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Rowan Cheung (@rowancheung)

Content / media · last checked 15 Oct 2026

The Rundown AI

● ClaimedAnnual revenue

TL;DR

  • Figure: $10M+ annual revenue (Annual revenue · Oct 2025 · founder X post)

    ≈ $833,333/month in USD — $10M+ per year ÷ 12.

  • Proof level: Claimed. The headline figure is a founder statement in a hiring post; Forbes gives different, lower numbers. No filings or payment-provider data available.
  • Our verdict (2/10, opinion): Huge audience built early from a very large X following; the AI-newsletter niche is now crowded. Part of the revenue is education ("teaching AI"), not AI product income.

Reported figure

$10M+ annual revenue

Proof level

Claimed

Agentic-ness (opinion)

3/5

Replicability (opinion)

2/10

The story

Rowan Cheung bootstrapped The Rundown off his X audience (per his posts); a detailed timeline is Research in progress.

  1. 2024 — $3M revenue; $7M projected for 2025 — Forbes profile (secondary)
  2. 20 Feb 2025 — 1M subscribers (audience, not revenue) — founder post on X
  3. 10 Oct 2025 — "$10M+ annual revenue", bootstrapped — founder post on X
  4. 14 Aug 2026 — 3M+ readers (audience, not revenue) — founder post on X

⚠ The founder's "$10M+" is above Forbes' 2025 projection, and "annual revenue" is not defined (run-rate vs calendar year).

Business model

  • What it sells: newsletter sponsorships and Rundown University (AI training).
  • To whom: sponsors (AI companies) and professionals learning AI.
  • Pricing: newsletter free (sponsor-funded); Rundown University ~$1,000/year (Forbes).
  • Acquisition: X (very large following), newsletter referrals.

Proof

Sources:

Why "Claimed": the headline figure is a founder statement in a hiring post; Forbes gives different, lower numbers. No filings or payment-provider data available.

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Agent blueprint

Our suggestion — not how the founder did it

  1. Step 1

    Research agent

    Scans AI news sources every morning

  2. Step 2

    Writer agent

    Drafts the daily issue for a human editor

  3. Step 3

    Sales agent

    Manages sponsor pipeline and bookings

  4. Step 4

    Course agent

    Turns popular topics into training modules

Human in charge

A person approves money, public posts and key decisions. We don't estimate how much human work this saves.

Agentic-ness today (our estimate): 3/5 · Difficulty: Very hard

Our take (opinion)

  1. Pick a narrower beat than "all of AI".
  2. Publish daily with agents doing research and first drafts.
  3. Grow on one social platform and with referrals.
  4. Sell sponsorships, then a paid course.
  • Skills: Fast curation, writing, audience growth
  • Audience needed: Yes — biggest factor

Risks & limits

  • Crowded niche.
  • Ad-market cyclicality.
  • Undefined revenue basis; self-reported.

Our verdict

Editorial opinion

2/10

Replicability today: 2/10. Huge audience built early from a very large X following; the AI-newsletter niche is now crowded. Part of the revenue is education ("teaching AI"), not AI product income. (Editorial opinion.)

Similar cases

Figures as reported by the cited sources on the dates shown. Verdict, ratings and blueprint are editorial opinion. Not financial advice. No income guarantees.