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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.

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The traditional, linear handoff from product (PRDs) to design to dev is too slow for AI's rapid iteration cycles. Leading companies merge these roles into smaller, senior teams where design and product deliver functional prototypes directly to engineering, collapsing the feedback loop and accelerating development.

AI tools are blurring the lines between roles like product management, UX design, and development. A single skilled individual can now leverage AI to handle tasks that previously required a three-person team, dramatically increasing individual productivity and changing organizational structures.

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.

The historical separation between product management, design, and engineering is dissolving. AI assistants handle the coding, allowing a single person to define the product (PM), ensure high-quality aesthetics and UX (designer), and direct the technical implementation (engineer), thus converging the three roles.

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.

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.

At the AI-native company Cursor, roles are "really muddy." Team members contribute based on individual strengths—like visual design or systems architecture—and use AI agents to bridge skill gaps and tie work together. This creates a more fluid and efficient team structure.

Top engineers are no longer just coding specialists. They are hybrids who cross disciplines—combining product sense, infrastructure knowledge, design skills, and user empathy. AI handles the specialized coding, elevating the value of broad, system-level thinking.

The traditional "assembly line" model of product development (PM -> Design -> Eng) fails with AI. Instead, teams must operate like a "jazz band," where roles are fluid, members "riff" off each other's work, and territorialism is a failure mode. PMs might code and designers might write specs.