These takes are frustrating.
October was a tough month for a lot of folks, with more jobs lost in a month since 2009 and in any October since 2003.
There seems to be two large camps trying to explain the why:
– People are failing to adapt so companies are saying toodles
– AI is taking/replacing jobs
Both takes have a problem, though.
The first one is the worst, honestly. Hoisting the blame or burden onto the workers is such victim blaming that it’s insulting.
Companies with major layoffs – Amazon, Target, Rivian, General Motors, etc. – are not making strategic, individual performance based reductions. They’re cutting swaths of departments and teams wholesale. In this environment, an individual can adapt all they want and it still not matter.
Is AI truly replacing any of these folks? Fully? Or even partially?
95% of AI deployments produce no or negative ROI. A report from Bain & Company says the AI sector needs to be grossing $2 trillion in the next 5 years in order to break even.
$2,000,000,000,000 by 2030 to just break even.
That’s more more than Amazon, Google, Microsoft, Apple, Nvidia and Meta’s grosses COMBINED.
So companies with those investments have to thread two needles: Creating cost savings somewhere today with an eye on marketing the value of AI in an environment with no ROI but a need to make a LOT of money in just a handful of years.
It’s a roadmap being followed in any number of companies:
- Throw money at the AI hype
- Overextend on capital expenses that can not be easily recouped in the short term
- Cut jobs (costs)
- Say it’s because your AI investments are creating efficiencies
- “If we can do it you can too”
- Sell all of the AI services!
Throw on top of all of this economic uncertainty, tariffs, tightening belts and wallets, and now growing unemployment and you’ve got a scary economic outlook going into the holidays.
My leave behind with all of that is: Let’s take a step back and evaluate what factors may really be driving these numbers instead of declaring outright that 150,000 folks were lazy in an effort to make the numbers fit a thought leadership narrative.
