AI Has Read More Code Than Anyone, and Never Understood a Line

In 1952, Grace Hopper sat down at a Mark I computer and finished the first compiler. The concept was simple: let a person write "ADD" and have the machine translate it into binary. Before that, programming meant speaking the machine's own language directly. After that, you spoke, and the machine translated. That chain extended for 70 years, all the way to today's AI code generators. Say "write me a function that takes a city name and returns today's weather forecast." Within seconds, working code appears, complete with docstrings, ready to run. The speed at which AI writes code already surpasses any human engineer. But none of that involves thinking. The model may have read more Python than every software engineer on earth combined. Its mechanism, though, is prediction: given billions of training examples, find the token most likely to follow statistically. It knows what typically comes after "city name input." Why that is, it can't tell you. Seventy years ago, Hopper's compiler was translating. Today's AI is still translating. The target has just shifted to your own words. There's a practical problem with this translation machine: developers accept roughly 30% of what it suggests. The other 70% gets read and deleted. And of that 30% that does get accepted, studies find around half carries known security vulnerabilities: syntactically clean, looks right, but hiding problems inside. That same week, the University of Chicago Law School announced something that looked like the opposite: starting this year, first-year students can't bring any devices to class, not even laptops. The school's position isn't that AI is bad. It's that students need a space without a translation machine first, where thinking has to do the work on its own. The approach is Socratic: professors ask, students answer in real time, reading cases themselves, tracing arguments themselves, finding the flaws in logic themselves. The point is that by the time students do use AI, they can actually judge whether what it produced is right. AI has pushed translation further than 1952 could have imagined. Chicago is saying: before you let AI do the translating, understand what you're translating first. Both things happening at once. Neither is wrong. That 70% of AI code suggestions that didn't make it: generated, then gone. Next semester, Chicago's first-year students walk in, desks clear, professor asks, they think.