Shipping still matters. We shipped an AI agent, which is rare right now. Users didn’t care. That isn’t a personal failure, it’s feedback. Value comes from outcomes and what we learn on the way. The real edge is the data we control and the speed we turn experiments into decisions.

In this talk, we treat “not used” and “did not help” as valid, valuable results. We use time-to-truth experiments with explicit kill criteria, so a no-value feature is retired quickly, not defended. We'll compare a product implemented as a simple orchestrated AI workflow, an AI agent, and using a commercial off-the-shelf offering. Building the best products means focusing on what competitors and big tech cannot copy: your data, connections, and unique insight. Even if a feature or app is canceled, the work done can leave durable wins across the platform, from cleaner data contracts to stronger monitoring and evaluation.

Learning outcomes
* Reframe identity: decouple self-worth from features, treat negative results as progress
* Design time-to-truth experiments with clear kill criteria and observability
* Choose buy versus build to avoid racing competitors or tech platform giants
* Leverage proprietary data as a differentiator