The creator of Claude Code prioritizes hiring generalists who possess skills beyond coding, such as product sense and a desire to talk to users. This 'full-stack' approach, where even PMs and data scientists code, fosters a more effective and versatile team.
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Lovable is moving away from the specialist, cross-functional squad model popularized by companies like Spotify, believing it creates decision-making bottlenecks. Instead, they hire "high slope" generalists with broad skills and good judgment who can own projects from start to finish, using AI to fill gaps.
Prioritize hiring generalist "athletes"—people who are intelligent, driven, and coachable—over candidates with deep domain expertise. Core traits like Persistence, Heart, and Desire (a "PhD") cannot be taught, but a smart athlete can always learn the product.
AI's productivity gains mean that on a lean, early-stage team, there is little room for purely specialized roles. According to founder Drew Wilson, every team member, including designers, must be able to contribute directly to the codebase. The traditional "design artifact" workflow is too slow.
Instead of siloing roles, encourage engineers to design and designers to code. This cross-functional approach breaks down artificial barriers and helps the entire team think more holistically about the end-to-end user experience, as a real user does not see these internal divisions.
The traditional tech team structure of separate product, engineering, and design roles is becoming obsolete. AI startups favor small teams of 'polymaths'—T-shaped builders who can contribute across disciplines. This shift values broad, hands-on capability over deep specialization for most early-stage roles.
Beyond speaking the same language as developers, an engineering background provides three critical PM skills: understanding architectural trade-offs to build trust, applying systems thinking to break down complex problems into achievable parts, and using root-cause analysis to look beyond user symptoms.
AI tools are collapsing the traditional moats around design, engineering, and product. As PMs and engineers gain design capabilities, designers must reciprocate by learning to code and, more importantly, taking on strategic business responsibilities to maintain their value and influence.
To effectively apply AI, product managers and designers must develop technical literacy, similar to how an architect understands plumbing. This knowledge of underlying principles, like how LLMs work or what an agent is, is crucial for conceiving innovative and practical solutions beyond superficial applications.
AI's rise means traditional product roles are merging. Instead of identifying as a PM or designer, focus on your core skills (e.g., visual aesthetics, systems thinking) and use AI to fill gaps. This 'builder' mindset, focused on creating end-to-end, is key for future relevance.
To deliver a high-stakes project on a tight deadline, an engineer took on product management responsibilities like defining scope and getting alignment. This ability to resolve ambiguity outside of pure engineering, which he calls the "product hybrid archetype," is a key differentiator for achieving senior-level impact.
