Choose AI projects carefully.
2 important reasons why 👇
1. Problem Space Understanding
Understanding a problem well takes a lot of effort. Its requires an accumulation of “real world” domain understanding and how the data represents that “real world”. Deep Problem Space understanding is key to building valuable AI products. If you stay focused on a single Problem Space for a while, you’ll often get compounding returns and better outcomes.
Each project is also an investment in a competency: Analytics, LSTM, bayesian statistics, simulation, time series forecasting, causal analysis, etc. Deep competencies takes time to develop, so decide which are most important to your long-term success and prioritize accordingly.
We pay a “learning curve tax” for each new problem area and competency.
Calculated choices ensure we get compounding returns.
Articles for Startup Product Leaders
Get 1 concise, actionable tip each week
Don’t take my word for it…
“Your posts are consistently hitting the nail on the head. Appreciate the experience.”
“Please keep sharing your experiences – a lot of your recent posts had great nuggets of value”
“You will be hard-pressed to find a smarter, more caring, empathetic executive.”
“Josh is a stellar Product professional. Out of all the books on his desk, I expected one to be authored by him.”