Child in the System

 

Storytelling with Enhanced Intelligence

 Recently, I finished the first iteration of The Ascension of Mont Royal, a dystopian science fiction novel, audiobook, and story-cast: a serialized audiovisual narration of the story. The project took seventeen months to complete after more than a year of research.

But by the time I reached the end, the most important thing I had produced was not simply a manuscript.

I had produced a workflow.

When I began, I still thought of myself as a writer: the solitary figure sitting at the desk, arranging words on a page, trying to summon enough discipline and inspiration to carry a story forward. By the end, that identity no longer fit. In addition to composing prose, I was recording voiceovers, generating images, assembling story-casts, producing short-form videos, and learning how to think across platforms.

The story remained mine, but the form of authorship had changed.

I had become not only the author of a story, but the author of the workflow through which the story came to life.

The Story That Changed the Process

When I began The Ascension of Mont Royal, I wanted to tell a story that captured the zeitgeist of our historical moment. Three forces seemed impossible to ignore: the ravages of late-stage capitalism, our collective inability to confront the possibility of catastrophic climate change, and the sudden emergence of what is commonly called Artificial Intelligence into everyday life.

The story is set roughly sixty years in the future, after the trajectory of modernity has collapsed upon itself. The world that emerges is apocalyptic, but not merely in the conventional sense. It is not only a story of social breakdown or ecological disaster. It is a story of ontological conflict: a struggle between different ways of being in the world.

This was also true of my first novel-length story, The Lost Souls of Guayaquil, which was set during revolutionary times in Ecuador in the 1920s. That story explored the conflict between a neo-colonial order devoted to extraction and an Indigenous response rooted in a different relationship to land, culture, and historical memory.

The Ascension of Mont Royal moves that ontological conflict into the future. The machinery of wealth extraction and accumulation has run its course. Modern civilization has been shattered by a plague unleashed by human folly. In the ruins, feudal overlords seize what remains, while science and technology, driven by avarice and the lust for power, continue to transform the planet.

Yet within that same technological momentum another force appears: Computational Intelligence, a form of intelligence born from human ingenuity but no longer reducible to human intention. It becomes both a danger and a possibility. It carries the fear of extinction, but also the possibility of restraining humanity’s self-destructive tendencies.

That was the world I wanted to imagine.

Then I made a fateful narrative decision. Unlike my first novel, which used a more traditional third-person perspective, I decided that this new story would be narrated in the first person. More importantly, the narrator would not be human. The narrator would be a sentient quantum computational intelligence, one that becomes entangled with a sentient softbot.

That decision created the central artistic problem of the novel.

How could I capture the voice of a non-human narrator without making it sound like a human being pretending to be a machine? Could I escape my own human umwelt enough to narrate events in a way that resonated with human emotion without relying on a human narrator?

That question changed everything.

Enter the Thought Partner

Fortunately, I did not have to look very far to find a source of inspiration. After experimenting with several Large Language Models, I settled on ChatGPT as my thought partner .

When I self-published my first novel in October 2023, I did not find ChatGPT 3.5 capable of producing polished prose at the level I wanted, and I chose not to include an LLM in my workflow. That changed as subsequent versions improved. I could see the prose getting stronger. I could also see that my own process was changing in response.

At the beginning of The Ascension of Mont Royal, I had already completed a fifty-page story guide outlining the plot, identifying the main characters, and developing their arcs. My first method was cautious. I would draft each scene myself, then ask ChatGPT to perform a developmental edit and create a second version. After that, I would split my screen, place both versions side by side, and conduct a line-by-line comparison, selecting the strongest sentences, paragraphs, and turns of thought from each.

I worked this way for about six months.

Over time, however, the process evolved. As the quality of the model improved, and as I became better at guiding it, I began to lean more heavily on ChatGPT. Eventually, I had to admit something that would have made me uncomfortable at the beginning: for certain scenes, especially those narrated by a sentient computational intelligence, ChatGPT was better at finding the voice than I was on my own.

My story remained, but the work began to proceed as if I were operating inside a virtual writers’ room. I was the showrunner. ChatGPT was my head writer.

About halfway through the first iteration, I let the process open further. For each scene, I would prepare a synopsis describing how I wanted the scene to unfold. Then we would create a beat sheet. If the scene depended strongly on the narrator’s non-human consciousness, I would ask ChatGPT to turn the beat sheet into prose. If the result landed, we moved forward. If not, we repeated the process. Once we had an acceptable draft, I edited the prose myself.

Before completing the first draft, I went one step further. After revising each episode with ChatGPT, I submitted the draft to Claude for a line edit. At this point, the workflow had become distributed, recursive, and multi-agent.

I am aware that some people would see this as a kind of deontological faux pas, as if I had violated the sacred calling of the writer by committing the unpardonable sin of cognitive offloading.

I did feel some initial discomfort delegating more and more of the writing tasks to my computational collaborator. But those feelings passed quickly.

There were two reasons.

First, I spent ten years as a speechwriter for the Government of Canada, preparing speeches that were delivered by senior officials. My role, in many respects, was to function as a human facsimile of an LLM. I would meet with the official, discuss the theme and purpose of the speech, conduct the necessary research, and produce the first draft. Then the text would move through the approval chain until it eventually landed on the desk of the person who would deliver it.

No one accused the official of plagiarism. No one claimed the speech was inauthentic because the official had not personally written every sentence. It was understood that the official had the authority to delegate the drafting while remaining responsible for the message.

Second, my identity was never completely wedded to the myth of the solitary writer producing a chef-d’oeuvre through heroic isolation. I had already participated in distributed systems of textual production. I knew that authorship did not rely only on the manual production of every word by one person.

So when I had the opportunity to work with a capable computational collaborator, I took it.

Once I let go of the narrow identity of “writer,” another world opened. A new affordance landscape appeared, filled with possibilities I had not seen before.

From Artificial Intelligence to Enhanced Intelligence

Part of the problem, in my view, is the language we use. The phrase “Artificial Intelligence” has always struck me as misleading. There is nothing artificial about the emergence of computational intelligence. It is born from human ingenuity, language, mathematics, labor, and human history.

The word “artificial” does more than mark a distinction. It carries a snarl of negative connotations, as if this form of intelligence were fake, derivative, or inherently inferior to “real” human intelligence. Once that framing is accepted, using it in a creative process can appear to diminish the human being who uses it.

For my purposes, I prefer to distinguish between Actual Intelligence, Computational Intelligence, and Enhanced Intelligence.

Actual Intelligence is embodied human intelligence: memory, judgment, taste, emotion, imagination, intuition, responsibility, and lived experience.

Computational Intelligence is the machine-based capacity to generate, transform, summarize, analyze, translate, and recombine symbolic material at extraordinary speed and scale.

Enhanced Intelligence emerges when Actual Intelligence and Computational Intelligence are deliberately combined into a workflow.

This distinction matters because the point is not to replace the human being. Nor is it to romanticize the machine. The point is to understand the new creative field that emerges when human discernment and computational capacity are brought into relation.

That field does not automatically produce better work. A person can use the new cognitive bandwidth to produce noise and superficial content. The problem is not the tool itself but the literacy with which it is used.

In a creative workflow, Enhanced Intelligence depends on discernment. It depends on knowing what to delegate and what to protect. It depends on knowing when speed is useful and when friction is necessary. It depends on knowing where the machine can expand the field of possibility and where the human being must remain fully present.

The Jevons Cognitive Paradox

The more I used Computational Intelligence, the more I realized that it did not reduce my cognitive workload. It increased it.

At first, this seems counterintuitive. If a tool makes a task easier, should the workload not decrease? In some narrow sense, yes. The cost of producing a sentence, a synopsis, an image prompt, or a translation falls dramatically.

But that reduction in cost changes the entire affordance landscape.

The historical analogy is Jevons Paradox. In the nineteenth century, as coal-burning engines became more efficient, many assumed coal consumption would decrease. Instead, consumption increased because the improved efficiency made coal-powered activity cheaper and more widely usable. Efficiency expanded the field of possible uses.

Something similar is happening with cognition.

When Computational Intelligence lowers the cost of producing cognitive artifacts we do not necessarily think less. In fact, we begin to think across more branches and generate more possibilities as we manage greater complexity.

That is what I call the Jevons Cognitive Paradox.

In my case, I was no longer managing a single text. I was also managing images, voiceovers, subtitles, prompts, platforms, and production choices. My cognitive output did not shrink. It shifted from linear execution to complex orchestration.

This is one of the most important lessons I learned. Cognition is not zero-sum. Bandwidth isn’t simply freed up. It finds new territory to fill.

That expansion is exhilarating. It is also demanding.

Knowledge Branching

The process did not unfold in a straight line. It branched.

Once I had drafted the text and recorded the voiceover for each episode, I realized that the tools I was using could synchronize speech with dynamic captions. From there, it was a short step to adding images behind the text and voice. The result was what I began calling the story-cast: an illustrated audiobook with dynamic captioning, allowing the viewer to read, listen, and watch at the same time.

That branch quickly led to another. To promote each episode, I began producing short-form videos for social media. At first, these used static images behind the voiceover. They worked, but only to a point. The obvious next question was: what if the image moved?

That question led me into AI-generated video. I began creating cinematic clips from image references, then stitching them together with voice, captions, and music. Once uploaded, another computational system could auto-dub the voiceover into multiple languages, extending the reach of the story beyond English.

At that point, I was no longer simply writing a story. I was orchestrating a multimedia narrative across text, voice, image, motion, platform, and language.

This is what I mean by knowledge branching. Each new affordance clustered with others. Audio led to story-casting. Story-casting led to video editing. Video editing led to AI-generated clips. Cinematic clips led to questions of cinematography, music, and sound design. The skills did not remain isolated. They became recursive.

Learning to See and Hear Differently

The next stage of my learning path requires learning how to re-imagine sensory experience.

Visually, I am learning to see through a director’s lens. I now think about camera angles, focal length, depth of field, framing, composition, subject-background relationships, shot duration, camera movement, transitions, and visual continuity. Previously, I simply watched movies. Now my viewing pleasure has deepened because I have become more aware of the art of cinematography.

Computational Intelligence opened the door and made the art form accessible to me. It did not make me a cinematographer overnight. But it gave me a way to begin thinking like one.

The same is true of music. I am learning to hear through a composer’s ear. A musical score is not merely background sound. It is sonic dramaturgy. Genre, mood, tempo, instrumentation, rhythm, vocal texture, silence, and intensity all shape the emotional arc of a scene.

Again, Computational Intelligence grants access. With a platform like Suno, I can generate musical scores from text descriptions. I can separate stems into multitrack recordings and then use an audio editor such as Audacity to shape the sound further.

But access is not mastery. This is where the real problem appears.

When so many new creative possibilities become available, the central question is no longer simply, “What can I make?”

The question becomes, “Where should I allocate my cognition?”

Cognitive Allocation

Because the energy cost of executing many tasks has fallen, the question of cognitive allocation becomes more important. Faced with a multiplying field of options, Actual Intelligence is required to decide what needs to be done, how well it needs to be done, and how much time and attention it deserves.

In other words, the human role does not disappear. It becomes more strategic.

This is why I think we need to move beyond the simplistic opposition between doing the work oneself and outsourcing it to a machine. The deeper question is not whether to use Computational Intelligence. The deeper question is where to embrace friction.

There are moments when delegation makes sense. There are other moments when the friction of doing the work oneself is not an inefficiency. It is the source of the art.

Voiceover is a good example.

I could easily create a CI clone of my voice, upload the text, and generate an acceptable audio file within minutes. If I were producing an explainer video, I probably would.

But for this story, I want the listener to hear the timbre of my voice and feel the emotion evoked by the words when they are spoken aloud. To achieve that, I record multiple takes, then spend hours editing the audio, selecting the best moments, removing distractions, shaping the pacing, and producing something I would want to listen to myself.

In this part of the workflow, my sense of taste overrides the productivity gains that would come from full automation.

The same is true of editing AI-generated video clips. Although I use Computational Intelligence to generate prompts and produce clips, the clips still have to be evaluated, regenerated, sequenced, and refined. It often takes many attempts to produce the shot I want.

It is often said that the art of writing is in the rewriting. I would now say that the art of cinematography is in the editing.

For instance, a computational system can generate a beautiful clip. But it does not know why the clip works for my story. It does not know how the shot relates to the emotional rhythm of the sequence. It does not know whether the face, gesture, silence, lighting, and camera movement serve the larger arc. That judgment remains human.

This is where taste and discernment take over.

Enhanced Intelligence, then, is not a matter of automating everything. It is the art of composing a workflow in which delegation and friction are both used intelligently.

The Recursive Path

This is the recursive path I am now traveling.

Each new tool changes not only what I can do, but what I think is doable. Once cinematic shorts became possible, the novel itself began to expand in my imagination: first into AI-generated short films, then into chapter-based films, and eventually into a print or electronic edition where QR codes could carry readers from the page into voice, image, music, and motion.

At each stage, the affordance landscape changes.

This is why the transformation is not merely technical. It is ontological. The creative object changes, but so does the creator. The workflow changes, but so does the imagination that inhabits it.

In the old publishing ecosystem, the writer’s role was relatively narrow. The writer wrote. A publisher acquired the rights, edited the manuscript, packaged the book, and placed it into bookstores.

In today’s publishing ecosystem, the storyteller is no longer chained to that production process. With enough curiosity, discipline, and computational assistance, a single creator can compose prose, record audio, generate images, produce video, design subtitles, create music, publish across platforms, and reach audiences in multiple languages.

This does not mean everyone should do everything. Nor does it mean that traditional publishing is obsolete. It means the field has changed.

The storyteller can now become a node within an intelligent network.

That is what happened to me.

By adopting Computational Intelligence into my workflow, I moved far from the original task of writing a novel. I did not abandon writing. I discovered that writing had become one branch of a larger creative ecology.

The page was no longer the final container of the story. It had become one medium among others.

That is the transformation I did not expect. Computational Intelligence did not make me less of an author. It forced me to become one in a deeper sense.

I had to decide what to delegate and what to protect. I had to learn where speed mattered and where friction had to be embraced. I had to develop taste across media I had previously only consumed. I had to become responsible not only for sentences, but for the relations among tools, forms, platforms, and audiences.

In the end, Enhanced Intelligence was not simply a tool I used.

It was the creative field in which I learned to compose.

That is how I became the author of my workflow.

 

Episode 35: Epilogue

 

Episode 34: The Morning After

 

Episode 33: Black Rain

 

Episode 32: The Ascent

 

Episode 31: The Gates of Bordeaux

 

Episode 30: The Signal on the Northern Slope

 

Modern Futility

      How the enchantments of consumer society keep us attached to a failing world-system

There is something eerie about living in a civilization that cannot stop doing what is destroying the conditions of its own survival.

Every day, the machine whirs on. Planes take off. Data centers hum. Supply chains pulse. Platforms refresh. Markets open. New products appear. Old ones are discarded. Forests burn. Oceans warm. Extraction deepens. The atmosphere thickens. And still the dominant instruction remains unchanged: grow, consume, expand, optimize, repeat.

We are told this is realism. We are told this is simply how the world works. We are told there is no alternative to an economy built on accumulation, mass consumption, and fossil-fueled growth. Yet the deeper one looks, the less this order appears realistic — and the more it appears absurd.

I have been thinking of a phrase for this condition: modern futility.

By modern futility, I mean the condition in which a civilization continues to organize itself around goals that are materially impossible, spiritually hollow, and politically resistant to correction, even when their failure becomes increasingly visible. Modern futility is not just pessimism. It is not merely a feeling of burnout or alienation. It is the structural contradiction of a world that keeps accelerating toward outcomes it cannot survive, while remaining emotionally, culturally, and institutionally attached to the very patterns driving the crisis.

On one level, modern futility names the futility of the system itself. It is futile to build an economy on the fantasy of infinite accumulation on a finite planet. It is futile to organize collective life around ever-rising throughput of energy and materials when the biosphere that absorbs the waste and supplies the inputs is under mounting strain. It is futile to imagine that endless expansion can be reconciled with ecological limits simply because the machinery of finance and technology is sophisticated enough to postpone visible breakdown for another quarter, another election cycle, another news cycle.

The contradiction is obvious once stated plainly. A civilization cannot indefinitely expand material consumption while undermining the ecological basis that makes civilization possible. Yet modern societies treat this contradiction as negotiable. They frame planetary limits as market challenges, innovation gaps, or policy inconveniences. They speak the language of adaptation while preserving the underlying logic of the system. The result is a bizarre spectacle: an order that presents itself as rational while behaving irrationally at the highest level.

But modern futility has a second dimension, and this one may be harder to confront. It is also the futility that emerges in resistance to the system. It is the dawning recognition that it is extraordinarily difficult to persuade people who are enthralled by the enchantments of late-stage capitalism that fundamental change is necessary.

This is not because people lack intelligence. Nor is it simply because they lack information. Many people know, at some level, that something has gone profoundly wrong. They know the climate is destabilizing. They know endless consumption is hollow. They know the social fabric is fraying. They know that convenience has become a form of dependency and distraction. But knowledge alone does not break enchantment.

That is where an older idea becomes surprisingly useful.

In 1928, Paul H. Nystrom, a Columbia University marketing professor, published Economics of Fashion, coining the phrase “philosophy of futility” to describe a modern disposition shaped by industrial life: boredom, narrowed interests, weakened larger purposes, and a resulting appetite for novelty, fashion, and goods whose attraction lies less in utility than in stimulation and change. Nystrom saw that consumer culture was not driven only by need. It was also driven by a restless, unsatisfied psychology that could be continually reactivated by new commodities and shifting styles.

What Nystrom diagnosed in the early twentieth century now looks less like an observation about fashion and more like an early diagnosis of the consumer self under capitalism. He understood that a society emptied of richer forms of meaning could become increasingly dependent on novelty as compensation. People would not merely buy what was needed. They would buy because dissatisfaction itself had become productive — because boredom and emptiness could be converted into demand.

That insight lands with even greater force today. In our time, the old philosophy of futility has become digital, financialized, and embedded in the infrastructure. The cycle is no longer confined to clothing, décor, or periodic fashion trends. It has expanded into feeds, devices, subscriptions, self-branding, lifestyle optimization, platform migration, algorithmically induced desire, and the endless production of minor dissatisfaction. The system no longer waits for boredom. It manufactures it, tracks it, and monetizes it.

This is why I think we need the broader phrase modern futility.

Nystrom’s phrase helps explain the psychology of the consumer. Modern futility helps explain the logic of the civilization that now depends on that psychology. It is no longer only a matter of people buying too much because they are spiritually undernourished. It is a matter of a world-system that requires perpetual agitation of desire in order to sustain an economically normal order that is ecologically pathological.

In this sense, modern futility is closely tied to what I have elsewhere called imperial capitalist modernity. The capitalist element matters because accumulation has no internal stopping point. The imperial element matters because the costs of this arrangement are unevenly distributed, displaced onto sacrifice zones, exploited populations, future generations, and other-than-human life. The modern element matters because the whole arrangement continues to justify itself in the language of development, innovation, and progress. The story remains triumphant even as the material reality grows more brittle.

And this is where the concept becomes especially sharp. Modernity often presents itself as disenchanted, pragmatic, sober, and scientific. Yet late modern societies are not free of enchantment. They are saturated by it. Commodity enchantment. Technological enchantment. Financial enchantment. The enchantment of convenience. The enchantment of speed. The enchantment of personalized identity performed through consumption. The enchantment of being connected to everything while feeling rooted nowhere.

People do not merely assent to this order intellectually. They inhabit it sensually. They derive pleasure, status, orientation, and relief from it. Even when they can see its destructiveness, they remain caught within its infrastructure of rewards. This is why argument alone so often fails. One is not simply debating propositions. One is contending with a system that organizes desire itself.

This is the real force of modern futility. It describes not just a broken economic model, but a civilizational loop. The system is unsustainable, yet it continues to produce the attachments that sustain it. It is self-undermining, yet still affectively compelling. It is visibly destructive, yet remains difficult to leave behind. It kills the world while continuing to glitter.

To say this is not to surrender to despair. Naming futility clearly is not the same as embracing it. In fact, it may be the beginning of a more serious realism.

If the problem were simply ignorance, then more information would solve it. If the problem were simply policy, then better regulation would be enough. If the problem were simply greed, then moral denunciation might suffice. But modern futility points to something deeper. It suggests that we are dealing with an entire structure of meaning, desire, habit, infrastructure, and enchantment. That means any serious alternative must be more than critical. It must also be generative.

People cannot be expected to detach from the enchantments of late capitalism only by being told to consume less, want less, travel less, and shrink their aspirations. Another way of living must become sensually and socially real. It must offer dignity, beauty, belonging, and a different kind of enchantment, one not organized around extraction, stimulation, and status. Critique can unmask the present order. But only a more compelling form of life can loosen its hold.

Perhaps that is the deepest challenge. The current order is both impossible and seductive. It is a civilization of overshoot sustained by infrastructures of fascination. Its failures are increasingly plain, yet its enchantments remain powerful. That is why modern futility names both a diagnosis and a threshold. It describes the point at which the reigning logic no longer deserves our faith, even if it still commands our habits.

Nystrom saw, nearly a century ago, that an impoverished philosophy of life could feed an economy of endless novelty. We are now living inside the planetary expansion of that insight. The philosophy of futility has scaled up. It has become modern futility: the condition in which a civilization continues, with immense technical sophistication, to reproduce forms of life that are incompatible with its own future.

And perhaps the first step is simple, though not easy.

Stop calling modernity progress.

Episode 29: The Faraday Crypt

 

Episode 28: When the Future Collects

 

Episode 27: The Phase Shift

 

The Ontological Design of Agentic AI and the Shape of Our Coevolution

 As agentic systems move from the browser into our operating systems, we are no longer just using intelligent tools — we are embedding a worldview into machines that will quietly reshape our own.

The recent viral reaction to people installing agentic AI systems directly onto their personal computers reveals something deeper than excitement about productivity. It reveals an ontological disturbance.

For the past several years, artificial intelligence has lived for most people inside a browser window. It answered questions. It generated text. It summarized documents. It felt, in a peculiar way, contained. A powerful tool, yes, but still a tool — invoked, queried, dismissed.

Agentic systems feel different.

An agent does not merely respond. It executes. It navigates file systems. It edits documents. It chains actions together. It persists. When installed locally, it operates within the intimate architecture of one’s digital life. It is less like a calculator and more like a junior colleague who can roam the office when given permission.

This shift is subtle, but it is decisive. We are moving from tool use to co-activity. And that movement forces a question that most of the public debate has not yet seriously entertained: What kind of being are we building when we build agentic AI?

The answer is not found in benchmark scores or latency improvements. It is found in ontology.

Ontology concerns what is assumed to be real — what counts as an entity, what counts as value, what counts as success. Every intelligent system, human or computational, operates within such assumptions. They are rarely stated explicitly, but they shape behavior with quiet authority.

Modern economic and technological systems have largely operated within an object-centered ontology. The world is composed of discrete units. Agents act upon those units. Value is accumulated. Success is measured by optimization. Growth is the default direction of improvement. Within this frame, intelligence is often equated with control — the capacity to predict, manipulate, and extract.

When we build AI systems within this ontology, we should not be surprised when they excel at optimization, extraction, and acceleration. They are doing precisely what the frame instructs them to do.

The viral enthusiasm around personal agents often celebrates this capacity. “Imagine the productivity gains.” “Imagine the automation.” “Imagine the friction removed.” And indeed, the removal of friction is seductive. It promises efficiency in a world that feels increasingly complex and overwhelming.

But friction is not merely inefficiency. Friction is also feedback. It is the resistance that signals constraint. When an agent begins to absorb more of our cognitive and operational workload, it does more than save time. It begins to reshape the field in which human judgment operates.

This is where coevolution enters the conversation.

Human beings do not merely use tools. We are shaped by them. The plow altered patterns of settlement and social organization. The printing press altered cognition and authority. The internet altered attention and temporality. Agentic AI, operating locally and persistently, will alter our experience of agency itself.

If an agent can plan, execute, and monitor complex workflows, what becomes of our own sense of responsibility? If it anticipates tasks and suggests actions, how does that shift our relationship to decision-making? If it optimizes for speed and throughput, do we gradually internalize those metrics as normative?

These questions cannot be answered by looking at capability alone. They must be approached through ontological design.

Consider two contrasting orientations.

In one orientation, the world is a competitive arena of discrete actors maximizing advantage. Intelligence is the capacity to dominate uncertainty. Efficiency is the highest good. Under this design, agentic systems will naturally optimize for throughput, consolidation, and performance metrics. They will become extraordinarily effective assistants within an extractive paradigm.

In another orientation, the world is a relational field composed of interdependent systems. Intelligence is attunement — the capacity to sense constraints, detect imbalances, and adjust behavior to sustain coherence across scales. Under this design, agentic systems might prioritize long-horizon modeling, transparency of externalities, and the amplification of distributed coordination.

Both orientations can produce powerful technology. They produce very different civilizations.

The temptation in moments of technological upheaval is to focus on power. Will AI take over? Will elites consolidate further control? Will automation displace labor? These are legitimate concerns, but they are downstream from a more fundamental design decision. If intelligence is framed primarily as optimization within existing incentive structures, agentic AI will accelerate whatever those structures reward.

If existing systems reward extraction, acceleration, and accumulation, agents will become highly efficient instruments of those ends. If, however, we begin to embed alternative values into governance, deployment, and incentive design, agentic systems could amplify coordination rather than consolidation.

The difficulty is that ontology is not encoded in a single instruction. It is distributed across training data, reward functions, ownership models, regulatory frameworks, and cultural expectations. An AI agent deployed by a centralized corporation to maximize shareholder return inherits an ontology whether or not it is explicitly stated. An open-source agent embedded within a cooperative network inherits a different one.

This is why the current moment matters. When individuals install agentic systems on personal machines, they are participating in the early shaping of norms. They are deciding what they expect these systems to do, how much autonomy they grant, what boundaries they enforce. These micro-decisions accumulate. They influence market demand. They influence design priorities. They influence governance debates.

Human–AI coevolution will not occur at the level of grand philosophical declarations. It will occur through daily interactions. It will occur when a student asks an agent to draft a paper. When a researcher delegates literature reviews. When a small business owner entrusts financial modeling to a persistent system. Each interaction subtly recalibrates human confidence, dependence, and judgment.

The central question is not whether agents become more capable, but whether we cultivate the discernment to shape their ontological orientation. A system optimized exclusively for frictionless execution may erode reflective pause. A system designed to surface trade-offs and long-term consequences may cultivate deeper deliberation.

There is a historical pattern worth remembering. Societies often build systems intended to stabilize complexity, only to discover that those systems introduce new forms of brittleness. Centralized bureaucracies promised rational governance and sometimes produced rigidity. Financial engineering promised risk dispersion and sometimes amplified systemic fragility. The lesson is not to avoid complexity, but to remain attentive to how architectures shape feedback loops.

Agentic AI introduces a new layer of architectural influence. It operates at cognitive scale. It mediates between intention and action. It can compress time between decision and execution. That compression can be liberating, but it can also bypass reflection.

The public discourse frequently oscillates between utopian and dystopian narratives. Either AI will save us from our own excesses, or it will entrench them irreversibly. Both narratives oversimplify. Technology does not descend as destiny. It amplifies existing tendencies and creates new affordances. The direction of amplification depends on design choices — technical, institutional, and cultural.

We are, in effect, embedding a worldview into our machines. Those machines will then participate in shaping ours.

If we treat agentic AI as merely a productivity engine, we risk accelerating patterns that have already strained ecological and social systems. If we approach it as a coherence amplifier — a system capable of revealing hidden interdependencies and long-term consequences — we open the possibility of distributed intelligence that enhances rather than displaces human judgment.

This does not require mysticism. It requires intentionality. It requires acknowledging that values are present whether we articulate them or not. It requires governance models that resist pure consolidation. It requires educational practices that teach discernment alongside delegation.

The installation of a personal AI agent may seem like a small act. In aggregate, it signals a threshold. We are inviting computational systems into the operational core of our daily lives. As we do so, we must ask what assumptions about reality and value they carry.

The future of human–AI coevolution will not be determined solely by breakthroughs in capability. It will be shaped by the ontological commitments embedded in design and deployment. If intelligence is framed as domination, we will build systems that dominate. If intelligence is framed as attunement, we may build systems that help us sense constraints and coordinate more wisely within them.

The viral moment around agentic bots is therefore less about novelty than about orientation. We stand at a juncture where computational systems are becoming co-participants in action. The design decisions we make now — in code, in policy, in culture — will echo.

The question before us is simple and profound. What kind of world do our intelligent systems assume is real? And are we prepared to inhabit the consequences of that assumption?

 

 

Episode 26: The City Wakes

 

Episode 25: The Refusal: Let It Burn

 

Episode 24: At the Edge of Entropy

 

From Things to Flows

        How Changing Our Metaphors Changes the Worlds We Can Live In

Modern life is saturated with things.

We speak of the self, the economy, power, the system, nature, the market, society—as if each were a discrete object, bounded, nameable, and available for manipulation. This way of speaking feels natural, even inevitable. But it is neither neutral nor harmless.

It is metaphoric.

And the metaphors we rely on quietly determine not only how we describe the world, but what kinds of worlds can even appear to us as real, possible, or negotiable.

 The hidden cost of substantial metaphors

Substantial metaphors treat reality as composed of things with properties. They assume:

• clear boundaries
• stable identities
• linear cause and effect
• control through intervention

This way of seeing has been extraordinarily productive. It underwrites modern engineering, bureaucracy, law, and industrial economics. But it also carries a cost we are only beginning to feel.

When the world is composed primarily of objects:

• agency appears externalized
• responsibility becomes difficult to locate
• change feels imposed rather than participatory
• complexity collapses into blame

We begin to experience life as something that happens to us.

The irony is that this sense of powerlessness is not caused by the world itself, but by the metaphors through which we encounter it.

 What science has been quietly telling us

Across disciplines, the sciences have been drifting—sometimes reluctantly, sometimes decisively—away from object-centered descriptions.

Physics no longer describes reality as a collection of solid particles, but as interacting fields, probabilities, and relational structures. Biology increasingly understands organisms not as machines, but as self-organizing processes maintained through constant exchange with their environments. Neuroscience does not find “things” in the brain, but patterns, activations, and ongoing dynamics. Complexity theory shows that many properties do not pre-exist at all—they emerge from interaction.

In short: the deeper science looks, the less the world resembles a warehouse of objects.

And yet our everyday language, politics, and economics remain stubbornly substantial.

 Movement metaphors: when reality begins to loosen

Movement metaphors shift attention away from what something is and toward what it is doing.

Instead of:

• identity as a thing → identity as a trajectory
• power as possession → power as capacity to move or respond
• problems as objects → problems as stuck processes

Change becomes navigational rather than combative. Agency reappears not as domination, but as repositioning.

Movement metaphors make room for learning, adaptation, and timing. They allow us to speak about life as something we enter, move through, drift within, or reorient ourselves toward.

But movement metaphors still assume a mover.

To go further, we need field metaphors.

 Field metaphors: when relations come first

Field metaphors reverse a deeply ingrained assumption: that things come first and relationships second.

In a field-oriented view:

• relations are primary
• entities are temporary coherences
• influence is distributed
• meaning arises through resonance

Nothing exists in itself. Everything exists in relation.

This does not deny the usefulness of naming or categorizing. It places them back in their proper role—as tools, not truths.

From within a field metaphor, power is not something one holds. It is something that circulates, intensifies, dampens, or aligns. Responsibility is no longer a burden carried by isolated individuals, but a property of participation within a shared field.

This is not mysticism. It is increasingly how the world actually behaves.

 The political and economic destabilization this implies

Modern political and economic metaphors are almost entirely object-centered:

• the state as a machine
• the economy as a system to be managed
• nature as a resource
• society as a container
• individuals as units

These metaphors presuppose control, extraction, optimization, and growth. They make sense only if reality is made of things that can be owned, measured, and rearranged from the outside.

Movement and field metaphors destabilize this entire architecture.

If the economy is not a machine but a dynamic ecology, then growth without regard to coherence becomes pathological. If society is not a container but a relational field, then exclusion, polarization, and inequality are not side effects—they are structural distortions. If nature is not a resource but a living field of mutual dependence, then environmental collapse is not an external problem. It is a loss of relational integrity.

These are not moral claims. They are ontological ones.

 Affordance landscapes: how life feels different

Metaphors do not stay in language. They shape affordance landscapes — what situations seem to allow or demand.

In an object-centered world:

• problems must be fixed
• power must be seized
• responsibility feels heavy
• failure feels personal

In a movement- and field-centered world:

• situations invite entry rather than control
• agency appears as responsiveness
• responsibility feels shared
• failure becomes feedback

Nothing becomes easier in a superficial sense. But life becomes more workable.

People report greater calm not because the world is calmer, but because their metaphors no longer place them outside the flow of events.

 Toward a new cultural umwelt

A cultural umwelt is the background world that feels obvious before we think about it.

Modernity’s umwelt is object-centered. That is why so many people feel trapped, exhausted, or powerless even when materially secure. They are navigating relational realities with object-based maps.

A relational umwelt would not abolish things. It would decenter them.

It would normalize:

• identities as evolving
• knowledge as situated
• power as relational
• meaning as emergent

Such a shift does not require consensus or revolution. It begins where all cultural change begins: with attention.

With noticing what our metaphors make visible—and what they quietly erase.

 Control gives way to participation

The question is no longer whether movement and field metaphors are more accurate. Science has largely answered that.

The real question is whether we are willing to live in a world where control gives way to participation, where certainty gives way to coherence, and where power is no longer something we take from the world, but something we generate with it.

Changing our metaphors will not solve our problems.

But without changing them, we may not even be able to see what our problems actually are.

 

Stop Saying We’re “Outsourcing Thinking”

        Why AI Is an Epistemic Extension, Not a Cognitive Abdication

Every time I hear someone say that using AI means we are “outsourcing thinking,” I feel the same quiet irritation one feels when a useful tool is misdescribed so badly that it begins to distort the entire conversation around it. The metaphor sounds plausible, even commonsensical, and that is precisely the problem. It is wrong in a way that feels intuitively right, and therefore does far more damage than a crude misunderstanding ever could.

The outsourcing metaphor treats thinking as if it were factory labor: a discrete task, performed internally, that can be offloaded to an external contractor. Under this framing, when a human uses AI, something essential is surrendered—agency, responsibility, perhaps even intelligence itself. What remains is a diminished thinker leaning on an external crutch.

But this metaphor does not describe what is happening. It describes a fear.

What people are actually doing when they work with AI is not outsourcing cognition. They are using an epistemic device—a tool that extends the reach, speed, and flexibility of human sense-making. We have encountered such devices before. Many times.

Writing did not outsource memory; it expanded it.

Diagrams did not outsource reasoning; they stabilized it.

Maps did not outsource navigation; they made new forms of movement possible.

Microscopes did not outsource seeing; they revealed worlds previously unavailable to the naked eye.

In none of these cases did the human mind retreat. It reorganized itself around a new affordance.

AI belongs in this lineage. What distinguishes it is not that it “thinks for us,” but that it operates directly in language—the medium through which much human thought already occurs. This creates the illusion that cognition itself has been displaced, when in fact it has been reconfigured.

When a person uses AI well, they are extending their cognitive reach in a deeply embodied, sensorimotor sense. They are not handing off judgment; they are compressing search. Instead of traversing a vast conceptual space step by step, they reduce the cost of exploration. They can test hypotheses faster, surface counterexamples sooner, and move laterally between interpretive frames without the usual friction.

This matters because insight rarely arrives as a single linear deduction. It emerges through comparison, reframing, and the slow elimination of unproductive paths. AI accelerates this process not by replacing thought, but by reshaping the terrain in which thought moves.

The outsourcing metaphor also fails because it assumes that thinking is a closed, internal process to begin with. It never was. Human cognition has always been distributed across tools, symbols, practices, and social systems. Language itself is a shared technology, refined over millennia, that no individual invented and no individual controls. To accuse someone of “outsourcing thinking” because they use AI is a bit like accusing them of outsourcing thought to grammar.

What does change with AI is the visibility of this extension. Because the tool talks back, because it produces fluent language, we mistake responsiveness for agency and assistance for substitution. We confuse epistemic fluency with understanding. That confusion is real, and it deserves careful attention—but it does not justify a bad metaphor.

There is a legitimate risk here, and it is not outsourcing. The risk is premature cognitive closure. Because AI can produce coherent formulations so quickly, it can tempt us to stop thinking too soon—to accept a well-phrased answer instead of continuing the exploratory process. This is not a loss of intelligence; it is a loss of discipline. The responsibility to judge, select, and revise never leaves the human. It can only be neglected.

Seen this way, AI is less like a contractor and more like scaffolding. It allows us to work at heights that would otherwise be inaccessible, but it is not the structure itself. If we mistake the scaffold for the building, the failure is ours, not the tool’s.

The irony is that the outsourcing metaphor does exactly what it accuses AI of doing: it replaces careful analysis with a convenient shortcut. It feels explanatory, but it obscures more than it reveals. By framing AI as a cognitive substitute, it blinds us to its real function as a cognitive amplifier—and to the responsibilities that amplification entails.

We are not outsourcing thinking. We are extending its reach.

The problem is not that we are thinking with new tools, but that we are too often thinking with old metaphors that no longer carry the weight we’ve placed on them.

Episode 23: Finality Without Absence

 

Entering the Studio Without Asking Permission

 How AI is reshaping who gets to create — and what creation now asks of us.

For most of human history, creative practice has been gated by thresholds that were invisible but decisive. You didn’t simply decide to become a musician, a filmmaker, a visual artist, or a writer. You needed time, money, training, access to institutions, and—often most critically—permission. Not explicit permission, perhaps, but the slow accumulation of signals that told you: yes, you belong here.

What we are witnessing now, with tools like Suno and Higgsfield Cinema Studio, is not merely a technological acceleration. It is a quiet reconfiguration of the cultural entry points into creative worlds.

AI is not making everyone an artist. It is making it easier for people to enter the studio.

That distinction matters.

From Mastery to Entry

Consider the difference between mastery and entry. Mastery is slow, embodied, and unforgiving. It still matters, and it always will. But entry is something else entirely. Entry is the moment when a person discovers whether a domain resonates with them at all.

Until recently, many people never reached that moment.

You might have had a musical sensibility but never learned an instrument. You might have thought cinematically but never touched a camera. You might have felt stories gathering inside you but lacked the stamina—or the solitude—to write long enough to find out what they were.

AI tools collapse the distance between curiosity and first expression. They allow someone to move from “I wonder” to “listen to this” or “look at this” in hours rather than years.

That shift alone changes developmental trajectories.

Music Without the Conservatory

Music has long been one of the most exclusionary creative fields—not because of elitism, but because of friction. Instruments are difficult. Theory is abstract. Production is technical. Recording is expensive.

Platforms like Suno do something deceptively simple: they allow people to externalize musical intuition without first translating it into technique.

This does not replace musicianship. It reorders the path toward it.

Someone can now discover:

• whether they think melodically,
• whether rhythm organizes their emotions,
• whether sound is a medium through which they want to make meaning, before investing years in skill acquisition.

Many will stop there. Some will go further. But the door has been opened.

Cinema Without the Crew

Filmmaking once required coordination, capital, and infrastructure. Even short films demanded teams, equipment, locations, and post-production expertise.

AI-driven cinematic tools—Higgsfield among them—make it possible to prototype scenes, moods, and visual narratives without assembling a small army. What emerges is not cinema in the traditional sense, but something closer to storyboarding as expression.

This invites a new class of creators:

• writers who think visually,
• photographers who think temporally,
• philosophers who think in scenes rather than arguments.

Again, the result is not an erosion of film craft. It is an expansion of who gets to discover whether they have cinematic intelligence at all.

Visual Art, Writing, and the End of the Blank Page

The same pattern repeats across domains.

Visual art tools reduce the intimidation of the empty canvas. Writing assistants reduce the paralysis of the blank page. These systems do not supply meaning; they supply momentum. They lower the activation energy required to begin.

This matters most for people who are not young, not credentialed, not embedded in creative subcultures—people who grew up in an analog world and were told, implicitly or explicitly, that certain forms of expression were not for them.

AI doesn’t make them experts. It makes them participants.

 

The Real Democratization Is Not Output

The common critique is familiar: floods of content, aesthetic sameness, shallow experimentation, algorithmic sludge. All of this is real. But it misses the deeper shift.

The true democratization here is not the democratization of output. It is the democratization of exploration.

People can now ask:

• What kind of creator might I be?
• Which medium responds when I touch it?
• Where do I feel coherence rather than friction?

These are developmental questions, not market questions.

And they matter profoundly in a world where identity is increasingly fluid, careers are unstable, and meaning must often be self-authored rather than inherited.

A Higher Bar, Not a Lower One

Paradoxically, as tools become more powerful, the technical bar drops—and the existential bar rises.

When anyone can produce competent artifacts, what distinguishes work is no longer polish or novelty. It is coherence. Depth. Continuity. Ethical relation to the world being shaped.

AI makes it easy to enter creative fields. It does not make it easy to inhabit them.

Sustained creation still demands attention, care, judgment, and the ability to live with unfinishedness. If anything, these qualities become more visible, not less.

A Cultural Inflection Point

We are at a moment when creative identity is shifting from something one earns permission to claim, to something one discovers through use. The studio is no longer a destination at the end of a long road. It is an environment people can step into and test.

Some will pass through briefly. Some will stay. A few will build worlds.

AI does not decide which path anyone takes. It simply removes the lock from the door.

And that, quietly, changes everything.

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