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.
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The most potent productivity gains from AI aren't just for junior staff. Seasoned professionals who combine deep domain expertise with adaptability are using AI to rapidly learn adjacent skills like design or marketing. This allows them to "collapse the skill stack" and single-handedly perform tasks that previously required multiple people.
AI tools are blurring the lines between roles like product management, UX design, and development. A single skilled individual can now leverage AI to handle tasks that previously required a three-person team, dramatically increasing individual productivity and changing organizational structures.
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.
Jensen Huang uses radiology as an example: AI automated the *task* of reading scans, but this freed up radiologists to focus on their *purpose*: diagnosing disease. This increased productivity and demand, ultimately leading to more jobs, not fewer.
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 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.
As AI tools empower individuals to handle tasks across the entire product development lifecycle, traditional, siloed roles are merging. This fundamental shift challenges how tech professionals define their value and contribution, causing significant professional anxiety.
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.
Contrary to the popular narrative, AI is not yet a primary driver of white-collar layoffs. Instead of eliminating roles, it's changing the nature of work within them. For example, analysts now spend time on different, higher-value activities rather than manual tasks, suggesting a shift in job content rather than a reduction in headcount.
The Jevons Paradox observes that technologies increasing efficiency often boost consumption rather than reduce it. Applied to AI, this means while some jobs will be automated, the increased productivity will likely expand the scope and volume of work, creating new roles, much like typewriters ultimately increased secretarial work.
