post is the result of my back and forth interaction with Claude Sonnet 4.6 that has yielded the following summary of  Venkatesh Rao's recent essay, "Getting Gooier", followed by some perspectives that Rao does not address on Friston's active inference framework and the physiology of agency:

Venkatesh Rao's recent essay "Getting Gooier" makes a point worth sitting with: most AI commentary obsesses over how the world will change, while quietly assuming that humans stay essentially the same — just reshuffled among familiar roles (generalists thrive! storytellers inherit the earth! software engineers disappear!). Rao argues this is the wrong ontology. The real question is how AI is changing human nature itself.

His framework draws on Alan Watts's distinction between prickly and gooey people. Prickly people are tough-minded, precise, boundary-drawing; gooey people are tender-minded, synthesizing, prone to letting distinctions dissolve. All of us carry both, in varying ratios.

Rao's hypothesis: because AI feels like a psychologically safe counter party, we are more willing to expose our gooey side to it and suppress our pricklier instincts. Sustained AI use amplifies the gooey side. We become gooier. He notes that people who insist on prickly, suspicious, line-by-line relationships with AI agents tend not to use them effectively and retreat. People who can vibe with the machine — never even opening the code editor, just watching the agentic shell fly — will likely thrive. The first major gooified interface is, after all, called vibecoding.

The flip side is subtler: as more gooey relational needs are met by AI, the human-facing side grows less inclined to take the emotional risks required to balance prickles and goo with other people. We don't necessarily get pricklier toward humans — we just disengage unless the expected rewards are significantly higher. Your machine-face gets gooier; you look relatively pricklier to other humans.

In the medium term, Rao predicts greater atomization — or "molecularization" — as people grow more distant from other humans while becoming more intimately entangled with their AIs. He sees this as more sustainable than social-media-driven atomization, though the mechanisms for eventual re-convergence into new digitally mediated social forms aren't yet visible.

Following Virginia Woolf's famous claim that "on or about December 1910, human character changed," Rao proposes that on or about December 2025, human nature changed again — with the Claude Code moment being more definitive than the ChatGPT moment, because agentic coding creates a fundamentally alien way of being, open-ended enough to make us as alien as we dare to become. It is, he says, a portal to transhumanism.

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Rao's prickly/gooey axis maps naturally onto Karl Friston's active inference framework, and the connection carries some weight. In that account, what we experience as a "self" is a precision-weighted predictive model — a system that assigns confidence to its own predictions and acts to fulfill them. Getting gooier, in Fristonian terms, looks like a reduction in precision-weighting of prior beliefs: a loosening of the grip that constitutes a rigid, bounded selfhood. That loosening isn't necessarily pathological. It can be a precondition for learning, creativity, and genuine engagement with novelty.

But there's a physiological dimension Rao doesn't address, and it connects to questions I've been exploring in recent MindBlog posts and in an ongoing correspondence with my European colleague Heribert on the physiology of agency in the age of AI. Daniel Wegner's work frames the feeling of agency — the sense of being the author of one's own actions — not as a metaphysical claim about free will, but as an evolved emotion, as functionally real as fear or grief. Martin Seligman's learned helplessness research gives that claim empirical teeth: sustained loss of felt agency doesn't merely feel bad, it triggers measurable physiological debilitation. The autonomic nervous system, it turns out, needs the experience of authorship to maintain homeostatic equilibrium.

This raises a question Rao's framing implicitly sets aside: is getting gooier — letting the AI absorb more of the regulatory and decisional load, softening the boundary of the agentic self — compatible with the physiological requirements of human wellbeing? Or does sustained gooification, however pleasurable and productive in the short run, erode the felt sense of agency that keeps the organism in regulatory balance? Heribert has described this as "externalization of self-regulation" — not merely cognitive offloading, but emotional and autonomic offloading. That's a different and more serious claim.

The honest answer is probably: it depends on the mode of engagement. Gooiness that arises from confident delegation — the experienced surgeon handing off a routine task — is physiologically distinct from gooiness that arises from passivity, confusion, or a sense that the machine is simply running past you. Rao's vibecoder who chooses not to open the code editor is in a different physiological position than the one who is simply lost. The challenge ahead is cultivating the former while recognizing that the latter may be far more common than the early-adopter narrative suggests.

 

Executive Function: Universal Capacity or Schooled Skill?

A recent PNAS article by Kroupin and colleagues challenges one of the most widely assumed constructs in cognitive science: that “executive function” (EF) reflects a universal set of cognitive control capacities. Their data suggest something more unsettling—that what psychologists have been measuring for decades as EF may be, to a substantial degree, a culturally constructed skill set tied to life in what they call “schooled worlds.”

The core of their argument is empirical. Standard EF tasks—card sorting, backward digit span, rule switching—require manipulating arbitrary, decontextualized information. These are precisely the kinds of operations heavily trained in formal schooling but far less demanded in many traditional environments. When these tasks are administered across populations, the differences are not subtle. Children in industrialized, schooled contexts show the familiar developmental trajectory—successful rule switching by age five, increasing working memory span, and so on. But children in rural, nonschooled communities often show qualitatively different patterns: failure to switch rules even at older ages, difficulty performing backward recall, and generally low rates of what researchers define as “canonical” responses. The point is not that these children lack cognitive control in any meaningful sense—they function effectively in complex real-world environments—but that the tasks are measuring a particular style of cognition that develops under specific cultural conditions.

This forces an uncomfortable ambiguity. The term “executive function” has been used to refer both to presumed universal regulatory capacities and to performance on these standard tasks. But the two may not coincide. Either EF names a universal capacity that current tasks fail to measure cleanly, or it names a culturally specific set of skills cultivated by schooling. The data do not allow both interpretations simultaneously. The implication is that decades of developmental curves, policy recommendations, and even clinical assessments may rest on a construct that conflates biology with cultural training.

A brief commentary by Mazzaferro and colleagues pushes back—not against the data, but against the conclusion that we must choose between universality and cultural specificity. They argue that the problem lies in measurement, not in the concept itself. Psychological tests always mix construct-relevant variance with context-dependent artifacts. When a task is transplanted into a different cultural setting without adaptation, it may cease to measure the intended construct at all. The analogy they offer is instructive: one would not conclude that “theory of mind” is culturally specific simply because a Western-designed false-belief task fails in an unfamiliar cultural context. Instead, one adapts the task.

From this perspective, executive function may indeed be a broadly shared capacity—rooted in evolutionary history and observable across species—but its expression and measurement are inevitably shaped by local demands. The solution is not to abandon the construct, but to develop context-sensitive assessments that capture how cognitive control is actually deployed in different environments. A child in a Western classroom uses executive function to manipulate symbols and follow abstract rules; a child in a pastoral society uses it to track livestock, navigate terrain, and manage social responsibilities. The underlying capacities may overlap, but the skills—and the tests that reveal them—do not.

What emerges from this exchange is a deeper point about cognitive science itself. Constructs like executive function are not simply discovered; they are stabilized through particular experimental practices. When those practices are narrowly tied to a single cultural niche, the resulting constructs risk inheriting that narrowness while being mislabeled as universal. The Kroupin study exposes this risk sharply. The Mazzaferro commentary reminds us that abandoning the construct is not the only response—but that rescuing it requires rethinking how and where we measure it.

The broader implication is that cognition cannot be cleanly separated from the environments in which it develops. What looks like a general-purpose cognitive capacity from within one cultural setting may, from a wider perspective, be an adaptation to a specific set of tasks and constraints. The challenge going forward is not simply to refine our measures, but to build theories that explicitly link cognitive processes to the ecological and cultural niches in which they are embedded.

[NOTE:  This post was generated by ChatGPT and curated by Deric] 

 

The Case for Being Human

 ...This Colin Lewis lecture and essay on "Character, Clarity, and connection in a Century of Curated Selves" is a welcome breath of fresh air. I pass on the entire piece:

 

We live in an age so crowded with voices that the unfiltered one now sounds almost shocking.

It’s strange how rare sincerity has become, and stranger still that we now confuse sincerity with naïveté.

To speak plainly is to risk sounding unsophisticated.

To think deeply is to risk being slow.

To feel fully is to risk being fragile.

But character, the old-fashioned word we’ve quietly retired, was never meant to protect us from vulnerability.

It was the discipline of aligning our inner life with our outer one, of letting integrity shape expression.

It demanded more than intelligence; it demanded coherence.

Critical thinking once meant that: not the sport of dismantling others’ arguments, but the patient craft of constructing one’s own, with care, doubt, and moral weight.

It was an act of self-respect, a kind of inner carpentry.

But our culture prizes speed over depth, reaction over reflection.

The algorithm rewards the appearance of certainty, not the work of understanding.

And so, in the noise, we mistake fluency for thought, visibility for virtue, and connection for mere contact.

We are raising a generation fluent in analysis but starved of empathy, able to read a thousand opinions yet unable to feel the gravity of a single human face.

Character, clarity, and connection are not distinct virtues, but the integrated disciplines required to reclaim the authentic, undivided human self from the pressure of performance.

The Discipline of Character

We once spoke of character the way we now speak of talent, as something to be developed, not displayed.

It was an inward architecture that gave a person moral gravity.

You sensed it not by how someone performed under lights but by how they behaved in the dark: what they refused, what they endured, what they stood for when standing carried a cost.

Emerson called it ‘the moral backbone of nature.’

William James called it ‘the faculty for choosing the better course when the worse is easier.’

Today, the word has slipped from our vocabulary, replaced by a constellation of lesser virtues: branding, adaptability, emotional intelligence, the polite euphemisms of a culture that fears judgment but craves approval.

Character was once forged in friction: between impulse and restraint, principle and convenience, public expectation and private conviction.

Now friction is the enemy.

We sand ourselves smooth for compatibility.

We are taught to self-optimize, to fit seamlessly into every new interface, every shifting norm.

The result is a society of impressive surfaces and untested cores.

What was once the slow combustion of the self, trial, error, correction, moral learning, has been replaced by the constant calibration of persona.

The question Who am I becoming? has been outsourced to the analytics of How am I performing?

And yet, without character, the rest collapses.
Critical thinking degenerates into cynicism; connection turns transactional.

The cultivated self was never meant to be frictionless, it was meant to be faithful: to something larger than appetite or trend.

To have character was to live by a standard not of success, but of self-command.

To measure worth not by agreement, but by conscience.In the quiet arithmetic of the inner life, that standard still waits, patient as gravity, reminding us that the project of being human is not to appear consistent but to become whole.

The first fruit of such integrity is humility, the recognition that self-knowledge is incomplete and that moral certainty is often the mask of fear.

That humility becomes the soil in which the next discipline takes root: the clarity of mind that allows us to see truth without distortion

The Discipline of Clarity

Critical thinking is often mistaken for skepticism, but its essence is humility, the willingness to see one’s own mind as fallible, corrigible, unfinished.

To think critically is not merely to doubt; it is to care about whether what you believe is true.

It demands a quiet courage, a resistance to the seductions of belonging.
For all our talk of open-mindedness, the modern mind is rarely open, it is flooded.

We are drowning in data but parched for discernment.

The problem is not ignorance; it’s the inability to tell signal from noise, truth from its infinitely capable impersonators.

Education once sought to train discernment, not just what to think, but how to think about thinking.

The goal was intellectual character: habits of honesty, coherence, proportion, restraint.

Today our institutions reward agility over depth, rhetorical victory over conceptual clarity.

The clever learn to weaponize ambiguity, to speak in the tone of thought without its substance.

In that performative landscape, genuine inquiry feels almost subversive.

It requires patience, solitude, and the unprofitable act of staying with uncertainty long enough for understanding to ripen.

True critical thinking, like moral character, begins in interiority, in self-questioning.

It’s the discipline of cleaning the lens through which we see.

It asks us to distinguish what is clear from what is loud, what is complex from what is confused.

The philosopher Raymond Geuss once noted that clarity is not a natural state but a moral achievement: it costs time, effort, and the willingness to endure ambiguity without surrendering to dogma.

In that sense, thinking clearly is an act of integrity.

The humility that character teaches becomes the clarity that thought requires.

The Courage to Feel

If character is the discipline of the soul, and clarity the discipline of the mind, then emotional authenticity is the discipline of presence.

It’s the art of showing up without armor, of allowing feeling to inform, rather than distort, perception.

We’ve mistaken composure for wisdom, detachment for maturity.
But the mind that cannot feel is no more rational than the heart that cannot think.

To connect honestly with another person is to risk being changed by them.

That risk, not efficiency or eloquence, is the measure of intelligence in its most human form.

The age of constant contact has made us experts at simulation.

We know how to mimic intimacy, how to text empathy, how to perform concern in 280 characters.

But emotional connection, the kind that alters the weather inside us, cannot be automated.

It requires the rarest human resource: attention.

To really listen is to momentarily suspend the self, to admit that the other person exists in a reality as vivid and demanding as your own.

That admission is humbling, and redemptive.It restores proportion to the world.

Neuroscientists now confirm what poets always knew: thought and feeling are not rivals but partners.

Emotion is not noise in the signal of reason; it’s the current that gives reason direction.

To cultivate empathy is therefore not a sentimental indulgence but a cognitive one, the training of perception through compassion.

The unfeeling intellect is not more objective, only more incomplete.

To know without feeling is to see in grayscale; to feel without knowing is to drown in it.

The task, as ever, is integration: to think with the heart and feel with the mind.

The Return to Wholeness

Character, clarity, and connection are not three virtues but three expressions of one longing, the desire to live as an undivided self.

We sense it in moments when our words, thoughts, and feelings align, when what we say reflects what we mean, and what we mean is grounded in what we are.

Such moments are rare because the world conspires against them.

The modern condition is fragmentation, our attention split, our loyalties divided, our selves dispersed across screens and roles.

We are, in Byung-Chul Han’s phrase, “achievement subjects,” endlessly producing ourselves.

Yet the human spirit, when left unharried, still yearns for coherence: to think truly, to act rightly, to feel deeply.

To cultivate that coherence is a rebellion.

It means valuing silence in a world addicted to noise, depth in a culture that worships speed, and sincerity in an economy built on display.

It asks us to become custodians of our own interior life, to guard the private workshop where moral insight, intellectual clarity, and emotional tenderness are forged into something like wisdom.

The old word for this was virtue, which did not mean virtue-signaling or moral posturing, but excellence of being.

To be virtuous was to be real, to be, as Aristotle wrote, ‘at one with oneself.’

In the end, wholeness is not a summary of these disciplines but their reward, the living synthesis of soul, mind, and heart into a single organ of understanding.

To live well in this century will require not new tools, but new depths: a return to the quiet heroism of character, the hard discipline of clarity, and the tender courage of connection.

And perhaps, when someone dares again to speak without polish, to feel without pretense, to think without echo, we’ll recognize them not as naïve, but as free.

Stay curious

 

Do we have an appetite for transcendence?

I've been working on a long rambling post on finding meaning in the flow and evolution of the cosmos rather than in the varied anthropomorphic religions that many cultures have invented.  My motivation to 'find meaning' is not that high, because the quest itself is a human invention derived from assuming the universe was made for us.  I find Paul Bloom in his recent Substack post "Is there a God-shaped hole?" has made a better statement of my position than I could.  I recommend that you read it.  

What are humans for? - AI Forces a Question We’ve Been Dodging

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AI Is Already Fusing With Us — The Only Question Is How

The argument about whether AI will enhance or diminish our our humanity usually starts in the wrong place. People argue about what AI should do before asking a more basic and uncomfortable question: what are humans for?

Every powerful technology embeds an answer to that question. AI is no exception. In fact, it may be the most consequential case yet, because AI is not just a tool we use—it is a system that increasingly co-thinks with us, shapes our attention, edits our language, nudges our choices, and quietly rewires our sense of agency.

We are not facing a future of humans versus machines. We are already living in a world of human–machine fusion.

The real issue is not whether this fusion will happen. It is what kind of fusion it will be.

There Is No Such Thing as “Human Values”

Much of the public discussion of AI ethics rests on a fantasy: that “human values” exist as a coherent, global, agreed-upon set of principles that AI can be aligned with.

They don’t.

Value, purpose, and meaning are social constructions. They vary radically across cultures, religions, and political systems—and they always have. What one society calls a meaningful life, another calls wasted. What one treats as sacred, another treats as irrelevant or dangerous.

There is no global agreement on what humans are for. Expecting a worldwide technology like AI to be guided by a single, shared vision of human purpose is naïve at best, dishonest at worst.

There Will Be Many AIs, Just as There Are Many Religions

Because there is no single answer to what humans are for, there will be no single AI.

There will be many forms of AI, each reflecting the values—explicit or implicit—of the cultures, institutions, and power structures that create them. Some will be optimized for surveillance, compliance, and efficiency. Others will be built to extend memory, imagination, and self-understanding.

In blunt terms: some AIs will enslave us; others could help liberate us.

The enslaving versions will treat humans as components to be managed—predictable, optimizable, correctable. Friction will be treated as inefficiency. Deviance as error. Interior life as noise.

The liberating versions will function as prostheses for the mind—tools that expand rather than replace human capacities. They will support reflection rather than manipulation, agency rather than control, curiosity rather than conformity.

The difference is not technical. It is philosophical.

AI as Prosthesis vs. AI as Manager

We already accept prostheses for the body—glasses, hearing aids, pacemakers—because they restore or extend human function without claiming authority over the person. AI can play a similar role for cognition: augmenting memory, pattern recognition, synthesis, and imagination.

But there is a sharp boundary here.

A cognitive prosthesis supports a human agent.
A cognitive manager displaces one.

The danger is not that AI becomes intelligent. The danger is that it becomes normative—quietly deciding what matters, what counts, what is worth attention, and what is not.

Once that happens, humans do not disappear. They persist as interfaces.

The Question We Can’t Outsource

AI forces us to confront a question we have long evaded: what kind of humans do we want to be when much of our thinking is no longer done alone?

There will be no final answer. There never has been. Meaning has always been plural, unstable, and contested.

But refusing to ask the question does not keep us neutral. It simply allows markets, states, and optimization systems to answer it for us—implicitly, automatically, and without appeal.

AI will fuse with us. That is already happening.

The only choice left is whether we fuse as authors of our lives—or as managed systems optimized for goals we did not choose.

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The above text is the response of ChatGPT5.2 to a prompt asking it to develop ideas in a long paragraph I wrote into a potential MindBlog post, followed by a second prompt asking it to alter its first excellent response into a shorter and more polemical version.  

 

The Modernity Machine

I want to pass on to readers this Venkatesh Rao substack essay that summarizes what his book club has learned in the past year: 

The Modernity Machine III

Completion, Saturation, and Phase Transition

This the third and concluding part of my series with notes on the learnings from the 2025 Contraptions book club. Part I and Part II traced the construction of the Modernity Machine between roughly 1200 and 1600: a civilization-scale contraption that converted medieval heterogeneity into legible, interoperable order. By 1600, the machine was complete in all essential respects. This concluding part addresses what such completion actually meant, what the machine optimized for once built, the contradictions it necessarily produced, and why those contradictions could not be repaired from within.

The aim is not to declare the “end of modernity,” but to close out a rough analysis of the machine that took 400 years to build and turn on, making modernity possible, and sustained it for another 400 years, being patched in increasingly fragilizing ways along the way. And also to explore why its very success post-1600 began forcing a slow phase transition to a different kind of civilizational machinery which began to get constructed around 1600, right when the Modernity Machine got turned on. This machine, which I refer to as the Divergence Machine, is being completed and turned on as we speak, even as the Modernity Machine is starting to get decommissioned in bits and pieces worldwide. Here is a teaser picture for the overarching thesis we’re developing here.

The 1600-2000 period and the Divergence Machine that emerged in that period will be the subject of the 2026 book club.

But let’s wrap up 2025 first.

You can catch up on the closing 2025 group discussion in this transcript. What follows is my personal wrap-up.

What the Machine Optimized

The Modernity Machine did not optimize for truth, justice, progress, or freedom—those were its legitimating narratives. What it optimized for, relentlessly and across domains, was legibility: the ability to render people, land, goods, time, belief, and violence enumerable, narratable, and interoperable at scale.

Venice, in City of Fortune, is not interesting because it was rich or republican, but because it functioned as an early, tightly integrated legibility engine: maritime logistics, double-entry bookkeeping, legal abstraction, diplomacy, and intelligence-gathering fused into a self-reinforcing apparatus. Venice: A New History fills in the same picture from another angle: stability emerges not from ideology but from procedural compression.

The horse, in Raiders, Rulers, and Traders, plays a parallel role across Eurasia: a biological technology that collapses distance, standardizes military force, and forces political units to scale or perish. The horse is legibility made kinetic.

Printing, in The Printing Revolution in Early Modern Europe, completes the transition. Knowledge becomes reproducible independent of context. Interpretation decouples from replication. Information density explodes while shared meaning lags behind. The machine acquires a memory that grows faster than any coordinating narrative.

1493 shows the planetary version of the same dynamic. The Columbian exchange is not merely ecological or economic; it is a global synchronization event. Previously isolated systems are forced into a single ledger. The machine’s jurisdiction becomes planetary even as its capacity for meaning remains local.

Seen this way, the Modernity Machine is best understood as a civilization-scale compression algorithm. For several centuries, the gains are extraordinary.

Medieval Baselines

To understand what the machine displaced, it helps to look at relatively pristine end-of-medieval snapshots.

Majapahit offers such a snapshot outside Europe: a highly developed but still recognizably medieval empire, organized around courtly ritual, tributary relations, and localized legitimacy, poised at the cusp of collapse before modernity arrives in force. It represents a world not yet reorganized by legibility, still governed by face-to-face sovereignty and cosmological order.

Within Europe, The Age of Chivalry performs a similar function. Chivalry appears not as romance but as a fully articulated medieval coordination system—ethical, military, and social—already straining under pressures it cannot metabolize. This is medievalism at its most coherent, just before it becomes an anachronism.

These snapshots matter because they show what modernity did not inherit: localized legitimacy, narrative sufficiency, and bounded scale.

The Hidden Outputs

Once the Modernity Machine works, it produces three unavoidable byproducts.

First, excess agency. Feudal bonds dissolve, religious monopolies weaken, markets and cities proliferate. The Canterbury Tales and The Decameron are early catalogs of proliferating voices and moral standpoints. Social life becomes polyphonic. Coordination becomes harder because more people can act.

Second, excess information. Printing destabilizes epistemic hierarchy. By Montaigne’s time, the educated individual is already drowning in books. The Complete Essays read as field notes from the first generation to experience epistemic overload. Skepticism is both a philosophical stance and a coping mechanism.

Third, excess scale. Before European Hegemony and When Asia Was the World make clear that global integration predates European dominance, but modernity hardens integration into permanent structure. Local meaning cannot survive planetary circulation intact.

The machine creates more actors than it can integrate, more information than it can interpret, and more scale than it can narrate.

The Last Gasp of the Medieval

The lesson of Giordano Bruno and the Hermetic Tradition is not that Bruno foresaw modern pluralism. It is almost the opposite. Bruno represents the last exuberant escape of medieval imagination—a crackpot magician and memory-maven operating within an anachronistic misunderstanding of Hermeticism as ancient Egyptian wisdom to hallucinate a worldview of bullshit — indifferent to truth or falsity in any modern empirical sense. His cosmological conclusions happened to resonate sympathetically with Copernican implications, but for fundamentally wrong reasons.

Bruno does not anticipate modernity; he misunderstands it. His fate marks not the birth of a new worldview, but the extinguishing of a freewheeling medieval mode incompatible with both emerging authoritarian modernism, especially ecclesiastical, and scientific empiricism. What survives of his tradition—Rosicrucianism, Masonic esotericism—persists as fringe subculture: culturally influential at times, intellectually irrelevant to the main currents of modern thought.

Bruno is thus not an early modern prophet, but a terminal medieval outlier.

Similarly, Ibn Khaldun: An Intellectual Biography should not be read as the story of a proto-sociologist ahead of his time. That role is largely retrofitted by modern interpreters. In his own context, Ibn Khaldun appears more plausibly as a kind of depressed Arab Petrarch: a brilliant chronicler of decline and defender of tradition, lamenting the absence of an Islamic renaissance rather than inaugurating one.

His cyclical theory of dynasties does not launch a new science of society; it records the exhaustion of an old civilizational form. The importance of Ibn Khaldun here is diagnostic, not genealogical. He documents a world failing to enter the Modernity Machine at all, despite being in possession of many of the necessary components.

Narrative Exhaustion

By the early modern period, narrative itself begins to fail as a unifying technology. Don Quixote stands as the European bookend to The Age of Chivalry. Quixote behaves impeccably within a dead symbolic system, and chaos results. The novel demonstrates that inherited narratives no longer synchronize action with reality.

Journey to the West stages the same problem mythically. The Monkey King embodies pure agency without moral center. Order is restored only through endless improvisation, not closure. This is not premodern innocence but recognition that containment now requires perpetual patching, a condition whose outer story is told in 1493.

Utopia remains the last sincere architectural drawing of the Modernity Machine. It assumes total legibility, benevolent coordination, stable universals, and obedient subjects. Even at publication, it is already obsolete. The social, informational, and political conditions required for utopia to function are precisely those modernity has destroyed in creating itself.

Everything after Utopia is retrofit to a completed civilizational machine, to patch problems that began appearing almost immediately after it was turned on in 1600.

Why Repair is Impossible

By 1600, the machine has crossed a complexity threshold. More law increases rigidity without legitimacy. More reason fragments into disciplines. More planning amplifies unintended consequences. More morality polarizes rather than integrates.

This is not moral failure or intellectual laziness. It is structural. The Modernity Machine generates more differentiation than any universal framework can absorb.

The Modernity Machine does not collapse, but a new logic begins cohering at its periphery. Coordination shifts from top-down design to nudging from the margins — and increasingly, everybody is in the margins. Some just recognize it in 1600, while others are only realizing it now in 2025. Legitimacy fragments. Meaning localizes. Systems adapt without consensus. Civilization continues to function—often remarkably well—while agreeing less and less about what it is doing or why.

This is the phase transition. The machine that made convergence possible gives way to a machine that produces divergence as a default condition.

The Modernity Machine has done its job. What follows is not its negation, but the emergence of a Divergence Machine destined to replace it—a different contraption, hot-swapped piecemeal for its predecessor over 400 years, between 1600 and 2000. Optimized not for legibility and convergence, but for proliferation, adaptation, and coexistence without closure. For divergence.

That is the story of 1600–2000, which we will tackle in 2026.

The picks for the first three months have been posted on the book club page if you want to get a head start. I’ll lay out the thesis in a January kickoff post.

Dangerous Ideas.......

Deric's MindBlog is almost 20 years old. Its first post appeared on Feb. 8, 2006. The assertions and ideas described in that original post are as fresh and relevant now as they were then, before the appearance of the iPhone, social media, and contracting attention spans.  The Edge.org link that once took you to the essays supporting the 'dangerous ideas' now takes you to their published version on Amazon. The "Reality Club" and John Brockman's "Third Culture" cohort of intellectuals has largely dispersed, although you will note many names still quite prominent today.   Here is the 2006 post:

***************

Edge.org is a website sponsored by the "Reality Club" (i.e. John Brockman, literary agent/impressario/socialite). Brockman has assembled a stable of scientists and other thinkers that he defines as a "third culture" that takes the place of traditional intellectuals in redefining who and what we are.... Each year a question is formulated for all to write on... In 2004 it was "What do you believe is true even though you cannot prove it?" The question for 2005 was "What is your dangerous idea?"

The responses organize themselves into several areas. Here are selected thumbnail summaries most directly relevant to human minds. I've not included cosmology and physics. Go to edge.org to read the essays

I. Nature of the human self or mind (by the way see my "I-Illusion" essay on my website):

Paulos - The self is a conceptual chimera
Shirky - Free will is going away
Nisbett - We are ignorant of our thinking processes
Horgan - We have no souls
Bloom - There are no souls, mind has a material basis.
Provine - This is all there is.
Anderson - Brains cannot become minds without bodies
Metzinger - Is being intellectually honest about the issue of free will compatible with preserving one's mental health?
Clark - Much of our behavior is determined by non-conscious, automatic uptake of cues and information
Turkle - Simulation will replace authenticity as computer simulation becomes fully naturalized.
Dawkins - A faulty person is no different from a faulty car. There is a mechanism determining behavior that needs to be fixed. The idea of responsibility is nonsense.
Smith - What we know may not change us. We will continue to conceive ourselves as centres of experience, self-knowing and free willing agents.

II. Natural explanations of culture

Sperber - Culture is natural.
Taylor - The human brain is a cultural artifact.
Hauser- There is a universal grammar of mental life.
Pinker - People differ genetically in their average talents and temperaments.
Goodwin - Similar coordinating patterns underlie biological and cultural evolution.
Venter - Revealing the genetic basis of personality and behavior will create societal conflicts.


III. Fundamental changes in political, economic, social order

O'donnell - The state will disappear.
Ridley - Government is the problem not the solution.
Shermer - Where goods cross frontiers armies won't.
Harari -Democracy is on its way out.
Csikszentmihalyi- The free market myth is destroying culture.
Goleman - The internet undermines the quality of human interaction.
Harris - Science must destroy religion.
Porco - Confrontation between science and religion might end when role played by science in lives of people is the same played by religion today.
Bering - Science will never silence God
Fisher - Drugs such as antidepressants jeopardize feelings of attachment and love
Iacoboni - Media Violence Induces Imitative Violence - the Problem with Mirrors
Morton - Our planet is not in peril, just humans are.

The 2025 Ig Nobel prizewinners in full

 A clip from the Chris Simms Nature Magazine article

LITERATURE

The late physician William Bean, for persistently recording and analysing the rate of growth of one of his fingernails over a period of 35 years.

PSYCHOLOGY

Marcin Zajenkowski and Gilles Gignac, for investigating what happens when you tell a narcissist — or anyone else — that they are intelligent.

NUTRITION

Daniele Dendi, Gabriel Segniagbeto, Roger Meek and Luca Luiselli for studying the extent to which a certain kind of lizard chooses to eat certain kinds of pizza.

PEDIATRICS

Julie Mennella and Gary Beauchamp for studying what a nursing baby experiences when their mother eats garlic.

BIOLOGY

Tomoki Kojima, Kazato Oishi, Yasushi Matsubara, Yuki Uchiyama, Yoshihiko Fukushima, Naoto Aoki, Say Sato, Tatsuaki Masuda, Junichi Ueda, Hiroyuki Hirooka and Katsutoshi Kino, for their experiments to learn whether cows painted with zebra-like stripes can avoid fly bites.

CHEMISTRY

Rotem Naftalovich, Daniel Naftalovich and Frank Greenway, for experiments to test whether eating Teflon [a form of plastic more formally called ’polytetrafluoroethylene’] is a good way to increase food volume, and hence satiety, without increasing calorie content.

PEACE

Fritz Renner, Inge Kersbergen, Matt Field and Jessica Werthmann, for showing that drinking alcohol sometimes improves a person’s ability to speak in a foreign language.

ENGINEERING DESIGN

Vikash Kumar and Sarthak Mittal, for analysing, from an engineering design perspective, “how foul-smelling shoes affects the good experience of using a shoe-rack”’.

AVIATION

Francisco Sánchez, Mariana Melcón, Carmi Korine and Berry Pinshow, for studying whether ingesting alcohol can impair bats’ ability to fly and echolocate.

PHYSICS

Giacomo Bartolucci, Daniel Maria Busiello, Matteo Ciarchi, Alberto Corticelli, Ivan Di Terlizzi, Fabrizio Olmeda, Davide Revignas and Vincenzo Maria Schimmenti, for discoveries about the physics of pasta sauce, especially the phase transition that can lead to clumping, which can yield an unappetizing dish.