The other day, China’s Huawei tech giant told the world it was “building an AI data lake to break down data silos” and create “seamless co-ordination between stable online models and agile offline iterations”. Whatever that means.

Before that, the HTX crypto exchange reported the equally baffling news that it had “fortified its security moat while leveraging sharp market insights and rapid execution to consistently capture alpha returns”.

And a top executive at the Nagarro AI engineering group said its clients wanted “strategic partners to help them reimagine their operational decision fabric”.

Moats, lakes, fabrics. The supply of fatuous, empty corporate bullshit never seems to wane, no matter how many times it is mocked, scorned and generally denounced.

So it is pleasing to see that someone may have finally found a use for it, or as Nagarro and co would say, a use case.

It turns out that measuring how receptive a person is to business bullshit could help companies decide which employees to promote and hire. 

This is the intriguing conclusion that Cornell University researcher Shane Littrell reaches in a study published in this month’s Personality and Individual Differences journal.

Littrell is one of a small number of scholars who have spent years analysing why some of us detest bullshit and some are impressed by it. 

His study is the first to look specifically at corporate twaddle, as opposed to the drivel spouted by, say, New Age gurus and the like. It confirms that being taken in by it is linked with lower levels of analytical thinking and poorer work-related decision making.

This supports my long-held view that the fewer people on the payroll who admire and spread bullshit the better. But how to spot them? Littrell’s paper offers an answer. It discusses a tool he has built and tested called the Corporate Bullshit Receptivity Scale that measures how susceptible people are to corporate claptrap by getting them to do things like rate various levels of waffle. 

Given the tool’s links with analytical thinking and decision-making, the study concludes it may be useful to see if it could help with “selection, hiring, or promotion”.

The system needs a lot more work before it could be ready for companies to use in the real world. Sadly, Littrell has been too busy to do it. But he would be thrilled if other researchers wanted to try it, he told me. “The more people doing work in this area, the better.”

I agree. Anything that could lead to less synergistic optimisation and pain points in the office has to be worth considering. Whether Littrell’s tool can be developed to the point that it is a foolproof aid to making hiring decisions is another thing. 

But it might not be any worse than some of the other tests employers keep foisting on hapless job applicants.

Companies continue to use versions of the Myers-Briggs personality test, even though there is very little data on how well the assessments measure personality and even less on how well they predict job performance.

Some outfits require job candidates to undergo extensive psychological evaluations that include Rorschach inkblot tests and drawing a person standing in the rain. A small drawing is an alleged sign of shyness. Rain shaped like teardrops supposedly suggests anxiety. 

Other firms have covertly checked on how well applicants treat reception desk staff when they arrive for an interview (Netflix) or deal with a waiter who mucks up a breakfast order (Charles Schwab).

Earlier this year, Luis von Ahn, the boss of the Duolingo language app, said the group asked taxi drivers bringing a job candidate into the office for an interview to report on how the passenger behaved.

One would-be chief financial officer who looked good on paper and had done well in interviews was rejected after he was mean to the driver, even though the company had been trying to fill the role for a year. When you’re trying to fill a staffing hole, it is “better to have a hole than an asshole”, von Ahn told a podcast.  

I admire the goal of screening out jerks, even if the sneakery involved in some tests is less admirable.

A reliable way to detect anyone who thinks it makes sense to talk about data lakes and decision fabrics would be blissful. It might not stamp out the problem entirely, but it could be a value-creation based level-setting for key stakeholders that could optimise shifting the dial.