PostHog will train AI models with your data (opt-in by default)

Summary

I really think we're on the verge of some of our best work through the next six months.Over the past year, we've started building more AI-powered features into PostHog, like our AI installation wizard, PostHog AI, and our MCP. They're all wildly popular, but they're only the start.PostHog's next chapter is about building more proactive, self-driving products. Products that surface answers and solutions for you, act on them, and improve over time. This is the vision for PostHog Code, which is now in beta. To enable this and more products like it, we want to try something new. We want to train models on data in PostHog.We have two goals here:Make our existing products smarter, more proactive, and useful to youBuild entirely new products, like PostHog Code, that help teams build better products, fasterThe first area we're interested in is session replay analysis. PostHog AI can already detect issues in replays, but it's expensive and doesn't scale well. We want replays to be as powerful at scale as they are for diagnosing the problems of individual users, and we think a model trained on the underlying data that powers replays will help us achieve this. Another idea I'm especially excited about is synthetic user testing – i.e. using our knowledge of user behavior to identify when users might get confused, or what flows might break, before you ship to production. As coding models improve, many people are seeing test and review workload increase hugely. We want to automate this, so you can focus on your product.And, if we can get better at predicting user behavior, we should be able to suggest changes that will improve conversion, and reduce user frustration, for features you've already shipped as well. If we can automate this work for you, you'll spend less time on manual analysis and burn fewer tokens in the process.Our ideas here are experimental. It will take iteration to figure out how to train models effectively, and what data is actually useful. But, so far, every ...