In a field as fast-moving as AI and ML, career decisions are often driven by whatever is hottest: the latest model, the biggest title, the most talked-about company. Eugene Yan's essay on career planning suggests a quieter but more durable approach. He argues that you should start from two questions: what do you deeply care about, and what do you excel at? A career built from the answer to both, rather than from external pressure, is more likely to be sustainable.
The essay itself is not AI-specific; it is general career advice. But that may be its strength for ML professionals. Many practitioners already have strong technical skills, yet struggle to choose between research, engineering, management, and other paths. Yan's framework reframes the decision as a personal alignment problem rather than an optimization problem. You are not simply picking the role with the most upside; you are picking the role that fits your values and leverages your comparative advantages.
The practical takeaway is not a checklist but a filtering principle. Before accepting a project or a job, pause to ask whether it uses your superpowers and serves what you care about. If both answers are yes, you are on solid ground. If only one, or neither, the role may be worth avoiding—no matter how exciting the technology around it seems.