In episode #334, Ben Murray breaks down how leading public SaaS and tech companies are reporting AI-driven value creation across their earnings calls. After analyzing more than 130 public tech earnings transcripts, Ben identifies five consistent themes in how incumbents communicate AI monetization, margin impact, revenue growth, and operational transformation to Wall Street.

These insights are critical for private SaaS and AI founders who want to understand how to position their own AI value story for Boards, investors, and future fundraising. As AI moves beyond the hype cycle, companies must clearly demonstrate monetization, adoption, and financial impact—not just vision and roadmap.

Why It Matters

Understanding how public companies frame AI value creation helps private founders avoid vague positioning and instead adopt investor-grade communication. These themes influence:

• Board reporting
• Fundraising narratives
• ARR and revenue forecasting
• Financial modeling
• Unit economics and cost structure decisions
• Long-term valuation strategy

As AI transitions from hype to monetization to full transformation, founders must adapt how they report AI’s contribution to performance and financial outcomes.

Resources Mentioned:

Reporting AI ARR: https://www.thesaascfo.com/ai-arr-vs-saas-arr-how-to-define-and-calculate/

SaaS Metrics Course: https://www.thesaasacademy.com/the-saas-metrics-foundation

In episode #333, Ben answers a foundational SaaS metrics question: Should expansion revenue be included in your Lifetime Value (LTV) calculation? Ben walks through the correct LTV formula and highlights how misalignment between LTV and CAC can distort your LTV:CAC ratio. He also covers when expansion should be included.

The episode provides a practical framework for SaaS founders, CFOs, and operators to ensure they calculate LTV accurately, compare it properly to CAC, and model unit economics using consistent, reliable inputs.

Key Topics Covered

• The correct LTV formula using average new-customer MRR × subscription gross margin
• Why the churn input should align with dollar-based metrics using 1 – Gross Revenue Retention (GRR)
• Why expansion revenue is deliberately excluded from LTV in most SaaS models
• How including expansion artificially inflates the LTV:CAC ratio
• The cost mismatch between acquiring new customers (CAC) and generating expansion revenue
• When PLG motions justify including limited, time-bound expansion revenue in LTV
• How organic upgrades differ from sales-assisted expansion
• How SaaS+ businesses must adjust their LTV formula to account for usage revenue
• The role of gross margin in determining true unit economics
• The importance of aligning metric definitions when evaluating customer profitability

Why This Matters

This episode is essential for:

• SaaS founders calculating LTV for budgeting, pricing, and forecasting
• CFOs, controllers, and FP&A leaders managing unit economics and CAC payback
• Finance teams modelling customer profitability and revenue expansion
• Operators working in PLG environments assessing organic expansion patterns
• Investors reviewing LTV:CAC ratios in diligence and portfolio monitoring
• Anyone building SaaS Plus (subscription + usage) revenue models

Resources Mentioned

Ben’s deep dive on SaaS+ LTV: https://www.thesaascfo.com/how-to-calculate-ltv-with-variable-revenue/

SaaS Metrics course: https://www.thesaasacademy.com/the-saas-metrics-foundation

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.

Key Topics Covered

• Why SaaS metrics still apply to AI companies, but with different economic inputs
• The impact of AI inference costs on gross margin and scalability
• Comparing a SaaS company at 80 percent gross margin vs. an AI company at 55 percent
• Why an AI company needs 6x the revenue to generate the same EBITDA
• How lower gross profit changes cash flow, EBITDA, and company valuation
• Why larger TAM and higher ACV potential in AI may offset lower margins
• How attacking labor budgets expands revenue opportunity for AI products
• The myth that SaaS metrics are “broken” for AI companies
• Understanding how COGS scale in SaaS vs. AI and why the math still works
• Evaluating OPEX profiles when modeling scale scenarios
• How to use the downloadable template to test scenarios for your own AI or SaaS business

Why This Matters

This episode is critical for:

• AI founders modeling their unit economics
• SaaS founders embedding AI and needing to understand margin changes
• CFOs, controllers, FP&A leaders, and finance teams navigating AI cost structures
• Investors assessing the scalability and valuation profile of AI companies
• Operators planning cash runway, revenue forecasts, and growth investment
• Understanding these financial dynamics early ensures you can forecast accurately, raise capital more effectively, and prepare for due diligence with confidence.

Resources Mentioned

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