The name of the game in worker productivity isn't merely about increasing it, it's about boosting demand for the output and the skills that drive that output. But let's throw another economic concept into the mix—saturation. How does increased productivity play out when demand is fully met? If everyone in your workforce becomes 50% more productive across the board, it sounds like a dream, unless demand is sated. When that happens, the price for these skills takes a nosedive. This isn’t necessarily what we want.
The narrative is changing—what skills does AI complement, thus enhancing productivity? Who reaps these benefits? How flexible is the demand for these upgraded skills? According to economist David Autor, AI can be a strange bedfellow—it's fantastic for productivity, not so much for wealth distribution. It's a myopic and potentially misleading view to talk about productivity without bringing saturation, elasticity, and wealth distribution to the table.
A recent study from MIT provides some intriguing insights into the interaction between generative AI, such as ChatGPT, and knowledge work. The study found that ChatGPT significantly boosted average productivity, primarily by elevating the productivity of less experienced or lower-ability workers. This uplift brought them closer to the productivity levels of their more skilled counterparts.
This impact of generative AI changes the structure of tasks. Work becomes more focused on generating ideas and editing, with generative AI smoothing the path to starting a project. This effect is particularly beneficial for those less familiar with a given task.
However, while generative AI reduces the human effort required for a task, this doesn't necessarily translate into an increase in creativity or innovation. This suggests that while AI can enhance productivity, its impact on creative outputs may be more nuanced and complex.
The notion of AI turning a novice coder into a coding wizard isn't as straightforward as it might seem. Novice programmers can become a wild card, bringing a wealth of unknown unknowns to the table, and potentially introducing errors and complexity.
In contrast, an experienced coder isn't automatically faster with AI assistance, especially when they're navigating novel, unfamiliar territory using complex code. Ultimately, how AI is harnessed hinges on the individual's perception of the problem, the AI tool, and their own skills.
