A post this summer by David Elkington on LinkedIn on the impacts of AI on an Ivy League class is truly terrifying. It’s not just overworked and lazy adults turning their lives over to hallucinatory chat, j’ai pétés and clods, but our next generation as well.
In his post, David Elkington summarized how Brown University professor Roberto Serrano has proved it. When, after 20 years of teaching a course, he offered his first take-home midterm, enrollment tripled and the average shot up almost 40% to 96 (A++). He then had his Teaching Assistants run the exam through ChatGPT, from various accounts, and they were able to reproduce the majority of proofs that dozens of students submitted.
So then the professor announced he would be returning to tradition, with an in-person final. Twenty percent of enrolled students dropped, and ten percent didn’t show up. The final average was 48.6, an F and a drop by half.
The majority of students had obviously not only outsourced their studying, but their thinking as well. After all, the course was “Welfare Ecomomics”. Formally defined as using microeconomic techniques to evaluate societal well-being and guide public policy, and relying on methods like Pareto efficiency to help analyze trade-offs, neither the math nor the analysis is particularly hard compared to other areas of economics, undergraduate mathematics, or even philosophy. The trick is a bit of study and a lot of logical, deep thought to think through and work out the consequences and likely reactions to a decision. Especially when there aren’t always absolutes like there are in physics and chemistry. When the answer is human intelligence, AI fails.
And that failure will carry forward into the real world.
Everyone forgets that computers were designed to compute — and do so at a speed and certainty that we cannot do as humans. That’s what they’re good at. We’ve spent over a trillion hours as a species researching computing science, writing, and re-writing code and not only improving the capability of computers but pushing the limits of what computation can do.
The first few decades were spent perfecting the hardware, the error correcting code, the operating systems, the core compilers, and testing methodology so that the chances of a well defined program on a stable operating system on well made hardware making a calculation error is now one in one quadrillion operations. Then decades were spent taking advantage of the fact that computers can repeat the same operation that many times flawlessly where as we, as humans, struggle to do the same calculation ten (10) times in a row without error. And we created not only business software to make our business operations more efficient, but communications software to make our personal lives more connected and easier, and education software to help us get smarter.
But then someone decided that if we created a big enough next-generation neural net and fed it enough data it would magically become intelligent, unleashed it on the world, and when it, for the first time since the concept of AI was conceived almost seven decades ago, natural language was produced that was indistinguishable from an average person (whose IQ was already deteorioating thanks to Twitter), someone else, of very limited intelligence, thought it was intelligent. Then the hype-mongers decided to proclaim this story as gospel, and convince the masses it was the word. And those who believe it will lead us to our collective downfall.
Only Human Intelligence (HI) will allow Procurement to deal with the constant turmoil of today’s world and adapt to the changing reality. Without it, they will not be able to secure supply at a cost the organization can afford, which is not only the core function of Procurement, but fundamentally the core function of a business. (If an organization cannot secure supply better than an individual, there is no reason for the business to exist.)
AI will be Procurement’s Downfall, and to be honest, at the rate things are going, Humanity’s downfall as well.

