Opinion - We Have Never Deskilled the Mind Like This Before
We Have Never Deskilled the Mind Like This Before
And we have no idea what happens next
History has a pattern for what happens when a new technology makes a skilled trade obsolete.
The factory floor ended the artisan economy. Before mass production, a blacksmith spent years as an apprentice, learning the feel of metal under a hammer, when to quench and when to let cool, how to read the color of heated steel. That knowledge lived in hands and in bodies. It transferred slowly, person to person, across decades. It was the kind of skill that couldn’t be written down because most of it wasn’t conscious. It was accumulated..
Then came factories. A stamping machine could produce in seconds what a journeyman spent years learning to make by hand. The craft didn’t disappear overnight. But the value of craft collapsed. The apprentice pipeline dried up. Why spend seven years under a master blacksmith when a factory line would hire you on Monday?
The knowledge succession broke. And nobody really noticed until the masters were gone.
The Digital Age Reversed It. Briefly.
When computing arrived, something unusual happened: skilled craft came back.
Software engineering rebuilt the artisan economy for the knowledge age. Junior developers wrote terrible code, got their PRs destroyed in review, debugged things they didn’t understand, got paged at 2am and figured out why production was on fire. That was the apprenticeship. That was how the knowledge transferred. Not through documentation or onboarding decks. Through the doing..
A senior engineer wasn’t just someone who knew more. They were someone with scar tissue. They’d shipped the bad architecture and lived with the consequences. They’d optimized prematurely and spent six months unwinding it. They’d built the system that couldn’t scale and been the one who had to explain why. The judgment that made senior engineers valuable wasn’t knowledge. It was earned intuition, built over years in the implementation layer.
The guild model came back. Junior, mid, senior, staff, principal. A legitimate apprenticeship ladder, hidden inside job titles.
Now We’re Doing It Again
This is not the first time technology has gone after cognitive work.
In the 1940s and 50s, “computer” was a job title held by human beings, people whose entire profession was mathematical calculation. Electronic computers eliminated that profession within a generation. Spreadsheets wiped out roughly 400,000 accounting clerk positions after 1980. Word processors dissolved the typing pool. Legal databases automated the citation research that had previously consumed entire workdays for junior associates.
Technology has been replacing specific cognitive tasks for decades. This is not new.
What is new, and it matters, is the generality of what’s happening now.
Every prior wave of cognitive automation targeted a narrow function. Spreadsheets did arithmetic. Legal databases did citation lookup. Word processors handled document formatting. Each tool was a scalpel: precise, domain-specific, bounded. The humans who lost those jobs had somewhere to go, because the automation only reached so far.
An LLM doesn’t have one PhD’s worth of pattern recognition. It has the distilled output of essentially every PhD who ever published, every Stack Overflow thread ever written, every codebase ever committed to a public repository. It writes code, drafts documents, analyzes architecture, explains tradeoffs, and reviews pull requests, not in one narrow lane but across all of them at once. This is a different kind of tool. Not a scalpel. Something more like a general solvent.
The implementation layer is going the way of the stamping machine. Not gone, but no longer where the value lives. No longer where you invest years of human time.
And here’s where the collective mythology kicks in: the engineers who already have deep systems knowledge are not threatened by this. They’re multiplied by it. The business case for hiring senior talent has never been stronger.
True enough. But it leaves something out.
The Problem Is Where Elite Engineers Come From
The multiplier narrative has a data problem.
METR, an AI safety research organization, ran a randomized controlled trial in mid-2025 with 16 experienced open-source developers and 246 real-world tasks. They found that AI tools made experienced developers 19% slower, not faster. The developers predicted a 24% speedup. They still believed AI had helped them. They were wrong. A separate field experiment by MIT, Microsoft, and Accenture, covering roughly 1,974 developers, found that junior developers gained 27–39% productivity from AI assistance while senior developers gained only 8–13%.
The “100x engineer” is largely an aspiration right now. The truth is context-dependent: seniors use AI better for architectural decisions; juniors benefit more on implementation tasks. Neither group gets a clean multiplier.
But set the productivity debate aside. The harder problem runs deeper.
Every senior engineer working today built their judgment in the implementation layer. They got there by being junior, then mid, then senior. They wrote the code. They owned the bugs. They did the work that AI is now going to do.
The hiring signal is already visible. A Stanford and ADP payroll study published in August 2025, covering millions of workers, found that employment for software developers aged 22–25 had dropped roughly 20% from its late-2022 peak, while workers aged 30 and up in the same AI-exposed fields saw employment grow. A Harvard study that same year, examining 285,000 firms and 62 million workers, found that companies adopting generative AI saw junior employment drop roughly 9–10% within six quarters. SignalFire reported a 50% decline in new role starts by people with less than a year of post-graduate experience at major tech firms between 2019 and 2024.
To be fair: post-pandemic correction, rising interest rates, and reduced venture capital are all real factors here. The Stanford study’s authors themselves noted they make no claim that AI is the sole cause. This is a multi-causal decline, not a clean AI story.
But some of it is clearly AI. Salesforce announced it would hire no new software engineers in 2025. Shopify’s CEO issued a memo requiring teams to prove AI can’t do a job before asking for headcount. The rationale in each case is the same: why grow the junior headcount when AI can absorb implementation work?
The economics are obvious. Each decision makes complete sense for the organization making it.
But organizations don’t exist in isolation. Industries do. And the industry is collectively making the same rational decision, which means the industry is collectively eliminating the environment where the next generation of senior engineers gets built.
Sociologists call this a tragedy of the commons. Labor economists would recognize it from Harry Braverman’s Labor and Monopoly Capital (1974), the foundational work on deskilling, which documented how industrial capitalism systematically separated the conception of work from its execution, concentrating judgment at the top and eliminating it from the bottom. The Communications of the ACM published a feature in 2025 explicitly applying Braverman’s framework to AI. Cal Newport invoked Braverman by name in January 2026 when warning about AI-driven deskilling. When Anthropic’s own January 2026 Economic Index report used “deskilling” as an analytical category to describe Claude’s effect on occupations, the concept went from academic framing to industry description.
The Compounding Problem Nobody Is Naming
There’s a term now circulating in research circles: never-skilling.
Not deskilling, where you had a skill and lost it. Never-skilling is what happens to the generation that enters the workforce after the training-pipeline tasks are already gone. They don’t lose a skill. They never develop it in the first place, because the work that would have built it is being handled by agents, reviewed by elites who are the last people to have gone through the full crucible.
The junior developers who do get hired today aren’t writing code from scratch and fixing their mistakes. They’re reviewing AI output. Catching the errors that surface. Learning, in some degree, to recognize what wrong looks like from the outside.
Nobody knows whether that builds the same quality of judgment. Ask any experienced engineer whether reading about concurrency bugs is the same as having caused one. Ask whether reviewing AI-generated infrastructure code teaches you the same things as being the one paged at 3am when that infrastructure fails.
Carnegie Mellon researcher Aniket Kittur has warned that AI is producing a loss of basic knowledge among engineers who rely on it without engaging with it. Matt Beane at UC Santa Barbara has spent years studying how AI tools disrupt the apprenticeship dynamics through which expertise actually transfers. Microsoft’s CTO Mark Russinovich and Scott Hanselman published a piece in the Communications of the ACM in February 2026 proposing a new preceptor-based training model for engineering, explicitly because the traditional path is breaking down. They called the knowledge succession concern “a hot topic” in conversations with customers.
IBM announced in February 2026 that it would triple its entry-level hiring. AWS CEO Matt Garman called the idea of replacing all juniors with AI “one of the dumbest things I’ve ever heard.” These are the loudest institutional counter-signals to the prevailing tide. Whether they shift anything at the industry level, or whether they’re outliers in a race to the bottom, is an open question.
The Question Nobody Is Asking
The conversation about AI and the future of work is almost entirely about the workers who exist today. Will senior engineers be replaced? Will mid-level roles survive? What do engineers need to learn to stay relevant?
These are real questions. But they’re not the most important question.
The most important question is about the workers who don’t exist yet.
The twelve-year-old who wants to build things. The computer science student learning to code in a world where AI writes most of the code. The career changer who wants to enter tech but finds the entry-level pipeline largely automated away. What is their path to senior? What replaces the decade of implementation work that built every senior engineer working today?
Researchers at Stanford, Carnegie Mellon, and UC Santa Barbara are asking this. Microsoft is asking it. IBM is acting on it. But there is no policy framework, no industry-wide response, no coordinated effort. Just individual companies making the rational short-term call, and a handful of voices warning that the aggregate result of those individual calls will be visible, and costly, in about fifteen years.
That’s how knowledge succession failures always work. They’re invisible until the last master retires.
What History Actually Teaches Us
When electronic computers displaced human computers in the 1940s and 50s, most of the displaced workers were still alive to retrain. When spreadsheets restructured accounting, the profession adapted: roughly 400,000 clerk positions gone, but about 600,000 new accountant roles created. The cognitive work didn’t vanish; it moved up the abstraction ladder.
Maybe that pattern holds here. Maybe the junior engineers who can’t find traditional entry-level implementation roles find work as AI supervisors, output reviewers, and systems monitors, accumulating in that work a different but equivalent kind of judgment.
Maybe. But the analogy that should worry people more is what happened to physical trades after the industrial revolution. The trades didn’t die. They contracted, specialized, and became luxury. The knowledge largely survived. But the pipeline that had once produced thousands of journeymen per generation now produces dozens. The craft is preserved as heritage, not as infrastructure.
If software implementation follows the same path, preserved by the people who love it and handled at scale by agents, the question is whether the pipeline that produces the elites who oversee those agents can sustain itself on craft-scale volume.
The industries that navigated these transitions without catastrophic knowledge loss did so because they made deliberate choices. They built institutions. Trade programs, certification bodies, structured apprenticeships. Things that kept the transfer pipeline alive even when the economics of the moment argued against it. They recognized that the market would not solve this on its own, because the market’s time horizon is a quarterly report and the knowledge succession problem plays out over fifteen years.
Software engineering as an industry has never had to make this choice before. The technology never moved fast enough to outpace the apprenticeship pipeline.
Now it might. And the window for deliberate choices is open right now, while the people who hold the knowledge are still working.
Michael Rishi Forrester has spent 25 years training engineers through platform shifts, from Red Hat to ThoughtWorks to AWS to cloud-native to AI. He’s a Principal Training Architect at KodeKloud, founder of The Performant Professionals, and has watched more “this changes everything” moments than he can count. He’s not sure this one is different. He’s not sure it isn’t.
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