Engineers, designers, and product managers now believe AI empowers them to perform the others' jobs. An engineer with AI can handle design and PM tasks, and vice versa. This isn't a threat but an opportunity for individuals to become multi-skilled and create immense value by combining domains.

In the age of AI, distinct roles like designer, PM, and engineer are converging. Long-term career success hinges on the ability to fluidly move between these disciplines and focus on shipping good software, rather than being confined by a rigid job title. Obsession with titles is a liability.

Even as AI allows a designer to code or a PM to prototype, the fundamental responsibilities of each role persist. Design champions the user, product management owns business outcomes, and engineering ensures system integrity. The tools converge, but the core mindsets do not.

As AI enables generalists, there's a danger of losing specialists. On one end, this means losing the deep craft and delight that expert designers bring. On the other, it means neglecting the complex engineering required to make a product work reliably for millions of users.

Netflix's CPTO observes that the value of narrow, deep specialization is declining. While still crucial for certain niche technologies, the preference is shifting toward adaptable generalists who can work across functions and stacks. The modern mindset is "I can learn that quickly" rather than sticking to one expertise.

Generative AI and low-code tools empower individuals to perform tasks previously owned by specialized roles, like a PM creating a functional prototype. This blurs traditional job descriptions. The critical skill shifts from mere tool proficiency to learning how to collaborate effectively in new, blended team structures.

With AI making code generation cheap, product taste is the key differentiator. In top AI teams, PMs are increasingly technical, using tools like Claude Code to build and iterate, making their role nearly identical to an engineer's.

AI development makes identifying the right use case and wrangling data the new bottlenecks, not coding. This flattens traditional hierarchies. The most effective teams are integrated 'tiger teams' where UX designers manage RAG files and developers talk to customers, valuing adaptability over rigid job descriptions.

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.