In the fast-moving AI space, the most critical career skill is the ability to rapidly experiment and update your own hard skills. This involves constantly testing new models on personal use cases to develop an intuitive "vibe evaluation" sense and quickly separating signal from noise. This applies to technical leaders as well as individual contributors.

The essential skill for AI PMs is deep intuition, which can only be built through hands-on experimentation. This means actively using every new LLM, image, and video model upon release to objectively understand its capabilities, limitations, and trajectory, rather than relying on second-hand analysis.

It's tempting to spend weeks setting up complex AI systems and skills before starting. This is a form of procrastination. The most effective way to learn AI tools is to jump straight into building a real-world application, learn from the errors, and iterate.

To effectively learn AI, one must make a conscious mindset shift. This involves consistently attempting to solve problems with AI first, even small ones. This discipline integrates the tool into daily workflows and builds practical expertise faster than sporadic, large-scale projects.

Don't try to learn everything about AI before starting ("Have, Do, Be"); the field moves too fast. Instead, adopt the identity of a builder ("Be"), start a project ("Do"), and you will acquire the necessary knowledge along the way ("Have"). This action-oriented approach is more effective.

AI operates on principles that are often counterintuitive to experienced professionals. Mastering AI means actively building new intuitions ('Fingerspitzengefühl') through experimentation, not relying on old mental models for how systems should integrate or behave.

To rapidly master a new domain like AI coding, skip the manuals and tutorials. The fastest path to developing an intuitive feel is to immediately start building a project, even a familiar one, with the new tools.

Theoretical knowledge from articles is insufficient for understanding AI models. True intuition is built through intensive, practical experimentation, such as feeding a model an entire codebase or extensive documentation. Pushing the AI to its limits is the fastest way to learn.

To stay current in a fast-moving field like AI, passive learning through articles and videos is insufficient. The key is active engagement: experimenting with new platforms, trying new features as they launch, and even building small applications to truly understand their capabilities and limitations.