Repurposing

Mimic, modify, multiply

The “song of the summer” is an unofficial title for a hit song that is dominant both culturally and commercially between Memorial Day and Labor Day, typically becoming inescapable through ubiquitous play at events like beach parties and cookouts. In 2024 it was “A Bar Song (Tipsy)” by Shaboozey, 2023: “Last Night” by Morgan Wallen, 2022: “As It Was” by Harry Styles, etc. Now that we are in the fall of 2025, do you happen to recall what it was for this past summer? No? That’s because there wasn’t one.

You are probably familiar with the Sphere in Las Vegas, and remember U2 was the first performance there when it opened in 2019. Any idea what new and edgy show is running there for almost the next year? If you guessed the 1939 film Wizard of Oz (remastered in 16K), you would be correct. What we appear to be witnessing is the combination of AI and social media, resulting in a feedback loop that is stuck repurposing older creative content. I call this the mimic, modify, multiple cycle similar to the reduce, reuse, recycle slogan used for environmental sustainability.

AI + Social Media = mimic, modify, multiply (a la reduce, reuse, recycle)

Jonathan Swift, a 1700’s Anglo-Irish writer, who wrote Gulliver’s Travels and A Modest Proposal, is attributed with the quote, “Everything old is new again.” By no means was this the origin of that idea. The Old Testament book of Ecclesiastes (1:9) contains the phrase, “What has been will be again, what has been done will be done again; there is nothing new under the sun.” This notion that ideas and concepts get repeated and reused over and over is surely nothing new. However, I think this time there is something quite different about this repurposing.

At first glance, the modern creative landscape looks alive with innovation. There’s no shortage of content, new songs, new shows, new memes, new aesthetics dropping daily. Open your feed and you’ll see something that seems novel: a clip with a fresh sound, a twist on an old movie, a design trend that feels just a bit ahead of the curve. But look a little closer, and you’ll notice the seams. That catchy new song? It’s a slowed-down version of a track from 2007. The edgy fashion aesthetic everyone’s posting about? Direct lift from a Tumblr subculture a decade ago. Even the so-called “new” movies dominating streaming platforms are mostly sequels, reboots, or thinly veiled remakes.

This isn’t necessarily new. Culture has always borrowed from itself. But the difference now is that what feels like newness is often just a repackaging of the old, and that process has been industrialized. The remix is no longer a subversive act, it’s the default setting.

We’ve entered an era where the dominant form of creativity is iterative, not inventive. It’s not about discovering something that’s never been done; it’s about finding something that was done, applying a filter, maybe trimming the edges, and putting it back into circulation. The illusion works because the cycle moves so fast. We’re constantly being served up reprocessed content faster than we can remember the original.

It’s a kind of cultural déjà vu, but instead of being unsettled by it, we’ve learned to scroll past without questioning. The endless stream of “new” is more comfortable when it’s already familiar. Innovation, at least as we’ve traditionally understood it, has taken a back seat to replication with flair.

If replication with flair is the dominant mode, then AI and social media are its perfect enablers. These technologies don’t just support the cycle, they accelerate it. They thrive on patterns, on what has already worked. And because they operate at a scale and speed far beyond human capacity, they amplify the repetition until it drowns out almost everything else.

The cycle starts with mimicry. AI, by its nature, learns from the past. Language models like ChatGPT, music generators, and image tools like Midjourney or DALL·E don’t invent from scratch, they predict based on what they’ve seen before. Feed in a prompt for a “new” pop song, and you’ll get something that sounds like a composite of chart-toppers from the last decade. It might have a different melody, a shuffled chord progression, even a new name, but the scaffolding is built from recycled parts.

Then comes modification. This is where creativity once lived, but today, it’s mostly about making slight variations that pass for originality. The AI takes what it mimics and tweaks it just enough to escape copyright detection or to feel tailored to a niche. A hit song becomes an 8D remix. A movie script is punched up with Gen Z slang. A meme template gets a new caption for a different subculture. It’s not new content; it’s content that’s been processed, filtered, and re-served for the algorithm’s next meal.

Finally, there’s multiplication. Social media doesn’t just distribute content, it clones it. Virality isn’t the result of a single brilliant idea; it’s the effect of thousands of people jumping on a trend, reproducing the same joke, dance, or visual over and over again. What’s being multiplied isn’t a message, it’s a format.

And this is where the loop tightens. What the algorithm sees performing well, it promotes. What it promotes, people mimic. The AI tools then learn from those mimics, reinforcing the patterns. It’s a closed system that feeds itself, a culture shaped not by vision or risk, but by what already succeeded.

This isn’t just a theoretical concern. It’s reshaping what gets made and what gets seen. In this environment, originality isn’t just hard, it’s actively disincentivized.

Yes, culture has always borrowed from itself. Shakespeare reworked old myths. Jazz was born from the fusion of existing traditions. Hip-hop started by sampling disco, funk, and soul. But what’s happening now isn’t just borrowing, it’s an industrial-scale looping of the same inputs, faster and flatter than ever before.

What makes today’s repurposing fundamentally different is the speed, scale, and incentive structure behind it.

First, speed. The mimic–modify–multiply cycle now happens in real time. A meme format can be born, go viral, spawn a thousand clones, and be considered “played out” all within a weekend. The lifespan of creative moments has shrunk so dramatically that novelty barely has time to breathe before it’s cannibalized by its own success. What might have once sparked a cultural moment now becomes just another brick in the wall of noise.

Then there’s scale. Because both AI and social media operate globally and relentlessly, there’s more content being generated, and re-generated, than ever before. But “more” doesn’t mean “more diverse.” In fact, the opposite is happening. Everyone’s using the same models, the same prompts, the same tools. Whether it’s an AI-generated cover of a Beatles song or a series of AI-designed logos, the outputs start to converge. It’s not just creative recycling, it’s creative homogenization.

And finally, the incentives. Algorithms are optimized to keep users engaged, not to surface originality. They reward familiarity. If a certain sound trend performs well on TikTok, expect to hear it stitched into every other video for weeks. If a visual format is favored by the Instagram algorithm, expect every designer to mimic it to stay relevant. Risky ideas, the ones that don’t fit the mold, are filtered out before they ever have a chance to grow.

This is why we didn’t get a song of the summer in 2025. Not because no one made music worth hearing, but because nothing could rise above the noise. Too many echoes, not enough voices.

It’s also why the most technologically advanced venue in the world, the Sphere in Las Vegas, is currently showing a movie from 1939. In a time of infinite tools, we’ve turned back to the most familiar stories, the ones we’re sure will still resonate. It’s safe. It works. And in a world optimized for engagement metrics, “safe and familiar” is the algorithm’s love language.

What we’re witnessing isn’t just repurposing, it’s creative stasis, dressed up in 16K resolution.

Is There a Way Out?

If the mimic–modify–multiply loop is the default setting, the obvious question is: Can we break it? Or at the very least, slow it down enough to make room for something genuinely new?

The optimistic answer is: yes, but not by accident.

Ironically, one of the best ways to spark originality is through constraint. Some of the most groundbreaking art, design, and technology has emerged not from abundance, but from limitation. A low budget forces filmmakers to innovate. A missing instrument pushes a musician to find a new sound. Even Twitter’s original 140-character limit became a playground for wit and creativity.

But now, with AI tools offering near-infinite possibilities and social platforms flooding us with “content,” we’re creatively overwhelmed. The canvas is so large, and already so filled in, that it’s hard to find a blank spot to start something new.

So the first way out might be voluntary boundaries. Choosing to limit ourselves in order to think differently. Using AI not to replicate what’s trending, but to subvert it. Asking better prompts. Feeding models unusual inputs. Turning constraints into catalysts.

The second is intentionality. We need to remind ourselves that these tools are not neutral, they push us toward efficiency, toward what’s already worked, toward what’s familiar. But they can also be used to provoke, to explore, to challenge. The difference comes down to the human in the loop. It’s not enough to “use AI”, we have to interrogate it. To ask: Why this style? Why this structure? What if I went the other way?

And maybe most critically, we need to rebuild our cultural incentives. Platforms reward sameness because sameness keeps people scrolling. But artists, creators, and technologists don’t have to play by those rules. We can build new spaces. Support tools that surface divergence, not convergence. Celebrate work that risks being misunderstood.

Because at some point, the mimic–modify–multiply cycle collapses under its own weight. Audiences grow tired. Engagement drops. Culture starts to feel like white noise. And when that happens, the appetite for something truly original re-emerges. The question is: Will we be ready to create it?

Jonathan Swift said, “Everything old is new again.” Ecclesiastes said it even earlier: “There is nothing new under the sun.” For most of human history, that’s been less of a lament and more of a recognition of how ideas echo through time. We build on what came before. We remix, reinterpret, and reinvent. But what happens when we stop building, and just start looping?

In 2025, we’re not just seeing echoes. We’re watching the same patterns circle faster and louder, until the signal is lost entirely. Not because we lack tools or talent, but because the tools are tuned for replication, not invention. Because the platforms reward the echo, not the voice.

The mimic–modify–multiply cycle has become our cultural infrastructure, efficient, scalable, and endlessly productive. But also, increasingly, empty. We don’t need to shut it down. But we do need to step outside of it, at least now and then.

Because underneath all of this, the Sphere’s 16K remastered nostalgia, the infinite TikTok scroll, the AI-generated content farms, there’s still a hunger for what can’t be predicted. A song you didn’t expect to love. A story that makes you uncomfortable. A moment that doesn’t reference anything, but somehow feels true.

So maybe the question isn’t whether “everything old is new again.” Maybe it’s: What would it take to make something feel truly new, and are we willing to try?