There is a new AI model, tool or framework being released virtually every single week. A better coding agent. A cheaper model. A new orchestration framework. A clever wrapper that promises to change everything. You would think that this abundance would make us all vastly more capable. In practice, I observe the opposite. Many of us spend more time evaluating which tool to use than actually using any of them. We read the benchmarks, watch the comparison videos, join the discussions on which model is marginally ahead this week and risk never doing the actual work. Analysis paralysis in an AI-first outfit. The two decade old paradox of choice is alive and well.

The way I handle it is to, first, timebox the evaluation and then commit for a longer period. Second, rather than looking at leaderboards, I try to choose based on what I use the tools actually for. Third, and maybe the one that matters most, I try to learn “the skill”. To decompose problems, prompt well and to verify output critically. That skill applies to every tool. And in my experience, the marginal gain from switching is nearly always smaller than the cost of the churn.

The winners in this era will not be the ones with the best tools. They will be the ones who picked something good enough and got moving while everyone else was still comparing spec sheets and waiting for clarity. As they say in the special forces community: a good plan violently executed now is better than a perfect plan executed next week.