To navigate the unpredictable AI landscape, Snowflake's CEO dismantled its specialized, multi-layered structure that had slowed down iteration. This shift prioritized accountability and shorter engineer-to-customer feedback loops, recognizing that speed and adaptability now trump carefully laid out strategies.

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.

As AI agents take over task execution, the primary role of human knowledge workers evolves. Instead of being the "doers," humans become the "architects" who design, model, and orchestrate the workflows that both human and AI teammates follow. This places a premium on systems thinking and process design skills.

The true challenge of AI for many businesses isn't mastering the technology. It's shifting the entire organization from a predictable "delivery" mindset to an "innovation" one that is capable of managing rapid experimentation and uncertainty—a muscle many established companies haven't yet built.

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.

Unlike the cloud-era "digital transformation," which often didn't change core employee workflows, the AI transformation is universal. It changes how every knowledge worker operates daily, making the shift more profound and akin to the move from paper to computers, fundamentally altering the nature of work itself.

Notion's CEO compares current AI adoption to swapping a water wheel for a steam engine but keeping the factory layout the same. The real gains will come from fundamentally rethinking workflows, meetings, and hierarchies to leverage AI that works 24/7, rather than just layering it onto existing processes.

The role of a top engineer is shifting from writing code to orchestrating multiple AI agents simultaneously. Notion's co-founder now queues tasks for AIs to work on while he's away, becoming a manager of AI talent rather than just an individual contributor, dramatically multiplying his leverage.

The idea that AI will enable billion-dollar companies with tiny teams is a myth. Increased productivity from AI raises the competitive bar and opens up more opportunities, compelling ambitious companies to hire more people to build more product and win.