From Ethan Mollick’s LinkedIn post about AI-generated sound effects to the recent Heise iX article on AI in UI design, it’s clear that AI is making inroads into every aspect of digital creation.

But as with any new technology, it’s crucial to approach these developments with a critical eye. The Heise iX article, for instance, shows that while ChatGPT with GPT-4 can provide seemingly insightful feedback on existing designs, it struggles to create usable wireframes from scratch. The results are completely overloaded, confusing, and consequently unsuitable.

This aligns with my observations about the limitations of text-to-image models like DALL-E 3. As I commented on the Heise forum:

The fact that this doesn’t work is conceptually based: the “steerability” of text-to-image models is, in my experience, always inadequate - which is also related to the fact that the prompt is always rewritten by the system first, presumably to avoid copyright infringement.

This limitation underscores a broader issue with many GPTs: they often promise more than they can deliver. It’s a reminder that while AI can be a powerful tool, it’s not a magic solution for complex creative tasks.

That said, there are ways to leverage AI effectively in UI/UX design. For instance, using GPT-4 to generate input for Mermaid Chart or draw.io can be more productive than asking for direct image generation. As always, the key is understanding the strengths and limitations of the tool you’re using.

In conclusion, while AI is undoubtedly changing the landscape of UI/UX design, it’s essential to approach these tools critically. They can be valuable aids in the creative process, but they’re not ready to replace human designers. As professionals, we need to stay informed about these developments, experiment with new tools, but also maintain a healthy skepticism about grandiose claims.