Even though AI enables PMs to code, it's an inefficient use of their time. Since code creation is cheap and product strategy is the new bottleneck, PMs should focus entirely on product work, not engineering tasks.

To handle increased code output from AI agents, engineering teams must shift platform efforts to strengthening their CI/CD pipeline. Braintrust pauses feature work to improve CI, viewing it as earning the right to move faster. A robust CI system is the foundation for AI-driven development.

Many non-technical PMs are stuck managing backlogs in tools like Jira, dependent on engineers. AI coding assistants like Claude Code empower them to contribute directly to the codebase, transforming their role from manager to builder.

Anthropic mandates that all people managers, regardless of their background, must actively build and ship product. This isn't just a "player-coach" model; it's a requirement to ensure leadership intimately understands the modern AI-native development process, enabling them to better invest in tooling and training.

To prevent a "ball of mud" codebase, OpenAI's system defines strict architectural layers using package boundaries and folder structures. By convention and tooling, different roles are restricted to specific layers—designers to the UI, PMs to business logic—ensuring modularity and preventing architectural decay.

To avoid shipping "slop" from AI coding assistants, the solution is building robust infrastructure. Automated checks and security guardrails prevent bad code from reaching production, acting as a programmatic senior engineer for the non-technical builder.

As AI tools lower the barrier to coding, the most effective PMs will evolve to contribute small code changes directly to the product. This blurs the lines between roles, unblocks small tasks, and deepens the PM's understanding of the product's construction.

To maximize speed, V0 operates with a "no handoffs" philosophy. Everyone, including designers and product managers, is expected to contribute code and submit their own pull requests. This "full-stack PM" model minimizes the coordination costs and wasted cycles of explaining changes.

Snap invested in its platform for over a decade, creating a robust codebase that allows non-engineers to contribute code safely. This reduces the blast radius of potential outages or performance regressions, allowing for faster iteration by breaking down traditional role barriers.