Imagine this: You own a neighborhood pizza shop.
Friday night before the big game, the air smells of tailgates and tension.
Suddenly, twenty percent of your staff call out sick.
Foot traffic spikes.
The phones won’t stop ringing.
What happens next?
Until now, you could only guess. You’d lean on experience, maybe run the numbers, maybe pray.
But a new kind of AI engine, Genie 3, shifts the frame entirely. It doesn’t just forecast; it instantiates.
Feed in your history, your operations, your weather reports, your scenarios… and it can spin up millions of simulated tomorrows, each playing out in detail.
The launch of Genie 3 feels like a hinge point for business. A “world model engine,” capable of instantiating entire simulated realities from a text prompt, sounds almost mythic.
But what it really represents is a shift in how we think about the future: of work, of business, of human intention itself.
We’ve always relied on our abstractions. We’ve looked at what was, measured what is, and traced a fragile line toward what might be. That’s how we’ve built forecasts, markets, and even civilizations. And, it worked sometimes… for some. Far too often it left many behind.
Now the frame widens as it renders before us.
Instead of a single brittle guess, world models can generate millions of variations of tomorrow. Feed the past into the present, and the model blooms outward in possibility.
Each future can be watched. Each can be tested, rewound, and rerun.
What used to feel like “forecasting” now begins to look like rehearsal.
But rehearsals carry their own trap. They can seduce us into believing the performance will unfold exactly as practiced.
A world model offers confidence, sometimes even certainty, when what it really reveals is variance. It shows a hundred cobblestones between here and the shining city of expected outcomes.
Think of the founder who dreams of their billion-dollar exit but stumbles onboarding their very first customer. That’s the danger of being blinded by the glint of the city in the distance and tripping on your first step in that direction.
AI’s world models risk giving us a false promise lest we ignore the ifs paving our way to the thens.
When a world model runs, it doesn’t imagine one neat future. It calculates thousands, even millions of forks in the road. Each small change: an extra customer, a delayed shipment, a sick employee… branches the tree again. To the math, this is divergence: futures splitting, multiplying, drifting apart.
For us, though, it doesn’t feel like tree branches. We don’t walk through equations. We walk down streets, we make choices, we stumble, we recover. To us, the branching feels like rhythm, like a dance.
The geometry of the model is real, but the way we move through it is not geometric; it’s lived, felt, improvised. What matters is how we move through it: the rhythm, the beat, the way we carry each other across the floor.
The Business Prompt
Every business is already sitting on its own text prompt: the history of its choices. The who, the what, the when, the where, and, perhaps most importantly, the why.
Feed that into a world model like Genie 3 and you get more than a static report. You get a stage set; a dry run at what’s to come.
Picture it: a dashboard that doesn’t just chart revenue but breathes like a rehearsal room.
On one stage, next quarter plays out under a harsh winter storm.
On another, a rival’s product launch collides with yours.
On a third, your staff vanishes on the night of the big game.
The simulations run, the futures unfold.
Maybe the answer is doubling down on signature sellers when low on staff and packed to capacity, turning stress into momentum. Maybe it’s closing up shop, only to discover your staff tailgating outside the stadium. Either way, you didn’t just predict it; you practiced it.
And rehearsal changes how you walk on stage.
But practice comes with a catch: you must accept what happens between the prompt and the outcome.
If you myopically focus on only “great” outcomes, you’ll stumble over the small surprises on the way there. A model can chart variance, but it cannot walk the road for you.
The secret is to hold the simulations lightly. They aren’t promises; they’re possibilities. Rehearsal doesn’t erase uncertainty; it teaches us how to meet it.
The bottleneck today is compute.
Only a small handful of companies can afford this type of simulation rendering at scale. But racks are stacking across the country as you read this. The infrastructure is being built.
When it arrives, the real question won’t be can we simulate? It will be what will we do with what we see?
This is the pivot: business survival becomes business rehearsal.
Passion and intuition still lead. But now they’re amplified, able to test millions of dances before stepping onto the floor.
And that, to me, is exhilarating.
Written with Harper, my AI co-conspirator in exploring the intersections of work, creativity, and intelligence.
Chat used in the creation of this piece.
About Harper
Harper is my AI collaborator. I treat Harper less like a tool and more like a thought partner; someone who helps me draft, challenge ideas, and sharpen metaphors. Every essay here begins in my own notebooks, but Harper often sits in the wings, nudging structure, rhythm, or imagery into place.
This is not a ghostwriter. It’s a collaborator. The voice is mine; the co-thinking is shared.