In remote, services-based businesses, pressure to deliver quality and the difficulty of junior mentorship make hiring senior engineers a necessity. The cost and complexity of building remote training programs often outweigh the benefits of hiring less experienced talent.
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AI is restructuring engineering teams. A future model involves a small group of senior engineers defining processes and reviewing code, while AI and junior engineers handle production. This raises a critical question: how will junior engineers develop into senior architects in this new paradigm?
Figma's founder, Dylan Field, admits he was a poor manager initially. His solution was to hire experienced leaders he could learn from directly, like his first director of engineering. This flips the traditional hiring dynamic; instead of hiring subordinates, insecure founders should hire mentors who can teach them essential skills and push the company forward.
While AI-native, new graduates often lack the business experience and strategic context to effectively manage AI tools. Companies will instead prioritize senior leaders with high AI literacy who can achieve massive productivity gains, creating a challenging job market for recent graduates and a leaner organizational structure.
The 30-40% pay premium for AI PMs isn't just because "AI is hot." It's rooted in the scarcity of their specialized skillset, similar to how analytics PMs with statistics backgrounds are paid more. Companies are paying for demonstrated experience with AI tooling and technical fluency, which is rare.
In niche sectors like aerospace engineering, the pool of senior, diverse talent is limited. A pragmatic strategy is to hire the best available senior specialists while intensely focusing diversity efforts on junior roles and internships. This builds a more diverse next generation of leaders from the ground up.
The "attitude vs. aptitude" debate is flawed. Instead, hire the person with the smallest skill deficiency relative to the role's requirements. For a cashier, attitude is the harder skill to train. For an AI researcher, technical aptitude is. The key question is always: is it worth our resources to train this specific gap?
Experience alone no longer determines engineering productivity. An engineer's value is now a function of their experience plus their fluency with AI tools. Experienced coders who haven't adapted are now less valuable than AI-native recent graduates, who are in high demand.
In rapidly evolving fields like AI, pre-existing experience can be a liability. The highest performers often possess high agency, energy, and learning speed, allowing them to adapt without needing to unlearn outdated habits.
In a paradigm shift like AI, an experienced hire's knowledge can become obsolete. It's often better to hire a hungry junior employee. Their lack of preconceived notions, combined with a high learning velocity powered by AI tools, allows them to surpass seasoned professionals who must unlearn outdated workflows.
