Technology & National Boundaries: A Civilization Mismatch

 

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.