It is nearly impossible to avoid the current hype around ChatGPT.  ChatGPT is an artificial intelligence content creator that generates a variety of outputs by answering questions fed to it.  You would have to be living under the proverbial rock not to hear the news stories – ripe with examples of the ChatGPT application passing MBA tests, and Law School exams.  The hype piqued my curiosity, so I decided to spend a cold February weekend testing out the application.  

Not knowing exactly how to use it – I decided to just have some fun. I prompted the application: “Create a short biography of Willie Mays”. It created two paragraphs of clean content highlighting May’s greatness as a baseball player.  I repeated the question and added “ In the style of Hemingway.” The response astonished me in its clarity and precision and likeness to Hemingway’s style. I asked other silliness such as “Visualize entropy” and it described an organized and disorganized living room as a metaphor for high and low entropy.

 While still figuring out its capabilities, I fed the app  a couple of paragraphs from this article to edit in the style of E.B. White. The result was impressive;  I was particularly impressed by the spartan, exacting use of each word – very much reminiscent of White. The ability of ChatGPT to create code and content, to edit, and to simply create from simple natural language prompts – stirred some long-lost memories.

Many, many years ago on a planet far, far away…I wrote a paper for my high school American History class. The paper was meant to emulate a well-researched college thesis, with a minimum of 50 pages, proper citations, a table of contents, and other related components. We were instructed to choose a topic that had either a historical or futuristic focus. Having just read Alvin Toffler’s best-selling “Future Shock,” I chose to write about the pace of technological change and its effects on society.

The End of the White Collar Class?

“Future Shock,” published in 1970, outlines the political, social, and technological impacts of rapid technological advancement. Toffler predicted the decline of traditional industries and the rise of knowledge-based careers leading to a constantly evolving job market where successful workers must be able to adapt and retrain quickly to maintain their employability. He foresaw the trend toward remote work, the gig economy, the Internet of Things, and even the planned obsolescence of products. Toffler’s insights, published more than 50 years ago, have proven to be largely accurate.

As I played more and more with ChatGPT, I could not help but wonder if this new tool and others like it would have a dramatic effect on the knowledge workers of today—the people foretold of in “Future Shock.” I tried to contextualize the impact of the technology. Would this automation be a help or a replacement for many of these workers? Could it help with some known labor shortages such as those in the supply chain? What would happen to the coders, content creators, illustrators, web designers, writers, and countless others engaged in careers that will be affected by this new tool?

This was the eureka connection to my high school thesis paper, I recalled considering the possibility that some forms of planned obsolescence might include people – expanding on the notion of technological unemployment first articulated by John Maynard Keynes. After a long career in supply chain and manufacturing, I clearly understand how the advance of breakthrough technology has shifted work. I have watched automated picking machines replace workers in warehouses, and robots replace legions of factory workers. And we are now on the cusp of automated driving vehicles that might replace truck drivers.

Through all of this “progress” I never once considered that white collar workers, the knowledge workers, would be impacted by advancing technology. I thought they were “safe”. I always assumed that the workers most likely to be “technologically unemployed” would be the folks working on a typical manufacturing production line, where a machine could be built or programmed to replace their physical labor.

After experimenting with my whimsical prompts, I gave ChatGPT a series of supply chain prompts such as : “Explain S&OP in simple terms.” Here again, I was amazed by the app’s near-perfect and grammatically accurate answer. ChatGPT was not a digital toy hardwired for fun and it is not just incremental improvement. It is a game changer. I was so enamored with this experience that I posted to LinkedIn my story of querying ChatGPT about S&OP. A former colleague, a senior marketing executive with a FinTech firm sent this reply to me:

“Saw your ChatGPT post. I’ve already started using it to write byline articles, but I would not say that publicly– I’d get scorched by copywriters, editors, etc. I’ve learned how to feed it to get decent fodder up front, and then I clean it up and add more nuanced info. It probably cuts article development time in half.”

My former colleague confirmed some of my concerns. ChatGPT is such game-changing technology that even in its embryonic form has already replaced human workers while improving outcomes. I don’t for a moment think the myriad of content creators or editors will lose their jobs immediately, but I can’t imagine many are happy with this new tool. They may eventually have to re-learn and re-tool to remain employable.

ChatGPT & Supply Chain Management

Channeling Toffler, I considered what this might mean in my own profession. What were the potential use cases within supply chain? Envisioning the possibilities of some natural language, quantitative ChatGPT “cousin” in the supply chain field, I can think of a hundred different ways I would leverage such a tool. As planners, we always search for that extra piece of data to help us perform our jobs more efficiently. Imagine someday using an app to inquire, “Where is the shipment of chemical X at the moment?” or “What is the forecast error for product Y?” or “Has product Z started being sold to Walmart?”

Imagine colleagues five years from now prompting their cell phones to “Generate a forecast for the new blue widget product line, using the red widget product line as an analog” or “Provide me with the economic and sustainability impacts of closing a warehouse in Memphis.” Then imagine a tool that could perform even more complex analyses: “What is the risk profile of supplier A?” or “What are the economic tradeoffs of a less than 100% fill level while serving Amazon?” When you consider all the data analyses that supply chain professionals perform on a daily basis, the opportunities for supply chain AI tools are limitless.

There is still much to learn as natural language artificial intelligence tools expand into many domains. To me, this is the real promise of Moore’s law – expansive computing power that provides digestible information at the speed of thought. The future is unknown (despite Toffler’s knack for prescience), but I suspect we will be talking about this breakthrough moment and its impacts for a long time.