Is There Demand for AI Profitability Analysis Tools?
JJitsev · x · 2026-07-15
While building AI products, the author wondered: how should AI companies measure true profitability, rather than just looking at revenue, MRR, or token usage.
He points out that under the same subscription price, the actual economics of different customers can vary significantly due to factors like model choice, retry rates, workflow complexity, inference costs, power-user behavior, and pricing strategies. Existing tools mostly focus on analytics or billing. The author wants a product layer that can answer questions like: which workflows are profitable, which customers are dragging down gross margins, which pricing needs adjusting first, where revenue is leaking, and which models have the biggest business impact.
To validate this idea, he built an interactive prototype called ProfitLens. It is currently just a demo, not a production-ready product, mainly used to validate demand. He also mentions entering it into the Emergent AI Builder Contest, hoping to get feedback from founders and engineers on whether the problem is real and what the first version should solve.
Related event: SOOFI benchmark claims challenged over leakage and baseline reporting(13 posts)→
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