There’s a peculiar moment in the lifecycle of transformative technology when it stops being remarkable and starts being ordinary. We’ve seen it happen with smartphones, GPS navigation, and online shopping. Now, quietly and without much fanfare, generative AI is crossing that same threshold.
Just two years ago, the ability to conjure coherent text from a simple prompt felt like magic. People shared screenshots of AI-generated content with the same wonder they once reserved for the first iPhone. Tech forums exploded with discussions about capabilities and limitations. Everyone had an opinion, usually a strong one, about what this technology meant for the future of work, creativity, and society itself.
But something has shifted. The novelty has worn off, replaced by something more subtle and perhaps more significant: expectation. Students now assume they can get help organizing their thoughts for essays. Developers expect instant code suggestions as they type. Marketers presume they can generate a dozen variations of ad copy before lunch. The technology hasn’t disappeared from our lives; it’s dissolved into them.This transition from spectacular to mundane follows a familiar pattern. Consider how we once marveled at the ability to video call someone on the other side of the world, and now we complain when the connection lags for a moment. Or how GPS navigation transformed from a luxury feature to something we expect in every ride-sharing app. The technology doesn’t become less impressive; we simply recalibrate our baseline for what’s normal.
The enterprise world reveals this shift most clearly. Companies that spent months debating whether to experiment with generative AI are now debating which of their dozen implementations to standardize on. The conversation has moved from “Should we?” to “How efficiently can we?” Customer service chatbots, document summarization tools, and automated report generation have migrated from pilot programs to production systems with surprising speed.
Even the skeptics have adjusted their stance. Early critics warned that AI would eliminate jobs, destroy creativity, and herald the end of human expertise. While some of those concerns persist, they’ve been tempered by reality. Instead of replacement, we’re seeing integration. The graphic designer still designs, but now sketches ideas faster. The programmer still programs, but debugs more efficiently. The writer still writes, but overcomes blank pages more easily.This normalization carries consequences worth examining. When technology becomes invisible infrastructure, we stop questioning it as rigorously. We accept its outputs without the scrutiny we once applied. The same healthy skepticism that made us carefully verify AI-generated information two years ago now competes with the convenience of trusting what appears on our screens. The technology hasn’t necessarily become more reliable; we’ve simply become more comfortable with it.
There’s also a generational dimension to this acceptance. For anyone who entered the workforce or university in the past two years, generative AI isn’t a disruptive innovation. It’s just a tool, like search engines were for millennials or calculators were for Gen X. They’re not impressed by its existence; they’re annoyed when it doesn’t work well. This cohort will likely push the technology in directions its creators never imagined, precisely because they lack the reverence that comes from remembering a world without it.
The regulatory landscape reflects this transition too. Early discussions about AI governance were abstract, focused on hypothetical scenarios and philosophical questions. Now they’re concrete, addressing specific issues like disclosure requirements, bias in automated decisions, and liability for generated content. The technology has become real enough that we need real rules for it.
Perhaps most telling is how we’ve stopped explaining it. A few years ago, mentioning that you’d used AI to help with a project required context and justification. Now it’s barely worth mentioning, like noting that you used spell-check or consulted Wikipedia. The technology has achieved what all successful tools eventually achieve: it’s become boring.This doesn’t mean generative AI has stopped evolving or that its societal implications have been fully resolved. Models continue to improve, new applications emerge, and thorny ethical questions remain unanswered. But the public conversation has shifted from breathless excitement to practical consideration. We’re no longer asking “What can this do?” We’re asking “How can I make this work better for what I need?”The transformation from miracle to mundane is complete when nobody thinks to comment on it. When students submit papers without mentioning they brainstormed with AI, when developers ship code without highlighting which parts they generated, when marketers run campaigns without celebrating their AI-assisted workflow—that’s when technology has truly arrived.
We’re there now. Generative AI has joined the ranks of technologies we use without thinking, expect without questioning, and only notice when they’re absent. It’s no longer the future. It’s simply part of how things work. And perhaps that’s the most revolutionary development of all.
