Google Cut the Team That Trains Engineers. What Kind of Livelihood Is AI Reaching?

This June, Google eliminated its entire "engineering education" department: the team responsible for compiling best practices and passing technical knowledge down to engineers. The old saying "learn a hard skill and you'll never go hungry" is starting to loosen in exactly this kind of place. In plain terms: companies used to have a group of people whose whole job was turning the experience accumulated by senior engineers into training materials, courses, and standards, then passing it on to newcomers. This training system was how technical knowledge flowed through an organization. Now Google has decided this no longer needs people to maintain it. Got a question? Ask AI. Code you don't understand? Throw it at AI. What used to rely on people passing it down has been taken over by another kind of system. What this really touches is the premise that makes a certain ability "valuable." High-complexity engineering skill has always been in hot demand, for a simple reason: people who can do it are hard to find. When people are hard to find, companies are willing to pay to train them and the market is willing to pay high salaries. That scarcity propped up the whole value. But when this ability slowly turns into a service you can pay for monthly, the question organizations ask changes, from "can I find the person who knows how to do this" to "now that we have this service, what should the team look like." A few students currently studying computer science compared their situation to the year the imperial examination essay was abolished at the end of the Qing dynasty: someone had spent twenty years memorizing the orthodox texts and was just about to sit the exam when the abolition order came down. They watch AI go from completing code all the way to planning architecture, assisting with testing, and debugging automatically, pushing forward, layer by layer, skills that used to take years to grind out. There are still classes to attend and exams to take, but in their hearts they know the distance is widening between what they're learning and what the market is now asking for. Even if you don't write code, this logic is worth keeping in mind: when a skill becomes a service you can summon on demand, just "being able to do it" isn't quite enough anymore. You have to move toward the "knowing how to judge how to use it, how to fold it into the work" end. If you happen to have AI tools at hand, try one thing: don't just treat it as a stand-in. Find something you already know how to do and let it help, then watch where it does well and where you still have to make the call yourself. The spots where you're unsure and have to decide in person are often exactly the value still left in your hands. The scholars at the end of the Qing were told the times had turned only after years of study. Today's students are a little different. They're watching the turn happen with their own eyes, yet they still have to finish the road in front of them first. When the rules are changing, the road itself doesn't change.