The First People to Go Through All of College Alongside AI Have Graduated

This summer a batch of new graduates walked out the school gates. They are the first group to go through college from start to finish alongside generative AI. ChatGPT only came out in the spring of their freshman year, and for the four years after that, their assignments had AI look over the drafts, their research had AI organize it, and their class discussions had AI comb through things beforehand. This tool went through college with them, and now it's heading into the working world with them too. And right around the time they got their diplomas, Google DeepMind's Hassabis said AGI (AI whose abilities fully catch up to humans) could arrive as early as 2029, just three years from now. The job market's attitude toward this class splits cleanly. On one side, companies are scrambling to hire AI-generation new graduates, the reasoning being that these people are more nimble and better with the tools than veterans who've worked twenty years. On the other side, at the very same time, companies are cutting entry-level openings, because those jobs are now cheaper to hand to AI. The two forces exist at once: this March, the unemployment rate for this age group spiked to 5.6%, a high rarely seen outside of pandemic years in the past decade or so. Both the scrambling-to-hire and the not-hiring are real. Why so split? Because the first thing AI eats is precisely that "entry-level" slice: looking things up, organizing, producing a first draft. These tasks used to be where newcomers practiced their craft. Now AI generates one in seconds. So the people who can hit the ground running with the tools right out of school become sought-after, while the entry-level openings whose contents happen to be replaceable by the tools slowly disappear. Jensen Huang has offered one angle on this. He said your major actually doesn't matter that much, because what mattered in the past will still matter in the future: the ability to tell a story, the instinct to hear, in the moment, "which question to ask," the taste to sense "something's off here." He thinks these will only get scarcer in the AI era. He uses the radiologist as an example: using AI to read images is just a "task," but the real "diagnosis" still has to come from a person, because it calls for judgment, and being able to recognize patterns isn't enough. This cuts across generations. Whether you just graduated this year or have been in the workforce for years, one thing you can set your mind at ease about: what AI takes is mostly the "just follow the procedure" part. What stays, and grows more valuable, is judgment. Which argument is worth digging deeper into, which question leads back to the same answer no matter how you circle it, whether to commit to a defensible decision when the information is murky and then wait. Next time you finish something with AI, don't rush to wrap it up. Turn back and ask yourself, "where could this be wrong?" That one question is the layer the tool can't learn yet, and the one you're building up. What that batch of graduates learned in school was partly the tools, and partly something with no course name, something that only grew out of spending four years among people. What they carried out the gates with them is that second part.