Top AI labs struggle to find people skilled in both ML research and systems engineering. Progress is often bottlenecked by one or the other, requiring individuals who can seamlessly switch between optimizing algorithms and building the underlying infrastructure, a hybrid skillset rarely taught in academia.

Since modern AI is so new, no one has more than a few years of relevant experience. This levels the playing field. The best hiring strategy is to prioritize young, AI-native talent with a steep learning curve over senior engineers whose experience may be less relevant. Dynamism and adaptability trump tenure.

The speaker credits his career success to being a well-rounded "product hybrid" with skills in data, software, product, and design. He argues this versatility, allowing him to move from debugging firmware to debating product strategy, is more valuable than deep specialization, quoting "specialization is for insects."

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.

Dylan Field predicts that AI tools will blur the lines between design, engineering, and product management. Instead of siloed functions, teams will consist of 'product builders' who can contribute across domains but maintain a deep craft in one area. Design becomes even more critical in this new world.

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.

In a fast-moving environment, rigid job descriptions are a hindrance. Instead of hiring for a specific role, recruit versatile "athletes" with high general aptitude. A single great person can fluidly move between delivery, sales, and product leadership, making them far more valuable than a specialist.

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.

Powerful AI assistants are shifting hiring calculus. Rather than building large, specialized departments, some leaders are considering hiring small teams of experienced, curious generalists. These individuals can leverage AI to solve problems across functions like sales, HR, and operations, creating a leaner, more agile organization.