ESSAY · 2026

We're living through a moment people keep calling "the next Gutenberg revolution." Artificial intelligence is racing ahead, data centers are spreading across farmland and river valleys, and powerful models are becoming the new engines of knowledge and decision-making.

What almost no one is talking about is the quiet divide this creates: between those who get the full versions of these systems and those who don't; between those who decide what information is visible and those who simply live inside whatever feed they are given; between the "cloud" and the ground beneath it — the land, water, species, and communities asked to make room for someone else's future. This is my attempt to sound an alarm before that divide becomes permanent.

THE GROUND BENEATH THE CLOUD

We talk about AI as if it lives in the sky. Press releases and TED talks say "the cloud," "the model," "the intelligence," as though these things float somewhere above the real world. But artificial intelligence sits on the earth. It sits in windowless warehouses packed with servers, in cooling systems that draw millions of gallons of water from rivers and aquifers, in transmission lines and substations built across fields and forests, in light that never turns off, washing out the night sky for miles around.

In Virginia, data centers already drive projections for electricity demand and water planning. Counties are being asked to convert hundreds of acres of farmland into industrial zones. Wells, streams, and ecosystems that have been stable for generations are suddenly in play. And yet, when you listen to national conversations about AI, you rarely hear about rivers, wells, or night. You hear about productivity, innovation, and "frontier models." The gap matters. If we don't name the ground, we won't see who is paying the price for the cloud.

INTELLIGENCE YOU CAN BUY

One of the most striking claims made recently by people inside the frontier labs is that intelligence can now be "purchased" with compute. In a talk given at a 2026 conference, an Anthropic researcher, Chloe Lubinski, put it almost exactly that way: with more money, which buys compute, "you can essentially purchase intelligence," kicking off a cycle that is very hard to stop. More money buys more chips; more chips buy more training runs; better models make more money; the loop spins faster.

That same loop also buys exclusion. The most capable systems today are tiered — internal versions inside the labs with full research capabilities, bespoke enterprise access for big companies, and limited public interfaces for everyone else. Which tier you see depends on who you are: a frontier researcher, a corporate customer, or an ordinary person paying a small subscription and talking to a partial version of the system. When people in power talk about "AI for everyone," they often mean AI assistants and tools for everyone — not equal access to the systems that will shape the laws, markets, and policies we all live under.

The most capable systems are increasingly tiered, and ordinary people rarely get the same access that wealthy institutions do. The risk is that frontier AI becomes a powerful institution's advantage, while the communities hosting the infrastructure get the least access to the very capabilities their land and water make possible.

WHO GIVES UP LAND, WHO GETS THE INTELLIGENCE?

From the vantage point of a rural county, the AI divide looks different than it does from a corporate campus. Land that once grew food or provided habitat is rezoned for data centers. Tax incentives are offered to global companies to build on that land. Promises of jobs and community benefits are made, often in rosy language and PowerPoint slides. And the long-term strain on water, grid stability, wildlife, and local character is not fully understood, let alone discussed in plain terms.

If you live near a proposed site, you may never be told which models your water is cooling, which firms will have access to the intelligence running on your land, or how much of that intelligence will be available to your local schools, libraries, or small businesses. You are asked to host the physical cost of someone else's future — and offered only a partial share of the power that future is supposed to deliver. It is a new kind of inequality: a ground divide over who gives up land, water, and night; and an intelligence divide over who gets full access to the systems that sit on that land. We cannot accept that the people who live nearest the infrastructure, and feel its impacts most directly, will be the ones with the least say and the least access.

FROM GUTENBERG TO AI

We've been here before. When Johannes Gutenberg's press appeared in the 15th century, it didn't just make books cheaper — it broke a monopoly. Suddenly, scripture, scholarship, and political ideas could move faster than bishops and kings. Ordinary people could encounter texts directly, instead of only hearing them summarized or filtered by clergy and scribes. Power noticed.

In response to the flood of printed material, the Catholic Church created the Index Librorum Prohibitorum — the Index of Prohibited Books. Beginning in 1559 and updated for centuries, the Index listed works that lay Catholics were forbidden to read or own without special permission. It targeted not only "heretical" theology, but scientific, philosophical, and literary works: Copernicus and Galileo on the cosmos, Spinoza on scripture and politics, Kant and Hume on reason and faith, Milton and many others whose ideas complicated official doctrine. The stated aim was protection — shielding believers from error. The practical effect was control: a centralized authority quietly deciding which ideas people were allowed to encounter.

It is vital to see this clearly. The Index was not simply a list of "bad books." It was a mechanism for curating reality — defining which parts of the world's thought would count as legitimate and which would effectively disappear from ordinary view.

Today, our "indexes" are rarely printed. They are paywalled journals and books that only some institutions can afford; training datasets that include certain histories and omit others; content policies and safety filters that quietly downrank or remove sensitive topics; recommendation systems that decide which news, commentary, and search results rise to the top — and which sink out of sight. We are told, once again, that this is for our protection: to prevent harm, hate, misinformation, and confusion. Some of that is true and necessary. But the deeper risk is the same: losing the truth of things when all information is curated by those who control us.

The difference is that the old Index was at least visible. People could know which authors had been banned and debate those choices. Today's digital and AI "indexes" are mostly hidden. We rarely see which topics have been flagged as too dangerous, too controversial, or too unprofitable. We rarely see which communities' stories were left out of the training data entirely. That is why building our own small, honest libraries now — from land, rivers, farms, and ordinary lives — is not nostalgia. It is self-defense.

OUR MORAL IMAGINATION IS TRAINING DATA

Frontier labs sometimes say that our "moral imagination" is the raw material their systems learn from: our stories, our values, our fears, our hopes. That is partly true — and partly misleading. Models learn from the language they are fed. If we fill the world with shallow entertainment and partisan noise, they will mirror that. If we preserve deep thinking about justice, ecology, conscience, and responsibility, they can mirror that too.

But models also learn from what is excluded: the histories we never teach, the local voices we never record, the ecosystems we treat as background instead of central. If the training data mostly reflects the perspectives of those who already have access and power, then even the most honest model will reproduce that imbalance. It will know a great deal about markets and consumption — and very little about wells running dry, pollinators losing habitat, or families living under permanent artificial light.

That is why, alongside data centers and models, we need something older and quieter: shelves where people can find banned, ignored, or inconvenient works; local histories that remember rivers, forests, and farms; spiritual and philosophical texts that ask harder questions than "how do we optimize this quarter?" The point is not to freeze the past. It is to ensure that when we train machines — and our own children — we are not feeding them a curated, flattened version of reality.

A SMALL LIBRARY AS A LINE OF DEFENSE

What can ordinary people do in the face of all this? We can't single-handedly rewrite corporate policies or global AI governance. We can't stop every data center or reverse every zoning decision. But we can do something both humble and radical: build and preserve a physical, human-chosen library of works that matter; teach our neighbors and children how to read them, argue with them, and hold them against whatever feed they are given; and insist that local institutions — libraries, schools, churches, councils — see access to deep knowledge as a responsibility, not a luxury.

That library should include foundational religious and wisdom texts from multiple traditions; philosophical works on ethics, power, and personhood; books that were once banned or targeted by systems like the Index; environmental and scientific works that describe the real, physical world; histories that remember both triumphs and harms; literature that trains empathy, imagination, and the ability to live with complexity; and serious analyses of technology, media, AI, and the governance of compute. This is not about agreeing with every author. Many of these books conflict with each other. That is the point. A curated truth wounds us because it removes the friction between competing perspectives. A living library preserves that friction so real thinking remains possible.

A CALL FROM THE GROUND

I'm writing this from the ground beneath the cloud — from a rural place watching data centers move closer, watching rivers and skies enter the vocabulary of "AI growth." I don't distrust intelligence. I don't fear machines in themselves. What I fear is the combination of concentrated power, curated information, and environmental disregard — the same pattern we have seen, again and again, in other eras, now accelerated by models and chips.

If we are indeed living through a new Gutenberg moment, then the most important question is not "how smart will AI become?" but: Who will be allowed to see the full range of ideas and histories? Who will be quietly shielded — or stripped — from them? And who will decide? We cannot fully control how labs and governments answer those questions. We can, however, refuse to leave them entirely in someone else's hands. By building small libraries, honest blogs, and local conversations — from the ground up — we keep alive the possibility that our children and neighbors will know more than one authorized story about the world. We give them a chance to see the land and water under their feet as part of the truth, not a footnote to someone else's cloud.

This divide is not abstract where I live. See what it means for Augusta County, Virginia →