AI is best understood not as something outside human knowledge, but as a new
formation within the collective knowledge ecosystem itself. In David Christian’s Big History frame, collective learning is humanity’s decisive
evolutionary advantage.
Humans differ from other species not merely because
individuals are intelligent, but because knowledge accumulates socially across
generations. Language, symbolic memory, writing, institutions, libraries,
science, and networks allow information to survive individual death and
compound over time.
A human being can leave instructions, warnings, equations,
myths, legal systems, recipes, and philosophical arguments for people not yet
born. Two hundred years later, another human may still retrieve the signal and
build on it. Other species can transmit behaviors socially, sometimes with
remarkable sophistication, but they cannot accumulate symbolic knowledge
outside living memory. No raven expects another raven two hundred years later
to recover a carefully labeled container of poisonous berries and continue the
experiment.
Human history therefore becomes cumulative in a way
biological evolution alone never could. In Christian’s framework, this is one
of the great emergent properties of the cosmos: matter organizes into stars,
stars into chemistry, chemistry into life, life into symbolic societies capable
of preserving and transmitting knowledge.
That idea already contains an implicit theory of emergence. Collective learning
is not located in any one brain. No single human contains civilization’s
knowledge. It arises from interactions among millions of minds distributed
across time. Knowledge is therefore not merely stored information but a
system-level property emerging from communication, cooperation, memory, and
transmission. Human civilization becomes, in effect, a distributed cognitive
process.
Similarly, Scott Page's work on Complexity emphasizes that
complex adaptive systems generate higher-order behavior through interactions
among diverse agents operating with partial information and local rules. Markets,
ecosystems, scientific communities, governments, and cultures are not centrally
designed intelligences. They are emergent systems. Their potential arises from
distributed interactions, feedback loops, adaptation, diversity, and continual
error correction.
Collective learning, viewed this way, is not a library. It is a living ecology
of signals, something closer to the dense techno-atmosphere now surrounding the
planet: satellites, fiber-optic cables, protocols, archives, recommendation
systems, decaying software, and a steadily growing cloud of informational
debris, not unlike the occasional flip-flop or stray wrench still orbiting
Earth long after the original space mission ended.
That distinction matters because knowledge does not spread merely because it is
true. It spreads because systems amplify certain signals, suppress others,
reward prestige, stabilize narratives, create incentives, and filter noise.
Scientific truths, political myths, social norms, and technological innovations
all propagate through complex social selection environments. Information
evolves socially much as organisms evolve biologically: through variation,
competition, replication, and selection.
This is where AI becomes historically serious.
AI is not merely another tool added to the knowledge system like a faster
search engine or a better encyclopedia. It intervenes directly in the
interaction layer from which collective intelligence emerges. It changes how
signals are selected, summarized, translated, prioritized, generated, and
circulated. In other words, AI does not simply add more information to the
system. It changes what people notice, repeat, trust, ignore, and build on.
That is a much larger claim.
Earlier cognitive technologies primarily extended storage and transmission.
Writing preserved memory. Printing scaled replication. Digital networks
accelerated distribution. AI is different because it begins participating in
intermediate cognition itself: summarizing, inferring, recommending, generating
explanations, synthesizing domains, and shaping interpretation in real time.
The medium no longer simply carries thought. It increasingly participates in
shaping thought.
This is why the relationship between humans and AI already feels recursive.
Every conversation with an AI system is, to some extent, a training exercise.
The models learn from accumulated human expression, but humans also start
adapting to the models in return: adjusting vocabulary, compressing thoughts
differently, reorganizing attention, even reshaping their sense of what a ‘good
explanation’ sounds like. The feedback loop is already underway. Some mornings,
before coffee, I suspect I’m running on an outdated operating system and
critically low on API tokens.
Complexity theory would recognize this immediately as feedback coupling inside
a complex adaptive system. Humans shape models; models reshape humans; both
evolve inside the same informational environment. The knowledge ecosystem
becomes recursive.
This recursive dimension makes the planetary metaphor unexpectedly useful —
provided it is handled carefully and not allowed to drift into Gaia gift-shop
mysticism. David Christian’s tone is important here. He speaks less like a
prophet than like a geologist. His sensibility is layered, historical,
material, and evolutionary. New forms of complexity emerge under the right
conditions over deep time. No incense necessary.
Viewed through that lens, human collective learning itself can be understood as
a late-forming layer at the Earth’s surface. Geological history produces
physical structures, chemical cycles, atmospheres, oceans, and biological
ecosystems. Human societies then generate another kind of layer: symbolic,
informational, institutional.
A thin but extraordinarily potent surface stratum composed
of language, archives, networks, science, law, media, and memory. AI now
emerges within that layer as one of its newest formations.
The metaphor of a “terrestrial cortex” works structurally if not literally. Not
planetary consciousness. That road gets mystical fast and usually ends in a
gift shop. But the Earth has undeniably developed increasingly complex systems
for storing, circulating, and processing information through one of its evolved
species. AI belongs inside that process.
Yet the more revealing metaphor may be fog.
AI thickens the informational atmosphere surrounding society. Signals multiply,
but so does haze. Information becomes more available while certainty becomes
strangely harder to pin down. Origins blur. Authority blurs. Sometimes even
reality itself starts feeling like it has passed through three summaries, two
recommendation engines, and a motivational LinkedIn post before reaching your
screen.
In a fog, navigation changes. People rely less on direct
perception and more on instruments, signals, maps, and collective guidance
systems. Something similar now happens in the informational environment. As
AI-generated interpretations and machine-mediated explanations proliferate,
humans increasingly experience reality through layers of processed abstraction.
The medium no longer simply carries information. It quietly begins shaping how
the information is perceived in the first place.
AI therefore becomes both archive and atmosphere, both memory and fog.
This tension cuts to the center of the problem. AI may deepen collective
intelligence by improving translation across domains, lowering barriers to
participation, accelerating synthesis, and helping societies process complexity
beyond ordinary human bandwidth. In that optimistic version, the emergent
intelligence of civilization becomes richer, more adaptive, and more
distributed.
But the opposite outcome is equally plausible.
Complex systems depend on diversity for resilience. Scott Page repeatedly
emphasizes that systems composed of varied models often outperform systems
built around uniform expertise.
If millions of people begin relying on the same handful of
model architectures, optimized toward similar patterns of fluency and
consensus, then cognitive diversity may shrink even while efficiency rises. The
result could be a civilization that becomes more coordinated but less
exploratory, more synchronized but more fragile.
The system grows increasingly efficient at distributing
ready-made interpretations, reducing the need for individuals to forage
cognitively for themselves. Ideas arrive pre-processed, like carefully
pre-chewed worms delivered directly into open beaks.
A system can become more organized without becoming wiser.
That may be the deepest risk. AI could intensify collective learning while
simultaneously degrading the epistemic conditions that make collective learning
self-correcting. Synthetic output may overwhelm verification. Fluency may
outrun truth. Feedback loops may become polluted by recursively generated
noise. Systems optimized for engagement may privilege coherence over accuracy.
Under those conditions, the collective intelligence of the system becomes
increasingly difficult to distinguish from large-scale imitation. In
sufficiently interconnected societies, a sufficiently sophisticated cognitive
malware may spread through institutions faster than many biological pathogens,
while leaving damage that is far harder to quarantine.
From both Big History and complexity theory, then, the real issue is not
whether AI accelerates collective learning. Of course it does. The real issue
is whether AI reorganizes the emergence dynamics of collective learning itself.
That is a civilizational question.
Human societies may be entering a phase in which cognition is no longer located
primarily within individual minds or even traditional institutions, but
distributed across recursive socio-technical systems that continuously shape
and reshape one another. Not machine consciousness. Not technological destiny.
Something more geological and systemic: new layers of organized complexity
forming within the informational surface of planetary civilization.
Whether that produces deeper collective intelligence or a denser and more
beautiful fog remains unresolved.
