Part 2: AI is Breaking SaaS Metrics

Why CAC Payback, the Rule of 40, and Gross Margin no longer tell the full story.

Traditional SaaS Success Metrics Break in the AI Era

Who should read this: If you’re a SaaS founder, product leader, operator, or investor, this article is for you. Whether you're running AI-native teams, building usage-based products, or evaluating growth-stage companies, you need new metrics that reflect how modern software businesses actually work. This post breaks down what’s no longer working, and suggests what can emerge in its place.

This is the second installment in the series, “AI - The End of Software As We Know It” exploring how AI is dismantling and reshaping the SaaS model.

In Part 1, “The Compression,” we looked at how software teams are shrinking while output is rising. This article builds on that shift by examining why the SaaS metrics we’ve used for the last 15 years no longer reflect how modern software businesses operate.

I also suggest a set of new metrics that enable operators and investors better understand the health of their company and business in the AI era.

The Metrics Are Still on the Dashboard. But They Don’t Reflect the Road Ahead.

In board and investor meetings across the software industry, a key set of metrics (aka “Unit Economics”) is used for those outside of the business to get a succinct and objective understanding of how well the business is performing.

I touched on some key metrics to measure Product Market Fit in an earlier article. Here’s a quick primer on the five core SaaS metrics that shaped the last era…the ones that need to evolve to meet the need of the AI era.

• CAC Payback: Measures how long it takes to recoup customer acquisition costs using gross profit. Commonly used to assess marketing efficiency and sales ROI. From a product perspective, this is influenced by the core value proposition and therefore price.

• LTV/CAC: Compares the lifetime value of a customer (based on expected revenue and churn) to the cost of acquiring them. Used to evaluate the return on growth investment. From a product perspective, this is commonly influenced by driving PLG motions to increase usage.

• Net Dollar Retention (NDR): Reflects revenue expansion, contraction, and churn over time within a customer base. Serves as a signal for product-market fit and upsell success. From a product perspective, this is driven by retention rates through product delight; and upsells and cross-sells via PLG and PLS motions.

• Gross Margin: Shows how much revenue remains after direct costs (COGS) are subtracted. Historically used to indicate software profitability and business model efficiency. From a product perspective, this is your core value proposition - how big is the customer problem you’re solving, and how often do you solve it.

• The Rule of 40: Combines growth rate and profitability (typically EBITDA margin) to offer a composite signal of SaaS health. A Rule of 40 score over 40 was considered strong. From a product perspective, the better your product experience and monetization hooks, the faster your ARR grows. This directly lifts the growth side of the Rule of 40.

Founders, operators, and investors built entire operating models and fundraises on these KPIs. But in the AI era of usage-based pricing, AI-native products, and infra-heavy cost structures, these metrics are breaking down (check out - Why AI Is Breaking Your SaaS Pricing Model).

These unit economics are still useful. But because how a company makes money is changing, they’re no longer sufficient.

Why These Metrics Are Breaking

Traditional SaaS metrics were designed around a simple, scalable model: fixed pricing, predictable revenue, and minimal marginal cost. But the AI-native cost model breaks these assumptions.

In today’s stack, revenue is volatile, cost of goods sold includes real-time compute, and customer value ramps unpredictably. And so pricing is no longer fixed; it flexes by engagement tier, output, or agent cost.

As a result, old metrics like CAC Payback and Gross Margin become misleading. They flatten out variability, obscure expansion dynamics, and mask infrastructure costs that scale nonlinearly. That’s why these metrics don’t need to be discarded, but they do need to be reinterpreted and augmented.

What’s Breaking, Why, and What to Do

These metrics didn’t fail. They’re just being asked to explain a reality they weren’t designed for. When cost structures become elastic, pricing becomes nonlinear, and usage patterns define value, traditional SaaS KPIs start to distort instead of clarify.

This section breaks down exactly where each legacy metric is falling short, and what we can use to augment or replace it.