Products that start as AI-light and transition to AI-heavy often struggle to integrate AI features seamlessly into their core functionality.

Instead of spending too much time on building an AI core product, launch smaller experiments to gather early user feedback before full integration.

When launching an AI product, it's crucial to experiment extensively first, as AI results can be unpredictable and exceed expectations.

Most AI applications today, particularly those labeled as AI agents, often have low operational costs due to their reliance on software and LLMs, which are becoming cheaper over time.

Many startups are focusing on selling tools for AI applications, but the reality is that many potential customers are still figuring out how to effectively use AI, leading to a disconnect between product offerings and actual market needs.

Using AI prototyping tools for production apps can lead to maintenance issues, as they require ongoing attention to bugs and regressions, making them less reliable than dedicated AI agent platforms.

AI is considered core to many businesses, leading them to prefer open-source models and tools to maintain control over their implementations.

The biggest concern for product managers regarding AI agents is cost, as many platforms price based on usage rather than value.

Many buyers of AI solutions are sophisticated and understand that the underlying technology is often simple, leading them to question the true costs of running these systems.