Boris Cherny's Blog

I am often wrong | Boris Cherny’s Blog

Boris Cherny's Blog

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I am often wrong
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.

Boris Cherny advocates for an iterative and rigorous framework for tackling problems, particularly in the context of building products in the age of artificial intelligence. His approach centers on a sequence of six steps applied to nearly every challenge: first, understanding the available information; second, gathering any missing information; third, defining the problem; fourth, defining a clear and simple approach to solve it; fifth, defining a goal; and finally, acting with urgency to achieve that goal. Cherny emphasizes that this process is not linear; when new information is acquired during the process, he must be prepared to revisit and redefine steps three through five, recognizing that this iterative adjustment, though sometimes perceived as challenging, is necessary for finding the correct solution.

He provides feedback to teams when they fail to adhere to this framework or execute certain steps poorly, noting that the most frequently observed failure modes involve failing to clearly define the problem and failing to define a clear and simple approach. Cherny points out that omitting clarity in defining the problem or the approach leads directly to overly complex plans and ambiguous success criteria, making external feedback particularly crucial for complicated problems. Furthermore, he is open to adjusting his own meta-process if he identifies flaws within it. Ultimately, Cherny expresses a positive view toward making mistakes, stating that embracing being wrong is beneficial because it facilitates a more precise definition of the problem, aids in discovering the optimal solution, accelerates learning, and ultimately leads to more effective problem resolution.