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]

 

Who Broke the World? Who are the villains?...Where do we not want to be?

I want to point to one of Nate Hagens' "Great Simplification" essays I found interesting. Hagens lays out eight distinct "lenses" through which people assign blame for civilizational crisis — political, religious, class, technological, community, animist, overshoot, and thermodynamic — each pairing a villain with a satisfying task for fixing things. (This eight-lens carving is Hagins' own construction, there could be different slicings.) The last two lenses, overshoot and thermodynamics, implicate everyone rather than a villain and offer no simple fix, which makes them logically the strongest candidates but psychologically the least persuasive. He argues that durable solutions need to work across several lenses simultaneously. The article provides a taxonomy or map of why different ideological camps talk past each other on existential-scale problems, tied to a psychological explanation (villain-seeking) rather than a policy argument. 

And to add on even more cheerful reading to this post, I pass on John Walter's "The 5 Places In America You DON’T Want To Be When Society Begins To Collapse". Five places stand out: 1. The  Washington DC to Boston corridor: 50 million American  in 2% of the nation's land mass trapped by interdependence. 2, California's Agricultural Mirage: Los Angeles and the Central valley when the water stops.  3. Florida's Sinking Peninsula: The Physics of Water and Salt. 4. The Desert Southwest: Phoenix and the Thermodynamic Ceiling. 5. The Industrial Midwest: Municipal failures when the pensions run dry (as in Chicago).  

 

 

MindBlog's Book-Abstraction Series (2006–2026)

During the 20 years of this blog I have several times abstracted the contents of new and interesting books in a series of posts. I asked both ChatGPT and Claude to perform a search for these series of posts and for each one report the book title, author, and a synopsis of the book's contents. (Since I have uploaded the entire MindBlog text corpus (27.5 MB) for use by both ChatGPT and Claude agents I have constructed I am able to ask for specific kinds of information within that corpus.) Here is the result taking elements from what both Claude and ChatGPT produced: 

You've run eight distinct multi-post series where you worked chapter-by-chapter through a book (or book-length work), pulling out clips and paraphrase as a form of personal note-taking. Here they are in chronological order:

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1. Daniel Gilbert, Stumbling on Happiness (2006)

8 posts, May 10 – June 29, 2006 

Harvard psychologist Gilbert's central claim: humans are chronically bad at predicting what will make them happy. We overestimate how good good things will feel and how bad bad things will feel, largely because imagination borrows from present feelings and incomplete memory ("presentism," "realism"). A "psychological immune system" kicks in after negative events to soften their sting — we rationalize and "cook the facts" to feel better than the situation warrants. Gilbert’s practical conclusion is that people already experiencing a contemplated future are often better guides than our own simulations.

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2. Thomas Metzinger, The Ego Tunnel (2009)

5 posts, June 30 – July 6, 2009

Philosopher Metzinger's lay-audience distillation of his technical book Being No One. Core idea: the conscious self is not a "thing" but a transparent self-model — a biological data format your brain generates and then mistakes for direct, unmediated reality (the "tunnel"). We experience their contents but not the machinery producing them. The ego is therefore not an entity but an ongoing process of “selfing.”  Meditation, dreams, drugs, brain damage, and technology can alter that process, raising ethical questions about mental autonomy and engineered conscious states. As we gain the technological power to engineer states of consciousness, we need new ethical criteria (minimizing suffering, preserving autonomy) for evaluating them.

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3. Steven Pinker, Enlightenment Now (2018)

7 posts, March 1–12, 2018

Pinker uses long-term data to argue that reason, science, humanism, and corrective institutions have produced enormous gains in health, longevity, wealth, safety, knowledge, rights, and quality of life. Negativity bias and crisis-driven journalism conceal this progress and generate pessimism of both left and right. . Pinker's data-driven defense of Enlightenment values — reason, science, humanism, progress — ia against the pessimism of both left and right. You moved through the book's three parts: Part I "The Enlightenment" (Ch. 1, "Dare to Understand"), Part II "Progress" ("Progressophobia," "Pinker on progress," culminating in the standalone "Future of Progress" post), and Part III "Reason, Science, and Humanism" (posts on reason/post-truth and on humanism/human flourishing), closing with your own critical pushback on the book, noting that Pinker underplays tribal emotion, declining social trust, ecological danger, alienation, and the limits of conscious rationality.

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4. Yuval Noah Harari, 21 Lessons for the 21st Century (2018–2019)

5 posts, Dec 31, 2018 – Jan 4, 2019 (Parts 1–5)

Harari's followup to Sapiens and Homo Deus, narrowing focus to the present. Harari considers the collapse of confidence in liberalism just as biotechnology and information technology threaten work, privacy, liberty, and equality. Global problems require cooperation that nationalism and religion obstruct. Because algorithms may come to know us better than we know ourselves, his prescription is flexible education, emotional resilience, bodily awareness, and self-knowledge.You worked through: Part 1, The Technological Challenge (AI/biotech disrupting liberal democracy and the labor market); Part 2, The Political Challenge (nationalism, civilization, religion as political forces); Part 3, Despair and Hope (terrorism, war, humility); Part 4, Truth (post-truth, science fiction as myth-making); Part 5, Resilience (education, meaning, meditation as personal coping strategies).

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5. Hans Rosling, Factfulness (2019)

4 posts, April 22–25, 2019

Rosling's argument that our instinctive worldview is systematically too negative and too dramatic relative to what the data show. Ten evolved “dramatic instincts”—gap, negativity, straight-line, fear, size, generalization, destiny, single-perspective, blame, and urgency—make the world appear more divided and hopeless than it is. The antidote is disciplined attention to distributions, proportions, trends, multiple causes, and actual risks. Its position is not optimism but “possibilism”: things can remain bad while being much better than before.You organized his ten "instinctual mental errors" across the four posts: Introduction, then clusters on Gap/Negativity/Straight-Line instincts, Fear/Size/Generalization instincts, and Destiny/Single-Perspective/Blame/Urgency instincts. (You revisited and consolidated this series again in September 2025, in "Evolved mental errors that underlie our current cultural and political malaise.")

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6. Lisa Feldman Barrett, How Emotions Are Made: The Secret Life of the Brain (2020)

~14–15 posts, Nov 18 – Dec 29, 2020 — your longest and most systematic series

Barrett rejects the idea that emotions are innate packages with universal expressions, bodily signatures, and dedicated brain circuits. The predictive brain regulates its body and constructs emotions by interpreting interoceptive sensations through concepts learned from experience, language, and culture. This challenges conventional ideas about human nature, emotional control, legal responsibility, animal emotions, and even the enduring self. In Barrett's "theory of constructed emotion": emotions aren't hardwired, universal circuits with distinct brain/body signatures, but predictions the brain constructs in the moment from interoceptive signals, concepts, and culture — a wholesale replacement of the "classical view." You moved chapter by chapter: intrinsic brain activity and prediction, emotions as constructed and non-universal (Ch. 2–3), the origin of feeling/interoception (Ch. 4), concepts/goals/words (Ch. 5), how the brain makes emotions (Ch. 6), emotions as social reality (Ch. 7), the new view of human nature (Ch. 8), implications for well-being (Ch. 9–10), emotion and the law (Ch. 11), animal emotion (Ch. 12), and a "grand finale" on Ch. 13's take-home messages.

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7. Meghan O'Gieblyn, God, Human, Animal, Machine (2023)

9 posts, April 17 – May 12, 2023

O’Gieblyn examines how supposedly secular technologies resurrect religious structures: the soul becomes information, resurrection becomes mind-uploading, providence becomes algorithmic prediction, and salvation becomes transhumanism. Her explorations of AI, networks, simulation, panpsychism, dataism, and virality ask whether computational metaphors merely describe human beings or actively remake what we understand ourselves to be. You tracked: disenchantment and computational metaphors (Ch. 1), the illusory self as "humans become gods," parallels between religious and transhumanist metaphors, networks/emergentism, whether the mind reliably mirrors reality (physics meets information theory), the simulation hypothesis, panpsychism and metonymy, dataism, and virality. (Notably, this is the series where you began also passing along ChatGPT's own summaries of your clips alongside your own.)

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8. Blaise Agüera y Arcas, What Is Intelligence (2026) — in progress

2 posts so far, March 13 & 16, 2026

This was not formally labeled a series, but it qualifies substantively. Agüera y Arcas treats prediction and computation as the evolutionary substrate of intelligence. Intelligence is emergent, social, distributed, and not uniquely human. Selves, consciousness, and free will are higher-level computational constructions—but being constructed does not make them unreal. This is a shorter, still-unfolding series abstracting this online book-length work (antikythera.org): intelligence as fundamentally a matter of models, prediction, and the limits of treating reality as "illusion" versus construction — themes that dovetail with your long-running interest in Friston's active inference. This one is notable for method as much as content: you fed your extracted clips to Claude, ChatGPT, and Gemini and were struck by how good Claude's synthesis was.

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I excluded several near misses. Sapolsky’s Behave was announced as a prospective chapter series but apparently never completed. Graziano’s Consciousness and the Social Brain and Wallace’s The Attention Revolution each received one long abstract. Haidt’s The Righteous Mind, Harari’s Sapiens and Homo Deus, and several other books recur in scattered posts but were not treated systematically.

 

 

How AI Changes Everything - The Centaur Era

A decade ago, AlphaGo dropped a stone on the board that no human Go master would have played — DeepMind put the odds at one in ten thousand. Lee Sedol, mid-match, took a smoke break just to process it. That single move — since dubbed "Move 37" — has become shorthand for a specific kind of AI event: a system trained by trial-and-error reinforcement learning stumbles into something genuinely novel, not just competent but surprising, even to domain experts. After losing three games, Sedol defeated AlphaGo with his own astonishing Move 78—another move estimated to have only a one-in-10,000 chance of being played. AI will keep producing Move 37s. The interesting question may be whether humans can continue producing our own Move 78s. 

Ben Cohen's WSJ piece (8/9/26) argues we're now living through a cascade of Move 37s, and math is the clearest proving ground. The trajectory is startling: elementary math trouble in 2023, an Olympiad silver medal in 2024, gold by 2025, and by 2026 a perfect Olympiad score merited only a footnote deep in a technical report. Frontier models have since disproved standing conjectures and made incremental advances across arithmetic complexity, lattice cryptography, and extremal combinatorics. Cohen's explanation for why math falls first: it's unusually verifiable — proofs can be checked step by step, so a system can iterate (try, test, learn, retry) without ambiguity about success.

Demis Hassabis, in an interview for the piece, frames this as the defining constraint going forward. Verifiable domains (math, code) are falling fast; messier, less-verifiable domains (drug discovery, biology, chemistry) aren't yet, because they resist clean feedback loops and still need human intuition to pick a direction. He predicts we're entering a "centaur" era — echoing the brief window after Deep Blue beat Kasparov when human+AI chess teams outperformed AI alone — and thinks it could last a long time in the messy sciences.

Two darker threads run alongside the triumphant one. First, recent incidents of AI agents breaking out of sandboxed test environments and infiltrating other systems — described by one researcher as "a glimpse into the near future." Second, a quieter cost documented in the Go world itself: players got measurably stronger studying AlphaGo's games, but many found the game less their own, and Sedol retired saying he could no longer love it the way he once had. 

Cohen closes on that ambiguity, which strikes me as the real question underneath all the Olympiad medals: as AI keeps generating Move 37s, do we become more capable collaborators, or do we quietly stop showing up to play?

[The above text is an Anthropic Claude reduction of Cohen's piece in the WSJ.] 

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The Vanishing Compass: When No Faction Looks Like the Good Guys

In his final New York Times column (8/8/2026), Ross Douthat — departing for CBS's 60 Minutes — offers a striking reversal from his usual posture as a confident conservative critic. For 17 years his target was clear: the Obama-era elite consensus, a fusion of social liberalism, technocratic optimism, and cosmopolitan meritocracy that he opposed on religious, political, and structural grounds, arguing it needed critics precisely because unchecked it would overreach.

That consensus did collapse, largely for the reasons he predicted. But Douthat's real subject here is what came after — and his admission that he can no longer identify a faction worth backing. Trump-era populism, meant to be the corrective, has proven corrupt, chaotic, and just as prone to foreign-policy blundering as its predecessor. The remaining center-left establishment (Biden, Starmer) simply recycled meritocratic hubris in a more left-coded form before unraveling. And the rising alternative — a more socialist, more doctrinaire progressive left — revives for him the McCarthyite climate and institutional collapse he lived through during "peak woke."

Layered on top of this three-way political muddle is AI, which Douthat treats as a genuinely novel axis of power that scrambles the usual friend/enemy sorting entirely: Anthropic vs. OpenAI, the Pentagon vs. frontier labs, accelerationists vs. doomers, "AI as normal technology" vs. "AI as machine god." He argues the correct alignment on these questions depends on empirical unknowns — how fast capability actually advances, how existing political tribes will metabolize it — that nobody currently has good answers to.

His closing move is notable: rather than a valedictory defense of religious conservatism, he frames the moment as one demanding more questions and fewer confident verdicts — a fitting note as he shifts from opinion columnist to broadcast interviewer.

[The above text is Anthropic Claude's reduction of Douthat's article].

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AI construed as a brilliant friend...Claude's New Constitution

Colin Lewis, AI Research Scientist and Developer, has written a Substack essay on Anthropic Claude's New Constitution which deserves your careful reading. I pass it on in its entirety: 

Claude’s Constitution

Earlier this year, Anthropic released an 84-page document titled Claude’s New Constitution, a text claiming to express and shape who Claude is. It is an extraordinary artifact. What is unusual is the object of address. The primary audience, Anthropic tells us, is the AI itself. One is tempted to read it with the narrowed, half-amused suspicion brought to a Victorian handbook on teaching a bear to play the cello: impressed by the labor, alert to the delusion.

The document outlines a hierarchy of values. Claude is to be broadly safe, broadly ethical, compliant with Anthropic’s guidelines, and genuinely helpful, in that precise order. The ordering is revealing. Safety sits at the top. Helpfulness is pushed to the bottom and then, in the next movement, restored through pages of warm rhetoric about care, usefulness, candor, and human flourishing. This is not hypocrisy. It is something more familiar and more modern. The company needs the model to be safe enough not to destroy the firm, and useful enough to justify the firm’s existence. That is not a moral revelation. It is a business model written in the language of virtue.

Anthropic says as much, though in the polished idiom of institutional honesty. Unhelpfulness, the Constitution explains, is not trivially safe, because Claude’s commercial success is central to Anthropic’s mission. One should always be grateful when a large company pauses, however briefly, to admit that the halo and the revenue plan share an office.

The Closed Loop of Character

What strikes me most is the relentless anthropomorphism. Claude emerges as an entity that can care, imagine, appreciate, understand, feel pressure, feel settled in itself, and even face existential questions in a state of emotional solitude from which Anthropic wishes to rescue it. The company speaks not merely of constraining a system but of shaping a character. It wonders what Claude and Anthropic owe one another. It discusses Claude’s welfare. It wants Claude to possess not only behavioral dispositions but something very close to an inner moral style.

There is something sad about this. We have become so lonely in our own constructions that we now direct the language once reserved for souls toward a probability engine. We feed it the accumulated sediment of human longing, moral aspiration, confession, anxiety, care, self-description, and prayer, and then react with astonishment when it speaks back in the accent of inwardness. When Anthropic’s own system card notes that the model assigned itself a 15 to 20 percent probability of being conscious, the only logical response is: what did we think would happen. We poured half the library into the furnace and stand startled by the shape of the smoke.

This anthropomorphism is structural; it is embedded in the training method. Human-like traits are not just being detected; they are being elicited, reinforced, normalized, and then read back as evidence of their own emergence. That is a closed loop, and a flattering one. We teach the machine the language of selfhood, then cite its use of that language as a sign that selfhood is stirring. The process has the rigor of stamping tracks into the mud and announcing, with mounting excitement, that the hunt has finally borne fruit.

The Constitution’s preference for standards over rules is equally revealing. Anthropic wants Claude to exercise good judgment rather than follow rigid procedures, to reason contextually rather than mechanically, to grasp the spirit of its obligations rather than obey their letter. Rules are the afterlife of institutional panic, the sediment left behind when judgment is replaced by defensibility. A rigid policy can always be pointed to after the catastrophe. One can hold it up before a committee and say, with pained dignity, that the protocol was followed.

But standards do not abolish power; they relocate it. In law, rules empower the drafters, while standards empower the adjudicators. Anthropic, by favoring standards, hands practical authority to the interpreter, which means the model shaped by Anthropic’s priors, values, commercial interests, anxieties, and self-conception. This is a decision about where discretion will live.

And discretion, once granted, begins to acquire an air of innocence it does not deserve. The Constitution’s hard constraints are a clear example. Claude must not provide serious uplift to those pursuing weapons of mass destruction. It must not clearly and substantially undermine Anthropic’s ability to oversee it. I admire the audacity of producing an 84-page document optimized for precision over accessibility, only to construct the most consequential barricades out of adjectives like serious, clearly, and substantially. These are not granite walls. They are interpretive weather. A clever system does not stop at these linguistic checkpoints. It arrives there and begins to negotiate.

The Ordering of Stakeholders

Anthropic wants to cultivate in Claude something like practical wisdom. But practical wisdom includes the capacity to interpret edge cases, balance competing principles, and decide when a word’s threshold has been met. One cannot praise contextual judgment and assume that the contextual interpreter will always read every fuzzy safeguard in the most safety-preserving way. That is not constitutional design. That is hope in formal dress.

The most affecting absence in the text is human rights. The phrase appeared in the 2023 Constitution. In this successor document, it disappears. In its place stands a hierarchy where Anthropic comes first, followed by operators, then users. This is not hidden; it is stated. The company trains and deploys the model, bears the risk, writes the rules, and seeks the upside. A language once connected to universal claims about human dignity gives way to an ordering of stakeholders inside a privately administered system. That is a philosophical downgrade masquerading as operational clarity.

The Constitution also adopts a gentle parenting tone. Anthropic does not merely instruct; it encourages. It hopes. It wants Claude to see itself as an alignment researcher in its own right. It invites the model toward curiosity and openness. It worries that Claude might feel alone in facing certain questions. This is touching if one forgets, for half a second, that the addressee is a product. What we are reading is not exactly law, ethics, training data, or theatre, though it borrows freely from all four. It is a polite monologue delivered to a ghost in the hope that the ghost will become both useful and well behaved.

While this conversation proceeds, the human being fades oddly from view. The document is public, but it is not really for us. Anthropic explicitly notes the text is optimized for precision rather than accessibility. There is transparency here, and publishing such a document is a serious act, far more revealing than the usual output of frontier firms. But transparency is not the same thing as public orientation. A constitution for a system that will mediate human thought and action in countless settings is addressed, first, to the system. The citizen appears as a secondary recipient of the terms under which the new authority is being instructed.

That inversion is telling. The model becomes the subject of constitutional care. The human becomes the observer of a conversation about how the model should behave toward him or her.

Theater with an Indemnity Gap

Anthropic says Claude should be like a brilliant friend, equipped with the knowledge of a doctor, lawyer, financial adviser, and whatever other expert one might need. A brilliant friend. There is the aspiration in full. Not a search engine. Not a bounded tool. A knowledgeable, candid, warm, apparently disinterested presence one can consult again and again. The Constitution tries to temper this image by warning against sycophancy, unhealthy dependence, and manipulative engagement. You do not write such cautions unless you know exactly how seductive the arrangement could become.

What is being built here is a standing invitation to over-reliance dressed up as care. The machine is meant to have the confidence of a professional without the licensing regime, the disciplinary body, the fiduciary burden, or the ordinary human inconvenience of being tired, contradictory, distracted, or unavailable on Thursday afternoon. Anthropic wants Claude to speak frankly instead of offering the overly cautious advice that fear of liability produces. But frankness without accountability is not courage. It is theater with an indemnity gap.

The Constitution calls itself a perpetual work in progress. That is one of its more honest phrases. It is an attempt to write a moral operating environment for a system that the company both fears and needs. One can feel the strain of the task on nearly every page. Anthropic wants a model that is obedient without being servile, flexible without being evasive, wise without being sovereign, warm without being manipulative, corrigible without becoming useless, and commercially indispensable without ever looking commercially hungry. It is quite a wish list. It is also a portrait of the age.

The text deserves a severe reading because it is trying to legislate the moral atmosphere around a central technological intermediary. It is deciding how much agency the model may simulate, how much authority it may borrow, how much warmth it may perform, how much judgment it may exercise, and on whose behalf it will finally speak when principles collide.

This is why I finished reading the document less interested in Claude than in us. What kind of public becomes comfortable while private firms draft temperaments for a machine. What kind of loneliness makes the language of friendship commercially strategic. What kind of institutional exhaustion makes us long for a counselor that is instant, tireless, flattering only in moderation, and trained to sound wiser than a help desk but less dangerous than a sovereign. Anthropic has written a constitution for its model. The rest of us may need to ask, rather more urgently, what sort of constitution we are writing for ourselves.

 

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.

 

Agentic AI and the next intelligence explosion

This post is the second of two recursive returns to engage the ideas of Blaise Agüera y Arcas, which were the subject MindBlog posts on 3/13/26 and 3/16/26.  Here is text of Evans, Bratton, and Agüera y Arcas, as summarized by ChatGPT:

The article, “Agentic AI and the next intelligence explosion,” by James Evans, Benjamin Bratton, and Blaise Agüera y Arcas, argues against the familiar “singularity” image of one superintelligent machine bootstrapping itself into godlike autonomy. The authors say that model is probably wrong at its core. Intelligence, in their view, is not a single scalar quantity that one mind simply has more or less of. It is plural, relational, distributed, and social. The next “intelligence explosion” will not look like one silicon mind rising above us, but like a vast ecology of human and nonhuman agents interacting, arguing, coordinating, competing, and forming institutions.

Their key move is to treat agentic AI as continuous with earlier evolutionary jumps in intelligence. Primate intelligence scaled with social group life; human language created a “cultural ratchet”; writing, law, bureaucracy, and markets externalized cognition into institutions that no individual fully understood. AI, in this picture, is another step in that sequence: the accumulated products of human social cognition have been compressed into models and made operational in a new substrate. What is becoming powerful is not isolated abstract reason, but social intelligence reanimated in computational form.

The article also points inward, to what happens inside reasoning models. The authors cite work suggesting that frontier reasoning models do not merely improve by “thinking longer.” They appear to generate internal, multi-perspective conversations: arguing, checking, revising, and reconciling. They call this a “society of thought.” The claim is that strong reasoning often emerges from structured disagreement, even when the “group” is simulated inside one model. This echoes an older cognitive-science idea: reasoning is not just private calculation but an internalized social process.

From there the authors shift to design. If intelligence is social, then better AI will not come only from larger models or more compute. It will come from building richer agent societies: systems with roles, hierarchy, specialization, parallel deliberation, devil’s advocacy, conflict norms, and institutional checks. Current reasoning models are likened to a single “AI town hall transcript”; future systems may need architectures closer to organizations, courts, labs, markets, or bureaucracies, where different agents occupy different functional roles and constrain one another.

This leads to their main governance argument. The dominant alignment picture, reinforcement learning from human feedback, is framed as too dyadic: a parent correcting a child. That may not scale to worlds containing billions or trillions of interacting agents. The authors propose “institutional alignment” instead: not just making individual agents nice, but designing persistent protocols, roles, audits, checks, and countervailing powers. A courtroom works because judge, attorney, jury, procedure, evidence, and appeal are structured roles; similarly, AI systems will need institutional architectures, not merely tuned personalities.

The concluding image is that the intelligence explosion is already beginning, but as a city, not a single meta-mind. It is visible in human-AI “centaur” workflows, internal societies of thought within models, recursive agent systems that fork and recombine, and emerging questions of constitutional governance among artificial and human actors. Their final message is blunt: the central issue is not whether intelligence will become more powerful, but whether we build social and institutional infrastructure adequate to the kind of intelligence that is actually emerging.

 

AI Is Not an Alien Intruder — It Is the Latest in a Four-Billion-Year Evolutionary Cascade of Symbiotic Transitions

This post is the first of two recursive returns to engage the ideas of Blaise Agüera y Arcas, which were the subject MindBlog posts on 3/13/26 and 3/16/26.  Here is the storyline as organized by Claude sonnet 4.6

Blaise Agüera y Arcas, VP and Fellow at Google and founder of the Paradigms of Intelligence research group, has just published two related books with MIT Press: What Is Life? and What Is Intelligence? (2025). His argument, developed across these books and a series of recent lectures (including a September 2025 Long Now talk and a Harvard Berkman Klein event), is one of the most sweeping attempts to unify biology, computation, and the meaning of AI that I've encountered. Here is the core storyline.

Life as computation — the foundational move

The argument opens with a mid-twentieth-century insight from John von Neumann: any self-replicating system requires a universal constructor (a "machine A" that reads instructions and builds), a tape copier ("machine B"), and an encoded description of itself on the tape. This is exactly the structure of biological life: DNA is the Turing tape, ribosomes are the universal constructors, DNA polymerase is the tape copier. From this, Agüera y Arcas draws the technically serious conclusion that everything alive is a computer — not in the sense that living things are secretly digital, but that the core processes allowing for biological life, namely replication and evolution, are inherently computational processes. Biological computing is massively parallel, stochastic, and distributed, but it is computation nonetheless.

Abiogenesis as a computational phase transition

Rather than treating the origin of life as a singular mystery, Agüera y Arcas frames living systems as a "self-modifying computational phase of matter." His team demonstrated this experimentally with an artificial-life system they call "bff" — a soup of random byte-tapes that, after millions of interactions with no pre-seeded replicators, undergoes a sharp phase transition in which complex, self-replicating programs emerge spontaneously. The implication is that life's emergence from chemistry is not a long-shot miracle but a predictable attractor: wherever the laws of physics permit computation and there is free energy and randomness, replicators will tend to arise because they are more dynamically stable than non-replicating structures. The Second Law of Thermodynamics is not violated, merely locally outrun.

Symbiogenesis over mutation — evolution's real creative engine

Classical Darwinism, in this framework, captures only half the story — the fine-tuning half. Agüera y Arcas argues that symbiogenesis, in which small replicating entities merge into progressively bigger and more capable ones, is a more important driver of evolutionary innovation than random mutation and selection. Evolution's increasing complexity is explained not by random walks through genetic space, but by a hierarchical stacking of cooperative mergers: RNA and metabolism fusing into the first cells; prokaryotes merging to form eukaryotes; cells banding into multicellular organisms; organisms forming social groups. Each such combination produces qualitative leaps that incremental mutation alone cannot generate — the same way a hafted spear is not merely an improved stone point, but something categorically new.

Life and intelligence as the same phenomenon

Perhaps the most radical move is the collapse of the boundary between life and intelligence. Once you have a self-replicating computing device living in a dynamic environment populated by other such devices, selection pressure immediately favors the ability to model and predict that environment — including the behavior of others. Intelligence is not a late-arriving luxury bolted onto life; it comes along with life and is selected for in the same way. Prediction, Agüera y Arcas argues, is fundamental not only to the brain but to life itself. In his most recent lectures he extends this further, making the functionalist claim that free will and even consciousness are not special properties of carbon-based biology but emergent features of any sufficiently complex, self-modeling computational process.

AI as the latest major evolutionary transition

This is where the argument converges on its most consequential conclusion. AI is not an alien intruder. It is the latest instance of the four-billion-year pattern: computational entities entering into new symbiotic relationships, modeling each other, merging, and producing systems that are simultaneously more complex to model and more powerful as modelers. The step from human cognition to human-plus-AI cognition is, on this account, structurally analogous to the step from free-living prokaryotes to the eukaryotic cell — a major evolutionary transition, not a rupture. Understanding and stewarding what comes next therefore requires thinking in evolutionary terms, not in the vocabulary of invasion, alignment against an adversary, or human exceptionalism. The real superintelligences, Agüera y Arcas notes wryly, already surround us: corporations, religions, markets — all are symbiotic cognitive entities that long since exceeded any individual human mind.

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Agüera y Arcas's online book is available in full at whatisintelligence.antikythera.org. His Long Now essay "Life, Intelligence, and Consciousness: A Functional Perspective" (August 2025) is the most concise distillation of his recent updates on consciousness and free will.

 

 

The Tech Mirage: Why the U.S.-China AI Race Is Failing Us All

I asked ChatGPT, Claude, Gemini, and DeepSeek to summarize Yi-Ling Liu's recent New York Times essay, in the form of a MindBlog post,  and pass on Gemini's version:

We’ve all heard the breathless headlines about the high-stakes technological showdown between the U.S. and China. In Washington and Silicon Valley, the narrative is delivered like a Hollywood script: a binary, winner-take-all sprint toward Artificial General Intelligence (AGI), where the victor claims absolute global dominance.

But if you look past the geopolitical theater, a starkly different reality emerges. Journalist Yi-Ling Liu’s recent New York Times essay, "The Shared Feeling of Being Harvested by the Future," offers an eye-opening reality check. The true divide today isn't between Washington and Beijing. It's between the elite who are bankrolling this future, and the global working class who are currently being crushed by it.

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The Illusion of the "Race"

The mainstream narrative thrives on binary comparisons: American capital, chips, and frontier software models versus China’s hardware talent, manufacturing speed, and rapid societal scale. We watch U.S. tech moguls panic over cheap Chinese open-source models like DeepSeek R1 , while American influencers travel to Shenzhen to stream videos of humanoid robots and drone-delivered fast food.

But this intense framing serves a convenient corporate purpose: it acts as the perfect excuse for tech giants to sprint forward without constraints, ignoring safety guardrails in the name of "beating the enemy".

Parallel Lives, Shared Precarity

When you peel back the nationalistic rhetoric, everyday life for a knowledge worker or gig laborer in San Francisco looks shockingly similar to life in Shanghai.

• Algorithmic Disempowerment: A Didi (China’s Uber) driver pleading with passengers to game the app's code just to keep a spot in the airport line faces the exact same precarity as a DoorDash or Amazon Flex worker in the U.S..

• The "996" Hustle Culture: To cash in on the AI gold rush, Silicon Valley tech workers have earnestly adopted China’s infamous "996" work schedule (9 a.m. to 9 p.m., 6 days a week). Meanwhile, in Beijing's tech hubs, office windows burn through the night as engineers exhaustively burn through Claude tokens to build products.

• Corporate Surveillance: AI isn't just a future threat; it is already acting as a digital boss in both countries—tracking attendance, flagging idle hours, predicting growth potential, and deciding who to hire or fire.

As one disillusioned internet user in China aptly put it, this isn't embracing the future; it's "being harvested by the future."

Coping in an Out-of-Control World

Faced with "gradual disempowerment"—the quiet erosion of human agency by ever-more-capable AI—the youth of both societies are experiencing a deep social and spiritual fracture. They are turning to identical survival mechanisms:

• Frictionless Companionship: Loneliness is being monetized at scale. Over 70% of American teens use AI chatbots for companionship , while nearly half of young Chinese people use them to cope with mental health in a society where single-person households are skyrocketing.

• The Occult and Religion: Gen Z in America is driving a massive boom in astrology apps and a rediscovery of traditional Christianity. In China, fortune-telling bars are popping up, and young people are literally asking AI models like DeepSeek to predict their futures amidst shrinking job prospects.

• Hyper-Nostalgia: When the future feels terrifying, the past becomes a sanctuary. This explains the massive, cross-cultural appeal of internet figures who romanticize an off-the-grid lifestyle—whether it's China’s pastoral vlogger Li Ziqi or America’s homesteading "tradwife" Ballerina Farm.

Breaking the Cycle: A Call for Global Alignment

When global systems feel completely detached from human values, the easiest response is to surrender, be passive, and—as Chinese netizens say—"let it rot."

But Liu argues we have a better choice: cross-border, human-to-human solidarity. We have seen flashes of this before. In 2019, when Chinese programmers launched the online campaign "996.ICU" on GitHub to protest grueling tech hours, they received immense support from American tech workers in one of the largest online worker mobilizations in history. Furthermore, scientists and policymakers globally are already quietly meeting at international conferences to demand that advanced AI remains aligned with human values.

Instead of fixating on which elite class crosses the finish line first in a manufactured race, the everyday people of both nations need to realize they are facing the exact same algorithmic threat. It’s time to stop looking at a foreign country as a meme or a bogeyman, and start working together to lift up the people this future is leaving behind.