Unlike coding, where context is centralized (IDE, repo) and output is testable, general knowledge work is scattered across apps. AI struggles to synthesize this fragmented context, and it's hard to objectively verify the quality of its output (e.g., a strategy memo), limiting agent effectiveness.
Notion's CEO analogizes today's human-paced organizations to walkable cities like Florence. AI will enable massive, continuously-operating "megacity" organizations. This creates vast new opportunities but comes at the cost of the familiar, legible rhythms of work like weekly meetings and quarterly planning.
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.
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.
As technology made marketing tasks more efficient (e.g., Google Ads), it democratized access, causing a 5x increase in marketing jobs since the 1970s. Box's CEO argues AI will have a similar effect on all knowledge work by lowering costs, which will dramatically increase overall demand for that work.
Box CEO Aaron Levy argues the focus on AI's return (R) is misplaced. The real leverage is making the initial investment (I) so low that companies can pursue projects previously deemed too expensive or risky, from custom software for small firms to new R&D initiatives, thus creating new value.