Think about those old computer graphics, the really early stuff. Not even video games, but the serious simulations. Like, the way a city planning model from the 1960s might have looked. Buildings weren’t complex shapes; they were often just colored blocks on a grid. A simple square, maybe a rectangle. That wasn’t just about how powerful the machine was.
It was a fundamental design choice. That grid, those limited block shapes, they imposed a constraint. They dictated what kind of city could even be shown. What futures were capable of being rendered on the screen. It was a pixel-level decision that quietly set boundaries for our imagination.
These early models, you know, they weren’t exactly ‘AI’ as we think of it today. But they were the first machines we trusted to tell us what might happen next. During the Cold War, generals fed scenarios into massive mainframes, trying to map out conflicts, predict troop movements. Economists tried to project markets, guess at growth.
They were giving us the first rough drafts of tomorrow. These weren’t just calculations. They were machine-mediated stories about what *could be*, establishing a quiet reliance on algorithms to pre-visualize our collective path. It started a habit, a way of seeing the future through a machine’s eyes.
But here’s the kicker. These systems, they had limits. Huge limits. Imagine trying to draw a detailed forest with only three crayons. What happens? You draw a lot of green blobs. The machine, like that kid with the crayons, took the easiest route.
Futures that fit neatly into its simple grids, its limited variables, its basic math – those were the ones it showed us. Not because they were more likely, or even more desirable. But because they were computationally *convenient*. It wasn’t about malice or foresight; it was simply the machine taking the path of least resistance, subtly imprinting these “easy” futures into our consciousness as plausible, even probable.
Look at how we still imagine cities today. Those sprawling, layered metropolises, often built on a grid. Or the classic sci-fi vision of space colonies, usually sleek, modular, self-contained units, repeating patterns. Where do those images come from?
Part of it, a big part, echoes back to those early models. It’s the ghost of a computationally simple idea from decades ago, rendered again and again until it became an ingrained, almost inescapable part of our contemporary imagination. We see it in movies, in urban planning proposals, in how we talk about progress.
So, what about all the other futures? The messy ones, the ones that didn’t fit a grid? The futures that were too complex, too human, too inconvenient for a 1960s mainframe to even begin to guess at? What possibilities did we just never bother to imagine, simply because the machines we used to help us imagine couldn’t draw them?
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