Aicut Affiliate Program for Faceless Video Creator Audiences

Automate & Grow Your Faceless Channel 🤖 Daily Automated Faceless Videos posted to your Channel 🖼️ Create AI Image, Fake Text, Reddit Story and Brainrot Videos ⏱️ Save hours of Video Editing 📈 Grow your Channel & Avoid Zero Views

Aicut

Pricing & Commission

Monthly Price Range:$19.99-$39.99/mo
Annual Plan:$299.99/year
Enterprise Pricing:No Enterprise Plan
Commission Rate:50%
Commission Duration:First 1 Month
Commission Type:one-time payout
Minimum Payout:$10

💡 We may earn a commission if you join. No extra cost to you.

Why Promote Aicut?

Aicut fits faceless-video educators, short-form creators, social automation publishers, and AI video reviewers because its product can be judged through finished content rather than feature claims alone. Current first-party material centers on generating videos with voiceover and captions for YouTube, TikTok, and Instagram, plus automation that can move approved content toward scheduled publishing. That creates a repeatable creator workflow to test.

A strong tutorial should create one representative video from source material, then inspect script structure, narration, captions, visual timing, and final export. If automation or scheduling is used, show the approval point before publication and document every correction. This keeps the review focused on production control instead of promising views, retention, channel growth, or monetization.

The current affiliate page publishes 20% commission on monthly subscriptions for life. It also says affiliates need $40+ to unlock the first payout and are paid on the 16th of each month. The program prohibits self-referrals, impersonating Aicut, and paid ads on Google, Bing, YouTube, or Meta; no public cookie duration is stated.

Those terms support a numeric 20% CTA, but the acquisition strategy must stay within Aicut's organic-promotion rules. Creator content should emphasize tutorials, watermarked examples, and authentic workflow demonstrations rather than paid-media arbitrage. Any unpublished attribution mechanics should remain unstated, and the product recommendation should be based on actual output quality and editing effort.