ARR is a Distraction

Why Profitability is the Reality that Matters in the AI Era

In the Cloud and SaaS era, ARR (Annual Recurring Revenue) was the North Star. It was a beautiful proxy because SaaS had a simple, linear relationship: build once, sell many times, with near-zero incremental costs. This led to the > 80% gross margins that fueled the venture decade.

But in the AI era, ARR is a distraction. It creates headlines without telling you if there’s a real business underneath. If you are valuing an AI company purely on a revenue multiple today, you aren’t just an optimist; you’re ignoring the laws of physics.

The “New Normal” is no longer about how many users you’ve captured, but how much Free Cash Flow (FCF) you can generate per unit of intelligence. In this world, tokens are the fundamental unit of both value and cost.

1. The Right Proxy: Gross Profit per Million Tokens

While SaaS focused on ARR, the AI-native proxy for success might be Gross Profit per Million Tokens.

In traditional SaaS, adding a new customer didn’t significantly increase your AWS bill. In AI, every query has a physical cost that often scales linearly (although there are economies of scale). If you have $10M in ARR but $8M in compute costs, you don’t have a software business - you have a low-margin infrastructure business that might depend on a lot of enterprise sale and project-integration work.

• The Misleading Nature of AI ARR: Huge ARR numbers often hide negative or razor-thin gross margins. A company with $50M in ARR and 20% margins is fundamentally less valuable than a “boring” SaaS company with $15M in ARR and 85% margins.

• Token Pass-Through: Many AI startups are essentially just “token resellers.” They pass through OpenAI’s or Anthropic’s costs with a thin markup. If your revenue growth is just a reflection of your increased API bill, you aren’t scaling; you’re just a highly-paid customer of the hyper scalers.

2. Token Consumption is Not Stickiness

In the SaaS world, MAUs (Monthly Active Users) and NRR (Net Revenue Retention) were proxies for habit. In AI, token consumption tells you nothing about stickiness. Even worse, high token usage often reflects a one-time burst of activity as enterprises test a new tool with their experimentation budgets. This is not the same as the deep, structural lock-in of a CRM or an ERP that also acts as a system of records.

• The Switching Cost Fallacy: Demand in the token area can shift rapidly. Because many AI startups are “thin wrappers” on the same foundational models, a customer can switch from one UI to another in a weekend (or even build their own UI with ease). If your only value is the “token path,” your moat is built on sand.

• Limited Lock-in: Tokens are a commodity. In SaaS, your data and integration with processes and other systems was the lock-in. In AI, the model is the engine, and if someone releases a faster, cheaper engine tomorrow, your “high-usage” customers will vanish.

3. Valuing Future Cash Flows: The “Rule of X”

We are moving from the “Rule of 40” to what Bessemer calls the “Rule of X,” which applies a multiplier to revenue growth while keeping a cold, hard eye on FCF margin.

The possibility of future cash flows in AI native companies depends on three things:

1. Model Routing: The ability to send simple queries to cheap models and only use “frontier” models when necessary.

2. Proprietary Data Moats: Using AI to generate unique insights that stay with the company, rather than just being a window (context) into an LLM.

3. Efficiency Gains: Progress in AI isn’t just about saving energy; it’s about the Output per Watt - delivering more intelligence for every joule of electricity consumed.

The Actionable Insight

Don’t let yourself be deflected by the “unlimited” model. The unlimited model in AI is a death sentence for margins. Look for business models that have blended pricing (platform fees + usage markups) and can prove and maintain a high Gross Profit per Million Tokens.

Success in this era isn’t about having the loudest headline or the biggest ARR spike. It’s about the ability to survive a “Great Squeeze” long enough to build a defensible engine of intelligence that actually returns cash to shareholders.

Growth starts the story, profit writes the ending.