An AI engineer's chatbot recommended a competitor's product, exposing why bare LLM wrappers fail. Real AI products need instrumentation, analytics, evaluation tools, and grading rubrics that capture data and build a proprietary moat over time. Anyone can ship a wrapper. Only systematic teams create products people keep using.
I talked to one AI engineer last week. They had built an AI chatbot for customer service that in a demo meeting to their execs actually recommended the competitor's product. You don't want to be that person. There's a big difference, a giant spectrum between LLM wrapper that's super generic and hey, we have gotten our instrumentation down, our analytics game down. We have gotten a systematic evals process with tooling and our team is trained up on it and we have all the right processes in place to capture traces, do open coding, update our grading rubric for the heuristics on what makes this good or bad, data failure taxonomy. That is the difference between super generic, low retention, low conversion, low revenue, low loyalty, and high revenue, high retention, high loyalty, high conversion. Because the difference there is the team has done the work to put in the foundation in place for objectively and measurably improving their AI product. LM application, your product and business being a commodity is whether or not you have that in place and are continuously building and compounding your own data moat that makes your AI product better over time as it gets more use. And that's the key lesson is anybody could ship a wrapper. To define and create a market category leader around an economically valuable problem that AI can solve, huge difference in the amount of capabilities and the knowledge to bring to build that foundation.
