Cal Newport's Blog, page 4

Dispatch from Herman Melville’s Farm

Growing up in New York, first in the city and then later in Albany, a young Herman Melville made frequent trips to stay with his uncle, Thomas Melvill, who lived on a farm near Pittsfield, in the Berkshire mountains of Western Massachusetts. In 1850, Thomas decided to sell his property. Melville, now with a young family of his own, arrived that summer for what they believed to be his final visit to the area.

It was during this fateful trip that Melville learned that the Brewster farm, consisting of 160 acres abutting his uncle’s plot, was up for sale. Fueled by impulse and nostalgia, he borrowed $3000 from his father-in-law and bought the property. He would come to call it Arrowhead in reference to native artifacts he found in its fields.

Melville’s plan for his time at Arrowhead was to write. He had recently published a series of bestselling adventure novels, drawing from the half-decade he spent wandering the Pacific as a sailor. He felt confident that his literary success would continue and the time was right to fully invest in this vision.

A few days ago, I travelled down to Arrowhead, now preserved by the Berkshire Historical Society, to better understand the writing-centered life that Melville constructed.

The original house is small, its second floor needing to fit Melville’s own family, as well as his mother and multiple sisters. He none-the-less claimed a sizable east-facing room for his office. Melville used a dining table to write, giving him ample room to spread out his books and notes. He pushed the table against a window offering a direct view of the hump-backed Mount Greylock in the distance:

(Legend has it that the whale-like appearance of the mountain inspired Moby Dick. We know this can’t be true because Melville conceived the novel before moving to Arrowhead, but his orientation toward the mountain, both physically and psychologically, clearly marks it as an important source of poetic inspiration for his work.)

Melville’s desk is flanked by bookshelves. A fireplace behind him boasts a poker forged from a whaling harpoon. According to the docent who led us on a tour, this setup, impressive as it is, was only temporary. Melville’s eventual plan was to raze the house and build a grander structure featuring a “writing tower.”

How did Melville make use of these spaces? We can gain some insight into his daily routine from a letter he wrote to a friend during this period:

I rise at eight–thereabouts–& go to my  barn–say good-morning to the horse, & give him his breakfast…My own breakfast over, I go to my work-room & light my fire–then spread my M.S.S. on the table–take one business squint at it, & fall to with a will.  At 2 1/2 p.m. I hear a preconcerted knock at my door, which (by request) continues till I rise & go to the door, which serves to wean me effectively from my writing, however interested I may be. . . .

The thirteen years Melville would spend at Arrowhead, writing half of each day at his dining table desk overlooking the mountains beyond, were the most productive of his career. The works he completed at Arrowhead included, most notably, Moby Dick, but also Pierre, the Confidence-Man, and Israel Potter, not to mention some of his best-known short stories, such as I and My Chimney, Benito Cereno, and Bartleby the Scrivener. (Tragically, these works were largely critical and commercial failures during Melville’s lifetime, leading him to eventually fall into debt before returning to New York to take a desk job. They wouldn’t become recognized as American classics until the early twentieth century.)

A couple weeks ago, I wrote a dispatch from the writing shed I was working from this July to help jumpstart a new book project. Melville’s Arrowhead provides a nice example of these same creative principles pushed toward a more notable extreme. Melville wanted to write, and knew that to do so at the level that could produce something of the caliber of Moby Dick would require great attention paid not just to what he was working on, but also where these efforts took place.

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In other news…

On the most recent episode of my Deep Questions podcast, I explored small habits that can lead to big results in the quest to find depth in a distracted world. (watch | listen)Meanwhile, for those who are still curious about my new book, Slow Productivity , Big Think just published a useful video in which I explain the book’s main principles.

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Dispatch from a Writing Shed

I’m writing this from a rental property, on a hillside overlooking the northern reach of the Taconic Mountains. A key feature of this property is a small outbuilding, designed and built by the current owner as a quiet place for visitors to work. Spanning, at most, twelve feet square, it features a daybed, a heating stove, and a desk arranged to look outward toward the distant peaks. A ceiling fan moves the air on muggy afternoons.

Here’s a view from the desk:

This rental property, in other words, includes a canonical example of one of my all-time favorite styles of functional architecture: the writing shed. (Indeed, as the owner told me, I’m not the first professional writer to use this space for this purpose in recent years.)

In my daily life in Takoma Park, Maryland, I don’t lack for interesting places to write. We designed the library in our house, which includes a custom-built Huston & Company library-style desk, specifically with writing in mind. (If you’re interested in what this looks like, the Spanish newspaper El País recently published a profile that includes a nice shot of me at my desk.) When I need a change of scenery while at home, I’ll also write on my front porch, where, during the grossest days of the DC summer, I’ll use a large floor fan to blow away the mosquitos and moderate the temperature. I also spend a considerable amount of time working amid the comforting din of our local coffee shop.

But as long-time readers of this newsletter know, I’ve always felt that there was something particularly special about the idea of writing in a quiet shed nestled in a quiet piece of natural property, such as what was enjoyed by Michael Pollan, David McCullough, and, perhaps my favorite example, E.B. White:

Which is all to say that I was excited, on arriving at this rental property, to spend a few weeks wrangling the early stages of a new book in a writing shed of my own.

So what have I learned so far?

Writing sheds don’t make the specific cognitive act of writing easier. It’s tempting to believe that the right aesthetics will usher in the muse and transport your efforts into a time-warping flow-state. But this doesn’t happen. Writing is still hard, requiring you to marshal multiple parts of your brain to work in synchronized and focused tandem toward the impossibly demanding task of producing well-crafted sentences.

But these sheds do seem to improve many of the general factors that surround this act. For example, they’re wonderfully effective at dampening the siren call of distraction. These rooms are used for a single purpose, so they lack the associations with other activities or interests that can so easily hijack your attention. The calming, natural environment beyond their windows also has a way of lulling the parts of your brain uninvolved in the writing task at hand into a harmless quiescence. Meanwhile, the novelty of their setting seems to lower the energy investment required to convince your brain to slip beyond its cacophonous inner-chatter and enter a deeper state more conducive to focus.

This all combines into a notable increase in mental stamina. Sessions that might have lasted ninety minutes at home can easily stretch to two or three hours amid the slow quiet of the shed. The writing is still hard, but it’s a more sustainable sort of hard.

There’s a lesson lurking here that extends beyond just writing: when it comes to cognitive work more generally, psychological factors matter. Whether you’re writing a book, or crafting computer code, or solving a business problem, or analyzing noisy data, you’re attempting to coax sustained abstract focus from a human brain not necessarily evolved for such intensely symbolic processing.

Of course elements like setting should really matter, as should other subtle elements such as how many total tasks you’re juggling, or the degree to which your day is necessarily fragmented by distraction. In knowledge work, productivity is about psychology as much as it is about tools and process. But we often ignore this reality.

As I can attest from personal experience, as I sit writing this essay, watching the clouds of an early morning rain shower clear off the distant mountains: If you really care about producing quality work, these softer factors matter.

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On Ultra-Processed Content

When I visited London last month, a large marketing push was underway for the paperback edition of Chris van Tulleken’s UK bestseller, Ultra-Processed People: Why Do We All Eat Stuff That Isn’t Food…and Why Can’t We Stop? It seemed to be prominently displayed in every bookstore I visited, and, as you might imagine, I visited a lot of bookstores.

Unable to ignore it, I eventually took a closer look and learned more about the central villain of van Tulleken’s treatise: ultra-processed food, a term coined in 2009 as part of a new food classification system, and inspired by Michael Pollan’s concept of “edible food-like substances.”

Ultra-processed foods, at their most damaging extreme, are made by breaking down core stock ingredients such as corn or soy into their basic organic building blocks, then recombining these elements into hyper-palatable combinations, rich in salt, sugar, and fat, soaked with unpronounceable chemical emulsifiers and preservatives.

As Chris van Tulleken points out, the problem with ultra-processed foods is that they’re engineered to hijack our desire mechanisms, making them literally irresistible. The result is that we consume way more calories than we need in arguably the least healthy form possible. Give me a bag of Doritos (a classic ultra-processed food) and I’ll have a hard time stopping until it’s empty. I’m much less likely to similarly gorge myself on, say, a salad or baked chicken.

I was thinking about this book recently as Scott Young and I were prepared to re-open our course, Life of Focus, for new registrations next week. One of the three month-long modules of this course focuses on implementing ideas from my book Digital Minimalism to help you regain control of your attention from the insistent attraction of screens.

It occurred to me that in this concept of ultra-processed food we can find a useful analogy for understanding both our struggles to disconnect, and for how we might succeed in this aspiration going forward.

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To elaborate this claim, I want to be more specific in analogizing food to media content. To start, we can connect passive text-based media, such as books and articles, to minimally processed whole foods. Linguistic encoding was the first information-bearing media our species developed; something we’ve been working with for over 5,000 years.

This timeframe, of course, is too short for evolutionary forces to apply, but it’s plenty long for us to have culturally adapted to this format. As with whole foods, consuming writing tends to make us feel better, and we rarely hear concerns about reading too much.

We can next compare twentieth-century electronic mass media — that is, radio and television — to moderately-processed food like white bread, dry pasta, and canned soups. As with processed foods, we weren’t prepared for the arrival of new mass media forms that where much easier to consume and much more superficially palatable.

As a result, for the first time in our species’s interaction with media, over-consumption became a problem. (In the 1960s, the average household television viewing jumped past five hours per day.) Many social critics and educators began to rightly lament this sudden intrusion of electronic media into our cultural landscape (see, for example, this and this and this).

Many of the new media forms built on the consumer internet that subsequently emerged in the late 1990s can be similarly classified as moderately-processed. These include podcasts, newsletters, and blog posts. As with television and radio, the content itself can be valuable, but often times it’s not, and the ease of its delivery requires vigilance to protect against over-consumption.

This then brings us back to ultra-processed foods, which as the twentieth century gave way to the twenty-first, began to increasingly dominate our diets with their lab-optimized hyper-palatability. The clear analogy here is to digital information offered through the social media platforms that vaulted into cultural supremacy in the 2010s.

As described, ultra-processed foods are created by first breaking down cheap stock foods into their basic elements, and then recombining these ingredients into something unnatural but irresistible. Something similar happens with social media content. Whereas the stock ingredients for ultra-processed food are found in vast fields of cheap corn and soy, social media content draws on vast databases of user-generated information — posts, reactions, videos, quips, and memes. Recommendation algorithms then sift through this monumental collection of proto-content to find new, hard to resist combinations that will appeal to users.

A feedback loop soon develops in which the producers of this stock content (that is, those posting to social media) adapt to what seems to better please the platforms, simplifying and purifying their output to more efficiently feed the algorithms’ goal of hijacking the human desire mechanisms.

In this way, the users of social media platforms simulate something like the food scientist’s ability to break down corn and reconstitute it into a hyper-palatable edible food-like substances. What is a TikTok dance mash up if not a digital Dorito?

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This analogy between food and media is useful because it helps us better understand responses to the latter. In the context of nutrition, we’re comfortable deciding to largely avoid ultra-processed food for health reasons. In making this choice, we do not worry about being labelled “anti-food,” or accused of a quixotic attempt to reject “inevitable progress” in food technology.

On the contrary, we can see ultra-processed good as its own thing — a bid for food companies to increase market share and profitability. We recognize it might be hard to avoid these products, as they’re easy and taste so good, but we’ll likely receive nothing but encouragement in our attempts to clean up our diets.

This is how we should think about the ultra-processed content delivered so relentlessly through our screens. To bypass these media for less processed alternatives should no longer be seen as bold, or radical, or somehow reactionary. It’s just a move toward a self-evidently more healthy relationship with information.

This mindset shift might seem subtle but I’m convinced that it’s a critical first step toward sustainably changing our interactions with digital distraction. Outraged tweets, aspirational Instagram posts, and aggressively arresting TikToks need not be seen as some unavoidable component of the twenty-first century media landscape to which we must all, with an exasperated sigh, adapt.

They’re instead digital Oreos; delicious, but something we should have no problem pushing aside while saying, “I don’t consume that junk.”

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In other news…

As mentioned, my online course Life of Focus (co-developed with Scott Young) is open for new registrations next week between June 24-28. This course draws on wisdom from my books Deep Work and Digital Minimalism, as well as Scott’s book Ultralearning. (Learn more here)In Slow Productivity news: As we reach the half-way point of 2024, my new book was selected by Amazon’s editors as their #1 Business Book of the year (so far)! I was also pleased to see it featured in a list of the nonfiction books “NPR staffers have loved so far this year” as well as in a New York Times article on “productivity books time-management experts actually use.” If you haven’t yet checked out my book yet, do so now…

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Manchester United Embraces Pseudo-Productivity

Earlier this month, Jim Ratcliffe, part owner and operations head for the storied English football club Manchester United, announced an end to the flexible work-from-home policy that the club’s approximately 1,000 employees had enjoyed since the beginning of the coronavirus pandemic. “If you don’t like it,” he said in a recent all staff meeting, “please seek alternative employment.”

Ratcliffe is not necessarily wrong to view remote work with skepticism. Having covered this topic extensively for The New Yorker, I don’t align myself with the crowd that automatically associates telecommuting with a self-evident pro-labor progressivism. Though I agree that flexible work arrangements will play an important role in the future of the knowledge sector, I also think that they’re hard to get right, and that we’re still in the early stages of figuring out how to implement them well — so for the moment, wariness is justified.

My problem with Ratcliffe’s return to office plan is instead the evidence he used to justify it. As reported by The Guardian, Ratcliffe supported his new policy by noting that when he experimented with a work-from-home Fridays program with another one of his companies, they measured a 20% drop in email traffic.

Here we find a pristine example of the central villain of my new book: a management philosophy called pseudo-productivity, which leverages visible activity as a crude proxy for useful effort.

Pseudo-productivity instantiates a double negative. Employers like Ratcliffe fear the idea of their employees not working at all; Tango dancing, so to speak, while still on the clock. If they see evidence that you’re doing something — anything, really — work related, then at the very least they know it’s not the case that you’re not working at all.

But in defending against this negative possibility, pseudo-productivity caps the ability to do something notably positive.

This follows because the easiest and most consistent way to demonstrate visible effort is to engage in rapid back-and-forth digital communication. This frenetic tending of inboxes and chat channels, however, makes it significantly harder to actually produce meaningful results.

Pseudo-productivity might prevent brazen slacking, but in doing so it impedes the type of results that ultimately matter most. It’s also exhausting for those caught in its twisted logic.

Ratcliffe’s goal shouldn’t be to increase his employees’ email traffic, but instead to find smarter measures of productivity that allow such a flawed metric to safely be ignored.

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Speaking of Slow Productivity, as well as storied British institutions, I just returned from a media tour in London, where, among other stops, I had a great conversation about my book on Chris Evan’s Breakfast Show (watch here).

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Do We Need AI to Revolutionize Work?

In recent months, I’ve been doing a fair number of interviews about my new book, Slow Productivity. I’m often asked during these conversations about the potential impact of artificial intelligence on the world of knowledge work.

I don’t talk much about AI in my book, as it focuses more on advice that individuals can put into place right now to escape busyness and find a more sustainable path toward meaningful accomplishment. But it’s a topic I do think a lot about in my role as a computer scientist and digital theorist, as well as in my recent journalism for The New Yorker (see, for example, this and this).

With this in mind, I thought I would share three current thoughts about the intersections of AI and office productivity…

First, the large language model tools drawing the bulk of the attention at the moment, including ChatGPT, Claude, and Gemini, will not, on their own, revolutionize knowledge work productivity.

Language models can help speed up administrative tasks. For example, you can use them to write initial drafts of an email or fix the language on an email you wrote quickly. They can also create a summary of what was discussed in a long chat transcript or help you brainstorm ideas.

This is useful, but not necessarily transformative. Other technologies have previously sped up the execution of administrative tasks (think: every major breakthrough of the personal computer revolution), but speeding up these tasks has a way of inducing even more to fall into their slipstream. The result is less a new productivity utopia than an even more intense level of freneticism.

(There are some interesting exceptions here. These models’ ability to produce ready-to-use computer code and bespoke images, as well as fully automate certain customer support interactions, could lead to immediate disruption in certain fields.)

Second, the real impact will come when artificial intelligence tools gain the ability to plan, including future prediction and the simulation of other minds. As I reported for The New Yorker (and summarized here) this will involve the combination of language models with other types of (non-neural network) models, like those used to explore moves in game-playing programs.

Such multi-strategy systems can go beyond speeding up administrative tasks and instead fully automate them. For example, instead of helping you draft an email, such a program might respond to an email entirely on your behalf. This would be a game changer.

(Mustafa Suleyman has argued that the real Turing Test that matters is whether a given AI can go off and earn $100,000 for you on the internet. I would argue the test that’s more relevant — and consequential — is whether an AI can empty your inbox.)

Third, while it’s true that the major players in this space are certainly working on these types of planning-enabled systems, there’s no need to wait for these new technologies to improve our professional lives. We can achieve their promised revolutions right now; not with fancy computer programs, but with new common sense rules and processes for how we manage workloads, and how we communicate about our efforts. I don’t need a trillion-parameter model to empty my inbox if I can prevent my inbox from getting so full in the first place.

My new book, this newsletter, and my podcast, among many other sources, all contain practical ideas for achieving such an overhaul of knowledge work. None of these ideas depend on radical new tools. They rely instead on new perspectives and common sense. Put another way: We don’t need transformer-based neural networks to revolutionize work, we just need a willingness to try something new.

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Speaking of books, my longtime friend and collaborator Scott Young has a fantastic new title out this week called: Get Better at Anything: 12 Maxims for Mastery. I think Scott has written the ultimate (and approachable) guide to getting better at the stuff that matters. If you’re interested in engineering a deeper life, you need this book in your proverbial toolkit. (Learn more.)

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Two Chances to See Me Next Week

I really enjoyed meeting so many of you at my Politics and Prose event a couple weeks ago. It was meaningful for me to be talking about my books in person again after having to launch my last title during a pandemic.

It was also a great opportunity to thank people for their support of Slow Productivity, which just yesterday landed #4 on the NYT’s monthly business bestseller list. (If you haven’t bought Slow Productivity yet, you should! If you read this newsletter, you’ll love it…)

It’s with all this in mind that I wanted to briefly share two upcoming opportunities to come meet me and hear me talk about Slow Productivity in the DC area next week!

On Monday (4/8) at 5:30pm I’ll be giving a book talk on campus at Georgetown University. The event, which is open to the public, will be held in the Fisher Colloquium (in the business school building on main campus), and will include plenty of time for questions. If you’re in the Georgetown area, please come attend! (Details.)

On Thursday (4/11) at 6:00pm I’ll be recording a “live” podcast at People’s Book, in my hometown of Takoma Park, MD (which is right at the DC border and easily accessible from metro). I’ve been looking forward to this event! I’ll do a deep dive segment, then producer Jesse and I will take live questions from the audience. This is also a chance to support my favorite independent bookstore (which is literally right around the corner from my podcast studio). This event is free and open to the public, but you should RSVP here if you’re planning on coming. (Details.)

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Can You Tweet Your Way to Impact?

Earlier this month, a group of scientists from universities around the world published the results of an ingeniously simple experiment in the journal PLoS ONE. Every month, for ten months, they randomly selected an article from a journal in their field to promote on their Twitter accounts, which, collectively, added up to around 230,000 followers. They then later compared the success of these tweeted articles with control articles randomly selected from the same issues.

The result? No statistically significant increase in citations in the promoted articles versus the controls. There was a difference, however, in the download numbers: more people took a look at the tweeted citations.

In this narrow look at social media and science a more general lesson about this technology emerges. Maintaining an aggressive presence in these online spaces can increase the number of people who temporarily encounter you or your work. But these encounters are often ephemeral, rarely leading to more serious engagement. It’s exciting to receive increased attention in the present, but it may have little effect on your impact in the future.

Book authors understand this lesson. It’s valuable, for example, for me to do a long-form podcast interview about my book, or to tell my long-time readers on this newsletter about it (ahem, see below). These activities are the equivalent of a professor giving a talk about their paper at an academic conference or posting an announcement in a relevant publication.

At the same time, more than a few authors have learned in recent years that large numbers of TikTok, or Twitter, or YouTube subscribers do not always amount to much in terms of sustained sales. The relationships with these social media audiences is different: less trust, more antsy energy; exciting, but shallow.

Content platforms that compensate your energies largely with the ego-boosting embrace of digital attention can be compelling and fun. They can also be useful for seeking out feedback or new connections (see my recent conversation with Adam Grant for a nice discussion of this point.) But don’t mistake them as somehow vital to your goal of finding a serious audience for your deepest efforts.

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Speaking of deep efforts, I have two quick administrative notes…

As part of a major article that I’m writing, I’m looking to hear from college students who have used tools like ChatGPT to help them write class paper assignments. If this describes you, please send me an email at author@calnewport.com with the subject “ChatGPT Writing.” You can assume the contents of your email will be confidential and off-the-record.My book Slow Productivity continues to chug along. It’s now spent its first three weeks on the Amazon Charts! If you have not yet bought the book, please consider doing so! (You can learn more here, or find it wherever books are sold.)

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ChatGPT Can’t Plan. This Matters.

A brief book update: I wanted to share that Slow Productivity debuted at #2 on the New York Times bestseller list last week! Which is all to say: thank you for helping this book make such a splash.

If you still haven’t purchased a copy, here are two nudges to consider: (1) due to the rush of initial sales, Amazon has temporarily dropped the hardcover price significantly, making it the cheapest it will likely ever be (US | UK); and (2) if you prefer audio , maybe it will help to learn that I recorded the audiobook myself. I uploaded a clip so you can check it out (US | UK).

Last March, Sebastien Bubeck, a computer scientist from Microsoft Research, delivered a talk at MIT titled “Sparks of AGI.” He was reporting on a study in which he and his team ran OpenAI’s impressive new large language model, GPT-4, through a series of rigorous intelligence tests.

“If your perspective is, ‘What I care about is to solve problems, to think abstractly, to comprehend complex ideas, to reason on new elements that arrive at me,'” he said, “then I think you have to call GPT-4 intelligent.”

But as he then elaborated, GPT-4 wasn’t always intelligent. During their testing, Bubeck’s team had given the model a simple math equation: 7*4 + 8*8 = 92. They then asked the model to modify a single number on the lefthand side so that the equation now equaled 106. This is easy for a human to figure out: simply replace the 7*4 with a 7*6.

GPT-4 confidently gave the wrong answer. “The arithmetic is shaky,” Bubeck explained.

This wasn’t the only seemingly simple problem that stumped the model. The team later asked it to write a poem that made sense in terms of its content, but also had a last line that was an exact reverse of the first. GPT-4 wrote a poem that started with “I heard his voice across the crowd,” forcing it to end with the nonsensical conclusion: “Crowd the across voice his heard I.”

Other researchers soon found that the model also struggled with simple block stacking tasks, a puzzle game called Towers of Hanoi, and questions about scheduling shipments.

What about these problems stumped GPT-4? They all require you to simulate the future. We recognize that the 7*4 term is the right one to modify in the arithmetic task because we implicitly simulate the impact on the sum of increasing the number of 7’s. Similarly, when we solve the poem challenge, we think ahead to writing the last line while working on the first.

As I argue in my latest article for The New Yorker, titled “Can an A.I. Make Plans?,” this inability for language models to simulate the future is important. Humans run these types of simulations all the time as we go through our day.

As I write:

“When holding a serious conversation, we simulate how different replies might shift the mood—just as, when navigating a supermarket checkout, we predict how slowly the various lines will likely progress. Goal-directed behavior more generally almost always requires us to look into the future to test how much various actions might move us closer to our objectives. This holds true whether we’re pondering life’s big decisions, such as whether to move or have kids, or answering the small but insistent queries that propel our workdays forward, such as which to-do-list item to tackle next.”

If we want to build more recognizably human artificial intelligences, they will have to include this ability to prognosticate. (How did Hal 9000 from the movie 2001 know not to open the pod bay doors for Dave? It must have simulated the consequences of the action.)

But as I elaborate in the article, this is not something large language models like GPT-4 will ever be able to do. Their architectures are static and feedforward, incapable of recurrence or iteration or on-demand exploration of novel possibilities. No matter how big we push these systems, or how intensely we train them, they can’t perform true planning.

Does this mean we’re safe for now from creating a real life Hal 9000? Not necessarily. As I go on to explain, there do exist AI systems, that operate quite differently then language models, that can simulate the future. In recent years, an increasing effort has been to combine these planning programs with the linguistic brilliance of language models.

I give a lot more details about this in my article, but the short summary of my conclusion is that if you’re excited or worried about artificial intelligence, the right thing to care about is not how big we can make a single language model, but instead how smartly we can combine many different types of digital cognition.

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Come See Me Saturday in DC + TikTok Falters

I know it’s been a minute since I’ve published one of my normal essays. I’ll be returning to these soon as the chaos of the Slow Productivity launch dissipates.

In the meantime, I wanted to share two quick notes: one about the book, and one about something interesting (but completely unrelated) that several of you have sent in my direction recently…

A note about the book

On Saturday, March 16th at 3:00pm, I’ll be appearing at Politics and Prose on Connecticut Avenue in Washington, DC. I’ll be joined in conversation with David Epstein, the New York Times bestselling author of Range. We’ll talk Slow Productivity and take questions from the audience. (For a preview, see my recent interview in Dave’s excellent newsletter.)

This is my first live event of the book tour, so if you’re in the DC area, I’d love to see you there! (More details.)

(You might also be interested in my most recent essay for The New Yorker, titled “How I Learned to Concentrate,” which discusses how my early years at MIT shaped almost everything I’ve written about ever since. I had fun writing this one: lots of Stata Center nostalgia!)

A note about something completely unrelated

Several readers have recently pointed me toward a fascinating article from the Wall Street Journal titled “Why Some 20-Somethings Are Saying No to TikTok.”

TikTok users between the ages of 18 to 24 dropped by around 9% between 2022 and 2023. Which is a lot for a single year.

The article’s author, Julie Jargon, talks to some of these ex-TikTok users to find out why they left. What she discovers is that many were unnerved by the application’s addictiveness.

One subject reported neglecting laundry and dishes to keep scrolling TikTok. Another reported that he lost the ability to do anything without the app in sight:

“He took out the trash while watching TikTok, but could only carry one bag at a time because his phone was in the other hand. When he cooked, he would stop chopping ingredients to scroll to the next video.”

This is all vaguely icky, but what caught my attention more was the fact that once these users broke their TikTok addiction, they were happy to move on. It was hard to put down, but ultimately not that important.

As I wrote in The New Yorker in 2022, this is the fatal flaw of TikTok. By focusing exclusively on addictiveness instead of slowly growing a hard-to-replicate social graph, like those that provide the foundation for legacy social platforms like Facebook and Instagram, TikTok gained lots of users quickly, but maintains only a weak grasp on them.

TikTok provides pure entertainment. If it unnerves you, as it has for the many 20-somethings who recently quit, there isn’t much cost to leaving — no careful collection of friend links or follower relationships to lose. You’re instead only walking away from an abstract stream of brain stem stimulation.

In my New Yorker piece I predicted that this trend toward pure distraction would lead to more turnover and tumult in the attention economy application space. We may be seeing the beginning of this trend starting to play out.

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How the Acquired Podcast Became a Sensation

My podcast producer recently turned me onto a show called Acquired, which features its co-hosts, Ben Gilbert and David Rosenthal, diving deep into the backstories of well-known brands and companies, from Porsche and Nike, to Amazon and Nintendo.

It turns out I was late to this party. In the eight years since Acquired was originally launched, it has grown into a huge hit. The show now serves more than 200,000 downloads per episode. As Rosenthal revealed in a Fast Company profile last summer, they now face the problem of their audience becoming too large for their advertisers to afford paying the full fair market price for their spots.

What interests me about Acquired, however, is less what they’ve accomplished than how they did it. The conventional wisdom surrounding new media ventures is that success requires frenetic busyness. You need to produce content perfectly-tailored to your audiences’ attention spans, master The Algorithm, exist on multiple platforms, and above all else, churn out content quickly to maximize your chances of stumbling into vibe-powered virality.

Acquired did none of this. Gilbert and Rosenthal’s podcasts are very long; the two-part treatment of Nintendo I just finished clocked in at a little under seven hours. They also publish on an irregular schedule, often waiting a month or more between episodes. Combine this with the reality that they largely ignore YouTube and have no discernible social media strategy, this venture should have long ago crashed and burned. But it instead keeps growing.

What does explain the success of Acquired? The answer is almost disappointingly simple: it’s really good. Gilbert and Rosenthal don’t just look into the histories of the companies they profile, they master them — tracking down obscure books, reading every relevant article, pouring through investor filings, interviewing people who were involved. Fast Company reported that for their episode on Nike, Rosenthal prepared a 39-page script and Gilbert created a 4,000-word document listing insights to mention during the taping.

The key to this quality is effort. Early in the show’s history, Gilbert and Rosenthal spent around 5 to 10 hours researching each episode. Today, this number has grown to around 100 hours, and for good reason. “What I do know is that every time we’ve done more work,” Rosenthal explained, “the reaction and the results, both in terms of what people say qualitatively and the numbers, go up.”

I’m telling this story because the growth of Acquired helps explain a seemingly curious choice I made in my new book, Slow Productivity. In this work, I present three principles for embracing a more sustainable and meaningful approach to your professional life. The first two principles are clearly related to slowness: “do fewer things” and “work at a natural pace.” The third, however, seems somewhat out of place: “obsess over quality.”

As the Acquired story emphasizes, however, it’s this third goal that supports the other two. When you decide to obsess over quality, as Gilbert and Rosenthal did with their podcast, slowness becomes self-evidently the only way forward. Gilbert and Rosenthal didn’t monkey around with YouTube, or social media strategies, or optimal customer growth strategies, because all of that fast effort would get in the way of the slow pursuit of excellence.

We’re used to the idea that slowing down might help improve the quality of what we do. But in many cases, this relationship can also exist in exactly the opposite direction.

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Speaking of Slow Productivity, the book comes out on March 5th, but if you’re thinking about buying it anyway, please consider pre-ordering it, as this really helps draw attention to the title. If you do pre-order, I want to thank you with some bonus material about the philosophy.

The process here is simple: (1) pre-order the book from your preferred book seller; (2) email your receipt to slow@preorderbonuses.com.

That’s it. We’ll verify your receipt and then immediately send you the bonuses. (More details, including how to pre-order a signed copy from my favorite local bookseller, are available here.)

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