Hiring managers often create AI-specific roles thinking it attracts experts. Instead, they should frame job descriptions around the complex problems the business needs to solve. This attracts true problem-solvers who can learn any necessary technology, rather than individuals skilled at keyword optimization.

Many skilled professionals are overlooked for promotions or new roles not because their work is subpar, but because they fail to articulate a compelling narrative around their accomplishments. How you frame your impact in interviews and promotion documents is as crucial as the impact itself.

Hiring documents often swing between demanding a "product unicorn" with an impossible mix of skills and a watered-down description so vague it attracts completely unqualified applicants. Both extremes fail to define the role effectively and create noise in the hiring process.

The language of job seeking has shifted. Descriptors like "seasoned," "passionate," or "cross-functional," and emphasizing years of experience, are now seen as fluff. Modern candidates must speak in terms of concrete actions and business outcomes they have driven, focusing on what they have shipped recently.

AI will create jobs in unexpected places. As AI accelerates the discovery of new drugs and medical treatments, the bottleneck will shift to human-centric validation. This will lead to significant job growth in the biomedical sector, particularly in roles related to managing and conducting clinical trials.

Establishing "non-negotiable" parameters early in a job search—such as geography, disease focus, or practice type—provides critical clarity. While it narrows the field of opportunities, this focus allows candidates to more efficiently identify the right-fit positions and avoid wasting time on unsuitable roles.

A new PhD hire at Meta was advised by his manager to embrace the "Software Engineer" title over "Data Scientist." The manager explained that SWEs held the real power and influence in the company, and pigeonholing himself into a less central role would limit his impact.

Many "AI Product Manager" jobs are standard PM roles with "AI" sprinkled in. A simple test is to replace every instance of "AI" with a random noun like "marble." If the description still largely makes sense or becomes nonsensical, it reveals the role lacks true AI-specific responsibilities.

Job descriptions are written to attract candidates, not to guide daily decision-making. Relying on them for operational clarity creates a gap between what's on paper and who actually decides, leading to career-killing confusion. They are the wrong artifact for clarifying real-time roles and responsibilities.