Have you noticed recently how everything looks and sounds the same? Ads, products, slogans, even „innovations,” all seem to echo one other.
Originality no longer shocks us. A breakthrough product launched on Monday, and by Friday, five competitors have reproduced its essence.
The same designs, the same ideas, the same voices, recycled through different faces, brands and screens. Everyone borrows, remixes and reposts until even their own ideas sound like echoes of someone else’s.
You might think it’s synchronicity, a collective rhythm, minds aligning in the ether, a shared pulse of innovation, but in reality, it’s the echo of an age where imitation moves faster than inspiration, the byproduct of an algorithmic mirror where ideas and trends replicate faster than they originate.
We are surrounded by copies of copies, yet starving for something real. Everyone claims to be original, yet everything seems strangely familiar. Ideas circle back faster than they’re born, and the space between inspiration and imitation has almost disappeared.
While traditionally, originality meant producing something completely new, an idea or creation that had never existed before, in today’s hyperconnected world, where everything references everything else, pure originality has become nearly impossible. Every idea, story structure, design, melody, or philosophical insight is connected to a lineage of influences.
Authenticity, by contrast, is about the truth of expression. It’s the coherence (the why and the how) behind the act of creation.
If two people or brands „borrow” the same idea (which happens frequently today), the authentic one transforms it through its own sensibility (its experiences, values, voice and perspective), taking it elsewhere, while the inauthentic one imitates without depth.
This is what we might call the ethical divide today. It all comes down to why they borrow what they borrow. In an economy saturated with information, imitation, and noise, the courage to take what already exists and make it real sounds like leadership. It builds resonance where originality cannot.
„It’s not where you take things from; it’s where you take them to.” says Jean-Luc Godard and the question is no longer who thought of them first, who invented them, but who makes them mean something.
From Picasso’s canvases to AI’s algorithms, the story of creativity has always been the story of transformation, and this story is now defining the future of business itself.
Originality is often misunderstood as producing something entirely new, but nothing arises ex nihilo. Every idea is connected to others; every image carries echoes. What distinguishes the authentic creator from the imitator is the depth of transformation, the degree to which influence is digested, not displayed.
Picasso didn’t invent from a vacuum. His radical forms and fractured faces were born from what he absorbed: Iberian sculpture, Cézanne’s geometry, African masks, El Greco’s elongations. He was a devourer of visual languages. Yet, rather than replicate them, he transfigured them.
If in the past, value came from scarcity, being the only one who could create or achieve something, nowadays, when everything can be copied instantly, the edge no longer comes from being the first or the only one, but from being the best transformer. The question is no longer whether you can create something new, but whether you can remain faithful while creating it.
Today, AI (the ultimate imitator) is the newest and most radical participant in this centuries-old conversation about authenticity, originality, and transformation. It doesn’t „invent” in the traditional sense, but rather absorbs and recombines everything humans have ever created, exposing the collective nature of innovation. It can now design logos, write code, compose music, and create advertising copy faster than any team of humans.
In this way, AI is the pure embodiment of the postmodern condition: Nothing is original. Everything is recombined.
This might terrify business leaders who equate creativity with originality. But the wise will see something else: AI is not killing creativity, it is democratizing it. It reveals that what makes a brand or leader distinct is not what it produces, but the intention and insight behind it.
- AI can write your mission statement, it can’t believe in your mission.
- It can design your campaign, it can’t feel your conviction.
- It can replicate your tone, it can’t replace your truth.
Think about it, when Picasso „stole,” he filtered what he took through his own psyche. His brushstroke, his energy, his rebellion, all made the theft authentic. AI, by contrast, has no inner life (no hunger, no trauma, no lived experience to transmute it into art). It can imitate authenticity, but it cannot inhabit it.
While AI can generate the form of authenticity, it lacks its inner cause. It can produce infinite variations of Picasso, but never the anguish, joy, or vision that moved his hand.
The truth is that AI gives us an infinite amount of material, but only human consciousness can assign meaning. Only we can say why what matters matters. That is why, today unlike in the past, value comes from trust, empathy and meaning, qualities that cannot be automated.
Now, if originality is dead and authenticity can’t be automated, you might ask, what is left for us? Let’s explore this in depth.
Well, today as we become directors of meaning, shaping how AI’s generative flood is filtered, contextualized, and transformed, our creative power must shift from production to curation, intention, and interpretation. In this sense, the human role returns to what it has always been: not to invent ex nihilo, but to choose with depth, to feel with precision, to transform with care.
The problem today is not imitation per se, but algorithmic narrowing. AI and recommendation systems often narrow the experience by:
- Optimizing for engagement (showing you what you already like)
- Learning from dominant data patterns (training bias)
- Collapsing novelty into statistical averages.
Humans today are overfitted to a single data distribution (our feeds, our cultural bubbles). This is analogous to a robot trained on the same 10 demonstrations of a single object in a single room. It masters this, but fails when the lighting or object changes.
In a 2024 paper on imitation learning for robotic manipulation (Lin et al., Data Scaling Laws in Imitation Learning for Robotic Manipulation), researchers discovered a powerful principle:
Performance improves with the diversity of experiences (objects and environments), but with diminishing returns from repeating the same demonstration.
In the world of robots:
- Each new environment or object significantly expands the power of generalization.
- Each repeated demonstration adds very little once the basics are learned.
In simple terms, robots learn faster and generalize better when exposed to many different situations, rather than seeing the same one perfected endlessly.
So variety fuels intelligence; repetition breeds stagnation. If this is relevant for robots, there may be something to learn for us humans as well. This technical statement about robot imitation learning becomes almost poetic when applied to human and cultural imitation today.
We live in an imitative loop (social networks, recommendation engines, generative models), where algorithms reflect, remix, and represent our own cultural data back to us. The „mirror” is recursive; we imitate what the algorithm amplifies, and the algorithm amplifies what we imitate. This mirrors the low-diversity regime in the paper: if a robot is shown with the same task over and over again, it perfects it, but it cannot generalize.
Similarly, when people or cultures are fed algorithmically optimized sameness (trending formats, viral aesthetics, repeated memes), they become perfect imitators within narrow bands, but lose adaptability and creative transfer, which is the very mechanism of originality.
In the world of human algorithms:
- Each new perspective, culture, or aesthetic paradigm (a diverse „environment”) reignites creativity.
- Each repeated meme, format, or algorithmic trend (a repeated „demonstration”) yields diminishing creative returns.
In human terms, this means that creativity improves with diversity of influences and contexts, not with more polished repetitions of one style.
If we model this as a power law, we can obtain a new model for innovation strategy:
Innovation ∝ (diversity of inputs)ᵅ
with 0 < ᵅ < 1 (diminishing returns, but still positive)
This means that our current digital culture is at the flat tail of that curve, where adding more content of the same kind yields almost no new creative output.
If originality decays when the distribution of data collapses, when everyone learns from the same fead, the same algorithm, the same mirrors, then the path back to originality is to re-diversify the data, not necessarily to escape imitation, but to expand what we imitate.
The robot’s lesson is clear: learning thrives on breadth, not repetition.
Applied to business this means:
- Imitate widely, not narrowly. Study and borrow ideas from different industries, geographies, and cultural systems, rather than benchmarking only against direct competitors.
- Diversify environments. Experiment with business models, channels, and audiences beyond your comfort zone. A strategy tested in only one context will not generalize when the environment changes.
- Encourage cross-domain synthesis. The most creative solutions often emerge at the intersection of different „objects”: design and data, art and logistics, storytelling and software.
Originality in business is not the absence of imitation, but the mastery of it: learning how to learn from multiple sources, how to adapt patterns from one domain to another, and how to recognize when imitation has become stagnation.
Companies that practice „large-scale imitation” turn copying into creativity, by connecting disparate perspectives into unique configurations.
From this perspective, if narrow imitation leads to similarity, meta-imitation, the ability to imitate across multiple systems, could be the new strategic advantage (the new competitive intelligence).
Here’s what this could mean:
- Use AI not merely to predict consumer behavior, but also to explore alternative possibilities and simulate divergent futures.
- Treat competitors not as templates to be copy, but as signals to be recombined into new forms.
- Build cultures that reward curiosity across boundaries (where a marketing team can learn from biologists, and a logistics team from jazz improvisation).
This is how your business regains its agility by generalizing across various „environments” in the marketplace, just as the adaptive robot generalizes across various rooms.
Most digital systems today are built to reinforce what already works. Algorithms reward engagement, efficiency, and predictability, conditions that create homogeneous ecosystems of products and ideas.
To break this loop, companies need to design for diversity in three layers:
Data diversity. Train AI models and decision-making systems on larger data sets, including outliers and minority signals. Avoid letting the same customer profiles or cultural inputs define the entire strategy.
Team diversity. Assemble teams that span disciplines, languages, and cognitive styles. Diversity in thinking is the human equivalent of „multiple learning environments.”
Strategic diversity. Run controlled experiments in multiple contexts, rather than endlessly optimizing in a single market niche. Think in portfolios of experiments, not individual workflows (single pipelines).
In short, stop asking algorithms what worked yesterday; start feeding them what might work elsewhere.
Therefore, imitation itself is not the enemy. In both robotics and human evolution, imitation is how intelligence begins. If for humans it is about intelligent mimicry (the ability to perceive structure, abstract meaning and reapply it in different domains), companies can reclaim imitation as a tool of intelligence, reframing it as translation, not duplication.
In both cases, creativity arises when imitation occurs across varied contexts, not within the same one. Just as a robot learns to pour water into any cup, not only the one it was shown, companies can learn to apply insights to any market or context, not only the one that birthed them.
Here are some examples:
- A fintech firm studying theater to improve storytelling in investor pitches.
- A logistics company learning from ant colonies to optimize distribution networks.
- A fashion brand drawing inspiration from robotics ergonomics to design adaptable garments.
While business, can learn from many „objects” (cultures, disciplines, media forms) rather than repeating the same trend, originality re-emerges as the intersection of diverse imitations, the creative synthesis of multiple mirrors.
The core message here it might be a challenge to modern business itself: Don’t perfect what you already know; expand what you can learn from.
Companies that aembrace this principle will become innovation generalists, capable of thriving in shifting conditions, because their intelligence is trained on diversity, not sameness.
In a marketplace of mirrors, where algorithms amplify the past, the only way forward is to imitate across, not within. The future of business creativity will not belong to the first to act, nor even the first to copy, but to those who can synthesize multiple mirrors into a single new reflection.
In summary, originality re-emerges not when we stop imitating, but when we imitate more intelligently, in many worlds, not just our own.
AI has the potential to both accelerate imitation and help restore diversity, depending on how it is used.
(a) The destructive mirror:
- Most AI systems are trained on the same cultural data. They amplify prevailing patterns, producing a feedback loop of similarity.
- This creates an „algorithmic monoculture”: everyone consumes and creates within a narrow band of styles and narratives.
- In this sense, AI accelerates the death of originality by being a perfect imitator that rewards imitation.
(b) The constructive multiplier:
However, the same principle that drives the efficiency of robot learning could revive originality if applied intentionally:
- Inject diversity into the data: AI can simulate many environments, styles, and cultural lenses, expanding the „object/environment” space for human creativity.
- Use AI as a partner, not a mirror: Ask AI to generate divergent, unlikely, or cross-domain combinations, rather than an optimized resemblance.
- Curate rather than consume: Instead of letting algorithms feed you what is statistically similar to your past, deliberately seek out dissimilarity, new contexts to „train” on.
Depending on whether it multiplies or narrows the perspectives it trains on, AI can collapse originality, or could become a platform for epistemic diversity, a counterforce to algorithmic homogenization.
Here’re some design implications worth considering:
- AI models could intentionally maximize internal diversity of responses (entropy sampling, not top-p optimization).
- Interfaces could include a „difference slider”: generate outputs that differ from your known preferences.
- Cultural datasets could be balanced across languages, aesthetics, and minority forms, expanding „environmental diversity” in the global ecosystem of imitation.
The robotics paper’s finding (a technical note on the laws of scaling) becomes a metaphor for the human condition in the digital age: Creativity follows a power law of diversity. Every new world you see multiplies your ability to act meaningfully in unseen worlds.
So the death of originality is not final, it’s simply a symptom of overfitting. And the cure is algorithmic divergence, a deliberate expansion of what, and whom, we imitate.
In other words, originality = breadth × synthesis.
Picasso taught us that art is theft made personal. Ai teaches us that creativity is collective. Modern businesses must learn both lessons: steal like an artist, but lead like a human.
***
The universe trends toward entropy; the human spirit trends toward pattern. Creativity lives in the tension between the two.
It may not always be on Friday, not at the same time or in the same format… but it will be weekly, always fresh and to the point.
In an entropic world, a touch of randomness keeps the system alive.
Keep it handy!
