Since the beginning of the industrial revolution, there have been several waves of automation, each in turn displacing workers and ushering in a new employment paradigm. The advent of Chat GPT and Generative Artificial Intelligence (GAI) has poised society for yet another upheaval in how work is done—raising productivity, but also its consequences, in unimaginable ways. Let’s look at a few past examples.
     The pen, the typewriter, the word processor. Prior to the twentieth century, all documents (except books printed by a press), ledgers, and letters were composed by hand. It was slow and laborious. Scriveners like Bob Cratchet earned mere pennies per day. Move ahead a hundred years. Huge secretarial pools using the much faster mechanical typewriter were the norm in offices. All reports and correspondence were hand-typed. Make a mistake? Retype it. But overall, it beat pen and ink.
     Then along came the IBM Selectric. Mistype something? Correct it right away, then continue typing. Word processors arose in the 1970s. Tasks that used to require clerical pools could now be accomplished by engineers, marketers, and small business people on their own.
     Animal domestication, the steam engine, the internal combustion engine. Draft animals allowed the transport of people and goods in greater quantity and distances than could be hand-carried. The advent of the steam engine ushered in an era when cartage could be transported by multiple tons. Freight and passenger trains increased trade and mobility by an order of magnitude. With the arrival of the internal combustion engine, ancillary industries prospered—steel, coal, rubber, manufacturing of the vehicle itself and all the various parts and components that go into it. In 2021 trucks hauled 10.93 billion tons of freight, 72.2% of total domestic tonnage shipped.
     The digital revolution. The design and manufacture of durable goods and services was upended within the past forty years. From 1970 to 2020 worker productivity in the US rose 61.8%. And now comes along Generative Artificial Intelligence. Beginning in the late 2000s, the emergence of deep learning drove progress and research in image and video processing, text analysis, speech recognition, and other tasks.
     A generative AI system applies unsupervised or self-supervised machine learning to a data set. The capabilities of a generative AI system depend on the content of the data set used.
     Text-based GAI systems are trained on words or sentences. They are capable of natural language processing, machine translation, and natural language generation and can be foundation models for other tasks. Data sets include BookCorpus, Wikipedia, and others.
     In addition to natural language text, large language models can be trained on programming language text, allowing them to generate source code for new computer programs.
     Generative AI can be trained on sets of images with text captions that identify the pertinent subject, such as “cat,” “dog,” “banana,” or “mountain.” They are commonly used for text-to-image generation and neural style transfer. For example, this type of system can render an image of a Van Gough painting in the style of Salvadore Dali.
     Training can be done on sequences of amino acids or molecular representations of DNA or proteins for protein structure prediction and drug discovery.
     Music-based GAI can learn the audio waveforms of recorded music along with text annotations to generate new musical samples based on text inputs.
     Generative AI trained on annotated video can generate video clips. This and image-based systems have generated the most controversy for their ability to compose deepfakes—digital accounts of events that never happened.
     GAI trained on the motions of a robotic system can generate new trajectories for motion planning. Commands like “pick up the hamburger with the spatula and place it on the open bun” would actuate a robotic arm.
     The Boeing company is using GAI in its design of NASA’s X-66, an experimental aircraft employing a radical new wing to increase fuel efficiency by at least 30%.                 Generative AI is used for book cover designs and illustrations, for generating plot outlines, marketing copy and book descriptions, draft emails and letters, and draft legal briefs. New search engines and personal digital assistants now use these systems.
     I expect Generative AI to displace those at the bottom rungs of the economic ladder. Ironically, lower wage workers may be hastening the change. The Great Resignation still affects hotels, restaurants, retail stores, warehouses, airlines, hospitals, trucking, local delivery, transit, and manufacturing.
     Many of these people were forced to leave their jobs during the COVID pandemic, and sought jobs with better pay, greater security and dignity. The goal of self-actualization, common among white collar employees in the late 20th and early 21st centuries, has trickled down to the labor class. The recent scarcity of workers closely matches the types of labor that lends itself to Generative AI automation.
     Where is this all leading us? Or rather, where is GAI leading us? My crystal ball is cloudy. There is a great deal of societal hand-wringing at present. But I can say this: traumatic worker and social dislocation accompanied each previous phase of automation. But from the perspective of hindsight, economists extoll the end results as a boon to society. Today, thanks to past technological advancements, we enjoy the most advanced economy in the history of civilization. The optimist in me says the same will happen with this impending tsunami. But with GAI, we can expect economic dislocation on a heretofore unprecedented scale.
     If we as a civilization want to reap the benefits of GAI, we need to start rethinking our educational system to prepare tomorrow’s workers for the inevitable changed economic landscape. On a personal level, many of us should consider moving to emerging industries, such as clean energy, or hydrogen-based transportation—or seek training for the coming changes in our current professions.
     Traditional sectors like law, engineering, and manufacturing will always exist, but in a radically different form. Lawyers may no longer require clerical help or even paralegals. Individual engineers and architects will give instruction to GAI to generate and document designs for all sorts of projects without the large teams utilized today.
     The proliferation of autonomous robotics will extend into increasingly smaller manufacturing firms. Those employees who remain will supervise and direct the machinery, rather than fabricate or assemble products. The service and hospitality sector will still be around but will follow a model similar to boutique establishments today, charging a premium price for exceptional quality and service. Fast food chains will become completely automated, a single “manager” running the entire operation. Even the trades will be affected. Individual carpenters, plumbers and electricians will always be in demand, but most large equipment will be automated. Truckers, delivery drivers, farm laborers and construction equipment operators will be vulnerable.
     That fish and chips I alluded to in the title of this article? I’ll order by voice command on my phone. It will be cooked and packaged robotically, placed into an autonomous delivery vehicle, and dropped off on my front porch by a small delivery bot. Have your digital assistant talk with my digital assistant. We’ll do lunch.

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     Want a deeper dive? Check out these sources, listed in the order of discussion.

36 Artificial Intelligence Examples Shaking Up Business Across Industries. Built In. Feb 2023. https://builtin.com/artificial-intelligence/examples-ai-in-industry
The History of Industrial Automation. Paramount Tool Company. 2023. https://www.paramounttool.com/the-history-of-industrial-automation/#:~:text=Steam%20engines%20allowed%20the%20beginnings,their%20business%20to%20run%20themselves.
Generative Artificial Intelligence. Wikipedia. 2023. https://en.wikipedia.org/wiki/Generative_artificial_intelligence