Generative AI content suffers from a pervasive uniformity that undermines creativity
Generative AI is impressive. There’s no denying that. But there’s also no denying a clear pattern: it produces work that looks and sounds the same. Whether you’re using it for video, images, or text, it tends to average things out. The result? Outputs that are safe, repetitive, and familiar. That might get the job done in basic use cases. But it doesn’t capture attention, and it definitely doesn’t push boundaries. Creative work, especially in business and media, thrives on uniqueness. Right now, AI isn’t giving us that, at least not consistently.
This problem doesn’t come from a flaw in the tech. It’s just how it’s designed. These models analyze billions of pieces of content and then deliver what sits closest to the average of what they’ve learned. That means if you ask AI to create a marketing video, design a product image, or write a press release, you get the “most statistically probable” version of that content. That’s not creativity. It’s optimization. To get closer to originality, people have to prompt the model with very specific, sometimes complicated, instructions. And even then, it only takes you slightly off the curve.
This issue matters as businesses increasingly depend on AI to scale content faster. If marketing leads, brand heads, or content strategists start publishing high volumes of AI-generated work, they risk losing the one thing that separates good content from noise, identity.
Mike Krieger, co-founder of Instagram and now Chief Product Officer at Anthropic, pointed this out recently. When asked whether a product like OpenAI’s Sora could replace social media platforms, he explained that for any AI-generated content to be compelling over time, it has to feel like it changes, evolves, and surprises. He said, “It must feel varied over time and not just sort of like, ‘Yeah, okay, I’ve kind of seen it before.’” And he’s right. Audiences won’t stick around for recycled ideas, even if they’re repackaged well.
If your brand depends on holding attention, and most successful ones do, you can’t afford to look and sound like everyone else. Right now, AI defaults to sameness. We need tools, processes, or breakthroughs that push AI from predictability to real imagination. Until then, humans still lead on originality.
The initial novelty of AI-generated content quickly fades as repetitive styles become overused
Generative AI can surprise you, once. Then it repeats itself. The first time a model creates an image in the style of a well-known artist or a unique visual concept, it feels fresh. But if that style gains traction and others begin using the same prompts, the results quickly become indistinguishable. This has already played out.
Take OpenAI’s Sora model. Early users began generating images using a Studio Ghibli-style prompt. It caught on fast. But within weeks, everyone was doing it. What began as novel turned into a visual echo chamber. Once the market was saturated with the same aesthetic, the energy drained out of it, and people moved on.
That’s the cycle. AI-generated content experiences trend fatigue, not because the tech isn’t powerful, but because its creative methods lead to convergence. The more a prompt works, the more likely others will adopt it. The more it’s adopted, the less distinct it becomes. As a result, widespread AI-generated content drifts toward uniformity at scale.
For business leaders and brand strategists, this creates risk. Using generative AI to build campaigns, product visuals, or consumer-facing experiences that rely on trends will only give you a short window of relevance before the crowd moves on. When everyone copies the same idea, no one stands out. You lose differentiation, and eventually, audience interest.
The solution isn’t to reject AI. It’s to use it differently. Leaders must focus on guiding creative teams and prompt engineers to emphasize variation, original thinking, and tailored prompts that are harder to mimic. Rapid ideation is nothing without sustained attention, and attention sticks with what you haven’t seen before.
When you use AI, the goal shouldn’t be to chase the last good idea. It should be to define the next one. This requires deliberate constraint-breaking, not just using what worked for someone else. Without that shift, AI-produced content will keep delivering short-lived novelty, not lasting brand value.
AI-generated text exhibits a standardized tone that diminishes differentiation
Business writing used to reflect the company behind it. Different voices, styles, and tones gave you a sense of who you were dealing with. That’s changing. As more teams rely on AI to write press releases, pitch emails, reports, and other material, the results are beginning to blend together. The format may be clean. The message may be clear. But it all starts to sound like it came from the same source.
AI isn’t deciding what your brand sounds like. It’s giving everyone the same polished template. This is a side effect of the fundamental mechanics of these models, they generate content based on recurring patterns found across huge amounts of text. That includes business articles, corporate emails, and marketing documents. So when you use AI to create something, you get what’s most statistically common for that use case. And that’s the issue.
It’s not that companies are becoming less creative. They’re adopting tools that unintentionally suppress voice in favor of consistency. For some industries, that might be fine. But for executives trying to scale brand visibility, establish authority, or connect with customers, sounding like everybody else is a disadvantage.
There’s a cost to blending in. Brand equity gets diluted when your message could just as easily come from five other players in your space. When every PR pitch feels like it came from the same agency, it’s harder for journalists or partners to recognize what makes your company stand out. When every leadership email sounds like an HR template, people stop reading.
You don’t need to eliminate AI from the writing process. What you need is human oversight that restores intent and voice. Use AI as a draft assistant, not your final word. Edit with purpose. Inject traits that reflect your leadership style, company values, or market position. Otherwise, you’re leaving your most visible communications in the hands of a system trained not to differentiate.
If standing out still matters, and it does, then you need to keep control of how your company sounds. AI can help, but it should not define you.
Generative AI must evolve from merely competent execution to genuine creative innovation
The core technology behind generative AI is solid, more than that, it’s groundbreaking. Models like Sora can simulate realistic physics in video generation. ChatGPT produces fast, coherent responses that often outperform human writing in structure and clarity. Image models can generate visuals that pass as studio-quality. These systems are miles ahead of where we were just a few years ago.
But execution alone isn’t enough. The current generation of AI systems is optimized for competence, accuracy, fluency, polish. What it lacks, consistently, is unpredictability. Surprise. Distinctiveness. Content either blends in with what’s already out there, or it feels slightly repackaged from something you’ve seen before. For real creative value, we need AI that can go beyond what the average user could produce with a few well-placed prompts.
That transition, from utility to originality, is not a small adjustment. It requires revisiting the architecture of the models, the diversity of their training data, and the way they’re prompted and refined. Right now, generative AI gives the illusion of creativity because it borrows from a wide range of existing material. But it rarely makes connections or choices that feel new or break convention.
For executive teams leaning into AI-powered content, this distinction is critical. These systems are great for speed, scale, and standardization. But those aren’t the qualities that define memorable product launches, high-impact campaigns, or landmark brand moments. If everything you release feels optimized but forgettable, you missed the mark.
Over time, improving technical quality will be the baseline. The real differentiator will be how AI can generate ideas and formats that shift perception. We’re not there yet. Until that happens, leaders should treat generative AI as a co-pilot, not a replacement for the human capacity to originate. Invest in people who know how to push these tools, structure better prompts, and challenge the defaults.
Creativity still demands intent. AI will get better. But the leap from competent output to original thinking will come when we stop asking it to do what’s expected, and start building systems that explore what hasn’t been done yet.
Key highlights
- Generative AI lacks distinctiveness: AI-generated content tends to average out creative input, resulting in visuals, videos, and text that feel repetitive. Leaders should ensure human input guides AI output to protect brand voice and creative differentiation.
- Novelty fades fast in AI content: Popular prompts and styles quickly become overused, leading to trend saturation. Executives should invest in creative frameworks that encourage prompt diversity and prevent reliance on short-lived visual trends.
- Brand voices are getting lost: AI-generated writing is flooding professional communication with generic, indistinct messaging. CMOs and comms teams should retain editorial control to preserve strategic tone and articulation of brand identity.
- AI is competent, not yet creative: While technically impressive, generative AI still falls short of delivering original or emotionally resonant content. Organizations should treat AI as a performance enhancer, not a creative lead, and continue prioritizing human-driven storytelling and innovation.
