When a team is too big

Summary

Lately I’ve been thinking a lot about these questions:What is a team?How large is too large and how small is too small?What qualifies a missions?How to weigh the cons and pros of specialist vs generalist in the age of GenAI?There’s a story for several years back that keeps back to my mind. What makes it interesting is the fact that there was no master plan. Yet with a few cultural elements, the story took such an interesting trajectory that it shaped my leadership model. Ever since, I have been an advocate of continuous improvement by preparing the environment instead of being the wise-ass who has the ultimate solution to all problems.Today I want to tell that story with these key takeaways:A generalist team is less likely to have internal dependencies acting as bottlenecks, blockage, and single point of failure. Generalist teams are more productive and resilient.Handovers introduce the risk of misunderstanding, delay, miscommunication, and diluted accountability. Clear ownership requires knowledge, mandate, and responsibility. It’s easier to implement with generalists.But as we’ll see generalism is not without problems either.🤖🚫 Note: No generative AI was used to create this content. This page is only intended for human consumption and is NOT allowed to be used for machine training including but not limited to LLMs. (why?)Many yeas ago I was part of a large team. 14 members to be exact.The problems started from the first standup: I didn't follow 90% of what everyone else was talking about. I thought it's because I'm new to the team but as time passed and I got closer to the other team members I learned that some others feel the same.It wasn't unusual to see someone visibly yawn or fall almost sleep during the standup. There was at least something in the round of standup that was completely irrelevant and boring to a few.In an effort to keep the standup short, we had a time limit per person paired with invented hand gestures to signal the person who is talking that ...