👋 Hey {{first_name|there}},
Last issue, we scored AI ideas on impact and feasibility (Lesson #59). There was a word buried inside "feasibility" doing more damage than the rest combined: cost. Specifically, what it costs to run for a single customer, and whether the price they pay covers it.
Why this matters
Traditional SaaS has a comfortable shape. Build the feature once, and the ten-thousandth user costs you a rounding error more than the tenth. Gross margins sit around 80%, and nobody loses sleep over cost-to-serve. AI features break that shape. Every call has a real, variable cost: tokens in, tokens out, the retrieval lookup, the retry when the first answer came back garbage. Usage doesn't just create value now. It creates spending, every single time.
So the feature everyone loved in the demo has a second life on your model-vendor bill, and that second life scales with the exact behavior you were trying to encourage. The better it works, the more they use it, and every use bills you again. At some volume, usually lower than you'd guess, the line where cost meets price gets crossed, and you're paying for the privilege of having engaged users.
🧭 The shift
From: "What does this feature cost us?" (one number, an average)
To: "What does it cost to serve our most expensive customer, and does their price cover it?"
The average is the comfortable number and the useless one. It hides the distribution, and the distribution is where the damage lives. A handful of power users running fifty times everyone else's volume will disappear inside a healthy-looking average, right up until the month they don't.