I shared this note with my team earlier this week, and am posting it here as well. I hope it is interesting or helpful for others working on building product in the age of AI.

Something that people learn quickly when they work with me is that my approach to pretty much every problem is: Understand the available information, gather missing information, define the problem, define a clear and simple approach to solve the problem, define a goal, and act with urgency to achieve the goal. Along the way, I will often learn new information. That means going back and redefining steps three through five, and repeating. This process is iterative and for complicated problems, it can take many tries to get right.

This can feel thrashy, but if you are aware that it's all part of the process, and that the only way to really solve a problem is to adjust when there is new data, then the churn is healthy. When there's new data, you have to update your priors. Most often, the failure mode I see is failure to clearly define the problem, and failure to define an approach that is clear and simple. All this to say, I love being wrong. It is my favorite, because it helps me more clearly define the problem, find the right solution, learn more quickly, and solve the problem.