When a company's conservative IT security stance stalls AI adoption, slowing down is not an option as competitors race ahead. For ambitious employees, the most practical answer is often to find a new role at a more progressive company, as changing yourself is easier than changing an organization.

Pursuing a more fulfilling career doesn't require risking financial ruin. Instead of taking a blind leap, you can vet a new direction by "trying it on"—shadowing professionals, conducting informational interviews, and testing the work in small ways to understand its reality before making a full transition.

You don't need to be an AI engineer today to contribute later. Strategic career paths include founding any tech company to learn entrepreneurial skills, gaining expertise in fields like diplomacy or forecasting, or joining key government institutions to be ready to integrate these tools when they arrive.

To transition into AI within your company without prior experience, proactively seek out nascent AI initiatives. By raising your hand for the "messy middle" where no one is an expert yet, you can learn on the job and establish yourself as a key player.

To pivot into an AI PM role without direct experience, create a case study by analyzing a past project you shipped. Articulate how AI could have enabled different features, improved outcomes, or changed the approach. This demonstrates applied thinking and initiative to recruiters.

A structured path to a top AI PM role moves from building prototypes to getting production experience. The final, critical step is to build a public brand by running evaluations on major open-source models (from Google, Meta, etc.) and publishing your findings and improvements.

This field is too immature for a standard career path with many job openings. Contributing effectively requires an entrepreneurial mindset to identify needs, create novel projects, and build solutions from the ground up, as opposed to simply applying for established roles.

The immediate career advantage in the AI era goes to employees who become internal AI champions. As CEOs mandate AI adoption, those who are already AI-native and can teach their teams to become more efficient will receive massive promotions and raises. This creates a clear path for advancement by leading the AI transition from within.

Instead of betting on a single AI timeline, plan your career across three plausible scenarios: 1) A short, fast-takeoff scenario where AI automates R&D by ~2027. 2) A medium timeline where this takes until the 2030s. 3) A long timeline where the current paradigm plateaus. This portfolio approach makes career strategy more robust.