Vercel's weekly demo days are not a competition but a congratulatory showcase where anyone can present an idea. This positive reinforcement encourages working in the open, attracting collaborators organically and creating a low-stakes environment for new projects to evolve.
AI tools are blurring the lines between roles. Vercel SVP Aparna Sinha notes that product managers can now build and test working products, not just prototypes. This allows for hyper-efficient, small teams—sometimes just one person—to achieve the output of a full squad.
Early-stage products have product managers aligned directly with engineering teams. As a product matures and finds market fit, the structure shifts. PMs become solution-focused, working across multiple engineering teams, complemented by horizontal teams for pricing and growth.
Instead of killing underperforming products, Vercel's culture encourages teams to find the valuable "nugget" within an idea and continuously iterate. Products don't die; they evolve through collaborative feedback, avoiding the typical "product cemetery" seen at other tech giants.
To maintain quality while iterating quickly, Vercel builds its own applications (like V0) on its core platform, becoming "customer zero." This internal usage forces them to solve real-world security, performance, and user experience problems, ensuring the underlying infrastructure is robust for external customers.
AI applications often have long waiting periods for model responses or user input, but traditional cloud platforms charge for this idle time. Vercel's "Fluid Compute" is designed so customers only pay when the application is actively processing, making it fundamentally more cost-effective for AI workloads.
Pure value-based pricing (e.g., per seat) fails for AI products due to unpredictable token costs from power users. Vercel's SVP of Product advises a hybrid model: one metric aligned with value (like seats) and another aligned with cost (like token usage) to ensure profitability.