Design processing
Dan wrote an interesting post with a somewhat clickbaity title; This Competition Exposed How AI is Reshaping Design:
I watched two designers go head-to-head in a high-speed battle to create the best landing page in 45 minutes. One was a seasoned pro. The other was a non-designer using AI.
If you can ignore the title (and the fact that Dan still actively posts on Twitter; something I find very hard to ignore), then there’s a really thoughtful analysis in there.
It’s less about one platform or tool vs. another more than it is a commentary on how design happens, and whether or not that’s changing in a significant way.
In particular, there’s a very revealing graph that shows the pros and cons of both approaches.
There’s no doubt about it, using a generative large language model helped a non-designer to get past the blank page. But it was less useful in subsequent iterations that rely on decision-making:
I’ve said it before and I’ll say it again: design is deciding. The best designers are the best deciders.
Dan finishes by saying that what he’d really like to see is an experienced designer/decider using these tools to turbo-boost their process:
AI raises the floor for non-designers. But can it raise the ceiling for designers?
Meanwhile, Matt has been writing about Vibe-designing. Matt is an experienced designer, but he’s not experienced with Figma. He’s found that he can work around that using a large language model:
Where in the past 30 years I might have had to cajole a more technically adept colleague into making something through sketches, gesticulating and making sound effects – I open up a Claude window and start what-iffing.
The “vibe” part of the equation often defaults to the mean, which is not a surprise when you think about what you’re asking to help is a staggeringly-massive machine for producing generally-unsurprising satisfactory answers quickly. So, you look at the output as a basis for the next sketch, and the next sketch and quickly, together, you move to something more novel as a result.
Interesting! Just as Dan insisted, the important work is making the decision and moving on to the next stage. If the actual outputs at each stage are mediocre, that seems to be okay, as long as they’re just good enough to inform a go/no-go decision.
This certainly seems more centaur-like than the usual boring uses of large language models to simply do what people are already doing.
Rich gets at something similar when he talks about using large language models for prototyping, where it’s okay if the code is kind of shitty:
If all you need is crappy code to try out a concept or a solution, then an LLM might well enable you (the designer) to do that.
Mind you, even if you do end up finding useful and appropriate ways to use these tools, you’re still using a tool built on exploitation and unfairness:
It’s hard (and reckless) to ignore the heartfelt and cogent perspective laid out by Miriam on the role of AI companies in the current geopolitical crisis:
When eugenics-obsessed billionaires try to sell me a new toy, I don’t ask how many keystrokes it will save me at work. It’s impossible for me to discuss the utility of a thing when I fundamentally disagree with the purpose of it.
