Links and Commentary 5/9/25

He worries about the long-term consequences of passively allowing 18-year-olds to decide whether to actively engage with their assignments. Would it accelerate the widening soft-skills gap in the workplace? If students rely on AI for their education, what skills would they even bring to the workplace? Lakshya Jain, a computer-science lecturer at the University of California, Berkeley, has been using those questions in an attempt to reason with his students. “If you’re handing in AI work,” he tells them, “you’re not actually anything different than a human assistant to an artificial-intelligence engine, and that makes you very easily replaceable. Why would anyone keep you around?” That’s not theoretical: The COO of a tech research firm recently asked Jain why he needed programmers any longer.

The ideal of college as a place of intellectual growth, where students engage with deep, profound ideas, was gone long before ChatGPT. The combination of high costs and a winner-takes-all economy had already made it feel transactional, a means to an end. (In a recent survey, Deloitte found that just over half of college graduates believe their education was worth the tens of thousands of dollars it costs a year, compared with 76 percent of trade-school graduates.) In a way, the speed and ease with which AI proved itself able to do college-level work simply exposed the rot at the core. “How can we expect them to grasp what education means when we, as educators, haven’t begun to undo the years of cognitive and spiritual damage inflicted by a society that treats schooling as a means to a high-paying job, maybe some social status, but nothing more?” Jollimore wrote in a recent essay. “Or, worse, to see it as bearing no value at all, as if it were a kind of confidence trick, an elaborate sham?”

So, I think the real question presented to faculty is “Why are we assigning the work we’re assigning?” If we’re asking students to do particular tasks because doing them is essential to the process of learning, then we should continue to do them even in a world with readily available “AI,” but also make clear to the students that that’s what they’re for. The ones who are interested in the opportunity to learn will still do the work, if they know that’s what it takes.

On the other hand, if we’re assigning particular tasks primarily to have a basis for assigning grades, that’s a different issue, and it’s probably worth examining how much we want to be in the grading business rather than the teaching business. A good deal of the fretting over LLMs as the death knell for higher education are fundamentally rooted in the same kind of credentialism that many of the fretters denounce in our students. I’m not sure that relationship has gotten as much thought as it deserves, and if there’s any silver lining to this cloudy moment, it might be as a spur to thinking about that issue.

Generative AI is in some ways only the last—and maybe most fatal—of a series of injuries and ailments stemming from decades of bad management in higher education, though faculty own some shared responsibility for some of those wounds. If you had invented a time machine in 2021 intending to bring ChatGPT into existence and you went to the year 1980, you couldn’t have done better to prepare universities for the massive adoption of generative AI as a method for cheating than to do what administrators proposed and faculty assented to over the. years since 1980.

Massive classes, built around a lecture pedagogy. The conversion of most teaching to contingency, with the loss of mentorship and curricular governance that resulted from that change. The spread of over-specialized credentialism and the narrowing of a wage premium to a smaller and smaller number of professional and white-collar jobs linked to credentialism. The willing acceptance of our role as gatekeepers and weed-outers, of being the alibi for the accelerating failure of the American political economy to provide a decent living standard for most of the country’s residents. Higher tuitions and the risk adversity they have helped to cultivate. The punishing accumulation of bullshit work processes within the academy and the disconnects between them and the core labor of faculty. Visions of austerity slamming into teaching and scholarship while administrative ranks grow seemingly without end. It all has led to many students at large universities feeling as if the university and its curriculum is little more than a credentials pinata to be whacked until it gives up the candy, and GPT is only the best and biggest stick ever provided for that purpose.

Before launching Cluely, Lee and Shanmugam raised $5.3 million from investors, which allowed them to hire two coders, friends Lee met in community college (no job interviews or LeetCode riddles were necessary), and move to San Francisco. When we spoke a few days after Cluely’s launch, Lee was at his Realtor’s office and about to get the keys to his new workspace. He was running Cluely on his computer as we spoke. While Cluely can’t yet deliver real-time answers through people’s glasses, the idea is that someday soon it’ll run on a wearable device, seeing, hearing, and reacting to everything in your environment. “Then, eventually, it’s just in your brain,” Lee said matter-of-factly. For now, Lee hopes people will use Cluely to continue AI’s siege on education. “We’re going to target the digital LSATs; digital GREs; all campus assignments, quizzes, and tests,” he said. “It will enable you to cheat on pretty much everything.”

Even still, it’s notable to me how different these numbers are from what you would think just listening to the zeitgeist. I think young people believe going to college should fundamentally change their life. Like, at least earn them an additional $25k a year. Instead, the bump is at best $16k annually for a typical STEM student, it’s often only a couple thousand bucks a year, and sometimes the return is zero or even negative.

This last paper also reminded me of some of the ideas in the Goldin and Katz paper from Day 2. Remember how that paper said that the reason why universities got so big at the beginning of the 20th century is because technology advanced in such a way that highly specifically trained workers became more valuable, so universities had to offer more specialized training? This last paper seems to suggest that trend is over. Now technology may be changing in such a way that doesn’t advantage highly specialized training.

What will that do to the market for institutes of higher education? Are we about to see a resurgence of interest in a general, broad, and dare I say liberal arts education?

What if we kind of inverted this story and then turned it inside out? What if, instead of schooling leading to generally a capable workforce, schooling was partially creating the job market and, therefore, driving the economy itself and the economic wellbeing of the populace? When I put it this way maybe it’s a bit crazy sounding, but I’d argue that this is what many important people who make decisions about education policy actually believed.

-Me in January

Attitudes towards reading are changing, with parents increasingly aligning their children’s reading with schoolwork rather than something fun to enjoy. This is particularly pronounced among Gen Z parents, where almost one in three (28%) see reading as a “more a subject to learn”, compared to one in five (21%) of Gen X parents. As the first generation to grow up with technology, Gen Z parents may turn to digital entertainment for fun rather than books.

The declining number of parents reading aloud to children, combined with the pressure associated with reading as something to learn rather than enjoy, may be contributing factors to the shift in how young people view reading. In 2024 almost one in three children aged 5-13 said reading is “more a subject to learn than a fun thing to do”, a significant rise from one in four (25%) in 2012. This is most pronounced among 11-13s, 35% of whom think reading is “more a subject to learn than a fun thing to do”.