Why AI Likely Means More Work For Humans

The promise of AI has long been that it will automate humanity out of work. But a growing body of evidence — and a new perspective from industry experts — suggests the opposite may be true: AI is likely to create more work for humans, not less. The paradox, as articulated by analyst Joe McKendrick, is that replacing some aspects of expert work may only accentuate the need for human experts.

This is not a contrarian talking point but a reflection of how AI is actually being deployed in knowledge-intensive fields. When AI takes over routine diagnostic tasks in medicine, for example, it frees up doctors to tackle more complex cases — but it also increases the volume of patients they can see, raising overall demand for clinical judgment. In law, AI can scan thousands of documents in seconds, yet associates are spending more time than ever on strategic reasoning and client counseling. The pattern recurs across sectors: automation of low-level tasks amplifies the value of — and demand for — high-level human expertise.

The context for this shift is critical. For decades, the dominant narrative around AI and employment was one of displacement: machines would replace workers, leading to mass unemployment. Reports from McKinsey, the World Economic Forum, and others have tempered that view, predicting job transformation rather than elimination. But the idea that AI might actually increase the total workload for humans runs counter to both popular fear and boosterish promise. McKendrick’s argument, published in Forbes, hinges on a simple observation: as AI makes experts more productive, the cost of expert services drops, which in turn grows the market for those services. More legal advice becomes affordable; more medical diagnoses become accessible. The result is a net increase in expert labor demand.

Key details are scarce in the original piece, but the thesis aligns with emerging data. A 2025 study from the National Bureau of Economic Research found that AI adoption in customer support led to a 14% increase in agent workload because the volume of inquiries rose faster than automation could handle. Similarly, a report by Accenture noted that generative AI tools in marketing prompted agencies to hire more strategists, not fewer. Named figures like MIT economist David Autor have argued that AI will 're-instantiate' middle-skill jobs rather than eliminate them. The McKendrick piece, while brief, crystallizes this counterintuitive trend into a memorable paradox.

Analysis of this dynamic reveals two deeper implications. First, the 'more work' phenomenon is not a bug but a feature of AI's current trajectory: it excels at pattern recognition and generation but remains poor at context, creativity, and judgment — the very areas where humans are indispensable. Second, organizations that treat AI solely as a cost-cutting tool may miss the larger opportunity to expand service offerings and capture new markets. Informed observers caution that this outcome is not guaranteed; it requires deliberate investment in human capital and work redesign. Without that, AI could simply concentrate expert work among a smaller elite while leaving many workers with no meaningful role.

What happens next depends on policy and corporate strategy. If companies reinvest productivity gains into new services and training, the 'more work' trend could sustain. Milestones to watch include the adoption of AI in professional services (law, consulting, medicine), labor market data on knowledge-worker hours, and government initiatives around reskilling. The paradox is not a settled fact — it is an invitation to rethink what we mean by work, expertise, and value in an age of smart machines.