2-16: ROAI and AI Unit Economics

Traditional SaaS operates on 80-90% gross margins because the marginal cost of computing a user action is near zero. AI products break this economic physics. Every prompt to an LLM invokes an intensive GPU inference cycle that costs real cents. If a user pays $20/month for a subscription, and runs 400 GPT-4 queries a month costing $0.05 each ($20 total), your margin is 0%. You are running a charity for OpenAI. Product Leaders must map token input/output costs, vector database storage costs, and embedding transit costs directly back to individual user pricing tiers.

In 2024, deploying an AI chatbot was enough to secure VC funding; it was an "Innovation Budget" experiment. In 2026, CFOs are demanding hard ROI - specifically ROAI. If you spend $1M developing an RAG-powered internal knowledge base and $50k/month in API costs, how many dollars of human labor did it actually replace or accelerate? ROAI forces teams to justify AI projects based on hard metric movement: FTE displacement, customer churn reduction, or direct new-revenue expansion. If the AI doesn't move the needle financially, the pilot dies.

You do not need GPT-4 Opus to summarize a 3-sentence email. Using frontier models for primitive tasks is economic malpractice. Advanced AI AI economics rely on "Model Routing." You deploy a fast, cheap model (like Claude 3 Haiku or Llama 3 8B) for 80% of simple classification and parsing tasks, and dynamically route only complex reasoning queries to the expensive frontier models. Combined with aggressive semantic caching (serving similar queries from a database instead of calling the API), you can slash enterprise AI costs by over 90% without degrading the user experience.