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'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.

AI tools lower the technical barrier for creating high-fidelity prototypes. This empowers designers, PMs, and engineers to contribute across traditional role boundaries, breaking down silos and fostering a more collaborative, cross-functional team dynamic.

Brex structures its AI teams into small pods, combining young, AI-native talent who think differently with experienced staff engineers who understand the existing codebase, product, and customer needs. This blends novel approaches with practical execution.

Anthropic leverages the low cost of execution in the AI era by building multiple potential product versions simultaneously. This "build all candidates" approach replaces lengthy spec-writing and low-bandwidth customer research, allowing them to pick the best functioning prototype directly.

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.

The industry has not standardized who owns AI prototyping. Three models are emerging: PM-led (leveraging deep customer knowledge), design-led (leveraging craft and speed), and collaborative (PMs and designers working together in the tool). Organizations should choose the model that best fits their team dynamics.

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.

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.