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With AI handling much of the low-level execution, human collaboration moves "up the stack." Teams focus on high-level feature concepts and strategy, rather than getting bogged down in fine-grained implementation details like PRDs or specific UI mockups.
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With AI making code generation cheap, the limiting factors for development velocity are now defining what to build (product) and ensuring its quality (review). Engineers will increasingly focus on high-level systems architecture rather than typing code.
AI tools are reducing the need for hyper-specialized roles in tech. A designer can now ship front-end code, and a PM can submit a simple PR. This shift allows companies like Thumbtack to move from 10-14 person 'pods' to 3-6 person teams, increasing speed and shared context.
As AI agents handle the mechanics of code generation, the primary role of a developer is elevated. The new bottlenecks are not typing speed or syntax, but higher-level cognitive tasks: deciding what to build, designing system architecture, and curating the AI's work.
Referencing the Dutch soccer strategy, Figma's design head describes a new dynamic where AI empowers individuals to cross into other domains. PMs can prototype and designers can ship code, creating a more resilient and faster team that eliminates single-person bottlenecks.
AI is blurring the lines on product teams. Product managers can now generate high-fidelity prototypes without designers and even commit simple code changes with AI assistance. This role compression accelerates the development cycle and changes team dynamics.
Their workflow clearly separates human-centric tasks from automated ones. PMs are "human in the loop" for discovery, alignment, and defining API abstractions. Once those strategic decisions are made, the process of writing production-ready code is largely automated via AI agents.
Collaboration is a bottleneck during the execution phase due to dependencies. AI tools empower individuals ("teams of one") to handle execution independently, freeing the team to collaborate more effectively at the start (discovery) and end (delivery, GTM).
By automating mechanical build tasks, AI liberates significant time in the development cycle. Teams can reallocate this time to more strategic upstream activities like planning and exploration, and downstream refinement, focusing on high-quality craft and polish.
The greatest leverage from AI comes not from accelerating individual tasks, but from improving information flow between teams. Use AI to create a "common brain"—a central repository of project knowledge and goals—to ensure alignment and drive efficiency at critical handoff points.
AI has commoditized idea generation and initial execution like creating docs, decks, and prototypes. The new critical bottleneck for teams is no longer creativity but establishing shared context. The challenge is ensuring everyone is "playing the same game" to enable faster, higher-quality decisions.
