Artificial Intelligence Through the Lens of Productivity
It goes that there are three ways to grow the economy: Population, Participation and Productivity. Population growth in Australia is rebounding back to its long-term trend after re-opening; and participation is strong but has limited upside.
Productivity is the efficiency of production of goods or services and productivity growth is a driver of increasing living standards. Increasing productivity helps to temper inflation and grow real wages. In Australia, productivity growth has been decreasing since the 90s.
There are quite rightly questions being asked about the Responsible use of Artificial Intelligence (AI) and its impact on fairness, bias, accuracy, ethics, transparency and more. A splendid book recommended by my colleagues is “Weapons of Math Destruction” by Cathy O’Neil if you’re interested in this space. It’s a great read which I highly recommend. And I love puns for titles.
Also, the impact of AI on the future of jobs and work is complicated and policy makers and researchers have a wide range of views and are looking at ways to minimise any negative impacts.
There are examples though where AI is being used successfully in increasing individual productivity. This shouldn’t be surprising. Productivity and new technology have always gone hand-in-hand. If we think about the automation of production in the Industrial revolution, or the impact of the Internet and the Information Age, both led to greater productivity and employment.
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| “Australian factory in the early 1900s”, (Aussie~mobs) https://www.flickr.com/photos/70994841@N07/7542555494 |
New use-cases for AI are constantly emerging, and will continue to emerge in the coming years, but two examples immediately come to mind. One is the work by the Victorian Department of Transport to use AI and computer vision to improve road transport operations. Less than 15% of the 1000+ livestreams can be manually watched at any one time, but AI can analyse all in real-time, providing “notification to our operators”, enabling “more focused, proactive incident response”.
Another newer example is the use of Generative AI and Large-Language Models (LLMs) to improve software developer productivity. Amazon CodeWhisperer is “trained on billions of lines of code and can generate code suggestions ranging from snippets to full functions in real time based on your comments and existing code.”
If we use the lens of productivity when talking about AI, we can see there are opportunities for ameliorating worker shortages, growing businesses, modernising the economy, and increasing national wealth.