Rediscovering the Edges: Why AI Feels Like Playing Again

AI isn’t about getting prompts right. It’s about rediscovering curiosity—testing what’s possible, staying open to surprise, and growing alongside a system that’s still unfolding. Leaders don’t need to be prompt engineers. They need to create the conditions for their teams to play.

Why This Matters

For the first time in decades, technology feels unfinished. Generative AI is less like software you install and more like a system you grow with. That’s a cool moment. It means the edges aren’t set and the organizations that lean into play and curiosity will find the biggest opportunities.

I grew up on a farm in rural Ontario. When something broke, you fixed it. When something needed doing, you did it. The weather was a constant variable, so plans never went perfectly. Outcomes mattered—at the end of the season, you either had food or you didn’t.

That kind of upbringing shapes you. It gave me a strong sense of agency: what you put in connects directly to what you get out. It also taught me to improvise, to tinker, to try.

Around that same time, I was also one of the first kids I knew to have a computer at home. My grandmother was a teacher, and thanks to a school board program, we ended up with a 286 personal computer. Turn it on, and you were staring at DOS. No friendly icons, no app store. Just a blinking prompt. To say holy shit is an understatement.

I don’t remember having instructions. I remember sitting with a friend and just trying things. Type this, hit enter. What happens? Sometimes nothing. Sometimes magic.

When Windows arrived, it felt unbelievable. I remember my grandmother crying when she first saw it. Pure wonder.

Then came the internet—dial-up, painfully slow, but full of possibility. I’d spend hours downloading games, figuring out how to install them through DOS even though Windows was sitting right there. I’d call a friend on the phone: “What are your settings? Did you get it to work?” We were piecing things together, building shared knowledge as we went.

“Technology was always a playground, and I learned by playing, not waiting for perfect instructions.”

I taught myself BASIC, then HTML and JavaScript. I built a website about llamas, because why not? I pulled apart toys in the workshop, rebuilt them, sat with my grandfather stripping wire for scrap. We had tools, space, and encouragement to make things. That mix of curiosity, trial and error, and hands-on work shaped how I see the world.

Looking back, I realize I’ve always had a passion for possibility. A bias toward “yes, and.” A willingness to explore where the edges are.

And for the first time in decades, AI feels like that again.

What We Lost Along the Way

Somewhere along the line, technology got too smooth. The iPhone just works. Apps update themselves. Cloud services hum in the background.

That’s been good for productivity and consumer ease, but we lost something: the willingness to tinker. To test, to play, to push at the boundaries. We stopped asking, “What else could this do?” and settled for “What it says on the box.”

Why AI Feels Different

Generative AI breaks that pattern. It’s not a finished product you download. It’s more like a system you grow alongside. Even the people who built transformers—the architecture behind tools like ChatGPT—don’t fully understand why they work as well as they do. That’s extraordinary.

“We don’t build AI—we grow with it.”

It means the edges aren’t set. We get to discover them.

Growing vs. Building

Most of our tech history has been about control: define requirements, build systems, lock them down. That works when you know the rules.

But AI isn’t like that. It’s probabilistic, dynamic, responsive. You don’t code every outcome, you explore what it can generate. That’s why the right stance is less “master the prompt” and more “stay open.”

AI is at its best when we treat it as collaborator, not calculator.

Lessons from the Farm

This all feels oddly familiar. Farming is also about working with what you can’t fully control. Weather, soil, markets—they don’t always bend to your plan. You prepare, you improvise, you adapt.

AI has that same quality. You can’t control every output. But you can shape, guide, and grow with it. You try, you learn, you adjust.

And just like farming, outcomes matter. If this tool can help us work better, think clearer, and make better decisions, why wouldn’t we use it? If we can accelerate impact, aren’t we obligated to at least be curious?

Why This Matters for Leaders and Teams

Leaders don’t need to be prompt engineers. What they need is to model curiosity. To create conditions where their teams can experiment safely. To remind people that the point isn’t “getting it right,” it’s “exploring what’s possible.”

That’s why in my workshops, I frame AI in three roles: efficiency tool, collaborator, and thinking partner. Saving time is nice, but the deeper value is in using AI to extend human capacity, to help us see and hold connections we’d miss, generate first drafts (prototypes) faster, or test ideas and iterate our thinking rapidly.

“Leaders don’t need certainty. They need to create conditions for curiosity.”

We need that help. All of us. The challenges we face in our organizations, communities, and society are too big to solve with the same capacity we’ve always had.

Back to the Edges

I still remember staring at that blinking DOS prompt, typing in commands, not knowing what would happen. Sometimes it worked. Sometimes it didn’t. Either way, it was learning.

That’s where we are again with AI. The edges aren’t set. The rules aren’t fully known. We have a chance to rediscover the spirit of play, to approach this technology with curiosity and wonder.

And if we do, we just might find better ways to do the work that matters most.

Closing thought

Strategy has always been design—imagining a future that’s better than today, then aligning ourselves to make it happen. AI is another material we can design with. The question is whether we’re willing to pick it up, tinker with it, and see what’s possible.