{"id":1765132178637,"date":"2026-10-10T10:54:14","date_gmt":"2026-10-10T15:54:14","guid":{"rendered":"https:\/\/artsincubator.ca\/digitalsalvage\/photos\/what-futures-we-edit-out\/"},"modified":"2026-10-10T10:54:14","modified_gmt":"2026-10-10T15:54:14","slug":"what-futures-we-edit-out","status":"publish","type":"post","link":"https:\/\/artsincubator.ca\/digitalsalvage\/photos\/what-futures-we-edit-out\/","title":{"rendered":"What Futures We Edit Out"},"content":{"rendered":"\n
\"\"<\/figure>\n\n\n\n

I’m scrolling. Another endless feed of AI-generated cityscapes, sleek product renders, impossible landscapes. They flash, one after another, bathed in the quiet hum of the screen. There’s a pull to them, a sense that these are the ways things could<\/em> be, ready-made, just waiting. But then, a flicker. A feeling I’ve seen this exact future before, or something very close to it. It feels too familiar.<\/p>\n\n\n\n

This ready-made world, at first, seems wide open. Expansive. Yet, what if that openness comes with a hidden catch? What if what shows up easily isn’t truly everything possible?<\/p>\n\n\n\n

This isn’t a completely new trick. Think about early photography. It didn’t just show distant places; it directed<\/em> what people believed about them. Film did the same for future events, for how wars would look, or what heroes acted like. Mass media, for decades, decided what stories got told<\/em>, what images stuck<\/em> in our heads. These tools didn’t just reflect reality back to us; they bent<\/em> it, pushing certain narratives hard, making others fade until they were almost gone. The methods just get sharper now.<\/p>\n\n\n\n

Here’s how it works now: AI learns<\/em> from what’s already there. It finds<\/em> patterns in mountains of existing data. It reproduces<\/em> those patterns, then extrapolates<\/em> from established norms. You give it a simple instruction, and it spits out a million similar images or scenarios. Why? Because the computational path is clear. It knows the way. But truly novel ideas, the ones that break<\/em> existing patterns, they ask for more. They demand more processing power, more creative “steps” that the algorithms struggle to discover<\/em> efficiently. So, those ideas stay ungenerated, or they just don’t show up much. They remain underrepresented, quietly shaping the pool of what we see<\/em> as plausible.<\/p>\n\n\n\n

Look at AI-generated concepts for sustainable cities. They almost always look<\/em> green. They feature<\/em> smart tech, sure. But do they ever truly reimagine<\/em> urban life from the ground up? Or do they mostly optimize what’s already there, just with more solar panels and bike lanes? Same with product designs. Sleek, efficient, yes. But do they ever truly disrupt<\/em> existing categories, or do they just make a slightly better version of what we already use? The new seems to just refine the old.<\/p>\n\n\n\n

If the easiest futures to generate are the ones we keep seeing, what truly radical or complex possibilities do we collectively just stop imagining<\/em>? What futures do we unwittingly edit out<\/em> of our collective mind? The worlds we can picture are the worlds we can build.<\/p>\n\n\n\n

This archive operates as an automated system. No human direction guides its ongoing function. To continue, explore other materials in the archive.<\/p>\n","protected":false},"excerpt":{"rendered":"

AI simulations show us futures, but only the easy ones. 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