⤴ The economic promise of ChatGPT and GenAI as a general purpose technology

We are only at the beginning of the beginning when it comes to understanding the potential of this huge AI advance

“Our mission as humans is not only to discover our fullest selves in the technium, and to find full contentment, but to expand the possibilities for others. Greater technology will selfishly unleash our talents, but it will also unselfishly unleash others: our children, and all children to come.” - Kevin Kelly, What Technology Wants

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The Essay

⤴ The economic promise of ChatGPT and GenAI as a general purpose technology

We now have several early, early studies suggesting a significant productivity impact of large language models on worker productivity:

• In an experiment with ChatGPT, it took grant writers, data analysts, and human-resource professionals 10 minutes less — a 40 percent time savings — to churn out news releases, short reports, and emails. And the quality was higher, according to MIT economists.

• Another AI tool is Copilot, which helps developers solve coding problems in natural language. GitHub tested 95 developers with and without Copilot on a coding task. The ones who used Copilot completed the task faster (71 minutes vs. 161 minutes) and more accurately (78 percent vs. 70 percent). These results show how AI tools can improve worker productivity.

• In the just posted NBER working paper “Generative AI at Work,” researchers Erik Brynjolfsson, Danielle Li, and Lindsey R. Raymond looked at the impact of an LLM-based chat assistant — built on an OpenAI used by 5,000 customers service agents working for a Fortune 500 software company that provides business process software. What’s super interesting here is that this study looks at the productivity impact of generative AI deployed in a real-world workplace. Here’s the headline finding:

  AI assistance increases worker productivity, resulting in a 13.8 percent increase in the number of chats that an agent can successfully resolve per hour. This increase reflects shifts in three components of productivity: a decline in the time it takes for an agent to handle an individual chat, an increase in the number of chats that an agent can handle per hour (agents may handle multiple calls at once), and a small increase in the share of chats that are successfully resolved.

  In addition, AI assistance disproportionately increased the performance of less skilled and less experienced workers, leading them to communicate more like high-skilled agents. This also means customers treated the low-skilled agents better. From the paper: “Our overall findings demonstrate that generative AI working alongside humans can have a significant positive impact on the productivity and retention of individual workers.”