Using AI to promote divergent thinking
A possible explanation for why we observe such a discrepancy between the productivity gains from AI tools of seasoned senior software engineers versus their less-experienced counterparts:
- Inexperienced problem solvers have a tendency to get stuck when using AI tools to solve complex problems because they bias towards convergent thinking: narrowing possibilities and looking for a specific solution which fits within their preconceptions.
- Experienced problem solvers who have adopted AI tools tend to use it to augment a scientific process, which includes building mental models, formulating hypotheses, and performing experiments. In this process, they use AI to accelerate divergent thinking: exploration and discovery of possibilities.
I've been thinking a lot lately about the significant discrepancy I've observed in how effective different people are at using AI for problem solving. I'm certainly not the only one; there seems to be a strong consensus building in my field (software engineering) that AI tools disproportionally augment the effectiveness of experienced people (e.g. those who developed their craft before AI tools became omnipresent), and that providing AI to people early in their career can actually slow them down- and impede their growth.
This certainly fits the observations I've made personally, but raises a lot of questions about what experienced people are doing so differently with AI. I've spent some time intermittently reflecting on my own use of AI, and also paying attention to how some of my younger colleagues are using it, and I think I have a working theory.