Thoughts on the taxonomy of thinking and making.
Prescriptive methodologies, such as GV’s Sprint or Design Thinking funnel us down the exact same way we always did things, just a little better and faster, or in other words flattening potholes on the way to the same destination.
I want to add to this another thought on the taxonomy of thinking and making. We arrive at any outcome 10, working through different steps (1–9). Everything below is focused on step 0.
In a world where intellectual property is as actionable and measurable as making a chair, or a car, I will define thinking as the individual and internal act of pulling together schemes and taxonomies to think about a problem (designing a board to play on). Things like picking a kernel of an idea, deciding on a set of resources, or remembering something you did as a child that relates to this project. More concretely this could mean that when asked to think about artificial intelligence for example, you might pull in tools, readings and metrics from neuroscience, plumbing and behavioral economics.
This step, the initial reflection point is fundamental to a future of human thinking.
- these schemas are internal. You might share them later, but so would your peer. You will both have a unique way of thinking about a problem, hence resulting in a diversity bonus
- machines are incapable of this type of cross–disciplinary, unstructured taxonomies. Which means thinking cannot be replicated, and your work can’t be automated.
Design thinking has nothing to do with thinking. It is all making. It an efficient (taylorist) way of getting at an outcome. That is fine, but it ignores so much of what we can bring to the table.
Wonderfully useless knowledge, true “new reality” innovation and cognitive diversity.
We lost the battle of optimization, machines do it better. And more so, the market is asking for more.
Nitzan Hermon is a designer and researcher of AI, human-machine augmentation and language. Through his writing, academic and industry work he is writing a new, sober narrative in the collaboration between humans and machines.