"Not enough people are talking about the inverted nature of AI. Most technology is created to solve a specific problem, then becomes more specialized as it scales. Generative AI is the opposite. No one asked for it, and it wasn't built to solve one clear problem, yet it's here, placing the burden on everyday people to figure out how to use it and why."
"Because most people are already competent at their jobs, they tend to project their weaknesses onto it: if you can't write, you think AI can make you a writer; if you can't design, you think it can make you a designer. We may already be seeing the bubble begin to deflate (with Microsoft scaling back its Copilot goals, scrutiny on AI stock inflation, and the number of users in some cases actually declining) as people realize AI can't turn them into something they're not."
Generative AI arrived as an atypical technology that was not built to solve one clear problem, creating an inverted adoption challenge where users must determine how and why to use it. Everyday workers project their weaknesses onto AI, expecting it to make them writers or designers, which fuels unrealistic expectations. Early indicators show a potential deflation of the AI bubble, including scaled-back Copilot ambitions, investor scrutiny over AI stock valuations, and declining user numbers in some cases. Separately, licensed IP and brand participation in generative video are enabling brands to seed characters, mascots and worlds for audiences to remix, with high-profile partnerships signaling a shift toward participatory brand presence.
Read at The Drum
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