The same AI: some companies use it to fire, others to hire

Start with two real stories from this year. Placed side by side, they don’t add up.

Story one: Meta laid off roughly 8,000 people in May — about 10% of its workforce. In that same internal memo, it raised its 2026 capex guidance by up to $10 billion, pushing the total to $145 billion, almost all of it going into AI data centers and chips. Zuckerberg wrote: “Success in the AI age is not guaranteed.”

Story two: Also in May, OpenAI put up $4 billion to stand up a dedicated “Deployment Company” and started hiring aggressively for a role called the Forward Deployed Engineer (FDE). Google posted 59 of the same openings at once — Google Cloud’s CEO even jumped on LinkedIn personally to recruit. Anthropic hasn’t cut a single person this year; its valuation has hit $380 billion, it has 2,300+ staff, and hundreds of roles are still open.

Same AI. In story one, the reason to fire people. In story two, the reason to hire them.

When one thing can simultaneously explain “that’s why we’re cutting headcount” and “that’s why we’re desperately recruiting,” it’s probably not the real reason for either. That’s what I want to get into: when every layoff announcement has the word “AI” in it, what is actually going on?

What laid-off employees read versus what their bosses meant

If you were laid off in the past six months, or someone near you was, you know the language by now: embracing AI, improving efficiency, restructuring for the future. The implication is that AI got so good it made your role redundant.

Put the numbers side by side and the story shifts.

Most of these layoffs are happening in the same quarters when these companies are reporting record profits. Not survival moves. Not a company bleeding out. The financials look great — record revenue, record margins — and they’re still cutting people. Meta’s own explanation didn’t even bother with euphemism: the layoffs are to “run the company more efficiently and to offset other investments we’re making.”

Translated into plain English: we want to spend $145 billion on GPUs and data centers, and the money has to come from somewhere. So we’re cutting your salary to pay for AI’s electricity bill. We’re not laying you off because AI can do your job. We’re laying you off because your paycheck is being reallocated to AI’s hardware.

The most candid framing I’ve seen came from an executive at a search firm. He said bosses can now finally and easily tell employees “I over-hired — that was my mistake” — because “the whole world now believes jobs are being replaced by machines.”

Sit with that for a second. It’s not saying AI has replaced jobs. It’s saying there’s a ready-made narrative that lets you cover for a prior hiring decision. AI isn’t the perpetrator here. It’s a remarkably convenient fig leaf. The past two years were flush — easy money, optimistic forecasts, rampant overhiring. Now comes the correction. “Blame AI” is a better story than “management miscalculated,” it doesn’t trigger boardroom accountability, and in many cases the stock price actually goes up.

Why does this cover story work so well? Because it flatters everyone in the room. For shareholders, “we’re using AI to drive efficiency” reads as a growth narrative — layoffs get interpreted as a positive, stock ticks up. For the board, nobody demands accountability from an executive who “embraced the technological wave.” For the public, “times are changing” sounds a lot more dignified than “we guessed wrong.” A management error, wrapped in AI language, transforms from something that should invite scrutiny into something that looks like foresight. There is no cheaper PR on the market.

Follow the money and it gets clearer still. This year, Meta, Amazon, Microsoft, and Google combined are putting roughly $725 billion into capex — up about 75% year over year — almost entirely for AI compute. Microsoft alone has cut roughly 4,800 people this year. When you’re moving that kind of capital, the fastest way to make the numbers work is to reduce headcount. AI’s invoice, in part, is being paid by the people who got laid off. So “we’re laying you off because of AI” might be more accurately read as: “we’re laying you off for AI.”

By May of this year, layoffs explicitly attributed to AI had hit 87,714 people — roughly 22% of all tech layoffs counted. Of that 22%, how much is AI genuinely displacing roles, and how much is AI simply getting cited as the narrative for cleaning up an old hiring mess? Nobody can separate those cleanly. And that ambiguity is precisely what makes the excuse so useful.

If AI really were eliminating jobs, AI companies would be shrinking first

This is the point I find most useful for cutting through the noise.

Accept the premise that AI is displacing human work — that it’s the actual driver behind this wave of cuts. Follow that logic forward: the companies most immersed in AI, using it hardest, most exposed to its displacement effects, should be the first to see their own headcount rendered unnecessary. They should be shrinking.

The opposite is happening.

• Anthropic has filed zero WARN notices this year, sent zero layoff memos. It’s in hypergrowth — over 2,300 staff, hundreds of open roles.
• OpenAI isn’t cutting. It’s spending $4 billion to set up a new company alongside Bain and McKinsey, whose singular purpose is to send people into other businesses to get AI actually deployed. It brought in roughly 150 Forward Deployed Engineers through an acquisition alone.
• Google is simultaneously posting dozens of FDE roles, each paying easily six figures.

If the people building the AI are hiring as fast as they can, the story that “AI makes humans unnecessary” falls apart before it even gets off the ground.

What’s more likely: AI is reshaping work, but not by swapping humans for machines. It’s shifting where value lives — some activities are being absorbed by AI, and a large volume of new, more valuable, distinctly human-required activities are appearing at the same time. The layoff wave and the hiring wave are two sides of the same coin, just being told as opposite stories by different companies.

The real variable was never the AI

We default to treating “AI” as a subject with agency — as if AI decided who leaves and who stays. It didn’t. Decisions are made by companies, by people. AI is the noun they push to the front of the sentence when it’s convenient.

Same technology. Meta invokes it to cut 8,000 people. OpenAI invokes it to hire hundreds. The difference isn’t the AI — it’s how each company sees AI, how they’re using it, and whether they’re being honest about their own choices.

• One company treats AI as a cost-reduction rationale: AI arrived, so I can hire fewer people, redirect the savings into compute, and incidentally write off years of overexpansion in one clean move.
• One company treats AI as a growth lever: AI arrived, so I need a whole new category of people to actually pull that lever, deliver it to customers, and turn it into revenue.

Same technology. Completely opposite responses. If you’re evaluating whether a company is worth working for, or worth investing in, the signal isn’t whether they have AI. It’s which side of that coin they’re on.

What the hiring frenzy is actually telling us

The Forward Deployed Engineer role deserves a closer look, because it functions like a probe — surfacing what’s genuinely scarce and genuinely valuable in this AI cycle.

FDEs aren’t training models. They’re not writing foundational algorithms — that’s a tiny slice of people at a handful of labs. What they’re doing is: walking into a real company, sitting down with business owners and frontline employees, figuring out where AI can actually create value in that specific context, redesigning the workflows around it, and making sure the whole thing holds — runs, sticks, produces sustained returns.

One piece of reporting on this role offered a verdict I think is exactly right:

This role is the clearest market signal yet — the hard part of AI has moved from building models to making them work inside a business.

That sentence contains more information than almost anything else written about AI this year.

Building models is an arms race for a tiny number of companies — irrelevant to most people. “Making AI produce value in a specific real-world context” requires enormous numbers of people. Those people don’t need to be able to train a neural network. But they need to understand the business, understand the people in it, know how to translate a vague pain point into a concrete problem that AI can actually solve, and then stay until it’s producing real results.

A concrete example makes this less abstract. Take an insurance company that wants to use AI to process claims. The model itself is off the shelf — anyone can call the API. The hard part is: which step in the claims process is the actual bottleneck? Which department is it stuck in? How do you encode the unwritten judgment calls that experienced adjusters carry in their heads? Who’s accountable when the model gets it wrong? How do you get frontline adjusters to actually use it rather than route around it? None of those questions are “the model isn’t capable enough.” Every one of them is “you need someone who understands this business, understands this workflow, and can fit AI into it.” The model is generic. The value is always embedded in a specific context. And contexts have to be worked through one by one, by a person. This is why the labs most aggressively pushing model capability are simultaneously spending billions to acquire the people who can “put the model inside the business” — because they know better than anyone that even the strongest model generates no revenue until it lands somewhere real.

Here’s the thing: many of the people caught in the Big Tech layoffs already have most of these capabilities, or could develop them with a relatively short pivot. AI hasn’t made those skills obsolete. It has made them more valuable than ever — the demand has just shifted from “maintain the old system” to “fit AI into the old system.”

What this means for the rest of us

A lot of words about other people’s situations. Time to make it concrete.

First: don’t let “AI replaced you” be the story you tell yourself — check whether it’s actually an excuse. A company booking record profits while pouring hundreds of billions into AI, which then cites AI to justify cutting your role — that is almost certainly not a verdict on your capabilities. It is a decision about how that company is allocating capital. Confusing those two things does real damage. A lot of people come out of these layoffs spiraling into “maybe I’m not good enough anymore” — and in many of these cases, that’s not the question at all. You didn’t lose to a machine. Your paycheck got reassigned to a server rack.

Second, and more important: find a way onto the side of the coin that’s hiring. That side isn’t short of people who can build AI. It’s short of people who can make AI produce value in real situations. There’s nothing mysterious about what that takes — it breaks down into specific, learnable things:

• Stop doing the work that AI will eventually automate; do the work that involves judging whether AI is doing the right thing, and whether it’s doing it correctly;
• Build a skill that’s hard to replicate: the ability to take an ambiguous business problem and describe it precisely enough that AI can actually solve it;
• Stop asking “will I be replaced?” and start asking “can I use AI to accomplish something that wasn’t possible before?” — the second question is what the hiring side is actually looking for.

AI won’t replace people. But it will reshuffle the deck: it will push out people who only do what AI can do, and lift up people who use AI to get things done. Strip away all the noise from this year’s contradictory headlines, and that’s the sentence underneath.

None of this is asking you to feel sympathy for Big Tech. And it’s not minimizing how much a layoff hurts — the bills are real. But the one time you can’t afford to be sloppy is when you’re trying to understand why it happened. Getting laid off often has nothing to do with losing to AI. It has to do with your company choosing to use AI’s name to cut costs, rather than using AI to generate more revenue. That’s their choice. It’s not your verdict.

The layoff memo and the job posting use the same word. They’re describing two completely different things. Don’t only read the half that scares you.