Difficulty in the design job market stems not from increased competition, but from companies seeking a perfect "puzzle piece" fit. They are over-filtering for extremely narrow, rigid profiles, often rejecting highly qualified but non-matching candidates.

Industry leaders from LinkedIn and Salesforce predict that AI will automate narrow, specialized tasks, fundamentally reshaping careers. The future workforce will favor 'professional generalists' who can move fluidly between projects and roles, replacing rigid departmental structures with dynamic 'work charts.'

Product management "range" is developed not by learning domain-specific facts, but by recognizing universal human behaviors that transcend industries—the desire for simplicity, convenience, or saving time. Working across different verticals hones this pattern-matching skill, which is more valuable than deep expertise in a world of accessible information.

Experts often view problems through the narrow lens of their own discipline, a cognitive bias known as the "expertise trap" or Maslow's Law. This limits the tools and perspectives applied, leading to suboptimal solutions. The remedy is intentional collaboration with individuals who possess different functional toolkits.

True diversification doesn't come from being a generalist, but from achieving undeniable mastery in one specific domain. This deep expertise becomes your leverage—your "in"—to access rooms, build credibility, and then expand horizontally into other ventures like production, investing, and brand partnerships.

Instead of diversifying randomly, a more effective strategy is to expand into adjacent verticals. Leverage your existing, happy clients for introductions into these parallel industries. This approach uses your established credibility and relationships as a bridge to new markets, lowering the barrier to entry.

Working at a startup early in your career provides exposure across the entire hardware/software stack, a breadth that pays dividends later. Naveen Rao argues that large companies, by design, hire for specific, repeatable tasks, which can limit an engineer's adaptability and holistic problem-solving skills.

Industry specialists can become trapped in an "echo chamber," making them resistant to paradigm shifts. WCM found their generalist team structure was an advantage, as a lack of "scar tissue" and a broader perspective allowed them to identify changes that entrenched specialists dismissed as temporary noise.

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.