Don't feel pressured to label every AI-powered enhancement as an "AI feature." For example, using AI to generate CSS for a new dark mode is simply a better way to build. The focus should be on the user benefit (dark mode), not the underlying technology, making AI an invisible, powerful tool.

The least intrusive way to introduce ads into LLMs is during natural pauses, such as the wait time for a "deep research" query. This interstitial model offers a clear value exchange: the user gets a powerful, free computation sponsored by an advertiser, avoiding disruption to the core interactive experience.

To grow an established product, introduce new formats (e.g., Instagram Stories, Google AI Mode) as separate but integrated experiences. This allows you to tap into new user behaviors without disrupting the expectations and mental models users have for the core product, avoiding confusion and accelerating adoption.

Instead of direct sales pitches, subtly promote a paid community by answering a member's question on your free podcast. This provides genuine value to all listeners while showcasing the quality of the community and creating authentic interest, without an overt sales pitch.

Instead of asking an AI tool for creative ideas, instruct it to predict how 100,000 people would respond to your copy. This shifts the AI from a creative to a statistical mode, leveraging deeper analysis and resulting in marketing assets (like subject lines and CTAs) that perform significantly better in A/B tests.

Ensure every product completion point offers a clear next step. Integrate invitations to your higher-tier offer (e.g., a membership) directly into the mini-course as a final lesson, a sidebar graphic, or triggered emails upon module completion. This creates a natural, non-pushy upsell path.

A key heuristic for identifying low-value "snake oil" AI products is an immediate paywall. If an AI tool is genuinely powerful and automated, it should offer a generous free tier or credits to demonstrate value (like ChatGPT or Suno). Forcing a credit card upfront suggests the product can't stand on its own and needs to lock in revenue before its lack of utility is discovered.

Because AI products improve so rapidly, it's crucial to proactively bring lapsed users back. A user who tried the product a year ago has no idea how much better it is today. Marketing pushes around major version launches (e.g., v3.0) can create a step-change in weekly active users.