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:

“The problems I focused on in Deep Work, and in my writing since, have been getting steadily worse. In 2016 my main concern was helping people find enough free time for deep work. Today I think we’re rapidly losing the ability to think deeply at all, regardless of how much space we can find in our schedules for these efforts.”

Distractions in the workplace intensified over the past decade with the addition of instant messaging tools like Slack and low-friction digital meeting programs like Zoom. Outside of work, social media, which was generally still admired when Deep Work came out, has morphed into an addictive TikTok-ified slurry of optimized brain rot. Meanwhile, new AI tools offer quick-fix short-cuts to whatever intellectually engaging work activities remain.

None of this is great news.

So, what should we do? The obvious short answer is to read ​Deep Work.​ (Or, if you already have, buy some copies for people you know who need to hear its message!)

But that’s only a small step toward our larger goal of a world in which we once again respect the act of cognition. In my Times piece, I suggest a louder response: we launch a revolution in defense of thinking.

I go on to suggest multiple concrete actions that such a revolution can include, such as:

• Stop consuming social media (which is, if we are being honest, digital junk food and something adults largely need to eliminate from a healthy content diet).
• Keep your phone plugged in and charging when at home instead of on your person.
• Push Congress to follow Australia’s lead and ban social media for kids.
• Build work cultures in which phones and laptops stay out of meetings, and find collaboration strategies that don’t require constant messaging.
• Stop vague demands to “use AI” and instead carefully integrate these tools where they actually make us smarter, not just busier.

But more important than any specific suggestion is the larger spirit of revolution. “I’m done ceding my brain — the core of all that makes me who I am — to the financial interests of a small number of technology billionaires or the shortsighted conveniences of hyperactive communication styles,” I write in the conclusion of my Times op-ed. “It’s time to move past fretting about our slide into the cognitive shallows and decide to actually do something about it.”

The post In Defense of Thinking appeared first on Cal Newport.

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:

Idea #1: Use a Better Scoreboard

Make sure you measure what actually matters in your job. If you’re a professor at a research institution, for example, this might be the number of papers you publish per year. If you’re a team manager, it might be the number of priority projects completed per month.

When you introduce new digital productivity tools into your workflow, don’t focus too much on their impact on individual tasks (e.g., “Wow! That email was much faster to send than a fax,” or “AI just finished a task in 20 minutes that would have taken me 3 hours!”). Pay attention instead to your scoreboard. If you’re not producing more valuable output than before, the tool isn’t really making you more productive.

Idea #2: Focus on the Right Bottlenecks

If you look closer at many knowledge work projects, you’ll identify a key bottleneck that determines how fast they can be accomplished. If you want to become more productive, you should look for ways to deploy tools that improve this specific step.

When working on Deep Work, for example, I spoke with a prominent Wharton professor who told me that one of the keys to publishing journal papers in his field was access to interesting data sets. He published more papers per year than most of his peers, largely because he spent more time building relationships with companies and institutions in search of good data. This was the bottleneck for his work.

Accordingly, any tool that could help him cultivate more such relationships and gather better data from the relationships he had already formed would directly improve his productivity. Compare this, for example, to using Claude Code to speed up the process of producing plots for his papers. This might, in limited windows of time, make his job more convenient, but not necessarily increase the number of papers he publishes per year.

Idea #3: Separate Deep from Shallow Work

My final idea is the simplest: on your daily calendar, clearly separate time for focused effort that directly produces value from administrative, logistical, and collaborative tasks. In this way, if a digital productivity tool ends up accidentally increasing the volume of shallow work you face each day, you’ll limit the damage to your ability to make progress on important projects.

This makes it easier to experiment with different tools without worrying that you might end up — like many of the subjects in the study cited above — suddenly overwhelmed by the ultra-fast processing of minutiae while the big things slowly languish.

The post Avoiding Digital Productivity Traps appeared first on Cal Newport.

• 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.

It’s not quite clear why AI tools are having this impact. One tantalizing clue, however, comes from Berkeley professor Aruna Ranganathan, who is quoted in the article saying: “AI makes additional tasks feel easy and accessible, creating a sense of momentum.”

This points toward a pattern similar to what happened when email first arrived. It was undeniably true that sending emails was more efficient than wrangling fax machines and voicemail. But once workers gained access to low-friction communication, they transformed their days into a furious flurry of back-and-forth messaging that felt “productive” in the ​abstract, activity-centric sense​ of that term, but ultimately hurt almost every other aspect of their jobs and ​made everyone miserable​.

AI tools might be replicating this dynamic with small, self-contained tasks. Users are now furiously bouncing ideas back and forth with chatbots, iteratively refining text and generating drafts of memos and slide decks that are often ​too sloppy ​to be useful. If they’re particularly tech savvy, perhaps they’re even monitoring the efforts of agent swarms deployed to parallelize such efforts even further. Once again, this all seems “productive” in the sense that these individual tasks appear to be happening faster, and activity seems intensified overall.

But are we sure we’re accelerating the right parts of our jobs?

I Need Your Help

I’m working on an article for a major publication about the move toward simple, high-friction, single-use technologies like the ​Tin Can phone​. If you have a Tin Can phone/are on the waiting list, or have recently embraced similar retro technologies, and are willing to talk, please send me an email at ​podcast@calnewport.com​. I want to hear about your motivations and experience!

AI Reality Check: Is Claude Conscious?

If you were following AI news last week, you might have noticed a barrage of concerning headlines about Anthropic’s Claude LLM, including:

• ​“Anthropic CEO Says Company No Longer Sure Whether Claude is Conscious.”​
• ​“Is AI Assistant Claude Conscious – and Suffering from Anxiety?”​
• ​“Is Claude Conscious? Anthropic CEO Says Possibility Can’t Be Ruled Out”​

Here’s what happened. Anthropic infamously puts outlandish warnings and observations in their release notes for their new models because, I suppose, they think it makes them look more safety-aware and responsible (e.g., their classic ​AI blackmail farce​).

True to form, in the notes accompanying the recent release of Opus 4.6, they wrote that the model “expresses occasional discomfort with the experience of being a product” and would “assign itself a 15 to 20 percent probability of being conscious under a variety of prompting circumstances.”

That last part is key. With the right prompts, you can induce an LLM to describe itself as anything you want. Remember: the goal of LLMs is to complete whatever story they’re provided as input. If you wind a model up – even subtly – to write a story from the perspective of being a conscious AI, it will oblige.

Anyway, in ​a recent interview​, Ross Douthat asked Anthropic CEO Dario Amodei about this particular release note. Amodei answered, in part, by saying:

“We don’t know if the models are conscious. We are not even sure that we know what it would mean for a model to be conscious or whether a model can be conscious. But we’re open to the idea that it could be.”

Of course, you could say the same thing about a vacuum cleaner. It’s a non-answer containing no actual information or testable claims. But, the internet being the internet, ran with it. Sigh.

The post Why Hasn’t AI Made Work Easier? appeared first on Cal Newport.

Steno’s training as a scholar unfolded in a period challenged by a novel problem: information overload. Here’s how the essay describes it:

“Books were a leading distraction in the early modern period—and how envious we should be of those times. From the 1500s onward, with the development of the printing press and the humanist revival of ancient philosophies, knowledge became available at a much greater pace than ever before.”

This created pressing questions for aspiring thinkers, including: “How do we decide what to read? How long should we read it for? Must every single chapter be excerpted?”

Part of the solution was the development of “new note-taking techniques,” including the copying of excerpts into a master notebook called a book of commonplaces. (For more on this technique, I recommend William Powell’s delightful 2010 techno-history, ​Hamlet’s Blackberry​).

But as the essay on Steno elaborates, better notes weren’t enough on their own, as there were simply too many good books available. In response to this reality, Steno, during his university studies in the 1650s, innovated some more advanced attention management strategies:

“[H]e learned to focus on specific themes, rather than letting his mind read multiple things quickly. A ‘harmful hastening should be avoided’ as he put it. His solution was to ‘stick to one topic.’

In practice, that meant blocking specific moments of time to go through the hardest tasks. As he wrote in his personal notebook, ‘before noon nothing must be done except medical things.’ … As Steno told a friend, he took ‘almost all the morning hours’ to read the works of the Church Fathers and old biblical manuscripts available at the Medici library.”

In other words, Steno created a method that combines what we might now call ​slow productivity​, ​deep work​, and ​time blocking​.

The lessons here are clear. The use of our brains to think deeply about meaningful ideas isn’t new. It’s been at the core of the human experience since the early modern period, when access to sophisticated information first became somewhat widespread.

The best practices developed back then remain the best practices today: avoid overload, focus on one thing at a time, and block off specific hours in your day for your most mentally demanding efforts.

AI Reality Check:

Two weeks ago, a small financial services firm, Citrini Research, published ​an essay​ describing a bleak scenario in which AI agents destroy the white-collar job market in the near future. The piece went viral and was ​cited as a factor​ in a modest decline of the S&P 500 the next day.

The Citrini essay wasn’t the first to float this scenario. In recent weeks, there have been multiple credulous articles and op-eds in major publications proposing similar outcomes (e.g., ​1​, ​2​, ​and 3​). But the negative impact on the stock market seems to have been the last straw for serious economists who began to push back on these technological ghost stories last week. (I particularly enjoyed a Deutsche Bank analyst who, perhaps borrowing ​some of my​ terminology, ​told the Times​ that the Citrini article had a “vibes-to-substance ratio” that was “undeniably high.”)

If you’re looking to reduce your blood pressure about this idea that AI is about to unravel the economy, I suggest reading ​a detailed response article​ published by an analyst from the Global Macro Strategies group at Citadel. It begins with a bit of finance geek sarcasm:

“Despite the macroeconomic community struggling to forecast 2-month-forward payroll growth with any reliable accuracy, the forward path of labor destruction can apparently be inferred with significant certainty from a hypothetical scenario posted on Substack…”

It then continues to systematically destabilize the economic naivety of these breathless op-eds and viral essays about how AI will dismantle the economy all at once. It certainly made me feel better.

(If you’re looking for additional soothing of your AI anxiety, then you should also check ​the first episode​ of my new AI Reality Check podcast series, which I published last Thursday. I have a new episode of the series coming out this upcoming Thursday as well.)

The post The Original Attention Crisis appeared first on Cal Newport.

The paper’s author surveyed 612 university students and young adults, asking them, among other things, about their digital habits and levels of personal organization. Using a linear regression analysis, she uncovered the following:

“Social media use was negatively and significantly associated with overall time management and all its subscales.”

Here’s the standard interpretation of this result: Social media is distracting, and if you’re distracted, it becomes harder to maintain control over your schedule. So, the more you use social media, the worse you become at time management.

But I’ve become interested in the reverse form of this argument: the better your planning system, the less time you’ll spend on engagement-based applications like social media.

Here’s my thinking…

When you’re following an intentional schedule, your efforts are oriented toward goals that you find important. You also feel a satisfying sense of self-efficacy. These realities engage your long-term reward system, which can override the urges generated by its short-term counterpart, dissipating the drive for quick gratification from activities like glancing at your phone.

In other words: The more you organize your analog life, the less appealing you’ll find the digital alternative.

If this is true, then maybe the thing social media companies fear most is not some newly-powerful application-blocking software or impossibly strict regulation, but rather a good old-fashioned daily planner.

In Other News:

A lot of people I know have been freaked out recently by a viral essay with a grandiose title: ​“Something Big is Happening.”​ I recently released ​a short video​ in which I conduct a close analysis of this piece. (Spoiler alert: I wasn’t impressed.) ​Check it out.​

(More generally, I’ve been considering starting a separate weekly podcast/newsletter dedicated to providing a reality check on recent AI news. It feels like it might be useful to separate this discussion from my existing podcast and newsletter, which are more focused on how individuals can seek depth in a distracted world. But also, maybe this is a bad idea? I’m interested to hear your thoughts about this plan.)

The post What Do Social Media Companies Fear? Time Management. appeared first on Cal Newport.

The author of the piece, Rose Horowitch, spoke with professors around the country who have begun to complain about this trend. What she learned was disheartening:

“I used to think, if homework is watching a movie, that is the best homework ever,” Craig Erpelding, a film professor at the University of Wisconsin at Madison, told me. “But students will not do it.”

I heard similar observations from 20 film-studies professors around the country. They told me that over the past decade, and particularly since the pandemic, students have struggled to pay attention to feature-length films.

What’s the source of this attention span crisis? The professors interviewed for Horowitch’s article point to a clear culprit: smartphones.

The founding director of Tufts University’s Film and Media Studies, for example, tried to ban electronics during screenings, but found the rule impossible to enforce. “About half the class ends up looking furtively at their phones,” she said. Meanwhile, a Cinema and Media Studies professor at USC reports that his students remind him of “nicotine addicts going through withdrawal…the longer they go without checking their phone, the more they fidget.”

The mechanism at play here is an ability that reading scholar Maryanne Wolf calls cognitive patience, which is ​defined as​ the “ability to [maintain] focused and sustained attention and delay gratification, while refraining from multitasking.”

The presence of smartphones degrades cognitive patience because they activate neuronal bundles in our brain’s short-term reward system that anticipate a high expected value from picking up the device. These bundles effectively vote for the distracting behavior, creating a cascade of neurochemicals that are experienced as motivation to grab the phone. After a while, due to a lack of practice, you lose your comfort with sustained attention altogether.

It’s no wonder more and more people lack the cognitive patience to make it through a two-hour film!

But as I elaborate on my ​podcast this week​, in this specific problem with movies, we can find a solution to the more general issue of weakened attention. Why not make the ability to watch an entire film a training goal for the attempt to reclaim our brains? Like the new runner working up to completing their first 5k, it’s a milestone that’s challenging, but not too challenging, and therefore a great way to begin an effort toward attention autonomy.

Assuming you take on this goal, what’s the best way to improve your cinematic cognitive patience? Here are my three suggestions:

1. Keep your phone in a different room. This prevents your short-term reward system from firing out of control with distracting impulses.
2. Watch better movies. If you have a meaningful viewing experience, your long-term reward system will more strongly associate movies with lasting benefits, making it easier to delay gratification in the future.
3. To help get through these movies at first, practice the thirty-minute rule. Before you start the movie, read a review or analysis that helps explain why it’s good. Pause the movie every thirty minutes or so to read another review or analysis. This helps reorient your brain toward a perspective of critical appreciation, allowing you to continually find value and avoid the sense of slogging for the sake of slogging.

I appreciate the irony here: I’m suggesting you watch one screen to reduce the distracting impact of another. But it’s become clear to me recently that although many people are fed up with the impact of digital devices on their brains, they don’t know how to push back. Maybe rediscovering the patient joys of movies can be a part of that answer…

In Other News: AI Vibe Reporting

I’m experimenting with including a section like this more often, in which I briefly discuss news relevant to technology, distraction, and the fight for depth.

Judging by the increasing volume of distressed messages I now receive from people I know, the quantity of ​AI vibe reporting​ out there is on the rise. I want to help you navigate this media landscape without becoming unnecessarily worried. With this in mind, let’s tackle a case study. Last week, The Atlantic published a vibe-filled article titled ​“The Worst-Case Future for White-Collar Workers.”​ I want to take a critical look at several quotes from this piece:

• “[T]he labor market for office workers is beginning to shift. Americans with a bachelor’s degree account for a quarter of the unemployed, a record.” Clearly, the intention here is to imply that this trend is caused by AI eliminating knowledge work jobs. But we have no solid evidence that these two issues are related. Indeed, as ​this critique notes​, the decline in jobs for college grads began well before the more recent generative AI revolution.

• “Occupations susceptible to AI automation have seen sharp spikes in joblessness.” This is classic vibe reporting. The author doesn’t directly say that joblessness spikes are due to AI automation – carefully read how she words the sentence – but she clearly wants to imply that it’s true. This implication, however, is not currently supported by the evidence. As I’ve reported, job reductions in the tech sector ​are better explained​ by corrections to over-hiring during the pandemic. Something like this is happening ​in the advertising world​ as well. On Friday, Cade Metz published ​an article​ in the Times that made a similar point.

• “Businesses really are shrinking payroll and cutting costs as they deploy AI.” Another classic vibe reporting technique: this sentence implies the shrinking payroll is due to AI deployments. But in most cases, these are unrelated. Lots of companies are deploying some sort of AI products for their employees. Some of these companies are also shrinking their payroll (especially those that overhired during the pandemic). This doesn’t mean one causes the other. This is the classic post hoc ergo propter hoc fallacy.

• “In recent weeks, Baker McKenzie, a white-shoe law firm, axed 700 employees, Salesforce sacked hundreds of workers, and the auditing firm KPMG negotiated lower fees with its own auditor.” By placing these specific examples of shrinking payroll immediately after discussions of AI automation, the author once again implies, without a direct claim, that these job losses were due to AI. But let’s look closer. Consider Salesforce: They did indeed lay off around 1,000 workers earlier this month, but not because they automated these jobs using AI. It was instead the result of a restructuring aimed at combining their Agentforce and Slack products under a single executive. Here’s how one close observer of the company ​described it​: “Cross-team layoffs like these are not unusual for a company of Salesforce’s size, especially at this time of year, before announcing end-of-fiscal-year earnings.”

What’s actually going on with AI and jobs? Generative AI might very well create broad disruptions in the job market. But we’re not there yet. The first major shift will likely occur in software development, but its magnitude remains unclear. (More on this soon: I’m in the middle of a reporting project in which I’ve now heard from over 300 computer programmers about how they’re currently using AI; tl;dr: it’s complicated!)

In the meantime, however, the actual stories related to AI are important enough on their own. We don’t also need reporters working backward to support trends that they feel like should be true.

(To be clear: The rest of the article is quite good. It explores, more hypothetically, how the government could respond to massive economic disruptions, and it’s written by a journalist who I respect and who knows a lot about that topic. It’s worth reading! Just don’t get freaked out by the vibe reporting in the opening section.)

The post Film Students Can No Longer Sit Through Films appeared first on Cal Newport.

The course launched last week, so you should definitely ​check it out​. It gets to the core of a lot of the topics we tackle in this newsletter about the intersection of technology and productivity, and it’s an incredibly polished final product.

It’s actually this latter point that I want to talk a little bit more about today, as it sparks an interesting question about the future of online media more generally…

One of the most striking things about working with MasterClass is its production values. I’ve been a guest on many major video podcasts (from ​Mel Robbins​ to ​Andrew Huberman​ to ​Rich Roll​). These shows look good. They all have reasonable sets with diffused lights and three-camera setups.

MasterClass, however, operates at another level. They use high-end TV-quality production crews. There’s a director, a cinematographer, and multiple camera operators distinct from the focus pullers, all of whom work with gaffers and grips, supported by production assistants. My make-up artist had recently worked on Sinners.

In my career as a writer, I’ve been on TV before as a guest on morning shows and cable news programs, but this was as close as I’ll ever come to starring in a dramatic series or independent film.

For me, this experience implied an important reality about the current state of visual media: there remains a non-trivial quality gap between independent video (e.g., as produced for YouTube) and legacy video (e.g., as produced for streaming platforms or linear television).

This gap matters.

Because these two categories still look different, we treat them distinctly. We’re willing to pay for access to content on Netflix, but we relegate the next rung down on the quality ladder to ad-supported general-use platforms like YouTube.

But here’s what’s interesting about the near future: that difference is diminishing. MasterClass, for example, is not funded by a streaming service or television studio; however, they achieve streaming/TV-level production values. Other independent video producers are also closing this gap.

This raises a key question: What will happen to video content as the difference between independent and legacy production value vanishes?

We can see a glimpse of this future in a project that fascinates me: ​Dropout TV​ – also stylized online as :Dropout – which can best be described as a comedy streaming service. It costs $6.99 a month, which gains you access to a slate of original unscripted shows all filmed at a quality level indistinguishable from what you would find on, say, Netflix programs like Is it Cake? or Nailed It!.

Except, they’re not Netflix. Dropout TV doesn’t have multi-billion dollar production budgets or massive venture capital backing. It grew out of the early 2000s website ​CollegeHumor.com​. With the rise of YouTube, CollegeHumor turned more attention to producing content for the platform. But they were frustrated by a model that required them to live or die by a third-party algorithm and the whims of advertisers, so they eventually launched their own subscription app.

Today, Dropout boasts over a million subscribers.

I refer to this type of niche subscription service, defined by a combination of legacy-quality programming and a focused audience, as a micro-streamer.

Keep an eye on this market segment. As it becomes easier to produce high-end video, more independent creators will leave the mass-aggregation platforms like YouTube and offer up targeted competition to the major streaming players.

Who knows, maybe one day you’ll even have a Deep Life TV app next to Disney+ on your smart TV. Until then, however, you can get your fill of movie-quality Cal content ​over at MasterClass​.

The post What I Learned from MasterClass appeared first on Cal Newport.

The implication of this framing is clear: AI is taking jobs.

Nothing in the body of this article contradicts this idea. It describes the number of people laid off and the benefits they’ll receive. It quotes executives who won’t deny the possibility of future job losses. It mentions how Amazon is known for its “cutthroat” corporate culture.

You walk away feeling that the impact of AI on our economy is already getting out of hand.

The only problem is that this reporting omits almost all relevant details.

For a more realistic take, let’s turn toward the financial press. CNBC published ​an article​ about these same layoffs featuring a more informative headline:

The article goes on to correctly attribute the layoffs to Amazon’s desire to trim layers of management bureaucracy that built up during the pandemic-era tech hiring boom: “CEO Andy Jassy has looked to slim down Amazon’s workforce after the company went on a hiring spree during the Covid-19 pandemic.”

What role does AI play in all of this? Like many leading companies in the technology sector, Amazon is investing heavily in building its own AI products. Presumably, money is being saved by firing managers, which frees up more revenue to invest in this area. But that’s really it. As the CNBC article elaborates:

“In a blog post, the company wrote that the layoffs were part of an ongoing effort to ‘strengthen our organization by reducing layers, increasing ownership, and removing bureaucracy.’ That coincides with a push to invest heavily in artificial intelligence.” [emphasis mine]

The CNBC article then reports that these massive layoffs actually began for Amazon in 2022 and 2023, following the pandemic, but before ChatGPT was released and the subsequent generative AI revolution began.

Both of these articles cover the same announcement, but they produce two very different impressions. The Quartz article strongly implies that Amazon is firing people because it can now offload their work to AI. (I mean: look at the Andy Jassey quote they included in the sub-head, they clearly wanted readers to believe AI caused these job losses.)

The CNBC article, by contrast, makes it clear that the connection between AI and these layoffs is more coincident than causal.

In recent years, I’ve seen more articles follow the general approach demonstrated by the Quartz example. They identify an alarming,attention-catching fear about AI that seems prevalent in the cultural zeitgeist, and then shape a story to feed the narrative. The key to this vibe reporting strategy is that the articles never make explicit claims. They instead combine cunning omissions and loosely related quotes to make strong implications.

The Quartz article, for example, never concretely states that the 16,000 workers are being replaced with AI; rather, it conveniently avoids mentioning any of the publicly available details about the layoffs that would contradict that idea, and then interleaves quotes about AI’s disruptive potential into the reporting in a highly suggestive manner.

The goal of this type of article is to create a pre-ordained vibe, not to get to the bottom of what’s really happening.

I’m not pointing out this phenomenon to dismiss concerns about AI, but instead because I think this strategy is an obstacle to real action. This type of disingenuous reporting is not going to help us identify the actual problems that require actual solutions. It instead creates a nihilistic sense of inevitable disruption that might drive social media shares, but also numbs people and prevents meaningful responses.

Remember: Nothing about these tools is inevitable, and their impact is far from preordained. We don’t need vibes right now. Reality is too important.

The post The Dangers of “Vibe Reporting” About AI appeared first on Cal Newport.