Harvard Business Review shares findings from an eight-month study examining the impact of AI tools on workplace performance. While the research confirms that AI drives a significant surge in productivity, it also reveals a troubling set of side effects, including cognitive fatigue, unsustainable working hours, and other emerging problems.

The post highlights a growing tension in the AI-augmented workplace: the same tools that boost output may simultaneously erode worker well-being. This finding is particularly relevant as organizations increasingly adopt AI at scale, raising important questions about how to balance productivity gains with sustainable work practices.

Tomas Friedmann shares a Wall Street Journal article highlighting a critical blind spot in the debate around AI and employment. The piece argues that workers are focused on the wrong threat — rather than worrying about whether AI will replace their jobs, they should be concerned about who will control the knowledge that companies extract and capture from their employees.

This reframes the AI-labor discussion from one of displacement to one of knowledge ownership and power dynamics, suggesting that the real danger lies in corporations using AI to systematically harvest employee expertise, potentially diminishing workers' leverage and autonomy in the process.

This California Management Review article by Dritjon Gruda and Brad Aeon systematically debunks seven common myths about AI and productivity using evidence from recent meta-analyses and systematic reviews rather than individual studies or vendor claims. The authors challenge the assumption that AI is a universal productivity enhancer, showing that gains are highly context-dependent — benefiting junior workers far more than experts, and often introducing quality regressions that offset speed improvements. Perhaps most strikingly, a meta-analysis of 106 experiments found that human-AI collaboration frequently underperforms either agent working independently, except in open-ended creative tasks like brainstorming.

The article also addresses AI's impact on creativity, finding that while AI-augmented humans achieve modestly higher novelty scores, this comes at the cost of dramatic declines in idea diversity — a homogenization effect that undermines innovation. At the macroeconomic level, a meta-analysis of 371 estimates found no robust relationship between AI adoption and aggregate productivity gains once methodological factors are controlled, echoing a modern version of the Solow paradox. Additional myths around automation bias, where high-reliability AI systems actually increase certain types of human error by 12%, and the stress paradox, where AI creates new forms of technostress rather than reducing workplace burden, round out the analysis.

The authors conclude with practical guidance: organizations should conduct evidence audits with task-specific metrics, implement a "creation versus evaluation" matrix for task triage, deploy adaptive governance structures with confidence disclosures, and maintain an equity lens to track distributional impacts across skill levels. The core message is that AI's productivity dividend is real but narrow — it materializes only for specific users, specific tasks, and specific workflow designs, demanding far more analytical rigor than most organizations currently apply.