Don't think of AI as replacing roles. Instead, envision a new organizational structure where every human employee manages a team of their own specialized AI agents. This model enhances individual capabilities without eliminating the human team, making everyone more effective.
Related Insights
The common fear of AI eliminating jobs is misguided. In practice, AI automates specific, often administrative, tasks within a role. This allows human workers to offload minutiae and focus on uniquely human skills like relationship building and strategic thinking, ultimately increasing their leverage and value.
As AI tools become operable via plain English, the key skill shifts from technical implementation to effective management. People managers excel at providing context, defining roles, giving feedback, and reporting on performance—all crucial for orchestrating a "team" of AI agents. Their skills will become more valuable than pure AI expertise.
While current AI tools focus on individual productivity (e.g., coding faster), the real breakthrough will come from systems that improve organizational productivity. The next wave of AI will focus on how large teams of humans and AI agents coordinate on complex projects, a fundamentally different challenge than simply making one person faster.
As AI agents handle technical execution, the most valuable human skill becomes ideation. Replit CEO Amjad Massad predicts this will dissolve rigid corporate hierarchies in favor of adaptable teams of generalists who collaborate with autonomous AI tools to bring ideas to life.
Treat advanced AI systems not as software with binary outcomes, but as a new employee with a unique persona. They can offer diverse, non-obvious insights and a different "chain of thought," sometimes finding issues even human experts miss and providing complementary perspectives.
Top-performing engineering teams are evolving from hands-on coding to a managerial role. Their primary job is to define tasks, kick off multiple AI agents in parallel, review plans, and approve the final output, rather than implementing the details themselves.
Don't view AI tools as just software; treat them like junior team members. Apply management principles: 'hire' the right model for the job (People), define how it should work through structured prompts (Process), and give it a clear, narrow goal (Purpose). This mental model maximizes their effectiveness.
The job of an individual contributor is no longer about direct execution but about allocation. ICs now act like managers, directing AI agents to perform tasks and using their judgment to prioritize, review, and integrate the output. This represents a fundamental shift in the nature of knowledge work.
Shift from departments staffed with people to a single owner who directs AI agents, automations, and robotics to achieve outcomes. This structure maximizes leverage and efficiency, replacing the old model of "throwing bodies" at problems.
The paradigm shift with AI agents is from "tools to click buttons in" (like CRMs) to autonomous systems that work for you in the background. This is a new form of productivity, akin to delegating tasks to a team member rather than just using a better tool yourself.
