THERE IS SOMETHING ABOUT ANTS

Every summer I try not to work. Notice I didn’t say I try to relax. To me, those are two very different things.

I’ve discovered that my brain doesn’t enjoy sitting still for very long. My body might be idle, but my mind wanders. Give me a deck chair and a week with nothing planned, and I’ll soon find myself reading papers, sketching diagrams, or trying to understand some obscure topic that has absolutely nothing to do with software architecture.

The trick is choosing something completely outside my day job. Otherwise, it’s work. And working in your free time is counterproductive. Right?

That idea comes from Richard Feynman. The Nobel Prize-winning physicist never treated vacations as periods of intellectual hibernation. Instead, he simply changed subjects when he was forced to take a vacation. He wandered into biology labs, experimented with art, learned to play the bongo drums, and happily spent time solving problems that had no obvious connection to quantum electrodynamics. He understood something many knowledge workers forget. Rest doesn’t always come from thinking less. Sometimes it comes from thinking differently.

I’ve borrowed that habit.

Last summer I disappeared into sociology and philosophy. I spent evenings wrestling with Pierre Bourdieu’s ideas about social capital, Habermas’ thoughts on communication, and Hannah Arendt’s writing on work, action, and the human condition. None of it helped me write better code the next morning. At least not directly.

Yet it quietly changed how I think about organizations, technology, and the people who use both.

The year before that, I explored the fascinating history of the hearing aids and how it has shaped much of the digital world as we know it today.

This year I ended up somewhere even stranger. Ants!

That may seem unusual for someone working with AI, software architecture, and digital transformation. My family certainly raised an eyebrow when books about ant colonies started appearing on the kitchen table, being smuggled to the beach and inserted into my weekly reading rotation.

The funny thing is that ants solve many of the same problems we spend our days discussing in technology.

They coordinate without central management. They adapt to changing environments. They distribute work. They recover from failure. They build surprisingly resilient systems from individuals following remarkably simple rules.

No ant understands the whole colony. Yet the colony behaves as though it does.

Researchers call this emergent behavior. Intelligence appears at the level of the system rather than inside any single individual. The colony finds the shortest route to food, reacts to threats, allocates workers, and expands into new territory without meetings, status reports, or carefully crafted roadmaps.

Sound familiar?

Many of today’s AI systems work in much the same way. Multi-agent systems rely on collections of specialized agents rather than one giant all-knowing model. Each agent has a narrow task, such as planning, searching, reasoning, writing, or testing. The interesting behavior appears when they cooperate.

It turns out evolution had a head start of roughly 140 million years, and the similarities don’t stop there.

Ant colonies don’t chase perfection. They experiment constantly. Individual ants make mistakes. Paths fail. Food sources disappear. The colony adjusts almost continuously through tiny local decisions rather than grand centralized plans.

That feels surprisingly modern.

Many organizations still treat digital transformation as a giant blueprint. Define everything up front, execute perfectly, and arrive exactly where the PowerPoint predicted two years earlier.

Nature has another opinion. Small experiments with continuous feedback and constant adaptation. It isn’t a bad strategy.

One lesson has surprised me more than anything else. Ants spend an astonishing amount of time doing… apparently nothing. (They are not familiar with Richard Feynman, I take it)

Researchers have discovered that many ants remain inactive for long periods. Earlier scientists assumed they were simply lazy. Today we think those inactive ants act as reserves. They step in when workers disappear, respond to unexpected events, or provide resilience during periods of stress.

My first reaction was exactly what you’d expect from someone working in software.

“That seems inefficient.”

Then I caught myself. There must be a method to this madness.

Modern organizations often chase one hundred percent utilization. Every calendar filled. Every backlog overflowing. Every engineer permanently busy. Every CPU squeezed for another percentage point.

Nature rarely works that way. Slack is not always waste. Sometimes it is resilience.

That idea has been quietly reshaping how I think about AI as well. We often ask how AI can make every process faster.Perhaps we should spend more time asking which parts deserve to stay slow. Reflection, exploration, and curiosity rarely fit neatly inside an optimization spreadsheet.

That brings me back to why I spend my summers wandering into unfamiliar territory.

Learning outside your profession creates strange connections that never appear inside your usual reading list. Sociology changed how I think about organizations. Philosophy changed how I think about responsibility. Ants are changing how I think about intelligence itself.

Not artificial intelligence. Just intelligence.

The kind that emerges when simple parts interact in surprising ways.

There’s another benefit. Reading far outside your field makes it much harder to fall into the trap of believing your industry invented every good idea. Software engineers have a habit of rediscovering concepts that biologists, economists, psychologists, and anthropologists explored decades ago. Looking sideways saves a lot of reinvention.

So every summer I pick something that feels almost absurdly disconnected from my work.

Next year it might be fungi or medieval shipbuilding. I have no idea.

I do know one thing. Every detour eventually finds its way back into my work, usually through a door I never expected. Who knew that a few million tiny insects would have so much to teach us about the future of AI?