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We have built 10 different AI products at PostHog like PostHog AI, our Wizard, Replay Vision, Scouts, and more. Combined, they are used by nearly 100k people every month. Along the way, we’ve learned a lot about building them, what works, and what doesn’t. Here are the 38 most important lessons:

1. AI can make your product worse. It can be slow and unreliable, or solve a problem nobody has (like chatting with your docs, any AI can do this). Just because it’s AI does not mean it’s worth building.

2. Consider whether you need to build AI at all. It’s often better to build for AI first, like an MCP server. These are easier to build and maintain. 5x more people use our MCP server daily than PostHog AI.

3. Look for jobs to be done, not demos. The best opportunities for AI are tasks that take 30+ seconds, use language or interfaces users don’t know (like SQL), or tasks users repeat 20+ times.

5. Rather than asking “how do we add AI to this?” ask “what is the agent-first version of this?” Doing this forces you to rethink the jobs to be done for an agent-first future and helps you avoid being disrupted by an AI-native competitor.

6. Building 2030-shaped products may require you to start fresh and break from years of cumulative UI and assumptions which are no longer valid or relevant.

7. Watch for customers rebuilding your product with AI. This reveals what features they actually want. When one of our largest customers built their own prototype of “PostHog 2.0” with our MCP and Claude Code, we knew we needed to add more customizable “canvas” artifacts to PostHog.