A Three-Part Series on Technology, Ownership, and What It Means to Live Well

Over the course of my life, I've watched three transformations reshape the world around me.

The first was physical.

As a child growing up in Tucson, I watched open desert where my friends and I built forts eventually become roads, parking lots, and a Walmart.

The second was digital.

As a college student in the mid-1990s, I watched computer labs, dial-up internet, and specialized software transform into personal computers connected to a rapidly expanding web.

The third is happening today.

Artificial intelligence has moved from science fiction into everyday life, raising questions about work, ownership, creativity, and what it means to be human.

Each of these transitions created anxiety.

Each created opportunity.

And each forced us to adapt.

This three-part series explores what AI may mean not only for our economy, but for our lives. We'll examine how technology changes markets, how individuals can adapt financially, and why the uniquely human qualities of meaning, purpose, and inspiration may become more important than ever.

In Part 1, we'll examine how intelligence itself is becoming increasingly accessible and scalable, and why this transformation reminds me of the day Walmart arrived in Tucson.

PART 1

When Intelligence Becomes a Commodity

Walmart, AI, and What We Risk Losing Along the Way

I was in high school when Walmart came to Tucson, but the story really begins much earlier.

As a kid in elementary school, there was a stretch of desert near my neighborhood where my friends and I built forts, rode our bikes, explored washes, and spent entire afternoons creating adventures. Looking back, I realize how much of childhood imagination is fueled by open space. There were no instructions, no screens, and no algorithms telling us what to think.

There was just possibility.

The desert wasn't economically productive. It wasn't generating tax revenue. It wasn't creating jobs. But it was creating something far more difficult to measure.

It was creating memories.

It was creating imagination.

It was creating inspiration.

By the time I was in high school, I watched that same desert disappear. Bulldozers arrived. The forts were gone. The trails were gone. The landscape that had been part of my childhood became roads, concrete, parking lots, traffic lights, and eventually a Walmart.

At the time, there was plenty of resistance.

People complained about products that were cheap and didn't last. Others objected because many of the goods weren't made in America. Local businesses worried about how they could compete against a company operating on a completely different scale.

Yet despite the criticism, the store thrived.

Including my own family, people shopped there.

Lower prices mattered.

Convenience mattered.

Economic reality proved stronger than cultural resistance.

And life moved on.

Looking back, that experience feels less like a retail story and more like a lesson about how societies adapt to efficiency.

Because Walmart wasn't really selling products.

It was selling efficiency.

The company figured out how to source labor more cheaply, move goods more efficiently, and deliver lower prices to consumers.

Whether people liked it or not, the economics were hard to ignore.

Today, when I think about artificial intelligence, that memory comes rushing back.

Not because AI is the same as Walmart.

But because the pattern feels familiar.

Walmart found cheaper labor.

Software found cheaper processes.

Artificial intelligence is finding cheaper intelligence.

For decades, we've watched technology reduce the cost of physical labor. Then we watched software reduce the cost of administrative work. Today, AI is beginning to reduce the cost of knowledge work itself.

The real disruption isn't happening in frontier models or open-source models.

The disruption is happening in the work.

AI is learning from millions of examples of human effort, judgment, communication, and expertise. In many ways, it's distilling intelligence from activities that once required uniquely human skill and making that intelligence increasingly accessible and scalable.

That doesn't mean humans become obsolete.

But it does mean intelligence itself is beginning to look more like a commodity.

And that's a profound shift.

Marc Andreessen famously wrote that software was eating the world.

Perhaps AI is taking the next step.

Not by eating the world.

But by learning from it.

By extracting patterns from human expertise and redistributing them at unprecedented scale.

As impressive as that sounds, it forces us to confront an uncomfortable question.

What happens when efficiency becomes the primary measure of value?

The debate around Walmart was filled with arguments about costs, prices, jobs, and economic growth.

The gains were easy to measure.

The losses were harder to quantify.

How do you measure a childhood memory?

How do you measure a place where kids learned creativity and independence?

How do you measure the value of open space that inspired imagination?

You probably can't.

And that is precisely the point.

Not everything important appears on a balance sheet.

Not everything valuable is economically productive.

The desert where my friends and I built forts wasn't profitable.

Yet decades later, I remember the desert.

I don't remember shopping at Walmart.

That doesn't mean Walmart was wrong.

It doesn't mean economic progress is bad.

The community adapted.

My family adapted.

I adapted.

In many ways, that's exactly what humans do. We adjust. We incorporate change into our lives. We learn. We move forward.

But adaptation shouldn't prevent reflection.

As AI accelerates, we should be asking not only what we're gaining, but also what we're giving up.

What happens if efficiency replaces discovery?

What happens if convenience replaces creativity?

What happens if algorithms increasingly shape our attention, our desires, and ultimately our decisions?

The most important questions about AI may not be technical.

They may not even be economic.

They may be deeply human.

Not "What can AI do?"

But rather:

What should humans continue doing?

Because if Walmart taught me anything, it's that we rarely stop transformational technologies and business models from arriving.

We adapt.

The more important challenge is preserving the things that inspired us before they arrived.

In Part 2, we'll explore what may be the most overlooked consequence of AI: the growing relationship between income and ownership, and why the future may belong not simply to those who earn income, but to those who systematically convert income into capital.