Study Hacks Blog

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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Most summers, my family and I retreat to New England for much of July. From a professional perspective, I see this as an exercise in ​seasonality​ (to use a term from my book ​Slow Productivity​), a way to recharge and recenter the creative efforts that sustain my work. This year, I needed all the help I could get. I had recently finished part one of my new book on the deep life and was struggling to find the right way to introduce the second.

During my first couple of days up north, I made rapid progress on the new chapter. But I soon began to notice some grit in the gears of my conceptual narrative. As I pushed forward in my writing, the gnashing and grinding became louder and more worrisome. Eventually, I had to admit that my approach wasn’t working. I threw out a couple thousand words, and went searching for a better idea.

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In a recent New Yorker review of Matt Richtel’s new book, How We Grow Up, Molly Fischer effectively summarizes the current debate about the impact phones and social media are having on teens. Fischer focuses, in particular, on Jon Haidt’s book, The Anxious Generation, which has, to date, spent 66 weeks on the Times bestseller list.

“Haidt points to a selection of statistics across Anglophone and Nordic countries to suggest that rising rates of teen unhappiness are an international trend requiring an international explanation,” Fischer writes. “But it’s possible to choose other data points that complicate Haidt’s picture—among South Korean teens, for example, rates of depression fell between 2006 and 2018.”

Fischer also notes that American suicide rates are up among many demographics, not just teens, and that some critics attribute depression increases in adolescent girls to better screening (though Haidt has addressed this latter point by noting that hospitalizations for self-harm among this group rose alongside rates of mental health diagnoses).

The style of critique that Fischer summarizes is familiar to me as someone who frequently writes and speaks about these issues. Some of this pushback, of course, is the result of posturing and status-seeking, but most of it seems well-intentioned; the gears of science, powered by somewhat ambiguous data, grinding through claims and counterclaims, wearing down rough edges and ultimately producing something closer and closer to a polished truth.

And yet, something about this whole conversation has increasingly rubbed me the wrong way. I couldn’t quite put my finger on it until I came across Ezra Klein’s interview with Haidt, released last April (hat tip: Kate McKay).

It wasn’t the interview so much that caught my attention as it was something that Klein said in his introduction:

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Last fall, I published a New Yorker essay titled, “What Kind of Writer is ChatGPT?”. My goal for the piece was to better understand how undergraduate and graduate college students were using AI to help with their writing assignments. 

At the time, there was concern that these tools would become plagiarism machines. (“AI seems almost built for cheating,” wrote Ethan Mollick in his bestselling book, Co-Intelligence. What I observed was somewhat more complex. 

The students weren’t using AI to write for them, but instead to hold conversations about their writing. If anything, the approach seemed less efficient and more drawn out than simply buckling down and filling the page. Based on my interviews, it became clear that the students’ goal was less about reducing overall effort than it was about reducing the maximum cognitive strain required to produce prose. 

“‘Talking’ to the chatbot about the article was more fun than toiling in quiet isolation,” I wrote. Normal writing requires sharp spikes of focus, while working with ChatGPT “mellowed the experience, rounding those spikes into the smooth curves of a sine wave.”

I was thinking about this essay recently, because a new research paper from the MIT Media Lab, titled “Your Brain on ChatGPT,” provides some support for my hypothesis. The researchers asked one group of participants to write an essay with no external help, and another group to rely on ChatGPT 4o. They hooked both groups to EEG machines to measure their brain activity.

“The most pronounced difference emerged in alpha band connectivity, with the Brain-only group showing significantly stronger semantic processing networks,” the researchers explain, before then adding, “the Brain-only group also demonstrated stronger occipital-to-frontal information flow.”

What does this mean? The researchers propose the following interpretation:

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