Factories exist to produce consistent, cost-effective products. That is the point. The relentless optimization of cost of goods sold is not a side effect of industrial production. It is the mandate. And it works, until it doesn’t. The reason products last so much less than they did twenty years ago is not that we forgot how to make durable things. It is that durability lost the cost argument. Quality is expensive. Variance is expensive. The system optimizes both out. What survives is the median product, built to a price, reliable enough to ship, and no more.

Modern schooling often behaves the same way. It batches children by age, sequences content for throughput, and optimizes for a predictable median. Sir Ken Robinson made this observation twenty years ago, and the metaphor stuck, not because it is clever but because it names incentives, not architecture. When a system must operate at scale under budget, policy, and staffing constraints, variance becomes expensive. The median becomes the target. Outliers become the problem.

That is how you get the loop so many families recognize.

A child with a spiky profile, gifted and struggling at the same time, or simply learning in a different sequence, is hard for a production line to interpret. The system cannot see internal state. It can only see outputs it knows how to count. Pacing, compliance, turn in rates, standardized measures, and classroom friction. When it cannot measure what is actually happening, it collapses complexity into a label. Lazy. Defiant. Behind. Broken. Sometimes worse. The misclassification is not incidental. It is structural. The factory cannot afford to treat every student as a special case, so it treats special cases as defects.

Twice exceptional programs were a serious attempt to address exactly this failure mode. 2e was not supposed to be a vibe. It was an operational category, a way to route support without denying capability.

Institutions rarely attack reforms head-on. They metabolize them. The common move is not to announce that everyone is 2e. It is more subtle. Fold 2e into the general program, justify the change as an opportunity for all, and quietly remove the differentiated pathways, expertise, and accountability that made 2e real. The label survives. The function does not. The specialist becomes a roaming consultant, the pull-out becomes a generic intervention block, and the documentation becomes a checkbox.

Spencer Silver at 3M spent years trying to make a strong adhesive and produced one that was too weak to hold permanently. By factory logic it was a failed batch. It sat in the lab for years because the system had no category for a glue that did not stick properly. A colleague with a different problem recognized the variance as the feature. The factory almost never found out what it had.

This pattern is familiar in M&A. Companies are often acquired to address a capability, culture, or talent gap. The acquirer gets what it wanted on paper, and then the organization takes over. Microsoft bought Hotmail to compete in web-based email. Hotmail ran on Linux. Microsoft ported it to Windows, the product degraded, and what had been acquired to solve a problem became an example of the problem. The engineers who built Hotmail watched what they had created get dismantled and left. The institution did not transform around the acquisition. The acquisition transformed into the institution, and the talent that made it valuable walked out with their badges.

The proof a program still exists is not whether the brochure mentions it. It is whether the supports remain distinct, staffed, and enforceable. When a category stops changing what adults do, the system reverts to default settings. Teach to the median, punish variance, treat the casualties as defects.

You can see the same dynamic in curriculum fights. When a system cannot reliably lift the floor, the path of least resistance is to lower the ceiling and call it equity. This is not cynical in intent. It is cynical in effect. Acceleration does not disappear. It moves off the books. Tutoring, test prep, schedule hacking, summer programs, parent advocacy. The families who can afford those channels use them. The families who cannot are left with the official story that the ceiling was lowered for their benefit. The median experience is preserved. The gap widens. Official metrics improve because the ceiling has been redefined.

None of this is morally mysterious. It is operational. What makes it damning is that schooling runs this population optimization model without the measurement and accountability that would make it legitimate.

Medicine is honest about something uncomfortable. Treatments have side effects. They do not affect everyone equally. Approval assumes some negative outcomes are acceptable in exchange for a greater good. But medicine only earns the right to make that utilitarian bargain because it is paired with surveillance and accountability. Trials, defined endpoints, adverse event reporting, label changes, and sometimes recalls. When a drug underperforms or causes unacceptable harm, the system has mechanisms to withdraw it.

Schooling borrows the utilitarian posture and skips the legitimacy conditions. There is no adverse event tracking for predictable harms like anxiety spirals, learned helplessness, disengagement, or the systematic grinding down of nonstandard profiles. When you ask what the rollback criteria are, you get a blank stare, because the system does not think in rollback terms. It thinks in throughput terms.

Here is a small, concrete example. One of my children has an accommodation plan tied to a documented set of specific needs. A teacher recently told us the plan would not be needed anymore because the child does not show ADHD signs. There is no ADHD diagnosis, and the plan is not based on ADHD. The teacher was not acting maliciously. They were acting normally inside a system that treats supports as vibes. In a system with real measurement, you do not withdraw support based on a vibe. You tie withdrawal to documented criteria, with a rollback plan if the criteria are wrong. This is not exotic engineering. It is basic change management. Define the hypothesis, define success, define failure, and pre-commit to the revert.

Schooling routinely does the opposite, and the response when things go sideways is not to revisit the decision. It is to escalate.

More pressure. More compliance. More labeling. The system treats opt-out as a containment breach rather than a performance signal, because enrollment and funding are coupled together. The institution has no incentive to register failure. It has strong incentives to frame failure as the students’.

So why does this cycle finally have a credible exit?

Because AI breaks the monopoly on instruction.

For most of modern history, if you wanted a coherent explanation, feedback loops, sequenced practice, and the ability to revisit a concept from a different angle without embarrassment, you needed the institution or you needed money. Those are the same thing for most families. AI makes those pieces abundant. It makes it cheaper to learn in a different order. It makes it cheaper to revisit a concept from five angles without being punished for needing a sixth. It reduces the penalty for variance in a way that nothing else in the past century has.

This is why models like Alpha School are worth watching, whatever you think of their specific implementation. They are proof that you can architect learning around mastery and coaching rather than batching and seat time. They are not just a new school brand. They are evidence that instruction is no longer scarce, and that the existing system’s grip on the delivery layer is loosening.

The tradeoff is real and worth being honest about. The devil you know versus the one you do not.

The existing system’s harms are normalized, which means they are mostly invisible. The new world introduces different risks. Dependency on opaque tools, misinformation at scale, AI-driven learning environments that are even more coercive than human ones because they optimize metrics nobody agreed to, and a widening gap between families who can navigate the options and those who cannot.

The credential layer will be the next fight. Institutions that lose control of instruction will shift to defending legitimacy. Seat time requirements, accreditation barriers, and the bureaucratic right to define what counts for the purposes of the next gate. If instruction becomes abundant, the last monopoly is not learning. It is recognition.

But the direction of travel is hard to reverse. Bureaucracy protects the status quo long past the point where the quo has lost its status. AI accelerates the expiration date. The more schooling responds to exits with escalation rather than adaptation, the more it will be outcompeted by systems that treat variance as signal rather than a defect.

I keep coming back to the medicine analogy, but with a sharper edge. In medicine, adverse events are data. In schooling, adverse events become discipline referrals and bad grades. One system updates on failure. The other system records the failure as the student.

AI is not a magic cure. But it is the first credible exit from a century-old loop. A factory that mistakes difference for defect, and calls the casualties the cost of scale.