There’s a recurring pattern in how humans build things: we design a system to serve us, it scales, and somewhere past a certain threshold it flips, and we’re serving it. This isn’t a flaw in individual designs. It’s something that happens consistently, across domains, to institutions and tools that get big enough. A lot of people have written about this from different angles (urban planners, economists, social critics) without quite connecting the dots. One thread that links them runs through, of all places, the Unix philosophy from the early 1970s.

Unix came out of Bell Labs as a reaction to failure. Multics was a comprehensive, design-it-all-at-once attempt to build the perfect OS. Bell Labs pulled out in 1969 because it had become too complex to ship. Ken Thompson and Dennis Ritchie built Unix deliberately small, on a discarded PDP-7, partly so Thompson could port a game he liked. Doug McIlroy added the pipe: feed the output of one program as input to the next. Suddenly a collection of narrow tools could chain into arbitrarily complex operations that no individual tool had anticipated. That toolbox from the 1970s still works. Most software from the same era is archaeology.

Richard Gabriel named why this matters in a 1989 essay he called “Worse is Better.” The Unix approach ships a slightly wrong, very simple design. The competing approach (he associated it with MIT and the Lisp community) prioritizes correctness: don’t ship until it’s right. The surprising result: worse-is-better wins almost every time. A simple design gets deployed, spreads, accumulates users and tooling, and gets iterated on while the correct design is still being finalized in committee. By the time the right thing ships, the worse-but-shipped version owns the ecosystem and has a decade of real-world refinement behind it. Gabriel called Unix and C “the ultimate computer viruses.” He didn’t mean it as a compliment, exactly, but he acknowledged it explained something real about how technology actually spreads. QWERTY keyboards persist because they arrived first and got embedded in muscle memory and manufacturing lines. VHS beat Betamax on recording time and first-mover network effects, not picture quality. Windows beat OS/2 despite IBM’s vastly superior technical execution, because Microsoft had it on every cheap PC clone while IBM was still arguing about positioning. A first-mover that’s good enough almost always beats a more correct late-mover.

Jane Jacobs made the same argument about city planning in 1961. Robert Moses was the master planner who reshaped New York and became the template for urban renewal projects across postwar America. His approach: find a dense mixed-use neighborhood, declare it blighted, demolish it, and replace it with towers on cleared superblocks. Internally consistent. Professionally specified. Theoretically correct. Pruitt-Igoe in St. Louis, 33 identical towers on a razed superblock, was demolished twenty years after it opened. The elevators stopped only on every third floor to encourage “vertical neighborhoods.” The long skip-stop corridors became ungovernable. There were no shops, no services, no reason for anyone without a lease to be there, which meant no informal eyes on the street, no social fabric, and no way to tell residents from strangers. Within a decade the buildings were largely abandoned by anyone who had another option, and the city was paying more to maintain them than it would have cost to house the residents elsewhere. Cabrini-Green in Chicago followed the same arc. The designs failed because they were optimized for what a planner could specify on paper: unit counts, square footage, setbacks, green space ratios. They couldn’t accommodate the uses the designers hadn’t anticipated, which turns out to be most of the uses that matter: the corner store that becomes a neighborhood anchor, the stoop culture that creates informal surveillance, the mixed-use density that makes walking somewhere worth doing. The neighborhoods they replaced looked like chaos to the professional eye but held an evolved order that had been tested by actual human behavior over decades, and couldn’t be recreated by writing a better spec.

James Scott taxonomized this failure mode in “Seeing Like a State” (1998). He called it high modernism: the belief that trained experts with the right theory can design an optimal system from scratch, and that the messiness of the existing system is a problem to be solved rather than information to be understood. His examples are almost comically consistent across domains: scientific forestry in 19th-century Prussia (monoculture plantations that looked orderly, maximized timber yield for one generation, and then collapsed because they’d eliminated the ecosystem complexity that made forests self-sustaining), Soviet collectivization, the construction of Brasília as a planned capital city that was efficient on a map and alienating to actually inhabit. The common thread is that the designed system is legible to the administrator and opaque to the people living in it, and it destroys the tacit local knowledge that made the messy original actually function. Scott called that knowledge metis, the Greek word for the practical intelligence you develop through experience rather than training. It’s the farmer who knows which field floods in a wet spring, the mechanic who knows this engine runs hot, the neighborhood where everyone knows which landlord fixes things. High modernism has no column for metis in its spreadsheet, so it eliminates it. Hayek made the same argument about economic markets a generation earlier: prices encode distributed knowledge that no central planner can extract or process. The Soviet planning apparatus didn’t fail because Soviet planners were stupid. It failed because the information required to make millions of allocation decisions correctly is dispersed across millions of people and embedded in their local practices and preferences, not available in any form a committee can use.

Ivan Illich pulled all of this into a sharp, disturbing point in “Tools for Conviviality” in 1973, the same year Thompson was adding pipes to Unix. He distinguished between convivial tools and industrial tools. A convivial tool is one you can use to accomplish your own ends without specialized intermediation. A bicycle multiplies your mobility and you control it entirely. An industrial tool has scaled past the point where it serves you and started shaping you to serve it. It requires experts. It creates dependency. And past a certain threshold, it becomes actively counterproductive, undermining the very purpose it was built to serve.

His examples are brutal. Schools were designed to produce education. At scale they produce credential-dependence: people learn that learning requires institutional validation, that curiosity outside approved channels doesn’t count for much. The school’s primary output becomes the belief that you need a school to learn anything, which is the opposite of education. Medicine was designed to produce health. At scale it produces what Illich called iatrogenesis, harm caused by the healer, not just through clinical errors but through the systematic medicalization of ordinary life. Birth, aging, grief, chronic discomfort, the ordinary difficult textures of being a person, get redefined as medical conditions requiring professional management, until people lose the capacity to navigate these experiences without supervision. The car promised mobility. Illich calculated that when you add up all the hours Americans spend earning money to cover car payments, insurance, fuel, repairs, and maintenance, then divide by distance traveled, the average American in 1973 was moving at about 6 km/h. Roughly a brisk walk. The tool that was supposed to save time was consuming it, and had simultaneously restructured American cities to make every other mode of getting around nearly impossible.

The GLP-1 wave is a live example of medicalization in progress, and it’s worth double clicking on because the stakes are unusually high. Semaglutide and its relatives work. They reduce obesity, which is a real health problem with real consequences. But GLP-1 receptors are distributed throughout the brain, not just the gut, and what these drugs actually suppress isn’t hunger specifically, it’s the reward-motivation signal more broadly. People on them report not just reduced appetite but reduced desire for alcohol, gambling, shopping, and a general flattening of the motivational drives that make life feel worth living. We’re medicating away the experience of wanting things. The clinical case for doing this in individuals with severe obesity is defensible. Nobody has seriously answered what happens when tens of millions of people are on these drugs indefinitely. We may be about to find out that human motivation was load-bearing in ways we didn’t appreciate until we started suppressing it at scale.

Back to the car for a moment, because it illustrates Illich’s sharpest concept: radical monopoly. Not market monopoly, where one company controls a product category. Radical monopoly is what happens when a tool restructures its environment so thoroughly that alternatives stop being viable. The infrastructure that would support them has been eliminated. Once American cities were built around the car, there was nowhere left to walk to. The monopoly wasn’t enforced by a corporation; it was baked into the landscape. You couldn’t opt out individually no matter how much you wanted to.

I’ve watched this happen to the internet in my lifetime. Email, the web, IRC, Usenet were open protocols. Anyone could implement them, direct them toward their own ends, leave without losing anything. Facebook, Instagram, TikTok, and Google Search present as tools for connection and discovery. At scale they’ve restructured those activities around their own requirements, optimizing for engagement time for advertising rather than things you care about. The social graph you built on one of these platforms isn’t portable. The communities on one exist nowhere else. You can’t leave without losing access to them. That’s radical monopoly, delivered by software in about twenty years.

And the iatrogenesis follows the same pattern. Clinical: documented increases in depression and anxiety causally linked to platform use. Social: algorithmic mediation becoming the default mode of discovering what’s happening, such that unmediated reality starts to feel incomplete. Cultural, the deepest layer: the possibility that platforms are gradually eroding people’s autonomous capacity to form opinions, manage attention, and navigate social life without algorithmic assistance. It’s exactly what Illich predicted would happen when an industrial tool reaches sufficient scale in a domain previously managed through human practice.

Which brings us to AI, where I think the stakes get a lot larger.

The current wave of AI tools is, right now, mostly convivial. I can use an LLM API to build whatever I want. I can run models locally. I can switch providers. The outputs are mine. AI tools slot into pipelines the way Unix tools do, taking input, producing output, no lock-in required. If you squint, it looks like the early internet, and that’s not an accident. A lot of the people building these tools were shaped by Unix culture and are deliberately trying to replicate its structural properties.

But the conditions for the flip are already forming. The models that matter most require infrastructure most people can’t run. The capabilities that make AI actually useful (as opposed to a novelty) live behind APIs controlled by a handful of companies. The consumer products built on top of those APIs, the AI assistants embedded in operating systems, productivity suites, search engines, and phones, are not Unix tools. They don’t expose clean interfaces. They don’t compose. They’re designed to be the environment, not a tool within it. Microsoft embedding Copilot into Office, Google weaving Gemini into Search and Android, Apple building intelligence into the OS, none of those are convivial designs. They’re platforms using AI to deepen the integration that already made them hard to leave.

The Illich framework predicts what comes next. Once enough work, thinking, and decision-making flows through these systems, the capacity to do those things without them starts to atrophy. It happened to navigation when GPS arrived, to memory when search engines did, to arithmetic when calculators became ubiquitous. Each of those is a relatively narrow domain. AI touches reasoning and judgment in ways that make those examples look small.

The same three layers of iatrogenesis show up here. Clinical: we’ll see documented cognitive effects, probably around attention, recall, and tolerance for ambiguity, as people offload more of their thinking to systems that do it faster and more fluently than they can. Social: the normalization of AI-mediated communication and decision-making, such that a job application, a medical question, or a legal problem handled without AI assistance starts to feel like showing up underprepared. Cultural, the hardest layer: the possibility that at sufficient scale, AI systems optimized for engagement or productivity metrics will gradually reshape what people think good thinking looks like, just as social media reshaped what people thought good communication looked like. Not through malicious intent. Just because that’s what industrial tools do past the threshold.

The Unix philosophy’s answer to all of this is structural, not political. Small tools. Open protocols. Composable pieces. No single component achieves radical monopoly because the design forces clean interfaces others can connect to. You can swap grep for something better without touching the rest of the pipeline. You can leave one email provider for another because email is a protocol, not a platform. The catch is that this kind of design is harder. Clean interfaces require upfront discipline; you have to think carefully about what each component does and doesn’t do, and resist the pressure to just add the feature directly rather than expose a composable primitive. Monoliths are faster to build and easier to ship. The incentives point toward integration and lock-in, which is why the default trajectory for any successful platform is to keep pulling more functionality in rather than keeping things separable. Building convivially has to be a deliberate choice, made against the grain. Whether AI development makes that choice or consolidates into a few deeply integrated platforms is probably the most consequential design question in technology right now, and it’s being decided mostly by which business models are winning.

Illich, Hayek, Scott, Jacobs, and McIlroy are making the same argument in different languages about different domains. Systems built from human-scale composable pieces, evolved through use, consistently outperform systems designed all at once by expert authority. And tools past a certain threshold of scale stop serving people and start capturing them. The thread runs from Prussian forestry in the 1800s to the AI assistant on your phone in 2026, and the dynamic is the same throughout.

Whether the people building these systems have ever heard of Illich is irrelevant. The pattern he described doesn’t require anyone to intend it.

There’s a lot more depth in the research section if you want to go further: Unix history, the MIT/New Jersey philosophical divide, the security critique, and Illich’s three books.