September 19, 2026
[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
- Act with urgency to achieve the goal
Along the way, I will often learn new information. That means going back and redefining #3-5, 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.
I apply something like these six steps for pretty much every problem, and pretty much every product (a product solves a problem for users). I apply this rough framework many times on most days.
Sometimes I will give feedback to people when they are missing steps in the framework, or are poorly executing some of the steps. I expect the same feedback in return. I try hard to give the feedback in real time, so the person/team can learn more quickly. Most often, the failure mode I see is (3) failure to clearly define the problem, and (4) failure to define an approach that is clear and simple. When one of these is missing, it leads to complex plans and unclear success criteria. For complicated problems, lack of clarity can be hard to spot if you’re the one making the plan, making it even more important to get feedback from people.
If part of this meta-process is meta-wrong, I am open to changing it.
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.