Ben Waber is one of the world’s leading experts on people analytics and organizational culture. He is the founder of Humanyze and a visiting scientist at the MIT Media Lab. We caught up in Tokyo to discuss the future of work and the impact of large language models (LLMs) on productivity.
In our discussion, Ben emphasizes the importance of considering systemic issues and inherent technological limitations when measuring productivity, arguing that companies often lack proper metrics for assessing the performance of knowledge workers. Despite the current hype around AI, we talked about the fundamental flaws of LLMs, such as their inability to reliably perform sequential reasoning and their lack of coherence, raising concerns about their application in critical areas like HR and legal departments.
Ben suggests that the most valuable work stems from interactions between people with diverse mindsets and experiences, as friction between different perspectives often leads to interesting ideas. He believes that technology should be used to shape these interactions creatively, both for individuals and organizations. While computational tools can assist in this process, humans will remain the driving force behind innovation for the foreseeable future.
5 Key Insights:
1. Task-level analysis of productivity is insufficient; consider systemic issues and the long-term impact of decisions on organizational outcomes.
2. LLMs have inherent limitations, such as the inability to reliably perform sequential reasoning and maintain coherence, making them unsuitable for tasks where truth and accuracy are critical.
3. Firing workers based on short-term productivity gains from LLMs may lead to long-term issues, as domain experts are best equipped to identify the tool's limitations.
4. Valuable work is probabilistic and often driven by interactions between people with diverse perspectives and experiences.
5. Technology should be used to foster creative interactions among employees, but humans will remain the primary source of innovation.
