Growth hacking for AI startups is rapid, data driven experimentation to move users and value quickly. Traditional marketing relies on established channels, longer campaigns, and brand building. For AI ventures, combine both: use fast product tests to prove value, then scale with credible messaging, governance, and enterprise readiness.

• Define a North Star metric: API activations or inferences per user.
• Product led onboarding: sandbox, guided prompts, instant value.
• Free tier with usage based pricing: tokens per month to test.
• In app prompts gallery: templates for common use cases.
• Referrals that reward first results shared by teammates.
• Use case catalogs: industry templates to shorten setup.
• Developer ecosystem: starter repos and samples.

• Brand credibility: benchmarks and customer stories.
• Thought leadership: technical posts and webinars for CTOs.
• Targeted ABM: vertical landing pages with ROI messaging.
• Events and PR: speak at conferences and issue updates.
• Case studies: measurable results from deployments.
• SEO and content: use case pages and tutorials.

• Focusing on vanity metrics without activation or retention.
• Onboarding friction that slows time to value for AI features.
• Relying on paid channels before the product proves value.
• Messaging misalignment with buyer personas such as data scientists or CIOs.
• Ignoring data privacy, governance, and explainability in claims.
• Weak developer experience missing samples, SDKs, and prompts.
• Not measuring long term value and customer lifetime impact.

• Choose a clear North Star metric and track it weekly.
• Run small, rapid experiments with plan do check act cycles.
• Launch a minimal lovable product for a niche vertical.
• Build a use case catalog with templates for 10 industries.
• Develop a strong developer experience with sample repos and prompts.
• Optimize onboarding with live model demos and guided prompts.
• Launch a referral program that rewards shares and tests results.