I often hear people say generative AI is useful for prototyping because it speeds up prototype creation. I strongly disagree with this, although I acknowledge that the speed gains from generative AI.

Protoypes should not be created in isolation. Talking to potential users will help guide what you prototype should do. AI lacks the ability to consider subjective user preferences that are described. While a AI prototype will be functional, it will do a poor job considering user preferences. AI also lacks the ability to convey the meaning of a prototype’s developer.

Quickly creating a prototype with generative AI can give a false impression about the state of a product. The prototype will need to be rewritten as AI is incapable of developing and maintaining user-facing software on it’s own. However, people will judge how quickly a final product can be delivered by the visual state of the prototype and how quickly it was made.

For example, for my website dmvboardgames.com, I made a logo using meeples to draw letters. and I use meeples elsewhere on the site. Meeples are used in many popular modern board games such as Carcassone. People typically think of games such as Monopoly when they hear about board games. I wanted a way to convey that my site was focused on events for modern board games, and didn’t want to use a more traditional board game symbol such as dice.

I also created a hexagonal background with light versions of colors similar to the ones used on the Catan board. Catan is a classic well-known example of a modern board game.

I had talked to enough people who played modern board games to know they were familiar with what a meeple looked like. I also realized that many people who didn’t play board games were somewhat familiar with Catan. Generative AI cannot talk to board game players or come up with a relevant design for a background. Users have also found that the hexagons and meeples looked interesting, even when they didn’t recognize what the represented.


I found a blog post that gives a great detailed overview on the problems with generative AI prototyping: https://www.frank.computer/blog/2026/03/prototyping-bottleneck.html