If Millennials had dude-bros with ‘entrepreneur’ in their Instagram bio, Gen-Z has ‘Artificial intelligence (AI) expert’. The premise of such a title is that one can be an authority in a field that is so quickly developing and so ever-changing. To centre this in the experience of Gen-Z, it’s that any AI tool must first be examined on its own for its merits, not the implications of the use of AI bogging down any possible use cases.
This is not to say AI experts don’t exist, but that the actual engineers and expert users and developers of machine learning tools who have a solid foundation in mathematics, statistics, computer science, and programming are far fewer than what LinkedIn profiles would have you believe. I look to the fact that the AI boom in which we find ourselves, while certainly a digital frontier we should all be eager to explore, is also fertile breeding ground for con-artists, grifters, and hustlers.
Everyone and their brother with a business degree and a six-month coding course under their belt seems to be calling themselves ‘AI business expert’ or ‘AI moneymakers’.
Now, I say none of this to state the obvious, but to point out how important it is to address AI with as much nuance and honesty as one can muster. If someone isn’t a computer scientist, coder, expert in linear algebra or otherwise worthy thought leader, they might start trying to sell their class on how to use Chat-GPT, leave social media and find anything you need to know for free on YouTube.
Maybe it’s my age specifically as an elder Gen-Z, right on the bubble and finding the wide use of such tools to simply be distasteful. I think it’s fine if you’re some small business that needs to write copy for a brochure, or even someone using it to write basic code for you to make your job a little easier.
What none of that allows for is the acknowledgment that the use of such tools are crutches that serve to increase productivity and, once again, play into the scarcity mindset that drives most industries. Thus, large language models (LLMs) emerge as a tool to accommodate the ever-growing markets of capitalism, without being used to address the problem they were originally created to solve.
When it comes to those calling themselves ‘expert’, I have first-hand experience. In my journalism degree, in one of my courses, our class was divided into groups that would then conceive a hypothetical journalism start-up. My group devised an AI-based fact-checking tool, due to the fact that written news’ publication speed is bogged down by the fact-checking process, in the view of some.
Ultimately, however, we lost out on marks because we could not properly explain the intricate coding techniques that such a fact-checking LLM would require to function (even though such detail was NOT in the rubric).
We explained to the panel judging us, two of whom were true AI experts, that we were researchers and writers — budding journalists — not the sci-tech professionals that could answer their questions, especially with only a few months of research.
Yet none of that addressed why fact-checking takes so long — for a frustratingly nuanced reason, that not all data can be publicly available at all times, obviously, for a variety of reasons. But it is for that exact reason ‘AI experts’ are cropping up all over the place: because people think AI is a panacea, when really, it’s only a stop-gap.