Adam Grant highlights new evidence suggesting that moderate AI consultation leads to the most original ideas, compared to either extensive or rare use. He argues that heavy reliance on AI doesn't just stifle critical thinking — it also constrains creativity. The sweet spot appears to be consulting AI sometimes rather than constantly or barely at all, reinforcing the idea that independent thought remains a foundational element of ingenuity.
Bernard Marr argues that AI will not replace humans but rather serve as a powerful tool for augmentation. He points to several fundamental limitations of AI: it cannot truly understand or experience emotional intelligence, it lacks the ability to conceive genuinely novel ideas, and it cannot make ethical or value-based judgments. While AI excels at processing data and identifying patterns, the human brain's ability to read context, make intuitive leaps, and understand nuanced societal needs remains unmatched.
Rather than framing the AI revolution as a zero-sum competition, Marr advocates for a human-AI partnership model. As AI automates routine and repetitive tasks, humans are freed to focus on higher-value work — strategic thinking, relationship building, and creative problem-solving. He contends that success in this new era belongs to those who develop distinctly human skills that complement AI, and that our humanity — our ability to connect, create, and care — becomes more valuable, not less, as technology advances.
Neil Perkin argues that AI's greatest value isn't just efficiency — it's serving as a thought partner that challenges assumptions, surfaces blind spots, and opens new lines of exploration. He warns against cognitive debt, the tendency to grow intellectually lazy by outsourcing thinking to AI, and advocates for systematizing approaches that combine AI with human cognition to generate genuinely new possibilities.
The post outlines several concrete techniques for using AI this way. Perspective triangulation involves curating a syllabus of source material and asking the model to contrast clashing viewpoints on a topic, forcing it to reason across tensions rather than averaging them. The Board of Brains method, borrowed from Anthropic's Lani Assaf, involves creating persistent AI projects that embody specific advisors or experts, each reviewing your work through their unique lens. Role-play debate simulates live multi-perspective discussions that reveal hidden assumptions.
Perkin also shares several prompting techniques: norm switching (applying norms from unrelated sectors), constraint reversal (imposing absurd constraints to force lateral thinking), counterfactual storyboards (narratives where key assumptions are inverted), forced analogy engines (mapping random domains to your challenge), negative inversion (listing spectacular failure modes then inverting them), and ladder up/ladder down (toggling between strategic 'why' and executional 'how' to expose gaps between intent and action). The overarching message is that intellectual curiosity becomes even more important in the AI era — using AI like a search engine means missing its potential to take your thinking to places you couldn't reach alone.
Trisociation is a creativity technique that combines three disparate concepts to generate novel, coherent ideas — extending Arthur Koestler's classic bisociation method by adding a third element. While the technique has existed in theory since Victor Papanek referenced it in Design for the Real World, it remained largely obscure due to the cognitive overload it imposes on human thinkers. The article argues that Large Language Models like ChatGPT and Claude have unlocked trisociation's potential, as they can seamlessly process and blend three unrelated concepts simultaneously without the cognitive biases or mental fatigue that limit human ideation.
The authors distinguish between open-ended trisociation (combining three random concepts for maximum novelty) and focused trisociation (anchoring one or two concepts to a specific domain like a product category). They demonstrate how the strategic selection of related versus unrelated concepts allows businesses to calibrate the balance between novelty and feasibility — choosing all unrelated terms yields breakthrough but potentially impractical ideas, while related terms produce incremental but commercially viable innovations. Practical applications span new venture ideation, product concept development, and attention-grabbing visual advertising, illustrated with examples like origami-inspired digital wellness platforms and bear-themed pizza ads.
Notably, companies like Ipsos and HYPE Innovation are already integrating trisociation into their creative workflows. The authors provide concrete implementation guidelines: define goals first, select concepts strategically, use AI outputs as raw starting points for human refinement, and maintain curated concept databases. The piece draws on 22 months of research and teaching across more than 10 organizations in Asia and North America.
Marty Cagan of Silicon Valley Product Group reflects on two years of generative AI's impact on product management, distinguishing between AI as a feature in products versus AI as a tool that changes how products are built. He acknowledges that despite a flood of announcements and bold predictions, the actual transformation has been slower than he hoped — his end-of-2023 predictions proved too optimistic about the pace of change.
Cagan highlights that most industry discussion focuses on delivery team product owners or feature team product managers, rather than empowered team product management, which is his primary area of interest. He notes that while AI tools clearly accelerate code creation and design work, faster production doesn't necessarily translate into better outcomes. The article poses several unresolved questions for product model companies, including how generative AI changes product discovery, which PM skills become more or less important, and whether AI will drive genuine innovation or merely incremental optimizations.
A key tension Cagan identifies is the pairing of strong product judgment with AI tools — he sees enormous potential when someone with deep product sense uses generative AI, but worries about the risks when people lacking foundational product skills rely on the same tools. Two years in, he concludes that while real progress has been made, the industry is still at the beginning of understanding AI's role in product management.
Tech industry analyst Tim Bajarin explores the dual-edged nature of generative AI's impact on human creativity, drawing from his personal experience as a self-described left-brain thinker who discovered new creative capabilities through AI tools. He argues that while AI serves as a powerful accelerator for creative professionals — generating design concepts, musical ideas, and text at unprecedented speed — it poses a serious risk of atrophying our creative muscles if over-relied upon. He draws parallels to how calculators dulled mental arithmetic and spell-checkers weakened spelling skills, warning that creators may become more skilled at prompt engineering than original thought.
Bajarin raises deeper concerns about the economics and motivation of creative industries. If AI can instantly replicate or surpass human output, the incentive for aspiring innovators to push boundaries diminishes. When businesses prioritize speed and cost over authenticity, human creators risk being sidelined. He calls for a multi-pronged approach — evolving legal frameworks for intellectual property, prioritizing skill development and critical thinking in education, and consciously using AI as an augmentation tool rather than a crutch. While optimistic about AI's possibilities, he emphasizes that the creative spirit is humanity's greatest asset and warns against allowing complacency to erode our collective capacity to imagine and invent.
A Wharton Human-AI Research (WHAIR) webinar explored the dual-edged impact of AI on creativity, featuring researchers from UCL, University of Exeter, and University of Washington. The panel found that while AI boosts individual productivity and raises baseline creative quality — particularly helping less-creative individuals produce more novel and useful work — it simultaneously narrows the overall diversity of ideas. When people start with AI-generated suggestions, they tend to anchor to them, leading to convergent rather than divergent thinking across teams.
The researchers emphasize that collaboration design is critical: having humans drive early ideation while AI assists with refinement and evaluation preserves idea diversity, whereas using AI at the start of the creative process leads to homogenized outputs. Panelists warned against over-reliance on AI as a "copilot," drawing an analogy to pilots losing manual skills through autopilot overuse. To maintain a competitive innovation edge, organizations should use multiple AI models with varied prompting strategies, encourage independent ideation before AI input, deploy AI as a critic rather than a generator, and establish clear norms about when AI should complement versus substitute for human creativity. The bottom line: companies that carefully structure human-AI collaboration will outperform those that treat AI as a default creative shortcut.
This Harvard Business Review article by Tojin T. Eapen, Daniel J. Finkenstadt, Josh Folk, and Lokesh Venkataswamy explores how generative AI — technologies capable of creating new content such as text, images, audio, and video — can be used to augment human creativity rather than simply replace human workers. The authors argue that while there is significant apprehension about AI displacing jobs, one of the largest opportunities lies in using these tools to promote divergent thinking and democratize innovation across businesses and governments.
The article, published in the July–August 2023 issue, positions generative AI not as a threat to creative professionals but as a powerful collaborator that can expand the range of ideas people generate and help overcome traditional barriers to innovation. The piece is notable for practicing what it preaches — all images in the article were themselves created using Midjourney, a generative AI tool, demonstrating the practical creative applications of the technology.
Ethan Mollick, a well-known researcher and commentator on AI capabilities, shares a post asserting that AI is "actually pretty good at ideas" as well. The post includes a link and an image, likely referencing research or evidence supporting the claim that AI systems can generate creative or novel ideas effectively.
This aligns with Mollick's ongoing exploration of how AI augments human creativity and ideation, challenging the common assumption that idea generation remains a uniquely human strength. The post suggests that AI's utility extends beyond execution and analysis into the realm of creative brainstorming and concept development.
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."
