Study Hacks Blog

Few recent technological innovations were better poised to become a productivity slam dunk than the personal computer. Spreadsheets, word processors, databases, presentation software, email – the list of programs that could vastly simplify common tasks seemed endless.

As I reported in ​a 2021 op-ed for WIRED​, however, reality proved more complicated. For example:

• A 1991 article in the New York Times quoted an economist who said that despite heavy spending on computers, “white-collar productivity has stagnated.” He concluded: “No longer are chief executives confident that throwing computers at their office staffs will result in greater efficiency.”
• A study covering the period from 1987 to 1993 calculated that computers added only 0.2 percentage points a year to business output growth, leading a contemporaneous summary to declare that the “impact of computers on recent productivity growth has been vastly overstated.”
• In his 1997 book, Why Things Bite Back, Edward Tenner argued that these findings underscored what he called a “productivity paradox.”

I thought about this article recently as I continue grappling with our current AI moment. It’s natural to assume that this technology, which clearly makes certain common professional activities easier, will make our working lives more productive overall.

But as the early years of desktop computing taught us: it’s not always so simple.

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The Financial Times ​recently reported​ an interview with Rebecca Hinds, head of the Work AI Institute. Hinds was discussing a new survey of 6,000 digital workers, which included the following arresting statistic: although respondents claimed that AI saved them 11 hours a week on average, only 13% reported any improvement in company performance.

Hinds offers three explanations for this paradoxical result:

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The leaders of AI companies often compare their technology to industrial automation: just as machines eliminated jobs that depended on human brawn, AI will eliminate jobs that rely on brains. Anthropic CEO Dario Amodei has suggested on multiple occasions that his AI-based tools will ​automate half​ of entry-level white-collar jobs. Not to be outdone, Mustafa Suleyman, the CEO of Microsoft AI, ​predicted ​in February that AI will deliver “human-level performance on most if not all professional tasks” within the next twelve to eighteen months.

As I​ recently discussed​ on an AI Reality Check episode of my podcast, however, some of these same leaders have started pulling back from automation discourse.

Two weeks ago, while appearing at a conference in Australia, ​Sam Altman said​ that he was “delighted” to be wrong about AI creating a “jobs apocalypse.” And Amodei is ​now saying​ that AI won’t replace jobs but will instead replace large parts of existing jobs, changing what employees do in their roles.

If automation is the wrong analogy, what could be better? In my most recent article for The New Yorker, which is titled ​“Instead of Taking Your Job, A.I. Might Transform It”​, I investigated possible answers.

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Last fall, the Notre Dame philosopher, Meghan Sullivan, participated in a closed-door meeting at the Vatican. She was there to discuss AI ethics with a group of religious thinkers, academics, and leading members of the technology industry.

As Sullivan recalls in ​a recent newsletter​, she attended an optional Catholic Mass the first morning, held in an ancient church. She was surprised to see one of the tech leaders sitting a few rows away in the pews. “[This was] the kind of guy you typically see in a black t-shirt and chinos,” she writes. “That morning he was dressed in a brown suit and tie, quietly taking in the sanctuary as the first rays of morning light filled the room.”

After the service concluded, they chatted. The tech leader, it turned out, was not Catholic. When Sullivan asked him why he was here, he gave the following answer:

“We’re building something that is going to change life as we know it. I want to make sure I keep in touch with what humans have always cared about. This is a place that takes care of those values.”

I found this interaction chilling. Not because of what it says about potential AI disruptions, but because of what it tells us about the engineers developing this technology.

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If you’ve been following technology news recently, you’ve probably noticed a sudden increase in references to a 19th-century economics theory called the ​Jevons Paradox​, which is named for the neoclassical economist William Stanley Jevons, and captures the observation that increasing the efficiency of a resource can lead to greater consumption.

Jevons first articulated this idea in an 1865 book, pithily titled, ​The Coal Question: An Inquiry Concerning the Progress of the Nation, and the Probable Exhaustion of Our Coal-Mines​. He argued that building more efficient steam engines – ones that required less fuel to generate the same power – would not solve the problem of England’s diminishing coal supplies. If you made the engines more efficient, Jevons predicted, people would find more applications for steam power, and even more coal would be burned overall.

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“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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