“Meta flattened and unflattened in under a year. That's not a company that got it wrong once. That's a company finding out that the ground is moving underneath them and they're trying to keep up.”
We Fixed It, You're Welcome · October 2026
Opening the episode, Melissa Eaton cited a Business Insider report that Meta is asking about 7,000 people in its applied AI division whether they'd like to go back to being managers, not long after moving them out of management.
Transcript
Yes, completely. Let's go.
…this labor market, don't go for the manager position. Be a valuable contributor, but don't let the company you work for add manager to your title because that might put you on the chopping block. But now, being a middle manager suddenly hot again? They're in demand. What the heck is going on here?
…without the authority or the title to go with it? And there's something underneath all of that. We're trying to answer that question with a playbook from the 90s, right? Flatten the org, cut the layers, grow the span of control. That's a decades-old org design way of thinking. But the AI tool driving this round didn't exist 18 months ago. And it's a different tool than it was six months ago. Meta flattened and unflattened in under a year. That's not a company that got it wrong once. That's a company finding out that the ground is moving underneath them and they're trying to keep up. So when AI can generate the Answer, who decides it's the right one? And who's left to help the people actually doing the work make sense of it? That's what we're here to unpack today. So let's go.
I mean, gosh, you have companies that made, even Meta is making sweeping declarations that, well, number one, that AI can fix everything. And number two, that they're not backing down from AI. They're not saying we're done with AI. They just found that you said the critical reasoning piece is gone…
I really think that, you know, especially at Meta, you know, this is really a reversal of a CX story in HR clothing, really, right? So since it's admitted internally that the AI rollout was atrocious for this applied AI team and undermined the trust of roughly 7,000 employees, leadership's now…
You know, one of the things that we need to separate in our mind is that there is something about efficiency, which machines actually help us to do. You know, so there's so much that machines can do to speed us up, make it efficiency. But then there is another element, which is effectiveness, you…
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