In 2022, I was hired to build AI operations at a health-tech startup. At the time, we were pioneering the use of AI in healthcare, which required significant human oversight until one day, it didn’t. GPT-4 launched and within a short period of time, I realized my role no longer made sense. My employer came to the same conclusion. There was no plan to retrain me or redeploy my skills into a new version of the work. My job simply disappeared.
I say this not as a cautionary tale, but as context. When I look at the wave of mass layoffs being justified as AI transformation, I’m not reading about it from a distance. I’ve been on the other side of that decision.
What I learned on the way down
What I understand now that I didn’t fully see then is that my employer wasn’t transforming. They were optimizing. Layoffs offer clean math. They deliver immediate cost savings and a simple story for boards eager to see returns on AI investments. What they don’t deliver is increased capacity, creative leverage, or new kinds of work. I was a cost that disappeared. The underlying capability question — what should this work become? — was never asked.
When companies like Meta and Microsoft cut tens of thousands of employees, many leaders frame it as a necessary step in becoming more “AI-native.” I recognize what’s actually happening. They are choosing the fastest path to efficiency instead of the harder path to reinvention. They are laying off their way to transformation because it’s easier than rewiring how work gets done. I know the difference between those two things firsthand.
What I did differently
Today I head up AI Operations at Pearl, an AI company for independent professionals, where we’ve taken a different path: upskilling employees, reshaping roles, and having uncomfortable conversations earlier than most companies are willing to. One of those conversations stands out.
I work closely with a technical writer who recently asked a question many employees are quietly thinking: “AI can do a lot of my work for me — so what is my job now?” She had realized that much of the value she provided — drafting, editing, and refining documentation — was now available to anyone using AI effectively. I recognized that moment immediately. I had lived it.
The difference this time was that we didn’t avoid the question. We answered it together. Today, she operates like an entire technical writing department with a team of AI agents that help her proofread, edit, and standardize content. She also owns our internal intranet, a function that often fails because it depends on constant manual updates. Instead of chasing teams for updates, she uses AI to collect, organize, and refresh content across departments — turning a usually stale system into a living source of truth. She has cut down the time typically required to maintain that system by 95% – entirely on her own.
The reason this worked is because we had already been talking candidly and early about how AI is changing work. Programs like our AI Champions initiative — which allots leaders across all departments 10% of their time to explore and build AI-powered workflows — have helped normalize experimentation and make it easier to have honest conversations about where roles evolve.
The pattern playing out at scale
This is the opportunity companies are missing. When leaders avoid redefining roles early, they create a moment where layoffs feel unavoidable. Teams wake up with hundreds of people whose old jobs no longer exist and no clear plan for what comes next. At that point, layoffs become a reaction to inaction. That is a failure of leadership, not a consequence of AI.
The companies that are truly transforming with AI are doing something far more difficult than issuing headcount reductions. They are acknowledging that work itself is changing and actively designing for it. They are retraining employees, redeploying them into new roles, and redefining what “good” work looks like in an AI-enabled environment.
This isn’t easy, especially at scale. It’s far simpler to tell every department to cut 20% of its staff and “figure it out.” Large organizations are optimized for that kind of directive. And when boards demand results in a single quarter, leaders often default to layoffs because they feel immediate and decisive.
But there’s a deeper risk: layoffs create a downward spiral. AI will continue to improve, so if each new wave of capability is met with another round of headcount reduction, companies steadily shrink themselves while relying more heavily on technology until there’s nothing left to transform. These companies will survive but won’t evolve. They become smaller versions of themselves, capable of doing the same amount of work with fewer people, while more adaptive organizations expand their scope and output with the same teams.
The divide is already forming
We are still early in this transition, but a clear divide is emerging. On one side are companies that treat AI as a justification for workforce reductions. On the other are companies that treat it as a catalyst for reinvention. The difference will come down to whether leaders choose transformation fueled by long-term capability building over short-term pressure.
The companies that navigate this well won’t be the ones that never faced disruption. They’ll be the ones that learned from it — and built the structures to handle the next wave before it arrived.
AI doesn’t just reduce labor. It multiplies what organizations can achieve when people are given the structure to evolve alongside it. I know that because I had to find that structure for myself — and because I’ve now helped someone else find it too. You can lay off your way to transformation and hope efficiency carries you forward. Or you can do the harder work. I know where the former one leads.
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