Study Hacks Blog

At a time when educators are increasingly concerned about technology’s impact in the classroom, the Washington Post published ​an op-ed​ with a contrarian tone. The piece, written by the journalism professor Stephen Kurczy, focuses on Green Bank, a small town in rural West Virginia, home to the world’s largest steerable radio telescope. Due to the sensitivity of this device, the entire area is a congressionally designated “radio quiet zone” in which cell service and WiFi are banned.

The thought of a disconnected life might sound refreshing, but as this op-ed argues, there’s one group for which this reality might be causing problems: the students in Green Bank’s combined elementary and middle school.

“Without WiFi, the 200 students couldn’t use Chromebooks or digital textbooks, or do research online,” Kurczy writes. “Teachers couldn’t access individualized education programs online or use Google Docs for staff meetings.”

Some teachers in the school are frustrated. “The ability to individualize learning with an iPad or a laptop – that’s basically impossible,” explained one teacher, quoted in the piece. “Without the online component of our curriculum fully working, it’s really detrimental to our instruction,” said another.

These concerns aren’t merely hypothetical. As Kurczy points out: “Green Bank consistently [posts] the lowest test scores in the county.” He quotes the school’s principal, who blames this on the students’ “lack of access to engaging technology.”

The message of this op-ed is clear. At a time when we’re rushing to condemn phones in classrooms, we should be careful not to extend this ire to other ed-tech innovations, as without these, students struggle.

It’s a tidy point. But is it true? I decided to dig a little deeper…

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Last fall, a Norwegian psychology professor named Lars Dehli was asked to give a lecture on intelligence. It had been a while since he had taught the topic, so he looked forward to revisiting it. As he explained in ​an essay​ about the experience, he decided to start the lecture by discussing the so-called Flynn Effect—the well-known phenomenon, first observed by James Flynn, whereby measured IQ scores have been steadily increasing since World War II. “It’s always fun to tell students that their generation is the smartest people who have ever lived,” Dehli wrote.

But as he gathered data to build an up-to-date chart, he was “very surprised” by what he discovered: “IQ has actually started to fall.”

Dehli was not the first person to notice this decline. In recent years, a growing number of researchers have been documenting what has become known as the Reverse Flynn Effect. Consider, for example, ​a recent paper​ published in the journal Intelligence that studied IQ scores over time in an American population. It found a steady decline in almost every intelligence metric studied as part of a 35-item assessment.

Here’s a chart that shows these declines broken out by education level:

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Last month, a 22-year-old entrepreneur named Emil Barr published a Wall Street Journal op-ed boasting a provocative title:​ “‘Work-Life Balance’ Will Keep You Mediocre.”​

He opens with a spicy take:

“I’m 22 and I’ve built two companies that together are valued at more than $20 million…When people ask how I did it, the answer isn’t what they expect—or want—to hear. I eliminated work-life balance entirely and just worked. When you front-load success early, you buy the luxury of choice for the rest of your life.”

As Barr elaborates, when starting his first company, he slept only three and a half hours per night. “The physical and mental toll was brutal: I gained 80 pounds, lived on Red Bull and struggled with anxiety,” he writes. “But this level of intensity was the only way to build a multimillion-dollar company.”

He ends the piece with a wonderfully cringe-inducing flourish. “I plan to become a billionaire by age 30,” he writes. “Then I will have the time and resources to tackle problems close to my heart like climate change, species extinction and economic inequality.”

(Hold for applause.)

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In the years since ChatGPT’s launch in late 2022, it’s been hard not to get swept up in feelings of euphoria or dread about the looming impacts of generative AI. This reaction has been fueled, in part, by the confident declarations of tech CEOs, who have veered toward increasingly bombastic rhetoric.

“AI is starting to get better than humans at almost all intellectual tasks,” Anthropic CEO Dario Amodei recently told Anderson Cooper. He added that half of entry-level white collar jobs might be “wiped out” in the next one to five years, creating unemployment levels as high as 20%—a peak last seen during the Great Depression.

Meanwhile, OpenAI’s Sam Altman said that AI can now rival the abilities of a job seeker with a PhD, leading one publication to plaintively ask, “So what’s left for grads?”​

Not to be outdone, Mark Zuckerberg claimed that superintelligence is “now in sight.” (His shareholders hope he’s right, as he’s reportedly offering compensation packages worth up to $300 million to lure top AI talent to Meta.)

But then, two weeks ago, OpenAI finally released its long-awaited GPT-5, a large language model that many had hoped would offer leaps in capabilities, comparable to the head-turning advancements introduced by previous major releases, such as GPT-3 and GPT-4. But the resulting product seemed to be just fine.

GPT-5 was marginally better than previous models in certain use cases, but worse in others. It had some nice new usability updates, but others that some found annoying. (Within days, more than 4,000 ChatGPT users signed a change.org petition asking OpenAI to make their previous model, GPT-4o, available again, as they preferred it to the new release.) An early YouTube reviewer concluded that GPT-5 was a product that “was hard to complain about,” which is the type of thing you’d say about the iPhone 16, not a generation-defining technology. AI commentator Gary Marcus, who had been predicting this outcome for years, summed up his early impressions succinctly when he called GPT-5 “overdue, overhyped, and underwhelming.”

This all points to a critical question that, until recently, few would have considered: Is it possible that the AI we are currently using is basically as good as it’s going to be for a while?

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A reader recently sent me ​a Substack post​ they thought I might like. “I bought my kids an old-school phone to keep smartphones out of their hands while still letting them chat with friends,” the post’s author, Priscilla Harvey, writes. “But it’s turned into the sweetest, most unexpected surprise: my son’s new daily conversations with his grandmothers.”

As Harvey continues, her son has adopted the habit of stretching out on the couch, talking to his grandmother on a retro rotary-style phone, the long cable stretching across the room. “There’s no scrolling, no distractions, no comparisons, no dopamine hits to chase,” she notes. “Instead he is just listening to stories, asking questions, and having the comfort of knowing someone who loves him is listening on the other end of the line.”

The post’s surface message is one about kids and technology. Harvey, defiantly pushed back against the culture of weary resignation surrounding our youth and phone use, and discovered something sacred.

But I think there’s a more general idea lurking here as well.

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In the wake of my recent (and inaugural) visit to Disneyland, I read Richard Snow’s history of the park, Disney’s Land. Early in the book, Snow tells a story that I hadn’t heard before. It fascinated me—not just for its details, but also, as I’ll soon elaborate, for its potential relevance to our current moment.

The tale begins in 1948. According to Snow, Disney’s personal nurse and informal confidant, Hazel George, had become worried. “[She] began to sense that her boss was sinking into what seemed to her to be a dangerous depression,” Snow writes. “Perhaps even heading toward what was then called a nervous breakdown.” 

The sources of this distress were obvious. Disney’s studio hadn’t had a hit since Bambi’s release in 1942, and the loss of the European markets during the war, as well as the economic uncertainty that followed in peacetime, had strained the company’s finances. Meanwhile, during this same period, Disney faced an animator strike that he took as a personal betrayal. “It seemed again to just be pound, pound, pound,” writes Snow. “Disney was often aggressive, abrupt, and when not angry, remote.”

Hazel George, however, had a solution. She knew about Disney’s childhood fascination with steam trains, so it caught her attention when she saw an advertisement in the paper for the Chicago Railroad Fair, which would feature exhibits from thirty different railway lines built out over fifty acres on the shore of Lake Michigan. She suggested Disney take a vacation to  see the fair. He loved the idea.

In Chicago, entranced by what he encountered, Disney felt a spark of the creative enthusiasm that had been missing throughout the war years. He just needed to find a way to harness it. Serendipitously, upon returning to Los Angeles, one of his animators, Ward Kimball, introduced him to a group of West Coast train enthusiasts who were building scale models of functioning steam trains large enough for an adult to ride on (think: cars roughly the length of a child’s wagon).

This, Disney decided, is what he needed to do.

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I want to present you with two narratives about AI. Both of them are about using this technology to automate computer programming, but they point toward two very different conclusions.

The first narrative notes that Large Language Models (LLMs) are exceptionally well-suited for coding because source code, at its core, is just very well-structured text, which is exactly what these models excel at generating. Because of this tight match between need and capability, the programming industry is serving as an economic sacrificial lamb, the first major sector to suffer a major AI-driven upheaval.

There has been no shortage of evidence to support these claims. Here are some examples, all from the last two months:

• Aravind Srinivas, the CEO of the AI company Perplexity, ​claims​ AI tools like Cursor and GitHub Copilot cut task completion time for his engineers from “three or four days to one hour.” He now mandates every employee in his company to use them: “The speed at which you can fix bugs and ship to production is scary.”
• ​An article in Inc​. confidently declared: “In the world of software engineering, AI has indeed changed everything.”
• Not surprisingly, these immense new capabilities are being blamed for dire disruptions. ​One article​ from an investment site featured an alarming headline: “Tech Sector Sees 64,000 Job Cuts This Year Due to AI Advancement.” No one is safe from such cuts. “Major companies like Microsoft have been at the forefront of these layoffs,” the article explains, “citing AI advancements as a primary factor.”
• My world of academic computer science hasn’t been spared either. A ​splashy Atlantic piece​ opens with a distressing claim: “The Computer Science-Bubble is Bursting,” which it largely blames on AI, a technology it describes as “ideally suited to replace the very type of person who built it.”

Given the confidence of these claims, you’d assume that computer programmers are rapidly going the way of the telegraph operator. But, if you read a different set of articles and quotes from this same period, a very different narrative emerges:

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