Study Hacks Blog

“Something is up in academic research,” ​write​ the members of an AI Task Force convened by the journal Organization Science. As they go on to elaborate:

“If you are an editor or reviewer at a journal these days, you probably already know this. The manuscripts are arriving in greater volume, with a particular feel that is hard to pin down. On the surface, the papers look the same as ever, but the writing feels weightless in a way that rarely describes academic writing…you find yourself scratching your head at the meaning the words are trying to convey.”

The culprit? The task force crunched the numbers and produced a clear answer. Starting in 2023, after ChatGPT became available, the number of submissions to Organization Science rapidly increased. At the same time, the percentage of submissions classified as using minimal AI has plummeted from near 100% down closer to 30%.

The impact of this shift on readability has been marked, with scores on a standard “reading ease” metric falling by 1.28 standard deviations between January 2021 and January 2026:

“Submissions have become far harder to read,” the Task Force reports. “This is counterintuitive. Most people assume that AI produces cleaner, more polished text. And in some narrow dimensions, it does…but on the measures that capture whether a reader can actually parse and absorb the prose, AI writing is worse…[using] longer words, more complex sentence structures, more jargon, and more nominalizations.”

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David Epstein, the #1 New York Times bestselling author of ​The Sports Gene​ and ​Range​, has a new book out called ​Inside the Box​. As with all of Epstein’s books, I really enjoyed it. He’s one of the best storytellers currently working in idea writing.

There was one chapter in particular, however, that captured my attention as being uniquely well-suited to the themes we discuss here. It focused on the ideas of a somewhat eccentric physicist-turned-management guru named Eliyahu Goldratt, who in the 1980s popularized a framework for understanding industrial productivity that he dubbed the “theory of constraints.”

Here’s how a non-profit established to promote Goldratt’s work summarizes it:

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Last week, Elizabeth Lopatto published an insightful article in The Verge. It boasted an intriguing title: ​“Silicon Valley has forgotten what normal people want.”​

“Within recent memory, people who made software and hardware understood their job was to serve their customers. It was to identify a need, and then fill it,” she writes. “But at some point following the financial crisis, would-be entrepreneurs got it into their heads that their job was to invent the future, and consumers’ job was to go along with that invented future.”

I certainly noticed this shift when it first began emerging. See, for example, my 2015 article titled, ​“It’s Not Your Job to Figure Out Why an Apple Watch Might Be Useful.”​ But it really picked up speed in the last half-decade. Here’s Lopatto with a needle-sharp summary of our current status quo:

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Late last year, the fantasy novelist Brandon Sanderson gave a talk at Dragonsteel Nexus, an annual conference organized by his media company. It was titled, ​“The Hidden Cost of AI Art.”​

As Sanderson explains, early in his address: “The surge of large language models and generative AI raises questions that are fascinating, and even if I dislike how the movement is going in relation to writing and art, I want to learn from the experience of what’s happening.”

Sanderson makes it clear that he disapproves of AI-generated art (“my stomach turns”), but he wants to understand better why this is the case. To do so, he begins considering and then ultimately dismissing a series of common objections:

• Does he dislike AI art because of the economic and environmental impacts? “Well, those do concern me, but if I’m answering honestly, I would still have a problem with it even if AI were not so resource hungry.”
• Does he dislike AI art because it’s trained on the work of existing artists? “ Well, I don’t like that. But even if it were trained using no copyrighted work, I’d still be concerned.”
• Does he just hate the idea of a machine replacing a person? Sanderson references the folk tale of John Henry attempting to beat a steam drill in a tunnel-digging competition that culminates in Henry’s death. “We respect him, but as a society we chose the steam drill. And I would too…The truth is, I’m more than happy to have steam engines drilling tunnels for me to drive through.”

So what is it?

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Ten years ago, I published ​Deep Work​. It was my second mainstream hardcover idea book. The previous title, ​So Good They Can’t Ignore You​, hadn’t sold as well as we hoped, so the expectations were lower for this follow-up.

This turned out to be freeing, as it allowed me to write Deep Work largely for myself – exploring the conceptual edges of the issues surrounding distraction that interested me most.

I was fascinated, for example, by the economic reality that so many knowledge work organizations systematically undervalued focus, and was convinced that this provided a massive opportunity for those willing to correct for this mistake. In this way, I saw myself as articulating something like Moneyball for the cubicle class. I also firmly believed that the act of thinking was at the core of the post-Paleolithic human experience; the source of our greatest ideas, satisfactions, and even moments of transcendence.

This mixture of the economic and philosophical was different from the typical book in this genre at the time. Readers probably expected that I would open on a breathless tale of an overworked executive, then regurgitate some stats about interruptions, before proceeding with long lists of tips calibrated to be practical, but also not too challenging, presented in a conversational tone and accompanied by clearly manipulated case studies.

But Deep Work was much weirder and more intense than that. Re-reading it recently, I was struck by how many of my stories had nothing to do with the knowledge sector at all. I quoted philosophers of religion and a blacksmith who forged swords with ancient techniques. I profiled a memory champion and discussed chavruta, the Jewish practice of studying Talmud or Torah in pairs. Rather than opening the book on a frustrated executive, I focused on Carl Jung’s efforts to break free from Sigmund Freud’s capriciousness. It was a direct look at the sources and ideas that most resonated with me.

This idiosyncratic approach seemed to reveal something fundamentally true about the problematic state of work at that time, as the book soon found an audience, going on to sell more than two million copies in over forty-five languages. (In its wake, So Good They Can’t Ignore You finally found its groove as well, quietly selling more than half a million copies, providing me with a dash of retrospective vindication.)

All of this led me recently to ask a natural follow-up question: How have things changed since that book first came out in 2016?

I tackled this query in ​a long-form essay​ I published in the New York Times over the weekend. My answer wasn’t optimistic:

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​Last week​ in this newsletter, I summarized some interesting results from ​a study​ that analyzed the behavior of 164,000 knowledge workers. It found that introducing AI tools increased administrative tasks by more than 90% while reducing deep work effort by almost 10%.

The problem, I concluded, was that digital productivity tools sometimes speed up the wrong tasks, which might feel efficient in the moment, but lead us to accomplish less over time. As I emphasized, AI is not the only technology to produce this paradoxical side effect —we saw something similar with email, mobile computing, and online meeting software as well.

So, what’s the solution to avoid these traps?

In ​today’s episode​ of my podcast, I suggested three ideas that might help. I want to summarize them here as well:

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I’ve been studying the intersection of digital technology and office work for quite some time. (I find it hard to believe that my book, ​Deep Work​, just passed its ten-year anniversary!?) Here’s a pattern I’ve observed again and again:

• A new technology promises to speed up some annoying aspects of our jobs.
• Everyone gets excited about freeing up more time for deep work and leisure.
• We end up busier than before without producing more of the high-value output that actually moves the needle.

This happened with the front-office IT revolution, and email, and mobile computing, and once again with video-conferencing.

I’m now starting to fear that we’re beginning to encounter the same thing with AI as well.

My worries were stoked, in part, by a recent article in the Wall Street Journal, titled ​“AI Isn’t Lightening Workloads. It’s Making Them More Intense.”​

The piece cites new research from the software company ActivTrak, which analyzed the digital activity of 164,000 workers across more than 1,000 employers. What makes the study notable is its methodology: it tracked individual AI users for 180 days before and after they began using these tools, providing clear insight into what changed. The results?

“ActivTrak found AI intensified activity across nearly every category: The time they spent on email, messaging and chat apps more than doubled, while their use of business-management tools, such as human-resources or accounting software, rose 94%.“

The one category where activity was not intensified, however, was deep work:

“[T]he amount of time AI users devoted to focused, uninterrupted work—the kind of concentration often required for figuring out complex problems, writing formulas, creating and strategizing—fell 9%, compared with nearly no change for nonusers.”

This is a worst-case scenario: you work faster and harder, but mainly on shallow, mentally taxing tasks (because of all the context shifting they require) that only indirectly help the bottom line compared to harder efforts.

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