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.  

2. Competencies 

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.

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