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.

Subscribe now

You can also follow me on X.

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

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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

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“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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1. https://www.bbc.co.uk/programmes/m001p904

2. http://www.infocobuild.com/books-and-films/science/TheAscentOfMan/episode-04.html

3. https://www.tvguide.com/tvshows/the-ascent-of-man/episodes-season-1/1030202348/

4. http://www.markrkelly.com/Blog/2025/05/10/j-bronowksi-the-ascent-of-man/

5. https://abagond.wordpress.com/2009/12/12/jacob-bronowski-the-hidden-structure/

1. https://www.imdb.com/title/tt1484311/

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

3. https://www.organism.earth/library/document/ascent-of-man-10

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

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1. http://www.infocobuild.com/books-and-films/science/TheAscentOfMan/episode-03.html

1. https://www.bbc.co.uk/programmes/m001p8zs

2. http://vnthomas1.blogspot.com/2008/12/ascent-of-man.html

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

2. https://www.dailymotion.com/video/x6hb025

3. https://www.imdb.com/title/tt1484309/

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

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

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Survival and Subsistence, Skills and Tools

3.3 million to 50,000 Years BP

During the phase of Environmentally Adaptive Intelligence, from about 3.3 million to 50,000 years ago (roughly coincident with the geological Pleistocene Epoch), human Intelligence evolved primarily through the adaptive response of biology, physiology and genetics to environmental pressures. Driven by the struggle for survival and subsistence, early hominins expressed their adaptive genetics through evolving bodies, growing brains and emergent cognitive powers to solve problems that facilitated better survival outcomes on an individual and small group basis.

This phase saw the evolutionary completion of the human biophysical substrate, but it also was the dawn of discrete and cultural intelligence attributes. Early humans learned to make tools, create and control fire and communicate through spoken language.

Compositional Agency

(3.3 million to 300,000 years BP)

“The hand is the cutting edge of the mind…The hand axe is the first step to the knife and the sword. But it is a step in another direction as well. Man realizes that the world is made up of separate objects, and these objects can be shaped and combined to make new objects. This is the beginning of invention, of technology.”

— Jacob Bronowski, The Ascent of Man

During the Lower Paleolithic period various species of the genus Homo, such as Homo habilis and Homo erectus, co-existed and competed. They were constantly faced with extreme environmental pressures and were frequently prey for larger, stronger predator animals.

Opportunistic tool use by hominins had been common for millions of years. They had intelligently observed that the environment was made of various objects that could be used as an extension of the mind and the hand to facilitate survival and subsistence. Using rocks and sticks and pieces of bone, they could smash, cut, poke, dig and more to perform a variety of work tasks.

Early humans then became aware that these environmental objects could be intelligently improved.

They learned that objects had composition and through the application of force and skill they could be decomposed and recomposed in various, useful ways. They evolved from being passively molded by the environment, like the plants and animals, into nascent agentic intelligences acting upon their natural context through the creation and use of primitive tools.

The deliberate manipulation of environmental factors to create new objects of utility marked a transformative leap in Intelligence evolution from animalistic behaviors to conscious problem-solving — the dawn of technology as an extension of the mind. Homo faber, “the human who makes”, was born through this new evolutionary Intelligence of Compositional Agency.

There are two essential components here that together represent a qualitative evolutionary leap relative to both the animals and the prior mode of opportunistic tool use.

Agency refers to the intentionality driving actions. It implies a conscious choice and a directed effort to manipulate the environment rather than a simple, instinctive reaction to it, distinguishing human cognition from that of lower primates and other animals.

Composition references the cognitive comprehension of the nature of things as being composed of smaller subunits of similar material as well as the observational awareness of the pattern of their construction. This understanding led hominins to agentic action upon the materials. The decomposing, recomposing and organizing of the elements of the environment into useful configurations represented an advanced level of cognitive engagement with the physical world encompassing the perception of possibilities and creation through experimentation.

Early tools were used to overcome survival and subsistence pressures in a variety of shaping, decomposing and recomposing applications, including:

• Butchering animal carcasses: Sharp stone flakes and chopping tools were used to cut through tough hides, remove meat from bones, and extract nutrient-rich marrow.

• Processing plant foods: Stone tools were used to dig up tubers and roots, crack open nuts, and strip bark from trees to access edible parts.

• Hunting and scavenging: While the extent of hunting in the Lower Paleolithic is debated, some researchers suggest that early hominins may have used tools like wooden spears or thrown stones to hunt small game or defend scavenged carcasses from other predators.

• Shelter and protection: Early hominins likely used tools to construct simple shelters, such as modifying natural features like rock overhangs or building basic structures from branches and vegetation. Tools could also have been used for defense against predators.

Through the intelligent observational awareness of materials composition and the agentic application of skill and force, early humans created these evolutionary adaptations and thereby began the process of separating themselves from the animal kingdom, initiating the long march towards increasing freedom from environmentally adaptive pressures and the gradual assumption of a primarily agentic status.

Causal Reasoning

(400,000–300,000 years BP)

Towards the end of the Lower Paleolithic period a relatively new species of hominin, Homo sapiens, was responsible for the next definitive leap of human Intelligence evolution when it learned to intentionally create and wield fire. While the opportunistic use of environmentally available fire had likely been common throughout the Lower Paleolithic, the intentional creation of fire is considered a significant milestone in human Intelligence evolution.

The creation of fire can be seen in part as an extension of the compositional cognitive abilities associated with tool making, but it also reflects a significantly more advanced form of intelligence. Breaking down, shaping, recomposing and arranging materials is a much simpler set of cognitive operations than those required for fire-making. The intentional production of fire is a complex technological innovation that requires a range of cognitive abilities including observation, abstraction, problem-solving, planning and most importantly, an understanding of cause and effect. Taken together, these cognitive attributes comprise the Intelligence evolution of Causal Reasoning.

Whereas primitive tool making involved the decomposition and recomposition of environmentally extant elements, fire-making represented the causal creation of an unavailable, non-existent element from other available elements, an act of distinct creativity.

The creation of fire by the striking of flint first required the observation that naturally occurring fire (from lightning or volcanos) and the spark seen when certain rocks were struck together both shared the properties of heat and light. It also required the observation that it was only certain kinds of rocks struck in specific ways that produced the effect. Similarly, the production of fire through intentional friction would have required the observational awareness that fire is hot and that when two pieces of wood are rubbed together, a similar heat is produced. Through Causal Reasoning the early Homo sapiens arrived at an understanding of the properties of different materials and the way that they interact with each other in a specific way to produce a spark or ember.

The conjuring of a flame must have seemed magical to the early human. Working with such a mysterious force with a seeming chimerical will of its own required a certain audacity that speaks to an inner spark of agentic power. While fire-making was an adaptive response to the environmental pressures of survival and subsistence, it was also a significant furtherance of human agency.

The ability to create and control fire provided a source of heat against the cold, light to extend the day, protection from fearsome predators and energy for the transformation of raw food. Using fire, humans proactively pushed back against the coldness, darkness, rawness and predatory dangerousness of their natural circumstances.

The cooking of food facilitated vastly improved survival and subsistence outcomes. Cooking made food safer to eat, easier to digest and more nutritious, supporting the growth and evolution of the human brain and other biological systems. It also fostered cognitive skills such as preparation, staging, planning and the perception of time.

Fire was likely the catalyst that bootstrapped early cultural attributes. It’s easy to imagine that the adept fire-maker enjoyed a significant status in early human society. At the same time, the creation and use of fire would have been the focal point of shared group interactions.

Cooking food with fire fostered the emergence of social structures through division of labor, task specialization and communal meals. Skill sets developed involving the creative combining of tools, vessels and heat to prepare and transform raw food into cooked meals.

The human relationship to fire was probably linked to the emergence of rudimentary language. Spoken language would have been required to communicate and share the knowledge of how to create and maintain fire, passing it down from generation to generation and between populations.

With the creation of tools and the mastery of fire early humans clearly distinguished themselves from other forms of life. Whereas plants and animals conform to environmental pressures, reshaping biology and behavior in order to survive, humans pushed back, decreasing and deflecting survival and subsistence pressures with intelligent creativity. While animals adapt physically and behaviorally to environmental pressures, humans adapted but also evolved their cognitive Intelligence.

In the subsequent period, the human species would push even further into its agency through the development of the first information technology, spoken language.

Representational Language

(300,000 to 50,000 years BP)

In the Lower Paleolithic period, early rudimentary verbal communication likely supported the emergence of tool and fire culture. Fossil and paleogenetic evidence suggests that during the Middle Paleolithic period humans developed the modern anatomical and genetic basis for verbal communication. The evolution of the physical basis for speech around 300,000 years ago was the final set of biological adaptations that established the present human physical body.

Coincident with these biological physical adaptations, early humans evolved an increasingly sophisticated and varied set of cultural technological and societal adaptations that dramatically improved survival and subsistence outcomes and further extended and established hominin agency:

• Scaling and complexification of tool and weapons making

• Organization and planning of communal hunts

• Establishment and building of primitive shelters

• Creation of clothing

In conjunction with the development of these ever more sophisticated survival and subsistence strategies early humans created verbal Representational Language. As each new human activity produced a growing body of knowledge and expertise it drove the development of a specific vocabulary that encoded the necessary information required for a given task or technology.

Word-smithing represented a qualitatively more advanced order of Intelligence. To evolve a socially accepted word that symbolizes a thing or action or idea requires an abstract conceptual space in which a thing that is not a sound is represented by the verbal making of a sound and the sound is understood to represent the thing.

The mention of the socially accepted word representing “lion” in a Middle Paleolithic encampment would invoke a mental image of the beast as well as the imagination of a chain of possible scenarios and potential actions associated with it. This abstract representational space of Intelligence emerges as a direct consequence of linguistic relativity.

A community of humans with a shared vocabulary then effectively creates and colonizes a noumenal realm in which possibilities, potentialities and counterfactual outcomes can be considered. In this space a wide range of advanced cognitive abilities such as abstraction, categorization, differentiation and generalization would be fostered.

A new span of temporality also opens up with the establishment of spoken language. As a storage medium for information, experience, meaning and belief, language creates a duration into the past and a projection into the future that extends beyond the present of an individual or group. Memory is vastly expanded and planning becomes possible.

Spoken language is likely to have engendered other forms of Representational Language. Early song, storytelling and myth probably had its origins in the late Middle Paleolithic.

The first signs of symbolism and ritualistic behaviors also emerged. Simple geometric engravings found in some Middle Paleolithic sites may have encoded early symbolic meaning. Burial sites may have been associated with symbolic beliefs about an afterlife. Jewelry such as pendants, beads, and personal ornaments made from materials such as bone, shell, ivory, and animal teeth show the beginnings of decorative self-adornment. Pigments were used to create coloring for skin, garments and artifacts suggesting ritualistic purpose. All of these cultural developments can be considered forms of language, Representational Language which stored and transmitted information, meaning, value and belief.

World Model

At the beginning of the Environmentally Adaptive Phase, the World Model of the early hominin was primarily centered around the immediate environment and basic survival needs, including the location of food sources, water, and potential dangers. There was little distinction between the world of the early human and that of the animals and the general environment. The perception of time was likely limited to the cyclical rhythms of nature and the patterns of day and night and seasonality. Social structures were simple and constrained to the local clan, and cultural knowledge was primarily transmitted through observation and imitation within small, kin-based groups.

By the end of the Environmentally Adaptive Phase, the human World Model had undergone dramatic expansion and refinement. The physical World Model was now mentally mapped and extended into the local hunting and foraging territory with awareness of competing social groups. With the development of tools and the control of fire the world was now an environment that could be intentionally manipulated and shaped. Spoken language expanded the World Model to include a new inner universe of symbolic thought, a space for the storage of knowledge, the working out of plans and the theory of mind that facilitated social interaction. Through spoken language and oral history a temporal light cone now included memory of the past and transmitted accumulated knowledge and skills across generations into an anticipated future. Humans now saw themselves as distinct and separate from the environment and other animals. Early cultural elements in the form of proto-artworks may suggest the beginnings of spiritual beliefs.

Conclusions

The Lower and Middle Paleolithic periods saw a progressive human Intelligence evolution marked by the development of the cognitive powers of Compositional Agency, Causal Reasoning and Representational Language. Each of these was associated with significant practical innovations that addressed survival and subsistence challenges. In the course of adaptively pushing back environmental pressures, humans expanded their agency. Coincident with this process, the hominin evolved into its current form, the biological, physiological and genetic modern Homo sapiens. By the end of the Middle Paleolithic period, humans had developed the basis in physical and cognitive Intelligence to transcend the limits of the Environmentally Adaptive Intelligence phase and enter into the far greater powers of Cultural Intelligence.

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(12,000–5,000 years BP)

Culture encompasses shared knowledge, languages, arts, beliefs, practices and structures communicated across generations. During the Neolithic period of the Cultural Intelligence evolution phase, civilizations formed around complex amalgamations of cultural attributes in given regions. Civilization can be seen as the large-scale, organized expression of culture in which cultural practices form the basis of persistent collective structures such as political and economic frameworks. By understanding civilization as an extension and elaboration of culture, we can better appreciate how Cultural Intelligence is not just about the accumulation of knowledge, but about the agentic use of it to create physical systems and social institutions that lay the basis for the next stage of human Intelligence evolution.

The Neolithic period saw a powerful resurgence of outwardly-directed energy as humans emerged from their long, wintery introspective phase to proactively shape their contextual environment. A significant stage of human Cultural Intelligence evolution unfolded as the vast glacial ice sheets receded, marked by the development of settled communities, agriculture, animal domestication, new practical innovations such as pottery and weaving and the construction of megalithic structures and sites.

Whereas in previous ages early hominins were molded by the intelligence of the natural environment, it was now humans who became the intelligent force of environmental pressure, laying claim upon the land and its inhabiting life force, intentionally harnessing and directing its power. This was the assertion of human agency and the birth of civilization.

The Compositional Agency stage involved the human cognition that the world is comprised of discrete objects and that those objects had composition. It also involved the human agentic activity of decomposing and recomposing environmentally available resources in complex and useful ways.

During the Civilizational Agency stage, humans recognized the objective nature of the land and all of its features and attributes, the plants, the animals and individualized and collective humanity. They then proceeded to exercise their agency by organizing and recomposing these elements into new and useful structures.

Humans extended Civilizational Agency over the land and began to settle into agricultural communities, establishing zones of dominion in which they were the uncontested power. The development of permanent settlements, agriculture and animal husbandry ended the predominance of the nomadic lifestyle. Humans intelligently evolved from adaptive hunter-gatherers to agentic builder-cultivators.

Cultural Intelligence began to aggregate around communal living in ways that would not have been possible under nomadic conditions. In a settled, persistent context cultural knowledge could be shared, developed and preserved across generations. Sedentary living allowed for increased social cooperation, specialized crafts and organizational knowledge applied at scale, enabling the creation of food surpluses. Pottery was created to prepare food and store surplus produce and transport it for trade.

Both agriculture and animal husbandry were creative and assertive extensions of human agentic intelligence into the realms of plant and animal life. Humans began to harness the biological intelligence powers of other species for their own development. The creation of bread wheat from native grasses and the evolution of dogs from wild wolves are prime examples of this process.

The intelligent human utilization of environmentally available resources such as stone, fire, bone and wood contrasts significantly with the agricultural domestication of plants and animals. The former required observation and utilization of the compositional nature of simple forms of matter whereas the latter involved the observation and appropriation of the properties and life cycles of complex organic intelligences. The planned cultivation of plants signified the directed harnessing of the life force itself. Likewise, the taming and breeding of animals extended the power of human intelligence to the governance and mastery of other sentient life forms and the beginning of a symbiotic relationship between humans and domesticated animals as labor, transportation, food and companionship.

The light of human observational awareness fell upon the plant kingdom and it was noticed that plants arose from seeds. Incipient agriculture in the vicinity of temporary settlements gradually led to intentional planting in permanent communities.

The domestication of wheat involved an ongoing directed process of selective breeding and experimentation, as Neolithic farmers carefully selected and propagated desirable traits over multiple generations. This process of artificial selection was a proto-scientific application of human intelligence to the manipulation of plant genetics. Some degree of serendipity was no doubt a factor, but each development, whether the result of conscious intent or chance, required the farmer to apply causal reasoning to the next iteration of selective cultivation. The creation of bread wheat was a highly agentic expression of human will and creativity.

In contrast, the human domestication of dogs from wolves was a more fluidly organic and serendipitous process, emerging from the complex interactions between the two intelligent species over time. This process was shaped by the agency and preferences of both humans and canines, as they adapted to each other’s presence and co-evolved new forms of social and ecological interaction.

Despite these differences, both processes were deeply cultural and social in nature, relying on the accumulated knowledge, practices and values of human societies over generations. The experiences of the domestication of wheat and dogs were pivotal in the development of human ecological intelligence. It is an extension of human intelligent awareness, understanding and will into and through the environment to a degree previously unrealized.

The Neolithic period thus represents a significant transformation in the nature and direction of human intelligence, as humans began to actively shape their environment and their relationships with other species through a combination of intentional experimentation and organic adaptation. The collective Cultural Intelligence of humanity evolved during the Neolithic Age to extend its agency beyond the species itself into the natural world, bringing the land, plants and animals within its growing sphere of influence and transforming them.

One of the salient aspects of Civilizational Agency was the construction of large scale megalithic structures and sites, a phenomenon seen at this stage of human Cultural Intelligence evolution across societies all over the world.

Sedentary living, agriculture and animal husbandry generated a rapid increase in population size and density. The availability of this new latent labor force was likely one of many factors that combined to result in the construction of megalithic structures and sites.

As noted in our study of the Cultural Meaning stage, in spite of the harsh conditions presented by Ice Age conditions, humans dedicated significant time and resources into externalizing inner meaning in the forms of Paleolithic artworks. Humanity next went from these intimate, personal and small group expressions of its nascent noumenal intelligence to large scale, collectivized long term construction projects. The Intelligence evolution of Cultural Meaning was amplified and writ large upon the surface of the earth during the Civilizational Agency stage in the form of the great human Megaliths.

The enormous stone structures and complexes were not cities for working or living, but rather served primarily ceremonial and spiritual functions. Their construction was frequently aligned with astronomical phenomena and embodied mathematical principles. The largest, most enduring and most physical projects of human prehistory were expressions of inner, ephemeral human noumenal intelligence.

With the Megalithic sites, the Temporal-Spatial dynamic seemed to predominate. Many of the features of these installations related to calendrical and celestial time keeping functions. The enduring construction of the colossal structures also speaks to the temporal element. Like the handprint outlines on Paleolithic cave walls, the Megaliths are a stamp of human presence on the landscape, a spatial registration of mortal immortality.

It is perhaps during this stage that the Discrete-Cultural Intelligence dynamic first becomes significant. It is rather clear that significant degrees of labor specialization and social stratification emerged. There were very likely classes of farmers, builders and ecclesiastics that grew up around the particular knowledge sets specific to agriculture, animal husbandry, construction and spiritual and celestial knowledge. It may be that the first leadership classes formed from among the shamanistic priests who governed the spiritual life that revolved around the megaliths.

If the human Cultural Intelligence phase of evolution can be analogized to an interplanetary space mission, the Cultural Meaning stage was the countdown preparation to launch and the Civilizational Agency phase was ignition and liftoff. Next humanity would rocket into the stratosphere with Agentic Force it expressed itself outwardly through newly discovered and invented Force carrying vectors.

(Part of a series and a larger work-in-progress)

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http://www.infocobuild.com/books-and-films/science/TheAscentOfMan/episode-02.html

https://naturedocumentaries.org/2816/harvest-seasons/

https://clumphd.com/the-ascent-of-man-episode-2-the-harvest-of-the-seasons/

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

https://abagond.wordpress.com/2009/11/21/jacob-bronowski-the-harvest-of-seasons/

https://usa.anarchistlibraries.net/library/james-c-scott-against-the-grain

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

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

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Prior to the age of Agentic Force, human cultural evolution had been relatively distributed and homogenous. Over long periods of time, features that arose in one region tended to arise in others and the slow diffusion of knowledge and cultural attributes led to a relatively flat distribution of cultural development.

The period of Agentic Force, however, saw the rise of complex civilizations defined by territorial boundaries and enforced by military power. As a result human Cultural Intelligence differentiated into distinct national and ethnic characteristics and specializations. Each pocket of human culture developed a special set of attributes that has been a source of resiliency as well as conflict throughout history.

While earlier societies exhibited more homogenous social structures, the era saw the establishment of distinct social classes, occupations, and roles within society. Specialization led to a depth of knowledge, skill and productivity that would not have been otherwise possible, but also resulted in inequality of power, status and wealth.

During the stage of Agentic Force, the first structured forms of social organization emerged. Human society self-organized into cohesive entities across every domain of human endeavor, including the specialized function classes of rulership, priesthood, intellectuals, military, managers, merchants, artists, artisans, laborers and slaves.

With regards to political structures, there was a shift from tribal leadership or small chiefdoms to centralized states with powerful rulers who were often considered divine or semi-divine figures. Examples include the pharaohs in Egypt, emperors in China, and kings in Mesopotamia. Bureaucratic systems to manage state affairs were instituted to collect taxes, maintain law and order, and oversee public works projects. Competition for resources, land, and control of trade routes drove the creation of military organizations and standing armies with advanced weaponry.

Economic systems developed around agricultural techniques, craft specialization, trade, commerce, currency and markets. Irrigation systems, plows, and crop rotation, led to increased food production and population growth. The expansion of trade networks, both local and long-distance, facilitated the exchange of goods, ideas, and technologies. This led to the growth of merchant classes and the development of economic specialization. The emergence of specialized crafts and industries, such as metalworking, pottery, weaving, and construction was supported by the surplus resources generated by agricultural intensification and trade. The development of standardized currencies and marketplaces facilitated economic exchange and the complexification of economics.

Social organization was a Force multiplier. The efficient division of labor into roles, the stratification of the population into classes and the centralization of authority into states gave birth to economics, increased productivity, accelerated innovation and facilitated the realization of large scale collective goals and projects.

Slavery

The Neolithic recruitment of animal Force as labor as well as the communal efforts involved in the construction of the great megalithic sites naturally evolved into the social organization of human labor Force during the Agentic Force stage. This included the widespread practice of slavery.

Abhorrent to our contemporary sensibilities, slavery seemed like a social good to the cultures of the time. In the Bronze Age, slavery was an accepted and normalized institution across various societies, integrated into economic, legal, and social structures. Slaves were primarily seen as property but were sometimes granted limited rights, depending on their role and society’s norms. Religious, legal, and administrative sources indicate a worldview in which slavery was considered a natural and necessary part of the social hierarchy. Captives of war often became slave labor.

War

Like slavery, wars of conquest are regarded as reprehensible when viewed through the lens of our present day worldview, but it was accepted as the normal course of human affairs in the ancient world. During the Agentic Force stage war and conquest were generally accepted and often celebrated as natural and were frequently justified through religious and ideological beliefs. Like slavery, war was deeply integrated into the cultural, social, and economic structures of the time, with kings and elites often using warfare to demonstrate their power, secure resources, and maintain their authority.

Warfare during the Agentic Force stage evolved significantly in scale, technology, and strategy. The rise of organized armies, advanced weaponry, and territorial ambitions transformed warfare into a systematic and professionalized institution. This period saw the rise and fall of empires, the development of formalized military strategies, and the increasing aggressiveness of campaigns driven by economic and political motivations. The increased scale and intensity of warfare reflected the broader societal changes of the period, including urbanization, technological innovation, and the emergence of complex social hierarchies.

Adaptation-Agency Dynamic and Agentic Force

It seems perhaps paradoxical that the same period which created marvelous cities, writing systems, mathematics, metal alloys, the wheel and complex social structures also manifested such destructive behaviors as war and slavery. But we may find an understanding of this apparent incongruity in the natural behavior of Intelligence as a general principle.

Intelligence, as a general principle, is expansive in nature. This may be the very significance of the “Big Bang” and the apparent expansion of the Universe. In all times and in all places, Intelligence tends towards its increase in scope, scale and depth.

Intelligence does not run in place or obtain in stasis. It is an active principle. There is an inherent “grow or die” imperative observably at work in all Intelligent Systems. That which is not growing is necessarily dying.

Human Cultural Intelligence seeks to spread, occupy space, reproduce and extend itself through time. The imperative of growth and expansion embedded in all human Intelligence — and Intelligence as a general principle — drove humans to extend agency over lands, resources, peoples and individuals. Viewed as a type of Intelligent System, it is propelled by the same inherent “expand or die” imperative that characterizes all Intelligence.

The Adaptation-Agency dynamic drove human Intelligence evolution forward from the earliest stage of Compositional Agency through to the stage of Agentic Force. At each stage, humans were forced to adapt to environmental pressures, intelligently countering with agentic responses. With the stage of Agentic Force humans moved largely beyond the survival and subsistence imperatives.

With the governor of environmental pressure almost entirely removed the Agentic Force of humanity was free to maximize its extension. The only regulating mechanism was then the opposition of other human agencies and the limits of self-destructive behavior. Adaptive pressures then originated from within the increasingly complex system of human Intelligence evolution itself. The primary force of adaptation became self-generated through human Cultural Intelligence.

Our contemporary worldview considers Force to be natural when it operates unconsciously as a consequence of universal law. Conversely, it is considered unnatural when directed by a conscious agent. But if conceptualized as an inherent feature of all Intelligent Systems, whether agentic or not, Force can be seen as the natural tendency of a system to explore the full range of its Adaptation-Agency dynamic over the course of its evolutionary process.

Slavery and war therefore represent the maximum extension of the Adaptation-Agency dynamic and its moment of highest relevancy in the history of human Intelligence evolution. Conquest involves the total extension of agency by one power over the lands, resources and people of another and the full adaptation of the conquered territory to the agency of the conqueror. Slavery involves the assumption of agency over the person of another and the full adaptation of the slave to will of the owner.

It can be argued that from this maximized state, the Adaptation-Agency dynamic has naturally tended to revert towards the mean. We regard war and slavery as abhorrent and primitive now because since the stage of Agentic Force the dynamic has been trending towards reversion. Even so, the drive to explore the maximium extension of the Adaptation-Agency dynamic is still present with us today in various forms, including sports and sexual role play. War also persists and the Military-Industrial complex shapes and drives economics and politics.

We are on the cusp of consciously comprehending the natural expansive imperative that unconsciously drove our species in the past. By becoming fully aware of the inherent imperative Force that has adaptively shaped our behavior in the past, we can now translate that collective experience into the Agency that consciously supersedes it with a new, superior form of human Intelligence. Freed from the compulsion of ancient adaptive pressures, we can self-design our next stage of human Intelligence Evolution and choose a fundamentally better future.

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