Have you felt it — that low-frequency hum beneath the surface of everyday life?
Technology is exponentially growing, and human evolution has become interdependent with the machine. It’s quietly rewriting the rules while some of us are still playing the old game.
The events that truly reshape history rarely begin with headlines. As Nassim Nicholas Taleb reminds us, the most disruptive forces — the Black Swans — tend to move in silence at first. They’re mistaken for background noise, dismissed as novelties. Only in hindsight do we grasp their full weight.
Artificial Intelligence may be one of those forces. But unlike the shocks of 9/11 or the 2008 financial crisis, AI’s transformation isn’t marked by a single disruption. It’s not a meteor. It’s erosion. A steady force, carving a new landscape while we’re busy tracing yesterday’s map.
We are already living through it — a silent avalanche in motion.
For decades, the 18-month doubling of computing power has driven exponential change, powering everything from pocket-sized supercomputers to machine learning breakthroughs. But exponential growth is deceptive. At first, each step seems incremental. Then, seemingly all at once, the curve goes vertical.
AI is riding that curve — not just progressing, but compounding. It learns from itself. It builds on each iteration with increasing velocity. What looks like a small advancement is often a leap masked by scale.
But this isn’t just about speed. It’s about how we play.
Simon Sinek makes the distinction between finite and infinite games. Finite games have winners, losers, and fixed rules. Infinite games evolve — the players shift, the rules change, and the objective is to keep playing.
AI doesn’t play to win. It plays to adapt. To continue. To improve. And that changes the nature of competition itself. The real advantage won’t go to those who control the board. It will go to those willing to co-create with a machine, to stay fluid, responsive, and open to constant transformation.
This is where game theory becomes more than academic. Once used to model economic behavior and military conflict, it now helps us understand how AI systems strategize, adjust, and evolve. These systems don’t just calculate — they respond. They anticipate. They play.
And while classical game theory is rooted in zero-sum thinking — for one to win, another must lose — AI invites a different approach. One where shared intelligence creates shared value. It’s less like a chessboard, more like jazz: improvised, relational, responsive.
And it’s already happening.
Artists are co-composing symphonies with AI.
Educators are generating customized lesson plans in minutes.
Doctors are using algorithms to detect diseases before symptoms emerge.
Small teams are deploying tools that once required entire institutions.
These aren’t anomalies. They’re signals — evidence that what once lived at the edge is becoming the new center.
This moment isn’t about reaching some end-point of intelligence. It’s about learning how to stay in the game — to evolve alongside systems that no longer wait for our permission to change. The task now isn’t control. It’s engagement — strategic, collaborative, and long-sighted.
What feels gradual, in retrospect, was an avalanche.
The shift is already underway. It’s just arriving the way it always does — quietly, from the edges.
