In episode #364, Ben Murray breaks down how SaaS finance teams should structure their chart of accounts to properly track inference costs, productivity AI, and agentic AI spend. As organizations shift from W-2 headcount to token costs and agentic software, your current expense coding may be out-of-date. If you can't see where the AI spend is going, you can't tie it to ROI — and you definitely can't make the case for going fully agentic.

• Why COGS is the right home for product inference costs (Claude, OpenAI, Gemini) — and why lumping them in with hosting is a mistake
• The three distinct AI spend buckets every SaaS CFO needs to track: direct COGS delivery costs, general productivity tools, and explicit labor substitution (agentic AI)
• Why agentic AI spend deserves its own GL account — and how that ties directly into your ROSE metric
• Where the tracking gets fuzzy: productivity tools vs. true labor displacement, and how to think about cause-and-effect as a CFO
• How AI spend reshapes the ROSE metric as orgs push toward $5M–$10M ARR per FTE targets
  Tune in to get the chart of accounts framework SaaS CFOs need before AI spend becomes too big to ignore — and too messy to measure.

Resources Mentioned

• ROSE Metric: https://www.thesaascfo.com/saas-rose-metric/

Is your SaaS company competing for funding in a market that's already decided AI wins? The Q1 2026 data is in — and the numbers are decisive.

If you're a SaaS founder thinking about your next raise — or a CFO modeling out valuation scenarios — understanding where investors are actually writing checks matters more than ever. In epsiode #363, Ben Murray covers:

• Which software categories dominated Q1 funding — AI infrastructure and vertical SaaS led at $4.6B and $4.5B respectively, and knowing why could sharpen your positioning
• Why enterprise pricing is the investor favorite — 59% of all capital flowed into enterprise-model companies, signaling exactly what target customer story VCs want to hear
• How Seed vs. Series A funding differs by category — Series A flipped toward vertical software and GRC, while Seed stayed heavy on AI infrastructure and DevOps
• What AI native vs. AI embedded actually means for classification — and why the distinction is shaping how investors evaluate your product
• Where to get the full Q1 2026 funding report — with searchable data across 552 rounds and $20B+ in tracked investment

Listen now to get the Q1 2026 funding breakdown — then download the full PDF report to see exactly where smart money is going before your next raise.

• Q1 2026 Funding Report PDF — available via Ben's newsletter: https://mailchi.mp/thesaascfo.com/investors-sent-a-message-in-1q26-ai-or-bust

Are you feeding raw financial data straight into AI and wondering why the results are inconsistent — or worse, just wrong?

AI is only as good as the data architecture underneath it. For SaaS CFOs and operators running monthly FP&A cycles, that means the order of operations matters enormously. Skip the deterministic compute layer, and your AI narrates garbage. Get the structure right, and suddenly AI can do what no human ever could — synthesize five years of retention schedules and SaaS metrics in seconds.

In episode #362, I'll cover:

• Why separating the 'thinking layer' (math) from the 'talking layer' (AI analysis) is the foundational principle for reliable SaaS financial AI — and what breaks when you skip it
• The pre-compute-everything rule: why you should never ask AI to calculate cohort retention, ARR, or MRR — and what you should ask it to do instead
• Why context beats prompts: how structured data inputs dramatically outperform one-off prompt experiments in repeatable FP&A workflows
• How constraints on what AI can and can't touch produce better output than better prompting — and why your context window size is quietly sabotaging your analysis
• The right mental model for AI in SaaS finance: a super-smart narrator that reads 1,000 computed data points — not an engine that replaces your metrics framework

If you're building or buying any AI layer on top of your SaaS financials, listen to this before you ship anything — these five lessons will save you weeks of bad output.

Resources Mentioned

• SoftwareMetrics.ai — Ben's five-pillar SaaS metrics platform