Aaron Levie, CEO of Box, observes a growing trend of product managers using AI to create functional product prototypes. He notes that this approach is accelerating the ideation phase by roughly 5-10X, primarily because ideas can be demonstrated much more quickly, which in turn sparks faster conversations and feedback loops. The post appears to be the beginning of a longer thread, hinting at a "but" that likely addresses caveats or limitations of this AI-driven prototyping approach.

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.