Steam-powered manufacturing had linked an entire production line to a single huge steam engine. As a result, factories were stacked on many floors around the central engine, with drive belts all running at the same speed. The flow of work around the factory was governed by the need to put certain machines close to the steam engine, rather than the logic of moving the product from one machine to the next. When electric dynamos were first introduced, the steam engine would be ripped out and the dynamo would replace it. Productivity barely improved.https://slate.com/culture/2007/06/what-the-history-of-the-electric-dynamo-teaches-about-the-future-of-the-computer.html
Eventually, businesses figured out that factories could be completely redesigned on a single floor. Production lines were arranged to enable the smooth flow of materials around the factory. Most importantly, each worker could have his or her own little electric motor, starting it or stopping it at will. The improvements weren’t just architectural but social: Once the technology allowed workers to make more decisions, they needed more training and different contracts to encourage them to take responsibility.
This is the second time this quote is appearing in a post on EFE. By the way, do read that earlier post, especially if you are in academia, and please let me know how your university has adjusted to the post pandemic world – have we just gone back to a fully offline world, or not?
But to come back to why I wanted to talk about this excerpt again – it is because The Economist asks an inevitable and obvious question regarding the deployment of AI in offices the world over:
Speculation about the consequences of ai—for jobs, productivity and quality of life—is at fever pitch. The tech is awe-inspiring. And yet ai’s economic impact will be muted unless millions of firms beyond Silicon Valley adopt it. That would mean far more than using the odd chatbot. Instead, it would involve the full-scale reorganisation of businesses and their in-house data. “The diffusion of technological improvements”, argues Nancy Stokey of the University of Chicago, “is arguably as critical as innovation for long-run growth.”https://www.economist.com/finance-and-economics/2023/07/16/your-employer-is-probably-unprepared-for-artificial-intelligence
Having technology is not the same as using it. And in fact people will take a long time to adopt to a new technology, and that for a variety of reasons. Some may be cultural, some may be about being comfortable with the “old” workflow, and some may be, well, irrational, plain and simple.
The article in The Economist gives the examples of Japan and France, and that section is well worth a read, but what is true for countries is true, of course, at the level of organizations and institutions too. Resistance to change is hard to overcome, and the diffusion of technology simply doesn’t happen as fast as some might hope. For example:
In 2017 a third of Japanese regional banks still used cobol, a programming language invented a decade before man landed on the moon. Last year Britain imported more than £20m-($24m-) worth of floppy disks, MiniDiscs and cassettes. A fifth of rich-world firms do not even have a website. Governments are often the worst offenders—insisting, for instance, on paper forms. We estimate that bureaucracies across the world spend $6bn a year on paper and printing, about as much in real terms as in the mid-1990s.https://www.economist.com/finance-and-economics/2023/07/16/your-employer-is-probably-unprepared-for-artificial-intelligence
But other factors are at play, beyond my simple list of factors from above (cultural reasons, inertia and irrationality). There may simply be no incentive to move to a better technology, if you are a business that is doing well in a sector with no young upstarts for competition. Particularly in the western world, it may simply be a case of an aging population that prefers to not learn new tricks. Governments may be ham-handed in terms of regulating the deployment of new technologies, and society may wish not to adopt technologies that save on labor. Costs, data privacy concerns, legal compliance issues, inevitable mistakes that AI will make – all are hurdles to be overcome.
The study of how this will change in the years to come will fascinate economists, sociologists, psychologists and many other -ists.
It is impossible to say how this will play out, but it will be a fascinating topic of study, that is for certain.
Further reading, if you are interested in an economic analysis of some of these issues.