The Application Layer Is the New Research Lab

In the pre-genAI era, vertical product teams handed insights to a separate R&D group, who shipped a new model two quarters later. That handoff is now a bug. Agentic systems are built from dozens of model calls, judges, tools, and harness decisions, and every one of those is a hyperparameter. The product surface and the training surface are the same surface. This talk argues that every vertical AI company is now its own applied research lab. I walk through what that function actually ships (custom judges, scenario benchmarks, data flywheels, harness tuning), where the thesis breaks (most domains are not Cursor), and how to staff for it without losing engineering velocity.