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👋 Hi, it’s Kyle Poyar and welcome to Growth Unhinged, my weekly newsletter exploring the hidden playbooks behind the fastest-growing startups. Today’s newsletter is a ✨ bonus ✨ edition.
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Rethinking SaaS metrics for AI
We’ve grown accustomed to the traditional set of SaaS metrics as just part of how to operate a B2B business. Here’s the thing: the traditional SaaS metrics playbook can be extremely misleading when it comes to building an AI-native business.
ARR, for example, was the lifeblood of SaaS. It’s becoming meaningless untrustworthy for AI-native companies.
It isn’t just ARR that no longer feels as powerful as it once did. Let’s look at a few other examples:
Classic product usage metrics were built for seat-based models where we expected people to be the main users. If AI is taking on digital labor, or if our products are used inside other products (ex: via Model Context Protocol or MCP), people shouldn’t need to be logging in everyday. Do we still need to obsess over DAUs, MAUs, or DAU:MAU?
LTV:CAC was always fun-with-numbers, and now we need to make it official. I don’t trust the LTV of any AI product right now, especially given AI experimentation budgets plus the epic shipping velocity of Anthropic and OpenAI. How much should we spend on customer acquisition when LTV is so unpredictable?
We’re seeing an explosion of mixed monetization models, lower margin profiles, and re-occurring revenue. Margin variability is the new normal. What happens if companies split revenue streams into platform (high margin) and tokens (low margin)? How should we treat Service-as-a-Software revenue versus pure software?
I’ve been interviewing more than a dozen of the top AI founders for an upcoming report about how AI-native companies grow (stay tuned 👀). What struck me was how the metrics these founders obsess over have started to shift, although there was almost no consistency in metrics from company to company.
The reality is that the old SaaS metrics still have a place, especially for companies selling subscriptions with high retention and healthy gross margins. But we need to start looking for next-era metrics that better define success.
Today’s post unpacks seven alternative metrics that feel more relevant and urgent for this next era of AI-native companies. These include both operational KPIs (which help you identify where to focus) and investor KPIs (which help you communicate the health of your overall business model).
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