AI Washing
Sam Altman admitted it out loud: companies are blaming AI for layoffs that have nothing to do with AI. He sees the washing. He doesn't see what's being washed.
You can watch the author express this essay on YouTube.
Sam Altman has made an admission. Speaking at the India AI Impact Summit last week, the CEO of OpenAI told CNBC-TV18 that companies are engaged in what he called “AI washing” — blaming artificial intelligence for layoffs that would have happened anyway.
This is a remarkable thing for the CEO of the world’s most prominent AI company to say out loud. It is also, when you sit with it for a moment, one of the most revealing statements anyone in Silicon Valley has made in years.
Because Altman is not simply observing that companies exaggerate AI’s role in their workforce decisions. He is inadvertently confessing something far more important: that the entire displacement narrative — the story that justifies a two-hundred-billion-dollar industry — is substantially, perhaps primarily, a fiction. And the truth underneath that fiction is one that no technology company wants to confront.
• • •
Watch the needle Altman is trying to thread. On one hand, he needs corporations to believe that OpenAI’s technology is powerful enough to replace expensive human labor and justify enormous licensing fees. The product must be seen as a substitute for human capability — that is what the customers are buying.
On the other hand, he would rather not be blamed for millions of eliminated jobs. “We’ll find new kinds of jobs, as we do with every tech revolution,” he assured the audience in India.
The problem is that both things cannot be true at once. You cannot sell a technology on its ability to replace human labor and then express surprise when companies use it — or claim to use it — to replace human labor. Unless, of course, the automation was never really the point. Unless the companies buying AI were doing something else entirely — something that predates artificial intelligence by decades — and AI simply gave them a more respectable story to tell.
That is exactly what is happening. And the data proves it.
• • •
According to Challenger, Gray & Christmas, approximately 55,000 layoffs in 2025 were attributed directly to AI. Significant — but less than one percent of all job losses for the year. A paper from the National Bureau of Economic Research found that ninety percent of executives said AI has had no impact on workplace employment over the past three years. Nine out of ten people actually making hiring and firing decisions.
Amazon cut 14,000 jobs while telling employees that AI meant the company would “need fewer people.” Six months later, the company walked it back — AI was not actually the reason. Announce layoffs. Cite AI. Wait six months. Admit AI was not the reason. The press release said innovation. The spreadsheet said cost reduction. The AI story was cover.
The freshest example is Block. On March 5, Jack Dorsey laid off approximately 4,000 employees — nearly half the company — and framed it as AI-driven transformation. Then Block’s own former head of communications Aaron Zamost published a response in the New York Times: the roles eliminated were disproportionately in policy and diversity functions. “Standard prioritization and cost management,” he called it, “not an AI-driven reinvention.” He also identified the investor dimension clearly — it matters less whether a company knows how to deploy AI and more whether investors believe it is on track to do so.
Altman calls this AI washing. I want to call it what it actually is: leadership laundering.
• • •
I have spent thirty-six years managing manufacturing operations across three continents. I have been in rooms where automation investments are approved, and on factory floors where the consequences land on actual human beings.
Here is what I can tell you from the plant floor: the vast majority of companies operate their workforce at a fraction of its cognitive capacity. Not because the workers are incapable. Because the organization has never bothered to develop, engage, or deploy the intelligence those workers bring to work every morning.
I call this the false baseline. The assumption embedded in every ROI calculation and automation business case — that the current performance of your workforce represents the full extent of what your workforce can do. It does not. In my experience, most organizations operate their people at thirty to forty percent of cognitive capacity. The remaining sixty to seventy percent goes home every night, unused, uninvited, and eventually unwanted.
This is what leadership laundering looks like in practice. A company spends years treating its workforce as a cost to be minimized. Workers are trained to execute, not to think. Their ideas are not solicited. Their intelligence — which appreciates with every year of experience — is systematically suppressed by management systems designed for control rather than capability.
Then AI arrives. And suddenly the company has a story. We are not eliminating jobs because we failed to develop our people. We are eliminating jobs because technology has made our people obsolete. The fault belongs to the future, not to us.
The worker who was never given a chance to demonstrate her full capability is now described as having been “replaced by AI.” The manager who never asked for her ideas is now a “change agent navigating digital transformation.” The executive who cut the training budget fifteen years ago is now “positioned for the future.”
You cannot claim AI replaced your workers’ contribution if you never measured that contribution. You cannot say a machine made your people redundant if you never made your people essential.
Altman sees the washing. He does not see what is being washed.
• • •
I know what happens when you go the other direction. At Royal Enfield in Chennai, I inherited a workforce the organization had largely written off — operating under low expectations, limited training, minimal engagement. By every automation consultant’s model, they were candidates for replacement.
We did not replace them. We deployed them. The same workforce. The same hands and minds. Different systems. Profits grew twentyfold. Not because we automated. Because we activated.
• • •
But Altman was not finished. Responding to concerns about AI’s enormous energy demands, he offered a comparison that reveals something far more troubling than corporate spin.
“It also takes a lot of energy to train a human,” he told the audience. “It takes, like, 20 years of life and all of the food you eat during that time before you get smart.”
Read that again. The CEO of the most powerful AI company on earth looked at twenty years of a child’s life and saw a training cost.
This is not a gaffe. This is the false baseline rendered as worldview. When you see a human being as something you train — when the food a child eats is an energy input and twenty years of scraped knees and bedtime stories and algebra homework are overhead — you have already made the decision that your technology ratifies. Humans are expensive, slow, inefficient models that happen to run on protein instead of silicon. The twenty years are a sunk cost. And if the machine produces that output faster and cheaper, the human becomes redundant.
I have three children. My eldest is a hematology-oncology fellow who fights cancer for a living. He was not trained to do this. He was formed — by family, by values, by watching his parents navigate three continents of uncertainty, by something in how he was raised that made him believe hard paths were worth walking. No loss function optimized for that. No gradient descent produced the moment he decided the hardest specialty in medicine was the one that mattered most. That was not a training outcome. That was a human being becoming himself.
Altman cannot see the difference. And that is not merely a personal failing. It is a structural one. When your entire business model depends on the premise that human cognition is a commodity — something that can be replicated, scaled, and sold by the token — you must eventually arrive at the conclusion that the original version is just an expensive prototype.
• • •
There is one dimension of this the sharpest critics have not fully reckoned with. The debate about AI and human knowledge has focused on the stock — the vast body of existing human output used to train these models. But the deeper crisis is about the flow. Not the knowledge that has been created, but the knowledge that would have been created if we kept investing in the people who produce it.
Every paper, manual, and insight that trained these models was paid for in human time. In human error. In human life. Now the industry proposes to replace the people who generated that material. In manufacturing, we have a name for what happens when a company eliminates its most experienced workers to cut costs and discovers five years later that nobody can diagnose the problems the veterans used to catch before they became catastrophes. We call it the capability gap. The knowledge lived in the hands and judgment of people who were told they were too expensive to keep.
AI is creating a civilizational capability gap. The models are getting better at reproducing what humans have already thought. They are getting no better at producing what humans have not yet thought — because genuine new knowledge is not a pattern in existing data. It is a break from existing patterns. It is what a forty-year machinist knows in year forty-one that surprises even him. You cannot train a model on knowledge that does not yet exist. And if you eliminate the people who would have produced it, that knowledge will never exist.
• • •
The AI washing will continue. Companies will keep citing artificial intelligence for workforce decisions that have nothing to do with artificial intelligence, because the alternative — admitting they never invested in the people they are now discarding — is an admission no earnings call can survive.
But some of us are watching. Some of us have been on the factory floor at midnight, and in the boardroom the next morning, and at the kitchen table where a boy decides to spend his life fighting cancer — and we know the difference between training a model and raising a human being.
Sam Altman is right that companies are AI washing. He is wrong about what is being washed. It is not the layoffs that need laundering. It is the decades of leadership failure that preceded them. It is the worldview that looks at a child and sees a cost. It is the poverty of imagination that mistakes efficiency for intelligence, output for purpose, and computation for life.
The intelligence was there. It was always there. It was already paid for. Not in kilowatt-hours. In love, in patience, in twenty years of cereal at the kitchen table.
Nobody bothered to unwrap it. That is not a technology story. It is a leadership story — and the leaders are the last ones who want you to know it.
Dr. Venki Padmanabhan is Plant Manager at Advanced Drainage Systems with 36 years of manufacturing leadership experience across three continents. He previously served as COO/CEO at Royal Enfield and COO at Ather Energy. His book, Already Paid For: Why Unlocking Frontline Intelligence Beats Automating Workers Away, is forthcoming. Subscribe to The Long Game at thelonggameforall.substack.com.