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Hacker News - Newest: "AI"

AI can't read an investor deck AI as an attorney? Student uses ChatGPT, Gemini to sue UW over alleged racial discrimination Hacking MCP Servers in AI Systems – The Rug Pull: Tool Changes After Approval GitHub - MeepCastana/KubeezCut: Free Web based video editor Can AI judge journalism? A Thiel-backed startup says yes, even if it risks chilling whistleblowers Coming soon: 10 Things That Matter in AI Right Now DARPA built an AI to fact-check enemy weapons claims What explains heterogeneity in AI adoption? When AI Meets Muscle: Context-Aware Electrical Stimulation Promises a New Way to Guide Human Movements - Department of Computer Science AI Changed How We Build. It Did Not Change What Matters. Linux rules on using AI-generated code - Copilot is OK, but humans must take 'full responsibility for the… Meta spins up AI version of Mark Zuckerberg to engage with employees Code Mode: Let Your AI Write Programs, Not Just Call Tools | TanStack Blog GitHub - Delavalom/graft: Go framework for building AI agents. Type-safe tools, multi-provider (OpenAI, Anthropic, Gemini, Bedrock), zero vendor SDKs. India's TCS tops estimates, says new AI models did not dent services demand Gen Z's fading AI hype Strong feeling: we are in a folded AI reality GitHub - machinarii/total-recall-catalog: A reference catalog of latest knowledge retrieval, memory & RAG systems GitHub - mensfeld/code-on-incus: Give each AI agent its own isolated machine with root, Docker, and systemd. Active defense detects and stops threats automatically.. Quantization, LoRA, and the 8% Problem: Benchmarking Local LLMs for Production AI Iran war: We spoke to the man making Lego-style AI videos that experts say are powerful propaganda Powell, Bessent discussed Anthropic's Mythos AI cyber threat with major U.S. banks GitHub - immartian/bellamem: Persistent belief-graph memory for AI agents. Retrieves decisive context by importance — not recency, not RAG, not /compact. recursive-mode: The Repo-Native Operating System for AI Engineering After the attack on Sam Altman's home, will AI CEO's go on the offensive? The biggest advance in AI since the LLM Opus 4.6 vs GPT 5.4 One Prompt Unity World Generation Test “AI polls” are fake polls Client Challenge Can AI be a 'child of God'? Inside Anthropic's meeting with Christian leaders
What your MRR is really worth: an AI margin calculator · ...
ermantrout · 2026-06-24 · via Hacker News - Newest: "AI"

The Study · a directional estimator

What you actually keep

The MRR screenshot is the most shared number in indie AI and the least useful. Revenue is what they pay you. Income is what is left after the model bill, the card fees, the refunds, the tax you are only holding, and the people who quietly leave. Put your numbers in. See the real one. It is the math from the economics piece, made clickable.

Your numbers

Monthly price

What one customer pays you per month.

$

Paying customers

How many are subscribed right now.

Model you build on

Sets a rough blended token price. Edit the number below if you know your real blend.

Blended token price

Auto-set by your model. Directional, last set Jun 2026.

$/ M tok

Tokens per user / month

Millions of tokens an average user burns.

M tok

Monthly churn

Share of customers who leave each month.

%

Sales on international cards

Higher card fees plus currency conversion.

%

Sales tax / VAT you remit

Set 0 if it is added on top and is not your liability.

%

The screenshot, minus everything

Gross MRR $10,000

You keep $3,260

That is 33% of the number you would screenshot.

Gross revenueprice × paying customers $10,000

Token billyour metered cost of goods − $5,000

Card feesprocessing, international, conversion − $540

Failed paymentsexpired and declined cards, no dunning − $900

Refunds and disputesreversed sales keep their fee, chargebacks − $300

Tax you remitflows through, flows back out − $0

What you actually keep $3,260

Per customer, you keep $6.52 of the $20.00 they pay. At 6.1% monthly churn, you replace about 31 customers a month just to stand still.

Your token cost is above your price. A user this heavy costs you more than they pay. A flat fee on a metered cost only works if you cap the heavy tail with rate limits.

These are starting points from the research, not your receipts. Change any of them to your real number, that is the point.

  • Failed payments % of gross treated as uncollected, the midpoint of common dunning-loss estimates. Baremetrics
  • Refunds and disputes % of gross since a reversed sale keeps its processing fee and a chargeback carries a flat cost. Stripe
  • Card fees 2.9% plus $0.30 domestic, rising toward 5.4% on the international share you set, plus a small conversion slice. Computed, not editable. Stripe
  • Token cost your tokens per user times the blended price you set. Real bills move with the input and output mix and with caching, and inference keeps falling, so treat it as directional, not a quote. Epoch AI
  • One month a snapshot of your current base. It is built to show the gap between gross and kept, not to file your taxes.

The token bill is the margin you do not have.

Mature SaaS runs at 70 to 80% gross margin because hosting amortizes toward zero. AI does the opposite: every query is metered, so inference becomes the dominant cost at scale. Industry AI gross margins sit near 52%, not 80%.

Cheap and self-serve is where churn is worst.

Products under $25 a month churn around 6.1% monthly, roughly half the base in a year. Over $250 a month, retention jumps near 70%. For most solo AI products the move is up-market, not more users.

The cheapest income is the income you already earned.

Failed and declined cards are 20 to 40% of all churn, and around 9% of MRR can simply fail to collect. Dunning, the automated retries and card-update nudges, claws it back with zero new customers.

Numbers are directional, not financial advice.