Ben Murray brings you actionable SaaS metrics lessons that he has learned through years of being in the SaaS CFO trenches. Whether you are new to SaaS or a SaaS veteran, learn the latest SaaS and AI metrics, finance, and accounting tactics that drive financial transparency and improved decision-making. Ben’s SaaS metrics blog consistently rates a 70+ NPS, and his templates have been downloaded over 100,000 times. There is always something to learn about SaaS and AI metrics.
In episode #381, Ben Murray covers the latest 2026 tech CFO compensation benchmarks across base, bonus, equity, and severance. If you set finance comp or negotiate your own, guessing at the market rate is expensive in both directions. Underpay and you risk losing your best finance leader. Overpay and you burn cash you cannot spare. This episode gives you the median numbers and the revenue tier splits that decide what competitive actually looks like. Why the median CFO package of $285K base, $100K target bonus, and $1M equity is only a starting point, and why company revenue size changes the whole picture How median base pay climbs across revenue tiers, from roughly $240K under $10M up to $375K at $100M to $250M, a premium of about 50 percent The gap between target and realized bonus, with CFOs hitting about 87 percent attainment while VPs of Finance and FP&A land closer to 50 percent Why equity is where packages really split, at a $1M median for CFOs versus $200K at the VP of Finance level, a 5x difference Where severance protection shows up, at the CFO and VP of Finance level, and where it disappears at the director level Tune in, then grab the full report and interactive benchmarks from the show notes before your next comp conversation or board meeting. Resources Mentioned Blog post and report: 2026 CFO compensation summary: https://www.thesaascfo.com/cfo-and-vp-finance-compensation-base-bonus-and-equity-benchmarks/ Full 2026 CFO compensation report: https://www.benchmarkit.ai/2026-finance-executive-compensation
Is gross revenue retention under attack at your SaaS company? The latest benchmark data says the ground has shifted under everyone. In episode #380, Ben Murray breaks down the latest SaaS gross revenue retention benchmarks from Ray Rike's Benchmarkit report, the same data set Ben uses to benchmark his own client base. GRR is one of the power three metrics, and it is hard to scale without it. Pricing models are changing; seat-based pricing is under pressure, and AI is reshaping how revenue holds. If your board still treats 88% median GRR as the baseline, you are benchmarking against last year's reality. Why median GRR fell from 88% to 84% year over year, and why the top quartile slipped from 95% to 91% Whether the 95% GRR "elite" rule of thumb still holds, backed by three years of top-quartile benchmark data Which pricing model retains revenue best, comparing pure subscription against usage-based and subscription-plus-usage Why vertical SaaS is outperforming horizontal SaaS on retention, with a 90% median GRR versus 84% How to benchmark GRR the right way by ACV segment instead of relying on dangerous aggregate numbers Tune in to see where your gross revenue retention really stands, before your next board meeting or investor update. Resources Mentioned Benchmarkit SaaS Metrics Benchmark Report, Ray Rike: https://www.benchmarkit.ai/2026-saas-ai-native-metrics Ben's KPI app: https://softwaremetrics.ai/ Ben's blog post on 2026 GRR benchmarks: https://www.thesaascfo.com/saas-grr-benchmark-2026/
AI ARR is easy to announce. Proving it is where most SaaS finance teams are about to get exposed. In episode #379, Ben Murray tackles the new bar for AI financial transparency and what it means for your next budget season. The public markets have already moved the goalposts. Launching AI was the 2024 story. Reporting AI ARR was the 2025 story. Now investors and boards want to see AI margins, customer outcomes, and proof that AI revenue is actually dropping to the bottom line. That same pressure is heading straight for private SaaS, and your board will bring it to budget season whether you are ready or not. Understand why AI ARR by itself no longer satisfies boards or investors, and what they now demand to see in the numbers. Separate pure AI revenue, AI-influenced revenue, and AI upsell so your reporting survives scrutiny, using clean SKUs, product IDs, and chart of accounts. Know which AI costs belong in COGS, including inference, infrastructure, and observability, so you can show your real AI margins. Walk into budget season ready for the board questions on AI revenue, AI cost, and margin by revenue stream. Instrument heavy, medium, and light AI users so you can defend margins and LTV to CAC as usage scales. Listen now and build the AI transparency your board will expect before budget season starts. Resources Mentioned Ben's blog posts on capturing AI costs in COGS: inference, infrastructure, and observability: https://www.thesaascfo.com/what-should-be-included-in-ai-cogs/ Ben's training on AI metrics: https://www.thesaasacademy.com/ai-finance-metrics-saas
Is your SaaS company stuck in the valuation doghouse while a handful of names trade at a massive premium? In episode #378, Ben Murray breaks down Meritech's June 2026 public software comps report and the widening valuation gap across SaaS. The median revenue multiple has fallen 64% from its pre-ZIRP peak, and most public software now trades below 5X. If you are a SaaS founder or CFO, the multiple attached to your business depends on a short list of traits the market now rewards. This episode shows you which ones, and why the rules quietly changed. Why only 9 of roughly 100 public software companies trade above a 10X revenue multiple, while 77 sit below 5X How the Rule of 40 shifted under the surface, with revenue growth now 3.3x more correlated with the multiple than free cash flow margin Why two companies with the same Rule of 40 score can trade at 7.3x versus 3.7x, depending entirely on how they got there What the top 9 share in common: free cash flow margins above 20% and ARR growth above 20% at the same time How AI exposure now sorts the market, and why a weak AI ARR story lands horizontal SaaS in the doghouse Tune in to see exactly what separates the premium names from the rest before you benchmark your own SaaS valuation. Resources Mentioned Meritech June 2026 Public Software Comps (Pulse Report): https://meritech.substack.com/p/meritech-software-pulse-12-june-2026 Ben's academy: https://www.thesaasacademy.com/
Everyone says seat-based pricing is dead, but do you actually have an outcome you can charge for? In episode #377, Ben Murray breaks down the 12 steps to building an outcome-based pricing plan, drawn from analyzing real, live outcome-based pricing pages and the fine print buried in their terms and conditions. Outcome pricing is complex to design and even harder for customers to understand: when are they charged, and where is the failure point at which they aren't? For SaaS founders and CFOs weighing a move to outcome- or agentic-AI pricing, getting the unit, success criteria, and spend controls right is the difference between a model customers trust and one that creates budget anxiety and billing disputes. How to decide whether you even have a billable outcome, and why a completed customer result is not the same as an activity. How to define the outcome unit and write success criteria twice, with real examples from Intercom's Fin, Help Scout's AI Answers, and Zendesk's 72-hour resolution window. Why failure forgiveness is a conversion tool, not just billing logic, and how measurement windows protect you from outcomes that unravel later. How to choose your commercial structure, anchor price to labor savings, revenue, or risk avoidance, and plan for the training lag before charges begin. Why spend controls and auditable billing events are non-negotiable, and how to know when outcome pricing is the wrong model entirely. Tune in for the full framework, then grab the deep-dive blog post before you design your next AI pricing plan. Resources Mentioned Ben's blog post: 12 Steps to Creating an Outcome-Based Pricing Plan: https://www.thesaascfo.com/how-to-build-outcome-based-pricing/
Is your 2027 software budget ready for the AI spend that's about to blow past every forecast you've built? In episode #376, Ben Murray covers five takeaways for CFOs from the Pricing I/O AI Pricing Report, produced in partnership with Benchmarkit, which surveyed 296 software buyers in Q1 2026. With budget season around the corner and demand for tokens, agentic AI, and tools like ChatGPT and Claude climbing fast, the gap between what buyers want and where AI pricing is heading has never mattered more. If you own a software budget or sell AI software, these findings reshape how you should think about predictability, governance, and the guardrails buyers are actually asking for. Why buyers rank predictable total cost as a top-3 priority, far above low entry price, and why the seat-based pricing obituary may be premature for enterprise deals. What the 89% budget-overrun rate really signals: a forecasting problem on the buy side, not vendors changing the rules after signing. Why credit and token pricing is the single hardest model to evaluate, and what Salesforce's new agentic work units mean for your bill. The surprising finding that IT, not Finance, owns AI cost risk, and why department-level allocation of token spend is the fix. Why buyers want soft caps, alerts, and approval steps over hard cutoffs, and where hard caps get genuinely painful in outcome-based pricing. Tune in to get the buyer-side data shaping AI pricing before you lock in your 2027 budget. Resources Mentioned Pricing I/O AI Pricing Report: https://www.benchmarkit.ai/widget/ai-pricing/cy-26?utm_source=TheSaaSCFO&utm_medium=Podcast&utm_campaign=TheSaaSCFO Ben's blog post: 12 Steps to Creating Your Outcome-Based Pricing: https://www.thesaascfo.com/how-to-build-outcome-based-pricing/
Do you actually know which of your AI customers are making you money and which are quietly destroying your gross margin? In episode #375, Ben Murray breaks down the shape of AI usage and why the distribution curve of your customers determines whether your AI subscription product is profitable. This is why Anthropic and GitHub changed their pricing. Heavy users on a flat subscription can quietly turn a 40% gross margin into a negative one, and most finance teams are not tracking token usage by customer in enough detail to see it coming. The three AI usage distribution scenarios every SaaS CFO needs to model: normal, right skew, and left skew, and what each does to your gross margin Why a right-skewed distribution means your light users are subsidizing your heavy users, and how to spot when that subsidy stops working How a left-skewed distribution can leave 80% of customers unprofitable and drag overall gross margin into the negatives Why median, mean, and P90 token usage by customer are now core SaaS finance metrics, not just product analytics What finance needs from product and engineering — usage by customer, model mix, input and output token pricing — to run real pricing scenario analysis Tune in before your next pricing review and find out where your AI margin is actually leaking. Resources Mentioned Ben's AI metrics course with the usage distribution template and free preview: https://www.thesaasacademy.com/ai-finance-metrics-saas AI readiness quiz: https://www.thesaasacademy.com/ai-finance-metrics-saas
The pricing model that built the SaaS industry is being replaced in real time. Is your finance team ready for what it does to your core metrics? In episode #374, Ben Murray breaks down the four SaaS P&L metrics that break when per-seat pricing dies. Public tech leaders are already shifting fast. ServiceNow now drives 50% of net new business from non-seat-based pricing, Workday is reporting hundreds of millions in AI ARR, and GitHub is moving Copilot to usage-based billing. If you are a SaaS CFO or finance leader still modeling on a single blended gross margin, your benchmarks are about to stop working. Why the AI product gross margin sits around 52% and how a 30% revenue mix shift can compress your blended margin by 10 to 15 points How AI COGS scale directly with product usage, breaking the near-zero incremental cost assumption traditional SaaS finance was built on Why one blended LTV no longer works once you have heavy, medium, and light AI usage cohorts, and how to rebuild LTV to CAC by cohort How CAC payback period shifts when gross margin is no longer a single number across the customer base The new frameworks finance teams need to model hybrid subscription plus usage and outcome-based pricing before the board notices the margin compression Tune in to get ahead of the pricing shift before your next forecast and board deck go out. Resources Mentioned Ben's blog post on the SaaS pricing revolution: https://www.thesaascfo.com/saas-per-seat-pricing/ Ben's AI course for SaaS finance leaders: https://www.thesaasacademy.com/ai-finance-metrics-saas
Is per-seat pricing dying a slow death, and is your SaaS expense structure ready for its replacement? In episode #373, Ben Murray breaks down the shift from per-seat subscriptions to usage and outcome-based pricing, and what it means for your finance org. Bloomberg projects subscription pricing falling from 60% to 30% of SaaS models over the next decade, while outcome-based pricing climbs from 10% to 60%. This is no longer a thesis on a slide. GitHub, Salesforce, Zendesk, Intercom, Figma, HubSpot, and others are already repricing, and public companies are reporting AI ARR in the hundreds of millions. If you cannot answer what your AI margins are when the board asks, you are already behind. See exactly how legacy SaaS leaders are repricing, from Zendesk charging per automated resolution to Salesforce billing per AI conversation plus flex credits, and what GitHub's June 1 move to token-based billing signals for the rest of the market. Understand why a single bucket of cloud hosting that blends traditional infrastructure with inference spend leaves you blind, and what instrumentation to put in place before budget season. Learn the questions your board will ask about AI margins, and how to answer whether low-usage customers are quietly subsidizing your heaviest users. Get the case for reconvening your pricing committee now to align product roadmap, AI features, and the expense framework that tracks them. Know which AI unit economics to track by revenue stream and by usage bucket so you can defend margin as your pricing model changes in real time. Listen now and put the tracking framework in place before the AI margin questions land on your desk. Resources Mentioned Ben's blog post: https://www.thesaascfo.com/saas-per-seat-pricing/ New course on AI unit economics and metrics: https://www.thesaasacademy.com/ai-finance-metrics-saas
Are you a legacy SaaS company quietly hoping for a recovery that isn't coming? In episode #372, Ben Murray breaks down two slides from Jason Lemkin's State of SaaS keynote at SaaStr Annual that every SaaS operator and CFO needs to confront. The four categories Lemkin laid out will tell you exactly where your company sits in the AI transition, and whether your ARR growth is real or borrowed time. If you're building, leading, or financing a SaaS business right now, this is the reality check that should reshape how you frame your strategy for the next board meeting. Understand the four SaaS+AI categories Jason Lemkin used to map every software company, and which one quietly signals the end of the road Learn why AI driving expansion revenue versus net new customer acquisition matters more than top-line ARR growth right now See which public SaaS companies are pulling off the AI-powered rocket ship growth and what they share Hear the "tired versus wired" narratives that separate operators stuck in 2024 talking points from those building what's next Get a clear lens for whether your AI features are real revenue drivers or just a story you're telling investors Tune in to find out where your company actually sits before the next board meeting forces the question. Resources Mentioned SaaStr Annual / Jason Lemkin: https://saastrannual.com/ Ben's new AI metrics course: https://www.thesaasacademy.com/ai-finance-metrics-saas
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. Resources Mentioned Ben's new AI course: https://www.thesaasacademy.com/ai-finance-metrics-saas ROSE metric: https://www.thesaascfo.com/saas-rose-metric/
Are AI inference costs already eating into your gross margin — and you can't even see them on your P&L? In episode #370, Ben Murray breaks down exactly what belongs in AI COGS for SaaS companies offering an AI-first or AI-infused product line. Inference bills are stacking up fast, infrastructure-layer spend is the surprise line item nobody priced in, and most finance teams haven't built the GL account structure to capture any of it cleanly. If you don't get the framework in place now, you'll be reporting AI gross margin you can't actually defend by next quarter — and your board will notice. The 5 cost categories every AI COGS framework needs — inference, model hosting/GPU infrastructure, the AI infrastructure layer, monitoring and observability, and AI-specific support Why AI inference costs deserve their own GL account — and shouldn't be buried inside your cloud hosting bill where they disappear The surprise cost line one industry report flagged as the #1 unexpected AI expense — hiding in data platform usage, networking, and egress How to structure your COGS cost centers so you can deliver clean margins by AI product line, not just lumped together at the company level Why token tracking by customer cohort (heavy / medium / light users) is now table stakes for any AI product sold as a subscription The deployed-engineer question: should AI support tickets sit with tech support or a specialized team — and how that decision rewires your margin model Tune in to get the AI COGS framework in place before your gross margin lands on a board slide you can't defend. Resources Mentioned Ben's new AI course: https://www.thesaasacademy.com/ai-finance-metrics-saas Ben's blog post: What Should Be Included in AI COGS: https://www.thesaascfo.com/what-should-be-included-in-ai-cogs/ SaaS Metrics Foundation course: https://www.thesaasacademy.com/the-saas-metrics-foundation
Did your AI bill just jump overnight — even though no one announced a price increase? In episode #369, Ben Murray breaks down the hidden AI price hike that's quietly hitting SaaS P&Ls this month. Anthropic shipped a new tokenizer underneath Claude Opus 4.7 — same menu pricing as 4.6, but real enterprise workloads are showing 12-27% higher effective cost, with some prompts consuming up to 35% more tokens for identical output. Most finance teams won't catch this variance until the invoice lands. If you're running AI in production, paying for Claude Code, or modeling AI COGS into next year's plan, this is the cost dynamic you need on your radar before the next board meeting. Why "same per-token pricing" doesn't mean same cost — and how a new tokenizer can quietly inflate your token consumption by 35% The real-world math: how a $50K/month API spend can balloon to $67K with zero changes to the pricing page What Anthropic's doubled Claude Code per-developer estimate ($6 → $13/day) signals about the end of subsidized AI pricing Why the era of "AI is just going to keep getting cheaper" assumptions is breaking down — and what that means for forecasting and runway The exact metrics to monitor in your Anthropic console today to catch token volume spikes before they hit your GL How to use the Inference Efficiency Ratio (revenue ÷ token costs in COGS) to measure AI margin if you're embedding AI into your product Why finance teams now need to document internal-use AI models the same way they document internal-use software Tune in before your next Anthropic invoice lands — and learn what to track now so AI variance doesn't become a board question. Resources Mentioned Dev.to article: https://dev.to/dev_tips/the-ai-price-hike-that-never-showed-up-on-the-pricing-page-your-bill-went-up-27-anyway-3mn5 Put your AI framework in place: https://www.thesaasacademy.com/ai-finance-metrics-saas
Can you actually prove what your AI product is doing for customers — or are you still pointing at token counts and hoping the board nods along? In episode #368, Ben Murray breaks down the four layers of AI measurement that every SaaS company needs to communicate internally and externally. Token usage is table stakes. The real question is whether you can move up the stack from consumption to work performed to verified outcomes to quantifiable P&L impact. Get this wrong, and your AI story falls apart in front of investors, customers, and your own finance team. Get it right, and you finally have ROI math a CFO will actually approve. Why AI inference belongs in COGS / DevOps — and what that means for the gross margin story behind your AI features and product lines How Salesforce's "agentic work units" framing on its latest earnings call signals where AI reporting is heading for the rest of SaaS Where true outcome-based pricing actually lives on the pricing page (HubSpot, Zendesk, and others) — and where Agentforce was really still usage-based in disguise How Layer 4 business impact replaces fuzzy ROI calculators with objective math What to show your board and investors at each layer so your AI value story holds up under scrutiny Tune in before your next board meeting — your AI story needs more than token counts. Resources Mentioned Ben's blog post on AI measurement and AI work units: https://www.thesaascfo.com/the-four-layers-of-ai-measurement-a-cfos-framework/ Ben's academy: https://www.thesaasacademy.com/
Salesforce just invented a new metric on their latest earnings call — not because they needed one, but because Wall Street didn't have the vocabulary to value what they built. In episode #366, Ben Murray breaks down Salesforce's Q4 FY2026 earnings call — not the financials, but the narrative architecture: a new unit of measurement for AI value (the AWU), a framing strategy designed to neutralize the biggest fear enterprise buyers have about AI, and three customer testimonials brought live onto the call. This is the communication playbook every SaaS operator can steal when explaining AI to boards, investors, and customers — at a time when the old metrics (tokens, MAUs, queries) no longer tell the value story. Why Salesforce introduced the Agentic Work Unit (AWU) — and what 2.4 billion AWUs against 19 trillion tokens reveals about the limits of token-based AI metrics The AWU-to-token ratio as a customer health signal — and why this is the metric your AI-enabled SaaS dashboard is missing The "humans and agents working together" framing that lets you sell AI capabilities without triggering the "we're going to lay people off" deal-killer How Wyndham's 8,300-hotel deployment, SharkNinja's 250,000 holiday-season engagements, and Lemkin's SaaStr transformation prove ROI when slides can't How to expand your SaaS metrics dashboard from 5 pillars to 6 — and the AI-era KPIs (AWUs, AI-attributed ARR, input-to-output ratios, customer outcome metrics) that belong in the new pillar Tune in before your next board meeting or AI sales pitch — and steal the vocabulary that's about to define the category. Resources Mentioned Salesforce Q4 FY2026 earnings call transcript Ben's 5-pillar SaaS metrics dashboard — and the upcoming 6-pillar AI-era expansion: https://www.thesaascfo.com/downloads/five-pillar-metrics-framework/
HubSpot's 50-cent bet may have just forced every SaaS founder to ask whether their current revenue model is still defensible. In episode #365, Ben Murray breaks down HubSpot's April 2nd announcement — slashing its Breeze customer agent from $1 to 50¢ per resolved conversation, plus a shift on its prospecting agent to $1 per qualified lead — and what this risk transfer means for SaaS revenue, forecasting, and the metrics CFOs need to start tracking. With Salesforce Agent Force hitting $800M in Q4 run rate and over 60% of bookings coming from existing-customer expansion, the question is no longer whether AI is reshaping SaaS pricing, but how fast and how unevenly. Ben pulls in his SEC filings research and a sharp counterpoint from Salesforce's own earnings call to show why the "SaaS is dead" narrative is overplayed. The two HubSpot pricing changes that signal a true risk transfer — and the 65% resolution rate (90% for top performers) that makes the bet credible Why "75% of AI agent vendors have no systematic approach to pricing" should put your pricing committee on notice this quarter The forecasting and metrics shift CFOs need to make as outcome-based pricing erodes predictable usage-based revenue — and the new KPIs that replace the old ones How Salesforce Agent Force's $800M Q4 run rate and 60%+ expansion bookings prove the AI revenue thesis — while Robin Washington's earnings call comment complicates the seat-erosion story The pricing reality check Ben pulled from analyzing 100+ SEC filings — and what it means for whether your ICP actually fits outcome-based pricing Listen before your next pricing committee meeting — and bring your CFO. The forecasting implications alone are worth the six minutes. Resources Mentioned Article from: https://thesaaslibrary.com/per-seat-pricing-dead-saas-2026/ SaaStr post by Jason Lemkin: https://www.saastr.com/salesforce-now-has-3-pricing-models-for-agentforce-and-maybe-right-now-thats-the-way-to-do-it/ Salesforce Q4 earnings call Ben's blog post: https://www.thesaascfo.com/your-ai-feature-is-quietly-destroying-your-gross-margin/
Is your AI SaaS company skating on thin ice because of exploding compute costs you're not tracking? In episode #365, Ben Murray tackles one of the most pressing financial challenges facing AI-first SaaS companies: the structural margin compression caused by LLM inference costs. Traditional SaaS was built on near-zero marginal cost per customer — that era is over. If you're building on top of AI, every prompt, query, and agentic workflow is a hard COGS line that scales with revenue, and if you're not managing it, it will quietly destroy your unit economics. Why AI-first SaaS companies are running 50–60% gross margins (vs. 70–80% for legacy SaaS) — and what Bessemer data shows about AI supernovas with margins as low as 25%. How inference and compute costs differ fundamentally from traditional SaaS COGS — and why they won't scale down the way hosting costs did Why token costs vary wildly (from $1–2 per million to $30–180+ for frontier models) and how that variability makes feature-level economics a CFO priority 5 tactical ways to reduce LLM spend: model routing, prompt caching, context compaction, semantic caching, and batch processing How to set up your GL accounts and COGS tracking to allocate inference costs by feature — so you actually understand the economics of what you've built Tune in before your next board meeting — because if you're not tracking AI inference costs at the feature level, you're flying blind on your most important unit economics. Resources Mentioned The SaaS CFO: https://www.thesaascfo.com/ Ray Rike — AI to ROI Newsletter: https://ai2roi.substack.com/ Tomas Tunguz: https://tomtunguz.com/ Fungies.io — 5 Ways to Save on LLM Costs: https://fungies.io
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. Resources Mentioned 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
