In SaaS your power user was free. In an AI product she's your largest bill.

Average AI product gross margin is 52% against the 80% that defined SaaS, and inference eats about 23% of revenue. Every growth instinct you inherited was formed in a world where your best user cost you nothing.

There's a rule most of us absorbed without ever being taught it: engagement is free. One more session, one more entry, one more day in the app — pure margin. That rule is why "north star metric" articles exist, why every dashboard puts DAU at the top, why nobody ever asked you what your heaviest cohort costs. It held for twenty years because the marginal cost of serving a software user really was approximately zero.

It doesn't hold for what you're building now, and the numbers on that are getting hard to argue with.

ICONIQ Capital's State of AI snapshot in January surveyed around 300 executives at companies building AI products. Average AI product gross margin: 52%, against the 70–80% that a mature SaaS business was expected to clear. At scaling-stage AI B2B companies, inference alone runs about 23% of revenue. Take the survey with the usual salt — self-reported, mostly B2B, published by an investor with a position in the outcome — but notice the direction before you dismiss it. That 52% is up from 41% in 2024. Margins are recovering, not collapsing. The headline everyone wrote about AI destroying software economics was directionally wrong; the interesting thing is subtler than that.

Here's the part that actually matters for a one-person product.

Your cost is now shaped like your usage, and your usage is not evenly distributed. It never was. You have a small tail of people who use the thing constantly, and in the old world that tail was your proof of product-market fit and cost you nothing to serve. In the new world that tail is your COGS. Your best cohort by retention can be your worst cohort by margin, and — this is the part that gets people — you almost certainly cannot see it. Your analytics tool reports engagement per user. Your model provider reports one number at the end of the month with no user attached to it. Nobody joins those two tables for you. So the flat $20 subscription keeps looking fine right up until the composition of your user base shifts.

Which brings me to the thing I'd push back on hardest, because it's the answer I hear whenever this comes up: costs are collapsing, so just wait.

The unit price genuinely is collapsing. GPT-4-class inference ran somewhere around $20–30 per million tokens in late 2022; equivalent performance is roughly $0.40–0.80 today. I'd treat the multipliers people quote with suspicion — I've seen 50x, 300x and 1,000x for the same three-year window, and which one you get depends entirely on which two models someone picked to compare. The direction is real. The round number is marketing.

But per-token price is only one half of your bill. The other half is tokens per task, and that is going the other way, fast. OpenRouter's own data has average prompt tokens per request up around 4x over thirteen months, with completion tokens nearly tripling. Reasoning models loop, re-check and reconsider; agentic flows make many calls where a chatbot made one. Comparisons floating around put a reasoning model's compute per task at tens of times a single-shot call on the same problem, and I don't fully trust any specific figure there, but the mechanism isn't in dispute.

So the price curve will not rescue you. Every saving it hands you, you will immediately spend — on reasoning, on longer context, on memory, on the multi-step version that finally works properly. That's not a failure of discipline. Better output costs more tokens. The savings arrive already earmarked.

Meanwhile the market is quietly repricing around this. Metronome's 2026 survey has 64% of SaaS companies billing at least partly by usage, up from 39% in 2024. A poll of 230 B2B software and AI companies found hybrid pricing — flat base plus a variable component — at 37%, up from 25% twelve months earlier. Both come from people who sell billing and pricing services, so read them as a direction rather than a measurement. The direction is that flat-rate is being abandoned by companies with finance teams, while indie makers are still shipping $20/month because it's the number that feels harmless.

What I'd actually do, and it's smaller than it sounds:

Compute cost per active user per month, not blended cost per request. The blended number hides exactly the thing you need to see.

Then split it by retention cohort. You are not looking for the average. You are looking for the margin on the users who love your product most, because those are the ones your growth plan is trying to make more of.

Before you ship a quality upgrade, price the new call first. "Make it smarter" is now a line item, and it's the easiest way to turn a working business into a broken one in a single release.

And if you do need a ceiling, design it into the product instead of bolting it on as a punishment. A natural stopping point — a session that ends, a reflection that concludes — reads as thoughtful. A hard cap that fires mid-use tells your most invested user she's used too much of the thing she's paying for. Same economics, completely different product.

The Murror version of this is uncomfortable and I'd rather say it than imply I'm above it. Our heaviest users are people going through something. The person journaling every day is in a hard month, and she is simultaneously the most expensive user to serve and the one I least want to meter. I don't have a clean answer to that. We made an unrelated decision last year to have the AI forget everything after thirty days, for trust reasons, and it happens to cap how much context every call carries — but I'd be rewriting history if I called that cost strategy. It wasn't. I got lucky, and getting lucky isn't a plan.

The honest summary is that the metric that used to mean "it's working" now means two things at once, and you need to be able to read both. Engagement is still the signal. It's just no longer free.