Finance is the canary in the coal mine. Unfortunately, what kills the canary eventually kills everything else. Finance is unforgiving, but it's not unique among enterprises adopting AI. Solving generally for finance opens the door to solving for many other industries. So, more concretely, what is the problem at hand? Agents and models don't know your business. They don't know your - enterprise context - internal knowledge graph - vertical regulations - best practices, and more. General agents haven't seen (or don't remember) your policies, your book, your incidents, or the hard-earned reasoning and insights your team has accumulated over the years. Grabbing an API key and piping your data into an agent's context window is fine for a PoC. However, it's miles from a production-ready system. Finance is the canary because: a) it's one of the largest enterprise markets in the world b) the regulator is already in the room c) the cost of being wrong can be quantified in real money, often on the same day. What replaces demoware is the same wherever you build: 1) a structured reasoning layer beneath the model that actually encodes the entities, the mechanisms, and the institutional context that pretraining didn't see and that weights don't represent. 2) something real for the model to reason against, which can be your guardrails, deterministic risk harness, and internal benchmarks and eval sets. Global markets move fast, so skipping this work shows its cost first. Every other domain will follow the infrastructure, techniques, and harnesses built for finance Great convo with @constkogan!