Generative AI like LLMs have been touted as a boon to collective productivity. But the authors argue that leaning into the hype too much could be a mistake. Assessments of productivity typically focus on the task level and how individuals might use and benefit from LLMs. Using such findings to draw broad conclusions about firm-level performance could prove costly. The authors argue that leaders need to understand two core problems of LLMs before adopting them company-wide: 1) their persistent ability to produce convincing falsities and 2) the likely long-term negative effects of using LLMs on employees and internal processes. The authors outline a long-term perspective on LLMs, as well as what kinds of tasks LLMs can perform reliably.
Large language models (LLMs) have been heralded as a boon to collective productivity. McKinsey boldly proclaimed that LLMs and other forms of generative AI could grow corporate profits globally by $4.4 trillion annually, and Nielsen trumpeted a 66% increase in employee productivity through the use of these same tools. Projections like these have made finding ways to use these tools — and turbocharge productivity — a top priority for many companies over the past year. While we are intrigued and impressed by this new technology, we advise cautious experimentation instead of wholesale company-wide adoption.