Early research suggests that AI can intensify workloads, leading employees to work faster and longer.

AI can contribute to information overload by making it easier to produce content that others must process.

Research suggests that productivity drops sharply after 50 hours of work per week.

Organizations can use AI-driven efficiency gains to reduce workloads rather than simply assign more tasks.

An influential man at the top of his field—we’ll call him John for now—has predicted that advances in technology will usher in a revolution defined by dramatic gains in productivity. John is so optimistic about these gains that he suggests his grandchildren’s generation will likely need to work only about 15 hours a week. He shares that the real challenge for this future generation will not be how to manage work, but rather what to do with all their newfound free time.

John might sound like an AI optimist, echoing what many have hoped: that advances in artificial intelligence would make people’s lives better and help curb the burnout crisis facing so many workers. Yet John’s comments came well before AI, or even modern computers, existed. John, better known as the influential economist John Maynard Keynes, made these predictions in the 1930s.

Just as the technological advances of the nearly 100 years since Keynes made his prediction have not reduced working hours to anything close to what he envisioned (American workers average 42.9 hours per week), a similarly troubling pattern is emerging with AI: rather than translating productivity gains into less work, technological advances too often lead us to do more work instead. For example, initial research has shown that the increased output that comes with leveraging AI does not necessarily reduce our workload. Instead, it can intensify it. One study of 200........

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