“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.”

Papers that are more difficult to read might be worth it if AI increased the amount of good science being produced. But this doesn’t seem to be the case. Organization Science is desk-rejecting (e.g., rejecting a paper before even sending it to peer reviewers) nearly 70% of manuscripts that made heavy use of AI. This number drops to 44% for papers written without AI.

Similarly, only 3.2% of high-AI papers are ultimately accepted compared to 12% of low-AI papers.

(It’s important to note here that the editors making these decisions do not themselves know the role of AI in the paper construction. These are retrospective analyses.)

All of this points to a distressing conclusion: generative AI tools are leading to many more poor paper submissions, which are taxing the time and patience of the community tasked with reviewing this research.

These tools make individual researchers’ lives easier in the moment (writing is hard!), but they are leading to worse outcomes for the field as a whole.

I tell this story because I think it’s a useful cautionary tale about AI. As I’ve been trying to argue from many different angles in recent weeks (e.g., ​1​ ​2​), making things faster or easier is not the same as making things better.

Sometimes there really is no shortcut to taking your time.

The post Easy is Overrated appeared first on Cal Newport.

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:

“Every system has a limiting factor or constraint. Focusing improvement efforts to better utilize this constraint is normally the fastest and most effective way to improve profitability.”

To borrow one of Goldratt’s examples, imagine you run a small assembly line that manufactures chicken coops following a step-by-step process – building the frame, attaching the roof, adding wire mesh, etc. Goldratt notes that the speed of this production is limited by whatever step is slowest; what he calls the “bottleneck.”

Speeding up other steps of the process won’t increase the rate at which you produce chicken coops, as the bottleneck still determines the overall efficiency. If, for example, putting on the roof is the slowest step, then adding more workers or better tools to earlier steps will lead to more partially-constructed coops piling up at the roofing station. To speed up the line, you need to move more resources to the weakest link.

Goldratt was primarily concerned with industrial production, but I think his theory of constraints provides insight into personal productivity, too.

Something I’ve long written about is the reality that many digital productivity tools paradoxically make us busier, rather than better at our jobs. Goldratt’s theory helps explain why.

When we deploy a digital tool like email to speed up communication, or generative AI to create (sloppy) slide presentations quickly, we don’t automatically become better at our jobs. If these steps don’t improve the bottleneck in our process – the key link where the real value is produced – then, as in the chicken coop example, they’re just as likely to create pile-ups and distraction, without actually boosting our true productivity.

This helps explain why ​email ended up an accidental disaster​, and the early returns on ​AI office tools have been mixed​ at best.

The theory of constraints implies a different way of thinking about getting better at our jobs. Don’t seek speed, or efficiency, or the avoidance of hard things. What ultimately matters more than anything else is how well we perform the deep steps that actually move the needle.

The post On Bottlenecks and Productivity appeared first on Cal Newport.

“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:

“In the place of problem-solving technology, companies have jumped on successive bandwagons like NFTs, the metaverse, and large language models. What these all have in common is that they are not built to really solve a market problem. They are built to make VCs and companies rich.”

Of these three examples, large language models clearly have the most potential utility. But this doesn’t let AI companies off the hook when it comes to figuring out and communicating those uses.

As Lopatto points out: “Normal people aren’t running around like chickens with their heads cut off, trying to automate every single part of their lives.“ Their biggest exposure to AI is using a tool like ChatGPT as a more verbose Google, or perhaps occasionally formatting an event itinerary. This is cool, and even useful, but at the moment it is probably less positively impactful in their lives than, say, the arrival of the iPod in the early 2000s.

But unlike an iPod, these same ordinary users are forced to hear about AI constantly; not just enthusiast tech bro nonsense, but dark, disturbing, relentless accounts about how everything is about to change in terrible ways that they can’t control.

This isn’t sustainable.

Generative AI has no shortage of ways that it might, with care, be shaped into genuinely useful products, but this shaping needs to actually happen before the hyper-scalers earn the right to continually harass the psyche of billions of people with breathless pronouncements. Most people don’t care that GPT 5.5, released late last week, underperformed Opus 4.7 on SWE-Bench Pro. They want the AI companies to let them know when they have a product that will actually and notably improve their lives, and until then, they want these companies to leave them alone and try their best not to ​crash the economy​.

As Lopatto concludes: “At some point, our Silicon Valley overlords forgot that in order for their vision of the future to be adopted, people had to want it.” They still have a lot of work to do.

AI Is Destroying the Job Market. Also, AI Is Saving the Job Market

I couldn’t help but add a quick additional note about AI to this week’s newsletter…

One of the big stories of the last year was the shrinking post-pandemic job market for recent college graduates. Many media outlets confidently offered an explanation for this shift: AI was automating the work of entry-level positions.

An ​article​ from last summer proclaimed that “AI is wrecking an already fragile job market for college graduates,” going on to note that “ChatGPT and other bots can do many of [the] chores” that used to be handled by entry-level workers. Another ​article​, published only two weeks ago, offered a stark warning: “college graduates can’t find entry-level roles in shrinking market amid rise of AI.”

But then, last week, new job numbers revealed that the entry-level job market for college graduates was rebounding, and hiring in this demographic is now projected to rise significantly. Whoops. I guess AI wasn’t actually automating those jobs. (​I told you so​.)

Does this mean the media will stop trying to force this technology into these more routine workforce narratives? If only wishing made it so. A recent Wall Street Journal ​article​ describing these positive numbers included the following line: “In some cases, artificial intelligence is spurring hires by enabling companies to expand services and product lines.”

So, let’s get this straight: AI is simultaneously contracting the job market for recent college graduates while also expanding the job market for recent college graduates.

Is there anything AI can’t do?

The post Who Asked For This? appeared first on Cal Newport.

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?

Sanderson ultimately lands on a more personal reason. Talking about his struggles with his first (failed) book manuscripts, he identifies the key value of art: it changes the artist who attempts it. As he elaborates:

“Maybe someday the language models will be able to write books better than I can. But here’s the thing: Using those models in such a way absolutely misses the point, because it looks at art only as a product. Why did I write [my first manuscript]?… It was for the satisfaction of having written a novel, feeling the accomplishment, and learning how to do it. I tell you right now, if you’ve never finished a project on this level, it’s one of the most sweet, beautiful, and transcendent moments. I was holding that manuscript, thinking to myself, ‘I did it. I did it.’”

As a writer myself, I’ve also been thinking about this question recently. I like Sanderson’s take, but I’ve been developing one of my own. I understand art to be an act of deep human communication, in which the artist uses a tangible medium, such as a page of prose or a painted canvas, to transmit a complex internal cognitive state from their brain to that of their audience.

It’s telepathy. And it’s one of the most beautiful and human things we do.

This makes the idea of reading a book written by a language model, or watching a film generated by a prompt, intrinsically absurd, if not anti-human. It’s the heroin needle providing a quixotic simulation of love.

What really struck me about Sanderson’s talk, however, was his conclusion. If art is deeply human, he argues, then it’s up to us to define it. “That’s the great thing about art – we define it, and we give it meaning,” he says. “The machines can spit out manuscript after manuscript after manuscript. They can pile them to the pillars of heaven itself. But all we have to do is say ‘no.’”

I’ve noticed a trend in recent AI commentary toward a certain nihilistic passivity. You probably know what I’m talking about – the now popular style of essay in which the author, with a sort of worldly weariness, lays out some grim scenario in which AI destroys something sacred, and then sort of just leaves it there, like a cat dropping a dead bird on the doorstep.

I’m getting tired of this meekness.

Sanderson reminds us that we have agency. In the areas that matter most, it’s us, not the whims of Sam Altman or Dario Amodei, that determine how we shape our existence. All we have to do is say “no.”

Correction:

In last week’s AI Reality Check episode of my podcast, I said the following:

“If you go back and look at the release notes for Anthropic’s earlier, less powerful opus 4.6 LLM, they say the following: their researchers used Opus to find, quote, ‘over 500 exploitable zero-day vulnerabilities, some of which are decades old.’ And let’s stop for a moment because that note, which was hidden in the system card for opus 4.6, is almost word for word what anthropic said about Mythos.”

Some of this wording was sloppy, so I want to clarify it here. I was referring to this report on Opus 4.6, which Anthropic published the same day it was released. This is not technically the system card for Opus 4.6, but it is accurately described as release notes (or perhaps supplementary release notes).

This report said: “Opus 4.6 found high-severity vulnerabilities, some that had gone undetected for decades.” In another place, it said: “So far, we’ve found and validated more than 500 high-severity vulnerabilities.” Both the title of the report and the conclusion refer to these vulnerabilities as “0-day.”

The specific quote I provided, however, does not appear in the report. It’s actually a summary of the report from this tweet. In my opinion, the summary is accurate, but the way I worded the above implies that it was actually found in the report, which it was not.

Thank you to the AI researcher who pointed out these issues. I appreciate corrections! You can always send concerns or notes to podcast@calnewport.com.

The post Brandon Sanderson vs. AI Art appeared first on Cal Newport.

The “stunning advance” was the release of Anthropic’s new LLM, named Claude Mythos. In a lengthy ​press release​, Anthropic announced that the model would be made available to a consortium of business partners, but not to the general public. To justify this decision, Anthropic cited their concerns about its effectiveness at finding security vulnerabilities in source code, noting: “AI models have reached a level of coding capability where they can surpass all but the most skilled humans at finding and exploiting software vulnerabilities.”

They go on to explain that Mythos “has already found thousands of high-severity vulnerabilities, including some in every major operating system and web browser.”

This announcement clearly rattled Friedman, who called Anthropic’s decision not to release the model a “terrifying warning sign,” writing:

“Holy cow! Superintelligent A.I. is arriving faster than anticipated, at least in this area…If this A.I. tool were, indeed, to become widely available, it would mean the ability to hack any major infrastructure system — a hard and expensive effort that was once essentially the province only of private-sector experts and intelligence organizations — will be available to every criminal actor, terrorist organization and country, no matter how small.”

Friedman was far from alone in this concern. Many major news outlets expressed similar unease about this scary new development, including ​one particularly anxiety-provoking headline​ that asked if Mythos was an “AI nightmare waiting to happen?”

So, what’s really going on here?

I thought it was worth taking a moment to look closer, not just to address the specific worries about Mythos, but also to help recalibrate, more generally, how those of us seeking depth in a distracted world should consume AI news.

~~~

When I talked to people who were spooked by Friedman’s column, they tended to be under the impression that this ability to find and exploit security vulnerabilities was a new phenomenon; a skill that emerged unexpectedly in Mythos, “terrifying” those who studied it.

In reality, security researchers have been worried about using LLMs for this purpose since the beginning of consumer LLMs.

Back in 2024, for example, IBM researchers published ​a splashy study​ about using GPT-4 to attack security vulnerabilities. They found that GPT-4 successfully exploited 87% of the vulnerabilities that it was presented, as compared to close to 0% for GPT 3.5. “Our findings raise questions around the widespread deployment of highly capable LLM agents,” they concluded.

To be fair, in the case of GPT-4, researchers were assessing whether an LLM could write code to exploit a known vulnerability. Mythos, however, can also find these vulnerabilities from scratch. But this isn’t new either.

Accompanying the release notes for Anthropic’s earlier Opus 4.6 LLM was ​the observation​ that Anthropic’s security team used the model to find “over 500 exploitable 0-day [vulnerabilities], some of which are decades old.” This is almost word-for-word what Anthropic said last week about Mythos, the main difference being that they replaced 500 with “thousands.”

We are not, therefore, talking about a new capability, but rather one that has been around for multiple years.

The relevant question then becomes, how much better is Mythos at finding vulnerabilities? It’s hard to tell for sure because Anthropic has kept their new model private. They did, however, release that Mythos scored 83.1% on a well-known cybersecurity benchmark. For comparison, Opus 4.6 scored 66.6% on this same test.

In general, benchmark results should be taken with a grain of salt as they represent specific (often narrow) tests that researchers can tune their models to pass. But even if we accept that this particular measure is useful, a sixteen percentage point increase seems to represent solid incremental progress more than a nightmarish leap.

When we turn our attention to actual results, the waters become even murkier. In a recent Substack post (​which is worth reading​), Gary Marcus rounds up responses from security researchers who took a closer look at the specific exploits that Anthropic reported that Mythos discovered. They were not impressed.

• Philo Groves, for example, ​noted​ that Mythos’s attention-grabbing attack on the Firefox browser required certain common security features to be disabled, and it built on results previously discovered by Opus. (“Shocker,” he concludes sardonically.)
• The CEO of the AI company HuggingFace then ​reported​ that they took all of the specific vulnerabilities that Anthropic highlighted and “ran them through small, cheap, open-weight models.” What did they find? “Those models recovered much of the same analysis.”

Since Marcus published his essay, I’ve come across several more similar findings:

• The AI security expert Stanislav Fort ran ​an experiment​ to see if existing, cheap open-weight models could find the same vulnerability in FreeBSD (an open-source operating system) that Anthropic touted as evidence of Mythos’s scary abilities to uncover bugs that had been hiding for decades. The result: all eight existing models they tested discovered the same issue.
• Meanwhile, the renowned security researcher Bruce Schneier ​weighed in​, similarly concluding: “You don’t need Mythos to find the vulnerabilities they found.”

And of course, it doesn’t help that a week before Anthropic released this supposedly super-powered vulnerability detector, they accidentally leaked the Claude Code source, and security researchers immediately found ​serious vulnerabilities​. (I guess Anthropic forgot to use Mythos to clean up their own software…)

~~~

What’s really happening?

It’s fair to say that LLMs have created significant cybersecurity concerns that researchers have been scrambling to address in recent years. It’s also fair to say, however, that we don’t yet have evidence that Claude Mythos significantly changed this reality. If anything, some of the early independent testing by security researchers implies that Mythos might be better understood as a version of Opus 4.6 tuned to perform better on a handful of benchmarks. And yet, many still took Anthropic at their word and covered this model’s release as a catastrophic event.

In a ​recent video​, the AI commentator Mo Bitar compared Anthropic’s model rollouts to Apple iPhone launches, where every year they resell you the same product with minor improvements. “Except here,” he adds, “the product is existential dread.”

And we keep falling for it.

I think we’ve entered a stage where we need to almost entirely discount any claims made by the AI companies themselves until we can independently verify what’s actually going on.

The post Is Claude Mythos “Terrifying” or Just Hype? appeared first on Cal Newport.

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.