The Number Goldman Sachs Didn’t Calculate

Reactive Essay | Source: “People Who Lose Their Job to AI Are in for a World of Pain, Goldman Sachs Report Finds”

By Joe Wilkins | Futurism | April 11, 2026

Goldman Sachs Research Note by Pierfrancesco Mei and Jessica Rindels, April 6, 2026

Somewhere in Ohio this week, a woman who spent fourteen years inspecting stormwater pipe opened her phone on break and read that Goldman Sachs had finally measured what losing your job to AI would cost. Earnings scarring. Delayed homeownership. Lower chance of marriage. She recognized every word. What she didn’t recognize was the number. Goldman said the damage was about $212,000 in lost lifetime earnings. She knew it was worse than that. She just didn’t have the language for how much worse. I do. And the number Goldman missed is not thousands. It is millions. Per worker. Let me show you.

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Goldman Sachs released a study this month on the “scarring” effects of AI-driven job displacement. Their economists, Pierfrancesco Mei and Jessica Rindels, examined four decades of individual-level data and found that workers displaced by technology suffer earnings growth nearly 10 percentage points slower than their peers over the following decade. They found delayed homeownership, slower wealth accumulation, and even lower rates of marriage. They called it scarring, and the data is devastating.

But Goldman measured the wrong wound.

They measured what happens to the worker after displacement. They never asked what the organization was doing with that worker’s intelligence before the displacement. They never calculated the economic value of the capability that was purchased through wages, warehoused for years, never deployed, and then discarded when the machine arrived. They performed a financial autopsy on the victim and never examined the system that created the injury.

Here is what they missed: the true economic cost of AI displacement is not the $212,000 in lost lifetime earnings per worker that their scarring data implies. It is the millions of dollars in foregone firm value that accumulates when you compare a worker whose intelligence was suppressed and then discarded against a worker whose intelligence was developed and then amplified by the same technology. The first worker gets displaced. The second worker wields AI as a power tool and generates compound value. Same worker. Same technology. The difference is formation — whether the organization invested in developing the capability it had already paid for through every paycheck. That difference, across a 25-year career in a single manufacturing facility, represents a gap of over $11 million in firm value per worker. For a 500-person plant, the number exceeds $5 billion. Goldman Sachs measured the scar. They missed the hemorrhage. That’s the argument.

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I have managed plants on three continents — GM, Mercedes-Benz, Royal Enfield, Ather Energy, and now Advanced Drainage Systems. In every single one, I watched the same pattern: workers arrived with intelligence the organization never intended to use. They were hired for their hands. Their minds were an externality. The job was designed to extract repetitive labor, not to develop judgment, problem-solving, or process insight. And every paycheck the company issued was, in economic terms, a payment for capability that the company then refused to deploy. I have stood on shop floors at 2 a.m. and watched operators solve problems that the engineering department couldn’t see — not because the engineers were incompetent, but because the operator had 15 years of pattern recognition the system never asked for.

At GM Lansing Grand River, I saw what happens when you reverse this. We achieved the JD Power Gold Plant Quality Award — not by replacing the workforce, but by trusting it. Same people. Same union. Same contract. The difference was that we treated their intelligence as an asset to be compounded, not a cost to be minimized. That is the difference between formation and extraction.

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Let me walk you through the economic model, because this is where the $212,000 turns into $11.5 million.

A worker arrives at a plant. She brings intelligence the organization could deploy — pattern recognition, judgment, process insight, the capacity to learn. The organization, operating under conventional task-based job design, deploys roughly 25 percent of that intelligence. The other 75 percent is purchased through wages and never deployed. That is Capability Capital — intelligence already paid for, systematically warehoused. Then AI arrives. The organization deploys it as a replacement, not a tool. The worker is displaced. Goldman measures the scarring: 10 percentage points of slower earnings growth over a decade, delayed homeownership, reduced wealth accumulation.

But that measurement captures only the worker’s visible wound. It misses two much larger numbers.

First: the cumulative value of the intelligence that was purchased but never used. Over 15 years, if the organization deployed even 80 percent of the worker’s capability instead of 25 percent, the firm value generated would have been dramatically higher — not by a small margin, but by multiples. The wasted Capability Capital for a single worker over a 25-year career, using conservative multipliers grounded in value-added-per-employee data, exceeds $10 million. That is money the firm already spent through wages and benefits. It was already paid for.

Second: the delta between a displaced worker and a deployed worker wielding AI. This is the number that should haunt every boardroom. A worker whose intelligence was developed over 15 years — whose judgment was sharpened, whose problem-solving authority was expanded, whose pattern recognition was trusted — does not get displaced by AI. She wields it. And the economic value of that formed-worker-plus-AI combination dwarfs what either the worker or the AI produc