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.

• Ben's new AI course: https://www.thesaasacademy.com/ai-finance-metrics-saas
• ROSE metric: https://www.thesaascfo.com/saas-rose-metric/