In episode #332, Ben Murray explains why AI companies with high inference costs and lower gross profit margins must scale dramatically faster—up to 6x larger—to match the financial performance of a comparable SaaS business. Using simple financial modeling and the core principles of SaaS economics, Ben breaks down how AI margins, variable COGS, and TAM expansion interact to shape the financial trajectory of AI-native companies.
This episode builds on a recent blog post and downloadable Excel model, both linked in the show notes.
Why SaaS metrics still apply to AI companies, but with different economic inputsThe impact of AI inference costs on gross margin and scalabilityComparing a SaaS company at 80 percent gross margin vs. an AI company at 55 percentWhy an AI company needs 6x the revenue to generate the same EBITDAHow lower gross profit changes cash flow, EBITDA, and company valuationWhy larger TAM and higher ACV potential in AI may offset lower marginsHow attacking labor budgets expands revenue opportunity for AI productsThe myth that SaaS metrics are “broken” for AI companiesUnderstanding how COGS scale in SaaS vs. AI and why the math still worksEvaluating OPEX profiles when modeling scale scenariosHow to use the downloadable template to test scenarios for your own AI or SaaS businessThis episode is critical for:
AI founders modeling their unit economicsSaaS founders embedding AI and needing to understand margin changesCFOs, controllers, FP&A leaders, and finance teams navigating AI cost structuresInvestors assessing the scalability and valuation profile of AI companiesOperators planning cash runway, revenue forecasts, and growth investmentUnderstanding these financial dynamics early ensures you can forecast accurately, raise capital more effectively, and prepare for due diligence with confidence.Full blog post on AI vs. SaaS economics: https://www.thesaascfo.com/the-real-economics-of-saas-versus-ai-companies/
SaaS Metrics Course: https://www.thesaasacademy.com/the-saas-metrics-foundation