First, more AI does not necessarily mean fewer jobs. Automation and mechanization have been around for centuries and are creating jobs while taking them away. The employment created can be direct, as in robotic production and surrounding infrastructure, or indirect, as when AI-assisted animation is sold and marketed.
Before the pandemic, Japan and the United States had full employment and a decent standard of living. The two were also two of the most mechanized societies in the world and had many robots.
Nevertheless, AI can bring big changes in compensation. Software can already write simple news articles, especially for standardized events such as results reports or sports scores. The salaries of journalists will fall accordingly, while those of specialists working on AI will increase.
But the benefits of AI don’t just accrue to players in the technology sector. AI makes many goods and services cheaper, which in turn benefits the poor and disadvantaged. If the software routes packages and shipments more efficiently, transportation costs will be lower. If software and AI programs save on electricity use, then it will be easier to mitigate climate change. As computational biology improves health care, the sick will benefit.
The people who need AI the least are the super-rich. They can already hire armies of servants to manage their obligations, schedules, etc. They do not need to economize on the use of human labor. The rest of us do, either directly or indirectly through the companies we patronize.
Another advantage for low-income groups is that current manifestations of AI generally do not replace the jobs of the poor. Many poor people hold jobs in the service sector or perform manual labor. These tasks are either difficult to automate (a robot gardener?) or, because wages are low, less profitable to automate.
It may be true that the costs of AI in the workforce – lost jobs – are more visible than the benefits of AI – new jobs and lower prices. So it’s no surprise that AI isn’t entirely popular.
AI is also going to require a lot of retraining. More jobs will require new skills that involve working with software, sometimes in the form of AI. It’s not like everyone has to know the intricacies of neural networks, but often this refresher course will be more daunting than learning how to use Starbucks’ new espresso machine.
On average, better educated individuals are more apt to retrain. The increase in the importance of retraining will therefore have significant inegalitarian effects, namely that the less educated may fall behind or be rehired at lower wages. This is the real concern of AI, which is not the same as “robots taking all our jobs”.
Ideally, continued advancements in AI will create many more jobs in education, training, and retraining. This is the optimistic scenario. Yet, given that so many Americans aren’t finishing their four-year college education — even though that could lead to better jobs and higher salaries — retraining probably won’t be so easy either. Penalizing those who fail to retrain may not be exactly the tonic that American society needs right now.
When thinking of AI in terms of investments, an obvious game is to try to pick the companies that will make breakthroughs. More generally, a very successful AI, all other things being equal, likely means higher prices for land and natural resources. There will be a lot more economic activity, which will consume more energy and take up more space.
The US and global economies are going through the early stages of a fundamental transformation, much of which will be driven by AI. When the history of our time is written, these will appear as the years when it all began.
More from Bloomberg Opinion:
• AI’s hold on humans is beginning to tighten: Parmy Olson
• AI pans my scenario. Can he crack Hollywood? : Trung Phan
• Confronting the potential of AI to create new chemical weapons: Lisa Jarvis
This column does not necessarily reflect the opinion of the Editorial Board or of Bloomberg LP and its owners.
Tyler Cowen is a Bloomberg Opinion columnist. He is a professor of economics at George Mason University and writes for the Marginal Revolution blog. He is co-author of “Talent: How to Identify the Energizers, Creatives and Winners in the World”.
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