AI to Fuel White-Collar Growth, Says Box CEO

Box CEO Aaron Levie has publicly countered the growing fear that artificial intelligence will hollow out the white-collar workforce. In a statement shared on social media, Levie asserted that AI is poised to increase, rather than reduce, job opportunities for knowledge workers. His argument centers on the economic reality that AI drastically lowers the cost of complex tasks, enabling businesses to undertake a volume of work that was previously unfeasible.

Levie's perspective stands in contrast to warnings from other technology executives who predict significant job displacement. By drawing parallels to historical economic shifts, he suggests that the current wave of automation will follow a familiar pattern of expansion rather than contraction. The following analysis explores Levie's reasoning, the economic theories he cites, and how his view compares to the broader industry consensus.

Levie's argument is rooted in a historical observation made nearly two centuries ago. To explain why he believes cheaper AI labor will lead to more work, Levie pointed to economist William Stanley Jevons. In 1865, Jevons observed that the advent of more efficient steam engines in England did not curb coal consumption; instead, it drove coal use higher. This phenomenon occurred because cheaper energy fueled the growth of new industries and expanded existing ones.

This dynamic, now known as the Jevons paradox, suggests that as technology increases the efficiency with which a resource is used, the total consumption of that resource increases rather than decreases. Levie argues that this same pattern has repeated itself throughout the history of computing. He notes that every major wave of cheaper technology—from mainframes to minicomputers to personal computers—dramatically expanded the adoption of computing.

Levie specifically highlighted the role of cloud computing in this progression. He explained that cloud technology erased many of the procurement and infrastructure advantages once held by large corporations. Consequently, tools such as accounting software, CRM systems, and marketing platforms became accessible to nearly any business, not just industry giants. Levie characterizes these previous efficiency gains as automating deterministic work—tasks with clear rules and predictable outcomes, such as accounting, record-keeping, and data processing.

According to Levie, the current wave of AI differs from previous technological shifts because it targets non-deterministic work. Unlike deterministic tasks, non-deterministic work involves judgment, creativity, and ambiguity. Levie lists examples such as reviewing contracts, writing software, designing marketing campaigns, and conducting research. Historically, these tasks required expensive human expertise and were difficult to automate or scale cost-effectively.

Levie argues that by sharply lowering the cost of this specific type of work, AI agents make it economically viable for companies to attempt projects they would have never justified before. He wrote, "Now, every business in the world has access to the talent and resources of a Fortune 500 company 10 years ago." As these non-deterministic tasks become cheaper, Levie predicts that companies will take on far more work, which in turn drives demand for white-collar jobs rather than eliminating them.

Furthermore, Levie addressed the fear of widespread replacement by highlighting a key constraint in the workflow. He stated, "The reality is that despite all the tasks that AI lets us automate, it still requires people to pull together the full workflow to produce real value." This suggests that while AI handles specific components of a task, human oversight and integration remain essential.

Levie's optimistic outlook contrasts sharply with predictions from other prominent figures in the technology and business sectors. The industry remains divided on the long-term impact of AI on the labor market. Some leaders and researchers have warned that AI will indeed hollow out white-collar jobs, leading to significant displacement.

Specifically, Anthropic CEO Dario Amodei and Ford CEO Jim Farley have warned that the technology could wipe out large numbers of white-collar roles. Conversely, figures such as OpenAI CEO Sam Altman, JPMorgan CEO Jamie Dimon, and Elon Musk have predicted outcomes ranging from major disruption to longer-term economic gains. Others, including Nvidia CEO Jensen Huang and Meta's outgoing chief AI scientist Yann LeCun, have predicted that AI will reshape how work is done rather than eliminate it outright.

Recent trends in the job market have provided evidence for both sides of the argument. Major tech companies such as HP, IBM, Salesforce, and Amazon have cut thousands of jobs amid AI-driven efficiency pushes. Meanwhile, KPMG's chief economist Diane Swonk has warned that the USA could face a "jobless boom" in 2026 as firms do more with fewer workers. However, some economists, such as Gbenga Ajilore of the Center on Budget and Policy Priorities, argue that the current white-collar downturn is more closely tied to high interest rates and a slowing economy than to AI itself.