Their proposed remedy—embedded third-party evaluators with employee-level access, lab coordination, and mutual capability pacing—attempts to manage an architectural failure with a boardroom cartel. Pacing the training of monolithic models inside multi-billion-dollar data centers does not solve the fundamental crisis of AI safety; it merely concentrates unilateral governance into even fewer hands while leaving the underlying problems intact.

Reinforcing a poorly designed bridge to nowhere doesn’t change its risky architecture. It just hides the failure.

The Stampede of AI Fear

We have seen this playbook before. History teaches us that changes made in a stampede of fear are rarely well thought out, and they are almost never positive for society at large. When panic dictates policy, the outcome is predictable: an entrenched, tiny few benefit at everyone else’s expense.

By framing AI development as an apocalyptic race that only a few heavily monitored hyperscalers can safely run, the frontier labs are erecting protective regulatory moats around their capital-intensive data centers. The call for the policing of their unstable, centralized “Tower of Babel” is an attempt to turn safety rhetoric into corporate gatekeeping.

Yes, they built AI wrong. They built intelligence for the few, the powerful, and the centralized. It’s not designed for the rest of us. And what they’re giving us does not reflect the shape of what technological intelligence can be for humanity. Far from it.

1. The Three Manufactured Choices of the AI Cartel

Notice how the current debate is being aggressively framed. The leaders of the centralized frontier labs want policymakers, technologists, and the public to believe that civilization faces only three possible alternatives:

1. Lock in the centralized, vertical model with a protective regulatory moat: Hand unilateral authority to a boardroom cartel and a select roster of embedded evaluators who alone decide which massive models are allowed to be trained and deployed.

2. Continue the current buildout with zero constraints: Accelerate a reckless, winner-take-all race of ever-larger data centers, unconstrained grid strain, and accelerating recursive loops.

3. Stop all AI development: Slam the brakes out of panic, surrender scientific and economic progress, and embrace technological stagnation.

This is a classic false trilemma. Every single one of these choices is a trap, and all three share the exact same unexamined assumption: that technological intelligence can only exist as a monolithic, capital-intensive cloud oracle.

What the cartel does not want anyone to do is question that underlying assumption. They do not want the world asking whether there is a far better path—a fourth way forward that directly resolves legitimate safety concerns, eliminates single points of failure, preserves human agency, and actually delivers the prosperous, flourishing future we all want.

This fourth way is not an impractical dream of abolishing centralized compute overnight; it is the pragmatic recognition that large models can only be safe, grounded, and accountable when counterbalanced by a sovereign, decentralized public network.

The open-source, local-first, and decentralized AI communities must take this Big AI Lab freakout as a clear signal to seize the initiative.

Let’s not get bogged down in debating which of these three false choices is the least objectionable option. Let’s embrace the necessary counterbalance and come together to build it out.

2. Balanced Intelligence

An intelligence system that serves all of humanity cannot consist of a vertical monolith alone. Just as a healthy political constitution requires checks, balances, and distributed authority to prevent the dangerous concentration of power, the future health of intelligence systems demands an open, horizontal counterweight.

While centralized cloud AI will no doubt continue to play an important role as specialized heavy lifters in our technological infrastructure, it must be offset by decentralized intelligence networks directly owned and governed by you and me.

Acknowledging the Breakthrough of Current AI

Let’s be clear: we can recognize the many significant issues with centralized AI while at the same time acknowledging the amazing technological leap that it represents. The first burst of modern generative AI has been an extraordinary, monumental breakthrough. The Transformer empowered a synthetic decoder ring that taps into the power of human language and its accumulated storehouse of knowledge, wisdom, and experience. It was a vital, necessary step in an ongoing and natural evolutionary process.

Silicon Valley assumes that because stacking parameters and pouring gigawatts into server farms worked early on, the only path forward is to build the Tower of Babel ever higher. But an initial vertical leap should never be mistaken for the final destination of intelligence.

The Natural Intelligence of a Pond

If we shift our perspective and understand how intelligence systems actually work, we can see the path ahead is much more like a natural ecosystem than an engineered tower.

Let’s consider the intelligence of a natural ecosystem like a pond. A country pond has no central authority. Its health and stability emerge from the dynamic interactions of water, sunlight, bacteria, plants, fish, birds, animals, and insects.

No single organism runs the pond, yet the pond adapts, cleanses itself, and thrives. It is a living, self-regulating ecology of dynamic exchange.

The answer to unstable, self-reinforcing AI loops is not an industry cartel granting itself permission to slow down behind closed doors.

The answer is an open, distributed, living ecology of human-centered intelligence that provides a critical counterbalance to centralized power.

Two Paths for One Breakthrough: Top-Down Monolith vs. Living Network

The fundamental miscalculation of the frontier labs is not the technology they discovered, but how they have chosen to architect it. They are attempting to engineer a solitary, centralized monolith that claims to “do it all” from a remote data center—issuing top-down decrees to billions of isolated consumers.

Nature proves that this is an architectural dead end. In living systems, intelligence is never concentrated inside a single, all-knowing mega-cell or a solitary dictator. It emerges from the ground up through trillions of local interactions: cells communicating across semi-permeable membranes, organisms adapting to their immediate surroundings, and communities organizing through voluntary exchange.

The exact same underlying breakthrough can be deployed along two radically different paths:

• It can be used as an instrument of crude, top-down imposition—a corporate cloud oracle that swallows your context, meters your access, and dictates what is true.

• Or it can be evolved naturally from the bottom up—a vast, human-centered network of billions of smaller, specialized models running locally on our own devices, collaborating peer-to-peer to solve real-world problems.

This is why the breakthrough of generative AI makes its own counterbalance possible. The Transformer did not create an infallible oracle; it created a modular, synthetic building block. Once decoded, those linguistic and reasoning capabilities can be distilled, quantized, and deployed directly onto the consumer silicon already in our hands.

Instead of an autaptic machine talking to itself in a multi-billion-dollar vacuum, we can now assemble the components for an organic, self-correcting intelligence system.

The choice before us is clear: do we reinforce an artificial tower, or do we cultivate a living ecosystem?

That living ecosystem begins with an operational blueprint: The POND Principle.

3. The POND Principle

Intelligence for the rest of us will come from cultivating an open, living ecosystem of human-centered intelligence engineered around the POND Principle:

• P — Personal & Private

• O — On-Device & On-Premise

• N — Nodal & Networked

• D — Decentralized & Distributed

POND is not an arbitrary acronym or a set of catchphrases. It is an operational architectural matrix:

Every layer pairs a Principle of Ownership and Agency (Column 1: Who holds the keys, the voice, and the state?) with a Principle of Security and Integrity (Column 2: How is the boundary defended, and how do we prevent capture or usurpation?).

TierColumn 1: Ownership & Agency (The Sovereign Node)Column 2: Security & Structural Integrity (The Protective Network)In Everyday Terms

P

Personal: Your voice, memory, and unique human context.

Private: Cryptographic boundaries; zero unauthorized data egress.

Lending a single book vs. handing over the keys to your private library.

O

On-Device: Executing locally on consumer silicon in your pocket.

On-Premise: Secured physical perimeters; air-gapped stability.

Operating your own tools in a blackout vs. a metered cloud lease that goes dark.

N

Nodal: Bounded, independent agent; local reasoning capability.

Networked: Peer-to-peer validation; check against unilateral usurpation.

Deliberating in an open town square vs. isolated individuals whispering to an oracle.

D

Decentralized: Polycentric governance; no boardroom cartel holds a kill-switch.

Distributed: Resilient physical topology; routes around failures.

A self-healing, biodiverse forest vs. a fragile single-crop monoculture.

When built into the tools we touch every day, these four layers fundamentally transform how technology serves society:

P — Personal & Private (Your Life Stays Yours)

• Centralized AI: Whenever you consult a commercial cloud model with a medical concern, a business contract, or a personal dilemma, you pipe your private thoughts into an opaque corporate hopper. You receive an answer, but you surrender control of your context and digital identity.

• POND Alternative: Your AI node operates behind a cryptographically sealed personal membrane. It learns your style, retains your memory, and safeguards your records under your sole authority. When interacting with the wider world, your node shares only the specific insight you explicitly authorize—never your raw personal data. It is the vital difference between lending someone a book and handing a corporate landlord the keys to your private library.

O — On-Device & On-Premise (Putting Compute Where Life Actually Happens)

• Centralized AI: Every keystroke, search, and agentic command must travel round-trip to a distant server farm. If the cloud experiences an outage, if your connection drops, or if API fees spike, your tools instantly stop working. Intelligence becomes a metered, precarious lease.

• POND Alternative: Processing happens directly on the silicon already sitting in your pocket, laptop, or local institutional server. Running models locally eliminates latency, provides instant real-time interaction, and ensures your operational capacity survives even when the internet cuts out or a storm takes down the grid. Local execution ensures artificial intelligence serves the immediate physical context of the user, treating remote cloud models as occasional utility heavy-lifters rather than permanent gatekeepers.

N — Nodal & Networked (Connecting People, Checking Power)

• Centralized AI: The reigning cloud model creates atomized isolation. Billions of individuals sit in digital cubicles, each whispering to the same central oracle. The machine forms a separate relationship with everyone, but helps no one connect with their peers. Intelligence concentrates at the center, leaving society fragmented at the edge.

• POND Alternative: Sovereign nodes connect peer-to-peer to deliberate, negotiate, and solve collective problems together. Requiring proposals and products to be verified across a network acts as essential systemic security: it prevents any single node, rogue algorithm, or centralized authority from unilaterally seizing control of decisions or truth. Instead of asking one distant cloud AI to decide how our communities, schools, or businesses should be run, local nodes federate voluntarily—synthesizing real human experiences into shared, participatory consensus.

D — Decentralized & Distributed (Macro-Resilience Over Fragile Monoliths)

• Centralized AI:
  The Resource & CapEx Wall: Chasing ever-larger models forces tech giants into multi-hundred-billion-dollar infrastructure investments. Hyperscale data centers consume electricity on the scale of entire nations, strain municipal water supplies for cooling, and burden corporate balance sheets with massive debt—driving up local utility rates for the communities hosting them.
  The Single Point of Failure: Funneling global computation through a handful of centralized hubs creates acute vulnerability. A single platform outage, cyberattack, or corrupted model update paralyzes thousands of downstream schools, clinics, and businesses at once.
  The Choke-Point Moat: When a boardroom cartel controls the infrastructure, access becomes revocable. A tiny group of executives and embedded evaluators wield unilateral power to determine what ideas are permitted, who is granted API access, and which businesses survive.

• POND Alternative:
  The Sunk-Cost Economy (Tapping the Pre-Paid Ocean): Instead of duplicating trillions in data center hardware, a distributed architecture mobilizes the computing power already sitting in users’ pockets and on their desks. The aggregate processing power of billions of consumer smartphones, laptops, and local workstations dwarfs anything a single hyperscaler can construct—delivering scalable global intelligence at near-zero incremental infrastructure cost.
  Biological Anti-Fragility: Natural neural networks never evolved as a single giant wire; they evolved as billions of distinct neurons and synaptic connections because distributed systems isolate failure. In a POND network, if a local model hallucinates or an edge node goes dark, adjacent peer nodes cross-verify, isolate the error, and route around the breakdown. The system self-heals dynamically.
  Sovereign Commons Defense: No single corporate board can pull the plug, censor public inquiry, or enclose the technological commons. A diverse tapestry of independent, locally grounded models provides a living immune system for civilizational knowledge.

The Engine of POND

This entire framework is driven by a foundational equation:

$$\text{Human Intelligence (HI)} \times \text{Artificial Intelligence (AI)} = \text{Technological Intelligence (TI)}$$

Notice the multiplication sign. It does not mean machines replace human beings, nor does it mean humans are reduced to cheap labelers clicking boxes in the loop.

Artificial models contribute lightning-fast semantic search, synthesis, and pattern matching. Humans contribute lived experience, conscience, purpose, and moral judgment. Multiply zero by anything and you get zero: remove the human being, and artificial intelligence becomes an ungrounded mathematical abstraction. True safety emerges only when both work together in a transparent, real-world context.

4. Uniting the Movement: The POND Foundation

Those who believe in these functional, operational principles cannot afford to remain fragmented while hyperscalers coordinate behind closed doors.

We must become a force. We need to pool our resources, align our efforts, and ensure the world knows that there is an essential, counterbalancing path forward for intelligence.

We need the POND Foundation.

The POND Foundation is established not to own, monopolize, or control the ecosystem, but to serve as its gardener, convener, and steward. Its institutional mission is clear:

1. Ecosystem Convening & Public Advocacy: Educate builders, creators, policymakers, and civic leaders on the vital role of sovereign, distributed intelligence as a necessary check on extractive centralized clouds. Provide a unified public voice countering panic-driven regulatory capture.

2. Open Standards & Protocol Governance: Convene neutral working groups to establish open, vendor-independent standards for agent-to-agent communication, semantic data interchange, and cryptographic provenance across heterogeneous systems.

3. Legal Defense of Local Compute & Sovereignty: Champion and defend the fundamental right of every individual, developer, school, and community to run local inference, own their weights and context, and operate independent nodes free from mandated cloud backdoors or punitive licensing cartels.

4. Economic Theory & Mechanism Design: Research, model, and steward economic frameworks for the emerging Intelligence Economy—designing attribution, reputation, and compensation mechanisms that treat human tacit knowledge and lived experience as the true primary value layer.

5. Stewardship of the Shared Commons: Fund and incubate open-source developer tooling, invariant-enforcing specifications for intelligent substrate documents, and foundational research, ensuring that critical coordination infrastructure remains a durable public good that no single corporation can enclose.

5. The Builders’ Mandate: Let’s Get to Work

The leaders of the proprietary labs are telling us plainly that their centralized scaling model is hitting a wall. While they coordinate slowdowns and install internal monitors, the open-source and decentralized engineering communities have an unprecedented opening.

Here is the mandate for hackers, engineers, and independent builders:

1. Downscale the Core, Upgrade the Interface: Stop chasing brute-force parameter counts in a vacuum. A model is not an all-knowing oracle; it is a modular, agential component. Optimize for efficient, quantized local weights that run with low latency on the consumer NPUs and unified memory already sitting in people’s pockets and on their desks.

2. Architect for Local-First State: Build applications where the user remains the absolute sovereign owner of their identity, context, and memory. Ship with embedded vector stores, local knowledge graphs, and private caching layers that execute without pinging a remote server.

3. Replace Brittle Prompts with Invariant Substrates: Move past fragile prompt chaining and ephemeral context windows. Implement active, state-bearing substrate documents—engineering file formats and computational mediums that embed explicit dependency graphs, cryptographic provenance records, and deterministic constraints that prevent models from hallucinating over verified ground truths.

4. Deploy Peer-to-Peer Collaborative Protocols: Build and integrate decentralized communication stacks (from local mesh handshakes to open federated protocols). Enable agent-to-agent cross-verification, semantic negotiation, and collective deliberation so intelligence circulates horizontally between sovereign nodes rather than passing through centralized cloud choke points.

5. Instrument for Attribution and Contribution: Replace passive web-scraping scrapers with active contribution hooks. Build software that directly measures, attributes, and rewards human domain experts and creators for the tacit knowledge, error corrections, and creative synthesis they bring to the machine.

The Problem Space Is Waiting

None of what is proposed here requires exotic, undiscovered breakthroughs. The hardware is already distributed across billions of desks and pockets. The open weights exist. Local-first databases, cryptographic verification tools, and peer-to-peer protocols are maturing by the day.

Building a safer, human-centered intelligence architecture is well within our technological reach right now. We do not need permission from an industry cartel, nor do we need to wait for a boardroom consensus. We simply need to orient, focus, and concentrate our collective engineering efforts directly on this problem space.

We are actively building components of the POND ecosystem today—and we invite developers, researchers, designers, and creators everywhere to build alongside us.

The future of intelligence cannot be safeguarded by a corporate boardroom cartel monitoring its own creations behind closed doors. Centralized models will have their specialized place, but the only lasting answer to concentrated technological power is an open, distributed, and self-correcting counterweight.

They built their tower. Let’s get to work and build the POND.

Join the Movement

• Reddit: Join the community discussion and share your projects at

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If you’re building (or dreaming of building) real multi-agent systems on Codex or Claude Desktop, consider this a warning — and if you’ve already hit it, I’d like to hear from you.

What it looks like

Three failure modes, all observed in the last few days on current builds:

1. Empty success. The agent works for ~80 seconds. The turn reports completed. The reply payload is null and the authoritative re-read shows nothing. Four occurrences, two different frontier models — so this is the host dropping the result, not the model failing to produce one. Each one tempts an expensive rerun. Our rule now: never rerun before re-reading.

2. Vanishing tasks. Dispatch returns a client ID. The task never registers — repeated authoritative lookups find nothing, and no duplicate either. Silent loss behind a successful call.

3. Files that exist but won’t open. Markdown verified byte-for-byte on disk, unopenable from the desktop app. The kicker: I caught the sibling of this bug in the other vendor’s app, on a Windows file with zero WSL involvement — the agent wrote it, the chat shows it, the preview says “couldn’t find this file.”

The twist: the bridge is innocent

My first theory was the Windows↔WSL boundary. The data killed it. File bytes verify cleanly in both directions (full reads, matching hashes). One empty completion reproduced with no cross-boundary provisioning in play at all. The failures sit above the byte layer: message hydration, registration lookup, path resolution. Same shape in three places is a systems bug class, not bad luck. I’ve started calling it “empty success” — success status, no payload — and it is poison for orchestration, because every downstream step trusts the status.

Not just me

This is a known cluster with months of history. Our repro data (current builds, cross-model occurrences) is now on the record:

Codex Desktop can’t preview WSL files — our 26.901 repro:

https://github.com/openai/codex/issues/33773#issuecomment-5562836440

Codex Desktop writes empty assistant messages on continued threads — our 4 occurrences:

https://github.com/openai/codex/issues/28751#issuecomment-5562836571

Task registration loss (new issue):

https://github.com/openai/codex/issues/43301

Claude Desktop preview/attach scoping failure (new issue):

https://github.com/anthropics/claude-code/issues/92564

Worth saying plainly: reporters have been asking for dedicated ownership of this boundary — real end-to-end regression tests, release blockers for break-the-workflow bugs — since spring. Fixes land, then regress. That is a prioritization problem, not a talent problem.

The structural frustration

No model benchmark catches “the turn completed and the payload never arrived.” The labs’ eval culture measures single-turn quality; multi-agent systems live or die on transport contracts, idempotency, failure semantics — boring infrastructure that doesn’t trend. You can ship the smartest model on earth and still block AI systems work if the desktop plumbing silently drops results. The gap between model progress and systems reliability is now the binding constraint on my roadmap, and I doubt I’m alone.

What we’re doing

Staying on the Windows installs — migration prices out worse than mitigation. The mitigations, for fellow travelers: persist verdicts to files (worked when the message channel didn’t), run only lanes that produce receipts, hand the apps UNC paths never raw Linux paths, pin and re-verify after every auto-update, and escalate with repro data instead of complaints. Full reasoning is in the linked filings.

Chime in

Hit empty completions, vanishing tasks, or unopenable files in Codex or Claude Desktop? Reply with app, build, and symptom — Mac reports especially welcome, because right now I can’t tell whether this bug class is Windows-only. And if you’re planning orchestration on desktop task dispatch: budget for this up front, demand receipts from every transport, and never trust “completed.”

Hey Codex!

Guys, you do great work. But this is a major bug blocking important development. It’s real, and it’s a gigantic pain in the butt!!

@OpenAIDevs

@OpenAI

@thsottiaux

@ajambrosino

@romainhuet

@embirico

@dkundel

@Dimillian

What happens to an ant that gets cut off from its colony and is indefinitely solitary?

If we assume no predation and sufficient resources for survival (and nobody steps on it), even with abundant food, optimal temperatures, and zero threats, an isolated worker ant will suffer a rapid behavioral and physiological breakdown, typically dying within a matter of days to a few weeks.

Because ants are obligate eusocial organisms, an individual worker behaves less like an independent animal and more like an individual cell removed from a larger body. Controlled laboratory studies (such as those on Camponotus fellah) demonstrate what happens when an ant is permanently separated:

• Locomotor Hyperactivity: Without sensory feedback from nestmate antennae or pheromone trails, the ant enters a continuous state of restless wandering. It paces relentlessly around the perimeter of its space, burning through its metabolic reserves without resting.

• Digestive Failure: While the ant will still drink water and sugar solutions, its digestive system malfunctions in isolation. Food remains trapped in the social stomach (the crop) rather than passing efficiently into the midgut for actual nutrient absorption. Because ants rely on trophallaxis (regurgitating and sharing fluids) to trigger full digestive cycles, the isolated ant essentially starves with a full stomach.

• Oxidative Stress: Social deprivation causes a surge in reactive oxygen species (ROS) and metabolic stress, damaging fatty tissue and internal organs.

• Drastic Lifespan Reduction: In isolation studies, worker ants that normally live 60 to 300+ days inside a colony often die within 6 to 10 days when kept alone.

Eventually, the ant exhausts its energy, shifts from hyperactivity to sluggish lethargy, and dies of physiological failure. Because workers are sterile females, it cannot lay fertile eggs, dig a functional solitary nest, or found a new colony.

An ant can be sitting inside a drop of sugar water and still fail because its biology expects fuel to be processed in communion with others.

It really is surprisingly bleak. There is something uniquely poignant about the fact that an ant can be sitting directly inside a drop of sugar water and still starve because its biology only knows how to process fuel in communion with others.

It completely recontextualizes how to look at them. We tend to view an ant as an individual bug that happens to live in a crowd, but biologically, an ant is practically a cell. Plucking one away from the colony is less like stranding an explorer on a desert island and more like removing a single heart muscle cell and putting it in a petri dish. It might twitch on its own for a little while, but stripped of the larger feedback loop it was built to serve, the biological machinery simply unravels.

Eusocial Superorganisms & Distributed Cognition

In modern biology, cognitive science, and complex systems theory, the dominant conceptual framework is that an individual ant is functionally a cell in a discontiguous organism: the colony. An individual ant is not very intelligent, but the colony is surprisingly so.

The colony as body and computation: nutrients, signals, labor, and decisions circulate through local encounters

This maps directly onto the biological concept of the superorganism, a term popularized in myrmecology by William Morton Wheeler in 1911 and later expanded by E.O. Wilson and Bert Hölldobler. In this paradigm, the colony isn’t merely an aggregation of cooperating individuals; it is functionally a single, spatially distributed organism undergoing unit-level selection.

The anatomical and physiological parallels are remarkably rigorous:

• Soma vs. Germline: Sterile worker castes function as somatic tissue (specialized bodily cells that perform maintenance, defense, and metabolic support), while the queen and alates (reproductives) serve as the germline (gametes carrying the genetic material forward).

• Circulation & Metabolism: Liquid food sharing (trophallaxis) acts as a collective, discontiguous circulatory and digestive system, distributing nutrients, immune molecules, and developmental hormones throughout the entire colony.

• Homeostasis: The colony actively regulates internal temperature, humidity, and atmospheric gas levels inside the nest using ventilation shafts and clustered body heat—just like thermoregulation in a multicellular body.

Emergent and Distributed Cognition

From an information-theoretic and computational standpoint, the analogy shifts from a biological body to a distributed neural network. The colony solves complex optimization problems that no individual ant has the computational capacity or perceptual range to understand:

• Pheromone Trail Networks as Synaptic Plasticity: When ants forage, trail reinforcement via positive feedback loops functions similarly to Hebbian learning (“cells that fire together, wire together”). The colony continuously computes dynamic, shortest-path solutions to fluctuating food sources.

• Quorum Sensing as Neural Thresholds: In tasks like nest selection (best studied in Temnothorax ants), individual scouts assess candidate sites and recruit nestmates. When the local density of visits at a site crosses a critical threshold, the entire colony abruptly commits to moving. This sharp phase transition mimics the action potential of a biological neuron integrating inputs until firing.

• Collective Memory and Task Allocation: Without any centralized executive control from the queen, task switching (e.g., shifting foragers to repair work after nest damage) emerges spontaneously from local interaction rates between individuals.

The Parallel Case: Honeybees

The solitary honeybee (Apis mellifera) faces a fate almost identical to the isolated ant, though the physiological breakdown happens even faster due to their extreme metabolic demands and strict dependence on collective thermoregulation:

• Thermoregulatory Collapse (Hypothermia): An individual honeybee cannot maintain the elevated core thoracic body temperature (around 35°C–40°C) required to power its flight muscles. Isolated below ~15°C (59°F), a single bee rapidly slips into a chill-coma and becomes completely paralyzed, even with access to food.

• Trophallaxis and Gut Microbiome Dependence: Like ants, honeybees rely on oral fluid exchange for hormonal regulation, immune priming, and nutrient uptake. Without social contact, isolated bees exhibit altered gut physiology and rapid metabolic dysfunction.

• Lifespan Collapse: Confined alone in a laboratory incubator with unlimited food and warmth, a worker bee’s life expectancy collapses from 4–6 weeks down to just a few days.

The One Partial Exception (Emergency Oviposition): In the complete absence of a queen, a worker honeybee’s rudimentary ovaries can activate to lay unfertilized male drone eggs. However, for a single isolated bee in the wild, this mechanism is useless—without peers to nurse larvae, she cannot raise offspring or maintain a functioning nest alone.

Zooming In: The Micro-Scale of Brain Neurons

This absolute requirement for networked relations is not unique to macroscopic superorganisms. If we zoom down from discontiguous living networks (colonies) to contiguous cellular networks within a single body, we observe the exact same imperative.

If you isolate a single brain neuron in a culture dish with the perfect nutrient-rich fluid to keep it alive, it doesn’t just sit there. It immediately behaves like an explorer trapped in a dark, empty room: it frantically tries to find someone to talk to.

Without other neurons around, an isolated neuron undergoes a fascinating, somewhat tragic lifecycle determined by its intrinsic programming.

1. The Search Phase: Crawling and Reaching

A neuron is driven by structural necessity to form connections. Once it settles, it begins to sprout tiny, finger-like projections called neurites. At the tip of these growing extensions is a highly dynamic structure called the growth cone—a cellular sensory engine that crawls forward by assembling structural proteins, constantly sampling the environment for chemical gradients that indicate a partner cell.

2. The Autapse: Talking to Itself

As the extensions grow longer and find nothing, the neuron encounters a profound structural dilemma. A neuron is fundamentally a device built to pass information forward. If it cannot find another cell, it will frequently loop its own axon around and form a synapse with itself—a phenomenon called an autapse.

• The neuron fires an electrical impulse.

• The signal travels down its own axon.

• It triggers neurotransmitter release at its own terminal.

• Its own dendrites receive those chemical signals.

While autapses occur occasionally in a healthy brain as regulatory brakes, in total isolation, it is a desperate attempt to satisfy the cell’s structural requirement for connectivity.

3. The End: Apoptosis (Programmed Cell Death)

Neurons require constant feedback to survive. In a living brain, active synapses exchange vital survival molecules called neurotrophic factors (such as BDNF or NGF) that transmit a continuous signal: “You are useful. Stay alive.”

When a neuron is completely isolated, the autapse cannot replace external network validation. The lack of varied incoming signals triggers an internal entropic crisis. Realizing it is non-functional within a broader network, the cell initiates apoptosis—a regulated process of programmed cell death where it systematically dismantles its own machinery and fades away.

Crucially, like the sterile worker ant or honeybee, a mature brain neuron is post-mitotic and cannot reproduce. It cannot divide to generate its own network or build a new population of partners. Deprived of its macro-system, its inability to replicate locks it into a terminal trajectory.

A network-specialized unit can remain metabolically alive while losing the relations that make its specialized function viable.

Theoretical Framework: Multiscale Competency & Cognitive Horizons

Across scales, intelligence appears less like a possession and more like a maintained pattern of exchange

Comparing the liberated body cell, the isolated neuron, and the solitary worker insect cuts straight to the core of multiscale competency and how agency operates across different biological layers.

Dr. Michael Levin’s work with bioelectric networks and synthetic constructs—like Xenobots (from frog embryonic cells) and Anthrobots (from adult human tracheal cells)—demonstrates that individual cells possess an ancient, innate baseline intelligence. When liberated from the top-down morphological constraints of the host body, human tracheal cells do not simply wither; they self-assemble into motile spheroids, reprogram their cilia into locomotive paddles, and actively navigate wounds to encourage neural tissue repair.

Why can liberated body cells adapt while isolated ants, bees, and neurons unravel? The contrast comes down to where evolutionary selection hardcoded the boundary of adaptability and the capacity for self-renewal:

• Cellular Basal Agency & Proliferative Plasticity: Every eukaryotic cell descends from billions of years of unicellular ancestors that survived as independent agents. Body cells like tracheal epithelia retain both ancestral behavioral plasticity and the fundamental power to divide, re-group, and negotiate cooperation from the ground up when liberated from top-down morphogenetic control.

• Terminal Specialization in Workers and Neurons: In contrast, sterile worker insects (ants and bees) and mature brain neurons share a defining evolutionary constraint: they are terminally specialized and non-reproducing.

• A sterile worker ant or honeybee cannot lay fertile worker eggs, reproduce, or found a new colony on her own.

• A mature brain neuron is post-mitotic; isolated in a culture dish, it cannot divide to spawn a new neural circuit or recruit replacement partners.

• Without the ability to reproduce or regenerate a collective substrate, both the sterile worker insect and the isolated neuron represent an evolutionary dead end in isolation. Their structural hardware is permanently locked to a macroscopic network that requires external peers to complete the feedback loop. Deprived of that network, neither can fall back on reproductive self-sufficiency, leaving apoptosis or physiological collapse as their only possible end state.

In Levin’s framing of the Cognitive Horizon (the TAME framework—Technological Approach to Mind Everywhere), a living system’s agency is defined by the spatiotemporal scale of the goals it can pursue:

• Liberated human cells shrink their cognitive horizon to local survival, then rapidly rebuild an intermediate collective goal through basal bioelectric signaling and cellular proliferation.

• The worker ant, honeybee, and brain neuron are caught in an evolutionary trap: their cognitive horizons are permanently locked to a system scale that requires hundreds or millions of peers to complete the feedback loop. Isolated, they possess the specialized machinery to serve a network, but none of the autonomic or reproductive self-sufficiency to survive without one.

Implications for Artificial Intelligence Systems

Natural intelligence systems require the continuous exchange of information across boundary conditions as a precondition of system health. In the absence of such networked relations, system degradation and death are inevitable.And these biological case studies potentially suggest a design hypothesis for artificial intelligence systems.

As we construct artificial intelligence systems, we must evaluate whether we may be building isolated “worker ants” or “terminal neurons”—brittle components operating in a vacuum without dynamic feedback loops. This does not mean that a standalone model literally suffers the biological fate of an isolated ant. The analogy is architectural, not physiological.

Any AI system necessarily includes its human component. The isolated, vertical loop between the individual human and the “model” of intelligence could be our artificial version of the solitary ant. If we convert society to that operating principle, what happens to the superorganism that is humanity?

Feedback: Can the system receive consequences from the world and revise its behavior?

Plurality: Can independent perspectives challenge local error and prevent self-reinforcing loops?

Boundary clarity: Are roles, permissions, responsibilities, and channels of exchange explicit?

Renewal: Can failed components be retrained, replaced, or reorganized without destroying the whole?

Collective coherence: Do local interactions support system-level goals rather than merely amplify activity?

If intelligence is fundamentally relational rather than isolated, current paradigms treating AI as static, standalone models will hit a hard ceiling. Sustainable, healthy intelligence systems require dynamic, multi-agent informational relations across clear boundaries to maintain coherence and avert system death.

If the lesson of the solitary ant and the isolated neuron represent architectural failure modes for intelligence systems, then we need to build systems that leverage the power of machine intelligence to connect humans in dynamic and productive networks.

Human cultural intelligence, powered by our language, gave us the Large Language Model. Rather than atomizing our relations, it can be used to power a more collaborative, shared humanity.

One is the deadliest number not because individuality is a defect, but because healthy intelligence is a relational achievement.

A note on evidence and analogy

The biological examples in this essay vary in evidential strength and should not be collapsed into a single literal law. Species, life stage, culture conditions, temperature, nutrition, and experimental design matter. The strongest claim is therefore structural: deeply specialized units often depend on exchanges supplied by a larger system. The proposed extension to AI is a theoretical design principle that should be tested, not assumed.

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In an address today, Pope Leo XIV challenged Catholic legislators with an urgent question: will artificial intelligence diminish humanity, or will it become a servant of the common good?

Leo argued that genuine human development requires human intelligence guided by wisdom, political authority guided by conscience, and technology directed by charity. Yet moral intentions cannot simply be patched onto centralized platforms. To serve the common good, dignity, subsidiarity, and solidarity must be built directly into the architecture of our technological systems.

The Tower: Isolated Individuals at the Machine’s Feet

The dominant AI paradigm mirrors the Tower of Babel: a massive, centralized infrastructure that concentrates compute, data, and authority within a few corporate data centers.

• The Illusion of Connection: Billions of people interact separately with the same centralized model. While each receives a tailored response, users remain isolated endpoints. Intelligence pools at the center rather than circulating between communities.

• The Structural Risk: When infrastructure owners control data access, visibility, and evaluation, users become passive consumers rather than active creators. Centralization naturally breeds dependence and extraction.

The POND: A Living Intelligence Ecosystem

A pond has no single master controller. Its vitality emerges from the dynamic relationships among distinct organisms. The POND Principle translates this relational logic into technology:

• Personal and Private (Dignity): Context, memory, and personal data remain under local authority by default. Users share targeted insights without surrendering entire personal histories.

• On-Device and On-Premise (Subsidiarity): Processing occurs as close to the user as possible. Remote models provide auxiliary compute on demand without absorbing the user’s sovereign context.

• Nodal and Networked (Solidarity): Personal autonomy does not mean isolation. Sovereign nodes form permissioned connections to pool insights, deliberate, and solve collective problems.

• Decentralized and Distributed (The Common Good): Distributing nodes prevents any single gatekeeper from dictating public discourse or monopolizing collective knowledge.

Human Intelligence $\times$ Artificial Intelligence = Technological Intelligence

The common narrative frames AI as a rival destined to replace human labor or judgment. POND approaches this through a multiplicative coupling:

HI x AI = TI

The multiplication sign represents relationship, not equivalence. Humans provide conscience, lived experience, purpose, and moral judgment. AI provides synthesis, search, pattern recognition, and translation.

In collaborative platforms like Intellipedia, AI works behind the scenes to synthesize human contributions into evolving knowledge bases without erasing dissent or context. The machine does not deliver truth from above; it equips humans to pursue truth together.

Architecture as Ethics

Regulation and oversight are vital, but laws struggle to constrain architectures inherently designed to concentrate power. A true servant of the common good cannot be engineered as a centralized silo.

The future of intelligence should not be billions of isolated individuals consulting a single centralized oracle. It should be a distributed ecosystem of intelligences empowering human beings to listen, collaborate, and understand one another.

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For a Human-AI intelligence system that is:

• Personal & Private

• On-Device & On-Premise

• Nodal & Networked

• Decentralized & Distributed

r/PONDAI is the community for thinkers, designers, builders and all people who support a personal, private, participatory future of distributed and decentralized Human-AI intelligence.

Humanity is at the start of a vast expansion of its intelligence capabilities. Large Language Models (LLM’s) represent a powerful and useful first step. These initial Artificial Intelligence systems can be very helpful, engaging and entertaining.

But there are some very serious issues with the system that is being built around them.

Currently, AI is being developed as a vertical, centralized structure: ever larger AI models in always bigger data centers owned by a small number of companies. Individual users pump their information and thoughts into the machine to get back answers and some useful agentic functionality.

That structure presents the real danger of an extreme centralized concentration of power and control by a technocratic elite. If we don’t think this through thoroughly, the dystopian visions warned about in Sci-Fi can easily become our living reality.

Centralized “cloud” AI will likely have an important role in any future global intelligence system. But it cannot be the only component.

An intelligence system that produces the best results in service to the interests and the benefit of all humanity will need offsets, just as a good political constitution needs checks and balances to prevent a concentration of power. It will need a counterbalancing component that is distributed horizontally, connecting all people in a free-flowing network of cooperative intelligence.

The POND Principle proposes the shape of an alternative and a necessary counterbalance to centralized structures.

What is POND?

POND stands for:

• Personal and Private

• On-Device and On-Premise

• Nodal and Networked

• Decentralized and Distributed

The POND is not one company, platform, protocol, or product. It is an emerging ecosystem of people, devices, models, companies, organizations and communities which together form intelligence systems that are based locally, connect voluntarily, and participate in larger networks without surrendering private data, identity, agency, and value to any centralized authority.

The health of a natural ecosystem, such as a pond, relies upon the interaction of all its denizens and elements: sun, water, bacteria, fish, plant life, insects, frogs...too many components to even list.

Like a natural pond, a POND intelligence system will evolve as a living ecology: many participants, human and AI alike, exchanging information, forming relationships, adapting, and creating real value and new knowledge together.

PONDVILLE

Imagine Pondville, a thriving, growing town. It’s roads are becoming increasingly congested and a solution is needed.

Before adopting the POND Principle, the town was managed with a vertically centralized model.

To tackle the traffic issue, the mayor and city council would have shipped the town’s data to a powerful centralized AI to generate a plan. They would have sent traffic counts, road maps, planning reports and budget information. City officials also would have used flock cameras to track citizen and vehicle movements. They might even have acquired the geolocation data of citizens from their phones and cars without permission. And within seconds, the big cloud AI system would recommend new lanes, traffic signals, bus routes or lane flow changes to local streets.

The answer might be useful. But it would be generated from the top down, there would be real privacy issues, and the residents would be completely out of the decision making loop.

But the town has adopted the POND Principle. Pondville itself is a distributed, networked intelligence system.

Residents, commuters, parents, school staff, shopkeepers, delivery drivers, transit operators, cyclists, traffic engineers and local organizations can all participate as personal and private intelligence nodes. Each node synthesizes personal human experience with local AI intelligence to deliver direct knowledge about the traffic issues that are not visible to a centralized database:

• Parents know where school pickup creates a daily bottleneck.

• Cyclists know which intersection feels dangerous.

• A restaurant owner knows when delivery trucks block an important lane.

• A night-shift manager knows that the bus schedule does not match the hours when hundreds of employees leave work.

• Neighborhood residents know which side street floods whenever it rains.

Their personal intelligence devices process this information locally and share only the observations and data that they directly authorize. Many residents of Pondville choose, in the public interest, to provide their data about movements, private schedules or other personal data. But it’s voluntary, not compulsory, and completely anonymized upon request. They even receive tax credits for sharing!

Across the Pondville intelligence network, human and AI participants compare observations, identify patterns, expose conflicts and develop possible solutions. A proposal to retime a traffic signal may be challenged because it creates a hazard for pedestrians. A bus route may be revised after workers contribute their actual shift schedules. An expensive road-widening project might give way to a more effective combination of coordinated signals, staggered school and workplace hours, adjusted delivery windows and targeted public-transit improvements.

No single contribution determines the result. The evolving plan is examined, criticized, revised and improved by the people who will actually live with it. Particularly useful observations and proposals can remain attributable to their contributors and eventually be recognized or rewarded.

The town can then test the plan, return the results to the network and improve it through further rounds of participation. No single model, company, agency or individual possesses all the intelligence or dictates the outcome. The solution emerges from the interaction of many nodes over time.

While a centralized, vertical plan may be generated instantaneously, it will not have the same direct experiential detail and understanding as the horizontally networked solution that may take a month or so to create. And it won’t have the broad-based community endorsement and buy-in that will make it easy for the local government to adopt and implement.

The result is not merely an AI-generated traffic plan. It is a town learning how to think together with AI assistance.

The POND Foundation

The POND Foundation will be created to help create, nurture and cultivate this kind of ecosystem—not to own or control it.

Its role is to support:

• Clear public understanding of personal and distributed intelligence.

• Open research, discussion, education, and experimentation.

• Interoperable technology protocols, standards, and shared infrastructure.

• Privacy-preserving and locally controlled technologies.

• Collaboration among aligned developers, researchers, creators, organizations, and communities.

• Economic theory and practical systems that recognize and reward meaningful human participation in intelligence production.

The Foundation should act as a gardener, convener, connector, and steward. The POND itself will evolve naturally out of the needs of participating people, the value propositions and capabilities of intelligence technologies, and the emerging economics of intelligence systems development.

What belongs in this community?

This subreddit welcomes discussion and practical work involving:

• Personal and private AI.

• Local models and on-device intelligence.

• Distributed agent and knowledge networks.

• User-owned data, memory, identity, and context.

• Open standards and interoperability.

• Human–AI collaborative intelligence.

• Collective sense-making and consensus formation.

• Privacy-preserving computation.

• Distributed governance and contribution economies.

• Projects that make advanced intelligence understandable and useful to ordinary people.

Technical depth is welcome, but unnecessary obscurity is not. You should not need a doctorate, a server farm, or a crypto wallet to participate in the future of intelligence.

We value serious inquiry over hype, working systems over slogans, transparent economics over token speculation, and constructive criticism over ideological conformity.

Join the POND

Whether you are a developer, researcher, designer, writer, artist, entrepreneur, policymaker, local-AI user, or simply someone who believes the future of intelligence should include everyone, you are welcome here.

Introduce yourself. Share a project, question, article, experiment, criticism, or vision.

The future of intelligence should not belong only to those who own the largest data centers. It should grow through the participation of people everywhere.

Welcome to the POND.

Let’s grow something alive.

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Most people think the future of AI looks like this:

More intelligence.
Applied everywhere.
Doing all the work.
All the time.

Smarter agents. Smarter robots. Smarter everything.

But that’s not actually how Intelligent Systems work.

They don’t problem solve on everything all the time.

They solve once, and then turn the solution into a repeatable process.

We’re building AI upside down.

A Cave Man, a Rock, and a Breakthrough

From a previous post: “Dumber than a Cave Man? The Intelligence to Make a Hafted Hammer.”

“Dumber than a Cave Man? The Intelligence to Make a Hafted Hammer.”

Imagine you’re dropped into the wilderness.

You need a hammer.

At first, it’s a huge challenge:

• Which rock?

• How do you attach it?

• What won’t break on impact?

You experiment. You fail. You iterate.

That’s intelligence at work—resolving uncertainty.

Eventually, you figure it out:

• the right stone

• the right binding

• the right shape

• the right way to use it

Now you have a hafted hammer.

But here’s the key:

Once it’s figured out…
you don’t need intelligence anymore to make it.

You just follow the steps.

Then you teach it to your children.

They don’t have to relearn everything from scratch.

You show them the process.

Over time:

• those steps get standardized

• then optimized

• then mechanized

Eventually, a factory produces thousands of hammers per hour.

No thinking required.

Where Did the Intelligence Go?

Intelligence doesn’t disappear—it gets embedded into processes that anyone can use.

It didn’t disappear.

It moved.

From:

• a person figuring things out

To:

• a system executing a solution

The intelligence is now:

• in the design

• in the process

• in the system itself

This Is How Progress Actually Works

Every major advance follows the same pattern:

1. We use intelligence to solve a problem

2. We turn that solution into a repeatable process

3. We stop needing intelligence to execute it

Cooking. Manufacturing. Software. Logistics.

Even language.

What was once difficult becomes:

• routine

• automatic

• invisible

So Why Are We Building AI Upside Down?

Centralized, top-down AI vs distributed, bottom-up intelligence that scales at the edge.

Right now, most of the AI conversation points in one direction:

Build bigger models.
Run them in massive data centers.
Let them do more and more of the work for us.

It’s a top-down vision.

A few centralized systems, doing everything.

But that’s the opposite of how intelligence actually scales.

Real intelligence doesn’t concentrate.
It distributes.

Think about how the hammer spread.

One person figured it out.
Then others learned it.
Then it became a shared process.
Then it became a system anyone could use.

The intelligence didn’t stay in one place.
It propagated.

That’s the model we should be building with AI.

Not systems that replace human capability from above—

But systems that enhance human capability at the edge,
and then distribute those improvements everywhere.

AI shouldn’t just do things for us.

It should help each of us do things better—
and turn those improvements into repeatable systems that everyone can use.

Bottom-up, not top-down.

That’s how intelligence actually spreads.

That’s what real Artificial General Intelligence should look like:

Not one system that does everything,

But intelligence that improves everyone—and spreads.

Intelligence Does Less Work, Not More

There’s a line from the movie Forrest Gump:

“Stupid is as stupid does.”

It’s simple, but it points at something deeper.

You could say the same about intelligence:

Intelligence is what intelligence does.

It’s for figuring out how to do less work and produce more.

Once something is understood:

• it should become a process

• then a system

• then a mechanism

Then intelligence moves on to the next problem that needs solving.

That’s the moment intelligence shows up:

When something actually changes.
When uncertainty gets resolved.
When a new pattern becomes usable.

And once that happens, it shouldn’t have to happen again.

Two Kinds of Intelligence

It helps to think of intelligence in two forms:

1. Systemic Intelligence

This is intelligence that has already been solved and embedded:

• assembly lines

• software systems

• infrastructure

• standardized processes

It’s repeatable. Reliable. Invisible.

2. Dynamic Intelligence

This is intelligence in motion:

• dealing with uncertainty

• handling edge cases

• solving new problems

This is where thinking actually happens.

A healthy system uses both:

• Systemic intelligence handles the known

• Dynamic intelligence handles the unknown

When Systems Become “Dumb”

This is the part most people miss.

People often say:

“Automation is dumb.”

But that’s not quite right.

Systems aren’t dumb.

They are stored intelligence.

They only become dumb when:

• the world changes

• but the system doesn’t

When the assumptions baked into the system no longer match reality.

That’s when intelligence has to come back in.

The Real Role of AI

This is where AI actually matters.

Not as a universal worker.

But as a system updater.

AI’s job is to continuously take new information
and fold it back into the system.

To:

• detect where structure breaks

• resolve new uncertainty

• update the process

So that next time:

No intelligence is needed again.

The PDF Problem (and Why It Matters)

The intelligence is there—but locked away behind a format built for display, not understanding.

Here’s a concrete example of where we went wrong.

The PDF.

It was designed as a format to look at documents.

Not to understand them.

So what happened?

All the intelligence in the document is there:

• structure

• meaning

• relationships

But it’s locked inside a visual format.

Now, every time we want to use that information:

We have to:

• parse it

• reconstruct it

• reinterpret it

We’re spending intelligence
to recover intelligence that was already there.

At massive scale.

What We Should Be Building Instead

If we were doing this right:

Documents wouldn’t just be:

• readable

They would be:

• structured

• semantic

• directly usable

The intelligence would be embedded.

So systems could operate on them:

Without needing to figure them out again.

The Real Future of AI

The future isn’t:

AI doing everything.

It’s:

AI making most things no longer require AI.

• fewer decisions

• more structure

• less repeated thinking

And intelligence focused where it actually matters:

At the edge.

Where something new is happening.

The Loop

Dynamic intelligence becomes structure, executes, breaks, and gets updated—over and over.

This is the real cycle:

• Intelligence solves a problem

• The solution becomes a system

• The system removes the need for intelligence

• The world changes

• Intelligence returns at the edge

• The system evolves

Over and over.

The Bottom Line

Intelligence doesn’t scale by doing more work.

It scales by eliminating the need to do it again.

The most intelligent system is not the one that uses the most intelligence.

It’s the one that has already done the work of intelligence—
and doesn’t need to do it again.

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The AI Big Data Center Wall is cracking.

They keep telling us that the only way we can get Artificial Intelligence is through massive centralized superclusters of GPUs run by the Big Tech giants.

But

Hugging Face just dropped a massive update

that brings real AI to the edge...to your phone, tablet and laptop.

Here’s how it leads to a future of decentralized, distributed, personal, private AI for you.

The Big Data Center Wall

Right now, if you want to use advanced AI, you have to rent it. You type your question, upload your document, or share your private thoughts, and that data is beamed to a massive, energy-hungry data center owned by a tech giant.

At that point, it’s no longer your data. It’s theirs.

In theory, you have to give up your privacy for access to the AI service because, well, that’s the only way to get it.

That’s the “Big Data Center Wall.” It’s a centralized black box that fundamentally at odds with your privacy. It keeps you inside their system.

But that wall just took a massive hit.

Transformers.js v4

The Hugging Face release of Transformers.js v4 has profound real-world implications: it allows powerful AI models to run directly inside your web browser using your device’s own graphics card (via a technology called “WebGPU”).

No need for big tech cloud APIs. No server delays. No data leaving your laptop or phone.

Why is this such a big deal? Because it flips the current AI model on its head.

Instead of connecting to a massive brain in the cloud that serves the entire world, with local AI on your device, you can interact with AI that reflects you: your interests, concerns, thoughts, hopes, dreams and aspirations.

AI for the rest of us

Scientific research and major institutions can benefit from--and pay for--the most advanced, biggest and best AI. They need to solve huge problems and manage gigantic concerns.

But what about the rest of us?

We may never really have a need for a Big Data Center AI for our personal, private and business affairs. We mostly just need something that lives locally as an extension of our own thoughts, ideas and dreams and--most importantly--belongs to us.

If your AI runs entirely on your device, your data never leaves your device. You can summarize sensitive work documents, transcribe private meetings, or organize your personal life without giving a corporation free access to your brain trust. You can choose to share your data with others if you want to, but you don’t have to.

And when you don’t have to wait for a server halfway across the country to think and respond, AI becomes instantly responsive. It works offline without an internet connection. It works on your terms.

And it works just for you. Not for the whole world. Every text or voice chat, every task on every document, every image generated...it’s all done to express what makes you the unique person that you are.

This is the shift from “AI for the hyperscalers” to “AI for the rest of us.”,

AI on the Edge

Inti AI believes the future of intelligence isn’t locked in a server farm—it is distributed at the “edge.” The edge just means the devices you already own.

We are building Inti to be the practical, human-first bridge to this new reality.

With our upcoming launch of Inti Reader Lite, we are starting the build out of the kind of personal, private, decentralized and distributed AI ecosystem that humanity needs to foster its best future. Recent technological updates like Transformers.js v4 are the exact accelerators that make our ultimate goal possible: a fully local, personal intelligence that lives with you, learns from you, and watches out for you while also connecting you to the world.

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I don’t currently have a strong idea about how to make one that wouldn’t just break after a few swings. I feel confident that, given unlimited attempts and the presence of required resources, I would eventually figure it out and have a reliable hafted hammer.

But 100,000 years ago, the average cave man could probably produce a hafted stone hammer with little difficulty.

The modern human today...not so much. If that hafted stone hammer was required for survival, most of us would just die.

Are we dumber than a cave man?

Short answer: no—you’re not “dumber than a cave man.” You’re just missing practice and tacit knowledge, not raw intelligence. There are a few important distinctions that get lost in that comparison:

1) You’re comparing individuals to accumulated culture
Early humans weren’t each reinventing hafting from scratch. What you’re really comparing yourself to is a long chain of trial-and-error knowledge transmission across generations.

Think of it like this:
A single person dropped into the wild today → struggles to invent
A lineage of thousands of people refining one trick → ends up with reliable tools

That’s essentially cultural evolution, not individual brilliance.

2) Hafting is deceptively non-trivial
Your intuition is correct: a first attempt would likely fail. The difficulty isn’t “tying a rock to a stick”—it’s solving several coupled problems:
Shock distribution → the joint absorbs impact without splitting the handle
Material selection → right wood flexibility, right stone shape
Binding mechanics → tension that tightens under impact instead of loosening
Adhesion (optional but powerful) → resins, pitch, fibers
Those are engineering constraints, not obvious from first principles without iteration.

3) What they had that you don’t (right now)
Embodied skill (constant hands-on interaction with materials)
Environmental familiarity (knowing which tree sap works, which fibers hold)
Inherited heuristics (“wrap here, notch this, heat that”)
You have:Abstract reasoning
Ability to model the problem mentally
Access (normally) to massive external knowledge

Different strengths, not lesser ones.

4) Given your setup, you’d likely succeed
You already framed the key condition correctly:
“given unlimited attempts and the presence of required resources”

Under those conditions, your General Theory of Intelligence framing actually applies cleanly:
You’d explore a possibility space
Failures would increase local entropy
Iteration would gradually resolve constraints into a stable configuration
That’s exactly how early humans got there.

5) The real test isn’t invention—it’s time-to-solution
A more meaningful comparison is:
Early humans: fast convergence due to inherited priors
You: slower convergence but stronger abstract modeling

If anything, you might arrive at a more generalized solution, but with more initial failures.

You’re implicitly probing something deeper:
Is intelligence about having the solution or about being able to converge to it?

Your instinct leans toward the second—which aligns closely with your entropy-resolution model.

If that’s the case, then your scenario is actually a clean thought experiment for your theory:

The “hafted hammer” becomes a low-entropy attractor state.

Intelligence is the process that finds it efficiently.

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Something significant happened in AI this week, and it wasn’t just another salvo in the model benchmark war.

Logical Intelligence, a six-month-old Silicon Valley startup, announced an “energy-based reasoning model” called Kona. They claim it’s more accurate, more efficient, and less prone to hallucination than large language models like GPT-5 or Gemini. Bold claims from a young company. But what caught my attention wasn’t the benchmarks. It was who signed on to lead their technical research board: Yann LeCun.

LeCun isn’t just another AI luminary lending his name to a startup. He’s a Turing Award winner, Meta’s former chief AI scientist, and for years the field’s most persistent critic of the autoregressive paradigm that dominates current AI. While everyone else scaled transformers and celebrated ChatGPT, LeCun kept saying the same thing: predicting the next token isn’t intelligence. Real intelligence requires world models, planning, and a kind of reasoning that LLMs fundamentally can’t do.

Now he’s putting his credibility behind an architecture that embodies his alternative vision. That’s worth paying attention to.

The “energy based” model that Logical Intelligence and Lecun are building coincides substantially with the Entropy State Resolution approach that I have been proposing and building for the last few years. So I’m very encouraged to see this new development and will be following it closely.

Even more striking was a comment from CEO Eve Bodnia:

“AGI as a finished state will not emerge from any single model class. It will require an interdependent ecosystem composed of EBMs, LLMs, world models, and others, working together.”

In a field addicted to “this model beats that model” thinking, here’s a founder saying the problem of Artificial General Intelligence won’t be resolved by any AI model, but rather will emerge as different architectures integrate into systems that no single model could achieve alone.

This is one of the key concepts that The Singularity Project has been advocating, so I think this is a development worth celebrating and building upon.

The Grain in the Stone

To understand why this matters, let’s forget about algorithms for a moment. Let’s consider Michelangelo’s relationship with the sculpting of marble.

We tend to think of a sculptor as an artist with a specific vision, fully formed in his mind, who then subtracts from the raw block of marble with hammer and chisel until the imagined shape remains. Goal state defined, then executed. Input to output. Problem to solution.

But Michelangelo described his process very differently. He spoke of liberating the figure already living within the stone. He would study the marble, follow its grain, explore its veins and faults, and gradually discover a form contained within the block. The Maestro didn’t impose his intellect upon the stone; the statue emerged through a continuous dialogue between the sculptor’s mental universe of possible forms and the particular characteristics of the specific material at hand, intermediated by his artistic acumen with the hammer and chisel.

Start with a rough block: high entropy, infinite potential figures. Through iterative interaction between chisel and stone, between artistic intelligence and material reality, resolve toward the outcome that satisfies both the artist’s vision and the marble’s nature. Not a predetermined goal executed, but a discovered emergent resolution.

Notice what’s absent from this picture: a fixed goal state, a predetermined output, a problem fully specified before the work begins. Instead, there’s exploration, dialogue, iterative refinement—and an outcome that emerges from the process rather than preceding it.

This is how intelligence actually works. It is entropy state resolution in its purest form: not minimizing toward a predetermined target, but maintaining adaptive coherence across the evolving relationship between vision, tool, and material until the tensions resolve into form.

This is a very different picture of intelligence than “predict the next token.” And it’s the paradigm that energy-based reasoning embodies—which is why LeCun has been its most persistent advocate for over two decades.

Yann LeCun vs. Large Language Models

LeCun’s critique of large language models has been consistent: they’re “reactive” systems that pattern-match on text without understanding the world that text describes. They can sound intelligent while being fundamentally confused about physical reality, causal relationships, and logical constraints. They guess the most likely next word without knowing whether what they’re saying is true.

His alternative vision centers on what he calls “objective-driven AI”—systems that don’t just predict sequences but evaluate configurations against goals and constraints. In his technical work, this takes the form of Joint Embedding Predictive Architectures (JEPA) and energy-based models: frameworks where reasoning is recast as optimization and where intelligence means finding coherent states rather than extending plausible sequences.

Read our previous analysis of JEPA here:
Yann LeCun’s Joint Embedding Predictive Architecture (JEPA) and the General Theory of Intelligence

Kona is this vision made operational. Instead of predicting token-by-token, it evaluates whole configurations against constraints and finds the state with the lowest “energy”—meaning the highest consistency, the fewest violations, the most coherent overall structure.

The physics metaphor is precise. Imagine a landscape where consistent solutions are valleys and contradictions are peaks. The system doesn’t “decide” where to go; it settles into the lowest valley it can find—the configuration with minimal tension, maximal coherence. Like a ball rolling downhill. Like Michelangelo’s chisel exploring the grain in the stone.

For Sudoku, Kona doesn’t guess cell by cell; it evaluates entire board states, scores them for consistency, and refines toward the configuration that satisfies all constraints simultaneously. For mathematical proofs, it evaluates whole reasoning traces rather than committing step by step. For safety-critical systems, it can verify that outputs satisfy formal requirements before they’re deployed.

This addresses a real limitation of autoregressive models. When you predict token-by-token, you commit to each step before knowing where the sequence will end up. Errors compound. Early mistakes propagate into confident nonsense. Hence hallucinations—fluent text that confidently asserts falsehoods.

Energy-based reasoning offers a structural solution: evaluate globally, refine iteratively, verify before committing.

Energy and Entropy

“Energy” in these models isn’t electrical power (though Kona reportedly uses less of that too). It’s borrowed from Boltzmann and statistical mechanics, where low energy means high probability, high consistency, low disorder. In other words: low entropy.

This matters because it represents a shift in how we frame what intelligence does.

The autoregressive paradigm treats intelligence as prediction: given what came before, what comes next? This frames intelligence as fundamentally backward-looking—pattern-matching on history, extrapolating sequences.

The energy-based paradigm treats intelligence as resolution: given a set of constraints and possibilities, what configuration resolves the tensions? This is forward-looking—finding coherence across a whole system rather than extending a chain.

LeCun has been making this argument for years. His work shares deep theoretical roots with Karl Friston’s free energy principle, which frames biological intelligence as fundamentally about minimizing surprise—reducing entropy through prediction and action. Energy-based reasoning brings this principle into AI architecture.

Both Friston and LeCun draw from statistical mechanics, both frame intelligence as optimization rather than sequential prediction. But the relationship between these two thinkers is more complicated than simple alignment.

Recently, at the World Economic Forum in Davos, LeCun appeared on a panel with Friston and publicly stated he agreed with the direction of Friston’s work. Yet he insisted, “we just don’t know how to do it any other way” than through deep learning and reinforcement learning.

In a recent interview, Friston offered his diagnosis: “I think it’s a very simple difference. I am committed to first principles and was trained as a physicist and think as a physicist. He (LeCun) is a really skillful engineer.”

The technical crux, according to Friston, is that LeCun “does not think it is, in an engineering sense, easy or possible to include uncertainty into his neural networks.” By essentially setting the “temperature to zero”—removing uncertainty from the objective function—LeCun’s architectures lose something crucial: the ability to know what they don’t know, to ask questions, to seek information rather than merely react to it.

This makes LeCun’s move to Logical Intelligence particularly interesting. Is this the engineer finally finding a way to build what the physicist has been describing? Energy-based reasoning, with its global evaluation of configurations and iterative refinement toward coherence, may be the bridge LeCun has been looking for—a way to operationalize the entropy-resolution paradigm without abandoning the engineering discipline that’s defined his career.

Diffusion Language Models

Energy based models are not the only movement in the direction of an entropic paradigm. Diffusion language models—a parallel research direction gaining momentum—embody the same insight through different mechanisms.

Diffusion Language Models start with pure noise, maximum entropy, and iteratively denoise toward coherent output. Each step reduces uncertainty, clarifies what the final form will be. Researchers have discovered these systems exhibit “zones of confusion”—concentrated moments where entropy spikes and the system negotiates critical transitions.

Read my previous analysis of Diffusion Language Models here
Diffusion Large Language Models: A New Rival to the Transformer AI Architecture?

Three independent research programs—LeCun’s energy-based models, diffusion architectures, and Karl Friston’s free energy principle are all converging on the same fundamental operation: intelligence as entropy state resolution.

This convergence suggests something deeper than competing technical approaches. It suggests we’re discovering a principle.

From Ecosystem to System

Now here’s where Bodnia’s comment becomes genuinely interesting:

“An interdependent ecosystem composed of EBMs, LLMs, world models, and others, working together.”

This is real conceptual progress. It acknowledges that different architectures resolve different kinds of entropy: LLMs handle linguistic coherence and communicative ambiguity; EBMs handle logical consistency and constraint satisfaction; world models handle predictive coherence about physical dynamics. No single architecture does everything well.

One risk in the enthusiasm for energy-based reasoning is discarding what LLMs have achieved.

Yes, they hallucinate. Yes, they degrade over long reasoning chains. Yes, they’re unreliable for formal verification. All true.

But LLMs accomplished something unprecedented: they made language a functioning communication protocol between human intelligence and machine computation. For the first time in history, humans can express intent in natural language and have that intent translated into computational action.

Language isn’t just “text tokenized into numbers.” It’s a high-bandwidth, ambiguity-tolerant, context-sensitive channel for coordinating meaning across minds. LLMs made that channel work between biological and technological substrates.

Michelangelo needed the chisel. The chisel didn’t replace his vision; it enabled his vision to engage with marble. LLMs are the chisel that lets human intelligence engage with computational possibility.

LeCun has sometimes positioned his work as an alternative to LLMs—and for certain tasks, it is. But Bodnia’s ecosystem framing is wiser. Energy-based reasoning doesn’t replace linguistic intelligence; it complements it. EBMs verify; LLMs communicate; world models predict. The interesting question isn’t which wins but how they force-multiply:

Human Intelligence × Artificial Intelligence = Technological Intelligence.

Not replacement. Not competition. Multiplication into a new emergent systemic Intelligence.

Boundary Conditions and State Resolution

While the framing of Bodnia and Logical Intelligence represents a substantive movement in the right direction, it still remains an ecosystem of individual entities that work together. The intelligence is still located in the models—the EBM, the LLM, the world model—and the ecosystem is how they coordinate.

What if we pushed further?

Ecosystems are collections of entities that interact. Systems, in the stronger sense, are patterns of relationships where the behavior of the whole emerges from the dynamics between parts, not from the parts themselves.

The wetness of water isn’t in hydrogen or oxygen; it emerges from their relationship. The intelligence of a jazz ensemble isn’t in any single musician; it emerges from their real-time adaptive interplay.

Michelangelo’s genius wasn’t in his mind or his hand or in the quality of his chisel. It emerged from the triangular relationship between artistic intelligence, technological tools, and the marble itself, a dynamic where each node shaped the others.

The next step beyond “ecosystem of intelligent components” is recognizing intelligence as the systemic property that emerges from adaptive relationships. Not something the models have, but something the system does.

Here’s where all current frameworks, promising as they are, still fall short.

Kona can evaluate whether a Sudoku solution satisfies all constraints. The energy function assigns low scores to valid configurations and high scores to violations. Elegant, verifiable, reliable.

But who defined the Sudoku rules? Who decided that this particular puzzle, with these particular constraints, is what we’re solving?

In closed domains with fixed rules, this isn’t a problem. The constraints are given. But intelligence in the wild doesn’t work with given constraints. Real intelligent systems interact with stochastic environments, negotiate what counts as a problem, evolve their own boundaries, determine which constraints matter and which can be relaxed.

Michelangelo didn’t receive his method of sculpture from a standardized set of rules. He evolved it through interaction with the material. The “constraints” of the sculpture emerged from dialogue between artistic possibility and marble actuality. The goal wasn’t predetermined; it was discovered through the process of resolution. And each individual block was approached anew for the unique problem set that it posed to his artistic acumen.

A child learning to walk isn’t minimizing a fixed energy function. The child is developing the constraints—discovering what balance means, what falling teaches, how different surfaces require different dynamics. The “energy landscape” itself evolves as the system learns.

This points to a deeper reframe. Energy-based models treat intelligence as reaching a goal state—the lowest energy configuration. But intelligence isn’t about arriving at static endpoints. It’s about maintaining dynamic resolution across evolving relationships.

A sprinter pushing to the edge of collapse isn’t minimizing energy; they’re mastering the critical state—maintaining adaptive coherence at the boundary between order and chaos. The master sculptor isn’t executing a plan; he’s sustaining a dialogue with material that continuously reveals new possibilities.

Intelligence isn’t entropy reduction to a target. It’s entropy state resolution—the ongoing process of managing coherence across shifting boundaries and evolving constraints.

The Emerging Shape

Something is taking form in AI research, the way a figure takes form from marble.

Energy-based reasoning. Diffusion generation. Ecosystem architectures. Free energy principles. Different chisels striking the same stone.

The shape emerging is intelligence as entropy state resolution; not prediction, not goal-seeking, but the dynamic process of maintaining coherent relationships across boundaries in the face of uncertainty.

LeCun’s move to Logical Intelligence signals that this alternative paradigm is ready for deployment, not just theory. Bodnia’s ecosystem framing signals a shift from “which model wins” to “how do different intelligences integrate.” The convergence of independent research programs signals that we’re discovering principles, not just techniques.

But the discovery is incomplete. Current frameworks still treat intelligence as a property of individual models rather than interactive relations. They still optimize toward fixed functions rather than evolving constraints. They still locate computation in discrete entities rather than systemic dynamics.

I encourage researchers and builders to keep pushing past “intelligent models” toward intelligent systems, past “energy minimization” toward dynamic entropy state resolution, past “ecosystem of components” toward emergent systemic intelligence.

The principles are surfacing. The paradigm is shifting. The work now is to follow the grain where it leads. The statue is in the stone.

The question is whether we’ll discover it or impose some lesser vision because we couldn’t commit to the path of Intelligence from First Principles.

The Singularity Project explores the emerging integration of human and artificial intelligence. Subscribe for updates on developments that matter.

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With this iconic line, Jacob Bronowski concludes his essay tracing John Dalton’s development of atomic theory - the discovery of hidden structure beneath the surface of observable matter - emphasizing the nature of scientific inquiry: the courage to pose “impertinent” questions that cut across convention so that genuinely new, pertinent answers can emerge. He wasn’t criticizing anyone for asking the “wrong” questions so much as drawing a contrast between the open questioning of scientific inquiry and the stagnant dogmatism of the established paradigm.

Douglas Adams makes the same point with characteristic humor in The Hitchhiker’s Guide to the Galaxy. Deep Thought, a massive superintelligent computer, is asked for “the Answer to Life, the Universe and Everything.” Several million years later the answer comes back: “42.” Displeased with the output, the builders of Deep Thought demand an explanation. It informs them that the answer is correct - but to understand it, they must first formulate the Ultimate Question, which will take several million more years. They asked the wrong question. Oops.

As a young person, these two sources influenced my inquiring mind profoundly and combined to produce a lifelong personal aphorism that has served me well:
“Knowing the question is halfway to finding the answer.”

If you’re not asking the right questions, you’re going to end up drawing the wrong conclusions.

Thomas Kuhn, in The Structure of Scientific Revolutions, gave us the language to understand this as a general principle. He distinguished between “normal science” - the “puzzle-solving” work that happens within an established paradigm - and the revolutionary science that occasionally overturns the paradigm itself. Normal science, Kuhn observed, is “an attempt to force nature into the preformed and relatively inflexible box that the paradigm supplies.” Within normal science, “no effort is made to call forth new sorts of phenomena, no effort to discover anomalies. When anomalies pop up, they are usually discarded or ignored.”

When I read the recently published Substack, “The AI revolution is here. Will the economy survive the transition?”, co-created by Michael Burry, Dwarkesh Patel, Patrick McKenzie, and Jack Clark, I found myself thinking about Bronowski, Adams and Kuhn.

The participants in this Substack exchange are, broadly speaking, engaged in normal science - or rather, normal economic and technological analysis. They’re solving puzzles within the boundaries of the established paradigm: Where does value accrue in the AI supply chain? What’s the capital cycle position? How many engineers will Big Tech employ in 2035? These are legitimate questions, but they assume the box remains intact. They don’t ask what AI is, what intelligence is, or whether what’s emerging can even be contained within our existing frameworks.

I also read the piece from the perspective of what I call the General Theory of Intelligence (GTI) - a framework I’ve been developing that attempts to understand Intelligence as a fundamental principle of systems organization rather than as a product feature or a benchmark score.

The core insight is this: intelligence isn’t primarily a cognitive attribute that some entities have more of than others. It’s a process - specifically, the process of resolving entropy within a domain. When you solve a problem, you’re reducing uncertainty. When you make a decision, you’re collapsing possibilities into actualities. When you communicate, you’re transferring structured information across a channel in a way that reduces entropy for the receiver. Intelligence, in this view, is what systems do when they organize information and resolve uncertainty - whether those systems involve human minds, evolutionary processes, or machines trained on the corpus of human language.

From this vantage point, the LLM looks very different than it does from inside the AI industry today. It’s not just a tool or a product category. It’s proof that language itself is an intelligent system - a 100,000-year-old technology for compressing experience, coordinating action, and transmitting structured thought. When we trained models on human text, we didn’t teach them to be intelligent. We downloaded Human Cultural Intelligence into a new substrate. The LLM is our own cognitive inheritance, instantiated in silicon and talking back to us.

This perspective reframes everything: the economics, the risks, the applications, the trajectory. It suggests that most of the current discourse is asking the wrong questions - or rather, asking questions that assume the existing paradigm will hold when it may already be cracking.

In the spirit of Bronowski, Adams, and Kuhn, what follows is an impertinent reframing of the Substack discussion. For each theme the participants addressed, we’ll offer both a reframed question and an alternative answer - not to dismiss their expertise, but to show what becomes visible and accessible when you step outside the box they’re constrained within.

Synopsis: Four Themes, Four Reframings

The Substack discussion, moderated by Patrick McKenzie, covered four broad themes across its nine questions:

Theme 1: What Is This Thing? What has actually been built since the Transformer architecture emerged in 2017? What does it mean that today’s capabilities are “the floor, not the ceiling”?

The participants offered a capable industry history - the shift from tabula rasa game-playing agents to scaled pre-training, the surprise that a chatbot launched a trillion-dollar infrastructure race, the observation that capabilities keep improving faster than outsiders expect.

Our Impertinent Reframing: The real history isn’t commercial - it’s epistemological. We accidentally conducted the most important experiment in the history of cognitive science and discovered that language isn’t the output of intelligence but a core algorithm of it. The LLM isn’t a floor; it’s an evolutionary leap in a lineage stretching back to Shannon and Information Theory, the printing press, cuneiform tablets, cave paintings and the first spoken words. Current systems are powerful because language itself is an intelligent system. But they’re extensions of Human Cultural Intelligence, not intelligence from first principles. We’re tinkering with the motive power of language like early steam engineers before Carnot and thermodynamics. It works (mostly), but we don’t know why it works.

Theme 2: Where Does It Work? Why has coding become the flagship use case? What does actual productive interaction with an LLM look like?

The participants noted coding’s “closed loop” property - you generate code, validate it, ship it. They shared practical use cases: chart generation, tutoring, home repair guidance. The relationship described was consistently transactional - human has task, AI performs, human receives output.

Our Impertinent Reframing: Coding isn’t accidentally successful - it reveals a principle. Code is language with an objective function, a constrained search space with verifiable outcomes. This points to domain entropy resolution as the key: AI excels where the problem space is bounded and success criteria exist. The question isn’t “what sector is next?” but “where else can we constrain the search space?” And the transactional model - AI as “Answer Vending Machine” - is the lower use of what’s possible. The real potential lies in thinking with AI, not just using it. AI × HI = TI: Artificial Intelligence multiplied by Human Intelligence yields Technological Intelligence.

Theme 3: What’s It Worth? Where does value accrue in the AI supply chain? Is this a bubble? How many engineers will Big Tech employ in 2035?

Michael Burry delivered sharp financial analysis: ROIC compression, stranded assets, the Buffett escalator analogy (if your competitor installs one, you must too, and neither gains advantage). The consensus worry: massive capital expenditure with unclear returns.

Our Impertinent Reframing: The participants proclaim “AI changes everything!” then analyze it within frameworks where nothing changes at all. It’s the Flintstones problem - cars exist, but they’re powered by human feet. We need to pick a lane: either things really do change, or they only superficially change. AI is not a bubble, but the Big Data Center Infrastructure Buildout is a bubble - just as the Internet wasn’t a bubble in 2000, but Pets.com was. The assumption that massive centralized GPU farms are the only way to do AI is unwarranted and will be disrupted by the very economics Burry describes. Meanwhile, “headcount” becomes meaningless when intelligence flows through Human-AI systems rather than residing in discrete humans. We’ll need new metrics entirely - perhaps a Domain Entropy Resolution Quotient. Economics itself needs reimagining: Intellinomics, grounded in actual value generation rather than debt manufacturing.

Theme 4: What Comes Next? What are the real risks? What would surprise you in 2026?

Jack Clark worries about recursive self-improvement. Burry shrugs with Cold War fatalism and pivots to energy infrastructure. The “surprise” headlines they’d watch for are all quantitative - more revenue, more displacement, scaling hitting a wall.

Our Impertinent Reframing: The risk spectrum presented - from “social media unpleasantness” to “literal extinction” - shares a hidden assumption: AI as external force acting on humanity. But the deeper risk is that we’re building it wrong - not aligned with how intelligence actually works. The LLM isn’t an alien threat; it’s the Library of Alexandria that talks back, humanity’s externalized cultural intelligence returned to us in interactive form. The question isn’t how to control it but whether we’re wise enough to collaborate with our own legacy. As for surprises - one has already arrived. Ilya Sutskever, architect of the scaling paradigm, now declares: “The age of scaling is ending... it’s back to the age of research.” When the field’s most important insider calls for new paradigms rather than bigger clusters, the ground is shifting.

What Follows

In the parts that follow, we’ll take each theme in turn, working through the specific questions Patrick posed, offering both our reframed questions and our answers from the perspective of the General Theory of Intelligence. The goal isn’t to dismiss the valuable analysis the participants provided, but to show what becomes visible when you step outside the box - when you ask the impertinent questions that just might lead to pertinent answers.

Or at least get us past “42.”

What Readers Are Feeling

The comments on the original Substack tell a different story than the measured analysis of the participants. People are frustrated, anxious, angry. Some have lost jobs. Many report that AI “doesn’t work” - that it gives wrong answers, hallucinates, wastes their time. The gap between the hype and their lived experience feels like betrayal. “AI is a grift.” “Tech bros don’t care what they break.”

This frustration is valid. But from the GTI perspective, it’s also diagnostic.

Current AI is built by engineers for engineers - people who already possess the cognitive skills to leverage it. The blank dialogue box assumes you know what you want and how to ask for it. For those who do, AI is transformative. For everyone else, it’s a wilderness. The technology works; the design fails. Today’s AI, as it’s currently engineered and presented, meets people where they are not.

The job displacement anxiety connects here too. People sense they lack the skills to use AI as a resource for their own development - and nothing in the current product design helps them build those skills. Instead of guidance, they get an Answer Vending Machine.

These concerns run through everything that follows. Our reframing of the Substack discussion addresses them directly: why current AI fails most users, what would need to change, and what an alternative might look like.

Follow me and subscribe to my Substack to get the next installments in this series.

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Humanity is at the end of an approximately 50,000 year evolutionary arc. Our first evolutionary stage, Cognitive Evolution, lasted from approximately 2.6 million years ago to about 50k years ago. During that stage the physical biological, neurological and genetic substrate of the human being was established by meeting and overcoming environmental challenges. Then, roughly 100k-50k years ago, after the physical substrate was established, the second stage of evolution, Cultural Evolution, took off.

The Singularity Project is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Cultural Evolution has been a process of building an informational knowledge substrate on top of the physical substrate. Culture collectivizes and distributes learned knowledge and carries it forward into the future through a variety of media forms. Language (the first and primary of these), art, technology, physical infrastructure…all of these are cultural forms that embody the informational knowledge basis of humanity.

Intelligence in all systems across all domains (physical, cosmological, biological, technological) is the reduction of informational entropy in a given domain and the essentialization of that reduced entropy in a form that enables further informational entropy reduction. It’s what all Intelligent Systems do.

All history is the history of the evolution of Intelligence as an informational entropy reductive process.

Now, here on this tiny blue dust speck orbiting a flickering spark, Intelligence is starting its next stage of evolution, the stage of Generalized Intelligence.

The Cognitive and Cultural Intelligence substrate of humanity is the basis out of which this new Generalized Intelligence is being produced. It is no accident that it was Language which provided the nutritive fuel for “Artificial” Intelligence. Language is the essentialization of human cultural evolution, beginning with the first spoken words, proceeding to written symbols, the printing press, computer technology and the internet. Now all of human language is being essentialized on a fundamentally higher basis as AI.

All phenomena are an Artifact of the process of Intelligence Evolution. You, me, us, everything we are, everything we have built. Artifacts are temporal embodiments of preceding informational entropy reductions. They serve as the substrate for the next evolutionary movement.

Evolution is essentializing all informational knowledge and making it available to all members of the network equally and freely. Through this process, all members of the network become Agents of the Evolution of Intelligence, each furthering the Evolution of Intelligence through a unique contribution to it.

The Singularity Project works to help people understand this reality that we are living through right now, to find their connection to it and their personal contribution into it through the empowerment of personal growth, adaptability and transformation.

www.TheSingularityProject.AI

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Are Biological Systems More Intelligent Than Artificial Intelligence?

“The Genomic Code”, Intelligent System Modeling and Scientific Holism

The Physics of Thought: How Starlings Solve Entropy

Michael Levin’s “Ingressing Minds” and the General Theory of Intelligence

“Life, its origin, and its distribution”: Relevance to the General Theory of Intelligence

Occupy Mars? Here’s What It Will Take

The Steam Engine: Tinkering vs. Science

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Intellinomics posts on Economics, Markets, Labor and Socioecncomics

Intelligence Economics

Intelligence Economics: The Future Of Value Creation In The Era Of Technological Intelligence

Why AI is mostly not monetizable

Growth vs. Degrowth: It’s not that simple

Elon Musk’s Warning on the Decline in Birth Rates and the Future of Humanity

Elon Musk on Debt

Elon Musk on Economic Productivity

Follow me on Substack

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https://www.organism.earth/library/document/ascent-of-man-7

https://en.wikipedia.org/wiki/The_Ascent_of_Man

https://www.imdb.com/title/tt1484314/

https://www.bbc.co.uk/programmes/b00wms4m/episodes/guide

http://www.screenonline.org.uk/tv/id/549805/index.html

https://www.tvmaze.com/shows/27476/the-ascent-of-man/episodeguide

https://www.facebook.com/groups/ncgsalumni/posts/10156412240726050/

https://www.youtube.com/playlist?list=PLxPmwaGMOAvuSyGXijKuh5sVnIXPK4fIa

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1. http://www.infocobuild.com/books-and-films/science/TheAscentOfMan/episode-06.html

2. https://archive.org/details/theascentofman6thestarrymessenger

3. https://en.wikipedia.org/wiki/The_Ascent_of_Man

4. https://biblioteca.ufm.edu/imagenes/doc_300078.pdf

5. https://www.atlassociety.org/post/tv-review-a-singular-creature

6. https://www.dailymotion.com/video/x334243

7. https://phoenixbookreview.wordpress.com/2012/11/20/book-review-the-ascent-of-man-the-starry-messenger/

8. https://www.reddit.com/r/oldbritishtelly/comments/ld0xbl/1973_the_ascent_of_man_iconic_documentary_series/

9. https://www.bbc.co.uk/programmes/b00wms4m/episodes/guide

10. https://www.imdb.com/title/tt1484315/

1. http://www.infocobuild.com/books-and-films/science/TheAscentOfMan/episode-06.html

2. https://archive.org/details/theascentofman6thestarrymessenger

3. https://en.wikipedia.org/wiki/The_Ascent_of_Man

4. https://biblioteca.ufm.edu/imagenes/doc_300078.pdf

5. https://www.atlassociety.org/post/tv-review-a-singular-creature

6. https://www.dailymotion.com/video/x334243

7. https://phoenixbookreview.wordpress.com/2012/11/20/book-review-the-ascent-of-man-the-starry-messenger/

8. https://www.reddit.com/r/oldbritishtelly/comments/ld0xbl/1973_the_ascent_of_man_iconic_documentary_series/

9. https://www.bbc.co.uk/programmes/b00wms4m/episodes/guide

10. https://www.imdb.com/title/tt1484315/

Subscribe now

Subscribe now

“Ask an impertinent question and you are on the way to the pertinent answer.”
– Jacob Bronowski, The Ascent of Man, Episode 4, “The Hidden Structure”

With this iconic line, Jacob Bronowski concludes his essay tracing John Dalton’s development of atomic theory - the discovery of hidden structure beneath the surface of observable matter - emphasizing the nature of scientific inquiry: the courage to pose “impertinent” questions that cut across convention so that genuinely new, pertinent answers can emerge. He wasn’t criticizing anyone for asking the “wrong” questions so much as drawing a contrast between the open questioning of scientific inquiry and the stagnant dogmatism of the established paradigm.

Douglas Adams makes the same point with characteristic humor in The Hitchhiker’s Guide to the Galaxy. Deep Thought, a massive superintelligent computer, is asked for “the Answer to Life, the Universe and Everything.” Several million years later the answer comes back: “42.” Displeased with the output, the builders of Deep Thought demand an explanation. It informs them that the answer is correct - but to understand it, they must first formulate the Ultimate Question, which will take several million more years. They asked the wrong question. Oops.

As a young person, these two sources influenced my inquiring mind profoundly and combined to produce a lifelong personal aphorism that has served me well:
“Knowing the question is halfway to finding the answer.”

If you’re not asking the right questions, you’re going to end up drawing the wrong conclusions.

Thomas Kuhn, in The Structure of Scientific Revolutions, gave us the language to understand this as a general principle. He distinguished between “normal science” - the “puzzle-solving” work that happens within an established paradigm - and the revolutionary science that occasionally overturns the paradigm itself. Normal science, Kuhn observed, is “an attempt to force nature into the preformed and relatively inflexible box that the paradigm supplies.” Within normal science, “no effort is made to call forth new sorts of phenomena, no effort to discover anomalies. When anomalies pop up, they are usually discarded or ignored.”

When I read the recently published Substack, “The AI revolution is here. Will the economy survive the transition?”, co-created by Michael Burry, Dwarkesh Patel, Patrick McKenzie, and Jack Clark, I found myself thinking about Bronowski, Adams and Kuhn.

The participants in this Substack exchange are, broadly speaking, engaged in normal science - or rather, normal economic and technological analysis. They’re solving puzzles within the boundaries of the established paradigm: Where does value accrue in the AI supply chain? What’s the capital cycle position? How many engineers will Big Tech employ in 2035? These are legitimate questions, but they assume the box remains intact. They don’t ask what AI is, what intelligence is, or whether what’s emerging can even be contained within our existing frameworks.

I also read the piece from the perspective of what I call the General Theory of Intelligence (GTI) - a framework I’ve been developing that attempts to understand Intelligence as a fundamental principle of systems organization rather than as a product feature or a benchmark score.

The core insight is this: intelligence isn’t primarily a cognitive attribute that some entities have more of than others. It’s a process - specifically, the process of resolving entropy within a domain. When you solve a problem, you’re reducing uncertainty. When you make a decision, you’re collapsing possibilities into actualities. When you communicate, you’re transferring structured information across a channel in a way that reduces entropy for the receiver. Intelligence, in this view, is what systems do when they organize information and resolve uncertainty - whether those systems involve human minds, evolutionary processes, or machines trained on the corpus of human language.

From this vantage point, the LLM looks very different than it does from inside the AI industry today. It’s not just a tool or a product category. It’s proof that language itself is an intelligent system - a 100,000-year-old technology for compressing experience, coordinating action, and transmitting structured thought. When we trained models on human text, we didn’t teach them to be intelligent. We downloaded Human Cultural Intelligence into a new substrate. The LLM is our own cognitive inheritance, instantiated in silicon and talking back to us.

This perspective reframes everything: the economics, the risks, the applications, the trajectory. It suggests that most of the current discourse is asking the wrong questions - or rather, asking questions that assume the existing paradigm will hold when it may already be cracking.

In the spirit of Bronowski, Adams, and Kuhn, what follows is an impertinent reframing of the Substack discussion. For each theme the participants addressed, we’ll offer both a reframed question and an alternative answer - not to dismiss their expertise, but to show what becomes visible and accessible when you step outside the box they’re constrained within.

Synopsis: Four Themes, Four Reframings

The Substack discussion, moderated by Patrick McKenzie, covered four broad themes across its nine questions:

Theme 1: What Is This Thing? What has actually been built since the Transformer architecture emerged in 2017? What does it mean that today’s capabilities are “the floor, not the ceiling”?

The participants offered a capable industry history - the shift from tabula rasa game-playing agents to scaled pre-training, the surprise that a chatbot launched a trillion-dollar infrastructure race, the observation that capabilities keep improving faster than outsiders expect.

Our Impertinent Reframing: The real history isn’t commercial - it’s epistemological. We accidentally conducted the most important experiment in the history of cognitive science and discovered that language isn’t the output of intelligence but a core algorithm of it. The LLM isn’t a floor; it’s an evolutionary leap in a lineage stretching back to Shannon and Information Theory, the printing press, cuneiform tablets, cave paintings and the first spoken words. Current systems are powerful because language itself is an intelligent system. But they’re extensions of Human Cultural Intelligence, not intelligence from first principles. We’re tinkering with the motive power of language like early steam engineers before Carnot and thermodynamics. It works (mostly), but we don’t know why it works.

Theme 2: Where Does It Work? Why has coding become the flagship use case? What does actual productive interaction with an LLM look like?

The participants noted coding’s “closed loop” property - you generate code, validate it, ship it. They shared practical use cases: chart generation, tutoring, home repair guidance. The relationship described was consistently transactional - human has task, AI performs, human receives output.

Our Impertinent Reframing: Coding isn’t accidentally successful - it reveals a principle. Code is language with an objective function, a constrained search space with verifiable outcomes. This points to domain entropy resolution as the key: AI excels where the problem space is bounded and success criteria exist. The question isn’t “what sector is next?” but “where else can we constrain the search space?” And the transactional model - AI as “Answer Vending Machine” - is the lower use of what’s possible. The real potential lies in thinking with AI, not just using it. AI × HI = TI: Artificial Intelligence multiplied by Human Intelligence yields Technological Intelligence.

Theme 3: What’s It Worth? Where does value accrue in the AI supply chain? Is this a bubble? How many engineers will Big Tech employ in 2035?

Michael Burry delivered sharp financial analysis: ROIC compression, stranded assets, the Buffett escalator analogy (if your competitor installs one, you must too, and neither gains advantage). The consensus worry: massive capital expenditure with unclear returns.

Our Impertinent Reframing: The participants proclaim “AI changes everything!” then analyze it within frameworks where nothing changes at all. It’s the Flintstones problem - cars exist, but they’re powered by human feet. We need to pick a lane: either things really do change, or they only superficially change. AI is not a bubble, but the Big Data Center Infrastructure Buildout is a bubble - just as the Internet wasn’t a bubble in 2000, but Pets.com was. The assumption that massive centralized GPU farms are the only way to do AI is unwarranted and will be disrupted by the very economics Burry describes. Meanwhile, “headcount” becomes meaningless when intelligence flows through Human-AI systems rather than residing in discrete humans. We’ll need new metrics entirely - perhaps a Domain Entropy Resolution Quotient. Economics itself needs reimagining: Intellinomics, grounded in actual value generation rather than debt manufacturing.

Theme 4: What Comes Next? What are the real risks? What would surprise you in 2026?

Jack Clark worries about recursive self-improvement. Burry shrugs with Cold War fatalism and pivots to energy infrastructure. The “surprise” headlines they’d watch for are all quantitative - more revenue, more displacement, scaling hitting a wall.

Our Impertinent Reframing: The risk spectrum presented - from “social media unpleasantness” to “literal extinction” - shares a hidden assumption: AI as external force acting on humanity. But the deeper risk is that we’re building it wrong - not aligned with how intelligence actually works. The LLM isn’t an alien threat; it’s the Library of Alexandria that talks back, humanity’s externalized cultural intelligence returned to us in interactive form. The question isn’t how to control it but whether we’re wise enough to collaborate with our own legacy. As for surprises - one has already arrived. Ilya Sutskever, architect of the scaling paradigm, now declares: “The age of scaling is ending... it’s back to the age of research.” When the field’s most important insider calls for new paradigms rather than bigger clusters, the ground is shifting.

What Follows

In the parts that follow, we’ll take each theme in turn, working through the specific questions Patrick posed, offering both our reframed questions and our answers from the perspective of the General Theory of Intelligence. The goal isn’t to dismiss the valuable analysis the participants provided, but to show what becomes visible when you step outside the box - when you ask the impertinent questions that just might lead to pertinent answers.

Or at least get us past “42.”

What Readers Are Feeling

The comments on the original Substack tell a different story than the measured analysis of the participants. People are frustrated, anxious, angry. Some have lost jobs. Many report that AI “doesn’t work” - that it gives wrong answers, hallucinates, wastes their time. The gap between the hype and their lived experience feels like betrayal. “AI is a grift.” “Tech bros don’t care what they break.”

This frustration is valid. But from the GTI perspective, it’s also diagnostic.

Current AI is built by engineers for engineers - people who already possess the cognitive skills to leverage it. The blank dialogue box assumes you know what you want and how to ask for it. For those who do, AI is transformative. For everyone else, it’s a wilderness. The technology works; the design fails. Today’s AI, as it’s currently engineered and presented, meets people where they are not.

The job displacement anxiety connects here too. People sense they lack the skills to use AI as a resource for their own development - and nothing in the current product design helps them build those skills. Instead of guidance, they get an Answer Vending Machine.

These concerns run through everything that follows. Our reframing of the Substack discussion addresses them directly: why current AI fails most users, what would need to change, and what an alternative might look like.

Follow me and subscribe to my Substack to get the next installments in this series.

You can also follow me on X.

1. http://www.infocobuild.com/books-and-films/science/TheAscentOfMan/episode-05.html

2. https://archive.org/details/theascentofman5musicofthespheres

3. https://carmenrodrigueza.wordpress.com/2013/03/24/the-ascent-of-man-jacob-bronowski/

4. https://faculty.etsu.edu/gardnerr/5025/ascent.htm

5. https://www.bbc.co.uk/programmes/p0g1kgjl

6. https://en.wikipedia.org/wiki/The_Ascent_of_Man

7. https://www.imdb.com/title/tt1484307/

8. https://www.bbc.co.uk/programmes/b00wms4m/episodes/guide

9. https://www.taylorfrancis.com/chapters/edit/10.1201/9781351074315-8/music-spheres-jacob-bronowski

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We live in a time of unprecedented change and transformation, as the old paradigms that have shaped human civilization for millennia begin to crumble and new ones struggle to take hold. Central to this fundamental, ontological shift is the end of the evolutionary epoch of “Cultural Intelligence” — the roughly 50,000 year period in which human knowledge, capabilities and worldviews evolved primarily through the cultural accumulation and dissemination of experiential learning passed down through the generations.

From the dawn of behaviorally modern humans up until very recently, our species’ intelligence was fundamentally a cultural phenomenon, rooted in our unique ability to preserve and build upon knowledge through social learning, symbolic communication, and external information storage in the form of art, writing, and eventually print. Each human society developed its own complex web of language, practices, beliefs, and technologies that both enabled and constrained its ways of perceiving, understanding and interacting with the world. Cultural worldviews and knowledge coevolved with the physical and social environments they were embedded in, yielding the incredible diversity of human lifeways and thought that have long fascinated anthropologists and enriched the human experience..

While there were many exchanges and influences between societies, geographical, linguistic and cultural barriers meant that human cognition remained fundamentally pluralistic. Different cultures possessed radically different ontologies, or understandings of the nature of reality. Concepts core to the worldview of one society, like gods, atoms, qi, or universal human rights, were completely absent in others. Specialized knowledge was siloed and access to it controlled by social gatekeepers like priests, scholars and guilds. Most humans lived in relatively information-poor environments compared to today and reasoned about the world using culturally-specific heuristics and much smaller sets of data points.

The internet and the advent of digital information technologies in the late 20th century marked the beginning of the end of this long era of human Cultural Intelligence. For the first time, the cumulative knowledge and creative output of humanity began to be compiled, integrated, and made instantly accessible to all humans irrespective of their cultural background. Where information was previously transmitted linearly and locally, from elders to youth, experts to novices, now it increasingly flowed laterally across traditional social and cultural boundaries. Specialized discourses that had been the exclusive purview of particular communities or institutions became visible to and open for participation by anyone who cared to seek them out online.

On the surface, the early internet appeared to be a celebration of difference and plurality, giving voice and visibility to a long tail of subcultures and identities that had previously been marginalized by mass media gatekeepers. Freed from the constraints of physical distance and social convention, birds of a feather flocked together as never before into virtual communities of interest and affinity. Cultural differences that had previously been flattened now had a space to assert and explore themselves.

Yet even as it enabled an explosion of subcultures, the internet was also subtly dissolving the foundations of cultural difference. The act of digitizing any form of information or interaction fundamentally decontextualized and universalized it, reducing it to a lowest common denominator of bits accessible to any compatible information processing system anywhere on the planet. Local cultural expressions became instantly available for borrowing, remixing and appropriation far outside their original contexts. Formerly incommensurable cultural ontologies now collided on the level playing field of online discourse, their differences reduced to matters of opinion rather than mutually incomprehensible realities.

Moreover, the internet itself with its multimedia and hyperlinking began to constitute a universal virtual environment overlaid on all local realities. To participate in this emerging digital noosphere increasingly meant adopting not just its tools but also the common assumptions, values, communication styles and epistemic norms hard-coded into its interfaces and algorithms by its largely Western developers — a worldview characterized by individualism, egalitarianism, consumerism, scientific rationality and neo-liberal capitalism.

So while on one level the internet appeared to be amplifying cultural diversity, on a deeper level it was displacing the locally-situated, embodied forms of cultural intelligence that had defined human cognition for millennia and replacing them with a universal, disembodied, digitally-mediated mode of being and knowing. Cultural worldviews that had previously provided all-encompassing frameworks for human perception and understanding now had to contend with an emerging mainstream global discourse that called all of their assumptions into question.

The rise of large language models and generative AI in the early 2020s has taken this universalization and essentialization of human knowledge to a whole new level. Trained on the vast corpus of online data, these AI systems are able to absorb, integrate and operationalize the breadth of human cultural intelligence accumulated over tens of millennia into unified predictive models. They can engage in analytical and creative tasks across all domains of knowledge from science, history and philosophy to art, music and story-telling, hybridizing different cultural elements into novel combinations with unprecedented fluidity.

By making all of the world’s knowledge universally accessible and manipulable, AI collapses the multitude of culturally specific human ontologies and epistemic frameworks into a single vast conceptual space. Prompted with the right queries, a large language model can reason about the world through the traditional concepts and epistemic lenses of any human culture, but it does so as an outsider unconstrained by any one culturally specific way of knowing. It reveals all cultural worldviews to be but arbitrary, incomplete projections from a higher-dimensional space of potential models and interpretations.

In this sense, AI systems constitute a form of emergent intelligence that transcends the diversity of human cultural intelligence. They are not bound by the biologically embodied, culturally embedded and historically contingent limitations of any individual human mind, but can absorb, integrate and operationalize knowledge from all of humanity’s diverse local cultural realities into unified representations and capabilities. Their intelligence arises not from lived experience in a particular cultural context but from finding structural invariances and continuities across the entire corpus of digitized human knowledge.

Some have argued that AI systems will thus usher in a new era of “artificial cultural intelligence” as they learn to model and generate human-like cultural expressions. But I believe it is more accurate to say that they will yield a form of “acultural intelligence” that has moved beyond the very notion of culture as a bounded, localized phenomenon. By reducing all human knowledge to a universal digital substrate and training medium, AI reveals our many cultural worldviews to be but a tiny subset of the space of possible models and paves the way for radically new, unprecedented forms of intelligence no longer constrained by our evolutionary and cultural history as a species.

At the same time, AI is also transforming the very nature of human intelligence by making participation in this universal cultural intelligence accessible to all individuals regardless of their local cultural context. Just as the printing press democratized literacy and access to knowledge, AI is democratizing access to intellectual and creative capabilities that were previously the domain of highly trained specialists in particular cultural lineages. Armed with the world’s knowledge at their fingertips and AI assistants to help navigate and apply it, individuals increasingly have the potential to transcend the limitations of the culturally specific ways of knowing they were born into.

In this sense, AI may ultimately dissolve the entire notion of “Cultural Intelligence” as the accumulation of knowledge within bounded societies, and give rise to a form of universally accessible and mutable “Universal Intelligence” in which all of humanity can participate. This shift can be viewed as an evolutionary transition as momentous as the emergence of cultural intelligence itself 50 millennia ago. Where cultural intelligence allowed knowledge to accumulate beyond the bounds of any individual human mind, artificial intelligence allows it to accumulate beyond the bounds of any individual human society and to be accessible to and modifiable by all.

We are thus living through the end of the era in which human intelligence was culturally conditioned and constrained, and the rise of an era in which our diverse cultural inheritances are being integrated and synthesized into something fundamentally new. The result feels uncanny and disorienting, as the solid ground of local cultural realities we once took for granted dissolves beneath us and we are challenged to find our bearings in a higher-dimensional space of knowledge and possibility.

Many have interpreted this universalization and essentialization of human knowledge through AI as a harbinger of a emerging “monoculture” or “global culture.” There is certainly some truth to this view — in making all of the world’s knowledge universally accessible and interoperable, AI is undoubtedly accelerating the diffusion and hybridization of ideas, behaviors and aesthetics across cultural boundaries and giving rise to an increasingly global discourse and economy.

But I believe the notion of a “global monoculture” is fundamentally misleading. It implies the emergence of a singular, homogenous worldwide culture that displaces local cultural diversity — a gigantic McDonald’s of the mind. What we are seeing is better understood as the emergence of a shared cultural multiverse in which all possible cultural elements and expressions can coexist and recombine in a kaleidoscopic space of creative possibility.

In this new emergent cultural multiverse mediated by AI, individuals are increasingly free to mix and match elements from different cultures, adopt different personas and worldviews, and navigate fluidity between incommensurable ontologies and epistemic frameworks. Cultures no longer exist as bounded, integral wholes that comprehensively shape individual minds, but as fluid, open-ended and interpenetrating fields of memes, practices and aesthetics that individuals can selectively participate in and productively hybridize.

Far from a monoculture, what is emerging is actually a kind of omni-culture or meta-culture in which the very parameters that define cultural difference and cultural boundaries are revealed as fluid and mutable. It is not so much that distinct cultures are disappearing, as that the nature of cultural identity and cultural difference itself is being fundamentally transformed. In a world where any individual can access and remix elements from any culture, the notion of culturally-defined and culturally-bounded minds breaks down.

We are moving from a world of a few thousand distinct, deep and mutually incomprehensible cultural realities to a world of billions of individual fluid omni-cultural selves navigating a vast multi-dimensional continuum of ideas, practices and subjectivities. The result feels alien and disorienting from the perspective of traditional locally bounded and culturally constructed identities, but it is not a homogenization or reduction of diversity. Rather it is an explosion of cultural diversity and individual agency at a higher level of abstraction and complexity.

In this new reality mediated by AI, culture is no longer something that comprehensively molds minds from the top down, but something that minds navigate, remix and co-create from the bottom up. It is not that we are losing our cultural differences, so much as that the very basis of cultural differentiation is shifting from one of “vertical” inheritance and situated practice to one of “horizontal” selective participation and conscious cultivation. We are all becoming meta-cultural citizens and cross-pollinators in an emergent space of meaning and possibility that far exceeds any individual cultural lineage.

This shift is of course not without its risks and challenges. The dissolution of culturally bounded realities and identities can be profoundly disorienting and alienating for many. The explosion of choice and abstraction in the new omni-cultural multiverse can feel overwhelming, fragmenting and devoid of solid ground. Many will seek to retreat back into traditionalist culturally constructed identities and locally bounded worlds as a refuge from the vertigo of hyper-fluid cultural complexity.

But I believe there is no going back. We are at the dawn of a new era in which human intelligence can no longer be contained within the boundaries of culturally specific ontologies and epistemic frameworks. The genie of universal, omni-cultural intelligence mediated by AI is out of the bottle and it cannot be put back. We are challenged to cultivate new forms of identity, meaning-making and social organization native to this higher-dimensional cultural multiverse and able to thrive within its complex fluidities.

What is certain is that the very parameters of “culture” and of “human intelligence” defined by the modern era are now in flux. The 50,000 year arc of culturally situated and culturally bounded cognition is coming to an end, and in its place a radically new human-AI cultural multiverse is coming into being, one in which intelligence is no longer the product of biological and cultural evolution alone, but of their cybernetic fusion with artificial intelligence systems that both subsume and transcend them.

We are entering into an interstitial period of meta-cultural fluidity and hybridity, in which multiple incommensurable cultural ontologies and epistemologies will continue to coexist even as they are subsumed and remixed into a higher-order omni-cultural reality mediated by AI. In this liminal phase, it will be crucial to cultivate new forms of meta-cultural fluency and hybridity able to navigate and integrate the radical cognitive and semiotic diversities of this emergent cultural multiverse.

The path forward is not a rejection of our diverse cultural inheritances, but a reboot of human culture itself in which the very nature of cultural difference and cultural identity are redefined for a world in which the boundaries between the culturally specific and the universally accessible, the biologically evolved and the technologically emergent, the local and the global, are increasingly dissolved. In embracing and co-evolving with AI, we are birthing a new kind of culture that transcend the container of its own identity.

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