Hide Your Intelligence
Blind Career Advice of the AI Age
Listen to the author narrate the essay to you.
A professor on a WSJ Op Ed tells workers to protect themselves by hiding their best thinking from their employers.
This Sunday, the Wall Street Journal published an Op ed piece by Matthew Call, an associate professor at Texas A&M, titled “Workers Are Afraid AI Will Take Their Jobs. They’re Missing the Bigger Danger.”
His argument: Enterprise AI systems — the Copilots and Einsteins embedded in your corporate workflows — are quietly capturing everything you know. Your problem-solving approaches, your workarounds, your hard-won expertise. The company records it, owns it, and can deploy it to your replacement. You are, in effect, training the system that makes you obsolete.
Professor Call is right about the diagnosis. I’ve watched this machinery operate across three continents and 36 years of manufacturing leadership. What he’s describing is real, and it’s accelerating.
But then he offers his solution, and I have to set down my coffee.
His advice to workers: Use personal AI tools. Do your strategic thinking on ChatGPT or Claude, off-platform, where the company can’t capture it. Keep your best insights on your personal laptop. When you walk out the door, your AI-enhanced capabilities walk with you.
In other words: Hide your intelligence from the people who pay you to use it.
This is what happens when academics study the workplace from the outside. You get a diagnosis that’s clinically accurate and a prescription that would kill the patient.
The Disease Is Older Than AI
Let me tell you what Professor Call is actually describing, because it didn’t start with enterprise AI. It started with Frederick Taylor and a stopwatch.
For over a century, the dominant management paradigm has treated worker knowledge as a raw material to be extracted. Time-and-motion studies. Best-practice documentation. Process standardization. Six Sigma. Lean. The language changes every decade. The logic never does: watch what the best workers do, write it down, systematize it, and then you don’t need those workers anymore. Or at least, you don’t need to pay them like you do.
AI is the latest and most sophisticated extraction tool. But the extraction mindset? That’s been the water we’ve been swimming in since the invention of the modern corporation.
Professor Call sees this clearly. What he doesn’t see is that his solution — hide your intelligence — accepts the extraction premise as permanent. He’s telling workers: the company will always try to strip-mine what you know, so get better at concealment. That’s not a new social contract. It’s a cold war. And cold wars don’t produce value. They produce paranoia, cynicism, and a workforce that gives you compliance instead of capability.
I’ve seen what that looks like. I’ve run plants where workers had learned, through bitter experience, that sharing a better way to do something meant the company would capture the method, speed up the line, and cut headcount. So they stopped sharing. They hoarded their intelligence. And management looked at the output and said: see, these workers don’t have ideas. Let’s automate.
That’s the death spiral. And Professor Call is prescribing its first stage as career advice.
What an Actual Solution Looks Like
I want to go back to his example, because it’s a good one. A senior software engineer debugs a system crash, develops a novel workaround, and the enterprise AI captures not just the solution but the problem-solving methodology. Now junior engineers can be guided through similar problems. The senior engineer, Call warns, has become “a lot less valuable” and “a lot more replaceable.”
Only if you’re an idiot about it.
Here’s what a company with a functioning brain would do:
First, pay the engineer for the intellectual contribution. She created a novel workaround with real economic value. It will save the company time and money every time a similar crash occurs. Acknowledge that. Compensate her for it — not with a pat on the back at the all-hands meeting, but with money. We do this for patent holders, for authors, for songwriters. We have entire legal frameworks for compensating people who create intellectual property. But when a software engineer or a machine operator creates knowledge that’s worth hundreds of thousands of dollars in saved downtime, we say thank you and move on. Or worse, we capture it silently and hope she doesn’t notice.
Second, upgrade her skills so the AI amplifies her value, not replaces it. If the AI now handles the class of problems she used to solve manually, that frees her to solve harder problems. Train her up. Give her the next challenge. Let the AI handle the routine diagnostics while she tackles the system architecture issues that the AI can’t touch. She becomes more valuable, not less. The AI isn’t her replacement. It’s her power tool.
Third, go get more customers. This is the part that drives me crazy, because it’s so obvious and so systematically ignored. If your workforce can now deliver higher-value output — if your senior engineer is solving harder problems and your junior engineers are ramping faster — you can serve markets you couldn’t reach before. You don’t shrink the headcount. You grow the topline. You go compete for the contracts you used to turn down because you didn’t have the capacity or the capability.
Fourth, improve margins because customers are happy to pay for the new value. The gains don’t come from cutting labor cost. They come from expanding what labor can deliver. Customers don’t pay premium prices for “we automated our workforce.” They pay premium prices for “we solve problems nobody else can solve.”
That’s it. That’s the whole model. Pay for the contribution, amplify the contributor, grow the market, capture the margin.
Why is this not the norm?
The $500 Billion Lie
It’s not the norm because there is a $500 billion industry — automation vendors, consulting firms, AI platform companies — that makes its money selling the opposite story. Their pitch: labor is a depreciating cost. It gets more expensive every year, it’s unreliable, it’s resistant to change, and technology can replace it at lower cost with higher consistency.
This pitch is failing at a spectacular rate. McKinsey’s own research shows 70% of corporate transformations fail. Bain found that 80% of companies believe they deliver superior experiences, while only 8% of their customers agree. The Cognizant “New Work, New World” report published last month found that the percentage of non-automatable tasks has dropped from 57% to 32% — but also acknowledges that “human involvement and adaptable operations continue to be vital to capturing the full value potential of AI.” Even the people selling the extraction model can’t make it work without the humans they’re supposedly replacing.
I know this because I’ve lived on both sides. I’ve been the plant manager implementing lean systems and automation projects. I’ve been the COO who inherited a factory full of suppressed intelligence and had to figure out how to unleash it.
At Royal Enfield in India, we didn’t automate our way to a 20x increase in profitability. We deployed worker intelligence — put frontline knowledge to work in design, quality, and process decisions. Production went from 50,000 to 113,000 units. Not because we replaced workers with machines, but because we treated workers as the most sophisticated machines on the floor — and finally used them at something closer to full capacity.
On a night shift at GM’s Lansing plant, I watched operators solve problems in real time that the engineering documentation couldn’t anticipate. One man could diagnose a failing motor by sound — a capability built over fifteen years of listening to that specific machine evolve. You cannot capture that in an enterprise AI system. It’s not a static dataset. It’s a living, adapting intelligence that gets more valuable with every shift.
The AI captured a snapshot. The worker is the stream.
The Real Question
Professor Call ends his piece by saying that AI is “breaking the traditional model of employment in real time faster than anyone realizes.” He’s right about the speed. But the traditional model was already broken.
It was broken the moment we decided that the purpose of management is to make workers replaceable rather than irreplaceable. It was broken when we built accounting systems that treat a $50,000 robot as a capital asset and a $50,000 worker as an operating expense. It was broken when business schools started teaching that the highest use of human intelligence is figuring out how to need less of it.
AI didn’t break the model. It’s revealing how broken it always was. And the response from the academy — hide your intelligence, negotiate your data rights, use personal tools so the company can’t capture what you know — is the logical endpoint of a system that pits Capital against Labor in a zero-sum contest over who controls the knowledge.
I refuse to accept that framing. Not because I’m naive. Because I’ve seen the alternative work.
The question isn’t who controls the knowledge AI captures. The question is whether we’re going to keep building organizations designed to make people obsolete — or finally build organizations worthy of the most sophisticated intelligence system on the planet.
There are 130 million Americans who show up to work every day in manufacturing, logistics, healthcare, construction, and services. Each one of them carries capabilities that took millions of years of evolution and decades of personal experience to develop. They are already here. They are already paid for.
The crime isn’t that AI might capture their knowledge.
The crime is that we never bothered to fully deploy it in the first place.
Venki Padmanabhan, PhD, has managed manufacturing operations across the U.S., Germany, and India for 36 years. He is currently Plant Manager at Advanced Drainage Systems in Wooster, Ohio, and founder of the Capability Capital Institute. His book, Already Paid For: How Deploying Frontline Intelligence Beats Replacing Employees, is forthcoming. He writes The Long Game on Substack.
Previously: COO/CEO at Royal Enfield, COO at Ather Energy, plant leadership at GM, Chrysler, and Mercedes-Benz.