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.

Laurel enables non-technical employees, including Product and Customer Success Managers, to build and ship full-stack features using agentic AI tools like Devin. This blurs traditional role boundaries and dramatically accelerates development cycles.

At OpenAI, teams of just one or two engineers leverage AI agents to own entire product lines. This model reduces human collaboration overhead and empowers engineers to make most micro-decisions autonomously, increasing speed and ownership.

The velocity at elite AI-native companies has radically accelerated. It is now possible to identify a critical user request, have a PM or engineer prototype a solution using tools like Claude Code, and ship a production-ready feature all within the same day.

At OpenAI, engineers use AI to build ideas instantly. This inverts the traditional product model, shifting the PM's role from upfront planning to evaluating already-built prototypes and deciding which ones to ship, dramatically accelerating development.

To adapt to AI-driven productivity, Block abandoned large, static feature teams for small squads of 1-6 people that can flexibly move between products. This structure, combined with cutting management layers by over 50%, allows for faster information flow and rapid, AI-powered development cycles.

By using AI to write and QA code, Condé Nast has redesigned its product development teams. Teams that were 10-12 people are now just 3-4, eliminating roles like technical project managers and QA engineers. These smaller, AI-augmented teams can move three times faster.

A new organizational model is emerging where companies create small, agile teams comprising a senior expert, an engineer, and a marketer. Empowered by AI tools, these pods can develop and launch new products in a week, a task that once required large teams and over six months.

To avoid choosing between deep research and product development, ElevenLabs organizes teams into problem-focused "labs." Each lab, a mix of researchers, engineers, and operators, tackles a specific problem (e.g., voice or agents), sequencing deep research first before building a product layer on top. This structure allows for both foundational breakthroughs and market-facing execution.