Ethan Mollick argues that generative AI is already good enough to significantly boost human creativity, particularly in idea generation. He explains that AI's quirks — including its tendency to hallucinate and produce strange outputs — are actually features rather than bugs in a brainstorming context, where weird ideas can spark novel thinking and bad ideas are easily filtered out. AI also brings vast knowledge that enables unique combinations and recombinations of existing concepts.
Mollick outlines several concrete techniques grounded in creativity research. Playing with constraints involves using AI to explore extreme users, impose arbitrary limitations on solutions, or consider opposites to break out of familiar thinking patterns. "Interviewing" the AI means having it role-play as different personas to surface problem areas you haven't considered. The "Yes, and…" improv technique works well with AI to iteratively build on ideas. He also advocates going for volume, leveraging AI's ability to rapidly generate large numbers of ideas, and making it weird by requesting outputs in unconventional formats — like a Johnny Cash song or a Seinfeld script — to stimulate creative thinking.
The post emphasizes that AI outputs don't need to be perfect or accurate to be useful in ideation; their primary value lies in pushing people beyond their familiar mental patterns. Mollick frames AI as a new and powerful "source of oddness" that anyone can access for free through tools like ChatGPT.
This paper presents a large-scale dynamic experiment with over 800 participants across 40+ countries investigating how exposure to AI-generated ideas from ChatGPT affects human creativity, idea diversity, and the evolution of collective thought. Using a variant of the Alternate Uses Task (AUT), a standard divergent thinking measure, participants brainstormed creative uses for everyday objects after viewing example ideas that were either human-generated (from prior participants) or AI-generated. The researchers manipulated two key factors: AI exposure level (none, low, or high) and AI disclosure (whether AI-generated ideas were labeled as such). The experiment's dynamic design, inspired by the "multiple-worlds" paradigm, allowed ideas from earlier participants to feed forward as stimuli for later ones, simulating how AI content compounds through a cultural feedback loop.
The key finding is that high AI exposure increased collective idea diversity but did not improve individual creativity — in short, AI made ideas different, not better. High AI exposure also accelerated the rate of change in idea diversity over time, a compounding effect with potentially significant long-term implications. There were no main effects of disclosure (labeling ideas as AI-generated), though interesting moderating effects emerged: self-identified creative individuals were less influenced by AI disclosure, while participants appeared more likely to knowingly adopt AI ideas when the task was difficult. The study contributes a novel experimental framework for studying passive AI exposure — the increasingly common scenario where people encounter AI-generated content without actively soliciting it — and suggests that while AI integration may enrich the diversity of a collective idea pool, it does not necessarily elevate the quality of individual contributions.
A Reddit user in the r/OpenAI community poses an open question to creative professionals about how they are actually integrating AI into their workflows. The post acknowledges the persistent stigma around AI in design, art, and creative work, and asks whether people are using it for tasks like brainstorming, references, copywriting, automating repetitive tasks, coding help, mood boards, or ideation — or whether most professionals are avoiding it due to reputation concerns.
The post is framed as a genuine inquiry rather than a debate prompt, seeking honest, real-world examples of AI adoption among creatives. Unfortunately, the saved page does not include any of the community responses, so only the original question is captured here.
A new study from Wharton researchers reveals a significant trade-off when using ChatGPT for creative tasks: while generative AI improves the quality of individual ideas, it substantially reduces the diversity of ideas across teams. The research, led by Mack Institute fellow Lennart Meincke and co-authored by professors Gideon Nave and Christian Terwiesch, found that in one experiment, only 6% of AI-generated ideas were unique compared to 100% in the human-only group. Participants using ChatGPT frequently converged on strikingly similar concepts, with several independently naming their toy invention "Build-a-Breeze Castle."
The underlying cause is twofold: the AI model draws from the same statistical distribution when given similar prompts, and users tend to frame their queries in similar ways. In 37 out of 45 comparisons, AI-assisted ideas were significantly less diverse than those produced by other methods. The researchers argue that diversity is essential for breakthrough innovation and warn against mistaking fluency for originality.
To mitigate this homogenization, the study recommends several practical strategies: varying prompts deliberately, using chain-of-thought prompting to break tasks into structured steps, starting brainstorming sessions with human ideas before introducing AI, and employing multiple AI models simultaneously. As Terwiesch noted, "Diversity is often overlooked, but it needs special protection — if you don't solve for it explicitly, you won't get it."
