Rethinking Unit Economics** The shift to API-first and generative AI features has created a new challenge for finance teams: ensuring profitability in a world where costs are no longer fixed. Anshuman Yadav, a strategic finance and AI leader, notes that every major shift in software models has forced finance to learn new math.
The move from on-prem servers to the cloud and then to SaaS required finance teams to adapt to new billing models. In the SaaS 1. 0 world, the goal was to acquire power users who would drive revenue while incurring minimal costs. However, with the introduction of generative AI features, these power users have become financial liabilities.
Each time they interact with AI features, it triggers a compute call, incurring costs that can add up quickly. To recover these costs, companies must charge accordingly. The Limitations of Aggregate Gross Margin Traditional aggregate gross margin analysis is no longer sufficient. Companies need to know the cost of a single "thought → query" to accurately price their AI features.
For instance, if an AI feature costs $0. 15 to answer a question and a user asks 100 questions a month, the company needs to charge at least $15 to recover its costs.
Anshuman Yadav is a strategic finance and AI leader with global SaaS, M⁘A, and ops experience. Kellogg MBA. Ex-civil engineer.Related perspectives: Visit website
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