Mar 29, 2025
Generative interface demos are everywhere on social media: type a question, watch the interface adapt to help you book a flight or look up a stock. These demos feed our fantasy of a magical, all-knowing black box—personalized and frictionless.
But while AI has transformed how we access information, it hasn’t changed how we process it. Data still needs to be structured into tables and forms so our brains can digest it. Most AI tools follow the same pattern: define, collect, clean, structure, generate. The hardest part isn’t displaying results—it’s deciding what to generate and keeping outputs consistent.
We often want the machine to do more, faster, with no tedious setup.
This mindset keeps resurfacing in how people talk about AI. There’s a persistent gap between the fantasy of agentic systems and the reality of what it takes to make them work. Many cling to the fantasy of the “do-it-all” machines. But behind the curtain, it’s often just cobbled-together grunt work—endless tweaking, testing, workarounds.
This isn’t a cynical take. Reinventing the familiar matters. There’s real value in adapting tools to new architectures. But let’s stop pretending any of this happens without human effort. Not yet, anyway.
I keep returning to Marshall McLuhan’s “the medium is the message.” Our tools shape how we see the world. What if AI handled ambiguity or responded to emotion instead of just commands? Could it help us externalize what we can’t name, becoming a thought partner rather than a mere assistant?
Working with AI can feel like a meditation on our own complexity, maybe even a gateway to understanding others. People have tried more ambient, aware experiences, but something’s still missing. Perhaps the real shift lies in transforming the mediums themselves—because it’s the mediums, not the messages, after all.
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