The economics of a one-person AI business (the real numbers) · Okane Land

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The Ledger · Income<br>The economics of a one-person AI business: what the MRR screenshots leave out<br>the editors · 11 min · receipts<br>AI product gross margin, 2026 (projected) ~52%, vs 70-80% for mature SaaS (ICONIQ)<br>Monthly churn, products under $25/mo 6.1% median, about half the base in a year (ChartMogul)<br>Inference price for a fixed capability falling ~50x per year (Epoch AI)<br>Indie products making $0 54%+, only ~5% clear $100k/year (Scraping Fish, 2022)

The MRR screenshot is the most shared number in indie AI and the least useful. What the research says about gross margins, churn, fees, and what a solo founder actually keeps.<br>Open any indie maker feed and you find the same image: a revenue dashboard, a green line going up, an MRR number circled in red. It is the most shared number in indie AI, and the least useful one. Revenue is what a customer pays. Income is what you keep after the model bill, the card fees, the refunds, the tax you are holding, and the customers who quietly leave. Those are very different numbers, and the gap between them is wider for an AI product than for almost anything else you could build.

Here is what the research actually says about the economics of a one-person AI business, and where the screenshots stop telling the truth.

Almost nobody gets the screenshot

Start with the survivor problem, because it shapes everything else. When Scraping Fish pulled every Indie Hackers product with Stripe-verified revenue in 2022, all 937 of them, more than half were making nothing at all, and only about 5% cleared roughly $100,000 a year, a level its author notes is “not that hard to earn as a software engineer in a full time job.” The conclusion was blunt: “success in the world of indie developers is an outlier business.”

Now compare that to the rooms where the screenshots come from. At MicroConf in 2025, a bootstrapper conference, founder Rob Walling reported that 28% of attendees were doing more than $100,000 in monthly recurring revenue. That is not a contradiction, it is selection: one number is the whole population, the other is the people who already won and bought a conference ticket. Watch the units too. MicroConf’s 28% is $100k a month. The Indie Hackers 5% is $100k a year.

The base rates are sobering even outside software. U.S. government data finds about 34.7% of business establishments born in 2013 were still operating ten years later, and roughly half survive five years. AI has made the first dollar faster: Stripe Atlas reports its 2025 cohort reached first payment in a median of 34 days, down from 38, with 20% charging a customer inside 30 days versus 8% in 2020. But getting to the first dollar quickly is not the same as reaching a number worth screenshotting.

The margin you do not have

Here is the part most pricing advice skips: an AI product is not an 80-percent-gross-margin SaaS business, and pretending it is will quietly bankrupt you.

Classic software is cheap to serve. Bessemer’s cloud benchmarks put the best SaaS gross margins at 80% and above, with a typical cloud business around 65 to 70%. AI is structurally lower. Andreessen Horowitz flagged this early. Its 2020 essay on the new business of AI pegged AI gross margins “often in the 50-60% range,” dragged down by the “25% or more of revenue” that goes to cloud and compute. Six years on, the gap has narrowed but not closed: ICONIQ’s 2026 State of AI snapshot reports average AI product gross margins of 41% in 2024, 45% in 2025, and a self-projected 52% in 2026, still well under the 70 to 80% a mature SaaS business takes for granted.

The reason is simple and it does not go away: your cost of goods is metered. Every query a user runs costs you tokens. SaaS hosting amortizes toward zero as you scale; inference does the opposite. ICONIQ finds model inference rising from 20% of AI product spend before launch to 23% at the scaling stage, becoming, in its words, “the dominant cost driver at scale.” Bessemer’s 2025 taxonomy makes the danger concrete: its usage-heavy “Supernova” companies run at about 25% gross margin, often negative, against roughly 60% for the more disciplined “Shooting Stars.”

This is the trap a flat monthly fee walks straight into. If you charge $20 a month and a power user burns $25 of tokens, you are paying them to use your product. a16z’s own partners note that the heaviest sliver of users drives a wildly disproportionate share of cost, and that rate limits on the top 5% cut spend “with limited revenue impact.” A flat fee on a metered cost is only safe if you cap the tail.

The tailwind: token prices are collapsing

The counterforce is real, and it is the best news in this whole piece. The price of a fixed amount of AI capability is falling faster than almost any input cost in business history. a16z calls it LLMflation: the cost to reach GPT-3-level quality fell from $60 per million tokens in late 2021 to $0.06 by late...