A Berkeley Haas study finds AI doesn't reduce work but intensifies it through 'task expansion.' Professionals use AI to venture into adjacent roles—like product managers writing code—widening their job scope and increasing total output, rather than simply doing their old job faster.

Research shows that instead of reducing work, AI often increases it through 'task expansion.' Employees use AI to take on work they previously delegated or outsourced, such as a product manager writing code, blurring roles and intensifying their workload.

Engaging with AI is a high-intensity mental workout, shifting the nature of work to 'cognitive synthesis.' Users, or 'neural athletes,' must constantly adjudicate between what the model says, what they know, and organizational needs, creating a new and profound cognitive strain.

Productivity models often wrongly assume time saved by AI is redeployed into other work. In reality, many employees use efficiency gains to finish early. This 'human slack' factor dampens macro-level productivity gains, except in highly driven fields like tech, where workers use it to work even more.

AI's true productivity leverage is not just speed but enabling more attempts. A human might get one shot at a complex task, whereas an AI-assisted workflow allows for three or more "turns at the wheel." The critical human skill shifts from initial creation to rapid review and refinement of these iterations.

A UC Berkeley study found employees using AI worked faster and took on broader tasks, leading to more hours worked, not fewer. AI offloads menial labor, making jobs more purpose-driven and motivating employees to do more, which increases stress and burnout.

When AI empowers non-specialists to perform complex tasks (e.g., marketers writing code), it creates a new, hidden workload for experts. These specialists must then spend significant time reviewing, correcting, and guiding the AI-assisted work from their colleagues, creating a new form of operational drag.

The primary benefit of AI for experienced users has evolved from efficiency gains to enabling entirely new tasks and boosting overall throughput. Time savings, once the top benefit, is now third, especially for heavy users focused on strategic value over simple task automation.

Developers using AI agents report unprecedented productivity but also a decline in job satisfaction. The creative act of writing code is replaced by the tedious task of reviewing vast amounts of AI-generated output, shifting their role to feel more like a middle manager of code.