The picture of the solution space in 3D makes a great point - we see a narrow hill that leads to a global maximum (i.e. a great result) in a solutions search space that otherwise has a very obvious & wide hill that produces "okay" results. Going from the okay & safe results to a great result means taking the risk of going back down the hill of shittier solutions.
He points out that generative AI will tend to produce results that land on that big wide hill. It's the safe hill, and has the most results. This is perhaps where taste (as a proxy of experience) trumps AI.
Interesting to tie this to the finishing stage of any work. I was definitely thinking about software development in that situation. I would argue it's similar to drawing as he mentions in the FAQ - we're solving a novel problem, as we start implementing a solution we might discover it is inappropriate and have to change to a different part of the solution space.
The picture of the solution space in 3D makes a great point - we see a narrow hill that leads to a global maximum (i.e. a great result) in a solutions search space that otherwise has a very obvious & wide hill that produces "okay" results. Going from the okay & safe results to a great result means taking the risk of going back down the hill of shittier solutions.
He points out that generative AI will tend to produce results that land on that big wide hill. It's the safe hill, and has the most results. This is perhaps where taste (as a proxy of experience) trumps AI.
Interesting to tie this to the finishing stage of any work. I was definitely thinking about software development in that situation. I would argue it's similar to drawing as he mentions in the FAQ - we're solving a novel problem, as we start implementing a solution we might discover it is inappropriate and have to change to a different part of the solution space.