There’s little hay to be made of the recent round of Big Tech layoffs. Tens of thousands of talented staff were almost simultaneously let go across the FAANG workforce and beyond (see Twitter), with Apple being among the only domestic tech giants that hasn’t frozen recruiting efforts as 2022 comes to a wrap.

Even still, Apple is taking a much more cautious approach to hiring today than it has in years past, while Meta and Amazon have shaved off—read: forcefully amputated—double-digit percentages from their workforce. 

It’s a major reversal of the “job seekers market” that characterized the start of the Covid-19 pandemic, when work-from-anywhere broadened access to top talent and jobs while creating a boom market for tech solutions that helped enable remote productivity. 

It’s also a major reversal for the long-term outlook of Big Tech. It might not be far-fetched to view these latest layoffs as akin to the “dotcom bubble” that burst at the turn of the Millenium. 

In that scenario, seemingly endless funds were funneled into nascent internet-born companies that staffed up in an arms race for talent. In fast order, the buzziest of those brands endured mass-layoffs and a slew of mergers/acquisitions before viable long-term “digital economy” businesses actually emerged. 

That’s not to say those experiencing these latest layoffs should look back to 2000 for an idea of what’s next for their former or future employers. While many corporate leaders are characterizing the layoffs as a “market correction” after following misguided forecasts during the pandemic, a look at the titles and departments targeted by the layoffs paints a different picture. 

Layoffs target humans-in-the-loop of ML and AI

For starters, engineering talent of all kinds were the first on the chopping block at Meta (nee Facebook), Twitter, Salesforce and even former unicorn startups Stripe and Lyft. These are the literal platform builders that not only helped shape the user interfaces we’re all familiar with, but helped make these spaces safe and enjoyable.

At Twitter, for instance, the company’s new leadership was almost proud to give pink slips to the ML Ethics, Transparency, and Accountability team led by Rumman Chowdhury, someone widely considered a rockstar in the ML space. While there is a LOT to unpack about Twitter’s current and future state, it’s worth noting that Meta similarly downsized their Probability unit, which itself focused on developing Meta’s ML infrastructure. 

While Meta’s 50-person Probability unit was less focused on ethics as an explicit marching order akin to the ML team Twitter offloaded, Probability literally represented the human-in-the-loop element required for steering successful ML and AI. Without delivering the data governance and oversight necessary to ensure ML and AI applications perform as expected—including accounting for data bias that’s been shown to deliver negative AI/ML outcomes on Facebook specifically—many of Meta’s ML-powered initiatives are essentially “steering blind.”

These layoffs emphasize how much humans literally sit at the center of technologies considered Artificial Intelligence. While the name implies “computers supplanting humans,” these systems run on models that require heavy human curation that’s anything but one-and-done. Rather, entire teams (ahem, Meta’s Probability unit) need to be tasked with handling ML infrastructure on a global scale—and the stakes of poor management couldn’t be higher. 

It’s especially noteworthy to emphasize too just how in-demand AI and ML talent seemingly remain: Look no further than any non-FAANG tech company’s career’s page for proof. So what should be the big takeaway behind all of these layoffs—beyond, in Twitter’s case, willful trolling?

Levering ML and AI talent for real-world solutions

A recent report from Protocol emphasized that while social media giants may be less keen to keep the talent they hoarded in boom times, Green Tech companies are more than happy to step in and leverage talent to build world-changing solutions. Similarly, continue to funnel money into tech startups fueled by ML and AI techniques that require the kind of experience dealing with unstructured data that former Meta and Twitter engineers have in spades.

So the real lesson here may be less an indictment on AI and ML and more of a repositioning—if not “market correction”—on the efficacy of social media. 

Both Meta and Twitter face an uncertain future because they’ve failed to make their platforms more enjoyable for users, while reporting increasingly disappointing ad revenues that underline their flailing business cases. It’s easy to pin this on TikTok and shifting consumer tastes, but it’s also a failure of these businesses to put their AI and ML practices to effective use in delivering pleasing outcomes for new consumer appetites. 

This paints a rosier picture for those recently laid off by Big Tech compared to those who were left jobless following the dotcom bubble burst. That’s because the skills these engineers have honed managing and curating unstructured data for a social media giant are widely applicable in the current job market. 

The lesson to bear in mind going forward is that social media may simply be evolving into something very different than what it was when Facebook and Twitter were at their peak valuation. On the flip side of that is that the market for tangible solutions addressing the myriad real-world struggles that social media has laid bare over the last decade is growing tangentially to social media’s descent. 

There are unfortunately bound to be similar headlines around layoffs and “bad quarters” across Big Tech going forward, but it’s still likely safe to characterize the current state of the workforce as reshuffling—at least for now.