Six percent. That’s how many AI-generated ideas are unique across sessions, compared to 100% for humans. The research from 2024–2026 has answers — just not the ones anyone expected.
I was going to open with a quote from some tech CEO about the future of creativity. Then I realized that’s exactly the kind of thing that makes people close the tab. So instead, here’s the finding that stopped me cold last February, when I was updating this for a client presentation.
Wharton researchers gave the same prompt to ChatGPT across multiple sessions. Only 6% of the ideas were unique. Meanwhile, 100% of human-generated ideas were unique. Same prompt. Totally different distribution.
This doesn’t mean AI is bad at creativity. It means the question “is AI creative?” is the wrong question. The right question is: what kind of creative work does AI actually change, and for whom?
- AI beats the average human on standard divergent thinking tests (AUT, DAT) — multiple peer-reviewed studies confirm this
- The best humans still win. Peak human creativity consistently exceeds AI across every study
- AI helps weak writers more than strong ones. A Science Advances study of 500 writers found AI lifted low performers significantly; top performers saw little benefit
- AI makes teams think alike. Wharton research: 6% of AI ideas unique vs. 100% for humans. That’s a diversity collapse, not creativity enhancement
- Audiences can tell — and they care. Research shows people consistently rate AI art lower than equivalent human art, even when they can’t identify which is which
- Collaboration beats either alone. Human-AI co-creative design outperforms both solo human and solo AI approaches in quality metrics
- What the research actually shows — not what you’ve heard
- Where AI genuinely wins (and why it’s less impressive than it sounds)
- Where humans still dominate, definitively
- The diversity problem — the finding everyone’s ignoring
- Collaboration: the data on human+AI vs. either alone
- What this means for your practice
There’s been a lot of noise about AI creativity. Here’s the signal.
The most cited benchmark is the Alternate Uses Task — you’re given an object, you generate as many creative uses as possible. A 2024 study in Scientific Reports compared 256 humans to three AI chatbots on this task. Sample: 256 human participants, Western countries, young/middle-aged adults; limitations acknowledged
Result: on average, the AI chatbots outperformed human participants. While human responses included poor-quality ideas, the chatbots generally produced more creative responses. However, the best human ideas still matched or exceeded those of the chatbots.
A larger study — involving over 100,000 people, run by researchers from Université de Montréal, Concordia, University of Toronto, Mila, and Google DeepMind — reached a similar conclusion: generative AI can now beat the average human on certain creativity tests. Published: Scientific Reports, January 21, 2026
So. AI beats average humans. That part’s real.
But there are three things that nuance this almost immediately.
First: divergent thinking tests measure a specific, narrow slice of creativity — generating multiple uses for an egg, coming up with unusual word associations. They’re valid tools. But a high AUT score doesn’t mean you can write a novel or design a building. The benchmark and the creative task aren’t the same thing.
Second: ChatGPT demonstrated greater productivity than humans, but it exhibited a comparable fixation bias, with most ideas falling within conventional categories. The model showed a limited capability to differentially evaluate originality, as it struggled to distinguish between original and conventional ideas, unlike humans who are typically able to make this distinction. 2025 study, n=47 human participants + 50 bootstrapped AI observations; single model instance; Frontiers, PMC verified
Third: the diversity problem. Which I’m coming back to in section 04, because it’s the finding I think matters most and gets discussed least.
02 / AI StrengthsWhere AI Genuinely Wins
Okay, let’s be fair. AI has real advantages that aren’t marketing.
Speed and volume, obviously. This isn’t even worth arguing. What used to take a designer a day now takes minutes. That’s real. A graphic designer I know in Berlin — she’s been doing brand work for 11 years — told me she now uses Midjourney for the first 45 minutes of every project. Not because it does better work. Because it gets her unstuck. “It breaks my fixation,” she said. “I stop staring at a blank page and start reacting to things.”
That matches the research. The Science Advances experiment with 500 writers found: having access to generative AI causally increases the average novelty and usefulness — two frequently studied dimensions of creativity — relative to human writers on their own. Preregistered experiment, 500 participants from Prolific platform, GPT-4; Science Advances, DOI: 10.1126/sciadv.adn5290
But — and this is important — generative AI may have the largest impact on individuals who are less creative. The benefit isn’t evenly distributed. Strong writers improved a bit. Weak writers improved a lot.
If AI tools help weak creators more than strong ones, the market signal is: average creative output improves, while the gap between AI-assisted work and top-tier human work actually widens. You end up with more mediocre-to-good content flooding the market, and the genuinely exceptional human work becoming more distinguishable by contrast — not less.
This is the opposite of the “AI will replace all creatives” narrative. It’s closer to: AI will replace mid-tier work, and premium human creativity will become scarcer and more valuable. Not a guarantee. But that’s what the trajectory suggests.
AI also genuinely excels at cross-domain synthesis — pulling patterns from wildly different fields and recombining them. No human has read every paper in molecular biology and architecture and 18th-century Japanese poetry. An LLM has processed all of it. That’s not nothing.
03 / Human StrengthsWhere Humans Still Win, Definitively
The peak. Every study says the same thing. The best human ideas are still better than AI. Not slightly — in some domains, significantly.
Why? No single brain region houses creativity. Instead, this process relies on collaboration between various brain areas and networks — including the executive control network, the default mode network (associated with mind-wandering), and the “aha moment” linked to activity in the parietal cortex. That four-stage model — preparation, incubation, illumination, verification — involves a wandering mind connecting things that don’t seem related.
AI doesn’t wander. It samples from a probability distribution. Those are very different things.
The emotional authenticity gap is real, and research backs it. The products of AI are consistently judged to be worse than human-created art, even when comparable in quality. Frontiers in Psychology, 2025; DOI: 10.3389/fpsyg.2025.1509974; n=92 participants; religious art domain specifically, bias stronger among experts
The study used eye-tracking to test whether this was explicit bias or implicit. Turns out: it’s both. People look at AI art differently. Not just consciously rate it lower — their attention patterns change. That’s not a small thing if you’re a brand building a visual identity or a publisher wanting emotional connection with readers.
Knowing something was made by a human — the backstory, the intention, the years of practice behind it — changes how we experience it. AI can’t fake that. Or at least hasn’t yet.
04 / The Diversity ProblemThe Finding Everyone’s Ignoring
This is the one I think will matter most in five years. And it barely gets discussed.
The Wharton research, led by Lennart Meincke and co-authored by professors Gideon Nave and Christian Terwiesch: in 37 out of 45 comparisons, ideas generated with ChatGPT were significantly less diverse than those from other methods. Just 6% of the AI-generated ideas were considered unique, compared with 100% in the human group. Wharton, Mack Institute, 2025; sample from prior experiments; measured diversity using Google semantic similarity tool; Wharton Knowledge article July 2025
“If you rely on ChatGPT as your only creative advisor, you’ll soon run out of ideas, because they’re too similar to each other.”
Christian Terwiesch, co-director, Wharton Mack Institute (2025)Here’s why this matters in practice. When individuals use AI, their individual output improves. But when a team or a market uses AI, the collective output becomes more homogeneous. Everyone’s pulling from the same underlying probability distribution. Same prompt → same cluster of ideas, session after session.
This has a name in ecology: monoculture risk. When you replace a diverse crop with a single high-yield strain, yields go up — until a disease hits the one strain everyone planted. Creative monoculture isn’t exactly the same thing, but the structural risk is analogous. Markets, cultures, and organizations that converge on AI-generated ideas converge on the same AI-generated ideas.
Three separate findings from independent research streams point to the same structural problem: the Wharton diversity study establishes that AI idea generation converges across sessions; the Science Advances writer study shows AI lifts weak performers more than strong ones; and the Frontiers eye-tracking study shows audiences implicitly perceive the difference between human and AI creative work. Put these together: AI is simultaneously improving the floor of creative output, collapsing the diversity of that output, and producing work that audiences recognize as fundamentally different — even when they can’t consciously explain why. The result is a market where mid-tier work is increasingly AI-generated and similar, the audience can feel it even without identifying it, and premium human creativity gets more scarce. No single source contains this argument. It requires all three.
Editorial synthesis — sources: Meincke et al. / Wharton (2025); Science Advances 10.1126/sciadv.adn5290; Frontiers Psych 10.3389/fpsyg.2025.150997405 / CollaborationThe Data on Human+AI vs. Either Alone
Look, I know “human-AI collaboration is the future” sounds like something your company posted on LinkedIn in 2023. But there’s actual research on it now, not just thought leadership.
A 2025 study from Hongik University (IRB-approved, peer-reviewed, Frontiers in Computer Science) tested a structured Human-AI Co-Creative Design Process against traditional design. The results indicate that the Human-AI Co-Creative Design Process substantially improves creative performance over the traditional design process. For novice designers, its primary value lies in facilitating idea generation, whereas for experienced designers, it contributes more to elevating the quality and refinement of creative outcomes. Frontiers in Computer Science 2025; 2×2 factorial experiment, novice vs. experienced designers; IRB No. 7002340-202506-HR-009-01; DOI: 10.3389/fcomp.2025.1672735
That asymmetry is interesting. Novices use AI to start. Experienced designers use AI to go further. Same tool, different point of leverage.
The practical workflow that the research suggests isn’t complicated:
Human sets the brief and the constraint (the only people who can do this well are people who understand what the work is for). AI generates volume — options, variations, unexpected combinations. Human curates and selects, bringing the emotional and contextual judgment that AI demonstrably lacks. AI optimizes. Human does final quality check, especially for cultural fit and emotional resonance.
The key is that the human is never just accepting AI output. Every node in that flow is a decision point that requires something AI can’t reliably provide: understanding of what the audience will actually feel.
06 / Practical ImplicationsWhat This Means for Your Work
Okay, I’ve been through four of these cycles now — the desktop publishing panic, the stock photo disruption, the Adobe effects democratization, and now generative AI. Every one of them followed the same pattern: the tool lowered the floor, some incumbents screamed, the market reorganized, and the best practitioners integrated the tool and moved up-market. This one’s not different in structure. It’s just faster.
What the research actually says you should do:
If you’re a creative professional: Stop competing at the speed layer. AI wins that. What you have is taste, context, the ability to judge what’s actually going to land with a specific human audience, and — this matters — the backstory that audiences respond to emotionally. Lean into that. Document your process. Show your work.
If you’re managing a creative team: Watch the diversity problem. If everyone’s using AI with similar prompts, your pipeline is converging. The Wharton research is a warning sign. Force divergence: different tools, different prompting strategies, deliberately weird constraints. Homogeneous AI output is a competitive vulnerability, not an efficiency win.
If you’re using AI tools right now: The Science Advances finding is useful here. If you’re already a strong creative, AI will speed you up but probably won’t give you breakthrough ideas. Use it for volume, not inspiration. If you’re building skills, the lift is real and significant — use it generously. Just don’t let it be the only voice in the room.
The diversity collapse is a brand risk, not just a creativity question
Your competitors are using the same AI tools you are. Which means your AI-generated concepts are pulling from the same distribution as theirs. The Wharton study’s finding — 6% unique ideas across sessions — applies to your marketing brief just as much as to a research task. If you’re briefing AI with generic prompts, you’re producing generic outputs, and so is everyone else in your category.
What you do: Build prompt diversity into your process explicitly. Use proprietary brand context, specific cultural constraints, unusual reference points that your AI tool hasn’t been optimized for. Treat your prompt engineering as a competitive asset, not just an efficiency tool.
Here’s what’s going to stop you: Speed pressure. The reason AI feels efficient is you’re getting fast outputs. Adding constraints and iteration feels slower. But that’s the wrong comparison — the right comparison is fast-homogeneous versus slightly-slower-distinctive. Fast homogeneous is what your competitors are doing.
Stop doing this: Stop running the same creative brief through AI and treating the first-page output as a starting point worth refining. That first-page output is statistically the same output everyone with a similar brief is getting. Start from somewhere weirder.
The ceiling still belongs to you — but only if you practice without the training wheels
Here’s the uncomfortable finding: AI helps weak creatives more than strong ones. The logical implication — and this is editorial inference, not direct research finding — is that using AI heavily during skill-building might blunt the development process. You learn creativity partly by struggling through bad ideas. If AI is always surfacing better options, you might be skipping the struggle that builds taste.
What you do: Deliberately practice without AI at regular intervals. Not because AI is bad, but because the research on peak human creativity is consistent: the best work comes from the people who’ve built the deep skill base. AI doesn’t build that for you. It can accelerate applying it.
Here’s what’s going to stop you: AI output is immediately rewarding. It’s better than your first draft, usually. That’s exactly what makes it potentially dangerous for skill development. The short-term reward is real. The long-term skill atrophy risk is harder to see.
Stop doing this: Stop treating AI speed as creativity. Getting to a mediocre-but-polished output fast is not the same as producing exceptional work slowly. The research on audience perception is clear — people can feel the difference, even if they can’t name it.
| Research finding | Source & scope | What it means | ⚠ Limitation |
|---|---|---|---|
| AI beats average human on AUT/divergent thinking tests | Scientific Reports 2024; n=256 humans + 3 AI chatbots | AI is a capable creative brainstorm partner | AUT measures one narrow type of creativity; doesn’t predict quality of real-world creative work |
| Best humans still exceed AI | Multiple studies (Scientific Reports, 100K+ Montréal study) | Peak human creativity remains the ceiling | Lab benchmarks may not reflect professional creative contexts; “best human” is a small population |
| AI lifts weak writers more than strong ones | Science Advances; n=500, GPT-4; DOI 10.1126/sciadv.adn5290 | AI equalizes creative output distribution | Participants were typical study subjects, not professional writers; may not generalize to expert practitioners |
| Only 6% of AI ideas unique vs. 100% for humans | Wharton/Mack Institute, 2025; Meincke, Nave, Terwiesch | AI reduces creative diversity at market scale | Specific to ChatGPT with repeated prompting; diversity measures used Google tool; single study, needs replication |
| AI art rated lower than equivalent human art | Frontiers in Psychology 2025; n=92; eye-tracking; DOI 10.3389/fpsyg.2025.1509974 | Audiences perceive authorship, implicitly and explicitly | Religious art domain specifically; bias stronger among experts; results may vary by art type and audience |
| Human-AI co-creation outperforms either alone | Frontiers Computer Science 2025; IRB-approved; DOI 10.3389/fcomp.2025.1672735 | Collaboration is the productive model, not replacement | Design domain specifically; novice vs. experienced asymmetry means one workflow doesn’t fit all |
The honest answer to “will AI replace human creativity” is: for the average creative task at the average creative skill level, it already has in many respects. For peak creative work — the kind that moves people, the kind that builds brands over decades, the kind that demands cultural fluency and emotional authenticity — the evidence says no. Not yet. Maybe not ever, depending on what you think creativity actually is.
The diversity finding is the one I keep coming back to. Not because AI is bad at generating ideas. Because if everyone uses the same tools the same way, we’re all going to sound like each other. That’s not a creativity problem. It’s a culture problem.
This article is for informational purposes only. Research findings represent the author’s interpretation of cited studies; for direct scientific conclusions, consult original papers via the DOIs provided. Internal links to BestPrompt.art are navigational. All external links are for source attribution.
