Applied Humanism: What Renaissance Thinkers Can Teach Us About AI

I build AI systems for a living. I teach people to use them, I help write governance frameworks for them, and I think constantly about what they mean for the organization I work for. Most software engineers are focused on a single question: What are we building? They evaluate models, benchmark performance, calculate ROI. These capabilities matter.

But I've become convinced that we need people asking a second question at the same time: What are we becoming? That second kind of seeing is humanistic work. And for guidance on how to do it, I've been returning to an unlikely source: the Renaissance.

The word humanism comes from studia humanitatis—the humanities—which Renaissance thinkers recovered from classical antiquity. Petrarch, often called the father of humanism, believed that reading Cicero and Virgil wasn't merely intellectual exercise but moral formation. The ancients had thought deeply about how to live, and their wisdom remained available to anyone who would engage it.

This was radical. It suggested that meaning and virtue weren't dispensed only through ecclesiastical authority but could be pursued through education, reflection, and the cultivation of character.

But here's what gets lost in popular accounts of Renaissance humanism: these weren't monks retreating to libraries. The humanists cared intensely about practical effect. They studied rhetoric because they wanted to persuade. They advised princes because they wanted to shape policy. They believed that a person formed by the humanities would act more wisely in the world—not escape from it. Civic virtue, for them, was the point.

The becoming question isn't a retreat from practical concerns. It's the most practical concern there is.

The Weakening of Humanistic Assumptions

We're living through a moment when humanistic assumptions have profoundly weakened—and the common thread is this: human judgment is increasingly treated as a problem to be solved rather than a capacity to be cultivated. Markets optimize around it. Algorithms route around it. Political discourse treats persuasion as naive when you can simply mobilize your base. Even universities have retreated from the humanities, the disciplines that once existed precisely to form judgment rather than replace it.

Meanwhile, technology poses genuinely new questions. If AI can write competent prose, what is the value of human writing? If algorithms can optimize decisions, what role remains for human judgment?

The instinct, for many technologists, is to dismiss these questions as hand-wringing—the anxious complaints of people being displaced. But we need to take them seriously. Not because AI will replace human judgment, but because how we use AI shapes what human judgment becomes. The question isn't whether humans remain in the loop. It's what kind of humans, with what capacities, exercising what kind of judgment.

The Renaissance humanists understood this about education: you don't just transmit information, you form identity. The same is true of technology adoption. Every tool teaches its users something through its design choices.

What Technology Adoption Teaches

Technology adoption shapes organizational culture. It sends signals about what's valued, what's rewarded, what kind of people thrive. An organization that adopts AI purely for efficiency becomes a place where efficiency is the highest value—where people learn to optimize, to move fast, to reduce friction. This isn't necessarily bad, but it is a choice, and it has consequences.

An organization that adopts AI humanistically becomes something different: a place where people are expected to exercise judgment, to take responsibility, and to think carefully about consequences. Where the question "should we?" is given as much weight as "can we?" Where the humans in the loop are genuinely empowered to be human—which means empowered to be slow, to be uncertain, to refuse the obvious optimization when something about it feels wrong.

This is what the second question forces you to see: not just the immediate capability gain, but the slow reshaping of human capacity that follows.

Luddism in a Toga

I can hear the objection: this is just technophobia dressed up in Renaissance costume. Another humanist wringing his hands while the builders build.

But I am a builder. I deploy these systems. I teach people to use them. I've seen what they can do, and I want more of it—more capability, more reach, more of the genuine good that comes from augmenting human capacity with machine intelligence. The question isn't whether to build. That's settled. The question is whether we're paying attention to what the building does to us.

The Luddites smashed looms because they saw their livelihoods threatened. That's not what I'm describing. I'm describing something more like what a master craftsman knows: that how you work shapes who you become. A carpenter who rushes every joint eventually loses the ability to see when a joint needs care. A writer who optimizes for engagement eventually loses the ear for prose that serves a different purpose. The tool doesn't just produce output. It molds perception.

This isn't an argument against capability. It's an argument for a certain kind of attention while building capability. The humanist question—what are we becoming?—doesn't slow the work down. It focuses the work on what is most valuable.

Most decisions should be optimized. Most processes should be made more efficient. The humanistic instinct isn't to resist optimization but to notice when optimization has become the only lens available—when "is this efficient?" has crowded out "is this wise?" or "is this what we should want?" The discipline is knowing when to ask the second kind of question. Not always. But sometimes. And not losing the capacity to recognize which is which.

The Practitioner's Discipline

Here's the deepest humanistic question for anyone doing this work: What is immersion in AI systems doing to you?

The danger isn't that you'll become a machine. It's subtler. It's that you'll start to see the world through the lens of optimization—that you'll lose patience with the slow, inefficient, gloriously human ways people muddle through problems. The occupational hazard of AI work is that it colonizes your perception.

Consider commercial lending. There's a version of AI adoption that keeps the process personal but makes it faster—the relationship officer still knows the borrower's business, still exercises judgment about factors that don't fit neatly into a credit model, but spends less time on document processing and data gathering. And there's another version that optimizes the human out entirely: faster still, more consistent, scalable in ways the first version never will be.

For a large institution competing on rates, the second version might make sense. But a smaller lender can't win on rates. What they have is the relationship—the officer who knows that this borrower's financials look weak because they just invested heavily in equipment that will pay off next quarter, or who senses something off despite clean numbers. Optimize that away and you haven't just lost a feature. You've eliminated the only advantage you had.

The danger isn't that someone decides to kill the relationship. It's that the metrics don't capture it, so it gradually stops being visible as a thing worth protecting. That's what colonized perception looks like: not malice, but a slow narrowing of what counts as important.

Keeping that question alive—what are we becoming?—requires counterweights: practices that resist the logic of efficiency, that insist on particularity and ambiguity, that develop patience rather than speed.

I print photographs in a darkroom. Not because the results are better than what I could achieve digitally—they're not, by most measures—but because the process resists everything my professional life rewards. Chemical development has fixed timing you cannot negotiate with. You stand in darkness, watching an image emerge on its own schedule, and no amount of urgency will make the silver halides react faster. The enlarger doesn't care about your deadline. The fixer takes exactly as long as it takes.

This is not productivity. It's practice—in the older sense, the way a pianist practices scales or a writer keeps a journal. The point isn't output. The point is maintaining a capacity that optimization erodes: the ability to work at a pace the work demands rather than the pace I prefer.

The specific practice matters less than the discipline of maintaining one. What matters is having something in your life that refuses to be optimized—that teaches you, over and over, that not everything worth doing submits to efficiency. Without that counterweight, the colonization is silent and complete.

The Urgency of the Humanities

Petrarch read Cicero because he believed the ancients had something to teach him about how to live. The questions they asked—about virtue, about civic responsibility, about what we owe each other—weren't antiquarian curiosities. They were urgent, practical necessities for anyone who wanted to act well in the world.

We build AI systems that will reshape how millions of people work, decide, and relate to one another. The question of how to live hasn't gotten smaller. It's gotten more urgent.

The humanities aren't a luxury we've outgrown. They're a discipline we desperately need—now more than ever, and especially for those of us building the machines. Not because they'll make us more cultured or well-rounded, but because they're how we learn to ask both questions: What are we building? and What are we becoming?

The Renaissance humanists understood that practical wisdom required formation—that you couldn't act well in the world without becoming a certain kind of person. They were right. The open question is whether we'll still believe that in ten years—or whether we'll have optimized our way out of understanding why it mattered.