If you're shipping AI product lines, are you measuring the two metrics that actually tell you whether your AI is making money — or burning it?
In episode #371, Ben Murray covers two AI unit economics metrics every SaaS CFO and founder should be tracking today: the Inference Expense Ratio and the Work-to-Inference Ratio. Traditional SaaS metrics aren't enough anymore — and a year from now, when your board, investors, and potential acquirers start asking for AI margin and efficiency data, the companies that built the chart-of-accounts structure now will have clean answers. Everyone else will be scrambling.
The Inference Expense Ratio (AI revenue ÷ inference cost) — and why you can start calculating this from your GL today if your chart of accounts is set up properly
The healthy benchmarks: 10:1 for AI-infused products, 5:1 for AI-native, and why 3:1 is the warning zone where inference is silently eating your gross margin
Why this metric only works if your chart of accounts cleanly separates AI revenue from non-AI revenue — and the SKU tagging that makes it possible
The Work-to-Inference Ratio — how Salesforce's "agentic work units" concept lets you measure whether your AI is getting more efficient over time
Why every AI product needs its own definition of a "work unit" — record updated, report generated, MCP called — and how the wrong definition will distort your margin trends
The chart-of-accounts evolution every SaaS company needs right now: from SaaS-only structure to SaaS + AI, with new GL accounts for inference cost in DevOps COGS
How the Inference Expense Ratio connects to Ben's ROSE metric — measuring revenue produced per dollar of employee, contractor, and agentic AI spend
Tune in to get the AI unit economics framework in place — before your board and investors start asking the questions you can't answer.
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