Designers often focus on selling their craft to design managers, but the final hiring decision frequently lies with product leaders. To succeed, designers must frame their value as a business investment, emphasizing the ROI and metric impact that resonates with the ultimate approver.

Presented with the "LinkedIn for AI" problem, the designer's first step isn't visual design. It's product strategy: clarifying the core objective (e.g., matchmaking, certification), identifying the target user groups (job seekers, employers), and defining what "a good match" even means in this new context.

In the fast-evolving world of AI, the most valuable trait in a designer is a deep-seated curiosity and the self-direction to learn and build independently. A designer who has explored, built, and formed opinions on new AI products is more valuable than one with only a perfect aesthetic.

Perplexity's VP of Design, Henry Modiset, states that when hiring, he values product intuition above all else. AI can generate options, but the essential, irreplaceable skill for designers is the ability to choose what to build, how it fits the market, and why users will care.

The common practice of hiring for "culture fit" creates homogenous teams that stifle creativity and produce the same results. To innovate, actively recruit people who challenge the status quo and think differently. A "culture mismatch" introduces the friction necessary for breakthrough ideas.

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?

Job seekers use AI to generate resumes en masse, forcing employers to use AI filters to manage the volume. This creates a vicious cycle where more AI is needed to beat the filters, resulting in a "low-hire, low-fire" equilibrium. While activity seems high, actual hiring has stalled, masking a significant economic disruption.

Standard application processes often filter out candidates with non-linear career paths. Bypassing these filters requires "warm networking"—building genuine connections with people inside a target company to let them see your potential as a human, not just a CV.

For cutting-edge AI problems, innate curiosity and learning speed ("velocity") are more important than existing domain knowledge. Echoing Karpathy, a candidate with a track record of diving deep into complex topics, regardless of field, will outperform a skilled but less-driven specialist.