Everyone's saying AI will kill SaaS — but is the SaaSpocalypse actually real, or just the latest wave of disruption that enterprise software has survived before?

If you're a SaaS founder or operator watching vibe-coded apps spin up overnight, the fear is real. But the narrative is missing something critical: enterprise software isn't just code, and the moats that protect your ARR aren't going away anytime soon. Understanding what actually protects your revenue — and what doesn't — is the difference between panic and a clear-headed strategy. Here's what will you'll learn in episode #361 with Ben Murray.

• Why enterprise software is far more than code — compliance infrastructure, security, governance, SLAs, and integrations take years to harden, and a weekend project won't replace that
• How your proprietary data moat is actually becoming more powerful in the AI era, not less — and why AI agents without that data context are starting from zero
• Why switching costs remain one of the strongest SaaS defensibility factors — and why even AI-native alternatives face massive operational barriers to displacement
• The real operational commitment behind SaaS that vibe-coded tools can't replicate: customer support, product development, distribution, and long-term value delivery
• Why internal vibe-coded tools face their own adoption ceiling — from data security concerns to IT compliance — so enterprise spend isn't fleeing as fast as the hype suggests

Tune in for the full bull case on SaaS survival — and get the frameworks from Ben's SaaSpocalypse blog post linked in the show notes.

• Ben's SaaSpocalypse Blog Post + Defensibility Frameworks: https://www.thesaascfo.com/the-saaspocalypse-ai-agents-vibe-coding-and-the-changing-economics-of-saas/

Is the “SaaSpocalypse” real—or just another wave of disruption you need to navigate?

If you’re building or scaling a SaaS company, the rapid rise of AI agents, lower barriers to entry, and shifting pricing models could directly impact your growth, revenue predictability, and competitive edge. Understanding these changes isn’t optional—it’s critical to staying relevant and defensible in an AI-driven market. Here's what you'll take away in episode #360 with Ben Murray.

• Understand how AI agents are reshaping the traditional SaaS interface and customer interaction

• Learn why barriers to entry are dropping fast—and what that means for competition

• Discover how evolving pricing models could impact your revenue and forecasting strategy

Tune in to uncover whether SaaS is truly at risk—and what you should do right now to stay ahead.

Resources:

• AI defensibility framework: https://www.thesaascfo.com/the-saaspocalypse-ai-agents-vibe-coding-and-the-changing-economics-of-saas/

Are finance teams implementing AI the wrong way?

In episode #359, Ben Murray argues that many CFOs and finance leaders are approaching AI backward—focusing too much on prompts and quick wins rather than building the foundational data infrastructure required for meaningful, repeatable insights.

Drawing from recent AI webinars and his experience building softwaremetrics.ai, Ben explains why SaaS metrics, retention, and cohort analysis should not rely on AI. Instead, these should be computed through structured, deterministic systems first—then enhanced with AI for deeper analysis and pattern recognition.

Resources Mentioned

• My new metrics engine: https://softwaremetrics.ai/
• My SaaSpocalypse post: https://www.thesaascfo.com/the-saaspocalypse-ai-agents-vibe-coding-and-the-changing-economics-of-saas/

What You’ll Learn

• Why prompt-driven AI workflows are not scalable in finance
• The difference between deterministic systems and AI-driven analysis
• Why you don’t need AI to calculate core SaaS metrics like retention or CAC payback
• The importance of structured data and clean data pipelines
• How AI should be layered on top of computed financial data—not raw inputs
• Why context windows and token usage matter when working with large datasets
• How AI can uncover insights (like expansion opportunities) that FP&A teams may miss

Why It Matters

• Prompt-based workflows create inconsistency and lack of auditability
• Without structured data, AI outputs are unreliable and not repeatable
• Finance teams risk “prompt fatigue” without building scalable systems
• Deterministic calculations ensure accuracy for critical SaaS metrics and reporting
• AI delivers the most value when used for analysis—not basic computation
• Efficient data handling reduces token costs and improves performance