Programming as Theory Building, Part II: When Institutions Crumble · cekrem.github.ioIn my previous post on Peter Naur’s “Programming as Theory Building”, I argued that a program is not its source code—it’s the shared mental model held by the people who built it. When those people leave (or never understood it in the first place), the theory dies, and you’re left with a codebase that works but nobody truly comprehends.<br>I’ve been thinking about this more, and I’ve come to believe the problem goes deeper than individual developers losing their edge. Yes, people are losing the ability to build theories. But the institutions where theory-building happens are being degraded too. Our teams, our companies, our profession. The whole ecosystem.<br>Software Teams Are Institutions
Link to heading<br>I recently read a draft paper by two Boston University law professors, Woodrow Hartzog and Jessica Silbey, called “How AI Destroys Institutions.” Their argument stopped me cold: AI systems aren’t tools that can be misused. Their core design is incompatible with how institutions function.<br>Now, when they say “institutions,” they’re talking about universities, the legal system, journalism, democracy. Big stuff. But as I read, I kept seeing my own world reflected back at me. Because software development teams are institutions, even if we don’t usually think of them that way.<br>Consider what makes an institution work:<br>Hierarchies of expertise : Junior developers learn from seniors, who learned from principals, who learned from architects. Knowledge flows through these relationships.<br>Shared purpose and rules : Coding standards, architectural decisions, domain models. The “rules of the game” that let a group of individuals function as a coherent team.<br>Decision-making with accountability : Code reviews, design discussions, ADRs. Points where someone can say “wait, that doesn’t fit our model” and actually be heard.<br>Human relationships and solidarity : The trust that makes you ask a teammate for help instead of flailing alone. The camaraderie that makes you want the team to succeed.<br>These are the machinery through which theory gets built and transmitted. Naur’s insight was about individual understanding, but that understanding doesn’t exist in a vacuum. It lives in the institution. In the conversations, the reviews, the mentoring, the shared struggle of building something together.<br>The Three Ways AI Degrades Institutions
Link to heading<br>Hartzog and Silbey identify three “destructive affordances” of AI. Not things AI might do if misused, but things AI does by design when deployed the way its creators intend:<br>1. AI Undermines Expertise
Link to heading<br>This one connects directly to what I wrote before about “reflexive AI usage” robbing us of growth opportunities. But the institutional angle makes it worse.<br>When juniors accept AI-generated code they don’t understand, they’re not building theory. Fine, we knew that. But they’re also not asking seniors for help, not struggling through code review, not having those hallway conversations where knowledge actually transfers. The institution’s pipeline for developing expertise—which took decades to build—gets bypassed entirely.<br>And here’s what gets me: AI can only look backwards. It’s trained on what already exists. The institution’s ability to evolve depends on humans doing the hard intellectual work of figuring out what doesn’t exist yet. New patterns, new approaches, new understanding in response to changing circumstances. When that atrophies, the institution ossifies.<br>As the paper puts it: when AI is “right,” people become less skilled. When AI is “wrong,” you need skilled people to catch and fix the errors. Either way, the institution suffers.<br>A recent MIT Media Lab study puts numbers to this. Researchers had students write essays, with one group using ChatGPT and others using traditional methods. The result? 83% of the ChatGPT group couldn’t quote anything they’d written. They didn’t remember. And they didn’t feel like the work was theirs.<br>That’s not just “they didn’t learn as much.” That’s they didn’t build any theory at all. The understanding never formed. And if it never formed, it can never be transferred to the next generation of the institution.<br>2. AI Short-Circuits Decision-Making
Link to heading<br>Institutions need friction. Not bureaucratic friction for its own sake, but the productive kind—moments where someone can push back, ask questions, challenge assumptions.<br>Think about a good code review. It’s not just checking for bugs. It’s a point of contestation where the team’s shared understanding gets tested and refined. “Why did you do it this way?” “Does this fit our domain...