Not the API costs. The development costs. Every time your team ships an AI feature, something happens before the first real line of product logic: you build infrastructure. Prompt templates. Response parsers. Validation logic. Retry handlers. Intent routers. All of it written by hand, for every object that needs to talk to an LLM. Nobody plans for this work. It shows up in the sprint as "a few integration tasks" and leaves three weeks later with its own file structure. Let's make it concrete Say you want to add AI to your CRM. Lead is already a Pydantic model — it has name, company, email, score, notes. The ask is simple: "given this email from a prospect, auto-fill the lead." Here's what the implementation actually costs: # Step 1: build the prompt LEAD_PROMPT = """ You are a CRM assistant. Extract lead information from the following email. Return a JSON object with these fields: name, company, email, score (0-100), notes. Do not include any explanation. Return only valid JSON.…