One of the stranger realizations that emerges from studying Big History and complexity theory is that technological progress and social maturity do not necessarily move at the same speed.
In fact, they often appear to move at dramatically
different speeds.
Humanity can map distant galaxies, sequence genomes,
and train large language models on significant portions of civilization’s
accumulated knowledge. At the same time, it remains perfectly capable of
organizing itself around tribal loyalties, centuries-old grievances, status
competitions, and disputes whose origins predate the printing press.
This creates a peculiar form of cognitive whiplash.
On one scale, we inhabit a civilization of astonishing
sophistication. On another, we remain a species of highly social primates
navigating incentives, identities, and narratives that would have been
recognizable to our ancestors thousands of years ago.
The contradiction is only apparent. Both realities are
true simultaneously.
Scott
Page would likely describe this as a consequence of complex adaptive
systems operating on multiple timescales. Technologies can evolve rapidly while
institutions, cultures, and governance structures adapt much more slowly. New
layers of complexity emerge long before older layers disappear.
The result is a civilization where the props often feel
futuristic but the setting still looks archaeological.
Bronze Age instincts coexist with medieval identities,
industrial institutions, global communication networks, and frontier artificial
intelligence. The layers accumulate faster than they are replaced.
This observation becomes especially relevant when
discussing AI.
Many current debates assume that the primary challenge
is technical: building capable systems, ensuring safety, increasing
performance, and managing deployment. Those are important concerns. Yet an
equally important question sits beneath them:
What happens when technologies begin operating at a
civilizational scale while governance remains organized around nations?
The mismatch is difficult to ignore.
The training data used by advanced AI systems is not
American knowledge, Chinese knowledge, or Argentine knowledge. It is the
accumulated symbolic residue of civilization itself: languages, books,
scientific papers, software repositories, journalism, philosophy, art,
documentation, and billions of human interactions flowing across borders.
The resource is transnational.
The disruption is transnational.
The governance remains national.
Which is a bit like discovering a new continent and
then insisting the most important question is which municipal office should
process the paperwork.
And that would be manageable if nations themselves
behaved like mature participants in a coordinated planetary project.
Unfortunately, we often seem determined to prove otherwise.
We can build systems that synthesize the knowledge of
billions of people, yet we still struggle to cooperate across borders, parties,
regions, and identities. Not because the problems are always impossibly
complex, but because incentives, prestige, short-term interests, and the
occasional outbreak of political chiquitaje remain remarkably durable features
of human affairs.
There is something profoundly puzzling about it.
A species capable of contemplating the origins of the
universe can still become hopelessly divided over symbolic disputes, procedural
squabbles, and status contests that, viewed from sufficient distance, look
suspiciously small. We no longer argue about the exact same goats that wandered
into the neighboring field centuries ago, but we continue to manufacture
functional equivalents with impressive creativity and enthusiasm.
Meanwhile, greed has not exactly retired from public
life. New technologies arrive, new fortunes emerge, and many leaders discover
once again that thinking in terms of the next election cycle, the next
quarterly report, or the next personal advantage feels more natural than
thinking at the scale of civilization. Not always. But often enough to matter.
The challenge is not that humanity lacks intelligence.
The challenge is that intelligence scales faster than
wisdom, and capability scales faster than coordination.
Politicians naturally propose national solutions
because nations are where political power resides. Taxation, regulation,
ownership structures, and redistribution mechanisms all operate through
existing states. Senator Bernie
Sanders’ proposal to tax extraordinary AI-driven gains and return a portion
of the benefits to the public deserves to be taken seriously in this context.
It recognizes something many observers across the political spectrum are
beginning to notice: AI systems derive value not only from private investment
but also from a vast reservoir of collective
human knowledge.
That insight is laudable.
It may even point toward a reasonable path for ensuring
that the benefits of increasingly capable systems are shared more broadly
rather than concentrated narrowly.
But here comes my “but.”
Even if Sanders’ proposal were implemented perfectly,
it would still confront the deeper challenge that the systems themselves
operate across borders while the mechanisms for redistribution remain tied to
individual nations. A national dividend may help address national consequences.
It does not fully answer the civilizational question.
This creates a peculiar asymmetry.
A sufficiently powerful AI system may affect labor markets in dozens of countries simultaneously. It may be trained on knowledge generated by people across the globe. The servers may sit in one jurisdiction, the investors in another, the users in hundreds more. The benefits and disruptions spread through a planetary informational network largely indifferent to political borders.
A similar
mismatch appears in public health. We often discuss outbreaks in distant
countries as though Marco Polo had just arrived in Venice with alarming tales
from a land beyond the edge of the known world. The fact that a pathogen can
now cross continents faster than Marco Polo crossed a village somehow does
little to diminish that feeling. We continue to treat many global health
threats as though they were unfolding on Uranus rather than within the same
densely connected civilization we inhabit.
The atmosphere does not care where a molecule originated. Viruses do not carry passports. Increasingly, informational systems appear equally indifferent to national borders.
This does not mean
nation-states become irrelevant. Governments still regulate, tax, negotiate,
and enforce. Companies remain subject to laws. Infrastructure exists in
physical places. Reality eventually cashes out into jurisdictions.
But the scale mismatch remains.
The problem is civilizational.
The available tools are largely
national.
Even if every country implemented excellent policies
tomorrow, the deeper question would remain unresolved.
Who owns the products of collective learning?
That question is far stranger than it first appears.
AI systems are built using private capital, private
engineering, and private risk-taking. Yet they are also built upon public
research, open-source software, scientific knowledge, language itself, and
centuries of accumulated human culture.
The training corpus looks suspiciously like a
civilization-scale commons.
This is why arguments about ownership feel different in
the AI era than they did in previous technological revolutions. The debate is
no longer only economic. It is epistemic.
Who owns the systems that increasingly mediate
knowledge, interpretation, memory, explanation, and attention?
That question begins to sound less like a debate about
factories and more like a debate about libraries, universities, communication
networks, and the informational infrastructure through which societies think.
Unfortunately, history offers little reassurance that
extraordinary capability automatically produces wise outcomes.
A civilization can become extraordinarily capable while
using both humans and machines in surprisingly stupid ways.
The Roman world produced remarkable engineering while
remaining trapped in recurring political dysfunction. The Industrial Revolution
transformed productivity while tolerating extraordinary human misery. The
internet connected billions of people and then devoted a meaningful portion of
its capacity to outrage optimization.
There is no law stating that intelligence, capability,
and wisdom must increase together.
Indeed, they often do not.
The future may not resemble the clean technological
trajectories imagined by either utopians or doomers. It may instead resemble a
civilization becoming progressively more capable while struggling to coordinate
around the consequences of its own success.
A civilization that can train frontier AI systems while
remaining politically fragmented.
A civilization that can model climate systems while
arguing about basic facts.
A civilization capable of mapping exoplanets while
still becoming trapped inside local incentive structures.
And perhaps, if we are being honest, a civilization
capable of generating endless new disagreements even after solving some of the
old ones. If ancient cities could spend generations arguing over whose goat
wandered into whose field, modern societies can certainly invent equally
passionate disputes over algorithms, data rights, and digital borders. The
names change. The coordination challenge remains.
This is not necessarily a sign of failure.
It may simply be the normal condition of complex
adaptive systems.
The truly remarkable fact is not that humans remain
tribal, emotional, and imperfect. The remarkable fact is that they have managed
to build global systems of cooperation despite those limitations.
Perhaps that is the real lesson of collective learning.
Humanity was never required to become wise before
becoming powerful.
It only had to become coordinated enough.
Whether wisdom eventually catches up remains an open
question.
