Like a programming language interpreter, GPT-3 translates the designer’s intent from a language they’re already familiar with (English) to one they need to learn (Figma’s information architecture, as manifested in its UI.) This can be easier for a new/busy designer, much like Python is easier and faster to work with than assembly language.
But that’s not “designing” — at least not any more than compiling Python code is “programming.” In both cases, all the system does is translate human intent into a lower level of abstraction. Sure, the process saves time — but the key is getting the intent part right. I’ll be convinced the system is “designing” when it can produce a meaningful output to a directive like “change the product page’s layout to increase conversions.”
There are two types of work: growth work and maintenance work.
Growth work involves making new things. It can be something big or small. In either case, growth work often follows a loose process.
Maintenance work is different. Maintenance work involves caring for the resources and instruments that make growth work possible. This includes tools, but also body and mind.
Maintenance is ultimately in service to growth. But effective growth can’t happen without maintenance. As with so many things, the ideal is a healthy balance — and it doesn’t come without struggle.
The design industry is an ecosystem. External design teams provide critical functions beyond augmenting internal design resources. Thought leadership — pushing the field’s boundaries — is indeed one of them.
Many practices and tools we take for granted — journey maps, personas, conceptual frameworks — were pioneered and/or popularized by ‘outies.’ Most of the field’s foundational books and blogs are by people outside ‘client’ organizations.
This isn’t because internal designers aren’t as clever or dedicated as their external colleagues. (Many ‘innies’ are former ‘outies.’) It’s because internal design roles are structurally misaligned with public thought leadership.
There is increasing concern that most current published research findings are false. The probability that a research claim is true may depend on study power and bias, the number of other studies on the same question, and, importantly, the ratio of true to no relationships among the relationships probed in each scientific field. In this framework, a research finding is less likely to be true when the studies conducted in a field are smaller; when effect sizes are smaller; when there is a greater number and lesser preselection of tested relationships; where there is greater flexibility in designs, definitions, outcomes, and analytical modes; when there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical significance.