there is no moat. in AI, anything good gets copied fast. so the best thing you can build is an unfair learning advantage around yourself. ⁠1. pick one problem you want to understand better than almost anyone else and stay close to it. build around it, talk to users, read complaints, watch competitors + keep seeing the same problem from different angles. ⁠2. ship constantly. every launch, post, feature, sales call + failed idea gives you another piece of information. the person with 100 real reps sees things the person with 10 reps can't. ⁠3. write down the decisions that matter. what did you believe, what did you choose, what happened afterward? after enough cycles, you start noticing which signals were useful and which ones fooled you. ⁠4. keep the lessons that only came from experience. weird customer objections, tiny product details, pricing mistakes, distribution tricks, workflows that worked better than expected. this is the stuff that never shows up in generic advice. ⁠5. build relationships with people who are close to the problem. customers, builders, operators, researchers. good relationships give you information earlier, better context + people to test ideas with. ⁠6. publish what you're learning. explaining something forces you to sharpen it, attracts other people working on the same problems + gives you a public record of how your thinking is changing. ⁠7. use AI to run this loop faster. research deeper, compare more examples, summarize calls, challenge assumptions, prototype ideas + review what happened. the model is available to everyone. what you feed into it comes from your own reps. keep doing this for long enough and you start to understand stuff like: which customers to ignore, which feature requests are fake demand, which channels work and why, when to ship, when to wait, and which problems are worth chasing in the first place.