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AI applications and large language models are insatiable consumers of history. They are educated by ingesting billions of online records, publications and comments, some ancient, others more recent, but always from the past. The rationale: The best way to make machines smart is by educating them on what humans have already learned (and often forgotten or overlooked).
The philosopher George Santayana is probably the best known prophet of this view of history as educator. Although his words were different, his opinion is widely expressed in this telling dictum: “Those who don’t learn the lessons of history are doomed to repeat them.”
It’s no surprise that Santayana’s message is generally seen as a warning. If we fail to learn from our past, we are likely to make the same mistakes over and over again. History’s retelling of human endeavors should be viewed as an instruction manual for the present and the future. Use the manual effectively and your odds of success go up dramatically in both dimensions.
That’s definitely accurate in the shaping human behavior. When applied to AI-powered products, however, the dictum can have exactly the opposite effect. In fact, at least some of those systems actually contradict Santayana. For them, the dictum should read “Those who learn the lessons of history are doomed to repeat them.” Unfortunately, online recruitment advertising is one of those cases.
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I’ve been studying online recruitment advertising since I launched my column for The Wall Street Journal in 1999. While there have been a few exceptions, job posts during that quarter-century plus period have been about as engaging as a brick. They may work with active job seekers (and even that’s debatable), but they are disdained and largely disregarded by passive talent.
These job posts began as the online equivalent of classified ad copy. After a couple of years, however, recruiters recognized that they were no longer limited by the expensive geography of a newspaper page, so they “improved” their ads by replacing them with position descriptions. These offers of employment were riddled with words only an HR bureaucrat could love, words such as Requirements and Responsibilities. They too largely failed to engage and motivate the vast majority of top performers. And sadly, their format, tone, word choice and content haven’t gotten any better.
And now, ta-dah, recruiters have ChatGPT, Claude, and Grok to help them write the same drivel only faster. As students of history – as consumers of all the previous (lousy) job postings in the annals of the internet – these AI applications now supercharge overworked recruiters’ productivity by rapidly creating job posts that are best described as Frankenads. They are a horror, both for employers seeking to add quality talent to their team and for job seekers trying to find the right next step for their career.
What are the attributes of a Frankenad? There are more than a few, but here are the most egregious:
• They aren’t structured as an ad (an invitation to learn more) but as a bureaucratic document with no upfront summary or hook to engage the reader.
• They are rife with requirements (what candidates must know and do) and lean on details about how the position in question will help to advance a person of talent.
• They use words that check boxes for SEO and/or DEI and omit language that will convey a sense of authenticity, empathy and a genuine interest in candidates.
Santayana died in 1952, so we can excuse his not allowing for the possibility that history can mislead as well as guide us. But that, in fact, is today’s reality. History is not always a reliable teacher, especially if it’s unfiltered by present day judgment. What does that mean for recruiters? Don’t plagiarize the past in your job postings; use some of that distinctive human creativity to craft something new and compelling, instead.
Food for Thought,
Peter
Peter Weddle has authored or edited over two dozen books and been a columnist for The Wall Street Journal. He is the Founder and CEO of TAtech: The Network for Talent Technology Solutions.
